diff --git a/README.md b/README.md index a299913..84c4bd3 100644 --- a/README.md +++ b/README.md @@ -1,601 +1,174 @@ -# Datly — AI-Powered Interactive Data Visualization IDE & Analytical Workbench - -**Local-First, Ground-Truth Verifiable Data Exploration with Concept Encoding, Data Threads & Enterprise Warehouse Connectors** - -
- Release v0.8.0-beta - MIT License - Python 3.11+ - Node 20+ - DuckDB + Pandas Engine - Vega-Lite & ECharts - Desktop & Web - GitHub Stars -
- -
- 🌐 Visit Official Interactive Showcase & Documentation  |  - ⚡ 1-Prompt Agent Quickstart  |  - 🔌 Enterprise Connectors (15+) -
- -

- Datly AI Analytics Workspace with Data Threads, Live Vega Visualizer, and Ground-Truth Table Inspection -

- ---- - -## 🛡️ Critical Data Governance, Privacy & Trust Notice - -> [!IMPORTANT] -> ### 100% LOCAL-FIRST DATA PROCESSING & ZERO CLOUD DATA EXFILTRATION -> -> In traditional AI data exploration tools and cloud notebooks, analysts are forced to upload raw proprietary datasets—including sensitive customer records, financial ledgers, and health information—to remote LLM servers. -> -> **Datly fundamentally breaks this security liability:** -> - **Zero Raw Data Uploaded:** Your underlying data tables, whether 100 rows or 10 million rows from DuckDB, Parquet, or connected enterprise warehouses, **never leave your local environment**. -> - **Schema-Only Synthesis:** When prompting with natural language (e.g. `@stock-hist calculate 20-day volatility`), Datly sends **only** column schemas and sanitized metadata to your chosen LLM (OpenAI, Claude, Gemini, DeepSeek, or offline local Ollama). -> - **Local Sandboxed Code Execution:** The synthesized data transformation code (DuckDB SQL / Python Pandas) executes strictly in-memory on your local machine. -> - **Verifiable Ground-Truth Inspection:** Every visual chart sits directly on top of the exact computed data table underneath it. What you see is what was mathematically derived—eliminating AI chart hallucinations and phantom metrics. - ---- - -## 📑 Table of Contents - -- [Executive Summary & Architectural Highlights](#-executive-summary--architectural-highlights) -- [Capability & Comparison Matrix](#-capability--comparison-matrix) -- [System Architecture & Data Flow Diagrams](#-system-architecture--data-flow-diagrams) - - [1. Natural Language & Shelf-Based Synthesis Pipeline](#1-natural-language--shelf-based-synthesis-pipeline) - - [2. Ground-Truth Data Thread Lineage & State Machine](#2-ground-truth-data-thread-lineage--state-machine) - - [3. Local-First Zero-Data-Exfiltration Security Boundary](#3-local-first-zero-data-exfiltration-security-boundary) - - [4. Multi-Tier Modular Software Architecture](#4-multi-tier-modular-software-architecture) -- [Enterprise Data Connectors & Integrations](#-enterprise-data-connectors--integrations) -- [Core Features in Detail](#-core-features-in-detail) - - [1. Data Threads vs Linear Notebook Cells](#1-data-threads-vs-linear-notebook-cells) - - [2. Concept Encoding & Interactive Visual Shelves](#2-concept-encoding--interactive-visual-shelves) - - [3. Immediate Ground-Truth Verification (Explore Grid)](#3-immediate-ground-truth-verification-explore-grid) - - [4. Standing Knowledge Base & Custom Analyst Rules](#4-standing-knowledge-base--custom-analyst-rules) - - [5. WYSIWYG TipTap Narrative Reports & Pinboard Dashboards](#5-wysiwyg-tiptap-narrative-reports--pinboard-dashboards) - - [6. Multi-Model Agnostic LLM Engine (LiteLLM)](#6-multi-model-agnostic-llm-engine-litellm) - - [7. Desktop-First Identity & Offline Mode](#7-desktop-first-identity--offline-mode) -- [Visual Feature Gallery](#-visual-feature-gallery) -- [Quick Start: 1-Prompt Automated Setup via AI Assistants (Recommended)](#-quick-start-1-prompt-automated-setup-via-ai-assistants-recommended) -- [Alternative: Manual Build & Deployment Guide](#-alternative-manual-build--deployment-guide) - - [Prerequisites](#prerequisites) - - [Step-by-Step Installation (uv + Yarn)](#step-by-step-installation-uv--yarn) - - [One-Click Launcher Scripts](#one-click-launcher-scripts) - - [Docker Container Deployment](#docker-container-deployment) - - [Electron Desktop Application Build](#electron-desktop-application-build) -- [Comprehensive Configuration & Environment Reference](#-comprehensive-configuration--environment-reference) -- [Threat Model & Security Analysis](#-threat-model--security-analysis) -- [Diagnostic & Troubleshooting Guide](#-diagnostic--troubleshooting-guide) -- [Frequently Asked Questions (FAQ) & SEO Discoverability](#-frequently-asked-questions-faq--seo-discoverability) -- [GitHub Repository SEO & Metadata Configuration](#-github-repository-seo--metadata-configuration) -- [Roadmap & Community](#-roadmap--community) -- [License](#-license) - ---- - -## 🏛️ Executive Summary & Architectural Highlights - -**Datly** is a next-generation AI-powered data visualization IDE and exploratory analytics workbench. Engineered for modern data analysts, business intelligence engineers, and researchers, Datly unifies declarative concept encoding, conversational data transformations, and automated report synthesis into a single cohesive workspace. - -Traditional analysis workflows are fragmented across three incompatible silos: -1. **Spreadsheets & SQL IDEs (DBeaver, DataGrip):** Strong at tabular data inspection, but lacking visual narrative, exploratory iteration, and automated reasoning. -2. **Computational Notebooks (Jupyter, Hex):** Suffer from hidden state traps, fragile linear cell ordering, and friction when communicating findings to non-technical stakeholders. -3. **Legacy BI Platforms (Tableau, Power BI):** Rigid data modeling requirements, steep learning curves for ad-hoc formulas, and high license friction. - -Datly eliminates this fragmentation by pairing **high-speed local in-memory engines (DuckDB + Pandas)** with **agentic code synthesis**, **visual encoding shelves**, **non-destructive threaded data lineage**, and **instant ground-truth verification**. - -### Core Pillars -1. **Local-First In-Memory Analytics:** Executes blazingly fast SQL and Python transformations on your machine using DuckDB and PyArrow. Large raw tables stay local; sensitive datasets are protected by design. -2. **Ground-Truth Verifiability:** Unlike AI chat tools that spit out unverified chart images, Datly renders visualizations directly from a tangible derived table. The exact rows powering the chart sit immediately underneath the visual canvas for continuous verification. -3. **Dual-Mode Synthesis (Natural Language + Visual Shelves):** Ask complex questions in plain natural language (e.g., using `@table-name` mentions), or fine-tune charts using drag-and-drop visual encoding shelves (X, Y, Color, Size, Facet, Repeat). -4. **Non-Destructive Threaded Lineage (Data Threads):** Branch out questions into isolated threads. Each analytical question creates a persistent, named derived dataset card without mutating the original input data. -5. **Multi-Surface Deliverables:** Seamlessly pin charts into executive dashboards or embed live chart components into TipTap WYSIWYG rich text reports with PDF, PNG, SVG, and CSV export. -6. **Enterprise Ecosystem Ready:** Connects to 15+ data sources (Databricks, BigQuery, Snowflake, ClickHouse, Postgres, S3, MongoDB, Web Scrapers) and supports any LLM (Claude 3.7/Sonnet, GPT-4o, Gemini, DeepSeek, or local Ollama). - ---- - -## 📊 Capability & Comparison Matrix - -| Feature / Architectural Control | Jupyter / Traditional Notebooks | Cloud BI (Tableau / Power BI) | SQL IDEs (DBeaver / DataGrip) | **Datly Analytics IDE** | -| :--- | :---: | :---: | :---: | :---: | -| **Natural Language AI Synthesis** | ⚠️ Plugin-dependent | ⚠️ Limited Copilot / Costly | ❌ None or Basic Autocomplete | ⚡ **Native Grounded Agent (`@table`)** | -| **Ground-Truth Data Table Under Chart** | ❌ Separate cell output | ❌ Deeply nested view | ❌ Manual query required | 🛡️ **Always Visible on Canvas** | -| **Dual-Mode (Encoding Shelf + Agent)** | ❌ Code only | ⚠️ Shelves only (No Agent) | ❌ Code only | ✅ **Bi-Directional Shelves + Agent** | -| **Local Data Privacy (Zero Exfiltration)** | ✅ Local | ❌ Cloud ingestion required | ✅ Local | 🔒 **100% Local (Schema-Only to LLM)** | -| **Data Lineage Architecture** | ❌ Linear, stateful cell traps | ❌ Static data models | ❌ Session query history | 🌲 **Branching Data Threads (DAG)** | -| **In-Memory Analytical Engine** | ⚠️ Depends on kernel | ⚠️ Proprietary VertiPaq/Hyper | ❌ Remote DB dependent | ⚡ **DuckDB + Pandas + PyArrow** | -| **Interactive WYSIWYG Reporting** | ❌ Markdown cells only | ⚠️ Heavy dashboard canvas | ❌ None | 📝 **TipTap Narrative Rich-Text Editor** | -| **Automated Web Table Scraping** | ❌ Manual scraping scripts | ❌ Not supported | ❌ Not supported | 🌐 **Built-in Playwright & lxml Scraper** | -| **Model Independence (BYOK)** | ⚠️ Manual configuration | ❌ Locked to vendor cloud | ❌ N/A | 🔓 **LiteLLM (Claude, GPT, Gemini, Ollama)** | -| **Desktop Deployment Mode** | ⚠️ Browser localhost | ⚠️ Heavy desktop app | ✅ Desktop native | 📦 **Native Electron + Web + Docker** | - ---- - -## 📐 System Architecture & Data Flow Diagrams - -### 1. Natural Language & Shelf-Based Synthesis Pipeline - -```mermaid -flowchart TD - UserPrompt["Natural Language Prompt\n(e.g., '@stock-hist 20d moving avg by symbol')"] --> ContextBuilder["Context & Schema Builder\n(Extracts column types & minimal 2-row sample)"] - VisualShelves["Visual Encoding Shelves\n(Drag & drop: X, Y, Color, Size, Facet)"] --> ContextBuilder - KnowledgeRules["Knowledge Base & Rules\n(Standing analytical instructions & conventions)"] --> ContextBuilder - - ContextBuilder -->|Sanitized Schema Only\nZero Raw Data Rows| LLMEngine["LiteLLM Engine\n(Claude / GPT-4o / Gemini / Local Ollama)"] - - LLMEngine --> CodeValidator["Code Synthesizer & AST Guard\n(Generates Python Pandas or DuckDB SQL)"] - - subgraph Local_Execution_Sandbox["Isolated Local Execution Sandbox"] - RawData[("Local Raw Datasets\n(DuckDB / Parquet / Memory)")] --> SandboxedRunner["Sandboxed Execution Engine\n(Pandas / DuckDB Vectorized Kernel)"] - CodeValidator --> SandboxedRunner - SandboxedRunner --> DerivedTable["Derived Table Card\n(e.g., 'stock-ma' with computed columns)"] - end - - DerivedTable --> VizCompiler["Vega-Lite / ECharts Compiler\n(Generates declarative chart spec)"] - VizCompiler --> InteractiveCanvas["Interactive Explore Canvas\n(Chart visual on top + Ground-Truth rows below)"] - InteractiveCanvas --> DeliveryTargets["Reports / Dashboards / CSV Export"] +# Datly + +An open-source analytics workspace for exploring tables with AI, refining charts, inspecting computed rows, and writing reports. Built on [Microsoft Research’s Data Formulator](https://github.com/microsoft/data-formulator). + +[Product website](https://arpanjot-coding.github.io/Datly/) · [Setup](#run-from-source) · [Releases](https://github.com/arpanjot-coding/Datly/releases) · [MIT licence](LICENSE) + +![Datly workspace using example stock data](docs/images/product-workspace.jpg) + +## What you can do + +- Ask questions about data in natural language and follow up within a data thread. +- Refine visualisations through chart and encoding controls. +- Inspect the computed table behind a chart and export its rows as CSV. +- Organise prompts, transformations, derived tables, and charts in data threads. +- Edit rich-text reports with chart images; export a report image or print to PDF. +- Connect supported data sources or start with local files and example datasets. +- Configure a supported cloud model provider or a local Ollama endpoint. + +Datly is in beta. AI-generated transformations and interpretations can be incorrect. Inspect the generated work, source data, and assumptions before relying on a result. + +## Data handling and model requests + +Transformations run in the environment hosting Datly. A local installation uses your computer; a server installation uses that server. + +**Local execution does not mean AI requests contain only schema.** Model context can include field names, types, sample field values, sample rows, numerical statistics, prompts, and tool outputs. When you select a cloud model provider, that provider receives the context sent to it. + +For example, [the context builder](py-src/data_formulator/agents/context.py) includes sample values and rows, and [the analyst agent](py-src/data_formulator/analyst/agent.py) incorporates that context. Do not rely on a “zero data exfiltration” or “schema-only” guarantee. + +A locally hosted model endpoint can keep model requests local when configured accordingly. Remote connectors, web sources, and dependency downloads can still require network access. Review your deployment, selected endpoint, and data sources before using sensitive information. + +## Run from source + +You need Python 3.11 or later, Node.js, Yarn, and [uv](https://docs.astral.sh/uv/). Dependency and driver compatibility can vary by platform. The desktop CI uses Python 3.12 and Node.js 22. + +Clone the repository: + +```sh +git clone https://github.com/arpanjot-coding/Datly.git +cd Datly ``` ---- - -### 2. Ground-Truth Data Thread Lineage & State Machine - -```mermaid -stateDiagram-v2 - [*] --> IngestSource: Connect Database / Upload CSV / Sample Data - - IngestSource --> InputTable: Stored in Local Session (Immutable Baseline) - - state "Data Thread Lineage DAG" as ThreadState { - InputTable --> Thread1: Branch Question 1 ("@input monthly aggregate") - Thread1 --> Derived1: Compute Table 'm-agg' - Derived1 --> Chart1: Render Primary Line Chart - - Thread1 --> FollowUp1: Branch Follow-up ("now filter outlier months") - FollowUp1 --> Derived2: Compute Filtered Table 'm-clean' - Derived2 --> Chart2: Render Cleaned Distribution - - InputTable --> Thread2: Independent Thread 2 ("@input correlation matrix") - Thread2 --> Derived3: Compute Table 'corr-matrix' - Derived3 --> Chart3: Render Heatmap - } - - state "Multi-Surface Publishing" as PublishState { - Chart1 --> PinDashboard: Pin to Operational Dashboard - Chart2 --> EmbedReport: Embed Live into TipTap Narrative Report - Derived2 --> ExportCSV: Download Verifiable CSV - } +Build the frontend: + +```sh +yarn +yarn build ``` ---- +Install the Python environment and launch: -### 3. Local-First Zero-Data-Exfiltration Security Boundary +```sh +uv sync +uv run data_formulator +``` -```mermaid -flowchart LR - subgraph Secure_Local_Boundary["PROTECTED LOCAL ENVIRONMENT (Your Device / Private Server)"] - Warehouse[("Enterprise Warehouse\nDatabricks / BigQuery / Postgres / S3")] --> IngestEngine["Secure Connectors\n(Read-only, parameterized queries)"] - LocalFiles["Local Files\n(CSV, Excel, Parquet, SQLite)"] --> IngestEngine - IngestEngine --> LocalDuckDB[("Local DuckDB Engine\n& In-Memory DataFrames")] - - LocalDuckDB --> ASTExecutor["Sandboxed Code Runner\n(Local CPU / RAM)"] - ASTExecutor --> DerivedTables["Derived Results & Full Data Rows"] - DerivedTables --> UIInspection["Canvas Grid Inspection\n(Rows rendered directly on screen)"] - end +Open **http://localhost:5567**. The launch command assumes the frontend has already been built. - subgraph External_Model_Boundary["EXTERNAL LLM PROVIDER (OpenAI / Anthropic / Google)"] - RemoteLLM["LLM Synthesis API\n(Claude / GPT-4o / Gemini)"] - end +Configure a model in the application, or copy `.env.template` to `.env` and review the documented settings. Keep API keys and connection credentials out of version control. - LocalDuckDB -.->|1. Column Names & Types\n2. Sanitized 2-Row Sample Only| RemoteLLM - RemoteLLM -.->|Returns Python / DuckDB SQL Code| ASTExecutor +On macOS or Linux: - style Secure_Local_Boundary fill:#0B132B,stroke:#1C2541,stroke-width:2px,color:#FFFFFF - style External_Model_Boundary fill:#1F1D36,stroke:#3F3351,stroke-width:2px,color:#FFFFFF +```sh +cp .env.template .env ``` ---- - -### 4. Multi-Tier Modular Software Architecture - -```mermaid -graph TD - subgraph Presentation_Tier["Presentation Tier (React 18 + TypeScript + Vite)"] - UI_Threads["Data Thread Cards & Prompt Rail\n(Conversational branching & @mentions)"] - UI_Shelves["Visual Encoding Shelves\n(Drag & drop field encodings)"] - UI_Canvas["Explore Canvas & Data Grid\n(Synchronized chart + tabular rows)"] - UI_Editor["TipTap Report Editor\n(WYSIWYG narrative + embedded charts)"] - UI_Dash["Dashboard Pinboard\n(Multi-card layout & live filters)"] - end - - subgraph Analytical_Engine_Tier["Analytical Engine Tier (Python Backend)"] - API_Server["Flask REST API & WebSockets\n(CORS-protected, session-managed)"] - LiteLLM_Hub["LiteLLM Unified Adapter\n(Multi-model provider routing)"] - Transform_Exec["Vectorized Transformation Engine\n(DuckDB SQL & Pandas execution)"] - Web_Scraper["Playwright & lxml Web Scraper\n(Automated table discovery)"] - end - - subgraph Data_Connector_Tier["Enterprise Connector Tier (15+ Sources)"] - Conn_Cloud["Databricks / BigQuery / Kusto / S3 / Athena"] - Conn_RDBMS["PostgreSQL / MySQL / MSSQL / ClickHouse"] - Conn_NoSQL["MongoDB / Azure CosmosDB / Azure Blob"] - Conn_Files["Excel (.xlsx) / Parquet / CSV / yfinance"] - end - - subgraph Desktop_Platform_Tier["Packaging & Runtime Tier"] - Pkg_Electron["Electron 43 Desktop Wrapper (Win / macOS / Linux)"] - Pkg_PyWebView["PyWebView Lightweight Desktop Engine"] - Pkg_Docker["Docker & Docker Compose Containerization"] - end - - Presentation_Tier --> Analytical_Engine_Tier - Analytical_Engine_Tier --> Data_Connector_Tier - Desktop_Platform_Tier --> Presentation_Tier - Desktop_Platform_Tier --> Analytical_Engine_Tier -``` +In PowerShell: ---- - -## 🔌 Enterprise Data Connectors & Integrations - -Datly features native, high-performance connectors with read-only parameterization and schema discovery: - -| Category | Supported Connector | Technology / Driver | Capability & Protocol | -| :--- | :--- | :--- | :--- | -| **Cloud Lakehouse** | **Databricks** | `databricks-sql-connector` | High-concurrency SQL Warehouse queries, Delta tables | -| **Cloud Warehouse** | **Google Cloud BigQuery** | `google-cloud-bigquery` | Service Account & OAuth authentication, nested fields | -| **Cloud Telemetry** | **Azure Data Explorer (Kusto)** | `azure-kusto-data` | KQL queries, fast time-series ingestion | -| **Serverless Query** | **AWS Athena & S3** | `boto3` | Direct Parquet / CSV S3 bucket queries via Presto engine | -| **High-Scale OLAP** | **ClickHouse** | `clickhouse-connect` | Vectorized analytical queries on billions of rows | -| **Relational SQL** | **PostgreSQL** | `psycopg2-binary` | Full schema browsing, SSL mode, parameterized queries | -| **Relational SQL** | **MySQL / MariaDB** | `pymysql` | Standard connection pooling, transactional inspection | -| **Relational SQL** | **Microsoft SQL Server (MSSQL)** | `mssql-python` | Windows Auth / SQL Auth, enterprise schemas | -| **Document NoSQL** | **MongoDB** | `pymongo` | JSON document extraction & automatic tabular flattening | -| **Cloud NoSQL** | **Azure Cosmos DB** | `azure-cosmos` | NoSQL API support, partitioned collection loading | -| **Cloud Storage** | **Azure Blob Storage** | `azure-storage-blob` | SAS token & Azure Identity managed parquet/csv loading | -| **BI Platforms** | **Apache Superset** | Superset REST API | Direct dataset import and dashboard chart mirroring | -| **Market Data** | **Yahoo Finance (`yfinance`)** | `yfinance` | Live historical ticker prices, financial statements, dividends | -| **Spreadsheets** | **Excel & OpenDocument** | `openpyxl` / `xlrd` | Multi-sheet detection, header inference, formula evaluation | -| **Web Scraping** | **Web Table Scraper** | `playwright` + `lxml` | Automated headless extraction of tables from live web URLs | - ---- - -## 🔒 Core Features in Detail - -### 1. Data Threads vs Linear Notebook Cells -Traditional notebooks execute sequentially top-to-bottom. If you re-run an earlier cell or branch an exploration, you overwrite the notebook's global state and risk corrupting subsequent calculations. -- Datly introduces **Data Threads**: each thread is an isolated analytical lineage branch. -- Asking a follow-up question builds directly on that thread's intermediate state. -- Starting a new thread branches cleanly from the original source without altering prior discoveries. -- Every thread card retains its natural language prompt, generated code, derived dataset, and visual representation. - -### 2. Concept Encoding & Interactive Visual Shelves -Datly unites the declarative expressive power of the Grammar of Graphics with AI automation: -- **Encoding Shelves:** Drag and drop data fields into shelves: **X-Axis**, **Y-Axis**, **Color (Grouping)**, **Size**, **Shape**, **Facet (Trellis)**, and **Repeat**. -- **Bi-Directional Synchrony:** Prompting the agent automatically updates the encoding shelves. Modifying an encoding shelf instantly adapts the Vega-Lite visualization without requiring an LLM round-trip. -- **Rich Chart Gallery:** Scatter, multi-series line, stepped curves, grouped bars, area charts, heatmaps, box plots, and geographic coordinates. - -### 3. Immediate Ground-Truth Verification (Explore Grid) -AI models can occasionally hallucinate aggregations or generate deceptive charts from misinterpreted data. -- In Datly, the **Explore Canvas** guarantees transparency: clicking any chart card opens the full interactive visualization with the **exact computed data table displayed directly underneath**. -- Inspect individual rows, sort columns, and confirm mathematical transformations with your own eyes. -- Use **Download CSV** to immediately export the derived table, or click **Quick Chart** to instantly visualize subsets. - -### 4. Standing Knowledge Base & Custom Analyst Rules -Datly allows you to establish continuous domain context without repeating instructions in every prompt: -- **Knowledge Rules Panel:** Define project-wide analytical rules (e.g., *"Fiscal year begins in April"*, *"Exclude cancelled orders where status = 'CANC'"*, *"Always format currency in USD thousands"*). -- Rules are automatically injected into the synthesis context for all subsequent prompt turns. -- Configure organization-specific color palettes, default aggregations, and business logic. - -### 5. WYSIWYG TipTap Narrative Reports & Pinboard Dashboards -Turn raw analytical discoveries into executive-ready communication artifacts: -- **Narrative Reports:** A built-in TipTap rich-text editor supporting headings, formatted tables, code snippets, callouts, and live embedded charts that stay dynamically linked to their underlying data threads. -- **Pinboard Dashboards:** Pin multiple charts side-by-side to construct live, interactive operational dashboards. -- **High-Fidelity Export:** One-click export to standalone PDF documents, presentation-ready PNG/SVG graphics, and clean CSV datasets. - -### 6. Multi-Model Agnostic LLM Engine (LiteLLM) -Never get locked into a single AI provider: -- Powered by **LiteLLM**, Datly seamlessly connects to **Anthropic Claude (3.7 Sonnet, 3.5 Sonnet)**, **OpenAI (GPT-4o, o1, o3-mini)**, **Google Gemini (2.5 Pro, 2.0 Flash)**, **DeepSeek (R1, V3)**, **Mistral**, **Groq**, and **Azure OpenAI**. -- **Offline / Local Inference:** Full support for local models via **Ollama**, **vLLM**, or **LocalAI**—enabling completely air-gapped, zero-internet data exploration. - -### 7. Desktop-First Identity & Offline Mode -Datly is engineered as a true daily-driver desktop IDE: -- Native **Electron application** with dedicated OS menus, hardware acceleration, and system tray integration. -- Cross-platform binaries for **Windows (.exe / NSIS)**, **macOS (.dmg / Apple Silicon & Intel)**, and **Linux (.AppImage / .deb)**. -- Lightweight **PyWebView mode** and zero-dependency local browser serving (`http://localhost:5173`). - ---- - -## 🖼️ Visual Feature Gallery - - - - - - - - - - -
- Multi-Source Data Ingestion -
15+ Data Lake & Warehouse Connectors - 		- Ground-Truth Tabular Canvas -
Ground-Truth Data Grid Under Chart -
- Interactive Dashboards -
Multi-Chart Pinboard Dashboards - 		- Narrative TipTap Reports -
WYSIWYG TipTap Narrative Reports -
- ---- - -## 🚀 Quick Start: 1-Prompt Automated Setup via AI Assistants (Recommended) - -Configuring full-stack data applications manually requires setting up Python 3.11+ virtual environments, compiling frontend TypeScript bundles, linking DuckDB native libraries, and installing data connectors. - -Modern agentic AI coding assistants—including **Claude Code**, **OpenAI Codex**, **Google Gemini**, **xAI Grok**, and **Cursor**—can initialize and launch Datly in a single automated step. - -### Universal AI Agent Prompt - -Open your AI coding assistant in this repository directory and paste the following prompt: - -```text -Set up and launch Datly: synchronize Python dependencies using uv (or pip), install frontend packages via yarn, compile the production bundle, and launch the Datly analytical workspace on localhost. +```powershell +Copy-Item .env.template .env ``` -### What AI Assistants Execute Automatically: -```mermaid -flowchart LR - Prompt["AI Assistant Prompt\n(Claude / Codex / Gemini / Grok / Cursor)"] --> EnvCheck["Verify Python 3.11+ & Node 20+"] - EnvCheck --> PySync["uv sync\n(DuckDB, Pandas, LiteLLM, Connectors)"] - PySync --> NodeBuild["yarn && yarn build\n(React 18 + Vite + TipTap + Vega)"] - NodeBuild --> ServerLaunch["uv run data_formulator\n(Local Server on :5000 / :5173)"] - ServerLaunch --> Ready["Workspace Open & Ready in Browser"] +To use a different backend port: + +```sh +uv run data_formulator --port 5568 ``` ---- - -## 🛠️ Alternative: Manual Build & Deployment Guide - -### Prerequisites -- **Python:** Version `3.11`, `3.12`, or `3.13` -- **Node.js:** Version `20.x` or newer (`node -v`) -- **Yarn:** Package manager (`npm install -g yarn`) -- **uv:** Modern high-speed Python package manager ([Astral uv](https://docs.astral.sh/uv/)) or standard `pip` - ---- - -### Step-by-Step Installation (uv + Yarn) - -1. **Clone the Repository:** - ```bash - git clone https://github.com/arpanjot-coding/Datly.git - cd Datly - ``` - -2. **Configure API Keys:** - Create your local `.env` configuration file from the template: - ```bash - cp .env.template .env - ``` - Add your preferred model provider keys (e.g. `ANTHROPIC_API_KEY`, `OPENAI_API_KEY`, or configure local Ollama). - -3. **Install & Build Frontend Assets:** - ```bash - yarn - yarn build - ``` - -4. **Synchronize Python Analytical Engine:** - - **Using uv (Recommended for 10x faster setup):** - ```bash - uv sync - uv run data_formulator - ``` - - **Using standard pip / venv:** - ```bash - python -m venv .venv - source .venv/bin/activate # Windows: .venv\Scripts\activate - pip install -e . - data_formulator - ``` - -5. **Access the Workspace:** - Open your browser to: **`http://localhost:5567`** (or **`http://localhost:5173`** during development). - ---- - -### One-Click Launcher Scripts - -Datly provides turnkey launcher scripts for instant zero-configuration startup: -- **Windows:** Double-click `local_server.bat` -- **macOS / Linux:** Execute `./local_server.sh` - -For active frontend or backend development with Hot Module Replacement (HMR): -```bash -# Terminal 1: Backend API with hot-reload +### Development + +Run the backend and frontend in separate terminals: + +```sh +# Backend uv run data_formulator --dev -# Terminal 2: Vite Frontend Dev Server +# Frontend yarn start ``` ---- - -### Docker Container Deployment +The Vite frontend uses http://localhost:5173 and proxies API requests to port 5567 by default. Set `API_PORT` when changing the backend port. -Deploy Datly as an isolated, self-hosted container on local hardware or private enterprise clusters: +### Docker -```bash -# Build and run with Docker Compose -docker-compose up --build -d +Install Docker with Docker Compose, clone the repository, and copy `.env.template` to `.env` using the command for your shell above. Review the configuration, then run: -# Open workspace in your browser -# URL: http://localhost:5567 +```sh +docker compose up --build ``` ---- +Open http://localhost:5567 after the services start. Building and dependency installation require network access. -### Electron Desktop Application Build +### Desktop -Compile Datly into native standalone desktop installers: +See [releases](https://github.com/arpanjot-coding/Datly/releases) for currently published downloads. -```bash -# Build native desktop package (Windows .exe, macOS .dmg, Linux .AppImage) -yarn electron:build +The [desktop workflow](.github/workflows/desktop-build.yml) contains Windows x64 and macOS builds. Electron packaging configuration also exists in [package.json](package.json); a configured target is not proof that a downloadable binary is available. The repository’s beta package version is declared in [pyproject.toml](pyproject.toml). + +## Supported data sources + +The [loader registry](py-src/data_formulator/data_loader/__init__.py) declares implementations for: + +- PostgreSQL, MySQL, Microsoft SQL Server, and ClickHouse. +- Databricks, BigQuery, AWS Athena, and Azure Data Explorer (Kusto). +- Amazon S3 and Azure Blob Storage. +- MongoDB and Azure Cosmos DB. +- Apache Superset datasets. +- Local folders, example datasets, and supported web sources. + +File-loading paths include CSV, Excel, and Parquet. Registered loaders can be unavailable if their driver dependencies are missing. Connections require suitable credentials, permissions, and network access. Snowflake is not registered as a built-in loader in this version. + +Optional browser rendering for web extraction needs the Python browser extra and Chromium: + +```sh +uv sync --extra browser +uv run python -m playwright install chromium ``` -Compiled installers will be placed in the `release-electron/` directory. - ---- - -## ⚙️ Comprehensive Configuration & Environment Reference - -| Environment Variable / Flag | Description | Default | Example / Options | -| :--- | :--- | :---: | :--- | -| `PORT` / `-p, --port` | Port for the local analytical API server | `5567` | `5567`, `8080` | -| `HOST` / `--host` | Network interface binding | `127.0.0.1` | `0.0.0.0` (for container / LAN access) | -| `SANDBOX` / `--sandbox` | Code execution environment backend | `local` | `local` (subprocess), `docker` | -| `DATA_FORMULATOR_HOME` / `--data-dir` | Workspace and persistent sessions root | `~/.data_formulator` | `/custom/path/to/data` | -| `MAX_DISPLAY_ROWS` / `--max-display-rows` | Maximum rows sent to UI for table inspection | `10000` | `1000`, `5000`, `10000` | -| `DISABLE_CUSTOM_MODELS` | Restrict LLMs to server-configured endpoints | `false` | `true`, `false` | -| `DISABLE_DATA_CONNECTORS` | Disable external database connector tabs | `false` | `true`, `false` | -| `FLASK_SECRET_KEY` | Session encryption entropy secret | *Auto-generated* | `hex-encoded-entropy-string` | -| `ANTHROPIC_API_KEY` | Anthropic Claude API Key | *None* | `sk-ant-api03-...` | -| `OPENAI_API_KEY` | OpenAI API Key | *None* | `sk-proj-...` | -| `GEMINI_API_KEY` | Google Gemini API Key | *None* | `AIzaSy...` | -| `OLLAMA_API_BASE` | Local Ollama endpoint for offline synthesis | `http://localhost:11434` | `http://127.0.0.1:11434` | - ---- -## 🛡️ Threat Model & Security Analysis +## Models + +The code includes provider handling for OpenAI, Azure OpenAI, Anthropic, Gemini, and Ollama. Configure your chosen model and credentials in the application. Availability and costs depend on the provider. + +Ollama’s default local endpoint is http://localhost:11434. A local model still needs to be installed and running, and its capability affects the quality of generated transformations. + +## Reports and dashboards + +The report editor uses TipTap. Chart embeds are images, rather than interactive chart widgets. Report images can be exported; PDF uses the browser print flow. + +The current dashboard implementation saves chart references and displays their type/title placeholders. It does **not** yet render an interactive multi-chart dashboard. See [DashboardView.tsx](src/views/DashboardView.tsx). -| Threat Vector | Potential Impact | Datly Technical Mitigation | -| :--- | :--- | :--- | -| **Raw Data Exfiltration to LLM Cloud** | Sensitive corporate or customer PII leaked to public model endpoints. | **Zero Raw Data Transfer:** Only column identifiers and a minimal 2-row sanitized preview are sent for code synthesis. Full datasets remain 100% local in DuckDB memory. | -| **Model Hallucination & Phantom Trends** | Business decisions made on false chart graphics generated by AI. | **Ground-Truth Data Canvas:** Charts are rendered strictly from local computed tables that remain open on screen under the visual. What you see is mathematically verified. | -| **Arbitrary Code Execution via AI** | Malicious or destructive scripts generated during prompt evaluation. | **AST Validation & Sandboxed Execution:** Synthesized transformation code is parsed into abstract syntax trees (AST) and executed within isolated in-memory Python / DuckDB processes. | -| **Credential & Connection String Leakage** | Database credentials or lakehouse secrets exposed in plain text. | All database connection strings and credentials are saved locally in private OS-level keystores or `.env` files; they are never logged or passed to LLM prompts. | -| **Cross-Site Scripting (XSS) in Reports** | Injected malicious code via shared analytical narrative documents. | **DOMPurify Sanitization:** All markdown, HTML, and TipTap report bodies undergo strict DOMPurify filtering before canvas rendering. | - ---- +## Product images -## 🔧 Diagnostic & Troubleshooting Guide - -| Symptom / Error | Root Cause | Step-by-Step Remediation | -| :--- | :--- | :--- | -| **`uv: command not found`** | Astral uv is not installed on system PATH. | Install uv via `curl -LsSf https://astral.sh/uv/install.sh \| sh` (macOS/Linux) or `powershell -c "irm https://astral.sh/uv/install.ps1 \| iex"` (Windows), or fallback to standard `pip install -e .`. | -| **`Playwright browser executable missing`** | Headless browser dependencies not installed for Web Scraper. | Run `uv run python -m playwright install chromium` or `python -m playwright install chromium`. | -| **`DuckDB native compilation error`** | C++ runtime or binary mismatch on legacy OS. | Ensure Python 3.11+ is running. DuckDB provides pre-compiled universal wheels; update pip with `python -m pip install --upgrade pip`. | -| **`LLM Model Connection Timeout / 401`** | Expired, invalid, or missing API keys in `.env`. | Verify `.env` file exists in project root. Check that `ANTHROPIC_API_KEY` or `OPENAI_API_KEY` has valid active quota. | -| **`Port 5567 / 5173 already in use`** | Another background server instance is active. | Pass custom port flag: `uv run data_formulator --port 5568` or terminate conflicting process via `lsof -i :5567` / `netstat -ano`. | -| **Database Connection Refused** | Remote database firewall blocking local IP or missing SSL certificate. | Verify credentials and firewall whitelist. In connection configuration, toggle **SSL Mode** or verify tunnel settings. | - ---- - -## 🔍 Frequently Asked Questions (FAQ) & SEO Discoverability - -### What is Datly and how does it differ from Microsoft Data Formulator? -Datly is an open-source, full-lifecycle AI data IDE and analytical workbench built upon the revolutionary research foundations of Microsoft Research's Data Formulator. While the original research prototype focused primarily on concept encoding, Datly elevates the platform into a comprehensive, professional enterprise data workspace—introducing 15+ native cloud warehouse connectors (Databricks, BigQuery, Snowflake, Postgres), TipTap narrative WYSIWYG report publishing, multi-chart pinboard dashboards, local-first zero-exfiltration security guarantees, and native cross-platform desktop installers. - -### Does Datly send my private database records to OpenAI or Anthropic? -**No.** Datly operates on a strict **local-first privacy architecture**. When you connect a database or load a CSV/Parquet file, the data is loaded directly into a local in-memory DuckDB database on your computer. When you ask a question using natural language, Datly transmits **only the column names and data types** (with an optional 2-row sanitized preview) to the LLM to write the query or transformation step. The resulting DuckDB SQL or Python code runs entirely on your local CPU. Your raw rows never leave your system. - -### Can I run Datly completely offline without any internet connection? -**Yes.** Datly integrates natively with local LLM runners via **Ollama**, **vLLM**, or **LocalAI**. By setting `OLLAMA_API_BASE=http://localhost:11434` and running a capable local open-weights coding model (such as `qwen2.5-coder:14b`, `llama3.3:70b`, or `deepseek-coder`), you can perform end-to-end data analysis in an entirely air-gapped, zero-internet environment. - -### How does Datly prevent AI chart hallucinations? -In conventional conversational analytics tools, AI models frequently guess summary numbers or directly generate misleading SVG/canvas images. In Datly, the AI is **never allowed to draw charts directly**. Instead, the AI writes verifiable code that executes on your real data to create a new, tangible **derived data table**. That derived table is displayed right on screen under the chart inside the **Explore Canvas**, allowing you to inspect the exact rows, formulas, and math supporting every visual insight. - -### Can I export my visualizations and reports? -Yes. Datly supports multi-format production exports: -- **Data:** Export any derived table or transformed dataset to clean CSV with one click. -- **Charts:** Export visualizations as publication-quality SVG vectors or high-resolution PNG images. -- **Reports:** Export full TipTap narrative reports—complete with analysis text, embedded charts, and metrics—into standalone PDF documents or markdown. - -### How does Datly compare to Hex, JupyterLab, Tableau, and Power BI? -- **vs JupyterLab / Hex:** Jupyter notebooks run in a sequential, linear, and mutable global namespace where re-running cells can create corrupt, irreproducible states. Datly uses non-destructive **Data Threads (DAG)**, so every question branches cleanly without overwriting upstream tables. Furthermore, Datly provides visual encoding shelves and an interactive data grid under every chart. -- **vs Tableau / Power BI:** Traditional BI tools require rigid semantic data modeling upfront, expensive proprietary cloud licenses, and steep learning curves for ad-hoc transformation calculations. Datly gives analysts immediate, flexible, code-level control through ordinary natural language, executes everything locally with DuckDB, and remains 100% open-source under the MIT license. -- **vs DBeaver / DataGrip:** While SQL IDEs excel at tabular queries, they lack visual narrative, exploratory AI reasoning, and integrated document synthesis. Datly combines the raw speed of a SQL IDE with the communication power of an executive analytics workbench. - -### Which LLM models and providers are supported? -Datly utilizes **LiteLLM** as its universal model gateway, supporting over 100+ model configurations out of the box: -- **Anthropic:** Claude 3.7 Sonnet, Claude 3.5 Sonnet, Claude 3.5 Haiku -- **OpenAI:** GPT-4o, GPT-4o-mini, o1, o3-mini -- **Google:** Gemini 2.5 Pro, Gemini 2.0 Flash, Gemini 1.5 Pro -- **Open Weights & Air-Gapped:** DeepSeek-R1, DeepSeek-V3, Qwen 2.5 Coder, Llama 3.3 (via Ollama, vLLM, or LocalAI) -- **Enterprise Cloud:** Azure OpenAI Service, AWS Bedrock, Google Vertex AI, Groq, Mistral AI - ---- - -## 🏷️ GitHub Repository SEO & Metadata Configuration - -To maximize visibility on GitHub Search, Google, and Bing, configure the following metadata in your GitHub repository settings (**Code** > ⚙️ **About**): - -- **Repository Description (158 chars):** - ```text - Open-source, local-first AI data visualization IDE & analytics workbench. Talk to tables, verify rows with DuckDB, and build reports. Zero data exfiltration. - ``` -- **Website URL:** - ```text - https://arpanjot-coding.github.io/Datly/ - ``` -- **Topics / Tags (Recommended 20 tags):** - ```text - data-visualization, ai, duckdb, data-ide, llm, vega-lite, data-formulator, business-intelligence, python, analytics, data-science, electron, local-first, tiptap, exploratory-data-analysis, pandas, echarts, bi, ollama, sql-ide - ``` - ---- - -## 🗺️ Roadmap & Community - -- [x] High-performance DuckDB vectorized in-memory transformation engine -- [x] Branching Data Threads DAG for non-destructive analytical lineage -- [x] 15+ Enterprise data connectors (Warehouses, SQL, NoSQL, Web Scrapers) -- [x] WYSIWYG TipTap narrative reporting with live embedded visual cards -- [x] Cross-platform Electron desktop distribution (Windows, macOS, Linux) -- [ ] Direct export of Data Threads into production dbt & SQL models -- [ ] Collaborative real-time multi-analyst workspace syncing -- [ ] Direct GitHub PR publishing for analytical reports - ---- - -## ⚖️ License - -Datly is open-source software released under the **MIT License**. +[Browse and download the product image library](https://arpanjot-coding.github.io/Datly/images/). -```text -MIT License - -Copyright (c) 2026 Datly Project & Contributors - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: +The product graphics use actual application captures already present in this repository, with new crops, framing, and explanatory labels. Example data is illustrative. The report graphic is explicitly labelled as an archived example of the earlier Data Formulator interface; current report editing uses TipTap. -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. +![Explore canvas and underlying rows](docs/images/product-explore.jpg) + +![Data threads](docs/images/product-threads.jpg) + +![Data inspection](docs/images/product-data.jpg) + +![Archived report example](docs/images/product-reports.jpg) + +Image provenance is recorded in [SOURCES.txt](docs/images/SOURCES.txt). Source SVGs, web images, and high-resolution PNGs are included in the image library. Existing screenshot files remain available. + +## Website development + +The product website is a buildless static site in `docs/`. Preview it without installing the application: + +It includes English, Russian, Simplified Chinese, Japanese, Vietnamese, Thai, Hindi, Arabic, French, German, Spanish, and Italian. Each language has a static HTML page with translated content and metadata, its own canonical URL, reciprocal `hreflang` links, and a sitemap entry. Arabic uses a right-to-left layout. Language selection is explicit; visitors are not redirected by IP or browser settings. + +Edit `docs/assets/home.template` and the matching `docs/locales/*.json` files, then regenerate pages with `node docs/assets/build-locales.mjs`. Generated HTML stays usable and indexable without JavaScript. The interactive gallery and status messages use the same locale dictionaries. + +The GitHub star count was verified as 249 on 2 November 2026. The site refreshes it through the public GitHub API and retains the dated count if the request fails or is rate-limited. Audience wording describes who Datly is built for; it does not claim verified customer adoption. + +```sh +python -m http.server 4387 --bind 127.0.0.1 --directory docs ``` ---- +Open http://localhost:4387. GitHub Pages should publish the `docs/` directory. The site does not call an analytics or geolocation service. Product claims are based on repository source; connector presence does not represent a live end-to-end integration test. + +## Licence and attribution -

- Datly Project · High-Assurance Exploratory Analytics & Data Visualization IDE · Built for Modern Data Teams -

+Datly is MIT licensed; see [LICENSE](LICENSE). It builds on Microsoft Research’s Data Formulator. Existing third-party screenshots and assets retain their original attribution. Model providers and connected infrastructure may have separate terms and costs. diff --git a/docs/.nojekyll b/docs/.nojekyll new file mode 100644 index 0000000..e69de29 diff --git a/docs/ar/index.html b/docs/ar/index.html new file mode 100644 index 0000000..da3113c --- /dev/null +++ b/docs/ar/index.html @@ -0,0 +1,95 @@ + + + +Datly — مساحة عمل مفتوحة المصدر لتحليل البيانات بالذكاء الاصطناعي + + + + + + + + + + + + + + + + + + + + + +
DatlyBETA
AR

اللغة

EnglishРусский简体中文日本語Tiếng Việtไทยहिन्दीالعربية FrançaisDeutschEspañolItaliano
+
+
مفتوح المصدر. مفتوح للاستكشاف.
+

أسئلة جيدة.
إجابات أوضح.

في بياناتك قصة. اكتشفها في مساحة عمل يدعمها الذكاء الاصطناعي وتربط أسئلتك ورسومك البيانية وصفوف البيانات التي تستند إليها.

ابدأ الاستكشاف ألقِ نظرة أقرب

برنامج مجاني ومفتوح المصدر · بترخيص MIT

للمحللين والباحثين وفرق البيانات.

+
تطوير على مرأى الجميع.تم التحقق من العدد في 2 نوفمبر 2026؛ ويُحدّث مباشرة.
249نجمة على GitHub16خيارات مصادر البيانات12لغات الموقعMITترخيص مفتوح المصدر
+
تنقّل أقل بين الأدوات. وفهم أعمق للبيانات.تطبيق فعلي · بيانات توضيحية
أسئلة ← تحويلات ← رسوم بيانية ← صفوفتحليلك، ضمن سياقه
+
مبني على أساس متين
DuckDBPythonVega-LiteReactTipTap
+

01 / من السؤال إلى الفهم

اتبع فضولك.
واحتفظ بالسياق.

انتقل من جدول إلى نتيجة مفيدة في مساحة عمل واحدة. اسأل وحسّن وافحص بينما يتشكّل تحليلك.

+
01

أحضر بياناتك

حمّل ملفًا محليًا، أو اتصل بقاعدة بيانات مدعومة، أو استكشف مثالًا مرفقًا.

+
02

اطرح السؤال التالي

صِف ما تريد استكشافه. حسّن رسمك البياني باستخدام عناصر التحكم في الترميز البصري أو بسؤال متابعة.

+
03

افحص، ثم اشرح

افحص الصفوف المشتقة، وصدّر ملف CSV، وأضف سياقًا لنتائجك في تقرير قابل للتحرير.

+

02 / نظرة أقرب

شاهد خطوات العمل.
وليس الإجابة فقط.

مشاهد فعلية للتطبيق تتيح لك النظر عن قرب. استكشف الميزات التي تجعل متابعة التحليل أسهل.

+ + + + +

الصفوف وراء
الخط البياني.

الرسم البياني نقطة بداية. افتح الجدول الذي يستند إليه لفحص القيم ومراجعة التحويل وتحديد المعنى الفعلي للنتيجة.

  • الرسم البياني والجدول المحسوب معًا
  • افحص الصفوف المشتقة وصدّرها
  • راجع النتائج قبل مشاركتها

لقطة فعلية للتطبيق، أعيد تأطيرها للوضوح. البيانات للتوضيح فقط؛ ويجب مراجعة التحليل الذي ينشئه الذكاء الاصطناعي.

+

03 / ابقَ مطّلعًا على الخطوات

الذكاء الاصطناعي يساعدك على الاستكشاف.
وأنت صاحب القرار.

التحليل الجيد يحتاج إلى أكثر من رسم بياني مقنع. يتيح لك Datly فحص العمل في مراحله الوسيطة.

+
تتبّع السؤال

حافظ على تسلسل
أفكارك.

تنظّم مسارات البيانات الأسئلة والتحويلات والجداول المشتقة والرسوم البيانية. تابع مسار البحث أثناء تطوّره.

استكشف مسارات البيانات
+
حسّن العرض

رسم بياني يمكنك
العمل عليه.

استخدم عناصر التحكم في الترميز البصري للمحاور والألوان وخصائص الرسم الأخرى. عدّل العرض كلما أصبح سؤالك أدق.

استكشف مساحة الرسم
+
اشرح النتيجة

أكثر من
لقطة شاشة.

اجمع السياق المكتوب وصور الرسوم البيانية في تقرير قابل للتحرير. احفظ صورة أو استخدم الطباعة في المتصفح لإنشاء PDF.

استكشف التقارير
+
+

04 / ابدأ بما لديك

بياناتك.
نقطة بداية مألوفة.

اعمل على الملفات ومصادر البيانات المدعومة. تعتمد الاتصالات على برامج التشغيل وبيانات الاعتماد والوصول المناسب إلى الشبكة.

ملف اليوم.
ومستودع بيانات غدًا.

ابدأ ببساطة مع ملف CSV أو مجموعة بيانات تجريبية. أضف اتصالًا مدعومًا عندما يحتاج سؤالك إلى المزيد.

اطّلع على تنفيذ الموصلات
    +
  • Pg
    PostgreSQLقاعدة بيانات علائقية
    • +
  • Db
    Databricksمستودع بيانات SQL
    • +
  • Bq
    BigQueryمستودع بيانات سحابي
    • +
  • Ch
    ClickHouseقاعدة بيانات تحليلية
    • +
  • My
    MySQLقاعدة بيانات علائقية
    • +
  • Ms
    SQL Serverقاعدة بيانات علائقية
    • +
  • S3
    Amazon S3تخزين الكائنات
    • +
  • Az
    Azure Blobتخزين الكائنات
    • +
  • At
    Athenaاستعلامات دون خادم
    • +
  • Mo
    MongoDBقاعدة بيانات مستندات
    • +
  • Co
    Cosmos DBقاعدة بيانات مستندات
    • +
  • Ku
    Azure Kustoمستكشف البيانات
    • +
  • Su
    Supersetاستيراد مجموعة بيانات
    • +
  • ↥
    ملفات محليةCSV وExcel وParquet
    • +
  • Wb
    مصادر الويباستخراج الجداول المدعوم
    • +
  • Ex
    مجموعات بيانات تجريبيةجرّب تحليلًا توضيحيًا
    • +

تُعرض جميع خيارات المصادر البالغ عددها 16. تصف الأسماء عمليات التنفيذ المتاحة، ولا تعني تكاملات معتمدة.

+

05 / اختر عن معرفة

معالجة محلية.
وحدود بيانات واضحة.

اقرأ شيفرة سياق النموذج
+
01 / التنفيذ

اعمل في بيئتك

يشغّل DuckDB وPython التحويلات في البيئة التي تستضيف Datly، بما في ذلك التثبيت المحلي على حاسوبك.

+
02 / سياق النموذج

اعرف ما يتلقاه الذكاء الاصطناعي

قد تتضمن طلبات الذكاء الاصطناعي مخطط البيانات وقيمًا وصفوفًا تجريبية ومطالبات ومخرجات الأدوات. يتلقى مزوّد النموذج السحابي السياق المرسل إليه.

+
03 / إعداداتك

اختر نموذجك

اتصل بمزوّد مدعوم أو اضبط نموذجًا محليًا عبر Ollama. تتطلب طلبات الذكاء الاصطناعي المحلية نقطة نهاية محلية؛ وتظل مصادر البيانات البعيدة بحاجة إلى الشبكة.

+
+

06 / سؤالك الجيد التالي

قليل من الإعداد.
وكثير مما يستحق الاستكشاف.

ابنِ التطبيق من المصدر وشغّل Datly في متصفحك. ابدأ بمجموعة بيانات تجريبية، ثم اضبط نموذجًا للاستكشاف بمساعدة الذكاء الاصطناعي.

  • Python 3.11 أو أحدث
  • Node.js وYarn للواجهة الأمامية
  • uv لبيئة Python
اقرأ دليل الإعداد اعرض الإصدارات 
# Get the project
+git clone https://github.com/arpanjot-coding/Datly.git
+cd Datly
+
+# Build the interface
+yarn
+yarn build
+
+# Install Python dependencies and launch
+uv sync
+uv run data_formulator
افتح localhost:5567 بعد التشغيل

+

بعض الأمور التي ينبغي معرفتها

قبل أن
تبدأ.

لديك أسئلة أو ملاحظات؟
+
ما هو Datly؟

Datly مساحة عمل مفتوحة المصدر لتحليل البيانات، مبنية على Data Formulator من Microsoft Research. تجمع الاستكشاف باللغة الطبيعية وعناصر التحكم البصري في الرسوم ومسارات البيانات وفحص الجداول والتقارير القابلة للتحرير. المشروع في مرحلة بيتا.

+
هل تبقى بياناتي على حاسوبي؟

ينفّذ التثبيت المحلي التحويلات على حاسوبك. قد تتضمن طلبات الذكاء الاصطناعي صفوفًا تجريبية وقيم حقول ومطالبات ومخرجات أدوات تُرسل إلى النموذج الذي ضبطته. إذا اخترت مزوّدًا سحابيًا، فسيتلقى هذا السياق. يمكن لنقطة نهاية نموذج محلية إبقاء طلبات النموذج محلية؛ راجع إعداداتك ومصادر البيانات المتصلة.

+
هل يضمن فحص الجدول إجابة صحيحة؟

لا. يساعدك الجدول على فحص القيم المحسوبة، لكن الذكاء الاصطناعي قد يختار تحويلًا أو تجميعًا أو تفسيرًا غير صحيح. راجع العمل المنشأ والبيانات الأساسية وافتراضات العمل قبل الاعتماد على النتيجة.

+
ما النماذج التي يمكنني استخدامها؟

تتضمن الشيفرة دعمًا لـ OpenAI وAzure OpenAI وAnthropic وGemini ونقاط نهاية Ollama المحلية. أنت تضبط المزوّد وبيانات الاعتماد. يعتمد توفر النماذج وقدراتها وتكاليف استخدامها على ذلك المزوّد.

+
هل يمكنني إنشاء تقارير ولوحات معلومات؟

يمكنك تحرير تقارير بنص منسّق مع صور الرسوم البيانية، أو تصدير صورة للتقرير، أو طباعته بصيغة PDF. يحفظ تنفيذ لوحة المعلومات الحالي مراجع الرسوم ويعرض عناصر نائبة للعناوين والأنواع؛ ولم تُنفّذ بعد لوحة متعددة الرسوم تعرض الرسوم فعليًا وتتيح التفاعل معها.

+
هل Datly مجاني؟

الشيفرة المصدرية لـ Datly متاحة بموجب ترخيص MIT. قد يفرض مزوّد النموذج أو البنية التحتية المتصلة أو الاستضافة تكاليف منفصلة. راجع تعليمات الإعداد والإصدارات في المستودع لمعرفة خيارات التثبيت الحالية.

+
+

في بياناتك قصة.

ابدأ بسؤال جيد.

ابدأ مع Datly
+
+ +

مساحة عمل Datly

لقطة فعلية للتطبيق باستخدام بيانات تجريبية.

 + + diff --git a/docs/assets/build-locales.mjs b/docs/assets/build-locales.mjs new file mode 100644 index 0000000..9028988 --- /dev/null +++ b/docs/assets/build-locales.mjs @@ -0,0 +1,44 @@ +import fs from 'node:fs'; +import path from 'node:path'; +import {fileURLToPath} from 'node:url'; +const root=fileURLToPath(new URL('../',import.meta.url)); +const template=fs.readFileSync(path.join(root,'assets/home.template'),'utf8'); +const base='https://arpanjot-coding.github.io/Datly/'; +const specs=[['en','en','English'],['ru','ru','Русский'],['zh','zh-Hans','简体中文'],['ja','ja','日本語'],['vi','vi','Tiếng Việt'],['th','th','ไทย'],['hi','hi','हिन्दी'],['ar','ar','العربية'],['fr','fr','Français'],['de','de','Deutsch'],['es','es','Español'],['it','it','Italiano']]; +const english=JSON.parse(fs.readFileSync(path.join(root,'locales/en.json'),'utf8')); +const esc=s=>s.replaceAll('&','&').replaceAll('<','<').replaceAll('>','>').replaceAll('"','"'); +const urlFor=key=>base+(key==='en'?'':key+'/'); +let built=0; +for(const [key,lang,native]of specs){ + const file=path.join(root,'locales',key+'.json');if(!fs.existsSync(file)){console.log('Waiting for '+key);continue;} + const copy=JSON.parse(fs.readFileSync(file,'utf8')); + const missing=Object.keys(english.translations).filter(k=>typeof copy.translations[k]!=='string'||!copy.translations[k].trim()); + if(missing.length)throw new Error(key+' missing '+missing.join('; ')); + const t=s=>copy.translations[s]??s; + const prefix=key==='en'?'':'../',canonical=urlFor(key); + let skip=0; + let html=template.match(/<[^>]+>|[^<]+/g).map(token=>{ + if(/^<(script|pre)\b/.test(token)){skip++;return token;} + if(/^<\/(script|pre)>/.test(token)){skip--;return token;} + if(skip)return token; + if(token.startsWith('<'))return token.replace(/(aria-label|alt|content)="([^"]+)"/g,(m,attr,text)=>attr+'="'+esc(t(text))+'"'); + const clean=token.trim();return clean&©.translations[clean]?token.replace(clean,esc(t(clean))):token; + }).join(''); + html=html.replace('',''); + html=html.replace(/(src|href)="(images\/|favicon\.svg|styles\.css|site\.js|site\.webmanifest)/g,(_,attr,asset)=>attr+'="'+prefix+asset); + html=html.replace('','\n'+specs.map(([route,code])=>'').join('\n')+'\n'); + html=html.replace('',''); + html=html.replace(/'; + }); + const menu='
'+key.toUpperCase()+'

'+esc(t('Language'))+'

'+specs.map(([route,code,label])=>''+label+(route===key?' ':'')+'').join('')+'
'; + html=html.replace('',menu); + const runtime={language:lang,translations:key==='en'?{}:copy.translations}; + html=html.replace('',''); + const dir=key==='en'?root:path.join(root,key);fs.mkdirSync(dir,{recursive:true});fs.writeFileSync(path.join(dir,'index.html'),html.trimEnd()+'\n'); + built++; +} +const sitemap='\n\n'+specs.map(([key])=>' '+urlFor(key)+'2026-11-02').join('\n')+'\n\n'; +fs.writeFileSync(path.join(root,'sitemap.xml'),sitemap); +console.log('Built '+built+' static language pages; '+specs.length+' language URLs in sitemap.'); diff --git a/docs/assets/build-product-images.mjs b/docs/assets/build-product-images.mjs new file mode 100644 index 0000000..761622a --- /dev/null +++ b/docs/assets/build-product-images.mjs @@ -0,0 +1,30 @@ +import fs from 'node:fs'; +import path from 'node:path'; +import { fileURLToPath } from 'node:url'; +const root = fileURLToPath(new URL('../images/', import.meta.url)); +const image = name => `data:image/${name.endsWith('.webp') ? 'webp' : 'png'};base64,${fs.readFileSync(path.join(root,name)).toString('base64')}`; +const esc = text => text.replaceAll('&','&'); +const logo = ``; +function card({name,title,subtitle,file,crop,number}) { + const [x,y,w,h] = crop; + const svg = `${logo}DatlyPRODUCT TOUR / ${number}${esc(title)}${esc(subtitle)}ACTUAL APPLICATION CAPTURE · ${file.includes('movies-report')?'ARCHIVED REPORT EXAMPLE':'EXAMPLE DATA'}datly / ${name}`; + let composed = svg; + if(name === 'product-explore') { + const start = composed.indexOf(''); + const end = composed.indexOf('', start) + 4; + const source = image(file); + composed = composed.slice(0,start) + `THE CHARTTHE COMPUTED ROWSA closer look at the databehind the visualisation.` + composed.slice(end); + } + if(name === 'product-reports') { + const start = composed.indexOf(''); + const end = composed.indexOf('', start) + 4; + composed = composed.slice(0,start) + `FROM CHART TO STORYGive the numbersa narrative.Write the finding.Bring a chart into the report.Explain the context.ARCHIVED DATA FORMULATOR EXAMPLECurrent report editing uses TipTap.` + composed.slice(end); + } + fs.writeFileSync(path.join(root,`${name}.svg`),composed); +} +card({name:'product-workspace',title:'One workspace. The whole question.',subtitle:'Explore your data, follow the analysis, and inspect the result.',file:'workspace.png',crop:[0,0,3840,2004],number:'01'}); +card({name:'product-explore',title:'Look closer. The numbers are right here.',subtitle:'A focused view of the Explore canvas and its underlying data table.',file:'workspace.png',crop:[2445,235,1360,1840],number:'02'}); +card({name:'product-threads',title:'Keep the path to your answer.',subtitle:'Questions, derived tables, and charts, organised into data threads.',file:'about-explore.png',crop:[720,325,1190,621],number:'03'}); +card({name:'product-data',title:'Get to know the table behind the chart.',subtitle:'Inspect data quality signals, browse rows, and export a CSV.',file:'about-surfaces.png',crop:[2040,240,1690,882],number:'04'}); +card({name:'product-reports',title:'Give your analysis a little more context.',subtitle:'An archived report example: written findings alongside chart images.',file:'screenshot-movies-report.png',crop:[2050,540,1240,647],number:'05'}); +console.log('Created five source-backed SVG product compositions.'); diff --git a/docs/assets/export-product-images.mjs b/docs/assets/export-product-images.mjs new file mode 100644 index 0000000..f3f0e41 --- /dev/null +++ b/docs/assets/export-product-images.mjs @@ -0,0 +1,21 @@ +// Usage: node docs/assets/export-product-images.mjs /absolute/path/to/node_modules/sharp/dist/index.mjs +// Keep authoring tools outside the application dependency tree. +import fs from 'node:fs/promises'; +import path from 'node:path'; +import { fileURLToPath, pathToFileURL } from 'node:url'; +if (!process.argv[2]) throw new Error('Pass the absolute path to the Sharp module.'); +const sharp=(await import(pathToFileURL(path.resolve(process.argv[2])).href)).default; +const root=fileURLToPath(new URL('../images/',import.meta.url)); +for(const key of ['workspace','explore','threads','data','reports']){ + const name='product-'+key, input=path.join(root,name+'.svg'); + await Promise.all([ + sharp(input).jpeg({quality:94,chromaSubsampling:'4:4:4'}).toFile(path.join(root,name+'.jpg')), + sharp(input).webp({quality:91,effort:6}).toFile(path.join(root,name+'.webp')), + sharp(input,{density:144}).png().toFile(path.join(root,name+'.png')) + ]); +} +const original=await fs.readFile(path.join(root,'product-workspace.svg'),'utf8'); +const social=original.replace('height="1100" viewBox="0 0 1600 1100"','height="900" viewBox="0 0 1600 900"').replace('One workspace. The whole question.','Good questions. Clearer answers.'); +await fs.writeFile(path.join(root,'social-card.svg'),social); +await sharp(Buffer.from(social)).jpeg({quality:94,chromaSubsampling:'4:4:4'}).toFile(path.join(root,'social-card.jpg')); +console.log('Exported JPG, WebP, 2x PNG, and social card assets.'); diff --git a/docs/assets/home.template b/docs/assets/home.template new file mode 100644 index 0000000..c3cafe0 --- /dev/null +++ b/docs/assets/home.template @@ -0,0 +1,82 @@ + + + +Datly — Open-source AI analytics workspace + + + + + + + + +
DatlyBETA
+
+
OPEN SOURCE. OPEN TO EXPLORATION.
+

Good questions.
Clearer answers.

Your data has a story. Find it with an AI-assisted workspace that keeps your questions, charts, and underlying rows connected.

Start exploring Take a closer look

Free, open-source software · MIT licensed

For analysts, researchers, and data teams.

+
Built in the open.Count verified 2 November 2026; refreshes live.
249stars on GitHub16Data source options12Website languagesMITOpen-source licence
+
A little less back and forth. A little more insight.ACTUAL APP · EXAMPLE DATA
Questions → transformations → charts → rowsYOUR ANALYSIS, IN CONTEXT
+
Built on a solid foundation
DuckDBPythonVega-LiteReactTipTap
+

01 / FROM QUESTION TO UNDERSTANDING

Follow your curiosity.
Keep the context.

Move from a table to a useful finding in one workspace. Ask, refine, and inspect as your analysis takes shape.

+
01

Bring your data

Load a local file, connect a supported database, or explore a bundled example.

+
02

Ask the next question

Describe what you want to explore. Refine your chart with visual encoding controls or a follow-up question.

+
03

Inspect, then explain

Check the derived rows, export a CSV, and give your findings context in an editable report.

+

02 / A CLOSER LOOK

See the work.
Not just the answer.

Real application views, with room to look closer. Explore the features that make the analysis easier to follow.

+ + + + +

The rows behind
the line.

A chart is a starting point. Open the underlying table to inspect the values, check a transformation, and decide what the result actually means.

  • Chart and computed table together
  • Inspect and export the derived rows
  • Review results before you share

Actual application capture, reframed for clarity. Example data is illustrative; AI-generated analysis requires review.

+

03 / STAY IN THE LOOP

AI helps you explore.
You make the call.

Good analysis depends on more than a convincing chart. Datly makes the intermediate work available for you to examine.

+
TRACE THE QUESTION

Keep your train
of thought.

Data threads organise questions, transformations, derived tables, and charts. Follow an investigation as it develops.

Explore data threads
+
REFINE THE VIEW

A chart you can
work with.

Use visual encoding controls for axes, colour, and other chart properties. Adjust the view as your question becomes more precise.

Explore the canvas
+
EXPLAIN THE FINDING

More than
a screenshot.

Combine written context with chart images in an editable report. Save an image or use your browser’s print flow for PDF.

Explore reports
+
+

04 / START WITH WHAT YOU HAVE

Your data.
A familiar starting point.

Work with files and supported data sources. Connections depend on the appropriate drivers, credentials, and network access.

A file today.
A warehouse tomorrow.

Start small with a CSV or a sample dataset. Bring in a supported connection when your question calls for more.

See connector implementation
    +
  • Pg
    PostgreSQLRelational database
    • +
  • Db
    DatabricksSQL warehouse
    • +
  • Bq
    BigQueryCloud warehouse
    • +
  • Ch
    ClickHouseAnalytics database
    • +
  • My
    MySQLRelational database
    • +
  • Ms
    SQL ServerRelational database
    • +
  • S3
    Amazon S3Object storage
    • +
  • Az
    Azure BlobObject storage
    • +
  • At
    AthenaServerless queries
    • +
  • Mo
    MongoDBDocument database
    • +
  • Co
    Cosmos DBDocument database
    • +
  • Ku
    Azure KustoData Explorer
    • +
  • Su
    SupersetDataset import
    • +
  • ↥
    Local filesCSV, Excel & Parquet
    • +
  • Wb
    Web sourcesSupported table extraction
    • +
  • Ex
    Example datasetsTry a sample analysis
    • +

Showing all 16 source options. Names describe implementations, not certified integrations.

+

05 / MAKE AN INFORMED CHOICE

Local processing.
Clear data boundaries.

Read the model context code
+
01 / EXECUTION

Work in your workspace

DuckDB and Python run transformations in the environment where you host Datly, including a local installation on your computer.

+
02 / MODEL CONTEXT

Know what AI receives

AI requests can include schema, sample values and rows, prompts, and tool outputs. A cloud model provider receives the context sent to it.

+
03 / YOUR CONFIGURATION

Choose your model

Connect a supported provider or configure a local model through Ollama. Local AI requests require a local endpoint; remote data sources still need network access.

+
+

06 / YOUR NEXT GOOD QUESTION

A little setup.
A lot to explore.

Build from source and launch Datly in your browser. Start with an example dataset, then configure a model for AI-assisted exploration.

  • Python 3.11 or later
  • Node.js and Yarn for the frontend
  • uv for the Python environment
Read the setup guide View releases 
# Get the project
+git clone https://github.com/arpanjot-coding/Datly.git
+cd Datly
+
+# Build the interface
+yarn
+yarn build
+
+# Install Python dependencies and launch
+uv sync
+uv run data_formulator
Open localhost:5567 after launch

+

A FEW THINGS TO KNOW

Before you
dive in.

Questions or feedback?
+
What is Datly?

Datly is an open-source analytics workspace built on Microsoft Research’s Data Formulator. It combines natural-language exploration, visual chart controls, data threads, table inspection, and editable reports. The project is in beta.

+
Does my data stay on my computer?

A local installation processes transformations on your computer. AI requests can include sample rows, field values, prompts, and tool outputs, which are sent to your configured model. If you select a cloud provider, that provider receives this context. A local model endpoint can keep model requests local; review your configuration and connected data sources.

+
Does inspecting the table guarantee a correct answer?

No. The table helps you inspect the computed values, but AI can still choose an incorrect transformation, aggregation, or interpretation. Review the generated work, underlying data, and business assumptions before relying on a result.

+
Which models can I use?

The code includes support for OpenAI, Azure OpenAI, Anthropic, Gemini, and local Ollama endpoints. You configure your provider and credentials. Model availability, capabilities, and any usage costs depend on that provider.

+
Can I create reports and dashboards?

You can edit rich-text reports with chart images, export a report image, or print to PDF. The current dashboard implementation saves chart references and displays title/type placeholders; a rendered, interactive multi-chart dashboard is not yet implemented.

+
Is Datly free?

Datly’s source code is available under the MIT licence. Your model provider, connected infrastructure, or hosting may have their own costs. Check the repository’s setup instructions and releases for the current installation options.

+
+

THERE’S A STORY IN YOUR DATA.

Start with a good question.

Get started with Datly
+
+ +

Datly workspace

Actual application capture using example data.

 + + diff --git a/docs/de/index.html b/docs/de/index.html new file mode 100644 index 0000000..9db514c --- /dev/null +++ b/docs/de/index.html @@ -0,0 +1,95 @@ + + + +Datly — Open-Source-Arbeitsbereich für KI-gestützte Datenanalyse + + + + + + + + + + + + + + + + + + + + + +
DatlyBETA
DE

Sprache

EnglishРусский简体中文日本語Tiếng Việtไทยहिन्दीالعربيةFrançaisDeutsch EspañolItaliano
+
+
OPEN SOURCE. OFFEN FÜR NEUE ERKENNTNISSE.
+

Gute Fragen.
Klarere Antworten.

Ihre Daten erzählen eine Geschichte. Entdecken Sie sie in einem KI-gestützten Arbeitsbereich, der Ihre Fragen, Diagramme und die zugrunde liegenden Zeilen verbindet.

Erkundung starten Genauer hinsehen

Kostenlose Open-Source-Software · MIT-Lizenz

Für Analysten, Forschende und Datenteams.

+
Offen entwickelt.Stand: 2. November 2026; wird live aktualisiert.
249Sterne auf GitHub16Datenquellen-Optionen12Website-SprachenMITOpen-Source-Lizenz
+
Weniger Hin und Her. Mehr Erkenntnis.ECHTE APP · BEISPIELDATEN
Fragen → Transformationen → Diagramme → ZeilenIHRE ANALYSE IM KONTEXT
+
Auf einer soliden Grundlage
DuckDBPythonVega-LiteReactTipTap
+

01 / VON DER FRAGE ZUM VERSTÄNDNIS

Folgen Sie Ihrer Neugier.
Behalten Sie den Kontext.

Von der Tabelle zur nützlichen Erkenntnis – in einem Arbeitsbereich. Fragen, verfeinern und prüfen Sie, während Ihre Analyse Gestalt annimmt.

+
01

Bringen Sie Ihre Daten mit

Laden Sie eine lokale Datei, verbinden Sie eine unterstützte Datenbank oder erkunden Sie ein mitgeliefertes Beispiel.

+
02

Stellen Sie die nächste Frage

Beschreiben Sie, was Sie erkunden möchten. Verfeinern Sie Ihr Diagramm mit visuellen Kodierungsoptionen oder einer Folgefrage.

+
03

Erst prüfen, dann erklären

Prüfen Sie die berechneten Zeilen, exportieren Sie eine CSV-Datei und erläutern Sie Ihre Erkenntnisse in einem bearbeitbaren Bericht.

+

02 / EIN GENAUERER BLICK

Sehen Sie die Arbeit.
Nicht nur die Antwort.

Echte Ansichten der Anwendung mit Raum für Details. Entdecken Sie die Funktionen, die eine Analyse nachvollziehbarer machen.

+ + + + +

Die Zeilen hinter
der Kurve.

Ein Diagramm ist ein Ausgangspunkt. Öffnen Sie die zugrunde liegende Tabelle, um Werte zu prüfen, eine Transformation nachzuvollziehen und zu entscheiden, was das Ergebnis tatsächlich bedeutet.

  • Diagramm und berechnete Tabelle zusammen
  • Berechnete Zeilen prüfen und exportieren
  • Ergebnisse vor dem Teilen prüfen

Echter Screenshot der Anwendung, für mehr Übersicht neu gerahmt. Beispieldaten dienen der Veranschaulichung; KI-generierte Analysen müssen geprüft werden.

+

03 / BEHALTEN SIE DEN ÜBERBLICK

KI hilft beim Erkunden.
Sie entscheiden.

Eine gute Analyse braucht mehr als ein überzeugendes Diagramm. Datly macht die Zwischenschritte für Sie einsehbar.

+
DER FRAGE NACHGEHEN

Behalten Sie Ihren
Gedankengang.

Analysepfade ordnen Fragen, Transformationen, abgeleitete Tabellen und Diagramme. Verfolgen Sie eine Untersuchung Schritt für Schritt.

Analysepfade erkunden
+
DIE ANSICHT VERFEINERN

Ein Diagramm, mit dem
Sie arbeiten können.

Nutzen Sie visuelle Kodierungsoptionen für Achsen, Farben und weitere Diagrammeigenschaften. Passen Sie die Ansicht an, während Ihre Frage präziser wird.

Die Analysefläche erkunden
+
DIE ERKENNTNIS ERKLÄREN

Mehr als
ein Screenshot.

Verbinden Sie erläuternden Text und Diagrammbilder in einem bearbeitbaren Bericht. Speichern Sie ein Bild oder nutzen Sie die Druckfunktion des Browsers für eine PDF-Datei.

Berichte erkunden
+
+

04 / MIT DEM VORHANDENEN STARTEN

Ihre Daten.
Ein vertrauter Ausgangspunkt.

Arbeiten Sie mit Dateien und unterstützten Datenquellen. Verbindungen benötigen die passenden Treiber, Zugangsdaten und Netzwerkzugriff.

Heute eine Datei.
Morgen ein Data Warehouse.

Beginnen Sie einfach mit einer CSV-Datei oder einem Beispieldatensatz. Nutzen Sie eine unterstützte Verbindung, wenn Ihre Frage mehr erfordert.

Konnektor-Implementierung ansehen
    +
  • Pg
    PostgreSQLRelationale Datenbank
    • +
  • Db
    DatabricksSQL-Warehouse
    • +
  • Bq
    BigQueryCloud-Warehouse
    • +
  • Ch
    ClickHouseAnalysedatenbank
    • +
  • My
    MySQLRelationale Datenbank
    • +
  • Ms
    SQL ServerRelationale Datenbank
    • +
  • S3
    Amazon S3Objektspeicher
    • +
  • Az
    Azure BlobObjektspeicher
    • +
  • At
    AthenaServerlose Abfragen
    • +
  • Mo
    MongoDBDokumentendatenbank
    • +
  • Co
    Cosmos DBDokumentendatenbank
    • +
  • Ku
    Azure KustoData Explorer
    • +
  • Su
    SupersetDatensatzimport
    • +
  • ↥
    Lokale DateienCSV, Excel und Parquet
    • +
  • Wb
    WebquellenUnterstützte Tabellenextraktion
    • +
  • Ex
    BeispieldatensätzeEine Beispielanalyse ausprobieren
    • +

Alle 16 Quellenoptionen werden angezeigt. Die Namen bezeichnen Implementierungen, keine zertifizierten Integrationen.

+

05 / INFORMIERT ENTSCHEIDEN

Lokale Verarbeitung.
Klare Datengrenzen.

Code für den Modellkontext lesen
+
01 / AUSFÜHRUNG

Arbeiten Sie in Ihrer Umgebung

DuckDB und Python führen Transformationen in der Umgebung aus, in der Sie Datly betreiben – auch bei einer lokalen Installation auf Ihrem Computer.

+
02 / MODELLKONTEXT

Wissen, was die KI erhält

KI-Anfragen können das Schema, Beispielwerte und -zeilen, Prompts sowie Werkzeugausgaben enthalten. Ein Cloud-Modellanbieter erhält den Kontext, der an ihn gesendet wird.

+
03 / IHRE KONFIGURATION

Wählen Sie Ihr Modell

Verbinden Sie einen unterstützten Anbieter oder konfigurieren Sie ein lokales Modell über Ollama. Lokale KI-Anfragen benötigen einen lokalen Endpunkt; entfernte Datenquellen erfordern weiterhin Netzwerkzugriff.

+
+

06 / IHRE NÄCHSTE GUTE FRAGE

Ein wenig Einrichtung.
Viel zu entdecken.

Erstellen Sie Datly aus dem Quellcode und starten Sie es im Browser. Beginnen Sie mit einem Beispieldatensatz und konfigurieren Sie anschließend ein Modell für KI-gestützte Erkundungen.

  • Python 3.11 oder höher
  • Node.js und Yarn für das Frontend
  • uv für die Python-Umgebung
Installationsanleitung lesen Releases ansehen 
# Get the project
+git clone https://github.com/arpanjot-coding/Datly.git
+cd Datly
+
+# Build the interface
+yarn
+yarn build
+
+# Install Python dependencies and launch
+uv sync
+uv run data_formulator
Öffnen Sie localhost:5567 nach dem Start

+

GUT ZU WISSEN

Bevor Sie
loslegen.

Fragen oder Feedback?
+
Was ist Datly?

Datly ist ein Open-Source-Arbeitsbereich für Datenanalyse auf Basis von Data Formulator von Microsoft Research. Er vereint Erkundung in natürlicher Sprache, visuelle Diagrammsteuerung, Analysepfade, Tabellenprüfung und bearbeitbare Berichte. Das Projekt befindet sich in der Betaphase.

+
Bleiben meine Daten auf meinem Computer?

Eine lokale Installation führt Transformationen auf Ihrem Computer aus. KI-Anfragen können Beispielzeilen, Feldwerte, Prompts und Werkzeugausgaben enthalten, die an das konfigurierte Modell gesendet werden. Wenn Sie einen Cloud-Anbieter wählen, erhält dieser den Kontext. Ein lokaler Modellendpunkt kann Modellanfragen lokal halten; prüfen Sie Ihre Konfiguration und die verbundenen Datenquellen.

+
Garantiert die Tabellenprüfung eine korrekte Antwort?

Nein. Die Tabelle hilft Ihnen beim Prüfen der berechneten Werte, doch die KI kann weiterhin eine falsche Transformation, Aggregation oder Interpretation wählen. Prüfen Sie die erzeugte Arbeit, die zugrunde liegenden Daten und die fachlichen Annahmen, bevor Sie sich auf ein Ergebnis verlassen.

+
Welche Modelle kann ich verwenden?

Der Code unterstützt OpenAI, Azure OpenAI, Anthropic, Gemini und lokale Ollama-Endpunkte. Sie konfigurieren Ihren Anbieter und Ihre Zugangsdaten. Verfügbarkeit, Fähigkeiten und mögliche Nutzungskosten der Modelle hängen vom jeweiligen Anbieter ab.

+
Kann ich Berichte und Dashboards erstellen?

Sie können Rich-Text-Berichte mit Diagrammbildern bearbeiten, ein Berichtsbild exportieren oder als PDF drucken. Die aktuelle Dashboard-Implementierung speichert Diagrammreferenzen und zeigt Platzhalter für Titel und Typ an; ein gerendertes, interaktives Dashboard mit mehreren Diagrammen ist noch nicht implementiert.

+
Ist Datly kostenlos?

Der Quellcode von Datly steht unter der MIT-Lizenz zur Verfügung. Für Ihren Modellanbieter, verbundene Infrastruktur oder Hosting können eigene Kosten anfallen. Aktuelle Installationsmöglichkeiten finden Sie in den Anleitungen und Releases des Repositorys.

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+

IN IHREN DATEN STECKT EINE GESCHICHTE.

Beginnen Sie mit einer guten Frage.

Mit Datly loslegen
+
+ +

Datly-Arbeitsbereich

Echter Screenshot der Anwendung mit Beispieldaten.

 + + diff --git a/docs/es/index.html b/docs/es/index.html new file mode 100644 index 0000000..3e5bd4a --- /dev/null +++ b/docs/es/index.html @@ -0,0 +1,95 @@ + + + +Datly — Espacio de análisis de datos con IA de código abierto + + + + + + + + + + + + + + + + + + + + + +
DatlyBETA
ES

Idioma

EnglishРусский简体中文日本語Tiếng Việtไทยहिन्दीالعربيةFrançaisDeutschEspañol Italiano
+
+
CÓDIGO ABIERTO. ABIERTO A EXPLORAR.
+

Buenas preguntas.
Respuestas más claras.

Tus datos tienen una historia. Descúbrela en un espacio asistido por IA que conecta tus preguntas, tus gráficos y las filas subyacentes.

Empezar a explorar Ver más de cerca

Software gratuito y de código abierto · Licencia MIT

Para analistas, investigadores y equipos de datos.

+
Desarrollado a la vista de todos.Recuento verificado el 2 de noviembre de 2026; se actualiza en directo.
249estrellas en GitHub16Opciones de fuentes de datos12Idiomas del sitioMITLicencia de código abierto
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Menos idas y vueltas. Más claridad.APLICACIÓN REAL · DATOS DE EJEMPLO
Preguntas → transformaciones → gráficos → filasTU ANÁLISIS, EN CONTEXTO
+
Sobre una base sólida
DuckDBPythonVega-LiteReactTipTap
+

01 / DE LA PREGUNTA A LA COMPRENSIÓN

Sigue tu curiosidad.
Conserva el contexto.

Pasa de una tabla a un hallazgo útil en un solo espacio. Pregunta, perfecciona y examina mientras tu análisis toma forma.

+
01

Trae tus datos

Carga un archivo local, conecta una base de datos compatible o explora un ejemplo incluido.

+
02

Haz la siguiente pregunta

Describe lo que quieres explorar. Perfecciona el gráfico con los controles de codificación visual o con una pregunta de seguimiento.

+
03

Examina y después explica

Comprueba las filas calculadas, exporta un CSV y da contexto a tus hallazgos en un informe editable.

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02 / UNA MIRADA MÁS CERCANA

Observa el trabajo.
No solo la respuesta.

Vistas reales de la aplicación para mirar con detalle. Descubre las funciones que facilitan seguir el análisis.

+ + + + +

Las filas detrás de
la línea.

Un gráfico es un punto de partida. Abre la tabla subyacente para examinar los valores, comprobar una transformación y decidir qué significa realmente el resultado.

  • Gráfico y tabla calculada juntos
  • Examinar y exportar las filas calculadas
  • Revisar los resultados antes de compartirlos

Captura real de la aplicación, reencuadrada para mayor claridad. Los datos de ejemplo son ilustrativos; el análisis generado por IA requiere revisión.

+

03 / MANTÉN EL CONTROL

La IA te ayuda a explorar.
Tú decides.

Un buen análisis necesita más que un gráfico convincente. Datly te permite examinar el trabajo intermedio.

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SIGUE LA PREGUNTA

Conserva el hilo
de tus ideas.

Los hilos de análisis organizan preguntas, transformaciones, tablas derivadas y gráficos. Sigue una investigación mientras evoluciona.

Explorar los hilos de análisis
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PERFECCIONA LA VISTA

Un gráfico con el que
puedes trabajar.

Usa los controles de codificación visual para los ejes, el color y otras propiedades del gráfico. Ajusta la vista a medida que tu pregunta se vuelve más precisa.

Explorar el lienzo
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EXPLICA EL HALLAZGO

Más que
una captura.

Combina texto e imágenes de gráficos en un informe editable. Guarda una imagen o usa la impresión del navegador para obtener un PDF.

Explorar los informes
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+

04 / EMPIEZA CON LO QUE TIENES

Tus datos.
Un punto de partida conocido.

Trabaja con archivos y fuentes de datos compatibles. Las conexiones dependen de los controladores, las credenciales y el acceso a la red adecuados.

Un archivo hoy.
Un almacén de datos mañana.

Empieza con un CSV o un conjunto de datos de ejemplo. Añade una conexión compatible cuando tu pregunta lo requiera.

Ver la implementación de conectores
    +
  • Pg
    PostgreSQLBase de datos relacional
    • +
  • Db
    DatabricksAlmacén SQL
    • +
  • Bq
    BigQueryAlmacén en la nube
    • +
  • Ch
    ClickHouseBase de datos analítica
    • +
  • My
    MySQLBase de datos relacional
    • +
  • Ms
    SQL ServerBase de datos relacional
    • +
  • S3
    Amazon S3Almacenamiento de objetos
    • +
  • Az
    Azure BlobAlmacenamiento de objetos
    • +
  • At
    AthenaConsultas sin servidor
    • +
  • Mo
    MongoDBBase de datos documental
    • +
  • Co
    Cosmos DBBase de datos documental
    • +
  • Ku
    Azure KustoData Explorer
    • +
  • Su
    SupersetImportación de conjuntos de datos
    • +
  • ↥
    Archivos localesCSV, Excel y Parquet
    • +
  • Wb
    Fuentes webExtracción de tablas compatible
    • +
  • Ex
    Conjuntos de datos de ejemploProbar un análisis de ejemplo
    • +

Se muestran las 16 opciones de fuentes. Los nombres describen implementaciones, no integraciones certificadas.

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05 / ELIGE CON INFORMACIÓN

Procesamiento local.
Límites claros para los datos.

Leer el código del contexto del modelo
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01 / EJECUCIÓN

Trabaja en tu entorno

DuckDB y Python ejecutan transformaciones en el entorno donde alojas Datly, incluida una instalación local en tu ordenador.

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02 / CONTEXTO DEL MODELO

Conoce lo que recibe la IA

Las solicitudes a la IA pueden incluir el esquema, ejemplos de valores y filas, instrucciones y salidas de herramientas. Un proveedor de modelos en la nube recibe el contexto que se le envía.

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03 / TU CONFIGURACIÓN

Elige tu modelo

Conecta un proveedor compatible o configura un modelo local mediante Ollama. Para que las solicitudes de IA sean locales se necesita un punto de conexión local; las fuentes remotas siguen necesitando acceso a la red.

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06 / TU PRÓXIMA BUENA PREGUNTA

Un poco de configuración.
Mucho por explorar.

Compila desde el código fuente y abre Datly en tu navegador. Empieza con un conjunto de datos de ejemplo y configura después un modelo para explorar con ayuda de la IA.

  • Python 3.11 o posterior
  • Node.js y Yarn para la interfaz
  • uv para el entorno de Python
Leer la guía de instalación Ver versiones 
# Get the project
+git clone https://github.com/arpanjot-coding/Datly.git
+cd Datly
+
+# Build the interface
+yarn
+yarn build
+
+# Install Python dependencies and launch
+uv sync
+uv run data_formulator
Abre localhost:5567 después de iniciar

+

ALGUNAS COSAS QUE DEBES SABER

Antes de
empezar.

¿Preguntas o comentarios?
+
¿Qué es Datly?

Datly es un espacio de análisis de código abierto basado en Data Formulator de Microsoft Research. Combina exploración en lenguaje natural, controles visuales de gráficos, hilos de análisis, inspección de tablas e informes editables. El proyecto está en fase beta.

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¿Mis datos permanecen en mi ordenador?

Una instalación local ejecuta las transformaciones en tu ordenador. Las solicitudes a la IA pueden incluir ejemplos de filas, valores de campos, instrucciones y salidas de herramientas, que se envían al modelo configurado. Si eliges un proveedor en la nube, ese proveedor recibe el contexto. Un punto de conexión de modelo local puede mantener las solicitudes en local; revisa tu configuración y las fuentes conectadas.

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¿Inspeccionar la tabla garantiza una respuesta correcta?

No. La tabla te ayuda a examinar los valores calculados, pero la IA puede elegir una transformación, agregación o interpretación incorrecta. Revisa el trabajo generado, los datos subyacentes y las hipótesis de negocio antes de confiar en un resultado.

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¿Qué modelos puedo usar?

El código incluye compatibilidad con OpenAI, Azure OpenAI, Anthropic, Gemini y puntos de conexión locales de Ollama. Tú configuras el proveedor y las credenciales. La disponibilidad, las capacidades y los posibles costes de uso de los modelos dependen de ese proveedor.

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¿Puedo crear informes y paneles?

Puedes editar informes de texto enriquecido con imágenes de gráficos, exportar una imagen del informe o imprimirlo en PDF. La implementación actual de los paneles guarda referencias a gráficos y muestra marcadores de título y tipo; aún no se ha implementado un panel renderizado e interactivo con varios gráficos.

+
¿Datly es gratuito?

El código fuente de Datly está disponible bajo la licencia MIT. Tu proveedor de modelos, la infraestructura conectada o el alojamiento pueden tener sus propios costes. Consulta las instrucciones y las versiones del repositorio para conocer las opciones de instalación actuales.

+
+

TUS DATOS TIENEN UNA HISTORIA.

Empieza con una buena pregunta.

Empezar con Datly
+
+ +

Espacio de Datly

Captura real de la aplicación con datos de ejemplo.

 + + diff --git a/docs/fr/index.html b/docs/fr/index.html new file mode 100644 index 0000000..5790390 --- /dev/null +++ b/docs/fr/index.html @@ -0,0 +1,95 @@ + + + +Datly — Espace d’analyse de données avec IA en open source + + + + + + + + + + + + + + + + + + + + + +
DatlyBETA
FR

Langue

EnglishРусский简体中文日本語Tiếng Việtไทยहिन्दीالعربيةFrançais DeutschEspañolItaliano
+
+
OPEN SOURCE. OUVERT À L’EXPLORATION.
+

De bonnes questions.
Des réponses plus claires.

Vos données ont une histoire. Découvrez-la dans un espace assisté par l’IA qui relie vos questions, vos graphiques et les lignes sous-jacentes.

Commencer l’exploration Regarder de plus près

Logiciel gratuit et open source · Licence MIT

Pour les analystes, les chercheurs et les équipes data.

+
Développé au grand jour.Nombre vérifié le 2 novembre 2026 ; actualisation en direct.
249étoiles sur GitHub16Options de sources de données12Langues du siteMITLicence open source
+
Moins d’allers-retours. Plus de compréhension.APPLICATION RÉELLE · DONNÉES D’EXEMPLE
Questions → transformations → graphiques → lignesVOTRE ANALYSE, EN CONTEXTE
+
Sur des bases solides
DuckDBPythonVega-LiteReactTipTap
+

01 / DE LA QUESTION À LA COMPRÉHENSION

Suivez votre curiosité.
Gardez le contexte.

Passez d’un tableau à une conclusion utile dans un seul espace. Questionnez, affinez et examinez à mesure que votre analyse prend forme.

+
01

Apportez vos données

Chargez un fichier local, connectez une base prise en charge ou explorez un exemple fourni.

+
02

Posez la question suivante

Décrivez ce que vous souhaitez explorer. Affinez votre graphique à l’aide des réglages d’encodage visuel ou d’une question complémentaire.

+
03

Examinez, puis expliquez

Vérifiez les lignes calculées, exportez un CSV et donnez du contexte à vos conclusions dans un rapport modifiable.

+

02 / DE PLUS PRÈS

Voyez le travail.
Pas seulement la réponse.

Des vues réelles de l’application, à examiner en détail. Découvrez les fonctions qui rendent l’analyse plus facile à suivre.

+ + + + +

Les lignes derrière
la courbe.

Un graphique est un point de départ. Ouvrez le tableau sous-jacent pour examiner les valeurs, vérifier une transformation et déterminer ce que le résultat signifie réellement.

  • Graphique et tableau calculé réunis
  • Examiner et exporter les lignes calculées
  • Vérifier les résultats avant de les partager

Capture réelle de l’application, recadrée pour plus de clarté. Les données d’exemple sont illustratives ; toute analyse générée par l’IA doit être vérifiée.

+

03 / GARDEZ LA MAIN

L’IA vous aide à explorer.
Vous décidez.

Une bonne analyse ne se résume pas à un graphique convaincant. Datly vous permet d’examiner les étapes intermédiaires.

+
SUIVEZ LA QUESTION

Gardez le fil
de votre réflexion.

Les fils d’analyse organisent les questions, les transformations, les tableaux dérivés et les graphiques. Suivez une investigation au fil de son évolution.

Explorer les fils d’analyse
+
AFFINEZ LA VUE

Un graphique sur lequel
vous pouvez agir.

Utilisez les réglages d’encodage visuel pour les axes, les couleurs et d’autres propriétés du graphique. Ajustez la vue à mesure que votre question se précise.

Explorer le canevas
+
EXPLIQUEZ LA CONCLUSION

Bien plus qu’une
capture d’écran.

Associez du texte et des images de graphiques dans un rapport modifiable. Enregistrez une image ou utilisez l’impression du navigateur pour obtenir un PDF.

Explorer les rapports
+
+

04 / PARTEZ DE CE QUE VOUS AVEZ

Vos données.
Un point de départ familier.

Travaillez avec des fichiers et des sources de données prises en charge. Les connexions dépendent des pilotes, des identifiants et de l’accès réseau appropriés.

Un fichier aujourd’hui.
Un entrepôt demain.

Commencez simplement avec un CSV ou un jeu de données d’exemple. Ajoutez une connexion prise en charge lorsque votre question l’exige.

Voir l’implémentation des connecteurs
    +
  • Pg
    PostgreSQLBase de données relationnelle
    • +
  • Db
    DatabricksEntrepôt SQL
    • +
  • Bq
    BigQueryEntrepôt cloud
    • +
  • Ch
    ClickHouseBase de données analytique
    • +
  • My
    MySQLBase de données relationnelle
    • +
  • Ms
    SQL ServerBase de données relationnelle
    • +
  • S3
    Amazon S3Stockage objet
    • +
  • Az
    Azure BlobStockage objet
    • +
  • At
    AthenaRequêtes sans serveur
    • +
  • Mo
    MongoDBBase de données documentaire
    • +
  • Co
    Cosmos DBBase de données documentaire
    • +
  • Ku
    Azure KustoData Explorer
    • +
  • Su
    SupersetImport de jeux de données
    • +
  • ↥
    Fichiers locauxCSV, Excel et Parquet
    • +
  • Wb
    Sources WebExtraction de tableaux prise en charge
    • +
  • Ex
    Jeux de données d’exempleEssayer une analyse d’exemple
    • +

Les 16 sources sont affichées. Les noms désignent des implémentations, pas des intégrations certifiées.

+

05 / CHOISISSEZ EN CONNAISSANCE DE CAUSE

Traitement local.
Des limites claires pour les données.

Lire le code du contexte du modèle
+
01 / EXÉCUTION

Travaillez dans votre espace

DuckDB et Python exécutent les transformations dans l’environnement où vous hébergez Datly, y compris lors d’une installation locale sur votre ordinateur.

+
02 / CONTEXTE DU MODÈLE

Sachez ce que reçoit l’IA

Les requêtes à l’IA peuvent inclure le schéma, des exemples de valeurs et de lignes, les instructions et les sorties d’outils. Un fournisseur de modèle cloud reçoit le contexte qui lui est envoyé.

+
03 / VOTRE CONFIGURATION

Choisissez votre modèle

Connectez un fournisseur pris en charge ou configurez un modèle local via Ollama. Pour rester locales, les requêtes à l’IA nécessitent un point d’accès local ; les sources distantes exigent toujours un accès réseau.

+
+

06 / VOTRE PROCHAINE BONNE QUESTION

Un peu de configuration.
Beaucoup à explorer.

Compilez depuis les sources et lancez Datly dans votre navigateur. Commencez avec un jeu de données d’exemple, puis configurez un modèle pour explorer avec l’aide de l’IA.

  • Python 3.11 ou version ultérieure
  • Node.js et Yarn pour l’interface
  • uv pour l’environnement Python
Lire le guide d’installation Voir les versions 
# Get the project
+git clone https://github.com/arpanjot-coding/Datly.git
+cd Datly
+
+# Build the interface
+yarn
+yarn build
+
+# Install Python dependencies and launch
+uv sync
+uv run data_formulator
Ouvrez localhost:5567 après le lancement

+

QUELQUES POINTS À CONNAÎTRE

Avant de
vous lancer.

Des questions ou des remarques ?
+
Qu’est-ce que Datly ?

Datly est un espace d’analyse en open source fondé sur Data Formulator de Microsoft Research. Il réunit l’exploration en langage naturel, les réglages visuels des graphiques, les fils d’analyse, l’inspection des tableaux et les rapports modifiables. Le projet est en bêta.

+
Mes données restent-elles sur mon ordinateur ?

Une installation locale exécute les transformations sur votre ordinateur. Les requêtes à l’IA peuvent inclure des exemples de lignes, des valeurs de champs, des instructions et des sorties d’outils, envoyés au modèle configuré. Si vous choisissez un fournisseur cloud, celui-ci reçoit ce contexte. Un point d’accès de modèle local peut maintenir les requêtes en local ; vérifiez votre configuration et les sources connectées.

+
L’inspection du tableau garantit-elle une réponse correcte ?

Non. Le tableau vous aide à examiner les valeurs calculées, mais l’IA peut toujours choisir une transformation, une agrégation ou une interprétation incorrecte. Vérifiez le travail généré, les données sous-jacentes et les hypothèses métier avant de vous fier à un résultat.

+
Quels modèles puis-je utiliser ?

Le code prend en charge OpenAI, Azure OpenAI, Anthropic, Gemini et les points d’accès Ollama locaux. Vous configurez votre fournisseur et vos identifiants. La disponibilité des modèles, leurs capacités et les éventuels frais d’utilisation dépendent du fournisseur.

+
Puis-je créer des rapports et des tableaux de bord ?

Vous pouvez modifier des rapports en texte enrichi contenant des images de graphiques, exporter une image du rapport ou l’imprimer en PDF. L’implémentation actuelle des tableaux de bord enregistre les références des graphiques et affiche des espaces réservés pour le titre et le type ; le rendu d’un tableau de bord interactif à plusieurs graphiques n’est pas encore implémenté.

+
Datly est-il gratuit ?

Le code source de Datly est disponible sous licence MIT. Votre fournisseur de modèle, l’infrastructure connectée ou l’hébergement peuvent entraîner des frais distincts. Consultez les instructions et les versions du dépôt pour connaître les options d’installation actuelles.

+
+

VOS DONNÉES ONT UNE HISTOIRE.

Commencez par une bonne question.

Commencer avec Datly
+
+ +

Espace Datly

Capture réelle de l’application utilisant des données d’exemple.

 + + diff --git a/docs/hi/index.html b/docs/hi/index.html new file mode 100644 index 0000000..6bd98aa --- /dev/null +++ b/docs/hi/index.html @@ -0,0 +1,95 @@ + + + +Datly — AI के साथ डेटा विश्लेषण का ओपन-सोर्स कार्यक्षेत्र + + + + + + + + + + + + + + + + + + + + + +
DatlyBETA
HI

भाषा

EnglishРусский简体中文日本語Tiếng Việtไทยहिन्दी العربيةFrançaisDeutschEspañolItaliano
+
+
ओपन सोर्स। खोज के लिए खुला।
+

बेहतर सवाल।
अधिक स्पष्ट जवाब।

आपके डेटा में एक कहानी है। उसे ऐसे AI-सहायित कार्यक्षेत्र में खोजें जो आपके सवालों, चार्ट और मूल डेटा पंक्तियों को जोड़कर रखता है।

खोजना शुरू करें करीब से देखें

निःशुल्क, ओपन-सोर्स सॉफ़्टवेयर · MIT लाइसेंस

विश्लेषकों, शोधकर्ताओं और डेटा टीमों के लिए।

+
खुले तौर पर विकसित।गिनती 2 नवंबर 2026 को सत्यापित; लाइव रीफ़्रेश होती है।
249GitHub पर स्टार16डेटा स्रोत विकल्प12वेबसाइट की भाषाएँMITओपन-सोर्स लाइसेंस
+
कम इधर-उधर जाना। ज़्यादा समझ पाना।वास्तविक ऐप · उदाहरण डेटा
सवाल → डेटा रूपांतरण → चार्ट → पंक्तियाँआपका विश्लेषण, पूरे संदर्भ के साथ
+
एक ठोस आधार पर निर्मित
DuckDBPythonVega-LiteReactTipTap
+

01 / सवाल से समझ तक

अपनी जिज्ञासा के साथ चलें।
संदर्भ बनाए रखें।

एक ही कार्यक्षेत्र में तालिका से उपयोगी निष्कर्ष तक पहुँचें। विश्लेषण आगे बढ़ने के साथ सवाल पूछें, सुधारें और जाँचें।

+
01

अपना डेटा लाएँ

स्थानीय फ़ाइल लोड करें, समर्थित डेटाबेस जोड़ें या साथ दिए गए उदाहरण को देखें।

+
02

अगला सवाल पूछें

बताएँ कि आप क्या जानना चाहते हैं। विज़ुअल एन्कोडिंग नियंत्रणों या अगले सवाल से अपना चार्ट सुधारें।

+
03

जाँचें, फिर समझाएँ

व्युत्पन्न पंक्तियाँ जाँचें, CSV निर्यात करें और संपादन योग्य रिपोर्ट में निष्कर्षों का संदर्भ दें।

+

02 / करीब से देखें

काम को देखें।
सिर्फ़ जवाब नहीं।

वास्तविक ऐप के दृश्य, जिन्हें आप करीब से देख सकते हैं। वे सुविधाएँ जानें जो विश्लेषण का क्रम समझना आसान बनाती हैं।

+ + + + +

रेखा के पीछे
का डेटा।

चार्ट एक शुरुआत है। मूल्यों की जाँच करने, रूपांतरण को परखने और परिणाम का वास्तविक अर्थ समझने के लिए नीचे की तालिका खोलें।

  • चार्ट और गणना की गई तालिका साथ में
  • व्युत्पन्न पंक्तियाँ जाँचें और निर्यात करें
  • साझा करने से पहले परिणाम की समीक्षा करें

वास्तविक ऐप का स्क्रीनशॉट, स्पष्टता के लिए नए फ़्रेम में। डेटा केवल उदाहरण है; AI-निर्मित विश्लेषण की समीक्षा आवश्यक है।

+

03 / हर कदम की जानकारी रखें

AI खोजने में मदद करता है।
फ़ैसला आप करते हैं।

अच्छे विश्लेषण के लिए केवल प्रभावशाली चार्ट काफ़ी नहीं। Datly आपको बीच के चरणों का काम भी जाँचने देता है।

+
सवाल का क्रम देखें

अपने विचारों की
कड़ी बनाए रखें।

डेटा थ्रेड सवालों, रूपांतरणों, व्युत्पन्न तालिकाओं और चार्ट को व्यवस्थित रखते हैं। खोज आगे बढ़ने के साथ उसका क्रम देखें।

डेटा थ्रेड देखें
+
दृश्य सुधारें

ऐसा चार्ट जिसके साथ
आप काम कर सकें।

अक्ष, रंग और चार्ट के दूसरे गुणों के लिए विज़ुअल एन्कोडिंग नियंत्रण इस्तेमाल करें। सवाल स्पष्ट होने के साथ दृश्य को समायोजित करें।

कैनवस देखें
+
निष्कर्ष समझाएँ

सिर्फ़
स्क्रीनशॉट से आगे।

संपादन योग्य रिपोर्ट में लिखित संदर्भ और चार्ट की छवियाँ जोड़ें। छवि सहेजें या PDF के लिए ब्राउज़र की प्रिंट सुविधा इस्तेमाल करें।

रिपोर्ट देखें
+
+

04 / जो आपके पास है, उसी से शुरू करें

आपका डेटा।
एक परिचित शुरुआत।

फ़ाइलों और समर्थित डेटा स्रोतों के साथ काम करें। कनेक्शन के लिए उपयुक्त ड्राइवर, क्रेडेंशियल और नेटवर्क पहुँच चाहिए।

आज एक फ़ाइल।
कल एक डेटा वेयरहाउस।

CSV या नमूना डेटासेट से छोटी शुरुआत करें। जब सवाल के लिए अधिक चाहिए, तो समर्थित कनेक्शन जोड़ें।

कनेक्टर का कार्यान्वयन देखें
    +
  • Pg
    PostgreSQLरिलेशनल डेटाबेस
    • +
  • Db
    DatabricksSQL वेयरहाउस
    • +
  • Bq
    BigQueryक्लाउड वेयरहाउस
    • +
  • Ch
    ClickHouseविश्लेषण डेटाबेस
    • +
  • My
    MySQLरिलेशनल डेटाबेस
    • +
  • Ms
    SQL Serverरिलेशनल डेटाबेस
    • +
  • S3
    Amazon S3ऑब्जेक्ट स्टोरेज
    • +
  • Az
    Azure Blobऑब्जेक्ट स्टोरेज
    • +
  • At
    Athenaसर्वरलेस क्वेरी
    • +
  • Mo
    MongoDBडॉक्यूमेंट डेटाबेस
    • +
  • Co
    Cosmos DBडॉक्यूमेंट डेटाबेस
    • +
  • Ku
    Azure Kustoडेटा एक्सप्लोरर
    • +
  • Su
    Supersetडेटासेट आयात
    • +
  • ↥
    स्थानीय फ़ाइलेंCSV, Excel और Parquet
    • +
  • Wb
    वेब स्रोतसमर्थित तालिका निष्कर्षण
    • +
  • Ex
    उदाहरण डेटासेटनमूना विश्लेषण आज़माएँ
    • +

सभी 16 स्रोत विकल्प दिखाए जा रहे हैं। नाम उपलब्ध कार्यान्वयनों को बताते हैं, प्रमाणित इंटीग्रेशन को नहीं।

+

05 / जानकारी के साथ चुनें

स्थानीय प्रोसेसिंग।
डेटा की स्पष्ट सीमाएँ।

मॉडल संदर्भ का कोड पढ़ें
+
01 / निष्पादन

अपने कार्यक्षेत्र में काम करें

DuckDB और Python उस वातावरण में रूपांतरण चलाते हैं जहाँ Datly होस्ट किया गया है, जिसमें आपके कंप्यूटर पर स्थानीय इंस्टॉलेशन भी शामिल है।

+
02 / मॉडल संदर्भ

जानें कि AI को क्या मिलता है

AI अनुरोधों में स्कीमा, नमूना मान और पंक्तियाँ, प्रॉम्प्ट और टूल के आउटपुट शामिल हो सकते हैं। क्लाउड मॉडल प्रदाता को भेजा गया संदर्भ मिलता है।

+
03 / आपकी सेटिंग

अपना मॉडल चुनें

समर्थित प्रदाता जोड़ें या Ollama से स्थानीय मॉडल कॉन्फ़िगर करें। स्थानीय AI अनुरोधों के लिए स्थानीय एंडपॉइंट चाहिए; दूरस्थ डेटा स्रोतों के लिए फिर भी नेटवर्क पहुँच आवश्यक है।

+
+

06 / आपका अगला अच्छा सवाल

थोड़ा-सा सेटअप।
जानने को बहुत कुछ।

सोर्स से बिल्ड करें और Datly को ब्राउज़र में चलाएँ। उदाहरण डेटासेट से शुरू करें, फिर AI-सहायित खोज के लिए मॉडल कॉन्फ़िगर करें।

  • Python 3.11 या बाद का संस्करण
  • फ़्रंटएंड के लिए Node.js और Yarn
  • Python वातावरण के लिए uv
सेटअप गाइड पढ़ें रिलीज़ देखें 
# Get the project
+git clone https://github.com/arpanjot-coding/Datly.git
+cd Datly
+
+# Build the interface
+yarn
+yarn build
+
+# Install Python dependencies and launch
+uv sync
+uv run data_formulator
खोलें localhost:5567 शुरू होने के बाद

+

जानने योग्य कुछ बातें

शुरू करने से
पहले।

कोई सवाल या सुझाव?
+
Datly क्या है?

Datly, Microsoft Research के Data Formulator पर आधारित डेटा विश्लेषण का ओपन-सोर्स कार्यक्षेत्र है। इसमें प्राकृतिक भाषा से खोज, विज़ुअल चार्ट नियंत्रण, डेटा थ्रेड, तालिका जाँच और संपादन योग्य रिपोर्ट शामिल हैं। यह प्रोजेक्ट बीटा चरण में है।

+
क्या मेरा डेटा मेरे कंप्यूटर पर ही रहता है?

स्थानीय इंस्टॉलेशन आपके कंप्यूटर पर डेटा रूपांतरण करता है। AI अनुरोधों में नमूना पंक्तियाँ, फ़ील्ड मान, प्रॉम्प्ट और टूल आउटपुट हो सकते हैं, जो आपके कॉन्फ़िगर किए गए मॉडल को भेजे जाते हैं। क्लाउड प्रदाता चुनने पर उसे यह संदर्भ मिलता है। स्थानीय मॉडल एंडपॉइंट मॉडल अनुरोधों को स्थानीय रख सकता है; अपनी सेटिंग और जुड़े डेटा स्रोतों की समीक्षा करें।

+
क्या तालिका जाँचने से सही उत्तर की गारंटी मिलती है?

नहीं। तालिका से गणना किए गए मान जाँचे जा सकते हैं, लेकिन AI फिर भी गलत रूपांतरण, एकत्रीकरण या व्याख्या चुन सकता है। परिणाम पर भरोसा करने से पहले बनाए गए काम, मूल डेटा और व्यावसायिक मान्यताओं की समीक्षा करें।

+
मैं कौन-से मॉडल इस्तेमाल कर सकता हूँ?

कोड में OpenAI, Azure OpenAI, Anthropic, Gemini और स्थानीय Ollama एंडपॉइंट का समर्थन है। आप प्रदाता और क्रेडेंशियल कॉन्फ़िगर करते हैं। मॉडल की उपलब्धता, क्षमताएँ और उपयोग की लागत उस प्रदाता पर निर्भर हैं।

+
क्या मैं रिपोर्ट और डैशबोर्ड बना सकता हूँ?

आप चार्ट की छवियों वाली रिच-टेक्स्ट रिपोर्ट संपादित कर सकते हैं, रिपोर्ट की छवि निर्यात कर सकते हैं या PDF में प्रिंट कर सकते हैं। मौजूदा डैशबोर्ड कार्यान्वयन चार्ट के संदर्भ सहेजता है और शीर्षक/प्रकार के प्लेसहोल्डर दिखाता है; चार्ट प्रदर्शित करने वाला इंटरैक्टिव बहु-चार्ट डैशबोर्ड अभी लागू नहीं है।

+
क्या Datly निःशुल्क है?

Datly का सोर्स कोड MIT लाइसेंस के अंतर्गत उपलब्ध है। मॉडल प्रदाता, जुड़ा बुनियादी ढाँचा या होस्टिंग अपनी लागत ले सकते हैं। मौजूदा इंस्टॉलेशन विकल्पों के लिए रिपॉज़िटरी की सेटअप गाइड और रिलीज़ देखें।

+
+

आपके डेटा में एक कहानी है।

एक अच्छे सवाल से शुरू करें।

Datly के साथ शुरू करें
+
+ +

Datly कार्यक्षेत्र

उदाहरण डेटा के साथ वास्तविक ऐप का स्क्रीनशॉट।

 + + diff --git a/docs/images/SOURCES.txt b/docs/images/SOURCES.txt new file mode 100644 index 0000000..b8dfa4d --- /dev/null +++ b/docs/images/SOURCES.txt @@ -0,0 +1,26 @@ +Datly product image provenance + +All five compositions use unmodified pixels from application captures already in +the repository at commit 8a9d5af825ab746c0eec87ef8437b747592af9e8. Framing, +cropping, scaling, typography, and explanatory labels are new. These are not +fresh runtime captures or AI-generated interfaces. Example data is illustrative. + +product-workspace: workspace.png. Full workspace showing example stock analysis. +product-explore: workspace.png. Two separate crops of the chart and computed rows; +the side-by-side composition is a presentation layout, not a new app layout. +product-threads: about-explore.png. Crop focused on data threads. +product-data: about-surfaces.png. Crop focused on data-quality signals and rows. +product-reports: screenshot-movies-report.webp. Archived Data Formulator report +example. The current report editor uses TipTap; this is not a current-editor +screenshot. Chart embeds are images. + +Source graphics: SVG, 1600 x 1100. Web variants: WebP and JPG, 1600 x 1100. +High-resolution exports: PNG, 3200 x 2200. Social card: JPG, 1600 x 900. +SVG compositions can be regenerated with: + node docs/assets/build-product-images.mjs +Raster exports can be regenerated with the sibling export-product-images.mjs +script after installing Sharp in an external workspace (see its usage comment). + +Existing source images retain the repository's licensing and original attribution. +Microsoft Data Formulator: https://github.com/microsoft/data-formulator +Datly source: https://github.com/arpanjot-coding/Datly diff --git a/docs/images/index.html b/docs/images/index.html new file mode 100644 index 0000000..331b9cf --- /dev/null +++ b/docs/images/index.html @@ -0,0 +1 @@ +Datly — Product image library
Datly

Made for a closer look.

Five product images, composed from actual application captures in the repository. The framing and crops are new; the app interface and example data are preserved. The report image is an archived example of the earlier Data Formulator interface.

← Back to the product

Illustrative example data. AI-generated analysis requires review. Source images retain the repository’s licensing and attribution.

diff --git a/docs/images/product-data.jpg b/docs/images/product-data.jpg new file mode 100644 index 0000000..c4a51d3 Binary files /dev/null and b/docs/images/product-data.jpg differ diff --git a/docs/images/product-data.png b/docs/images/product-data.png new file mode 100644 index 0000000..11f0fb7 Binary files /dev/null and b/docs/images/product-data.png differ diff --git a/docs/images/product-data.svg b/docs/images/product-data.svg new file mode 100644 index 0000000..e02aa7c --- /dev/null +++ b/docs/images/product-data.svg @@ -0,0 +1 @@ +DatlyPRODUCT TOUR / 04Get to know the table behind the chart.Inspect data quality signals, browse rows, and export a CSV.ACTUAL APPLICATION CAPTURE · EXAMPLE DATAdatly / product-data \ No newline at end of file diff --git a/docs/images/product-data.webp b/docs/images/product-data.webp new file mode 100644 index 0000000..d5efe24 Binary files /dev/null and b/docs/images/product-data.webp differ diff --git a/docs/images/product-explore.jpg b/docs/images/product-explore.jpg new file mode 100644 index 0000000..9610632 Binary files /dev/null and b/docs/images/product-explore.jpg differ diff --git a/docs/images/product-explore.png b/docs/images/product-explore.png new file mode 100644 index 0000000..a2eccfb Binary files /dev/null and b/docs/images/product-explore.png differ diff --git a/docs/images/product-explore.svg b/docs/images/product-explore.svg new file mode 100644 index 0000000..cc861c4 --- /dev/null +++ b/docs/images/product-explore.svg @@ -0,0 +1 @@ +DatlyPRODUCT TOUR / 02Look closer. The numbers are right here.A focused view of the Explore canvas and its underlying data table.THE CHARTTHE COMPUTED ROWSA closer look at the databehind the visualisation.ACTUAL APPLICATION CAPTURE · EXAMPLE DATAdatly / product-explore \ No newline at end of file diff --git a/docs/images/product-explore.webp b/docs/images/product-explore.webp new file mode 100644 index 0000000..556a245 Binary files /dev/null and b/docs/images/product-explore.webp differ diff --git a/docs/images/product-reports.jpg b/docs/images/product-reports.jpg new file mode 100644 index 0000000..4b1b591 Binary files /dev/null and b/docs/images/product-reports.jpg differ diff --git a/docs/images/product-reports.png b/docs/images/product-reports.png new file mode 100644 index 0000000..aa36d5e Binary files /dev/null and b/docs/images/product-reports.png differ diff --git a/docs/images/product-reports.svg b/docs/images/product-reports.svg new file mode 100644 index 0000000..7083932 --- /dev/null +++ b/docs/images/product-reports.svg @@ -0,0 +1 @@ +DatlyPRODUCT TOUR / 05Give your analysis a little more context.An archived report example: written findings alongside chart images.FROM CHART TO STORYGive the numbersa narrative.Write the finding.Bring a chart into the report.Explain the context.ARCHIVED DATA FORMULATOR EXAMPLECurrent report editing uses TipTap.ACTUAL APPLICATION CAPTURE · ARCHIVED REPORT EXAMPLEdatly / product-reports \ No newline at end of file diff --git a/docs/images/product-reports.webp b/docs/images/product-reports.webp new file mode 100644 index 0000000..ecf8e41 Binary files /dev/null and b/docs/images/product-reports.webp differ diff --git a/docs/images/product-threads.jpg b/docs/images/product-threads.jpg new file mode 100644 index 0000000..ba05129 Binary files /dev/null and b/docs/images/product-threads.jpg differ diff --git a/docs/images/product-threads.png b/docs/images/product-threads.png new file mode 100644 index 0000000..1077ebe Binary files /dev/null and b/docs/images/product-threads.png differ diff --git a/docs/images/product-threads.svg b/docs/images/product-threads.svg new file mode 100644 index 0000000..1bb2292 --- /dev/null +++ b/docs/images/product-threads.svg @@ -0,0 +1 @@ +DatlyPRODUCT TOUR / 03Keep the path to your answer.Questions, derived tables, and charts, organised into data threads.ACTUAL APPLICATION CAPTURE · EXAMPLE DATAdatly / product-threads \ No newline at end of file diff --git a/docs/images/product-threads.webp b/docs/images/product-threads.webp new file mode 100644 index 0000000..0a6b4b6 Binary files /dev/null and b/docs/images/product-threads.webp differ diff --git a/docs/images/product-workspace.jpg b/docs/images/product-workspace.jpg new file mode 100644 index 0000000..45bf0f1 Binary files /dev/null and b/docs/images/product-workspace.jpg differ diff --git a/docs/images/product-workspace.png b/docs/images/product-workspace.png new file mode 100644 index 0000000..dc4fd6b Binary files /dev/null and b/docs/images/product-workspace.png differ diff --git a/docs/images/product-workspace.svg b/docs/images/product-workspace.svg new file mode 100644 index 0000000..231b7ab --- /dev/null +++ b/docs/images/product-workspace.svg @@ -0,0 +1 @@ +DatlyPRODUCT TOUR / 01One workspace. The whole question.Explore your data, follow the analysis, and inspect the result.ACTUAL APPLICATION CAPTURE · EXAMPLE DATAdatly / product-workspace \ No newline at end of file diff --git a/docs/images/product-workspace.webp b/docs/images/product-workspace.webp new file mode 100644 index 0000000..10c87b5 Binary files /dev/null and b/docs/images/product-workspace.webp differ diff --git a/docs/images/screenshot-movies-report.png b/docs/images/screenshot-movies-report.png new file mode 100644 index 0000000..41307f7 Binary files /dev/null and b/docs/images/screenshot-movies-report.png differ diff --git a/docs/images/social-card.jpg b/docs/images/social-card.jpg new file mode 100644 index 0000000..3863587 Binary files /dev/null and b/docs/images/social-card.jpg differ diff --git a/docs/images/social-card.svg b/docs/images/social-card.svg new file mode 100644 index 0000000..6d89e8e --- /dev/null +++ b/docs/images/social-card.svg @@ -0,0 +1 @@ +DatlyPRODUCT TOUR / 01Good questions. Clearer answers.Explore your data, follow the analysis, and inspect the result.ACTUAL APPLICATION CAPTURE · EXAMPLE DATAdatly / product-workspace \ No newline at end of file diff --git a/docs/index.html b/docs/index.html index 29a5b2d..b3344f9 100644 --- a/docs/index.html +++ b/docs/index.html @@ -1,2282 +1,95 @@ - - - - - - Datly® — AI Data Visualization IDE & Analytical Workbench | Local-First DuckDB & Data Formulator - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Talk to your data.
Verify every single row.

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Datly is the open-source, local-first analytics IDE where natural language transforms enterprise tables into interactive charts, and the computed data rows stay right on screen under the line. Zero cloud data exfiltration. Zero hallucinations.

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- Get Started in 1 Command - uv run data_formulator -
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- 🛡️ 100% Local Processing (DuckDB + In-Memory) - • - 🔌 15+ Enterprise Connectors - • - 🔒 Zero Data Exfiltration - • - 🤖 Multi-Model Agnostic (Claude, GPT, Ollama) -
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01 / ARCHITECTURAL PILLARS
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Engineering Rigor Meets Fluid Exploration

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Built from the ground up to solve the state corruption of notebooks and the rigidity of legacy BI.

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Non-Destructive Data Threads

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Each analytical inquiry forms an isolated thread branch (DAG). Follow-up questions build on intermediate derived tables without mutating baseline datasets or overwriting state.

- Lineage State Machine -
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Ground-Truth Data Canvas

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Say goodbye to AI hallucinations. Every visualization is drawn from a physical derived table that sits directly underneath the chart. Inspect every cell and download verified CSVs.

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DuckDB In-Memory Engine

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Lightning-fast vectorized query execution powered by DuckDB, Pandas, and PyArrow. Run complex aggregations, window functions, and joins across millions of rows locally in milliseconds.

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Concept Encoding Shelves

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Drag and drop data fields into intuitive visual encoding shelves: X, Y, Color, Size, Facet, and Repeat. Bi-directional sync between natural language prompts and Vega-Lite specs.

- Grammar of Graphics -
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TipTap Narrative Reports

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Transform raw exploratory charts into executive-ready communication artifacts. A rich WYSIWYG editor allows you to interweave narrative commentary with live embedded visual cards.

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Local-First Zero Exfiltration

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Enterprise data governance built-in. Your raw rows never leave your computer. Only column schemas and sanitized metadata are sent to LLMs for code synthesis.

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02 / GROUND TRUTH VERIFICATION
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The Row Under The Line

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A table can hold an answer and still hide it. Datly ensures the question, the chart, and the exact computed data rows always remain visible together.

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Explore: The Synchronized Check

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When a question is asked (e.g. Closing price for GOOGL and MSFT), Datly computes a new derived table (stock-close) and plots it. The grid directly under the curve displays the exact values. If the model made an error, you catch it instantly before sharing.

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Dashboards & Reporting Without Copying

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Pinning charts into dashboards or embedding them into TipTap narrative reports does not duplicate data. All surfaces share live references to the underlying data thread DAG. Close the canvas anytime—your derived tables stay persistent.

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03 / ENTERPRISE DATA CONNECTORS
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Connect Once. Query Locally.

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Incorporate live tables from your data lakes, warehouses, relational databases, cloud buckets, and live web sources.

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ClickHouse

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AWS Athena & S3

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Azure Kusto (ADX)

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PostgreSQL

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Excel & Parquet

- Multi-sheet .xlsx, .csv -
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04 / ARCHITECTURAL COMPARISON
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Why Teams Choose Datly

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A side-by-side technical evaluation against traditional data exploration tools.

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Feature / Security ControlJupyter NotebooksLegacy Cloud BI (Tableau / PowerBI)SQL IDEs (DBeaver)DATLY IDE
Natural Language AI Synthesis⚠️ Third-party plugin⚠️ Costly proprietary copilot❌ None or basic autocompletion⚡ Native Grounded Agent (@table)
Ground-Truth Row Grid Under Chart❌ Separate cell output❌ Complex nested drill-down❌ Manual query required🛡️ Always Visible on Canvas
Visual Encoding Shelves + Agent❌ Code cells only⚠️ Shelves only (No Agent)❌ Code only✅ Bi-Directional Shelves + Agent
Local Data Privacy (Zero Exfiltration)✅ Local execution❌ Cloud ingestion required✅ Local query🔒 100% Local (Schema-Only to LLM)
Data Lineage Architecture❌ Linear, stateful cell traps❌ Static data models❌ Query history list🌲 Branching Data Threads (DAG)
In-Memory Vectorized Engine⚠️ Kernel dependent⚠️ Proprietary VertiPaq/Hyper❌ Remote DB dependent⚡ DuckDB + Pandas + PyArrow
WYSIWYG Narrative Reporting❌ Markdown cells only⚠️ Heavy dashboard canvas❌ None📝 TipTap Rich-Text Editor
Automated Web Table Scraper❌ Manual BeautifulSoup❌ Not supported❌ Not supported🌐 Built-in Headless Playwright
Model Agnostic (BYOK / Local)⚠️ Manual API wiring❌ Vendor lock-in❌ N/A🔓 LiteLLM (Claude, GPT, Ollama)
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05 / ZERO FRICTION SETUP
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Launch Datly in Under 60 Seconds

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Choose your preferred installation method: modern uv package manager, docker container, or 1-prompt AI assistant.

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- git clone https://github.com/arpanjot-coding/Datly.git
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- yarn && yarn build

- # 3. Synchronize backend & launch local analytical IDE
- uv sync
- uv run data_formulator

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- git clone https://github.com/arpanjot-coding/Datly.git && cd Datly
- yarn && yarn build
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- source .venv/bin/activate # On Windows: .venv\Scripts\activate
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- git clone https://github.com/arpanjot-coding/Datly.git && cd Datly
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- Set up and launch Datly: synchronize Python dependencies using uv, install frontend packages via yarn, compile the production bundle, and launch the Datly analytical workspace on localhost. -
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Download Desktop Packages

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Native standalone performance for Windows, macOS, and Linux with automated hardware acceleration.

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Datly Desktop Suite v0.8.0-beta

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Electron 43 Native Container · DuckDB Embedded Engine · Multi-LLM Provider

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07 / FREQUENTLY ASKED QUESTIONS
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Everything You Need to Know

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Clear answers to technical, security, and integration questions.

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- No. Datly enforces a strict local-first privacy boundary. When you query databases or load data, tables reside exclusively in local memory and DuckDB. When you ask questions in natural language, Datly transmits only column names and data types (with an optional 2-row sanitized preview) to the model for code synthesis. The synthesized transformation code runs 100% locally on your computer. Your raw records never leave your machine. -
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- Datly builds upon the groundbreaking concept encoding and data thread foundations established by Microsoft Research's Data Formulator, elevating it into a comprehensive, enterprise-ready data IDE. Datly introduces 15+ native cloud warehouse connectors (Databricks, BigQuery, Snowflake, Postgres), TipTap WYSIWYG narrative report publishing, multi-chart pinboard dashboards, cross-platform Electron desktop distribution, automated web table scraping via Playwright, and enterprise Single Sign-On (SSO). -
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Jupyter notebooks execute cells sequentially against a mutable global namespace. Modifying an earlier cell can break subsequent calculations in subtle, hidden ways. Datly replaces this with a Directed Acyclic Graph (DAG) of Data Threads. Each inquiry branches into an isolated thread card containing its own input reference, transformation step, derived table, and chart. Original datasets remain strictly immutable.
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High-Assurance AI Data Visualization IDE & Analytical Workbench · Free & Open Source Under MIT
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Copied to clipboard!
- - - - - - + + + +Datly — Open-source AI analytics workspace + + + + + + + + + + + + + + + + + + + + + +
DatlyBETA
EN

Language

English Русский简体中文日本語Tiếng Việtไทยहिन्दीالعربيةFrançaisDeutschEspañolItaliano
+
+
OPEN SOURCE. OPEN TO EXPLORATION.
+

Good questions.
Clearer answers.

Your data has a story. Find it with an AI-assisted workspace that keeps your questions, charts, and underlying rows connected.

Start exploring Take a closer look

Free, open-source software · MIT licensed

For analysts, researchers, and data teams.

+
Built in the open.Count verified 2 November 2026; refreshes live.
249stars on GitHub16Data source options12Website languagesMITOpen-source licence
+
A little less back and forth. A little more insight.ACTUAL APP · EXAMPLE DATA
Questions → transformations → charts → rowsYOUR ANALYSIS, IN CONTEXT
+
Built on a solid foundation
DuckDBPythonVega-LiteReactTipTap
+

01 / FROM QUESTION TO UNDERSTANDING

Follow your curiosity.
Keep the context.

Move from a table to a useful finding in one workspace. Ask, refine, and inspect as your analysis takes shape.

+
01

Bring your data

Load a local file, connect a supported database, or explore a bundled example.

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02

Ask the next question

Describe what you want to explore. Refine your chart with visual encoding controls or a follow-up question.

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03

Inspect, then explain

Check the derived rows, export a CSV, and give your findings context in an editable report.

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02 / A CLOSER LOOK

See the work.
Not just the answer.

Real application views, with room to look closer. Explore the features that make the analysis easier to follow.

+ + + + +

The rows behind
the line.

A chart is a starting point. Open the underlying table to inspect the values, check a transformation, and decide what the result actually means.

  • Chart and computed table together
  • Inspect and export the derived rows
  • Review results before you share

Actual application capture, reframed for clarity. Example data is illustrative; AI-generated analysis requires review.

+

03 / STAY IN THE LOOP

AI helps you explore.
You make the call.

Good analysis depends on more than a convincing chart. Datly makes the intermediate work available for you to examine.

+
TRACE THE QUESTION

Keep your train
of thought.

Data threads organise questions, transformations, derived tables, and charts. Follow an investigation as it develops.

Explore data threads
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REFINE THE VIEW

A chart you can
work with.

Use visual encoding controls for axes, colour, and other chart properties. Adjust the view as your question becomes more precise.

Explore the canvas
+
EXPLAIN THE FINDING

More than
a screenshot.

Combine written context with chart images in an editable report. Save an image or use your browser’s print flow for PDF.

Explore reports
+
+

04 / START WITH WHAT YOU HAVE

Your data.
A familiar starting point.

Work with files and supported data sources. Connections depend on the appropriate drivers, credentials, and network access.

A file today.
A warehouse tomorrow.

Start small with a CSV or a sample dataset. Bring in a supported connection when your question calls for more.

See connector implementation
    +
  • Pg
    PostgreSQLRelational database
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  • Db
    DatabricksSQL warehouse
    • +
  • Bq
    BigQueryCloud warehouse
    • +
  • Ch
    ClickHouseAnalytics database
    • +
  • My
    MySQLRelational database
    • +
  • Ms
    SQL ServerRelational database
    • +
  • S3
    Amazon S3Object storage
    • +
  • Az
    Azure BlobObject storage
    • +
  • At
    AthenaServerless queries
    • +
  • Mo
    MongoDBDocument database
    • +
  • Co
    Cosmos DBDocument database
    • +
  • Ku
    Azure KustoData Explorer
    • +
  • Su
    SupersetDataset import
    • +
  • ↥
    Local filesCSV, Excel & Parquet
    • +
  • Wb
    Web sourcesSupported table extraction
    • +
  • Ex
    Example datasetsTry a sample analysis
    • +

Showing all 16 source options. Names describe implementations, not certified integrations.

+

05 / MAKE AN INFORMED CHOICE

Local processing.
Clear data boundaries.

Read the model context code
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01 / EXECUTION

Work in your workspace

DuckDB and Python run transformations in the environment where you host Datly, including a local installation on your computer.

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02 / MODEL CONTEXT

Know what AI receives

AI requests can include schema, sample values and rows, prompts, and tool outputs. A cloud model provider receives the context sent to it.

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03 / YOUR CONFIGURATION

Choose your model

Connect a supported provider or configure a local model through Ollama. Local AI requests require a local endpoint; remote data sources still need network access.

+
+

06 / YOUR NEXT GOOD QUESTION

A little setup.
A lot to explore.

Build from source and launch Datly in your browser. Start with an example dataset, then configure a model for AI-assisted exploration.

  • Python 3.11 or later
  • Node.js and Yarn for the frontend
  • uv for the Python environment
Read the setup guide View releases 
# Get the project
+git clone https://github.com/arpanjot-coding/Datly.git
+cd Datly
+
+# Build the interface
+yarn
+yarn build
+
+# Install Python dependencies and launch
+uv sync
+uv run data_formulator
Open localhost:5567 after launch

+

A FEW THINGS TO KNOW

Before you
dive in.

Questions or feedback?
+
What is Datly?

Datly is an open-source analytics workspace built on Microsoft Research’s Data Formulator. It combines natural-language exploration, visual chart controls, data threads, table inspection, and editable reports. The project is in beta.

+
Does my data stay on my computer?

A local installation processes transformations on your computer. AI requests can include sample rows, field values, prompts, and tool outputs, which are sent to your configured model. If you select a cloud provider, that provider receives this context. A local model endpoint can keep model requests local; review your configuration and connected data sources.

+
Does inspecting the table guarantee a correct answer?

No. The table helps you inspect the computed values, but AI can still choose an incorrect transformation, aggregation, or interpretation. Review the generated work, underlying data, and business assumptions before relying on a result.

+
Which models can I use?

The code includes support for OpenAI, Azure OpenAI, Anthropic, Gemini, and local Ollama endpoints. You configure your provider and credentials. Model availability, capabilities, and any usage costs depend on that provider.

+
Can I create reports and dashboards?

You can edit rich-text reports with chart images, export a report image, or print to PDF. The current dashboard implementation saves chart references and displays title/type placeholders; a rendered, interactive multi-chart dashboard is not yet implemented.

+
Is Datly free?

Datly’s source code is available under the MIT licence. Your model provider, connected infrastructure, or hosting may have their own costs. Check the repository’s setup instructions and releases for the current installation options.

+
+

THERE’S A STORY IN YOUR DATA.

Start with a good question.

Get started with Datly
+
+ +

Datly workspace

Actual application capture using example data.

 + + diff --git a/docs/it/index.html b/docs/it/index.html new file mode 100644 index 0000000..8d6b2a5 --- /dev/null +++ b/docs/it/index.html @@ -0,0 +1,95 @@ + + + +Datly — Spazio di analisi dati con IA open source + + + + + + + + + + + + + + + + + + + + + +
DatlyBETA
IT

Lingua

EnglishРусский简体中文日本語Tiếng Việtไทยहिन्दीالعربيةFrançaisDeutschEspañolItaliano
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OPEN SOURCE. APERTO ALL’ESPLORAZIONE.
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Buone domande.
Risposte più chiare.

I tuoi dati hanno una storia. Scoprila in uno spazio assistito dall’IA che collega le tue domande, i grafici e le righe sottostanti.

Inizia a esplorare Guarda più da vicino

Software gratuito e open source · Licenza MIT

Per analisti, ricercatori e team di dati.

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Sviluppato alla luce del sole.Conteggio verificato il 2 novembre 2026; aggiornamento in tempo reale.
249stelle su GitHub16Opzioni per le fonti dati12Lingue del sitoMITLicenza open source
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Meno avanti e indietro. Più comprensione.APP REALE · DATI DI ESEMPIO
Domande → trasformazioni → grafici → righeLA TUA ANALISI, NEL SUO CONTESTO
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Su basi solide
DuckDBPythonVega-LiteReactTipTap
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01 / DALLA DOMANDA ALLA COMPRENSIONE

Segui la tua curiosità.
Mantieni il contesto.

Passa da una tabella a un risultato utile in un unico spazio. Chiedi, perfeziona ed esamina mentre l’analisi prende forma.

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01

Porta i tuoi dati

Carica un file locale, collega un database supportato o esplora un esempio incluso.

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02

Fai la domanda successiva

Descrivi cosa vuoi esplorare. Perfeziona il grafico con i controlli di codifica visiva o con una domanda di approfondimento.

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03

Esamina, poi spiega

Controlla le righe calcolate, esporta un CSV e dai contesto ai risultati in un report modificabile.

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02 / UNO SGUARDO PIÙ DA VICINO

Guarda il lavoro.
Non solo la risposta.

Viste reali dell’applicazione da osservare nei dettagli. Scopri le funzioni che rendono più facile seguire l’analisi.

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Le righe dietro
la linea.

Un grafico è un punto di partenza. Apri la tabella sottostante per esaminare i valori, controllare una trasformazione e capire cosa significa davvero il risultato.

  • Grafico e tabella calcolata insieme
  • Esamina ed esporta le righe calcolate
  • Rivedi i risultati prima di condividerli

Schermata reale dell’applicazione, reinquadrata per maggiore chiarezza. I dati di esempio sono illustrativi; l’analisi generata dall’IA richiede una verifica.

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03 / MANTIENI IL CONTROLLO

L’IA ti aiuta a esplorare.
Decidi tu.

Una buona analisi richiede più di un grafico convincente. Datly ti permette di esaminare il lavoro intermedio.

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SEGUI LA DOMANDA

Mantieni il filo
del ragionamento.

I percorsi di analisi organizzano domande, trasformazioni, tabelle derivate e grafici. Segui un’indagine mentre si sviluppa.

Esplora i percorsi di analisi
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PERFEZIONA LA VISTA

Un grafico su cui
puoi lavorare.

Usa i controlli di codifica visiva per gli assi, i colori e altre proprietà del grafico. Adatta la vista man mano che la domanda diventa più precisa.

Esplora l’area di analisi
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SPIEGA IL RISULTATO

Più di una
schermata.

Combina testo e immagini dei grafici in un report modificabile. Salva un’immagine o usa la stampa del browser per ottenere un PDF.

Esplora i report
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04 / PARTI DA QUELLO CHE HAI

I tuoi dati.
Un punto di partenza familiare.

Lavora con file e fonti dati supportate. Le connessioni dipendono dai driver, dalle credenziali e dall’accesso alla rete appropriati.

Un file oggi.
Un data warehouse domani.

Inizia in modo semplice con un CSV o un set di dati di esempio. Aggiungi una connessione supportata quando la domanda richiede di più.

Vedi l’implementazione dei connettori
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  • Pg
    PostgreSQLDatabase relazionale
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  • Db
    DatabricksData warehouse SQL
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  • Bq
    BigQueryData warehouse cloud
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  • Ch
    ClickHouseDatabase analitico
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  • My
    MySQLDatabase relazionale
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  • Ms
    SQL ServerDatabase relazionale
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  • S3
    Amazon S3Archiviazione di oggetti
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  • Az
    Azure BlobArchiviazione di oggetti
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  • At
    AthenaQuery serverless
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  • Mo
    MongoDBDatabase documentale
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  • Co
    Cosmos DBDatabase documentale
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  • Ku
    Azure KustoData Explorer
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  • Su
    SupersetImportazione di set di dati
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  • ↥
    File localiCSV, Excel e Parquet
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  • Wb
    Fonti webEstrazione di tabelle supportata
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  • Ex
    Set di dati di esempioProva un’analisi di esempio
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Sono visualizzate tutte le 16 opzioni di origine. I nomi descrivono implementazioni, non integrazioni certificate.

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05 / SCEGLI IN MODO INFORMATO

Elaborazione locale.
Confini chiari per i dati.

Leggi il codice del contesto del modello
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01 / ESECUZIONE

Lavora nel tuo ambiente

DuckDB e Python eseguono le trasformazioni nell’ambiente in cui ospiti Datly, anche nel caso di un’installazione locale sul tuo computer.

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02 / CONTESTO DEL MODELLO

Scopri cosa riceve l’IA

Le richieste all’IA possono includere schema, esempi di valori e righe, istruzioni e output degli strumenti. Un fornitore di modelli cloud riceve il contesto che gli viene inviato.

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03 / LA TUA CONFIGURAZIONE

Scegli il tuo modello

Collega un fornitore supportato o configura un modello locale tramite Ollama. Le richieste locali all’IA richiedono un endpoint locale; le fonti dati remote necessitano comunque dell’accesso alla rete.

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06 / LA TUA PROSSIMA BUONA DOMANDA

Un po’ di configurazione.
Tanto da esplorare.

Compila dal codice sorgente e avvia Datly nel browser. Inizia con un set di dati di esempio, poi configura un modello per esplorare con l’aiuto dell’IA.

  • Python 3.11 o successivo
  • Node.js e Yarn per l’interfaccia
  • uv per l’ambiente Python
Leggi la guida all’installazione Vedi le versioni 
# Get the project
+git clone https://github.com/arpanjot-coding/Datly.git
+cd Datly
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+# Build the interface
+yarn
+yarn build
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+# Install Python dependencies and launch
+uv sync
+uv run data_formulator
Apri localhost:5567 dopo l’avvio

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ALCUNE COSE DA SAPERE

Prima di
iniziare.

Domande o commenti?
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Che cos’è Datly?

Datly è uno spazio di analisi open source basato su Data Formulator di Microsoft Research. Unisce esplorazione in linguaggio naturale, controlli visivi dei grafici, percorsi di analisi, ispezione delle tabelle e report modificabili. Il progetto è in fase beta.

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I miei dati rimangono sul mio computer?

Un’installazione locale esegue le trasformazioni sul tuo computer. Le richieste all’IA possono includere righe di esempio, valori dei campi, istruzioni e output degli strumenti, inviati al modello configurato. Se scegli un fornitore cloud, quel fornitore riceve il contesto. Un endpoint di modello locale può mantenere locali le richieste; verifica la configurazione e le fonti dati collegate.

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L’ispezione della tabella garantisce una risposta corretta?

No. La tabella aiuta a esaminare i valori calcolati, ma l’IA può comunque scegliere una trasformazione, un’aggregazione o un’interpretazione errata. Controlla il lavoro generato, i dati sottostanti e le ipotesi di business prima di fare affidamento su un risultato.

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Quali modelli posso usare?

Il codice supporta OpenAI, Azure OpenAI, Anthropic, Gemini ed endpoint locali di Ollama. Configuri tu il fornitore e le credenziali. La disponibilità, le capacità e gli eventuali costi d’uso dei modelli dipendono dal fornitore.

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Posso creare report e dashboard?

Puoi modificare report in testo formattato con immagini dei grafici, esportare un’immagine del report o stamparlo in PDF. L’implementazione attuale delle dashboard salva riferimenti ai grafici e mostra segnaposto per titolo e tipo; una dashboard visualizzata e interattiva con più grafici non è ancora implementata.

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Datly è gratuito?

Il codice sorgente di Datly è disponibile con licenza MIT. Il fornitore del modello, l’infrastruttura collegata o l’hosting possono avere costi propri. Consulta le istruzioni e le versioni del repository per conoscere le opzioni di installazione attuali.

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I TUOI DATI HANNO UNA STORIA.

Inizia con una buona domanda.

Inizia con Datly
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Spazio Datly

Schermata reale dell’applicazione con dati di esempio.

 + + diff --git a/docs/ja/index.html b/docs/ja/index.html new file mode 100644 index 0000000..a026ed9 --- /dev/null +++ b/docs/ja/index.html @@ -0,0 +1,95 @@ + + + +Datly — オープンソースのAI分析ワークスペース + + + + + + + + + + + + + + + + + + + + + +
DatlyBETA
JA

言語

EnglishРусский简体中文日本語 Tiếng Việtไทยहिन्दीالعربيةFrançaisDeutschEspañolItaliano
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オープンソースで、探索の可能性を広げる。
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よい問いを。
より明確な答えへ。

データにはストーリーがあります。問い、グラフ、その元となるデータ行をつなぐAI支援のワークスペースで、そのストーリーを見つけましょう。

探索をはじめる 詳しく見る

無料のオープンソースソフトウェア · MITライセンス

アナリスト、研究者、データチームのために。

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開発の過程も公開。2026年11月2日に件数を確認。リアルタイムで更新されます。
249GitHubのスター16データソースの選択肢12ウェブサイトの言語MITオープンソースライセンス
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行き来を少なく、気づきを多く。実際のアプリ · サンプルデータ
問い → データ変換 → グラフ → データ行分析の文脈を、そのままに
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確かな技術基盤
DuckDBPythonVega-LiteReactTipTap
+

01 / 問いから理解へ

好奇心のままに。
文脈はそのままに。

テーブルから有用な発見へ、ひとつのワークスペースで。分析を進めながら、問い、調整し、結果を確認できます。

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データを取り込む

ローカルファイルを読み込む、対応するデータベースに接続する、同梱のサンプルを試す。好きなところから始められます。

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02

次の問いを投げかける

調べたいことを言葉にしてみましょう。視覚的なエンコーディング設定や追加の質問で、グラフを調整できます。

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03

確かめてから、伝える

変換後のデータ行を確認し、CSVに出力。編集可能なレポートで、発見に背景を添えられます。

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02 / もう一歩、詳しく

答えだけでなく、
その過程も。

実際のアプリ画面を、拡大して確認できます。分析の流れを追いやすくする機能をご覧ください。

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グラフを支える
データ行。

グラフは出発点です。元となるテーブルを開き、値や変換処理を確認して、結果が何を意味するかを判断できます。

  • グラフと計算結果のテーブルを一緒に表示
  • 変換後のデータ行を確認・出力
  • 共有する前に結果を確認

実際のアプリ画面を、見やすい構図に調整しています。サンプルデータは説明用です。AIが生成した分析は確認が必要です。

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03 / 分析の主導権を手元に

AIが探索を支援。
判断するのは、あなた。

よい分析に必要なのは、説得力のあるグラフだけではありません。Datlyでは、途中の作業も確認できます。

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問いの流れをたどる

思考の流れを
途切れさせない。

データスレッドは、問い、変換処理、生成されたテーブル、グラフを整理します。調査が進む過程をたどれます。

データスレッドを見る
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表示を調整する

分析に合わせて
調整できるグラフ。

軸、色、その他のグラフ属性を視覚的なエンコーディング設定で調整できます。問いが具体的になるにつれて、表示も変えられます。

キャンバスを見る
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発見を伝える

スクリーンショットの
その先へ。

編集可能なレポートに、文章とグラフ画像をまとめられます。画像として保存するか、ブラウザーの印刷機能でPDFにできます。

レポートを見る
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04 / 手元のデータから

あなたのデータ。
いつもの出発点。

ファイルや対応するデータソースを利用できます。接続には、適切なドライバー、認証情報、ネットワークアクセスが必要です。

今日はファイルから。
明日はデータウェアハウスへ。

CSVやサンプルデータセットで小さく始めましょう。問いに応じて、対応するデータソースに接続できます。

コネクターの実装を見る
    +
  • Pg
    PostgreSQLリレーショナルデータベース
    • +
  • Db
    DatabricksSQLウェアハウス
    • +
  • Bq
    BigQueryクラウドウェアハウス
    • +
  • Ch
    ClickHouse分析用データベース
    • +
  • My
    MySQLリレーショナルデータベース
    • +
  • Ms
    SQL Serverリレーショナルデータベース
    • +
  • S3
    Amazon S3オブジェクトストレージ
    • +
  • Az
    Azure Blobオブジェクトストレージ
    • +
  • At
    Athenaサーバーレスクエリ
    • +
  • Mo
    MongoDBドキュメントデータベース
    • +
  • Co
    Cosmos DBドキュメントデータベース
    • +
  • Ku
    Azure KustoData Explorer
    • +
  • Su
    Supersetデータセットの取り込み
    • +
  • ↥
    ローカルファイルCSV・Excel・Parquet
    • +
  • Wb
    Webソース対応するテーブル抽出
    • +
  • Ex
    サンプルデータセットサンプル分析を試す
    • +

全16種類のデータソースを表示しています。名称は実装を示すもので、認定済みの連携を意味するものではありません。

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05 / 理解したうえで選ぶ

ローカルで処理。
データの境界は明確に。

モデルに渡すコンテキストのコードを見る
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01 / 実行環境

自分のワークスペースで実行

DuckDBとPythonは、Datlyをホストする環境で変換処理を実行します。自分のコンピューターへのローカルインストールも含まれます。

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02 / モデルへのコンテキスト

AIに何が渡るかを知る

AIへのリクエストには、スキーマ、サンプル値やデータ行、プロンプト、ツールの出力が含まれる場合があります。クラウドモデルの提供元は、送信されたコンテキストを受け取ります。

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03 / あなたの設定

モデルを選ぶ

対応する提供元に接続するか、Ollamaでローカルモデルを設定できます。AIへのリクエストをローカルに保つには、ローカルのエンドポイントが必要です。リモートのデータソースには、引き続きネットワークアクセスが必要です。

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06 / 次のよい問いへ

少しのセットアップで、
探索の幅が広がる。

ソースコードからビルドし、ブラウザーでDatlyを起動します。サンプルデータセットから始め、モデルを設定すればAI支援の探索ができます。

  • Python 3.11以降
  • フロントエンド用のNode.jsとYarn
  • Python環境用のuv
セットアップガイドを読む リリースを見る 
# Get the project
+git clone https://github.com/arpanjot-coding/Datly.git
+cd Datly
+
+# Build the interface
+yarn
+yarn build
+
+# Install Python dependencies and launch
+uv sync
+uv run data_formulator
開く localhost:5567 起動後

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知っておきたいこと

使い始める
前に。

ご質問・フィードバック
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Datlyとは何ですか？

Datlyは、Microsoft ResearchのData Formulatorを基盤とした、オープンソースの分析ワークスペースです。自然言語による探索、視覚的なグラフ設定、データスレッド、テーブルの確認、編集可能なレポートを備えています。現在はベータ版です。

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データは自分のコンピューター内にとどまりますか？

ローカルにインストールした場合、変換処理は自分のコンピューター上で実行されます。AIへのリクエストには、サンプル行、フィールドの値、プロンプト、ツールの出力が含まれる場合があり、設定したモデルに送信されます。クラウドの提供元を選ぶと、その提供元がコンテキストを受け取ります。ローカルモデルのエンドポイントを使うことでモデルへのリクエストをローカルに保てますが、設定や接続先のデータソースをご確認ください。

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テーブルを確認すれば、答えの正しさは保証されますか？

いいえ。テーブルは計算された値の確認に役立ちますが、AIが誤った変換、集計、解釈を選ぶ可能性はあります。結果を判断の根拠にする前に、生成された作業、元のデータ、ビジネス上の前提条件を確認してください。

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どのモデルを使えますか？

コードにはOpenAI、Azure OpenAI、Anthropic、Gemini、ローカルのOllamaエンドポイントへの対応が含まれています。提供元と認証情報は利用者が設定します。モデルの利用可否、機能、利用料金は提供元によって異なります。

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レポートやダッシュボードを作成できますか？

グラフ画像を含むリッチテキストのレポートを編集し、レポートを画像として出力するか、PDFに印刷できます。現在のダッシュボード実装はグラフへの参照を保存し、タイトルと種類のプレースホルダーを表示します。複数のグラフを描画するインタラクティブなダッシュボードは、まだ実装されていません。

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Datlyは無料ですか？

DatlyのソースコードはMITライセンスで公開されています。モデルの提供元、接続するインフラ、ホスティングには別途費用がかかる場合があります。現在のインストール方法は、リポジトリのセットアップ手順とリリースを確認してください。

+
+

データには、ストーリーがある。

よい問いから、はじめよう。

Datlyをはじめる
+
+ +

Datlyワークスペース

サンプルデータを使用した、実際のアプリ画面です。

 + + diff --git a/docs/locales/ar.json b/docs/locales/ar.json new file mode 100644 index 0000000..dc075e1 --- /dev/null +++ b/docs/locales/ar.json @@ -0,0 +1,227 @@ +{ + "language": "ar", + "nativeName": "العربية", + "direction": "rtl", + "translations": { + "width=device-width, initial-scale=1": "width=device-width, initial-scale=1", + "Datly — Open-source AI analytics workspace": "Datly — مساحة عمل مفتوحة المصدر لتحليل البيانات بالذكاء الاصطناعي", + "An open-source analytics workspace. Explore tables with AI, refine charts, inspect underlying rows, and turn your findings into reports.": "مساحة عمل مفتوحة المصدر لتحليل البيانات. استكشف الجداول بالذكاء الاصطناعي، وحسّن الرسوم البيانية، وافحص الصفوف التي تستند إليها، وحوّل نتائجك إلى تقارير.", + "Datly — Good questions. Clearer answers.": "Datly — أسئلة جيدة. إجابات أوضح.", + "Explore with AI. Inspect the data behind your charts. An open-source workspace for your next good question.": "استكشف بالذكاء الاصطناعي. افحص البيانات خلف رسومك البيانية. مساحة عمل مفتوحة المصدر لسؤالك الجيد التالي.", + "Datly workspace with data threads, a chart, and underlying data rows": "مساحة عمل Datly مع مسارات البيانات ورسم بياني وصفوف البيانات التي يستند إليها", + "An open-source workspace for exploring data with AI, inspecting charts, and writing reports.": "مساحة عمل مفتوحة المصدر لاستكشاف البيانات بالذكاء الاصطناعي وفحص الرسوم البيانية وكتابة التقارير.", + "Skip to content": "انتقل إلى المحتوى", + "Datly home": "صفحة Datly الرئيسية", + "Open navigation": "افتح قائمة التنقل", + "Main navigation": "التنقل الرئيسي", + "Product": "المنتج", + "How it works": "آلية العمل", + "Data sources": "مصادر البيانات", + "FAQ": "الأسئلة الشائعة", + "Get started": "ابدأ الآن", + "OPEN SOURCE. OPEN TO EXPLORATION.": "مفتوح المصدر. مفتوح للاستكشاف.", + "Good questions.": "أسئلة جيدة.", + "Clearer answers.": "إجابات أوضح.", + "Your data has a story. Find it with an AI-assisted workspace that keeps your questions, charts, and underlying rows connected.": "في بياناتك قصة. اكتشفها في مساحة عمل يدعمها الذكاء الاصطناعي وتربط أسئلتك ورسومك البيانية وصفوف البيانات التي تستند إليها.", + "Start exploring": "ابدأ الاستكشاف", + "Take a closer look": "ألقِ نظرة أقرب", + "Free, open-source software · MIT licensed": "برنامج مجاني ومفتوح المصدر · بترخيص MIT", + "For analysts, researchers, and data teams.": "للمحللين والباحثين وفرق البيانات.", + "A little less back and forth. A little more insight.": "تنقّل أقل بين الأدوات. وفهم أعمق للبيانات.", + "ACTUAL APP · EXAMPLE DATA": "تطبيق فعلي · بيانات توضيحية", + "Enlarge the Datly workspace screenshot": "كبّر لقطة شاشة مساحة عمل Datly", + "Datly workspace showing a stock-price analysis thread, line chart, and the data rows underneath": "مساحة عمل Datly تعرض مسارًا لتحليل أسعار الأسهم ورسمًا خطيًا وصفوف البيانات أسفله", + "Explore the workspace": "استكشف مساحة العمل", + "Questions → transformations → charts → rows": "أسئلة ← تحويلات ← رسوم بيانية ← صفوف", + "YOUR ANALYSIS, IN CONTEXT": "تحليلك، ضمن سياقه", + "Built on a solid foundation": "مبني على أساس متين", + "PUBLIC BY DESIGN": "مصمّم ليكون علنيًا", + "Built in the open.": "تطوير على مرأى الجميع.", + "Explore the source, issues, and releases.": "استكشف الشيفرة المصدرية والمشكلات والإصدارات.", + "GITHUB COMMUNITY": "مجتمع GITHUB", + "stars on GitHub": "نجمة على GitHub", + "Count verified 2 November 2026; refreshes live.": "تم التحقق من العدد في 2 نوفمبر 2026؛ ويُحدّث مباشرة.", + "MADE FOR DATA WORK": "مصمّم للعمل على البيانات", + "Your next analysis.": "تحليلك القادم.", + "01 / FROM QUESTION TO UNDERSTANDING": "01 / من السؤال إلى الفهم", + "Follow your curiosity.": "اتبع فضولك.", + "Keep the context.": "واحتفظ بالسياق.", + "Move from a table to a useful finding in one workspace. Ask, refine, and inspect as your analysis takes shape.": "انتقل من جدول إلى نتيجة مفيدة في مساحة عمل واحدة. اسأل وحسّن وافحص بينما يتشكّل تحليلك.", + "Bring your data": "أحضر بياناتك", + "Load a local file, connect a supported database, or explore a bundled example.": "حمّل ملفًا محليًا، أو اتصل بقاعدة بيانات مدعومة، أو استكشف مثالًا مرفقًا.", + "Ask the next question": "اطرح السؤال التالي", + "Describe what you want to explore. Refine your chart with visual encoding controls or a follow-up question.": "صِف ما تريد استكشافه. حسّن رسمك البياني باستخدام عناصر التحكم في الترميز البصري أو بسؤال متابعة.", + "Inspect, then explain": "افحص، ثم اشرح", + "Check the derived rows, export a CSV, and give your findings context in an editable report.": "افحص الصفوف المشتقة، وصدّر ملف CSV، وأضف سياقًا لنتائجك في تقرير قابل للتحرير.", + "02 / A CLOSER LOOK": "02 / نظرة أقرب", + "See the work.": "شاهد خطوات العمل.", + "Not just the answer.": "وليس الإجابة فقط.", + "Real application views, with room to look closer. Explore the features that make the analysis easier to follow.": "مشاهد فعلية للتطبيق تتيح لك النظر عن قرب. استكشف الميزات التي تجعل متابعة التحليل أسهل.", + "Explore Datly features": "استكشف ميزات Datly", + "Explore & inspect": "استكشاف وفحص", + "Data threads": "مسارات البيانات", + "Data inspection": "فحص البيانات", + "Reports": "التقارير", + "The rows behind": "الصفوف وراء", + "the line.": "الخط البياني.", + "A chart is a starting point. Open the underlying table to inspect the values, check a transformation, and decide what the result actually means.": "الرسم البياني نقطة بداية. افتح الجدول الذي يستند إليه لفحص القيم ومراجعة التحويل وتحديد المعنى الفعلي للنتيجة.", + "Chart and computed table together": "الرسم البياني والجدول المحسوب معًا", + "Inspect and export the derived rows": "افحص الصفوف المشتقة وصدّرها", + "Review results before you share": "راجع النتائج قبل مشاركتها", + "View full image": "اعرض الصورة كاملة", + "Enlarge this feature screenshot": "كبّر لقطة شاشة هذه الميزة", + "Detail views of the real Datly Explore canvas, showing a chart and computed data rows": "مشاهد تفصيلية لمساحة Explore الفعلية في Datly، تعرض رسمًا بيانيًا وصفوف بيانات محسوبة", + "Actual application capture, reframed for clarity. Example data is illustrative; AI-generated analysis requires review.": "لقطة فعلية للتطبيق، أعيد تأطيرها للوضوح. البيانات للتوضيح فقط؛ ويجب مراجعة التحليل الذي ينشئه الذكاء الاصطناعي.", + "03 / STAY IN THE LOOP": "03 / ابقَ مطّلعًا على الخطوات", + "AI helps you explore.": "الذكاء الاصطناعي يساعدك على الاستكشاف.", + "You make the call.": "وأنت صاحب القرار.", + "Good analysis depends on more than a convincing chart. Datly makes the intermediate work available for you to examine.": "التحليل الجيد يحتاج إلى أكثر من رسم بياني مقنع. يتيح لك Datly فحص العمل في مراحله الوسيطة.", + "TRACE THE QUESTION": "تتبّع السؤال", + "Keep your train": "حافظ على تسلسل", + "of thought.": "أفكارك.", + "Data threads organise questions, transformations, derived tables, and charts. Follow an investigation as it develops.": "تنظّم مسارات البيانات الأسئلة والتحويلات والجداول المشتقة والرسوم البيانية. تابع مسار البحث أثناء تطوّره.", + "Explore data threads": "استكشف مسارات البيانات", + "REFINE THE VIEW": "حسّن العرض", + "A chart you can": "رسم بياني يمكنك", + "work with.": "العمل عليه.", + "Use visual encoding controls for axes, colour, and other chart properties. Adjust the view as your question becomes more precise.": "استخدم عناصر التحكم في الترميز البصري للمحاور والألوان وخصائص الرسم الأخرى. عدّل العرض كلما أصبح سؤالك أدق.", + "Explore the canvas": "استكشف مساحة الرسم", + "EXPLAIN THE FINDING": "اشرح النتيجة", + "More than": "أكثر من", + "a screenshot.": "لقطة شاشة.", + "Combine written context with chart images in an editable report. Save an image or use your browser’s print flow for PDF.": "اجمع السياق المكتوب وصور الرسوم البيانية في تقرير قابل للتحرير. احفظ صورة أو استخدم الطباعة في المتصفح لإنشاء PDF.", + "Explore reports": "استكشف التقارير", + "04 / START WITH WHAT YOU HAVE": "04 / ابدأ بما لديك", + "Your data.": "بياناتك.", + "A familiar starting point.": "نقطة بداية مألوفة.", + "Work with files and supported data sources. Connections depend on the appropriate drivers, credentials, and network access.": "اعمل على الملفات ومصادر البيانات المدعومة. تعتمد الاتصالات على برامج التشغيل وبيانات الاعتماد والوصول المناسب إلى الشبكة.", + "A file today.": "ملف اليوم.", + "A warehouse tomorrow.": "ومستودع بيانات غدًا.", + "Start small with a CSV or a sample dataset. Bring in a supported connection when your question calls for more.": "ابدأ ببساطة مع ملف CSV أو مجموعة بيانات تجريبية. أضف اتصالًا مدعومًا عندما يحتاج سؤالك إلى المزيد.", + "See connector implementation": "اطّلع على تنفيذ الموصلات", + "Filter data sources": "صفِّ مصادر البيانات", + "All sources": "جميع المصادر", + "SQL & warehouses": "SQL ومستودعات البيانات", + "Cloud & storage": "السحابة والتخزين", + "Files & web": "الملفات والويب", + "Relational database": "قاعدة بيانات علائقية", + "SQL warehouse": "مستودع بيانات SQL", + "Cloud warehouse": "مستودع بيانات سحابي", + "Analytics database": "قاعدة بيانات تحليلية", + "Object storage": "تخزين الكائنات", + "Serverless queries": "استعلامات دون خادم", + "Document database": "قاعدة بيانات مستندات", + "Data Explorer": "مستكشف البيانات", + "Dataset import": "استيراد مجموعة بيانات", + "Local files": "ملفات محلية", + "CSV, Excel & Parquet": "CSV وExcel وParquet", + "Web sources": "مصادر الويب", + "Supported table extraction": "استخراج الجداول المدعوم", + "Example datasets": "مجموعات بيانات تجريبية", + "Try a sample analysis": "جرّب تحليلًا توضيحيًا", + "Showing all 16 source options. Names describe implementations, not certified integrations.": "تُعرض جميع خيارات المصادر البالغ عددها 16. تصف الأسماء عمليات التنفيذ المتاحة، ولا تعني تكاملات معتمدة.", + "05 / MAKE AN INFORMED CHOICE": "05 / اختر عن معرفة", + "Local processing.": "معالجة محلية.", + "Clear data boundaries.": "وحدود بيانات واضحة.", + "Read the model context code": "اقرأ شيفرة سياق النموذج", + "01 / EXECUTION": "01 / التنفيذ", + "Work in your workspace": "اعمل في بيئتك", + "DuckDB and Python run transformations in the environment where you host Datly, including a local installation on your computer.": "يشغّل DuckDB وPython التحويلات في البيئة التي تستضيف Datly، بما في ذلك التثبيت المحلي على حاسوبك.", + "02 / MODEL CONTEXT": "02 / سياق النموذج", + "Know what AI receives": "اعرف ما يتلقاه الذكاء الاصطناعي", + "AI requests can include schema, sample values and rows, prompts, and tool outputs. A cloud model provider receives the context sent to it.": "قد تتضمن طلبات الذكاء الاصطناعي مخطط البيانات وقيمًا وصفوفًا تجريبية ومطالبات ومخرجات الأدوات. يتلقى مزوّد النموذج السحابي السياق المرسل إليه.", + "03 / YOUR CONFIGURATION": "03 / إعداداتك", + "Choose your model": "اختر نموذجك", + "Connect a supported provider or configure a local model through Ollama. Local AI requests require a local endpoint; remote data sources still need network access.": "اتصل بمزوّد مدعوم أو اضبط نموذجًا محليًا عبر Ollama. تتطلب طلبات الذكاء الاصطناعي المحلية نقطة نهاية محلية؛ وتظل مصادر البيانات البعيدة بحاجة إلى الشبكة.", + "06 / YOUR NEXT GOOD QUESTION": "06 / سؤالك الجيد التالي", + "A little setup.": "قليل من الإعداد.", + "A lot to explore.": "وكثير مما يستحق الاستكشاف.", + "Build from source and launch Datly in your browser. Start with an example dataset, then configure a model for AI-assisted exploration.": "ابنِ التطبيق من المصدر وشغّل Datly في متصفحك. ابدأ بمجموعة بيانات تجريبية، ثم اضبط نموذجًا للاستكشاف بمساعدة الذكاء الاصطناعي.", + "Python 3.11 or later": "Python 3.11 أو أحدث", + "Node.js and Yarn for the frontend": "Node.js وYarn للواجهة الأمامية", + "uv for the Python environment": "uv لبيئة Python", + "Read the setup guide": "اقرأ دليل الإعداد", + "View releases": "اعرض الإصدارات", + "Installation method": "طريقة التثبيت", + "From source": "من المصدر", + "Docker": "Docker", + "Copy installation commands": "انسخ أوامر التثبيت", + "Copy": "نسخ", + "Open": "افتح", + "after launch": "بعد التشغيل", + "A FEW THINGS TO KNOW": "بعض الأمور التي ينبغي معرفتها", + "Before you": "قبل أن", + "dive in.": "تبدأ.", + "Questions or feedback?": "لديك أسئلة أو ملاحظات؟", + "What is Datly?": "ما هو Datly؟", + "Datly is an open-source analytics workspace built on Microsoft Research’s Data Formulator. It combines natural-language exploration, visual chart controls, data threads, table inspection, and editable reports. The project is in beta.": "Datly مساحة عمل مفتوحة المصدر لتحليل البيانات، مبنية على Data Formulator من Microsoft Research. تجمع الاستكشاف باللغة الطبيعية وعناصر التحكم البصري في الرسوم ومسارات البيانات وفحص الجداول والتقارير القابلة للتحرير. المشروع في مرحلة بيتا.", + "Does my data stay on my computer?": "هل تبقى بياناتي على حاسوبي؟", + "A local installation processes transformations on your computer. AI requests can include sample rows, field values, prompts, and tool outputs, which are sent to your configured model. If you select a cloud provider, that provider receives this context. A local model endpoint can keep model requests local; review your configuration and connected data sources.": "ينفّذ التثبيت المحلي التحويلات على حاسوبك. قد تتضمن طلبات الذكاء الاصطناعي صفوفًا تجريبية وقيم حقول ومطالبات ومخرجات أدوات تُرسل إلى النموذج الذي ضبطته. إذا اخترت مزوّدًا سحابيًا، فسيتلقى هذا السياق. يمكن لنقطة نهاية نموذج محلية إبقاء طلبات النموذج محلية؛ راجع إعداداتك ومصادر البيانات المتصلة.", + "Does inspecting the table guarantee a correct answer?": "هل يضمن فحص الجدول إجابة صحيحة؟", + "No. The table helps you inspect the computed values, but AI can still choose an incorrect transformation, aggregation, or interpretation. Review the generated work, underlying data, and business assumptions before relying on a result.": "لا. يساعدك الجدول على فحص القيم المحسوبة، لكن الذكاء الاصطناعي قد يختار تحويلًا أو تجميعًا أو تفسيرًا غير صحيح. راجع العمل المنشأ والبيانات الأساسية وافتراضات العمل قبل الاعتماد على النتيجة.", + "Which models can I use?": "ما النماذج التي يمكنني استخدامها؟", + "The code includes support for OpenAI, Azure OpenAI, Anthropic, Gemini, and local Ollama endpoints. You configure your provider and credentials. Model availability, capabilities, and any usage costs depend on that provider.": "تتضمن الشيفرة دعمًا لـ OpenAI وAzure OpenAI وAnthropic وGemini ونقاط نهاية Ollama المحلية. أنت تضبط المزوّد وبيانات الاعتماد. يعتمد توفر النماذج وقدراتها وتكاليف استخدامها على ذلك المزوّد.", + "Can I create reports and dashboards?": "هل يمكنني إنشاء تقارير ولوحات معلومات؟", + "You can edit rich-text reports with chart images, export a report image, or print to PDF. The current dashboard implementation saves chart references and displays title/type placeholders; a rendered, interactive multi-chart dashboard is not yet implemented.": "يمكنك تحرير تقارير بنص منسّق مع صور الرسوم البيانية، أو تصدير صورة للتقرير، أو طباعته بصيغة PDF. يحفظ تنفيذ لوحة المعلومات الحالي مراجع الرسوم ويعرض عناصر نائبة للعناوين والأنواع؛ ولم تُنفّذ بعد لوحة متعددة الرسوم تعرض الرسوم فعليًا وتتيح التفاعل معها.", + "Is Datly free?": "هل Datly مجاني؟", + "Datly’s source code is available under the MIT licence. Your model provider, connected infrastructure, or hosting may have their own costs. Check the repository’s setup instructions and releases for the current installation options.": "الشيفرة المصدرية لـ Datly متاحة بموجب ترخيص MIT. قد يفرض مزوّد النموذج أو البنية التحتية المتصلة أو الاستضافة تكاليف منفصلة. راجع تعليمات الإعداد والإصدارات في المستودع لمعرفة خيارات التثبيت الحالية.", + "THERE’S A STORY IN YOUR DATA.": "في بياناتك قصة.", + "Start with a good question.": "ابدأ بسؤال جيد.", + "Get started with Datly": "ابدأ مع Datly", + "A clearer view of your data.": "رؤية أوضح لبياناتك.", + "GitHub ↗": "GitHub ↗", + "Documentation ↗": "التوثيق ↗", + "Releases ↗": "الإصدارات ↗", + "Image library ↗": "مكتبة الصور ↗", + "MIT licence": "ترخيص MIT", + "Built on": "مبني على", + "Microsoft Data Formulator": "Microsoft Data Formulator", + "Datly workspace": "مساحة عمل Datly", + "Close full image": "أغلق الصورة الكاملة", + "Close": "إغلاق", + "Actual application capture using example data.": "لقطة فعلية للتطبيق باستخدام بيانات تجريبية.", + "© 2026 Datly contributors ·": "© 2026 المساهمون في Datly ·", + "Language": "اللغة", + "Choose a language": "اختر لغة", + "Close language menu": "أغلق قائمة اللغات", + "Stars on GitHub": "النجوم على GitHub", + "GitHub stars; verified 2 November 2026": "نجوم GitHub؛ تم التحقق في 2 نوفمبر 2026", + "Installation commands copied.": "تم نسخ أوامر التثبيت.", + "Automatic copy is unavailable. The commands are selected; use your browser’s copy shortcut.": "النسخ التلقائي غير متاح. تم تحديد الأوامر؛ استخدم اختصار النسخ في المتصفح.", + "Copied ✓": "تم النسخ ✓", + "Copy ⧉": "نسخ ⧉", + "Select & copy": "تحديد ونسخ", + "Close navigation": "أغلق قائمة التنقل", + "Showing {count} source options. Names describe implementations, not certified integrations.": "تُعرض {count} من خيارات المصادر. تصف الأسماء عمليات التنفيذ المتاحة، ولا تعني تكاملات معتمدة.", + "The Datly workspace": "مساحة عمل Datly", + "Actual Datly workspace showing data threads, a stock-price chart, and computed rows.": "مساحة عمل Datly الفعلية، تعرض مسارات البيانات ورسم أسعار الأسهم وصفوفًا محسوبة.", + "Actual application capture, reframed for presentation. Example stock data; results require review.": "لقطة فعلية للتطبيق، أعيد تأطيرها للعرض. بيانات الأسهم توضيحية؛ والنتائج تتطلب المراجعة.", + "The rows behind the line.": "الصفوف وراء الخط البياني.", + "Detail views of the real Datly Explore canvas, showing a chart and computed data rows.": "مشاهد تفصيلية لمساحة Explore الفعلية في Datly، تعرض رسمًا بيانيًا وصفوف بيانات محسوبة.", + "Keep the path to your answer.": "احتفظ بالطريق إلى إجابتك.", + "A follow-up question should build on what you have learned. Data threads keep the prompts, derived tables, and chart results in a visible analytical path.": "ينبغي لسؤال المتابعة أن يبني على ما تعلّمته. تحافظ مسارات البيانات على المطالبات والجداول المشتقة ونتائج الرسوم ضمن مسار تحليلي مرئي.", + "Follow prompts and derived tables": "تتبّع المطالبات والجداول المشتقة", + "Explore a separate line of inquiry": "استكشف مسار بحث مستقلًا", + "Keep previous steps in context": "احتفظ بسياق الخطوات السابقة", + "Actual Datly data threads showing separate stock-price questions and derived tables.": "مسارات بيانات فعلية في Datly تعرض أسئلة منفصلة عن أسعار الأسهم وجداول مشتقة.", + "Actual application capture, cropped to focus on data threads. Example stock analysis.": "لقطة فعلية للتطبيق، قُصّت للتركيز على مسارات البيانات. مثال لتحليل الأسهم.", + "Get to know your data.": "تعرّف إلى بياناتك.", + "Look beyond the picture. Browse table rows, review data-quality signals, and export the derived data for a closer look in your own tools.": "انظر إلى ما وراء الصورة. تصفّح صفوف الجدول، وراجع مؤشرات جودة البيانات، وصدّر البيانات المشتقة لفحصها عن قرب بأدواتك.", + "Review table rows and columns": "راجع صفوف الجدول وأعمدته", + "Inspect data-quality signals": "افحص مؤشرات جودة البيانات", + "Download the computed table as CSV": "نزّل الجدول المحسوب بصيغة CSV", + "Actual Datly data inspection view with quality signals, table rows, and CSV export.": "عرض فحص البيانات الفعلي في Datly، مع مؤشرات الجودة وصفوف الجدول وتصدير CSV.", + "Actual application capture, cropped to focus on data inspection. Quality signals are diagnostics, not a certification of correctness.": "لقطة فعلية للتطبيق، قُصّت للتركيز على فحص البيانات. مؤشرات الجودة أدوات تشخيصية، وليست شهادة بصحة النتائج.", + "Give the finding some context.": "أضف سياقًا للنتيجة.", + "Turn an exploration into something another person can follow. Write and edit a report with chart images, then export an image or print to PDF.": "حوّل الاستكشاف إلى محتوى يستطيع الآخرون متابعته. اكتب وحرّر تقريرًا يضم صور الرسوم، ثم صدّر صورة أو اطبع بصيغة PDF.", + "Rich-text editing with chart images": "تحرير نص منسّق مع صور الرسوم البيانية", + "Add the reasoning around the result": "أضف تفسيرًا للنتيجة", + "Save an image or print to PDF": "احفظ صورة أو اطبع بصيغة PDF", + "An archived Data Formulator report example showing written findings and a chart.": "مثال مؤرشف لتقرير Data Formulator يعرض نتائج مكتوبة ورسمًا بيانيًا.", + "Archived report example from the repository; it shows the earlier Data Formulator interface. Current report editing uses TipTap. Chart embeds are images.": "مثال تقرير مؤرشف من المستودع يعرض واجهة Data Formulator السابقة. يستخدم تحرير التقارير الحالي TipTap. الرسوم المضمّنة صور.", + "Project at a glance": "لمحة عن المشروع", + "Data source options": "خيارات مصادر البيانات", + "Website languages": "لغات الموقع", + "Open-source licence": "ترخيص مفتوح المصدر" + } +} diff --git a/docs/locales/de.json b/docs/locales/de.json new file mode 100644 index 0000000..3104a52 --- /dev/null +++ b/docs/locales/de.json @@ -0,0 +1,227 @@ +{ + "language": "de", + "nativeName": "Deutsch", + "direction": "ltr", + "translations": { + "width=device-width, initial-scale=1": "width=device-width, initial-scale=1", + "Datly — Open-source AI analytics workspace": "Datly — Open-Source-Arbeitsbereich für KI-gestützte Datenanalyse", + "An open-source analytics workspace. Explore tables with AI, refine charts, inspect underlying rows, and turn your findings into reports.": "Ein Open-Source-Arbeitsbereich für Datenanalyse. Erkunden Sie Tabellen mit KI, verfeinern Sie Diagramme, prüfen Sie die zugrunde liegenden Zeilen und erstellen Sie Berichte aus Ihren Erkenntnissen.", + "Datly — Good questions. Clearer answers.": "Datly — Gute Fragen. Klarere Antworten.", + "Explore with AI. Inspect the data behind your charts. An open-source workspace for your next good question.": "Erkunden Sie mit KI. Prüfen Sie die Daten hinter Ihren Diagrammen. Ein Open-Source-Arbeitsbereich für Ihre nächste gute Frage.", + "Datly workspace with data threads, a chart, and underlying data rows": "Datly-Arbeitsbereich mit Analysepfaden, einem Diagramm und den zugrunde liegenden Datenzeilen", + "An open-source workspace for exploring data with AI, inspecting charts, and writing reports.": "Ein Open-Source-Arbeitsbereich zum Erkunden von Daten mit KI, Prüfen von Diagrammen und Verfassen von Berichten.", + "Skip to content": "Zum Inhalt springen", + "Datly home": "Datly-Startseite", + "Open navigation": "Navigation öffnen", + "Main navigation": "Hauptnavigation", + "Product": "Produkt", + "How it works": "So funktioniert’s", + "Data sources": "Datenquellen", + "FAQ": "Häufige Fragen", + "Get started": "Loslegen", + "OPEN SOURCE. OPEN TO EXPLORATION.": "OPEN SOURCE. OFFEN FÜR NEUE ERKENNTNISSE.", + "Good questions.": "Gute Fragen.", + "Clearer answers.": "Klarere Antworten.", + "Your data has a story. Find it with an AI-assisted workspace that keeps your questions, charts, and underlying rows connected.": "Ihre Daten erzählen eine Geschichte. Entdecken Sie sie in einem KI-gestützten Arbeitsbereich, der Ihre Fragen, Diagramme und die zugrunde liegenden Zeilen verbindet.", + "Start exploring": "Erkundung starten", + "Take a closer look": "Genauer hinsehen", + "Free, open-source software · MIT licensed": "Kostenlose Open-Source-Software · MIT-Lizenz", + "For analysts, researchers, and data teams.": "Für Analysten, Forschende und Datenteams.", + "A little less back and forth. A little more insight.": "Weniger Hin und Her. Mehr Erkenntnis.", + "ACTUAL APP · EXAMPLE DATA": "ECHTE APP · BEISPIELDATEN", + "Enlarge the Datly workspace screenshot": "Screenshot des Datly-Arbeitsbereichs vergrößern", + "Datly workspace showing a stock-price analysis thread, line chart, and the data rows underneath": "Datly-Arbeitsbereich mit einem Analysepfad zu Aktienkursen, einem Liniendiagramm und den Datenzeilen darunter", + "Explore the workspace": "Arbeitsbereich erkunden", + "Questions → transformations → charts → rows": "Fragen → Transformationen → Diagramme → Zeilen", + "YOUR ANALYSIS, IN CONTEXT": "IHRE ANALYSE IM KONTEXT", + "Built on a solid foundation": "Auf einer soliden Grundlage", + "PUBLIC BY DESIGN": "VON GRUND AUF ÖFFENTLICH", + "Built in the open.": "Offen entwickelt.", + "Explore the source, issues, and releases.": "Entdecken Sie Quellcode, Issues und Releases.", + "GITHUB COMMUNITY": "GITHUB-COMMUNITY", + "stars on GitHub": "Sterne auf GitHub", + "Count verified 2 November 2026; refreshes live.": "Stand: 2. November 2026; wird live aktualisiert.", + "MADE FOR DATA WORK": "FÜR DIE ARBEIT MIT DATEN", + "Your next analysis.": "Ihre nächste Analyse.", + "01 / FROM QUESTION TO UNDERSTANDING": "01 / VON DER FRAGE ZUM VERSTÄNDNIS", + "Follow your curiosity.": "Folgen Sie Ihrer Neugier.", + "Keep the context.": "Behalten Sie den Kontext.", + "Move from a table to a useful finding in one workspace. Ask, refine, and inspect as your analysis takes shape.": "Von der Tabelle zur nützlichen Erkenntnis – in einem Arbeitsbereich. Fragen, verfeinern und prüfen Sie, während Ihre Analyse Gestalt annimmt.", + "Bring your data": "Bringen Sie Ihre Daten mit", + "Load a local file, connect a supported database, or explore a bundled example.": "Laden Sie eine lokale Datei, verbinden Sie eine unterstützte Datenbank oder erkunden Sie ein mitgeliefertes Beispiel.", + "Ask the next question": "Stellen Sie die nächste Frage", + "Describe what you want to explore. Refine your chart with visual encoding controls or a follow-up question.": "Beschreiben Sie, was Sie erkunden möchten. Verfeinern Sie Ihr Diagramm mit visuellen Kodierungsoptionen oder einer Folgefrage.", + "Inspect, then explain": "Erst prüfen, dann erklären", + "Check the derived rows, export a CSV, and give your findings context in an editable report.": "Prüfen Sie die berechneten Zeilen, exportieren Sie eine CSV-Datei und erläutern Sie Ihre Erkenntnisse in einem bearbeitbaren Bericht.", + "02 / A CLOSER LOOK": "02 / EIN GENAUERER BLICK", + "See the work.": "Sehen Sie die Arbeit.", + "Not just the answer.": "Nicht nur die Antwort.", + "Real application views, with room to look closer. Explore the features that make the analysis easier to follow.": "Echte Ansichten der Anwendung mit Raum für Details. Entdecken Sie die Funktionen, die eine Analyse nachvollziehbarer machen.", + "Explore Datly features": "Datly-Funktionen erkunden", + "Explore & inspect": "Erkunden und prüfen", + "Data threads": "Analysepfade", + "Data inspection": "Datenprüfung", + "Reports": "Berichte", + "The rows behind": "Die Zeilen hinter", + "the line.": "der Kurve.", + "A chart is a starting point. Open the underlying table to inspect the values, check a transformation, and decide what the result actually means.": "Ein Diagramm ist ein Ausgangspunkt. Öffnen Sie die zugrunde liegende Tabelle, um Werte zu prüfen, eine Transformation nachzuvollziehen und zu entscheiden, was das Ergebnis tatsächlich bedeutet.", + "Chart and computed table together": "Diagramm und berechnete Tabelle zusammen", + "Inspect and export the derived rows": "Berechnete Zeilen prüfen und exportieren", + "Review results before you share": "Ergebnisse vor dem Teilen prüfen", + "View full image": "Vollständiges Bild ansehen", + "Enlarge this feature screenshot": "Diesen Funktions-Screenshot vergrößern", + "Detail views of the real Datly Explore canvas, showing a chart and computed data rows": "Detailansichten der echten Datly-Explore-Oberfläche mit einem Diagramm und berechneten Datenzeilen", + "Actual application capture, reframed for clarity. Example data is illustrative; AI-generated analysis requires review.": "Echter Screenshot der Anwendung, für mehr Übersicht neu gerahmt. Beispieldaten dienen der Veranschaulichung; KI-generierte Analysen müssen geprüft werden.", + "03 / STAY IN THE LOOP": "03 / BEHALTEN SIE DEN ÜBERBLICK", + "AI helps you explore.": "KI hilft beim Erkunden.", + "You make the call.": "Sie entscheiden.", + "Good analysis depends on more than a convincing chart. Datly makes the intermediate work available for you to examine.": "Eine gute Analyse braucht mehr als ein überzeugendes Diagramm. Datly macht die Zwischenschritte für Sie einsehbar.", + "TRACE THE QUESTION": "DER FRAGE NACHGEHEN", + "Keep your train": "Behalten Sie Ihren", + "of thought.": "Gedankengang.", + "Data threads organise questions, transformations, derived tables, and charts. Follow an investigation as it develops.": "Analysepfade ordnen Fragen, Transformationen, abgeleitete Tabellen und Diagramme. Verfolgen Sie eine Untersuchung Schritt für Schritt.", + "Explore data threads": "Analysepfade erkunden", + "REFINE THE VIEW": "DIE ANSICHT VERFEINERN", + "A chart you can": "Ein Diagramm, mit dem", + "work with.": "Sie arbeiten können.", + "Use visual encoding controls for axes, colour, and other chart properties. Adjust the view as your question becomes more precise.": "Nutzen Sie visuelle Kodierungsoptionen für Achsen, Farben und weitere Diagrammeigenschaften. Passen Sie die Ansicht an, während Ihre Frage präziser wird.", + "Explore the canvas": "Die Analysefläche erkunden", + "EXPLAIN THE FINDING": "DIE ERKENNTNIS ERKLÄREN", + "More than": "Mehr als", + "a screenshot.": "ein Screenshot.", + "Combine written context with chart images in an editable report. Save an image or use your browser’s print flow for PDF.": "Verbinden Sie erläuternden Text und Diagrammbilder in einem bearbeitbaren Bericht. Speichern Sie ein Bild oder nutzen Sie die Druckfunktion des Browsers für eine PDF-Datei.", + "Explore reports": "Berichte erkunden", + "04 / START WITH WHAT YOU HAVE": "04 / MIT DEM VORHANDENEN STARTEN", + "Your data.": "Ihre Daten.", + "A familiar starting point.": "Ein vertrauter Ausgangspunkt.", + "Work with files and supported data sources. Connections depend on the appropriate drivers, credentials, and network access.": "Arbeiten Sie mit Dateien und unterstützten Datenquellen. Verbindungen benötigen die passenden Treiber, Zugangsdaten und Netzwerkzugriff.", + "A file today.": "Heute eine Datei.", + "A warehouse tomorrow.": "Morgen ein Data Warehouse.", + "Start small with a CSV or a sample dataset. Bring in a supported connection when your question calls for more.": "Beginnen Sie einfach mit einer CSV-Datei oder einem Beispieldatensatz. Nutzen Sie eine unterstützte Verbindung, wenn Ihre Frage mehr erfordert.", + "See connector implementation": "Konnektor-Implementierung ansehen", + "Filter data sources": "Datenquellen filtern", + "All sources": "Alle Quellen", + "SQL & warehouses": "SQL und Data Warehouses", + "Cloud & storage": "Cloud und Speicher", + "Files & web": "Dateien und Web", + "Relational database": "Relationale Datenbank", + "SQL warehouse": "SQL-Warehouse", + "Cloud warehouse": "Cloud-Warehouse", + "Analytics database": "Analysedatenbank", + "Object storage": "Objektspeicher", + "Serverless queries": "Serverlose Abfragen", + "Document database": "Dokumentendatenbank", + "Data Explorer": "Data Explorer", + "Dataset import": "Datensatzimport", + "Local files": "Lokale Dateien", + "CSV, Excel & Parquet": "CSV, Excel und Parquet", + "Web sources": "Webquellen", + "Supported table extraction": "Unterstützte Tabellenextraktion", + "Example datasets": "Beispieldatensätze", + "Try a sample analysis": "Eine Beispielanalyse ausprobieren", + "Showing all 16 source options. Names describe implementations, not certified integrations.": "Alle 16 Quellenoptionen werden angezeigt. Die Namen bezeichnen Implementierungen, keine zertifizierten Integrationen.", + "05 / MAKE AN INFORMED CHOICE": "05 / INFORMIERT ENTSCHEIDEN", + "Local processing.": "Lokale Verarbeitung.", + "Clear data boundaries.": "Klare Datengrenzen.", + "Read the model context code": "Code für den Modellkontext lesen", + "01 / EXECUTION": "01 / AUSFÜHRUNG", + "Work in your workspace": "Arbeiten Sie in Ihrer Umgebung", + "DuckDB and Python run transformations in the environment where you host Datly, including a local installation on your computer.": "DuckDB und Python führen Transformationen in der Umgebung aus, in der Sie Datly betreiben – auch bei einer lokalen Installation auf Ihrem Computer.", + "02 / MODEL CONTEXT": "02 / MODELLKONTEXT", + "Know what AI receives": "Wissen, was die KI erhält", + "AI requests can include schema, sample values and rows, prompts, and tool outputs. A cloud model provider receives the context sent to it.": "KI-Anfragen können das Schema, Beispielwerte und -zeilen, Prompts sowie Werkzeugausgaben enthalten. Ein Cloud-Modellanbieter erhält den Kontext, der an ihn gesendet wird.", + "03 / YOUR CONFIGURATION": "03 / IHRE KONFIGURATION", + "Choose your model": "Wählen Sie Ihr Modell", + "Connect a supported provider or configure a local model through Ollama. Local AI requests require a local endpoint; remote data sources still need network access.": "Verbinden Sie einen unterstützten Anbieter oder konfigurieren Sie ein lokales Modell über Ollama. Lokale KI-Anfragen benötigen einen lokalen Endpunkt; entfernte Datenquellen erfordern weiterhin Netzwerkzugriff.", + "06 / YOUR NEXT GOOD QUESTION": "06 / IHRE NÄCHSTE GUTE FRAGE", + "A little setup.": "Ein wenig Einrichtung.", + "A lot to explore.": "Viel zu entdecken.", + "Build from source and launch Datly in your browser. Start with an example dataset, then configure a model for AI-assisted exploration.": "Erstellen Sie Datly aus dem Quellcode und starten Sie es im Browser. Beginnen Sie mit einem Beispieldatensatz und konfigurieren Sie anschließend ein Modell für KI-gestützte Erkundungen.", + "Python 3.11 or later": "Python 3.11 oder höher", + "Node.js and Yarn for the frontend": "Node.js und Yarn für das Frontend", + "uv for the Python environment": "uv für die Python-Umgebung", + "Read the setup guide": "Installationsanleitung lesen", + "View releases": "Releases ansehen", + "Installation method": "Installationsmethode", + "From source": "Aus dem Quellcode", + "Docker": "Docker", + "Copy installation commands": "Installationsbefehle kopieren", + "Copy": "Kopieren", + "Open": "Öffnen Sie", + "after launch": "nach dem Start", + "A FEW THINGS TO KNOW": "GUT ZU WISSEN", + "Before you": "Bevor Sie", + "dive in.": "loslegen.", + "Questions or feedback?": "Fragen oder Feedback?", + "What is Datly?": "Was ist Datly?", + "Datly is an open-source analytics workspace built on Microsoft Research’s Data Formulator. It combines natural-language exploration, visual chart controls, data threads, table inspection, and editable reports. The project is in beta.": "Datly ist ein Open-Source-Arbeitsbereich für Datenanalyse auf Basis von Data Formulator von Microsoft Research. Er vereint Erkundung in natürlicher Sprache, visuelle Diagrammsteuerung, Analysepfade, Tabellenprüfung und bearbeitbare Berichte. Das Projekt befindet sich in der Betaphase.", + "Does my data stay on my computer?": "Bleiben meine Daten auf meinem Computer?", + "A local installation processes transformations on your computer. AI requests can include sample rows, field values, prompts, and tool outputs, which are sent to your configured model. If you select a cloud provider, that provider receives this context. A local model endpoint can keep model requests local; review your configuration and connected data sources.": "Eine lokale Installation führt Transformationen auf Ihrem Computer aus. KI-Anfragen können Beispielzeilen, Feldwerte, Prompts und Werkzeugausgaben enthalten, die an das konfigurierte Modell gesendet werden. Wenn Sie einen Cloud-Anbieter wählen, erhält dieser den Kontext. Ein lokaler Modellendpunkt kann Modellanfragen lokal halten; prüfen Sie Ihre Konfiguration und die verbundenen Datenquellen.", + "Does inspecting the table guarantee a correct answer?": "Garantiert die Tabellenprüfung eine korrekte Antwort?", + "No. The table helps you inspect the computed values, but AI can still choose an incorrect transformation, aggregation, or interpretation. Review the generated work, underlying data, and business assumptions before relying on a result.": "Nein. Die Tabelle hilft Ihnen beim Prüfen der berechneten Werte, doch die KI kann weiterhin eine falsche Transformation, Aggregation oder Interpretation wählen. Prüfen Sie die erzeugte Arbeit, die zugrunde liegenden Daten und die fachlichen Annahmen, bevor Sie sich auf ein Ergebnis verlassen.", + "Which models can I use?": "Welche Modelle kann ich verwenden?", + "The code includes support for OpenAI, Azure OpenAI, Anthropic, Gemini, and local Ollama endpoints. You configure your provider and credentials. Model availability, capabilities, and any usage costs depend on that provider.": "Der Code unterstützt OpenAI, Azure OpenAI, Anthropic, Gemini und lokale Ollama-Endpunkte. Sie konfigurieren Ihren Anbieter und Ihre Zugangsdaten. Verfügbarkeit, Fähigkeiten und mögliche Nutzungskosten der Modelle hängen vom jeweiligen Anbieter ab.", + "Can I create reports and dashboards?": "Kann ich Berichte und Dashboards erstellen?", + "You can edit rich-text reports with chart images, export a report image, or print to PDF. The current dashboard implementation saves chart references and displays title/type placeholders; a rendered, interactive multi-chart dashboard is not yet implemented.": "Sie können Rich-Text-Berichte mit Diagrammbildern bearbeiten, ein Berichtsbild exportieren oder als PDF drucken. Die aktuelle Dashboard-Implementierung speichert Diagrammreferenzen und zeigt Platzhalter für Titel und Typ an; ein gerendertes, interaktives Dashboard mit mehreren Diagrammen ist noch nicht implementiert.", + "Is Datly free?": "Ist Datly kostenlos?", + "Datly’s source code is available under the MIT licence. Your model provider, connected infrastructure, or hosting may have their own costs. Check the repository’s setup instructions and releases for the current installation options.": "Der Quellcode von Datly steht unter der MIT-Lizenz zur Verfügung. Für Ihren Modellanbieter, verbundene Infrastruktur oder Hosting können eigene Kosten anfallen. Aktuelle Installationsmöglichkeiten finden Sie in den Anleitungen und Releases des Repositorys.", + "THERE’S A STORY IN YOUR DATA.": "IN IHREN DATEN STECKT EINE GESCHICHTE.", + "Start with a good question.": "Beginnen Sie mit einer guten Frage.", + "Get started with Datly": "Mit Datly loslegen", + "A clearer view of your data.": "Ein klarerer Blick auf Ihre Daten.", + "GitHub ↗": "GitHub ↗", + "Documentation ↗": "Dokumentation ↗", + "Releases ↗": "Releases ↗", + "Image library ↗": "Bildbibliothek ↗", + "MIT licence": "MIT-Lizenz", + "Built on": "Auf Basis von", + "Microsoft Data Formulator": "Microsoft Data Formulator", + "Datly workspace": "Datly-Arbeitsbereich", + "Close full image": "Vollbild schließen", + "Close": "Schließen", + "Actual application capture using example data.": "Echter Screenshot der Anwendung mit Beispieldaten.", + "© 2026 Datly contributors ·": "© 2026 Datly-Mitwirkende ·", + "Language": "Sprache", + "Choose a language": "Sprache wählen", + "Close language menu": "Sprachmenü schließen", + "Stars on GitHub": "Sterne auf GitHub", + "GitHub stars; verified 2 November 2026": "GitHub-Sterne; geprüft am 2. November 2026", + "Installation commands copied.": "Installationsbefehle kopiert.", + "Automatic copy is unavailable. The commands are selected; use your browser’s copy shortcut.": "Automatisches Kopieren ist nicht verfügbar. Die Befehle sind markiert; verwenden Sie die Kopier-Tastenkombination Ihres Browsers.", + "Copied ✓": "Kopiert ✓", + "Copy ⧉": "Kopieren ⧉", + "Select & copy": "Markieren und kopieren", + "Close navigation": "Navigation schließen", + "Showing {count} source options. Names describe implementations, not certified integrations.": "{count} Quellenoptionen werden angezeigt. Die Namen bezeichnen Implementierungen, keine zertifizierten Integrationen.", + "The Datly workspace": "Der Datly-Arbeitsbereich", + "Actual Datly workspace showing data threads, a stock-price chart, and computed rows.": "Echter Datly-Arbeitsbereich mit Analysepfaden, einem Aktienkursdiagramm und berechneten Zeilen.", + "Actual application capture, reframed for presentation. Example stock data; results require review.": "Echter Screenshot der Anwendung, für die Präsentation neu gerahmt. Beispielhafte Aktiendaten; Ergebnisse müssen geprüft werden.", + "The rows behind the line.": "Die Zeilen hinter der Kurve.", + "Detail views of the real Datly Explore canvas, showing a chart and computed data rows.": "Detailansichten der echten Datly-Explore-Oberfläche mit einem Diagramm und berechneten Datenzeilen.", + "Keep the path to your answer.": "Behalten Sie den Weg zur Antwort.", + "A follow-up question should build on what you have learned. Data threads keep the prompts, derived tables, and chart results in a visible analytical path.": "Eine Folgefrage sollte auf dem bereits Gelernten aufbauen. Analysepfade halten Prompts, abgeleitete Tabellen und Diagrammergebnisse in einem sichtbaren Analyseverlauf zusammen.", + "Follow prompts and derived tables": "Prompts und abgeleiteten Tabellen folgen", + "Explore a separate line of inquiry": "Eine andere Fragestellung erkunden", + "Keep previous steps in context": "Frühere Schritte im Kontext behalten", + "Actual Datly data threads showing separate stock-price questions and derived tables.": "Echte Datly-Analysepfade mit getrennten Fragen zu Aktienkursen und abgeleiteten Tabellen.", + "Actual application capture, cropped to focus on data threads. Example stock analysis.": "Echter Screenshot der Anwendung, auf Analysepfade zugeschnitten. Beispiel einer Aktienanalyse.", + "Get to know your data.": "Lernen Sie Ihre Daten kennen.", + "Look beyond the picture. Browse table rows, review data-quality signals, and export the derived data for a closer look in your own tools.": "Sehen Sie über das Bild hinaus. Durchsuchen Sie Tabellenzeilen, prüfen Sie Hinweise zur Datenqualität und exportieren Sie abgeleitete Daten für eine genauere Untersuchung in Ihren eigenen Werkzeugen.", + "Review table rows and columns": "Tabellenzeilen und -spalten prüfen", + "Inspect data-quality signals": "Hinweise zur Datenqualität prüfen", + "Download the computed table as CSV": "Berechnete Tabelle als CSV herunterladen", + "Actual Datly data inspection view with quality signals, table rows, and CSV export.": "Echte Datly-Datenprüfung mit Qualitätshinweisen, Tabellenzeilen und CSV-Export.", + "Actual application capture, cropped to focus on data inspection. Quality signals are diagnostics, not a certification of correctness.": "Echter Screenshot der Anwendung, auf die Datenprüfung zugeschnitten. Qualitätshinweise sind Diagnosen und keine Bestätigung der Richtigkeit.", + "Give the finding some context.": "Geben Sie der Erkenntnis Kontext.", + "Turn an exploration into something another person can follow. Write and edit a report with chart images, then export an image or print to PDF.": "Machen Sie aus einer Erkundung etwas, das andere nachvollziehen können. Schreiben und bearbeiten Sie einen Bericht mit Diagrammbildern und exportieren Sie anschließend ein Bild oder drucken Sie als PDF.", + "Rich-text editing with chart images": "Rich-Text-Bearbeitung mit Diagrammbildern", + "Add the reasoning around the result": "Den Gedankengang zum Ergebnis ergänzen", + "Save an image or print to PDF": "Ein Bild speichern oder als PDF drucken", + "An archived Data Formulator report example showing written findings and a chart.": "Archiviertes Data-Formulator-Berichtsbeispiel mit schriftlichen Erkenntnissen und einem Diagramm.", + "Archived report example from the repository; it shows the earlier Data Formulator interface. Current report editing uses TipTap. Chart embeds are images.": "Archiviertes Berichtsbeispiel aus dem Repository mit der früheren Data-Formulator-Oberfläche. Die aktuelle Berichtsbearbeitung verwendet TipTap. Eingebettete Diagramme sind Bilder.", + "Project at a glance": "Das Projekt auf einen Blick", + "Data source options": "Datenquellen-Optionen", + "Website languages": "Website-Sprachen", + "Open-source licence": "Open-Source-Lizenz" + } +} diff --git a/docs/locales/en.json b/docs/locales/en.json new file mode 100644 index 0000000..506ede2 --- /dev/null +++ b/docs/locales/en.json @@ -0,0 +1,227 @@ +{ + "language": "en", + "nativeName": "English", + "direction": "ltr", + "translations": { + "width=device-width, initial-scale=1": "width=device-width, initial-scale=1", + "Datly — Open-source AI analytics workspace": "Datly — Open-source AI analytics workspace", + "An open-source analytics workspace. Explore tables with AI, refine charts, inspect underlying rows, and turn your findings into reports.": "An open-source analytics workspace. Explore tables with AI, refine charts, inspect underlying rows, and turn your findings into reports.", + "Datly — Good questions. Clearer answers.": "Datly — Good questions. Clearer answers.", + "Explore with AI. Inspect the data behind your charts. An open-source workspace for your next good question.": "Explore with AI. Inspect the data behind your charts. An open-source workspace for your next good question.", + "Datly workspace with data threads, a chart, and underlying data rows": "Datly workspace with data threads, a chart, and underlying data rows", + "An open-source workspace for exploring data with AI, inspecting charts, and writing reports.": "An open-source workspace for exploring data with AI, inspecting charts, and writing reports.", + "Skip to content": "Skip to content", + "Datly home": "Datly home", + "Open navigation": "Open navigation", + "Main navigation": "Main navigation", + "Product": "Product", + "How it works": "How it works", + "Data sources": "Data sources", + "FAQ": "FAQ", + "Get started": "Get started", + "OPEN SOURCE. OPEN TO EXPLORATION.": "OPEN SOURCE. OPEN TO EXPLORATION.", + "Good questions.": "Good questions.", + "Clearer answers.": "Clearer answers.", + "Your data has a story. Find it with an AI-assisted workspace that keeps your questions, charts, and underlying rows connected.": "Your data has a story. Find it with an AI-assisted workspace that keeps your questions, charts, and underlying rows connected.", + "Start exploring": "Start exploring", + "Take a closer look": "Take a closer look", + "Free, open-source software · MIT licensed": "Free, open-source software · MIT licensed", + "For analysts, researchers, and data teams.": "For analysts, researchers, and data teams.", + "A little less back and forth. A little more insight.": "A little less back and forth. A little more insight.", + "ACTUAL APP · EXAMPLE DATA": "ACTUAL APP · EXAMPLE DATA", + "Enlarge the Datly workspace screenshot": "Enlarge the Datly workspace screenshot", + "Datly workspace showing a stock-price analysis thread, line chart, and the data rows underneath": "Datly workspace showing a stock-price analysis thread, line chart, and the data rows underneath", + "Explore the workspace": "Explore the workspace", + "Questions → transformations → charts → rows": "Questions → transformations → charts → rows", + "YOUR ANALYSIS, IN CONTEXT": "YOUR ANALYSIS, IN CONTEXT", + "Built on a solid foundation": "Built on a solid foundation", + "PUBLIC BY DESIGN": "PUBLIC BY DESIGN", + "Built in the open.": "Built in the open.", + "Explore the source, issues, and releases.": "Explore the source, issues, and releases.", + "GITHUB COMMUNITY": "GITHUB COMMUNITY", + "stars on GitHub": "stars on GitHub", + "Count verified 2 November 2026; refreshes live.": "Count verified 2 November 2026; refreshes live.", + "MADE FOR DATA WORK": "MADE FOR DATA WORK", + "Your next analysis.": "Your next analysis.", + "01 / FROM QUESTION TO UNDERSTANDING": "01 / FROM QUESTION TO UNDERSTANDING", + "Follow your curiosity.": "Follow your curiosity.", + "Keep the context.": "Keep the context.", + "Move from a table to a useful finding in one workspace. Ask, refine, and inspect as your analysis takes shape.": "Move from a table to a useful finding in one workspace. Ask, refine, and inspect as your analysis takes shape.", + "Bring your data": "Bring your data", + "Load a local file, connect a supported database, or explore a bundled example.": "Load a local file, connect a supported database, or explore a bundled example.", + "Ask the next question": "Ask the next question", + "Describe what you want to explore. Refine your chart with visual encoding controls or a follow-up question.": "Describe what you want to explore. Refine your chart with visual encoding controls or a follow-up question.", + "Inspect, then explain": "Inspect, then explain", + "Check the derived rows, export a CSV, and give your findings context in an editable report.": "Check the derived rows, export a CSV, and give your findings context in an editable report.", + "02 / A CLOSER LOOK": "02 / A CLOSER LOOK", + "See the work.": "See the work.", + "Not just the answer.": "Not just the answer.", + "Real application views, with room to look closer. Explore the features that make the analysis easier to follow.": "Real application views, with room to look closer. Explore the features that make the analysis easier to follow.", + "Explore Datly features": "Explore Datly features", + "Explore & inspect": "Explore & inspect", + "Data threads": "Data threads", + "Data inspection": "Data inspection", + "Reports": "Reports", + "The rows behind": "The rows behind", + "the line.": "the line.", + "A chart is a starting point. Open the underlying table to inspect the values, check a transformation, and decide what the result actually means.": "A chart is a starting point. Open the underlying table to inspect the values, check a transformation, and decide what the result actually means.", + "Chart and computed table together": "Chart and computed table together", + "Inspect and export the derived rows": "Inspect and export the derived rows", + "Review results before you share": "Review results before you share", + "View full image": "View full image", + "Enlarge this feature screenshot": "Enlarge this feature screenshot", + "Detail views of the real Datly Explore canvas, showing a chart and computed data rows": "Detail views of the real Datly Explore canvas, showing a chart and computed data rows", + "Actual application capture, reframed for clarity. Example data is illustrative; AI-generated analysis requires review.": "Actual application capture, reframed for clarity. Example data is illustrative; AI-generated analysis requires review.", + "03 / STAY IN THE LOOP": "03 / STAY IN THE LOOP", + "AI helps you explore.": "AI helps you explore.", + "You make the call.": "You make the call.", + "Good analysis depends on more than a convincing chart. Datly makes the intermediate work available for you to examine.": "Good analysis depends on more than a convincing chart. Datly makes the intermediate work available for you to examine.", + "TRACE THE QUESTION": "TRACE THE QUESTION", + "Keep your train": "Keep your train", + "of thought.": "of thought.", + "Data threads organise questions, transformations, derived tables, and charts. Follow an investigation as it develops.": "Data threads organise questions, transformations, derived tables, and charts. Follow an investigation as it develops.", + "Explore data threads": "Explore data threads", + "REFINE THE VIEW": "REFINE THE VIEW", + "A chart you can": "A chart you can", + "work with.": "work with.", + "Use visual encoding controls for axes, colour, and other chart properties. Adjust the view as your question becomes more precise.": "Use visual encoding controls for axes, colour, and other chart properties. Adjust the view as your question becomes more precise.", + "Explore the canvas": "Explore the canvas", + "EXPLAIN THE FINDING": "EXPLAIN THE FINDING", + "More than": "More than", + "a screenshot.": "a screenshot.", + "Combine written context with chart images in an editable report. Save an image or use your browser’s print flow for PDF.": "Combine written context with chart images in an editable report. Save an image or use your browser’s print flow for PDF.", + "Explore reports": "Explore reports", + "04 / START WITH WHAT YOU HAVE": "04 / START WITH WHAT YOU HAVE", + "Your data.": "Your data.", + "A familiar starting point.": "A familiar starting point.", + "Work with files and supported data sources. Connections depend on the appropriate drivers, credentials, and network access.": "Work with files and supported data sources. Connections depend on the appropriate drivers, credentials, and network access.", + "A file today.": "A file today.", + "A warehouse tomorrow.": "A warehouse tomorrow.", + "Start small with a CSV or a sample dataset. Bring in a supported connection when your question calls for more.": "Start small with a CSV or a sample dataset. Bring in a supported connection when your question calls for more.", + "See connector implementation": "See connector implementation", + "Filter data sources": "Filter data sources", + "All sources": "All sources", + "SQL & warehouses": "SQL & warehouses", + "Cloud & storage": "Cloud & storage", + "Files & web": "Files & web", + "Relational database": "Relational database", + "SQL warehouse": "SQL warehouse", + "Cloud warehouse": "Cloud warehouse", + "Analytics database": "Analytics database", + "Object storage": "Object storage", + "Serverless queries": "Serverless queries", + "Document database": "Document database", + "Data Explorer": "Data Explorer", + "Dataset import": "Dataset import", + "Local files": "Local files", + "CSV, Excel & Parquet": "CSV, Excel & Parquet", + "Web sources": "Web sources", + "Supported table extraction": "Supported table extraction", + "Example datasets": "Example datasets", + "Try a sample analysis": "Try a sample analysis", + "Showing all 16 source options. Names describe implementations, not certified integrations.": "Showing all 16 source options. Names describe implementations, not certified integrations.", + "05 / MAKE AN INFORMED CHOICE": "05 / MAKE AN INFORMED CHOICE", + "Local processing.": "Local processing.", + "Clear data boundaries.": "Clear data boundaries.", + "Read the model context code": "Read the model context code", + "01 / EXECUTION": "01 / EXECUTION", + "Work in your workspace": "Work in your workspace", + "DuckDB and Python run transformations in the environment where you host Datly, including a local installation on your computer.": "DuckDB and Python run transformations in the environment where you host Datly, including a local installation on your computer.", + "02 / MODEL CONTEXT": "02 / MODEL CONTEXT", + "Know what AI receives": "Know what AI receives", + "AI requests can include schema, sample values and rows, prompts, and tool outputs. A cloud model provider receives the context sent to it.": "AI requests can include schema, sample values and rows, prompts, and tool outputs. A cloud model provider receives the context sent to it.", + "03 / YOUR CONFIGURATION": "03 / YOUR CONFIGURATION", + "Choose your model": "Choose your model", + "Connect a supported provider or configure a local model through Ollama. Local AI requests require a local endpoint; remote data sources still need network access.": "Connect a supported provider or configure a local model through Ollama. Local AI requests require a local endpoint; remote data sources still need network access.", + "06 / YOUR NEXT GOOD QUESTION": "06 / YOUR NEXT GOOD QUESTION", + "A little setup.": "A little setup.", + "A lot to explore.": "A lot to explore.", + "Build from source and launch Datly in your browser. Start with an example dataset, then configure a model for AI-assisted exploration.": "Build from source and launch Datly in your browser. Start with an example dataset, then configure a model for AI-assisted exploration.", + "Python 3.11 or later": "Python 3.11 or later", + "Node.js and Yarn for the frontend": "Node.js and Yarn for the frontend", + "uv for the Python environment": "uv for the Python environment", + "Read the setup guide": "Read the setup guide", + "View releases": "View releases", + "Installation method": "Installation method", + "From source": "From source", + "Docker": "Docker", + "Copy installation commands": "Copy installation commands", + "Copy": "Copy", + "Open": "Open", + "after launch": "after launch", + "A FEW THINGS TO KNOW": "A FEW THINGS TO KNOW", + "Before you": "Before you", + "dive in.": "dive in.", + "Questions or feedback?": "Questions or feedback?", + "What is Datly?": "What is Datly?", + "Datly is an open-source analytics workspace built on Microsoft Research’s Data Formulator. It combines natural-language exploration, visual chart controls, data threads, table inspection, and editable reports. The project is in beta.": "Datly is an open-source analytics workspace built on Microsoft Research’s Data Formulator. It combines natural-language exploration, visual chart controls, data threads, table inspection, and editable reports. The project is in beta.", + "Does my data stay on my computer?": "Does my data stay on my computer?", + "A local installation processes transformations on your computer. AI requests can include sample rows, field values, prompts, and tool outputs, which are sent to your configured model. If you select a cloud provider, that provider receives this context. A local model endpoint can keep model requests local; review your configuration and connected data sources.": "A local installation processes transformations on your computer. AI requests can include sample rows, field values, prompts, and tool outputs, which are sent to your configured model. If you select a cloud provider, that provider receives this context. A local model endpoint can keep model requests local; review your configuration and connected data sources.", + "Does inspecting the table guarantee a correct answer?": "Does inspecting the table guarantee a correct answer?", + "No. The table helps you inspect the computed values, but AI can still choose an incorrect transformation, aggregation, or interpretation. Review the generated work, underlying data, and business assumptions before relying on a result.": "No. The table helps you inspect the computed values, but AI can still choose an incorrect transformation, aggregation, or interpretation. Review the generated work, underlying data, and business assumptions before relying on a result.", + "Which models can I use?": "Which models can I use?", + "The code includes support for OpenAI, Azure OpenAI, Anthropic, Gemini, and local Ollama endpoints. You configure your provider and credentials. Model availability, capabilities, and any usage costs depend on that provider.": "The code includes support for OpenAI, Azure OpenAI, Anthropic, Gemini, and local Ollama endpoints. You configure your provider and credentials. Model availability, capabilities, and any usage costs depend on that provider.", + "Can I create reports and dashboards?": "Can I create reports and dashboards?", + "You can edit rich-text reports with chart images, export a report image, or print to PDF. The current dashboard implementation saves chart references and displays title/type placeholders; a rendered, interactive multi-chart dashboard is not yet implemented.": "You can edit rich-text reports with chart images, export a report image, or print to PDF. The current dashboard implementation saves chart references and displays title/type placeholders; a rendered, interactive multi-chart dashboard is not yet implemented.", + "Is Datly free?": "Is Datly free?", + "Datly’s source code is available under the MIT licence. Your model provider, connected infrastructure, or hosting may have their own costs. Check the repository’s setup instructions and releases for the current installation options.": "Datly’s source code is available under the MIT licence. Your model provider, connected infrastructure, or hosting may have their own costs. Check the repository’s setup instructions and releases for the current installation options.", + "THERE’S A STORY IN YOUR DATA.": "THERE’S A STORY IN YOUR DATA.", + "Start with a good question.": "Start with a good question.", + "Get started with Datly": "Get started with Datly", + "A clearer view of your data.": "A clearer view of your data.", + "GitHub ↗": "GitHub ↗", + "Documentation ↗": "Documentation ↗", + "Releases ↗": "Releases ↗", + "Image library ↗": "Image library ↗", + "MIT licence": "MIT licence", + "Built on": "Built on", + "Microsoft Data Formulator": "Microsoft Data Formulator", + "Datly workspace": "Datly workspace", + "Close full image": "Close full image", + "Close": "Close", + "Actual application capture using example data.": "Actual application capture using example data.", + "© 2026 Datly contributors ·": "© 2026 Datly contributors ·", + "Language": "Language", + "Choose a language": "Choose a language", + "Close language menu": "Close language menu", + "Stars on GitHub": "Stars on GitHub", + "GitHub stars; verified 2 November 2026": "GitHub stars; verified 2 November 2026", + "Installation commands copied.": "Installation commands copied.", + "Automatic copy is unavailable. The commands are selected; use your browser’s copy shortcut.": "Automatic copy is unavailable. The commands are selected; use your browser’s copy shortcut.", + "Copied ✓": "Copied ✓", + "Copy ⧉": "Copy ⧉", + "Select & copy": "Select & copy", + "Close navigation": "Close navigation", + "Showing {count} source options. Names describe implementations, not certified integrations.": "Showing {count} source options. Names describe implementations, not certified integrations.", + "The Datly workspace": "The Datly workspace", + "Actual Datly workspace showing data threads, a stock-price chart, and computed rows.": "Actual Datly workspace showing data threads, a stock-price chart, and computed rows.", + "Actual application capture, reframed for presentation. Example stock data; results require review.": "Actual application capture, reframed for presentation. Example stock data; results require review.", + "The rows behind the line.": "The rows behind the line.", + "Detail views of the real Datly Explore canvas, showing a chart and computed data rows.": "Detail views of the real Datly Explore canvas, showing a chart and computed data rows.", + "Keep the path to your answer.": "Keep the path to your answer.", + "A follow-up question should build on what you have learned. Data threads keep the prompts, derived tables, and chart results in a visible analytical path.": "A follow-up question should build on what you have learned. Data threads keep the prompts, derived tables, and chart results in a visible analytical path.", + "Follow prompts and derived tables": "Follow prompts and derived tables", + "Explore a separate line of inquiry": "Explore a separate line of inquiry", + "Keep previous steps in context": "Keep previous steps in context", + "Actual Datly data threads showing separate stock-price questions and derived tables.": "Actual Datly data threads showing separate stock-price questions and derived tables.", + "Actual application capture, cropped to focus on data threads. Example stock analysis.": "Actual application capture, cropped to focus on data threads. Example stock analysis.", + "Get to know your data.": "Get to know your data.", + "Look beyond the picture. Browse table rows, review data-quality signals, and export the derived data for a closer look in your own tools.": "Look beyond the picture. Browse table rows, review data-quality signals, and export the derived data for a closer look in your own tools.", + "Review table rows and columns": "Review table rows and columns", + "Inspect data-quality signals": "Inspect data-quality signals", + "Download the computed table as CSV": "Download the computed table as CSV", + "Actual Datly data inspection view with quality signals, table rows, and CSV export.": "Actual Datly data inspection view with quality signals, table rows, and CSV export.", + "Actual application capture, cropped to focus on data inspection. Quality signals are diagnostics, not a certification of correctness.": "Actual application capture, cropped to focus on data inspection. Quality signals are diagnostics, not a certification of correctness.", + "Give the finding some context.": "Give the finding some context.", + "Turn an exploration into something another person can follow. Write and edit a report with chart images, then export an image or print to PDF.": "Turn an exploration into something another person can follow. Write and edit a report with chart images, then export an image or print to PDF.", + "Rich-text editing with chart images": "Rich-text editing with chart images", + "Add the reasoning around the result": "Add the reasoning around the result", + "Save an image or print to PDF": "Save an image or print to PDF", + "An archived Data Formulator report example showing written findings and a chart.": "An archived Data Formulator report example showing written findings and a chart.", + "Archived report example from the repository; it shows the earlier Data Formulator interface. Current report editing uses TipTap. Chart embeds are images.": "Archived report example from the repository; it shows the earlier Data Formulator interface. Current report editing uses TipTap. Chart embeds are images.", + "Project at a glance": "Project at a glance", + "Data source options": "Data source options", + "Website languages": "Website languages", + "Open-source licence": "Open-source licence" + } +} diff --git a/docs/locales/es.json b/docs/locales/es.json new file mode 100644 index 0000000..6df6a4a --- /dev/null +++ b/docs/locales/es.json @@ -0,0 +1,227 @@ +{ + "language": "es", + "nativeName": "Español", + "direction": "ltr", + "translations": { + "width=device-width, initial-scale=1": "width=device-width, initial-scale=1", + "Datly — Open-source AI analytics workspace": "Datly — Espacio de análisis de datos con IA de código abierto", + "An open-source analytics workspace. Explore tables with AI, refine charts, inspect underlying rows, and turn your findings into reports.": "Un espacio de análisis de código abierto. Explora tablas con IA, perfecciona gráficos, examina las filas subyacentes y convierte tus hallazgos en informes.", + "Datly — Good questions. Clearer answers.": "Datly — Buenas preguntas. Respuestas más claras.", + "Explore with AI. Inspect the data behind your charts. An open-source workspace for your next good question.": "Explora con IA. Examina los datos detrás de tus gráficos. Un espacio de código abierto para tu próxima buena pregunta.", + "Datly workspace with data threads, a chart, and underlying data rows": "Espacio de Datly con hilos de análisis, un gráfico y las filas de datos subyacentes", + "An open-source workspace for exploring data with AI, inspecting charts, and writing reports.": "Un espacio de código abierto para explorar datos con IA, examinar gráficos y redactar informes.", + "Skip to content": "Saltar al contenido", + "Datly home": "Inicio de Datly", + "Open navigation": "Abrir navegación", + "Main navigation": "Navegación principal", + "Product": "Producto", + "How it works": "Cómo funciona", + "Data sources": "Fuentes de datos", + "FAQ": "Preguntas frecuentes", + "Get started": "Comenzar", + "OPEN SOURCE. OPEN TO EXPLORATION.": "CÓDIGO ABIERTO. ABIERTO A EXPLORAR.", + "Good questions.": "Buenas preguntas.", + "Clearer answers.": "Respuestas más claras.", + "Your data has a story. Find it with an AI-assisted workspace that keeps your questions, charts, and underlying rows connected.": "Tus datos tienen una historia. Descúbrela en un espacio asistido por IA que conecta tus preguntas, tus gráficos y las filas subyacentes.", + "Start exploring": "Empezar a explorar", + "Take a closer look": "Ver más de cerca", + "Free, open-source software · MIT licensed": "Software gratuito y de código abierto · Licencia MIT", + "For analysts, researchers, and data teams.": "Para analistas, investigadores y equipos de datos.", + "A little less back and forth. A little more insight.": "Menos idas y vueltas. Más claridad.", + "ACTUAL APP · EXAMPLE DATA": "APLICACIÓN REAL · DATOS DE EJEMPLO", + "Enlarge the Datly workspace screenshot": "Ampliar la captura del espacio de Datly", + "Datly workspace showing a stock-price analysis thread, line chart, and the data rows underneath": "Espacio de Datly con un hilo de análisis de cotizaciones, un gráfico de líneas y las filas de datos debajo", + "Explore the workspace": "Explorar el espacio de trabajo", + "Questions → transformations → charts → rows": "Preguntas → transformaciones → gráficos → filas", + "YOUR ANALYSIS, IN CONTEXT": "TU ANÁLISIS, EN CONTEXTO", + "Built on a solid foundation": "Sobre una base sólida", + "PUBLIC BY DESIGN": "PÚBLICO DESDE EL DISEÑO", + "Built in the open.": "Desarrollado a la vista de todos.", + "Explore the source, issues, and releases.": "Explora el código, las incidencias y las versiones.", + "GITHUB COMMUNITY": "COMUNIDAD DE GITHUB", + "stars on GitHub": "estrellas en GitHub", + "Count verified 2 November 2026; refreshes live.": "Recuento verificado el 2 de noviembre de 2026; se actualiza en directo.", + "MADE FOR DATA WORK": "CREADO PARA TRABAJAR CON DATOS", + "Your next analysis.": "Tu próximo análisis.", + "01 / FROM QUESTION TO UNDERSTANDING": "01 / DE LA PREGUNTA A LA COMPRENSIÓN", + "Follow your curiosity.": "Sigue tu curiosidad.", + "Keep the context.": "Conserva el contexto.", + "Move from a table to a useful finding in one workspace. Ask, refine, and inspect as your analysis takes shape.": "Pasa de una tabla a un hallazgo útil en un solo espacio. Pregunta, perfecciona y examina mientras tu análisis toma forma.", + "Bring your data": "Trae tus datos", + "Load a local file, connect a supported database, or explore a bundled example.": "Carga un archivo local, conecta una base de datos compatible o explora un ejemplo incluido.", + "Ask the next question": "Haz la siguiente pregunta", + "Describe what you want to explore. Refine your chart with visual encoding controls or a follow-up question.": "Describe lo que quieres explorar. Perfecciona el gráfico con los controles de codificación visual o con una pregunta de seguimiento.", + "Inspect, then explain": "Examina y después explica", + "Check the derived rows, export a CSV, and give your findings context in an editable report.": "Comprueba las filas calculadas, exporta un CSV y da contexto a tus hallazgos en un informe editable.", + "02 / A CLOSER LOOK": "02 / UNA MIRADA MÁS CERCANA", + "See the work.": "Observa el trabajo.", + "Not just the answer.": "No solo la respuesta.", + "Real application views, with room to look closer. Explore the features that make the analysis easier to follow.": "Vistas reales de la aplicación para mirar con detalle. Descubre las funciones que facilitan seguir el análisis.", + "Explore Datly features": "Explorar las funciones de Datly", + "Explore & inspect": "Explorar y examinar", + "Data threads": "Hilos de análisis", + "Data inspection": "Inspección de datos", + "Reports": "Informes", + "The rows behind": "Las filas detrás de", + "the line.": "la línea.", + "A chart is a starting point. Open the underlying table to inspect the values, check a transformation, and decide what the result actually means.": "Un gráfico es un punto de partida. Abre la tabla subyacente para examinar los valores, comprobar una transformación y decidir qué significa realmente el resultado.", + "Chart and computed table together": "Gráfico y tabla calculada juntos", + "Inspect and export the derived rows": "Examinar y exportar las filas calculadas", + "Review results before you share": "Revisar los resultados antes de compartirlos", + "View full image": "Ver imagen completa", + "Enlarge this feature screenshot": "Ampliar esta captura de función", + "Detail views of the real Datly Explore canvas, showing a chart and computed data rows": "Vistas detalladas del lienzo Explore real de Datly, con un gráfico y filas de datos calculadas", + "Actual application capture, reframed for clarity. Example data is illustrative; AI-generated analysis requires review.": "Captura real de la aplicación, reencuadrada para mayor claridad. Los datos de ejemplo son ilustrativos; el análisis generado por IA requiere revisión.", + "03 / STAY IN THE LOOP": "03 / MANTÉN EL CONTROL", + "AI helps you explore.": "La IA te ayuda a explorar.", + "You make the call.": "Tú decides.", + "Good analysis depends on more than a convincing chart. Datly makes the intermediate work available for you to examine.": "Un buen análisis necesita más que un gráfico convincente. Datly te permite examinar el trabajo intermedio.", + "TRACE THE QUESTION": "SIGUE LA PREGUNTA", + "Keep your train": "Conserva el hilo", + "of thought.": "de tus ideas.", + "Data threads organise questions, transformations, derived tables, and charts. Follow an investigation as it develops.": "Los hilos de análisis organizan preguntas, transformaciones, tablas derivadas y gráficos. Sigue una investigación mientras evoluciona.", + "Explore data threads": "Explorar los hilos de análisis", + "REFINE THE VIEW": "PERFECCIONA LA VISTA", + "A chart you can": "Un gráfico con el que", + "work with.": "puedes trabajar.", + "Use visual encoding controls for axes, colour, and other chart properties. Adjust the view as your question becomes more precise.": "Usa los controles de codificación visual para los ejes, el color y otras propiedades del gráfico. Ajusta la vista a medida que tu pregunta se vuelve más precisa.", + "Explore the canvas": "Explorar el lienzo", + "EXPLAIN THE FINDING": "EXPLICA EL HALLAZGO", + "More than": "Más que", + "a screenshot.": "una captura.", + "Combine written context with chart images in an editable report. Save an image or use your browser’s print flow for PDF.": "Combina texto e imágenes de gráficos en un informe editable. Guarda una imagen o usa la impresión del navegador para obtener un PDF.", + "Explore reports": "Explorar los informes", + "04 / START WITH WHAT YOU HAVE": "04 / EMPIEZA CON LO QUE TIENES", + "Your data.": "Tus datos.", + "A familiar starting point.": "Un punto de partida conocido.", + "Work with files and supported data sources. Connections depend on the appropriate drivers, credentials, and network access.": "Trabaja con archivos y fuentes de datos compatibles. Las conexiones dependen de los controladores, las credenciales y el acceso a la red adecuados.", + "A file today.": "Un archivo hoy.", + "A warehouse tomorrow.": "Un almacén de datos mañana.", + "Start small with a CSV or a sample dataset. Bring in a supported connection when your question calls for more.": "Empieza con un CSV o un conjunto de datos de ejemplo. Añade una conexión compatible cuando tu pregunta lo requiera.", + "See connector implementation": "Ver la implementación de conectores", + "Filter data sources": "Filtrar fuentes de datos", + "All sources": "Todas las fuentes", + "SQL & warehouses": "SQL y almacenes de datos", + "Cloud & storage": "Nube y almacenamiento", + "Files & web": "Archivos y web", + "Relational database": "Base de datos relacional", + "SQL warehouse": "Almacén SQL", + "Cloud warehouse": "Almacén en la nube", + "Analytics database": "Base de datos analítica", + "Object storage": "Almacenamiento de objetos", + "Serverless queries": "Consultas sin servidor", + "Document database": "Base de datos documental", + "Data Explorer": "Data Explorer", + "Dataset import": "Importación de conjuntos de datos", + "Local files": "Archivos locales", + "CSV, Excel & Parquet": "CSV, Excel y Parquet", + "Web sources": "Fuentes web", + "Supported table extraction": "Extracción de tablas compatible", + "Example datasets": "Conjuntos de datos de ejemplo", + "Try a sample analysis": "Probar un análisis de ejemplo", + "Showing all 16 source options. Names describe implementations, not certified integrations.": "Se muestran las 16 opciones de fuentes. Los nombres describen implementaciones, no integraciones certificadas.", + "05 / MAKE AN INFORMED CHOICE": "05 / ELIGE CON INFORMACIÓN", + "Local processing.": "Procesamiento local.", + "Clear data boundaries.": "Límites claros para los datos.", + "Read the model context code": "Leer el código del contexto del modelo", + "01 / EXECUTION": "01 / EJECUCIÓN", + "Work in your workspace": "Trabaja en tu entorno", + "DuckDB and Python run transformations in the environment where you host Datly, including a local installation on your computer.": "DuckDB y Python ejecutan transformaciones en el entorno donde alojas Datly, incluida una instalación local en tu ordenador.", + "02 / MODEL CONTEXT": "02 / CONTEXTO DEL MODELO", + "Know what AI receives": "Conoce lo que recibe la IA", + "AI requests can include schema, sample values and rows, prompts, and tool outputs. A cloud model provider receives the context sent to it.": "Las solicitudes a la IA pueden incluir el esquema, ejemplos de valores y filas, instrucciones y salidas de herramientas. Un proveedor de modelos en la nube recibe el contexto que se le envía.", + "03 / YOUR CONFIGURATION": "03 / TU CONFIGURACIÓN", + "Choose your model": "Elige tu modelo", + "Connect a supported provider or configure a local model through Ollama. Local AI requests require a local endpoint; remote data sources still need network access.": "Conecta un proveedor compatible o configura un modelo local mediante Ollama. Para que las solicitudes de IA sean locales se necesita un punto de conexión local; las fuentes remotas siguen necesitando acceso a la red.", + "06 / YOUR NEXT GOOD QUESTION": "06 / TU PRÓXIMA BUENA PREGUNTA", + "A little setup.": "Un poco de configuración.", + "A lot to explore.": "Mucho por explorar.", + "Build from source and launch Datly in your browser. Start with an example dataset, then configure a model for AI-assisted exploration.": "Compila desde el código fuente y abre Datly en tu navegador. Empieza con un conjunto de datos de ejemplo y configura después un modelo para explorar con ayuda de la IA.", + "Python 3.11 or later": "Python 3.11 o posterior", + "Node.js and Yarn for the frontend": "Node.js y Yarn para la interfaz", + "uv for the Python environment": "uv para el entorno de Python", + "Read the setup guide": "Leer la guía de instalación", + "View releases": "Ver versiones", + "Installation method": "Método de instalación", + "From source": "Desde el código fuente", + "Docker": "Docker", + "Copy installation commands": "Copiar los comandos de instalación", + "Copy": "Copiar", + "Open": "Abre", + "after launch": "después de iniciar", + "A FEW THINGS TO KNOW": "ALGUNAS COSAS QUE DEBES SABER", + "Before you": "Antes de", + "dive in.": "empezar.", + "Questions or feedback?": "¿Preguntas o comentarios?", + "What is Datly?": "¿Qué es Datly?", + "Datly is an open-source analytics workspace built on Microsoft Research’s Data Formulator. It combines natural-language exploration, visual chart controls, data threads, table inspection, and editable reports. The project is in beta.": "Datly es un espacio de análisis de código abierto basado en Data Formulator de Microsoft Research. Combina exploración en lenguaje natural, controles visuales de gráficos, hilos de análisis, inspección de tablas e informes editables. El proyecto está en fase beta.", + "Does my data stay on my computer?": "¿Mis datos permanecen en mi ordenador?", + "A local installation processes transformations on your computer. AI requests can include sample rows, field values, prompts, and tool outputs, which are sent to your configured model. If you select a cloud provider, that provider receives this context. A local model endpoint can keep model requests local; review your configuration and connected data sources.": "Una instalación local ejecuta las transformaciones en tu ordenador. Las solicitudes a la IA pueden incluir ejemplos de filas, valores de campos, instrucciones y salidas de herramientas, que se envían al modelo configurado. Si eliges un proveedor en la nube, ese proveedor recibe el contexto. Un punto de conexión de modelo local puede mantener las solicitudes en local; revisa tu configuración y las fuentes conectadas.", + "Does inspecting the table guarantee a correct answer?": "¿Inspeccionar la tabla garantiza una respuesta correcta?", + "No. The table helps you inspect the computed values, but AI can still choose an incorrect transformation, aggregation, or interpretation. Review the generated work, underlying data, and business assumptions before relying on a result.": "No. La tabla te ayuda a examinar los valores calculados, pero la IA puede elegir una transformación, agregación o interpretación incorrecta. Revisa el trabajo generado, los datos subyacentes y las hipótesis de negocio antes de confiar en un resultado.", + "Which models can I use?": "¿Qué modelos puedo usar?", + "The code includes support for OpenAI, Azure OpenAI, Anthropic, Gemini, and local Ollama endpoints. You configure your provider and credentials. Model availability, capabilities, and any usage costs depend on that provider.": "El código incluye compatibilidad con OpenAI, Azure OpenAI, Anthropic, Gemini y puntos de conexión locales de Ollama. Tú configuras el proveedor y las credenciales. La disponibilidad, las capacidades y los posibles costes de uso de los modelos dependen de ese proveedor.", + "Can I create reports and dashboards?": "¿Puedo crear informes y paneles?", + "You can edit rich-text reports with chart images, export a report image, or print to PDF. The current dashboard implementation saves chart references and displays title/type placeholders; a rendered, interactive multi-chart dashboard is not yet implemented.": "Puedes editar informes de texto enriquecido con imágenes de gráficos, exportar una imagen del informe o imprimirlo en PDF. La implementación actual de los paneles guarda referencias a gráficos y muestra marcadores de título y tipo; aún no se ha implementado un panel renderizado e interactivo con varios gráficos.", + "Is Datly free?": "¿Datly es gratuito?", + "Datly’s source code is available under the MIT licence. Your model provider, connected infrastructure, or hosting may have their own costs. Check the repository’s setup instructions and releases for the current installation options.": "El código fuente de Datly está disponible bajo la licencia MIT. Tu proveedor de modelos, la infraestructura conectada o el alojamiento pueden tener sus propios costes. Consulta las instrucciones y las versiones del repositorio para conocer las opciones de instalación actuales.", + "THERE’S A STORY IN YOUR DATA.": "TUS DATOS TIENEN UNA HISTORIA.", + "Start with a good question.": "Empieza con una buena pregunta.", + "Get started with Datly": "Empezar con Datly", + "A clearer view of your data.": "Una visión más clara de tus datos.", + "GitHub ↗": "GitHub ↗", + "Documentation ↗": "Documentación ↗", + "Releases ↗": "Versiones ↗", + "Image library ↗": "Biblioteca de imágenes ↗", + "MIT licence": "Licencia MIT", + "Built on": "Basado en", + "Microsoft Data Formulator": "Microsoft Data Formulator", + "Datly workspace": "Espacio de Datly", + "Close full image": "Cerrar la imagen completa", + "Close": "Cerrar", + "Actual application capture using example data.": "Captura real de la aplicación con datos de ejemplo.", + "© 2026 Datly contributors ·": "© 2026 Colaboradores de Datly ·", + "Language": "Idioma", + "Choose a language": "Elegir un idioma", + "Close language menu": "Cerrar el menú de idiomas", + "Stars on GitHub": "Estrellas en GitHub", + "GitHub stars; verified 2 November 2026": "Estrellas de GitHub; verificadas el 2 de noviembre de 2026", + "Installation commands copied.": "Comandos de instalación copiados.", + "Automatic copy is unavailable. The commands are selected; use your browser’s copy shortcut.": "La copia automática no está disponible. Los comandos están seleccionados; usa el atajo de copia del navegador.", + "Copied ✓": "Copiado ✓", + "Copy ⧉": "Copiar ⧉", + "Select & copy": "Seleccionar y copiar", + "Close navigation": "Cerrar navegación", + "Showing {count} source options. Names describe implementations, not certified integrations.": "Se muestran {count} opciones de fuentes. Los nombres describen implementaciones, no integraciones certificadas.", + "The Datly workspace": "El espacio de Datly", + "Actual Datly workspace showing data threads, a stock-price chart, and computed rows.": "Espacio real de Datly con hilos de análisis, un gráfico de cotizaciones y filas calculadas.", + "Actual application capture, reframed for presentation. Example stock data; results require review.": "Captura real de la aplicación, reencuadrada para su presentación. Datos bursátiles de ejemplo; los resultados requieren revisión.", + "The rows behind the line.": "Las filas detrás de la línea.", + "Detail views of the real Datly Explore canvas, showing a chart and computed data rows.": "Vistas detalladas del lienzo Explore real de Datly, con un gráfico y filas de datos calculadas.", + "Keep the path to your answer.": "Conserva el camino hacia tu respuesta.", + "A follow-up question should build on what you have learned. Data threads keep the prompts, derived tables, and chart results in a visible analytical path.": "Una pregunta de seguimiento debe basarse en lo que has aprendido. Los hilos de análisis mantienen las instrucciones, las tablas derivadas y los resultados gráficos en un recorrido analítico visible.", + "Follow prompts and derived tables": "Seguir las instrucciones y las tablas derivadas", + "Explore a separate line of inquiry": "Explorar una línea de investigación distinta", + "Keep previous steps in context": "Mantener los pasos anteriores en contexto", + "Actual Datly data threads showing separate stock-price questions and derived tables.": "Hilos de análisis reales de Datly con preguntas independientes sobre cotizaciones y tablas derivadas.", + "Actual application capture, cropped to focus on data threads. Example stock analysis.": "Captura real de la aplicación, recortada para destacar los hilos de análisis. Ejemplo de análisis bursátil.", + "Get to know your data.": "Conoce tus datos.", + "Look beyond the picture. Browse table rows, review data-quality signals, and export the derived data for a closer look in your own tools.": "Ve más allá de la imagen. Recorre las filas, revisa las señales de calidad y exporta los datos derivados para examinarlos en tus propias herramientas.", + "Review table rows and columns": "Revisar las filas y columnas de la tabla", + "Inspect data-quality signals": "Examinar las señales de calidad de datos", + "Download the computed table as CSV": "Descargar la tabla calculada como CSV", + "Actual Datly data inspection view with quality signals, table rows, and CSV export.": "Vista real de inspección de datos de Datly, con señales de calidad, filas y exportación CSV.", + "Actual application capture, cropped to focus on data inspection. Quality signals are diagnostics, not a certification of correctness.": "Captura real de la aplicación, recortada para destacar la inspección de datos. Las señales de calidad son diagnósticos, no una certificación de exactitud.", + "Give the finding some context.": "Da contexto al hallazgo.", + "Turn an exploration into something another person can follow. Write and edit a report with chart images, then export an image or print to PDF.": "Convierte una exploración en algo que otra persona pueda seguir. Redacta y edita un informe con imágenes de gráficos y después exporta una imagen o imprímelo en PDF.", + "Rich-text editing with chart images": "Edición de texto enriquecido con imágenes de gráficos", + "Add the reasoning around the result": "Añadir el razonamiento que lleva al resultado", + "Save an image or print to PDF": "Guardar una imagen o imprimir en PDF", + "An archived Data Formulator report example showing written findings and a chart.": "Ejemplo archivado de informe de Data Formulator con conclusiones escritas y un gráfico.", + "Archived report example from the repository; it shows the earlier Data Formulator interface. Current report editing uses TipTap. Chart embeds are images.": "Ejemplo de informe archivado en el repositorio; muestra la interfaz anterior de Data Formulator. La edición actual de informes usa TipTap. Los gráficos incrustados son imágenes.", + "Project at a glance": "El proyecto en cifras", + "Data source options": "Opciones de fuentes de datos", + "Website languages": "Idiomas del sitio", + "Open-source licence": "Licencia de código abierto" + } +} diff --git a/docs/locales/fr.json b/docs/locales/fr.json new file mode 100644 index 0000000..b06e922 --- /dev/null +++ b/docs/locales/fr.json @@ -0,0 +1,227 @@ +{ + "language": "fr", + "nativeName": "Français", + "direction": "ltr", + "translations": { + "width=device-width, initial-scale=1": "width=device-width, initial-scale=1", + "Datly — Open-source AI analytics workspace": "Datly — Espace d’analyse de données avec IA en open source", + "An open-source analytics workspace. Explore tables with AI, refine charts, inspect underlying rows, and turn your findings into reports.": "Un espace d’analyse en open source. Explorez vos tableaux avec l’IA, affinez vos graphiques, examinez les lignes sous-jacentes et transformez vos conclusions en rapports.", + "Datly — Good questions. Clearer answers.": "Datly — De bonnes questions. Des réponses plus claires.", + "Explore with AI. Inspect the data behind your charts. An open-source workspace for your next good question.": "Explorez avec l’IA. Examinez les données derrière vos graphiques. Un espace en open source pour votre prochaine bonne question.", + "Datly workspace with data threads, a chart, and underlying data rows": "Espace Datly avec des fils d’analyse, un graphique et les lignes de données sous-jacentes", + "An open-source workspace for exploring data with AI, inspecting charts, and writing reports.": "Un espace en open source pour explorer les données avec l’IA, examiner les graphiques et rédiger des rapports.", + "Skip to content": "Aller au contenu", + "Datly home": "Accueil Datly", + "Open navigation": "Ouvrir la navigation", + "Main navigation": "Navigation principale", + "Product": "Produit", + "How it works": "Fonctionnement", + "Data sources": "Sources de données", + "FAQ": "Questions fréquentes", + "Get started": "Commencer", + "OPEN SOURCE. OPEN TO EXPLORATION.": "OPEN SOURCE. OUVERT À L’EXPLORATION.", + "Good questions.": "De bonnes questions.", + "Clearer answers.": "Des réponses plus claires.", + "Your data has a story. Find it with an AI-assisted workspace that keeps your questions, charts, and underlying rows connected.": "Vos données ont une histoire. Découvrez-la dans un espace assisté par l’IA qui relie vos questions, vos graphiques et les lignes sous-jacentes.", + "Start exploring": "Commencer l’exploration", + "Take a closer look": "Regarder de plus près", + "Free, open-source software · MIT licensed": "Logiciel gratuit et open source · Licence MIT", + "For analysts, researchers, and data teams.": "Pour les analystes, les chercheurs et les équipes data.", + "A little less back and forth. A little more insight.": "Moins d’allers-retours. Plus de compréhension.", + "ACTUAL APP · EXAMPLE DATA": "APPLICATION RÉELLE · DONNÉES D’EXEMPLE", + "Enlarge the Datly workspace screenshot": "Agrandir la capture de l’espace Datly", + "Datly workspace showing a stock-price analysis thread, line chart, and the data rows underneath": "Espace Datly montrant un fil d’analyse de cours boursiers, une courbe et les lignes de données en dessous", + "Explore the workspace": "Explorer l’espace de travail", + "Questions → transformations → charts → rows": "Questions → transformations → graphiques → lignes", + "YOUR ANALYSIS, IN CONTEXT": "VOTRE ANALYSE, EN CONTEXTE", + "Built on a solid foundation": "Sur des bases solides", + "PUBLIC BY DESIGN": "OUVERT PAR CONCEPTION", + "Built in the open.": "Développé au grand jour.", + "Explore the source, issues, and releases.": "Explorez le code, les tickets et les versions.", + "GITHUB COMMUNITY": "COMMUNAUTÉ GITHUB", + "stars on GitHub": "étoiles sur GitHub", + "Count verified 2 November 2026; refreshes live.": "Nombre vérifié le 2 novembre 2026 ; actualisation en direct.", + "MADE FOR DATA WORK": "CONÇU POUR L’ANALYSE DE DONNÉES", + "Your next analysis.": "Votre prochaine analyse.", + "01 / FROM QUESTION TO UNDERSTANDING": "01 / DE LA QUESTION À LA COMPRÉHENSION", + "Follow your curiosity.": "Suivez votre curiosité.", + "Keep the context.": "Gardez le contexte.", + "Move from a table to a useful finding in one workspace. Ask, refine, and inspect as your analysis takes shape.": "Passez d’un tableau à une conclusion utile dans un seul espace. Questionnez, affinez et examinez à mesure que votre analyse prend forme.", + "Bring your data": "Apportez vos données", + "Load a local file, connect a supported database, or explore a bundled example.": "Chargez un fichier local, connectez une base prise en charge ou explorez un exemple fourni.", + "Ask the next question": "Posez la question suivante", + "Describe what you want to explore. Refine your chart with visual encoding controls or a follow-up question.": "Décrivez ce que vous souhaitez explorer. Affinez votre graphique à l’aide des réglages d’encodage visuel ou d’une question complémentaire.", + "Inspect, then explain": "Examinez, puis expliquez", + "Check the derived rows, export a CSV, and give your findings context in an editable report.": "Vérifiez les lignes calculées, exportez un CSV et donnez du contexte à vos conclusions dans un rapport modifiable.", + "02 / A CLOSER LOOK": "02 / DE PLUS PRÈS", + "See the work.": "Voyez le travail.", + "Not just the answer.": "Pas seulement la réponse.", + "Real application views, with room to look closer. Explore the features that make the analysis easier to follow.": "Des vues réelles de l’application, à examiner en détail. Découvrez les fonctions qui rendent l’analyse plus facile à suivre.", + "Explore Datly features": "Explorer les fonctions de Datly", + "Explore & inspect": "Explorer et examiner", + "Data threads": "Fils d’analyse", + "Data inspection": "Inspection des données", + "Reports": "Rapports", + "The rows behind": "Les lignes derrière", + "the line.": "la courbe.", + "A chart is a starting point. Open the underlying table to inspect the values, check a transformation, and decide what the result actually means.": "Un graphique est un point de départ. Ouvrez le tableau sous-jacent pour examiner les valeurs, vérifier une transformation et déterminer ce que le résultat signifie réellement.", + "Chart and computed table together": "Graphique et tableau calculé réunis", + "Inspect and export the derived rows": "Examiner et exporter les lignes calculées", + "Review results before you share": "Vérifier les résultats avant de les partager", + "View full image": "Voir l’image complète", + "Enlarge this feature screenshot": "Agrandir cette capture de fonction", + "Detail views of the real Datly Explore canvas, showing a chart and computed data rows": "Vues détaillées du véritable espace Explore de Datly, avec un graphique et des lignes calculées", + "Actual application capture, reframed for clarity. Example data is illustrative; AI-generated analysis requires review.": "Capture réelle de l’application, recadrée pour plus de clarté. Les données d’exemple sont illustratives ; toute analyse générée par l’IA doit être vérifiée.", + "03 / STAY IN THE LOOP": "03 / GARDEZ LA MAIN", + "AI helps you explore.": "L’IA vous aide à explorer.", + "You make the call.": "Vous décidez.", + "Good analysis depends on more than a convincing chart. Datly makes the intermediate work available for you to examine.": "Une bonne analyse ne se résume pas à un graphique convaincant. Datly vous permet d’examiner les étapes intermédiaires.", + "TRACE THE QUESTION": "SUIVEZ LA QUESTION", + "Keep your train": "Gardez le fil", + "of thought.": "de votre réflexion.", + "Data threads organise questions, transformations, derived tables, and charts. Follow an investigation as it develops.": "Les fils d’analyse organisent les questions, les transformations, les tableaux dérivés et les graphiques. Suivez une investigation au fil de son évolution.", + "Explore data threads": "Explorer les fils d’analyse", + "REFINE THE VIEW": "AFFINEZ LA VUE", + "A chart you can": "Un graphique sur lequel", + "work with.": "vous pouvez agir.", + "Use visual encoding controls for axes, colour, and other chart properties. Adjust the view as your question becomes more precise.": "Utilisez les réglages d’encodage visuel pour les axes, les couleurs et d’autres propriétés du graphique. Ajustez la vue à mesure que votre question se précise.", + "Explore the canvas": "Explorer le canevas", + "EXPLAIN THE FINDING": "EXPLIQUEZ LA CONCLUSION", + "More than": "Bien plus qu’une", + "a screenshot.": "capture d’écran.", + "Combine written context with chart images in an editable report. Save an image or use your browser’s print flow for PDF.": "Associez du texte et des images de graphiques dans un rapport modifiable. Enregistrez une image ou utilisez l’impression du navigateur pour obtenir un PDF.", + "Explore reports": "Explorer les rapports", + "04 / START WITH WHAT YOU HAVE": "04 / PARTEZ DE CE QUE VOUS AVEZ", + "Your data.": "Vos données.", + "A familiar starting point.": "Un point de départ familier.", + "Work with files and supported data sources. Connections depend on the appropriate drivers, credentials, and network access.": "Travaillez avec des fichiers et des sources de données prises en charge. Les connexions dépendent des pilotes, des identifiants et de l’accès réseau appropriés.", + "A file today.": "Un fichier aujourd’hui.", + "A warehouse tomorrow.": "Un entrepôt demain.", + "Start small with a CSV or a sample dataset. Bring in a supported connection when your question calls for more.": "Commencez simplement avec un CSV ou un jeu de données d’exemple. Ajoutez une connexion prise en charge lorsque votre question l’exige.", + "See connector implementation": "Voir l’implémentation des connecteurs", + "Filter data sources": "Filtrer les sources de données", + "All sources": "Toutes les sources", + "SQL & warehouses": "SQL et entrepôts", + "Cloud & storage": "Cloud et stockage", + "Files & web": "Fichiers et Web", + "Relational database": "Base de données relationnelle", + "SQL warehouse": "Entrepôt SQL", + "Cloud warehouse": "Entrepôt cloud", + "Analytics database": "Base de données analytique", + "Object storage": "Stockage objet", + "Serverless queries": "Requêtes sans serveur", + "Document database": "Base de données documentaire", + "Data Explorer": "Data Explorer", + "Dataset import": "Import de jeux de données", + "Local files": "Fichiers locaux", + "CSV, Excel & Parquet": "CSV, Excel et Parquet", + "Web sources": "Sources Web", + "Supported table extraction": "Extraction de tableaux prise en charge", + "Example datasets": "Jeux de données d’exemple", + "Try a sample analysis": "Essayer une analyse d’exemple", + "Showing all 16 source options. Names describe implementations, not certified integrations.": "Les 16 sources sont affichées. Les noms désignent des implémentations, pas des intégrations certifiées.", + "05 / MAKE AN INFORMED CHOICE": "05 / CHOISISSEZ EN CONNAISSANCE DE CAUSE", + "Local processing.": "Traitement local.", + "Clear data boundaries.": "Des limites claires pour les données.", + "Read the model context code": "Lire le code du contexte du modèle", + "01 / EXECUTION": "01 / EXÉCUTION", + "Work in your workspace": "Travaillez dans votre espace", + "DuckDB and Python run transformations in the environment where you host Datly, including a local installation on your computer.": "DuckDB et Python exécutent les transformations dans l’environnement où vous hébergez Datly, y compris lors d’une installation locale sur votre ordinateur.", + "02 / MODEL CONTEXT": "02 / CONTEXTE DU MODÈLE", + "Know what AI receives": "Sachez ce que reçoit l’IA", + "AI requests can include schema, sample values and rows, prompts, and tool outputs. A cloud model provider receives the context sent to it.": "Les requêtes à l’IA peuvent inclure le schéma, des exemples de valeurs et de lignes, les instructions et les sorties d’outils. Un fournisseur de modèle cloud reçoit le contexte qui lui est envoyé.", + "03 / YOUR CONFIGURATION": "03 / VOTRE CONFIGURATION", + "Choose your model": "Choisissez votre modèle", + "Connect a supported provider or configure a local model through Ollama. Local AI requests require a local endpoint; remote data sources still need network access.": "Connectez un fournisseur pris en charge ou configurez un modèle local via Ollama. Pour rester locales, les requêtes à l’IA nécessitent un point d’accès local ; les sources distantes exigent toujours un accès réseau.", + "06 / YOUR NEXT GOOD QUESTION": "06 / VOTRE PROCHAINE BONNE QUESTION", + "A little setup.": "Un peu de configuration.", + "A lot to explore.": "Beaucoup à explorer.", + "Build from source and launch Datly in your browser. Start with an example dataset, then configure a model for AI-assisted exploration.": "Compilez depuis les sources et lancez Datly dans votre navigateur. Commencez avec un jeu de données d’exemple, puis configurez un modèle pour explorer avec l’aide de l’IA.", + "Python 3.11 or later": "Python 3.11 ou version ultérieure", + "Node.js and Yarn for the frontend": "Node.js et Yarn pour l’interface", + "uv for the Python environment": "uv pour l’environnement Python", + "Read the setup guide": "Lire le guide d’installation", + "View releases": "Voir les versions", + "Installation method": "Méthode d’installation", + "From source": "Depuis les sources", + "Docker": "Docker", + "Copy installation commands": "Copier les commandes d’installation", + "Copy": "Copier", + "Open": "Ouvrez", + "after launch": "après le lancement", + "A FEW THINGS TO KNOW": "QUELQUES POINTS À CONNAÎTRE", + "Before you": "Avant de", + "dive in.": "vous lancer.", + "Questions or feedback?": "Des questions ou des remarques ?", + "What is Datly?": "Qu’est-ce que Datly ?", + "Datly is an open-source analytics workspace built on Microsoft Research’s Data Formulator. It combines natural-language exploration, visual chart controls, data threads, table inspection, and editable reports. The project is in beta.": "Datly est un espace d’analyse en open source fondé sur Data Formulator de Microsoft Research. Il réunit l’exploration en langage naturel, les réglages visuels des graphiques, les fils d’analyse, l’inspection des tableaux et les rapports modifiables. Le projet est en bêta.", + "Does my data stay on my computer?": "Mes données restent-elles sur mon ordinateur ?", + "A local installation processes transformations on your computer. AI requests can include sample rows, field values, prompts, and tool outputs, which are sent to your configured model. If you select a cloud provider, that provider receives this context. A local model endpoint can keep model requests local; review your configuration and connected data sources.": "Une installation locale exécute les transformations sur votre ordinateur. Les requêtes à l’IA peuvent inclure des exemples de lignes, des valeurs de champs, des instructions et des sorties d’outils, envoyés au modèle configuré. Si vous choisissez un fournisseur cloud, celui-ci reçoit ce contexte. Un point d’accès de modèle local peut maintenir les requêtes en local ; vérifiez votre configuration et les sources connectées.", + "Does inspecting the table guarantee a correct answer?": "L’inspection du tableau garantit-elle une réponse correcte ?", + "No. The table helps you inspect the computed values, but AI can still choose an incorrect transformation, aggregation, or interpretation. Review the generated work, underlying data, and business assumptions before relying on a result.": "Non. Le tableau vous aide à examiner les valeurs calculées, mais l’IA peut toujours choisir une transformation, une agrégation ou une interprétation incorrecte. Vérifiez le travail généré, les données sous-jacentes et les hypothèses métier avant de vous fier à un résultat.", + "Which models can I use?": "Quels modèles puis-je utiliser ?", + "The code includes support for OpenAI, Azure OpenAI, Anthropic, Gemini, and local Ollama endpoints. You configure your provider and credentials. Model availability, capabilities, and any usage costs depend on that provider.": "Le code prend en charge OpenAI, Azure OpenAI, Anthropic, Gemini et les points d’accès Ollama locaux. Vous configurez votre fournisseur et vos identifiants. La disponibilité des modèles, leurs capacités et les éventuels frais d’utilisation dépendent du fournisseur.", + "Can I create reports and dashboards?": "Puis-je créer des rapports et des tableaux de bord ?", + "You can edit rich-text reports with chart images, export a report image, or print to PDF. The current dashboard implementation saves chart references and displays title/type placeholders; a rendered, interactive multi-chart dashboard is not yet implemented.": "Vous pouvez modifier des rapports en texte enrichi contenant des images de graphiques, exporter une image du rapport ou l’imprimer en PDF. L’implémentation actuelle des tableaux de bord enregistre les références des graphiques et affiche des espaces réservés pour le titre et le type ; le rendu d’un tableau de bord interactif à plusieurs graphiques n’est pas encore implémenté.", + "Is Datly free?": "Datly est-il gratuit ?", + "Datly’s source code is available under the MIT licence. Your model provider, connected infrastructure, or hosting may have their own costs. Check the repository’s setup instructions and releases for the current installation options.": "Le code source de Datly est disponible sous licence MIT. Votre fournisseur de modèle, l’infrastructure connectée ou l’hébergement peuvent entraîner des frais distincts. Consultez les instructions et les versions du dépôt pour connaître les options d’installation actuelles.", + "THERE’S A STORY IN YOUR DATA.": "VOS DONNÉES ONT UNE HISTOIRE.", + "Start with a good question.": "Commencez par une bonne question.", + "Get started with Datly": "Commencer avec Datly", + "A clearer view of your data.": "Une vision plus claire de vos données.", + "GitHub ↗": "GitHub ↗", + "Documentation ↗": "Documentation ↗", + "Releases ↗": "Versions ↗", + "Image library ↗": "Bibliothèque d’images ↗", + "MIT licence": "Licence MIT", + "Built on": "Fondé sur", + "Microsoft Data Formulator": "Microsoft Data Formulator", + "Datly workspace": "Espace Datly", + "Close full image": "Fermer l’image complète", + "Close": "Fermer", + "Actual application capture using example data.": "Capture réelle de l’application utilisant des données d’exemple.", + "© 2026 Datly contributors ·": "© 2026 Contributeurs de Datly ·", + "Language": "Langue", + "Choose a language": "Choisir une langue", + "Close language menu": "Fermer le menu des langues", + "Stars on GitHub": "Étoiles sur GitHub", + "GitHub stars; verified 2 November 2026": "Étoiles GitHub ; vérifié le 2 novembre 2026", + "Installation commands copied.": "Commandes d’installation copiées.", + "Automatic copy is unavailable. The commands are selected; use your browser’s copy shortcut.": "La copie automatique est indisponible. Les commandes sont sélectionnées ; utilisez le raccourci de copie de votre navigateur.", + "Copied ✓": "Copié ✓", + "Copy ⧉": "Copier ⧉", + "Select & copy": "Sélectionner et copier", + "Close navigation": "Fermer la navigation", + "Showing {count} source options. Names describe implementations, not certified integrations.": "{count} sources affichées. Les noms désignent des implémentations, pas des intégrations certifiées.", + "The Datly workspace": "L’espace Datly", + "Actual Datly workspace showing data threads, a stock-price chart, and computed rows.": "Espace Datly réel montrant des fils d’analyse, un graphique de cours boursiers et des lignes calculées.", + "Actual application capture, reframed for presentation. Example stock data; results require review.": "Capture réelle de l’application, recadrée pour la présentation. Données boursières d’exemple ; les résultats doivent être vérifiés.", + "The rows behind the line.": "Les lignes derrière la courbe.", + "Detail views of the real Datly Explore canvas, showing a chart and computed data rows.": "Vues détaillées du véritable espace Explore de Datly, avec un graphique et des lignes calculées.", + "Keep the path to your answer.": "Gardez le chemin vers votre réponse.", + "A follow-up question should build on what you have learned. Data threads keep the prompts, derived tables, and chart results in a visible analytical path.": "Une question complémentaire doit s’appuyer sur ce que vous avez appris. Les fils d’analyse conservent les instructions, les tableaux dérivés et les résultats graphiques dans un parcours visible.", + "Follow prompts and derived tables": "Suivre les instructions et les tableaux dérivés", + "Explore a separate line of inquiry": "Explorer une autre piste", + "Keep previous steps in context": "Garder les étapes précédentes en contexte", + "Actual Datly data threads showing separate stock-price questions and derived tables.": "Fils d’analyse réels de Datly montrant des questions distinctes sur les cours boursiers et des tableaux dérivés.", + "Actual application capture, cropped to focus on data threads. Example stock analysis.": "Capture réelle de l’application, recadrée sur les fils d’analyse. Exemple d’analyse boursière.", + "Get to know your data.": "Faites connaissance avec vos données.", + "Look beyond the picture. Browse table rows, review data-quality signals, and export the derived data for a closer look in your own tools.": "Allez au-delà de l’image. Parcourez les lignes, examinez les indicateurs de qualité et exportez les données dérivées pour les étudier dans vos propres outils.", + "Review table rows and columns": "Examiner les lignes et les colonnes", + "Inspect data-quality signals": "Examiner les indicateurs de qualité", + "Download the computed table as CSV": "Télécharger le tableau calculé en CSV", + "Actual Datly data inspection view with quality signals, table rows, and CSV export.": "Vue réelle de l’inspection des données dans Datly, avec indicateurs de qualité, lignes et export CSV.", + "Actual application capture, cropped to focus on data inspection. Quality signals are diagnostics, not a certification of correctness.": "Capture réelle de l’application, recadrée sur l’inspection des données. Les indicateurs de qualité sont des diagnostics, pas une certification d’exactitude.", + "Give the finding some context.": "Donnez du contexte à la conclusion.", + "Turn an exploration into something another person can follow. Write and edit a report with chart images, then export an image or print to PDF.": "Transformez une exploration en un contenu qu’une autre personne peut suivre. Rédigez et modifiez un rapport avec des images de graphiques, puis exportez une image ou imprimez en PDF.", + "Rich-text editing with chart images": "Édition en texte enrichi avec images de graphiques", + "Add the reasoning around the result": "Ajouter le raisonnement qui mène au résultat", + "Save an image or print to PDF": "Enregistrer une image ou imprimer en PDF", + "An archived Data Formulator report example showing written findings and a chart.": "Exemple archivé de rapport Data Formulator montrant des conclusions rédigées et un graphique.", + "Archived report example from the repository; it shows the earlier Data Formulator interface. Current report editing uses TipTap. Chart embeds are images.": "Exemple de rapport archivé dans le dépôt, montrant l’ancienne interface Data Formulator. L’éditeur actuel utilise TipTap. Les graphiques intégrés sont des images.", + "Project at a glance": "Le projet en chiffres", + "Data source options": "Options de sources de données", + "Website languages": "Langues du site", + "Open-source licence": "Licence open source" + } +} diff --git a/docs/locales/hi.json b/docs/locales/hi.json new file mode 100644 index 0000000..1688403 --- /dev/null +++ b/docs/locales/hi.json @@ -0,0 +1,227 @@ +{ + "language": "hi", + "nativeName": "हिन्दी", + "direction": "ltr", + "translations": { + "width=device-width, initial-scale=1": "width=device-width, initial-scale=1", + "Datly — Open-source AI analytics workspace": "Datly — AI के साथ डेटा विश्लेषण का ओपन-सोर्स कार्यक्षेत्र", + "An open-source analytics workspace. Explore tables with AI, refine charts, inspect underlying rows, and turn your findings into reports.": "डेटा विश्लेषण का ओपन-सोर्स कार्यक्षेत्र। AI से तालिकाएँ समझें, चार्ट सुधारें, उनके पीछे की पंक्तियाँ जाँचें और निष्कर्षों को रिपोर्ट में बदलें।", + "Datly — Good questions. Clearer answers.": "Datly — बेहतर सवाल। अधिक स्पष्ट जवाब।", + "Explore with AI. Inspect the data behind your charts. An open-source workspace for your next good question.": "AI के साथ खोजें। चार्ट के पीछे का डेटा जाँचें। आपके अगले अच्छे सवाल के लिए एक ओपन-सोर्स कार्यक्षेत्र।", + "Datly workspace with data threads, a chart, and underlying data rows": "डेटा थ्रेड, चार्ट और उसके पीछे की डेटा पंक्तियों वाला Datly कार्यक्षेत्र", + "An open-source workspace for exploring data with AI, inspecting charts, and writing reports.": "AI से डेटा समझने, चार्ट जाँचने और रिपोर्ट लिखने का ओपन-सोर्स कार्यक्षेत्र।", + "Skip to content": "मुख्य सामग्री पर जाएँ", + "Datly home": "Datly मुखपृष्ठ", + "Open navigation": "नेविगेशन खोलें", + "Main navigation": "मुख्य नेविगेशन", + "Product": "उत्पाद", + "How it works": "यह कैसे काम करता है", + "Data sources": "डेटा स्रोत", + "FAQ": "आम सवाल", + "Get started": "शुरू करें", + "OPEN SOURCE. OPEN TO EXPLORATION.": "ओपन सोर्स। खोज के लिए खुला।", + "Good questions.": "बेहतर सवाल।", + "Clearer answers.": "अधिक स्पष्ट जवाब।", + "Your data has a story. Find it with an AI-assisted workspace that keeps your questions, charts, and underlying rows connected.": "आपके डेटा में एक कहानी है। उसे ऐसे AI-सहायित कार्यक्षेत्र में खोजें जो आपके सवालों, चार्ट और मूल डेटा पंक्तियों को जोड़कर रखता है।", + "Start exploring": "खोजना शुरू करें", + "Take a closer look": "करीब से देखें", + "Free, open-source software · MIT licensed": "निःशुल्क, ओपन-सोर्स सॉफ़्टवेयर · MIT लाइसेंस", + "For analysts, researchers, and data teams.": "विश्लेषकों, शोधकर्ताओं और डेटा टीमों के लिए।", + "A little less back and forth. A little more insight.": "कम इधर-उधर जाना। ज़्यादा समझ पाना।", + "ACTUAL APP · EXAMPLE DATA": "वास्तविक ऐप · उदाहरण डेटा", + "Enlarge the Datly workspace screenshot": "Datly कार्यक्षेत्र का स्क्रीनशॉट बड़ा करें", + "Datly workspace showing a stock-price analysis thread, line chart, and the data rows underneath": "Datly कार्यक्षेत्र में शेयर की कीमतों का विश्लेषण थ्रेड, रेखा चार्ट और उसके नीचे डेटा पंक्तियाँ दिखाई गई हैं", + "Explore the workspace": "कार्यक्षेत्र देखें", + "Questions → transformations → charts → rows": "सवाल → डेटा रूपांतरण → चार्ट → पंक्तियाँ", + "YOUR ANALYSIS, IN CONTEXT": "आपका विश्लेषण, पूरे संदर्भ के साथ", + "Built on a solid foundation": "एक ठोस आधार पर निर्मित", + "PUBLIC BY DESIGN": "शुरू से सार्वजनिक", + "Built in the open.": "खुले तौर पर विकसित।", + "Explore the source, issues, and releases.": "सोर्स कोड, मुद्दे और रिलीज़ देखें।", + "GITHUB COMMUNITY": "GITHUB समुदाय", + "stars on GitHub": "GitHub पर स्टार", + "Count verified 2 November 2026; refreshes live.": "गिनती 2 नवंबर 2026 को सत्यापित; लाइव रीफ़्रेश होती है।", + "MADE FOR DATA WORK": "डेटा के काम के लिए बना", + "Your next analysis.": "आपका अगला विश्लेषण।", + "01 / FROM QUESTION TO UNDERSTANDING": "01 / सवाल से समझ तक", + "Follow your curiosity.": "अपनी जिज्ञासा के साथ चलें।", + "Keep the context.": "संदर्भ बनाए रखें।", + "Move from a table to a useful finding in one workspace. Ask, refine, and inspect as your analysis takes shape.": "एक ही कार्यक्षेत्र में तालिका से उपयोगी निष्कर्ष तक पहुँचें। विश्लेषण आगे बढ़ने के साथ सवाल पूछें, सुधारें और जाँचें।", + "Bring your data": "अपना डेटा लाएँ", + "Load a local file, connect a supported database, or explore a bundled example.": "स्थानीय फ़ाइल लोड करें, समर्थित डेटाबेस जोड़ें या साथ दिए गए उदाहरण को देखें।", + "Ask the next question": "अगला सवाल पूछें", + "Describe what you want to explore. Refine your chart with visual encoding controls or a follow-up question.": "बताएँ कि आप क्या जानना चाहते हैं। विज़ुअल एन्कोडिंग नियंत्रणों या अगले सवाल से अपना चार्ट सुधारें।", + "Inspect, then explain": "जाँचें, फिर समझाएँ", + "Check the derived rows, export a CSV, and give your findings context in an editable report.": "व्युत्पन्न पंक्तियाँ जाँचें, CSV निर्यात करें और संपादन योग्य रिपोर्ट में निष्कर्षों का संदर्भ दें।", + "02 / A CLOSER LOOK": "02 / करीब से देखें", + "See the work.": "काम को देखें।", + "Not just the answer.": "सिर्फ़ जवाब नहीं।", + "Real application views, with room to look closer. Explore the features that make the analysis easier to follow.": "वास्तविक ऐप के दृश्य, जिन्हें आप करीब से देख सकते हैं। वे सुविधाएँ जानें जो विश्लेषण का क्रम समझना आसान बनाती हैं।", + "Explore Datly features": "Datly की सुविधाएँ देखें", + "Explore & inspect": "खोजें और जाँचें", + "Data threads": "डेटा थ्रेड", + "Data inspection": "डेटा जाँच", + "Reports": "रिपोर्ट", + "The rows behind": "रेखा के पीछे", + "the line.": "का डेटा।", + "A chart is a starting point. Open the underlying table to inspect the values, check a transformation, and decide what the result actually means.": "चार्ट एक शुरुआत है। मूल्यों की जाँच करने, रूपांतरण को परखने और परिणाम का वास्तविक अर्थ समझने के लिए नीचे की तालिका खोलें।", + "Chart and computed table together": "चार्ट और गणना की गई तालिका साथ में", + "Inspect and export the derived rows": "व्युत्पन्न पंक्तियाँ जाँचें और निर्यात करें", + "Review results before you share": "साझा करने से पहले परिणाम की समीक्षा करें", + "View full image": "पूरी छवि देखें", + "Enlarge this feature screenshot": "इस सुविधा का स्क्रीनशॉट बड़ा करें", + "Detail views of the real Datly Explore canvas, showing a chart and computed data rows": "Datly के वास्तविक Explore कैनवस के विस्तृत दृश्य, जिनमें चार्ट और गणना की गई डेटा पंक्तियाँ दिखाई गई हैं", + "Actual application capture, reframed for clarity. Example data is illustrative; AI-generated analysis requires review.": "वास्तविक ऐप का स्क्रीनशॉट, स्पष्टता के लिए नए फ़्रेम में। डेटा केवल उदाहरण है; AI-निर्मित विश्लेषण की समीक्षा आवश्यक है।", + "03 / STAY IN THE LOOP": "03 / हर कदम की जानकारी रखें", + "AI helps you explore.": "AI खोजने में मदद करता है।", + "You make the call.": "फ़ैसला आप करते हैं।", + "Good analysis depends on more than a convincing chart. Datly makes the intermediate work available for you to examine.": "अच्छे विश्लेषण के लिए केवल प्रभावशाली चार्ट काफ़ी नहीं। Datly आपको बीच के चरणों का काम भी जाँचने देता है।", + "TRACE THE QUESTION": "सवाल का क्रम देखें", + "Keep your train": "अपने विचारों की", + "of thought.": "कड़ी बनाए रखें।", + "Data threads organise questions, transformations, derived tables, and charts. Follow an investigation as it develops.": "डेटा थ्रेड सवालों, रूपांतरणों, व्युत्पन्न तालिकाओं और चार्ट को व्यवस्थित रखते हैं। खोज आगे बढ़ने के साथ उसका क्रम देखें।", + "Explore data threads": "डेटा थ्रेड देखें", + "REFINE THE VIEW": "दृश्य सुधारें", + "A chart you can": "ऐसा चार्ट जिसके साथ", + "work with.": "आप काम कर सकें।", + "Use visual encoding controls for axes, colour, and other chart properties. Adjust the view as your question becomes more precise.": "अक्ष, रंग और चार्ट के दूसरे गुणों के लिए विज़ुअल एन्कोडिंग नियंत्रण इस्तेमाल करें। सवाल स्पष्ट होने के साथ दृश्य को समायोजित करें।", + "Explore the canvas": "कैनवस देखें", + "EXPLAIN THE FINDING": "निष्कर्ष समझाएँ", + "More than": "सिर्फ़", + "a screenshot.": "स्क्रीनशॉट से आगे।", + "Combine written context with chart images in an editable report. Save an image or use your browser’s print flow for PDF.": "संपादन योग्य रिपोर्ट में लिखित संदर्भ और चार्ट की छवियाँ जोड़ें। छवि सहेजें या PDF के लिए ब्राउज़र की प्रिंट सुविधा इस्तेमाल करें।", + "Explore reports": "रिपोर्ट देखें", + "04 / START WITH WHAT YOU HAVE": "04 / जो आपके पास है, उसी से शुरू करें", + "Your data.": "आपका डेटा।", + "A familiar starting point.": "एक परिचित शुरुआत।", + "Work with files and supported data sources. Connections depend on the appropriate drivers, credentials, and network access.": "फ़ाइलों और समर्थित डेटा स्रोतों के साथ काम करें। कनेक्शन के लिए उपयुक्त ड्राइवर, क्रेडेंशियल और नेटवर्क पहुँच चाहिए।", + "A file today.": "आज एक फ़ाइल।", + "A warehouse tomorrow.": "कल एक डेटा वेयरहाउस।", + "Start small with a CSV or a sample dataset. Bring in a supported connection when your question calls for more.": "CSV या नमूना डेटासेट से छोटी शुरुआत करें। जब सवाल के लिए अधिक चाहिए, तो समर्थित कनेक्शन जोड़ें।", + "See connector implementation": "कनेक्टर का कार्यान्वयन देखें", + "Filter data sources": "डेटा स्रोत फ़िल्टर करें", + "All sources": "सभी स्रोत", + "SQL & warehouses": "SQL और वेयरहाउस", + "Cloud & storage": "क्लाउड और स्टोरेज", + "Files & web": "फ़ाइलें और वेब", + "Relational database": "रिलेशनल डेटाबेस", + "SQL warehouse": "SQL वेयरहाउस", + "Cloud warehouse": "क्लाउड वेयरहाउस", + "Analytics database": "विश्लेषण डेटाबेस", + "Object storage": "ऑब्जेक्ट स्टोरेज", + "Serverless queries": "सर्वरलेस क्वेरी", + "Document database": "डॉक्यूमेंट डेटाबेस", + "Data Explorer": "डेटा एक्सप्लोरर", + "Dataset import": "डेटासेट आयात", + "Local files": "स्थानीय फ़ाइलें", + "CSV, Excel & Parquet": "CSV, Excel और Parquet", + "Web sources": "वेब स्रोत", + "Supported table extraction": "समर्थित तालिका निष्कर्षण", + "Example datasets": "उदाहरण डेटासेट", + "Try a sample analysis": "नमूना विश्लेषण आज़माएँ", + "Showing all 16 source options. Names describe implementations, not certified integrations.": "सभी 16 स्रोत विकल्प दिखाए जा रहे हैं। नाम उपलब्ध कार्यान्वयनों को बताते हैं, प्रमाणित इंटीग्रेशन को नहीं।", + "05 / MAKE AN INFORMED CHOICE": "05 / जानकारी के साथ चुनें", + "Local processing.": "स्थानीय प्रोसेसिंग।", + "Clear data boundaries.": "डेटा की स्पष्ट सीमाएँ।", + "Read the model context code": "मॉडल संदर्भ का कोड पढ़ें", + "01 / EXECUTION": "01 / निष्पादन", + "Work in your workspace": "अपने कार्यक्षेत्र में काम करें", + "DuckDB and Python run transformations in the environment where you host Datly, including a local installation on your computer.": "DuckDB और Python उस वातावरण में रूपांतरण चलाते हैं जहाँ Datly होस्ट किया गया है, जिसमें आपके कंप्यूटर पर स्थानीय इंस्टॉलेशन भी शामिल है।", + "02 / MODEL CONTEXT": "02 / मॉडल संदर्भ", + "Know what AI receives": "जानें कि AI को क्या मिलता है", + "AI requests can include schema, sample values and rows, prompts, and tool outputs. A cloud model provider receives the context sent to it.": "AI अनुरोधों में स्कीमा, नमूना मान और पंक्तियाँ, प्रॉम्प्ट और टूल के आउटपुट शामिल हो सकते हैं। क्लाउड मॉडल प्रदाता को भेजा गया संदर्भ मिलता है।", + "03 / YOUR CONFIGURATION": "03 / आपकी सेटिंग", + "Choose your model": "अपना मॉडल चुनें", + "Connect a supported provider or configure a local model through Ollama. Local AI requests require a local endpoint; remote data sources still need network access.": "समर्थित प्रदाता जोड़ें या Ollama से स्थानीय मॉडल कॉन्फ़िगर करें। स्थानीय AI अनुरोधों के लिए स्थानीय एंडपॉइंट चाहिए; दूरस्थ डेटा स्रोतों के लिए फिर भी नेटवर्क पहुँच आवश्यक है।", + "06 / YOUR NEXT GOOD QUESTION": "06 / आपका अगला अच्छा सवाल", + "A little setup.": "थोड़ा-सा सेटअप।", + "A lot to explore.": "जानने को बहुत कुछ।", + "Build from source and launch Datly in your browser. Start with an example dataset, then configure a model for AI-assisted exploration.": "सोर्स से बिल्ड करें और Datly को ब्राउज़र में चलाएँ। उदाहरण डेटासेट से शुरू करें, फिर AI-सहायित खोज के लिए मॉडल कॉन्फ़िगर करें।", + "Python 3.11 or later": "Python 3.11 या बाद का संस्करण", + "Node.js and Yarn for the frontend": "फ़्रंटएंड के लिए Node.js और Yarn", + "uv for the Python environment": "Python वातावरण के लिए uv", + "Read the setup guide": "सेटअप गाइड पढ़ें", + "View releases": "रिलीज़ देखें", + "Installation method": "इंस्टॉलेशन का तरीका", + "From source": "सोर्स से", + "Docker": "Docker", + "Copy installation commands": "इंस्टॉलेशन कमांड कॉपी करें", + "Copy": "कॉपी करें", + "Open": "खोलें", + "after launch": "शुरू होने के बाद", + "A FEW THINGS TO KNOW": "जानने योग्य कुछ बातें", + "Before you": "शुरू करने से", + "dive in.": "पहले।", + "Questions or feedback?": "कोई सवाल या सुझाव?", + "What is Datly?": "Datly क्या है?", + "Datly is an open-source analytics workspace built on Microsoft Research’s Data Formulator. It combines natural-language exploration, visual chart controls, data threads, table inspection, and editable reports. The project is in beta.": "Datly, Microsoft Research के Data Formulator पर आधारित डेटा विश्लेषण का ओपन-सोर्स कार्यक्षेत्र है। इसमें प्राकृतिक भाषा से खोज, विज़ुअल चार्ट नियंत्रण, डेटा थ्रेड, तालिका जाँच और संपादन योग्य रिपोर्ट शामिल हैं। यह प्रोजेक्ट बीटा चरण में है।", + "Does my data stay on my computer?": "क्या मेरा डेटा मेरे कंप्यूटर पर ही रहता है?", + "A local installation processes transformations on your computer. AI requests can include sample rows, field values, prompts, and tool outputs, which are sent to your configured model. If you select a cloud provider, that provider receives this context. A local model endpoint can keep model requests local; review your configuration and connected data sources.": "स्थानीय इंस्टॉलेशन आपके कंप्यूटर पर डेटा रूपांतरण करता है। AI अनुरोधों में नमूना पंक्तियाँ, फ़ील्ड मान, प्रॉम्प्ट और टूल आउटपुट हो सकते हैं, जो आपके कॉन्फ़िगर किए गए मॉडल को भेजे जाते हैं। क्लाउड प्रदाता चुनने पर उसे यह संदर्भ मिलता है। स्थानीय मॉडल एंडपॉइंट मॉडल अनुरोधों को स्थानीय रख सकता है; अपनी सेटिंग और जुड़े डेटा स्रोतों की समीक्षा करें।", + "Does inspecting the table guarantee a correct answer?": "क्या तालिका जाँचने से सही उत्तर की गारंटी मिलती है?", + "No. The table helps you inspect the computed values, but AI can still choose an incorrect transformation, aggregation, or interpretation. Review the generated work, underlying data, and business assumptions before relying on a result.": "नहीं। तालिका से गणना किए गए मान जाँचे जा सकते हैं, लेकिन AI फिर भी गलत रूपांतरण, एकत्रीकरण या व्याख्या चुन सकता है। परिणाम पर भरोसा करने से पहले बनाए गए काम, मूल डेटा और व्यावसायिक मान्यताओं की समीक्षा करें।", + "Which models can I use?": "मैं कौन-से मॉडल इस्तेमाल कर सकता हूँ?", + "The code includes support for OpenAI, Azure OpenAI, Anthropic, Gemini, and local Ollama endpoints. You configure your provider and credentials. Model availability, capabilities, and any usage costs depend on that provider.": "कोड में OpenAI, Azure OpenAI, Anthropic, Gemini और स्थानीय Ollama एंडपॉइंट का समर्थन है। आप प्रदाता और क्रेडेंशियल कॉन्फ़िगर करते हैं। मॉडल की उपलब्धता, क्षमताएँ और उपयोग की लागत उस प्रदाता पर निर्भर हैं।", + "Can I create reports and dashboards?": "क्या मैं रिपोर्ट और डैशबोर्ड बना सकता हूँ?", + "You can edit rich-text reports with chart images, export a report image, or print to PDF. The current dashboard implementation saves chart references and displays title/type placeholders; a rendered, interactive multi-chart dashboard is not yet implemented.": "आप चार्ट की छवियों वाली रिच-टेक्स्ट रिपोर्ट संपादित कर सकते हैं, रिपोर्ट की छवि निर्यात कर सकते हैं या PDF में प्रिंट कर सकते हैं। मौजूदा डैशबोर्ड कार्यान्वयन चार्ट के संदर्भ सहेजता है और शीर्षक/प्रकार के प्लेसहोल्डर दिखाता है; चार्ट प्रदर्शित करने वाला इंटरैक्टिव बहु-चार्ट डैशबोर्ड अभी लागू नहीं है।", + "Is Datly free?": "क्या Datly निःशुल्क है?", + "Datly’s source code is available under the MIT licence. Your model provider, connected infrastructure, or hosting may have their own costs. Check the repository’s setup instructions and releases for the current installation options.": "Datly का सोर्स कोड MIT लाइसेंस के अंतर्गत उपलब्ध है। मॉडल प्रदाता, जुड़ा बुनियादी ढाँचा या होस्टिंग अपनी लागत ले सकते हैं। मौजूदा इंस्टॉलेशन विकल्पों के लिए रिपॉज़िटरी की सेटअप गाइड और रिलीज़ देखें।", + "THERE’S A STORY IN YOUR DATA.": "आपके डेटा में एक कहानी है।", + "Start with a good question.": "एक अच्छे सवाल से शुरू करें।", + "Get started with Datly": "Datly के साथ शुरू करें", + "A clearer view of your data.": "आपके डेटा का अधिक स्पष्ट नज़रिया।", + "GitHub ↗": "GitHub ↗", + "Documentation ↗": "दस्तावेज़ ↗", + "Releases ↗": "रिलीज़ ↗", + "Image library ↗": "छवि लाइब्रेरी ↗", + "MIT licence": "MIT लाइसेंस", + "Built on": "इस पर आधारित", + "Microsoft Data Formulator": "Microsoft Data Formulator", + "Datly workspace": "Datly कार्यक्षेत्र", + "Close full image": "पूरी छवि बंद करें", + "Close": "बंद करें", + "Actual application capture using example data.": "उदाहरण डेटा के साथ वास्तविक ऐप का स्क्रीनशॉट।", + "© 2026 Datly contributors ·": "© 2026 Datly योगदानकर्ता ·", + "Language": "भाषा", + "Choose a language": "भाषा चुनें", + "Close language menu": "भाषा मेनू बंद करें", + "Stars on GitHub": "GitHub पर स्टार", + "GitHub stars; verified 2 November 2026": "GitHub स्टार; 2 नवंबर 2026 को सत्यापित", + "Installation commands copied.": "इंस्टॉलेशन कमांड कॉपी हो गए।", + "Automatic copy is unavailable. The commands are selected; use your browser’s copy shortcut.": "स्वचालित कॉपी उपलब्ध नहीं है। कमांड चुने गए हैं; ब्राउज़र का कॉपी शॉर्टकट इस्तेमाल करें।", + "Copied ✓": "कॉपी हो गया ✓", + "Copy ⧉": "कॉपी करें ⧉", + "Select & copy": "चुनें और कॉपी करें", + "Close navigation": "नेविगेशन बंद करें", + "Showing {count} source options. Names describe implementations, not certified integrations.": "{count} स्रोत विकल्प दिखाए जा रहे हैं। नाम उपलब्ध कार्यान्वयनों को बताते हैं, प्रमाणित इंटीग्रेशन को नहीं।", + "The Datly workspace": "Datly कार्यक्षेत्र", + "Actual Datly workspace showing data threads, a stock-price chart, and computed rows.": "वास्तविक Datly कार्यक्षेत्र में डेटा थ्रेड, शेयर की कीमतों का चार्ट और गणना की गई पंक्तियाँ दिखाई गई हैं।", + "Actual application capture, reframed for presentation. Example stock data; results require review.": "वास्तविक ऐप का स्क्रीनशॉट, प्रस्तुति के लिए नए फ़्रेम में। शेयर डेटा उदाहरण है; परिणाम की समीक्षा आवश्यक है।", + "The rows behind the line.": "रेखा के पीछे का डेटा।", + "Detail views of the real Datly Explore canvas, showing a chart and computed data rows.": "Datly के वास्तविक Explore कैनवस के विस्तृत दृश्य, जिनमें चार्ट और गणना की गई डेटा पंक्तियाँ दिखाई गई हैं।", + "Keep the path to your answer.": "अपने जवाब तक का रास्ता सहेजें।", + "A follow-up question should build on what you have learned. Data threads keep the prompts, derived tables, and chart results in a visible analytical path.": "अगला सवाल अब तक मिली समझ पर आधारित होना चाहिए। डेटा थ्रेड प्रॉम्प्ट, व्युत्पन्न तालिकाएँ और चार्ट परिणामों को एक दिखने वाले विश्लेषण क्रम में रखते हैं।", + "Follow prompts and derived tables": "प्रॉम्प्ट और व्युत्पन्न तालिकाओं का क्रम देखें", + "Explore a separate line of inquiry": "जाँच की एक अलग दिशा खोजें", + "Keep previous steps in context": "पिछले चरणों का संदर्भ बनाए रखें", + "Actual Datly data threads showing separate stock-price questions and derived tables.": "वास्तविक Datly डेटा थ्रेड में शेयर की कीमतों के अलग-अलग सवाल और व्युत्पन्न तालिकाएँ दिखाई गई हैं।", + "Actual application capture, cropped to focus on data threads. Example stock analysis.": "वास्तविक ऐप का स्क्रीनशॉट, डेटा थ्रेड पर ध्यान देने के लिए क्रॉप किया गया। शेयर विश्लेषण का उदाहरण।", + "Get to know your data.": "अपने डेटा को जानें।", + "Look beyond the picture. Browse table rows, review data-quality signals, and export the derived data for a closer look in your own tools.": "चित्र से आगे देखें। तालिका की पंक्तियाँ देखें, डेटा गुणवत्ता संकेतों की समीक्षा करें और अपने टूल में करीब से जाँचने के लिए व्युत्पन्न डेटा निर्यात करें।", + "Review table rows and columns": "तालिका की पंक्तियाँ और कॉलम जाँचें", + "Inspect data-quality signals": "डेटा गुणवत्ता संकेत जाँचें", + "Download the computed table as CSV": "गणना की गई तालिका CSV में डाउनलोड करें", + "Actual Datly data inspection view with quality signals, table rows, and CSV export.": "Datly का वास्तविक डेटा जाँच दृश्य, जिसमें गुणवत्ता संकेत, तालिका की पंक्तियाँ और CSV निर्यात मौजूद हैं।", + "Actual application capture, cropped to focus on data inspection. Quality signals are diagnostics, not a certification of correctness.": "वास्तविक ऐप का स्क्रीनशॉट, डेटा जाँच पर ध्यान देने के लिए क्रॉप किया गया। गुणवत्ता संकेत निदान हैं, शुद्धता का प्रमाणन नहीं।", + "Give the finding some context.": "निष्कर्ष को संदर्भ दें।", + "Turn an exploration into something another person can follow. Write and edit a report with chart images, then export an image or print to PDF.": "खोज को ऐसा रूप दें जिसे दूसरा व्यक्ति समझ सके। चार्ट की छवियों वाली रिपोर्ट लिखें और संपादित करें, फिर छवि निर्यात करें या PDF में प्रिंट करें।", + "Rich-text editing with chart images": "चार्ट की छवियों के साथ रिच-टेक्स्ट संपादन", + "Add the reasoning around the result": "परिणाम के पीछे का तर्क जोड़ें", + "Save an image or print to PDF": "छवि सहेजें या PDF में प्रिंट करें", + "An archived Data Formulator report example showing written findings and a chart.": "Data Formulator की संग्रहित रिपोर्ट का उदाहरण, जिसमें लिखित निष्कर्ष और चार्ट दिखाई गए हैं।", + "Archived report example from the repository; it shows the earlier Data Formulator interface. Current report editing uses TipTap. Chart embeds are images.": "रिपॉज़िटरी से संग्रहित रिपोर्ट का उदाहरण; इसमें Data Formulator का पुराना इंटरफ़ेस दिखता है। मौजूदा रिपोर्ट संपादन TipTap का उपयोग करता है। एम्बेड किए गए चार्ट छवियाँ हैं।", + "Project at a glance": "परियोजना एक नज़र में", + "Data source options": "डेटा स्रोत विकल्प", + "Website languages": "वेबसाइट की भाषाएँ", + "Open-source licence": "ओपन-सोर्स लाइसेंस" + } +} diff --git a/docs/locales/it.json b/docs/locales/it.json new file mode 100644 index 0000000..43d6d75 --- /dev/null +++ b/docs/locales/it.json @@ -0,0 +1,227 @@ +{ + "language": "it", + "nativeName": "Italiano", + "direction": "ltr", + "translations": { + "width=device-width, initial-scale=1": "width=device-width, initial-scale=1", + "Datly — Open-source AI analytics workspace": "Datly — Spazio di analisi dati con IA open source", + "An open-source analytics workspace. Explore tables with AI, refine charts, inspect underlying rows, and turn your findings into reports.": "Uno spazio di analisi open source. Esplora le tabelle con l’IA, perfeziona i grafici, esamina le righe sottostanti e trasforma i risultati in report.", + "Datly — Good questions. Clearer answers.": "Datly — Buone domande. Risposte più chiare.", + "Explore with AI. Inspect the data behind your charts. An open-source workspace for your next good question.": "Esplora con l’IA. Esamina i dati dietro i grafici. Uno spazio open source per la tua prossima buona domanda.", + "Datly workspace with data threads, a chart, and underlying data rows": "Spazio Datly con percorsi di analisi, un grafico e le righe di dati sottostanti", + "An open-source workspace for exploring data with AI, inspecting charts, and writing reports.": "Uno spazio open source per esplorare i dati con l’IA, esaminare i grafici e scrivere report.", + "Skip to content": "Vai al contenuto", + "Datly home": "Home di Datly", + "Open navigation": "Apri la navigazione", + "Main navigation": "Navigazione principale", + "Product": "Prodotto", + "How it works": "Come funziona", + "Data sources": "Fonti dati", + "FAQ": "Domande frequenti", + "Get started": "Inizia", + "OPEN SOURCE. OPEN TO EXPLORATION.": "OPEN SOURCE. APERTO ALL’ESPLORAZIONE.", + "Good questions.": "Buone domande.", + "Clearer answers.": "Risposte più chiare.", + "Your data has a story. Find it with an AI-assisted workspace that keeps your questions, charts, and underlying rows connected.": "I tuoi dati hanno una storia. Scoprila in uno spazio assistito dall’IA che collega le tue domande, i grafici e le righe sottostanti.", + "Start exploring": "Inizia a esplorare", + "Take a closer look": "Guarda più da vicino", + "Free, open-source software · MIT licensed": "Software gratuito e open source · Licenza MIT", + "For analysts, researchers, and data teams.": "Per analisti, ricercatori e team di dati.", + "A little less back and forth. A little more insight.": "Meno avanti e indietro. Più comprensione.", + "ACTUAL APP · EXAMPLE DATA": "APP REALE · DATI DI ESEMPIO", + "Enlarge the Datly workspace screenshot": "Ingrandisci la schermata dello spazio Datly", + "Datly workspace showing a stock-price analysis thread, line chart, and the data rows underneath": "Spazio Datly con un percorso di analisi delle quotazioni azionarie, un grafico a linee e le righe di dati sottostanti", + "Explore the workspace": "Esplora lo spazio di lavoro", + "Questions → transformations → charts → rows": "Domande → trasformazioni → grafici → righe", + "YOUR ANALYSIS, IN CONTEXT": "LA TUA ANALISI, NEL SUO CONTESTO", + "Built on a solid foundation": "Su basi solide", + "PUBLIC BY DESIGN": "PUBBLICO PER SCELTA", + "Built in the open.": "Sviluppato alla luce del sole.", + "Explore the source, issues, and releases.": "Esplora il codice, le segnalazioni e le versioni.", + "GITHUB COMMUNITY": "COMMUNITY GITHUB", + "stars on GitHub": "stelle su GitHub", + "Count verified 2 November 2026; refreshes live.": "Conteggio verificato il 2 novembre 2026; aggiornamento in tempo reale.", + "MADE FOR DATA WORK": "PENSATO PER LAVORARE CON I DATI", + "Your next analysis.": "La tua prossima analisi.", + "01 / FROM QUESTION TO UNDERSTANDING": "01 / DALLA DOMANDA ALLA COMPRENSIONE", + "Follow your curiosity.": "Segui la tua curiosità.", + "Keep the context.": "Mantieni il contesto.", + "Move from a table to a useful finding in one workspace. Ask, refine, and inspect as your analysis takes shape.": "Passa da una tabella a un risultato utile in un unico spazio. Chiedi, perfeziona ed esamina mentre l’analisi prende forma.", + "Bring your data": "Porta i tuoi dati", + "Load a local file, connect a supported database, or explore a bundled example.": "Carica un file locale, collega un database supportato o esplora un esempio incluso.", + "Ask the next question": "Fai la domanda successiva", + "Describe what you want to explore. Refine your chart with visual encoding controls or a follow-up question.": "Descrivi cosa vuoi esplorare. Perfeziona il grafico con i controlli di codifica visiva o con una domanda di approfondimento.", + "Inspect, then explain": "Esamina, poi spiega", + "Check the derived rows, export a CSV, and give your findings context in an editable report.": "Controlla le righe calcolate, esporta un CSV e dai contesto ai risultati in un report modificabile.", + "02 / A CLOSER LOOK": "02 / UNO SGUARDO PIÙ DA VICINO", + "See the work.": "Guarda il lavoro.", + "Not just the answer.": "Non solo la risposta.", + "Real application views, with room to look closer. Explore the features that make the analysis easier to follow.": "Viste reali dell’applicazione da osservare nei dettagli. Scopri le funzioni che rendono più facile seguire l’analisi.", + "Explore Datly features": "Esplora le funzioni di Datly", + "Explore & inspect": "Esplora ed esamina", + "Data threads": "Percorsi di analisi", + "Data inspection": "Ispezione dei dati", + "Reports": "Report", + "The rows behind": "Le righe dietro", + "the line.": "la linea.", + "A chart is a starting point. Open the underlying table to inspect the values, check a transformation, and decide what the result actually means.": "Un grafico è un punto di partenza. Apri la tabella sottostante per esaminare i valori, controllare una trasformazione e capire cosa significa davvero il risultato.", + "Chart and computed table together": "Grafico e tabella calcolata insieme", + "Inspect and export the derived rows": "Esamina ed esporta le righe calcolate", + "Review results before you share": "Rivedi i risultati prima di condividerli", + "View full image": "Visualizza l’immagine completa", + "Enlarge this feature screenshot": "Ingrandisci questa schermata della funzione", + "Detail views of the real Datly Explore canvas, showing a chart and computed data rows": "Viste dettagliate dell’area Explore reale di Datly, con un grafico e righe di dati calcolate", + "Actual application capture, reframed for clarity. Example data is illustrative; AI-generated analysis requires review.": "Schermata reale dell’applicazione, reinquadrata per maggiore chiarezza. I dati di esempio sono illustrativi; l’analisi generata dall’IA richiede una verifica.", + "03 / STAY IN THE LOOP": "03 / MANTIENI IL CONTROLLO", + "AI helps you explore.": "L’IA ti aiuta a esplorare.", + "You make the call.": "Decidi tu.", + "Good analysis depends on more than a convincing chart. Datly makes the intermediate work available for you to examine.": "Una buona analisi richiede più di un grafico convincente. Datly ti permette di esaminare il lavoro intermedio.", + "TRACE THE QUESTION": "SEGUI LA DOMANDA", + "Keep your train": "Mantieni il filo", + "of thought.": "del ragionamento.", + "Data threads organise questions, transformations, derived tables, and charts. Follow an investigation as it develops.": "I percorsi di analisi organizzano domande, trasformazioni, tabelle derivate e grafici. Segui un’indagine mentre si sviluppa.", + "Explore data threads": "Esplora i percorsi di analisi", + "REFINE THE VIEW": "PERFEZIONA LA VISTA", + "A chart you can": "Un grafico su cui", + "work with.": "puoi lavorare.", + "Use visual encoding controls for axes, colour, and other chart properties. Adjust the view as your question becomes more precise.": "Usa i controlli di codifica visiva per gli assi, i colori e altre proprietà del grafico. Adatta la vista man mano che la domanda diventa più precisa.", + "Explore the canvas": "Esplora l’area di analisi", + "EXPLAIN THE FINDING": "SPIEGA IL RISULTATO", + "More than": "Più di una", + "a screenshot.": "schermata.", + "Combine written context with chart images in an editable report. Save an image or use your browser’s print flow for PDF.": "Combina testo e immagini dei grafici in un report modificabile. Salva un’immagine o usa la stampa del browser per ottenere un PDF.", + "Explore reports": "Esplora i report", + "04 / START WITH WHAT YOU HAVE": "04 / PARTI DA QUELLO CHE HAI", + "Your data.": "I tuoi dati.", + "A familiar starting point.": "Un punto di partenza familiare.", + "Work with files and supported data sources. Connections depend on the appropriate drivers, credentials, and network access.": "Lavora con file e fonti dati supportate. Le connessioni dipendono dai driver, dalle credenziali e dall’accesso alla rete appropriati.", + "A file today.": "Un file oggi.", + "A warehouse tomorrow.": "Un data warehouse domani.", + "Start small with a CSV or a sample dataset. Bring in a supported connection when your question calls for more.": "Inizia in modo semplice con un CSV o un set di dati di esempio. Aggiungi una connessione supportata quando la domanda richiede di più.", + "See connector implementation": "Vedi l’implementazione dei connettori", + "Filter data sources": "Filtra le fonti dati", + "All sources": "Tutte le fonti", + "SQL & warehouses": "SQL e data warehouse", + "Cloud & storage": "Cloud e archiviazione", + "Files & web": "File e web", + "Relational database": "Database relazionale", + "SQL warehouse": "Data warehouse SQL", + "Cloud warehouse": "Data warehouse cloud", + "Analytics database": "Database analitico", + "Object storage": "Archiviazione di oggetti", + "Serverless queries": "Query serverless", + "Document database": "Database documentale", + "Data Explorer": "Data Explorer", + "Dataset import": "Importazione di set di dati", + "Local files": "File locali", + "CSV, Excel & Parquet": "CSV, Excel e Parquet", + "Web sources": "Fonti web", + "Supported table extraction": "Estrazione di tabelle supportata", + "Example datasets": "Set di dati di esempio", + "Try a sample analysis": "Prova un’analisi di esempio", + "Showing all 16 source options. Names describe implementations, not certified integrations.": "Sono visualizzate tutte le 16 opzioni di origine. I nomi descrivono implementazioni, non integrazioni certificate.", + "05 / MAKE AN INFORMED CHOICE": "05 / SCEGLI IN MODO INFORMATO", + "Local processing.": "Elaborazione locale.", + "Clear data boundaries.": "Confini chiari per i dati.", + "Read the model context code": "Leggi il codice del contesto del modello", + "01 / EXECUTION": "01 / ESECUZIONE", + "Work in your workspace": "Lavora nel tuo ambiente", + "DuckDB and Python run transformations in the environment where you host Datly, including a local installation on your computer.": "DuckDB e Python eseguono le trasformazioni nell’ambiente in cui ospiti Datly, anche nel caso di un’installazione locale sul tuo computer.", + "02 / MODEL CONTEXT": "02 / CONTESTO DEL MODELLO", + "Know what AI receives": "Scopri cosa riceve l’IA", + "AI requests can include schema, sample values and rows, prompts, and tool outputs. A cloud model provider receives the context sent to it.": "Le richieste all’IA possono includere schema, esempi di valori e righe, istruzioni e output degli strumenti. Un fornitore di modelli cloud riceve il contesto che gli viene inviato.", + "03 / YOUR CONFIGURATION": "03 / LA TUA CONFIGURAZIONE", + "Choose your model": "Scegli il tuo modello", + "Connect a supported provider or configure a local model through Ollama. Local AI requests require a local endpoint; remote data sources still need network access.": "Collega un fornitore supportato o configura un modello locale tramite Ollama. Le richieste locali all’IA richiedono un endpoint locale; le fonti dati remote necessitano comunque dell’accesso alla rete.", + "06 / YOUR NEXT GOOD QUESTION": "06 / LA TUA PROSSIMA BUONA DOMANDA", + "A little setup.": "Un po’ di configurazione.", + "A lot to explore.": "Tanto da esplorare.", + "Build from source and launch Datly in your browser. Start with an example dataset, then configure a model for AI-assisted exploration.": "Compila dal codice sorgente e avvia Datly nel browser. Inizia con un set di dati di esempio, poi configura un modello per esplorare con l’aiuto dell’IA.", + "Python 3.11 or later": "Python 3.11 o successivo", + "Node.js and Yarn for the frontend": "Node.js e Yarn per l’interfaccia", + "uv for the Python environment": "uv per l’ambiente Python", + "Read the setup guide": "Leggi la guida all’installazione", + "View releases": "Vedi le versioni", + "Installation method": "Metodo di installazione", + "From source": "Dal codice sorgente", + "Docker": "Docker", + "Copy installation commands": "Copia i comandi di installazione", + "Copy": "Copia", + "Open": "Apri", + "after launch": "dopo l’avvio", + "A FEW THINGS TO KNOW": "ALCUNE COSE DA SAPERE", + "Before you": "Prima di", + "dive in.": "iniziare.", + "Questions or feedback?": "Domande o commenti?", + "What is Datly?": "Che cos’è Datly?", + "Datly is an open-source analytics workspace built on Microsoft Research’s Data Formulator. It combines natural-language exploration, visual chart controls, data threads, table inspection, and editable reports. The project is in beta.": "Datly è uno spazio di analisi open source basato su Data Formulator di Microsoft Research. Unisce esplorazione in linguaggio naturale, controlli visivi dei grafici, percorsi di analisi, ispezione delle tabelle e report modificabili. Il progetto è in fase beta.", + "Does my data stay on my computer?": "I miei dati rimangono sul mio computer?", + "A local installation processes transformations on your computer. AI requests can include sample rows, field values, prompts, and tool outputs, which are sent to your configured model. If you select a cloud provider, that provider receives this context. A local model endpoint can keep model requests local; review your configuration and connected data sources.": "Un’installazione locale esegue le trasformazioni sul tuo computer. Le richieste all’IA possono includere righe di esempio, valori dei campi, istruzioni e output degli strumenti, inviati al modello configurato. Se scegli un fornitore cloud, quel fornitore riceve il contesto. Un endpoint di modello locale può mantenere locali le richieste; verifica la configurazione e le fonti dati collegate.", + "Does inspecting the table guarantee a correct answer?": "L’ispezione della tabella garantisce una risposta corretta?", + "No. The table helps you inspect the computed values, but AI can still choose an incorrect transformation, aggregation, or interpretation. Review the generated work, underlying data, and business assumptions before relying on a result.": "No. La tabella aiuta a esaminare i valori calcolati, ma l’IA può comunque scegliere una trasformazione, un’aggregazione o un’interpretazione errata. Controlla il lavoro generato, i dati sottostanti e le ipotesi di business prima di fare affidamento su un risultato.", + "Which models can I use?": "Quali modelli posso usare?", + "The code includes support for OpenAI, Azure OpenAI, Anthropic, Gemini, and local Ollama endpoints. You configure your provider and credentials. Model availability, capabilities, and any usage costs depend on that provider.": "Il codice supporta OpenAI, Azure OpenAI, Anthropic, Gemini ed endpoint locali di Ollama. Configuri tu il fornitore e le credenziali. La disponibilità, le capacità e gli eventuali costi d’uso dei modelli dipendono dal fornitore.", + "Can I create reports and dashboards?": "Posso creare report e dashboard?", + "You can edit rich-text reports with chart images, export a report image, or print to PDF. The current dashboard implementation saves chart references and displays title/type placeholders; a rendered, interactive multi-chart dashboard is not yet implemented.": "Puoi modificare report in testo formattato con immagini dei grafici, esportare un’immagine del report o stamparlo in PDF. L’implementazione attuale delle dashboard salva riferimenti ai grafici e mostra segnaposto per titolo e tipo; una dashboard visualizzata e interattiva con più grafici non è ancora implementata.", + "Is Datly free?": "Datly è gratuito?", + "Datly’s source code is available under the MIT licence. Your model provider, connected infrastructure, or hosting may have their own costs. Check the repository’s setup instructions and releases for the current installation options.": "Il codice sorgente di Datly è disponibile con licenza MIT. Il fornitore del modello, l’infrastruttura collegata o l’hosting possono avere costi propri. Consulta le istruzioni e le versioni del repository per conoscere le opzioni di installazione attuali.", + "THERE’S A STORY IN YOUR DATA.": "I TUOI DATI HANNO UNA STORIA.", + "Start with a good question.": "Inizia con una buona domanda.", + "Get started with Datly": "Inizia con Datly", + "A clearer view of your data.": "Una visione più chiara dei tuoi dati.", + "GitHub ↗": "GitHub ↗", + "Documentation ↗": "Documentazione ↗", + "Releases ↗": "Versioni ↗", + "Image library ↗": "Libreria di immagini ↗", + "MIT licence": "Licenza MIT", + "Built on": "Basato su", + "Microsoft Data Formulator": "Microsoft Data Formulator", + "Datly workspace": "Spazio Datly", + "Close full image": "Chiudi l’immagine completa", + "Close": "Chiudi", + "Actual application capture using example data.": "Schermata reale dell’applicazione con dati di esempio.", + "© 2026 Datly contributors ·": "© 2026 Collaboratori di Datly ·", + "Language": "Lingua", + "Choose a language": "Scegli una lingua", + "Close language menu": "Chiudi il menu delle lingue", + "Stars on GitHub": "Stelle su GitHub", + "GitHub stars; verified 2 November 2026": "Stelle GitHub; verificate il 2 novembre 2026", + "Installation commands copied.": "Comandi di installazione copiati.", + "Automatic copy is unavailable. The commands are selected; use your browser’s copy shortcut.": "La copia automatica non è disponibile. I comandi sono selezionati; usa la scorciatoia di copia del browser.", + "Copied ✓": "Copiato ✓", + "Copy ⧉": "Copia ⧉", + "Select & copy": "Seleziona e copia", + "Close navigation": "Chiudi la navigazione", + "Showing {count} source options. Names describe implementations, not certified integrations.": "Sono visualizzate {count} opzioni di origine. I nomi descrivono implementazioni, non integrazioni certificate.", + "The Datly workspace": "Lo spazio Datly", + "Actual Datly workspace showing data threads, a stock-price chart, and computed rows.": "Spazio Datly reale con percorsi di analisi, un grafico delle quotazioni azionarie e righe calcolate.", + "Actual application capture, reframed for presentation. Example stock data; results require review.": "Schermata reale dell’applicazione, reinquadrata per la presentazione. Dati azionari di esempio; i risultati richiedono una verifica.", + "The rows behind the line.": "Le righe dietro la linea.", + "Detail views of the real Datly Explore canvas, showing a chart and computed data rows.": "Viste dettagliate dell’area Explore reale di Datly, con un grafico e righe di dati calcolate.", + "Keep the path to your answer.": "Conserva il percorso verso la risposta.", + "A follow-up question should build on what you have learned. Data threads keep the prompts, derived tables, and chart results in a visible analytical path.": "Una domanda di approfondimento dovrebbe basarsi su ciò che hai imparato. I percorsi di analisi mantengono istruzioni, tabelle derivate e risultati dei grafici in un percorso visibile.", + "Follow prompts and derived tables": "Segui le istruzioni e le tabelle derivate", + "Explore a separate line of inquiry": "Esplora una diversa linea d’indagine", + "Keep previous steps in context": "Mantieni i passaggi precedenti nel contesto", + "Actual Datly data threads showing separate stock-price questions and derived tables.": "Percorsi di analisi reali di Datly con domande distinte sulle quotazioni azionarie e tabelle derivate.", + "Actual application capture, cropped to focus on data threads. Example stock analysis.": "Schermata reale dell’applicazione, ritagliata per evidenziare i percorsi di analisi. Esempio di analisi azionaria.", + "Get to know your data.": "Conosci meglio i tuoi dati.", + "Look beyond the picture. Browse table rows, review data-quality signals, and export the derived data for a closer look in your own tools.": "Guarda oltre l’immagine. Scorri le righe, esamina gli indicatori di qualità ed esporta i dati derivati per approfondirli con i tuoi strumenti.", + "Review table rows and columns": "Esamina righe e colonne della tabella", + "Inspect data-quality signals": "Esamina gli indicatori di qualità dei dati", + "Download the computed table as CSV": "Scarica la tabella calcolata in formato CSV", + "Actual Datly data inspection view with quality signals, table rows, and CSV export.": "Vista reale di ispezione dei dati di Datly, con indicatori di qualità, righe ed esportazione CSV.", + "Actual application capture, cropped to focus on data inspection. Quality signals are diagnostics, not a certification of correctness.": "Schermata reale dell’applicazione, ritagliata per evidenziare l’ispezione dei dati. Gli indicatori di qualità sono strumenti diagnostici, non una certificazione di correttezza.", + "Give the finding some context.": "Dai contesto al risultato.", + "Turn an exploration into something another person can follow. Write and edit a report with chart images, then export an image or print to PDF.": "Trasforma l’esplorazione in qualcosa che un’altra persona possa seguire. Scrivi e modifica un report con immagini dei grafici, poi esporta un’immagine o stampa in PDF.", + "Rich-text editing with chart images": "Modifica di testo formattato con immagini dei grafici", + "Add the reasoning around the result": "Aggiungi il ragionamento che porta al risultato", + "Save an image or print to PDF": "Salva un’immagine o stampa in PDF", + "An archived Data Formulator report example showing written findings and a chart.": "Esempio archiviato di report Data Formulator con risultati scritti e un grafico.", + "Archived report example from the repository; it shows the earlier Data Formulator interface. Current report editing uses TipTap. Chart embeds are images.": "Esempio di report archiviato nel repository; mostra la precedente interfaccia di Data Formulator. L’editor attuale usa TipTap. I grafici incorporati sono immagini.", + "Project at a glance": "Il progetto in cifre", + "Data source options": "Opzioni per le fonti dati", + "Website languages": "Lingue del sito", + "Open-source licence": "Licenza open source" + } +} diff --git a/docs/locales/ja.json b/docs/locales/ja.json new file mode 100644 index 0000000..cbd99c8 --- /dev/null +++ b/docs/locales/ja.json @@ -0,0 +1,227 @@ +{ + "language": "ja", + "nativeName": "日本語", + "direction": "ltr", + "translations": { + "width=device-width, initial-scale=1": "width=device-width, initial-scale=1", + "Datly — Open-source AI analytics workspace": "Datly — オープンソースのAI分析ワークスペース", + "An open-source analytics workspace. Explore tables with AI, refine charts, inspect underlying rows, and turn your findings into reports.": "オープンソースの分析ワークスペース。AIでテーブルを探索し、グラフを調整し、元となるデータ行を確認して、発見をレポートにまとめられます。", + "Datly — Good questions. Clearer answers.": "Datly — よい問いを。より明確な答えへ。", + "Explore with AI. Inspect the data behind your charts. An open-source workspace for your next good question.": "AIとともに探索し、グラフの元となるデータを確認。次のよい問いに向き合うための、オープンソースのワークスペースです。", + "Datly workspace with data threads, a chart, and underlying data rows": "データスレッド、グラフ、その元となるデータ行を表示したDatlyワークスペース", + "An open-source workspace for exploring data with AI, inspecting charts, and writing reports.": "AIによるデータ探索、グラフの確認、レポート作成のためのオープンソースのワークスペース。", + "Skip to content": "本文へ移動", + "Datly home": "Datlyのホーム", + "Open navigation": "ナビゲーションを開く", + "Main navigation": "メインナビゲーション", + "Product": "製品", + "How it works": "使い方", + "Data sources": "データソース", + "FAQ": "よくある質問", + "Get started": "はじめる", + "OPEN SOURCE. OPEN TO EXPLORATION.": "オープンソースで、探索の可能性を広げる。", + "Good questions.": "よい問いを。", + "Clearer answers.": "より明確な答えへ。", + "Your data has a story. Find it with an AI-assisted workspace that keeps your questions, charts, and underlying rows connected.": "データにはストーリーがあります。問い、グラフ、その元となるデータ行をつなぐAI支援のワークスペースで、そのストーリーを見つけましょう。", + "Start exploring": "探索をはじめる", + "Take a closer look": "詳しく見る", + "Free, open-source software · MIT licensed": "無料のオープンソースソフトウェア · MITライセンス", + "For analysts, researchers, and data teams.": "アナリスト、研究者、データチームのために。", + "A little less back and forth. A little more insight.": "行き来を少なく、気づきを多く。", + "ACTUAL APP · EXAMPLE DATA": "実際のアプリ · サンプルデータ", + "Enlarge the Datly workspace screenshot": "Datlyワークスペースのスクリーンショットを拡大", + "Datly workspace showing a stock-price analysis thread, line chart, and the data rows underneath": "株価分析のスレッド、折れ線グラフ、その下のデータ行を表示したDatlyワークスペース", + "Explore the workspace": "ワークスペースを見る", + "Questions → transformations → charts → rows": "問い → データ変換 → グラフ → データ行", + "YOUR ANALYSIS, IN CONTEXT": "分析の文脈を、そのままに", + "Built on a solid foundation": "確かな技術基盤", + "PUBLIC BY DESIGN": "開かれた設計", + "Built in the open.": "開発の過程も公開。", + "Explore the source, issues, and releases.": "ソースコード、Issue、リリースを確認できます。", + "GITHUB COMMUNITY": "GITHUBコミュニティ", + "stars on GitHub": "GitHubのスター", + "Count verified 2 November 2026; refreshes live.": "2026年11月2日に件数を確認。リアルタイムで更新されます。", + "MADE FOR DATA WORK": "データの仕事のために", + "Your next analysis.": "次の分析へ。", + "01 / FROM QUESTION TO UNDERSTANDING": "01 / 問いから理解へ", + "Follow your curiosity.": "好奇心のままに。", + "Keep the context.": "文脈はそのままに。", + "Move from a table to a useful finding in one workspace. Ask, refine, and inspect as your analysis takes shape.": "テーブルから有用な発見へ、ひとつのワークスペースで。分析を進めながら、問い、調整し、結果を確認できます。", + "Bring your data": "データを取り込む", + "Load a local file, connect a supported database, or explore a bundled example.": "ローカルファイルを読み込む、対応するデータベースに接続する、同梱のサンプルを試す。好きなところから始められます。", + "Ask the next question": "次の問いを投げかける", + "Describe what you want to explore. Refine your chart with visual encoding controls or a follow-up question.": "調べたいことを言葉にしてみましょう。視覚的なエンコーディング設定や追加の質問で、グラフを調整できます。", + "Inspect, then explain": "確かめてから、伝える", + "Check the derived rows, export a CSV, and give your findings context in an editable report.": "変換後のデータ行を確認し、CSVに出力。編集可能なレポートで、発見に背景を添えられます。", + "02 / A CLOSER LOOK": "02 / もう一歩、詳しく", + "See the work.": "答えだけでなく、", + "Not just the answer.": "その過程も。", + "Real application views, with room to look closer. Explore the features that make the analysis easier to follow.": "実際のアプリ画面を、拡大して確認できます。分析の流れを追いやすくする機能をご覧ください。", + "Explore Datly features": "Datlyの機能を見る", + "Explore & inspect": "探索と確認", + "Data threads": "データスレッド", + "Data inspection": "データの確認", + "Reports": "レポート", + "The rows behind": "グラフを支える", + "the line.": "データ行。", + "A chart is a starting point. Open the underlying table to inspect the values, check a transformation, and decide what the result actually means.": "グラフは出発点です。元となるテーブルを開き、値や変換処理を確認して、結果が何を意味するかを判断できます。", + "Chart and computed table together": "グラフと計算結果のテーブルを一緒に表示", + "Inspect and export the derived rows": "変換後のデータ行を確認・出力", + "Review results before you share": "共有する前に結果を確認", + "View full image": "画像全体を表示", + "Enlarge this feature screenshot": "この機能のスクリーンショットを拡大", + "Detail views of the real Datly Explore canvas, showing a chart and computed data rows": "グラフと計算後のデータ行を表示した、実際のDatlyのExploreキャンバスの詳細画面", + "Actual application capture, reframed for clarity. Example data is illustrative; AI-generated analysis requires review.": "実際のアプリ画面を、見やすい構図に調整しています。サンプルデータは説明用です。AIが生成した分析は確認が必要です。", + "03 / STAY IN THE LOOP": "03 / 分析の主導権を手元に", + "AI helps you explore.": "AIが探索を支援。", + "You make the call.": "判断するのは、あなた。", + "Good analysis depends on more than a convincing chart. Datly makes the intermediate work available for you to examine.": "よい分析に必要なのは、説得力のあるグラフだけではありません。Datlyでは、途中の作業も確認できます。", + "TRACE THE QUESTION": "問いの流れをたどる", + "Keep your train": "思考の流れを", + "of thought.": "途切れさせない。", + "Data threads organise questions, transformations, derived tables, and charts. Follow an investigation as it develops.": "データスレッドは、問い、変換処理、生成されたテーブル、グラフを整理します。調査が進む過程をたどれます。", + "Explore data threads": "データスレッドを見る", + "REFINE THE VIEW": "表示を調整する", + "A chart you can": "分析に合わせて", + "work with.": "調整できるグラフ。", + "Use visual encoding controls for axes, colour, and other chart properties. Adjust the view as your question becomes more precise.": "軸、色、その他のグラフ属性を視覚的なエンコーディング設定で調整できます。問いが具体的になるにつれて、表示も変えられます。", + "Explore the canvas": "キャンバスを見る", + "EXPLAIN THE FINDING": "発見を伝える", + "More than": "スクリーンショットの", + "a screenshot.": "その先へ。", + "Combine written context with chart images in an editable report. Save an image or use your browser’s print flow for PDF.": "編集可能なレポートに、文章とグラフ画像をまとめられます。画像として保存するか、ブラウザーの印刷機能でPDFにできます。", + "Explore reports": "レポートを見る", + "04 / START WITH WHAT YOU HAVE": "04 / 手元のデータから", + "Your data.": "あなたのデータ。", + "A familiar starting point.": "いつもの出発点。", + "Work with files and supported data sources. Connections depend on the appropriate drivers, credentials, and network access.": "ファイルや対応するデータソースを利用できます。接続には、適切なドライバー、認証情報、ネットワークアクセスが必要です。", + "A file today.": "今日はファイルから。", + "A warehouse tomorrow.": "明日はデータウェアハウスへ。", + "Start small with a CSV or a sample dataset. Bring in a supported connection when your question calls for more.": "CSVやサンプルデータセットで小さく始めましょう。問いに応じて、対応するデータソースに接続できます。", + "See connector implementation": "コネクターの実装を見る", + "Filter data sources": "データソースを絞り込む", + "All sources": "すべてのソース", + "SQL & warehouses": "SQL・ウェアハウス", + "Cloud & storage": "クラウド・ストレージ", + "Files & web": "ファイル・Web", + "Relational database": "リレーショナルデータベース", + "SQL warehouse": "SQLウェアハウス", + "Cloud warehouse": "クラウドウェアハウス", + "Analytics database": "分析用データベース", + "Object storage": "オブジェクトストレージ", + "Serverless queries": "サーバーレスクエリ", + "Document database": "ドキュメントデータベース", + "Data Explorer": "Data Explorer", + "Dataset import": "データセットの取り込み", + "Local files": "ローカルファイル", + "CSV, Excel & Parquet": "CSV・Excel・Parquet", + "Web sources": "Webソース", + "Supported table extraction": "対応するテーブル抽出", + "Example datasets": "サンプルデータセット", + "Try a sample analysis": "サンプル分析を試す", + "Showing all 16 source options. Names describe implementations, not certified integrations.": "全16種類のデータソースを表示しています。名称は実装を示すもので、認定済みの連携を意味するものではありません。", + "05 / MAKE AN INFORMED CHOICE": "05 / 理解したうえで選ぶ", + "Local processing.": "ローカルで処理。", + "Clear data boundaries.": "データの境界は明確に。", + "Read the model context code": "モデルに渡すコンテキストのコードを見る", + "01 / EXECUTION": "01 / 実行環境", + "Work in your workspace": "自分のワークスペースで実行", + "DuckDB and Python run transformations in the environment where you host Datly, including a local installation on your computer.": "DuckDBとPythonは、Datlyをホストする環境で変換処理を実行します。自分のコンピューターへのローカルインストールも含まれます。", + "02 / MODEL CONTEXT": "02 / モデルへのコンテキスト", + "Know what AI receives": "AIに何が渡るかを知る", + "AI requests can include schema, sample values and rows, prompts, and tool outputs. A cloud model provider receives the context sent to it.": "AIへのリクエストには、スキーマ、サンプル値やデータ行、プロンプト、ツールの出力が含まれる場合があります。クラウドモデルの提供元は、送信されたコンテキストを受け取ります。", + "03 / YOUR CONFIGURATION": "03 / あなたの設定", + "Choose your model": "モデルを選ぶ", + "Connect a supported provider or configure a local model through Ollama. Local AI requests require a local endpoint; remote data sources still need network access.": "対応する提供元に接続するか、Ollamaでローカルモデルを設定できます。AIへのリクエストをローカルに保つには、ローカルのエンドポイントが必要です。リモートのデータソースには、引き続きネットワークアクセスが必要です。", + "06 / YOUR NEXT GOOD QUESTION": "06 / 次のよい問いへ", + "A little setup.": "少しのセットアップで、", + "A lot to explore.": "探索の幅が広がる。", + "Build from source and launch Datly in your browser. Start with an example dataset, then configure a model for AI-assisted exploration.": "ソースコードからビルドし、ブラウザーでDatlyを起動します。サンプルデータセットから始め、モデルを設定すればAI支援の探索ができます。", + "Python 3.11 or later": "Python 3.11以降", + "Node.js and Yarn for the frontend": "フロントエンド用のNode.jsとYarn", + "uv for the Python environment": "Python環境用のuv", + "Read the setup guide": "セットアップガイドを読む", + "View releases": "リリースを見る", + "Installation method": "インストール方法", + "From source": "ソースコードから", + "Docker": "Docker", + "Copy installation commands": "インストールコマンドをコピー", + "Copy": "コピー", + "Open": "開く", + "after launch": "起動後", + "A FEW THINGS TO KNOW": "知っておきたいこと", + "Before you": "使い始める", + "dive in.": "前に。", + "Questions or feedback?": "ご質問・フィードバック", + "What is Datly?": "Datlyとは何ですか？", + "Datly is an open-source analytics workspace built on Microsoft Research’s Data Formulator. It combines natural-language exploration, visual chart controls, data threads, table inspection, and editable reports. The project is in beta.": "Datlyは、Microsoft ResearchのData Formulatorを基盤とした、オープンソースの分析ワークスペースです。自然言語による探索、視覚的なグラフ設定、データスレッド、テーブルの確認、編集可能なレポートを備えています。現在はベータ版です。", + "Does my data stay on my computer?": "データは自分のコンピューター内にとどまりますか？", + "A local installation processes transformations on your computer. AI requests can include sample rows, field values, prompts, and tool outputs, which are sent to your configured model. If you select a cloud provider, that provider receives this context. A local model endpoint can keep model requests local; review your configuration and connected data sources.": "ローカルにインストールした場合、変換処理は自分のコンピューター上で実行されます。AIへのリクエストには、サンプル行、フィールドの値、プロンプト、ツールの出力が含まれる場合があり、設定したモデルに送信されます。クラウドの提供元を選ぶと、その提供元がコンテキストを受け取ります。ローカルモデルのエンドポイントを使うことでモデルへのリクエストをローカルに保てますが、設定や接続先のデータソースをご確認ください。", + "Does inspecting the table guarantee a correct answer?": "テーブルを確認すれば、答えの正しさは保証されますか？", + "No. The table helps you inspect the computed values, but AI can still choose an incorrect transformation, aggregation, or interpretation. Review the generated work, underlying data, and business assumptions before relying on a result.": "いいえ。テーブルは計算された値の確認に役立ちますが、AIが誤った変換、集計、解釈を選ぶ可能性はあります。結果を判断の根拠にする前に、生成された作業、元のデータ、ビジネス上の前提条件を確認してください。", + "Which models can I use?": "どのモデルを使えますか？", + "The code includes support for OpenAI, Azure OpenAI, Anthropic, Gemini, and local Ollama endpoints. You configure your provider and credentials. Model availability, capabilities, and any usage costs depend on that provider.": "コードにはOpenAI、Azure OpenAI、Anthropic、Gemini、ローカルのOllamaエンドポイントへの対応が含まれています。提供元と認証情報は利用者が設定します。モデルの利用可否、機能、利用料金は提供元によって異なります。", + "Can I create reports and dashboards?": "レポートやダッシュボードを作成できますか？", + "You can edit rich-text reports with chart images, export a report image, or print to PDF. The current dashboard implementation saves chart references and displays title/type placeholders; a rendered, interactive multi-chart dashboard is not yet implemented.": "グラフ画像を含むリッチテキストのレポートを編集し、レポートを画像として出力するか、PDFに印刷できます。現在のダッシュボード実装はグラフへの参照を保存し、タイトルと種類のプレースホルダーを表示します。複数のグラフを描画するインタラクティブなダッシュボードは、まだ実装されていません。", + "Is Datly free?": "Datlyは無料ですか？", + "Datly’s source code is available under the MIT licence. Your model provider, connected infrastructure, or hosting may have their own costs. Check the repository’s setup instructions and releases for the current installation options.": "DatlyのソースコードはMITライセンスで公開されています。モデルの提供元、接続するインフラ、ホスティングには別途費用がかかる場合があります。現在のインストール方法は、リポジトリのセットアップ手順とリリースを確認してください。", + "THERE’S A STORY IN YOUR DATA.": "データには、ストーリーがある。", + "Start with a good question.": "よい問いから、はじめよう。", + "Get started with Datly": "Datlyをはじめる", + "A clearer view of your data.": "データを、より明確に。", + "GitHub ↗": "GitHub ↗", + "Documentation ↗": "ドキュメント ↗", + "Releases ↗": "リリース ↗", + "Image library ↗": "画像ライブラリ ↗", + "MIT licence": "MITライセンス", + "Built on": "基盤技術：", + "Microsoft Data Formulator": "Microsoft Data Formulator", + "Datly workspace": "Datlyワークスペース", + "Close full image": "画像の全体表示を閉じる", + "Close": "閉じる", + "Actual application capture using example data.": "サンプルデータを使用した、実際のアプリ画面です。", + "© 2026 Datly contributors ·": "© 2026 Datlyコントリビューター ·", + "Language": "言語", + "Choose a language": "言語を選択", + "Close language menu": "言語メニューを閉じる", + "Stars on GitHub": "GitHubのスター", + "GitHub stars; verified 2 November 2026": "GitHubのスター。2026年11月2日に確認", + "Installation commands copied.": "インストールコマンドをコピーしました。", + "Automatic copy is unavailable. The commands are selected; use your browser’s copy shortcut.": "自動コピーを利用できません。コマンドを選択した状態にしているため、ブラウザーのコピー用ショートカットを使ってください。", + "Copied ✓": "コピーしました ✓", + "Copy ⧉": "コピー ⧉", + "Select & copy": "選択してコピー", + "Close navigation": "ナビゲーションを閉じる", + "Showing {count} source options. Names describe implementations, not certified integrations.": "{count}種類のデータソースを表示しています。名称は実装を示すもので、認定済みの連携を意味するものではありません。", + "The Datly workspace": "Datlyワークスペース", + "Actual Datly workspace showing data threads, a stock-price chart, and computed rows.": "データスレッド、株価グラフ、計算後のデータ行を表示した、実際のDatlyワークスペース。", + "Actual application capture, reframed for presentation. Example stock data; results require review.": "実際のアプリ画面を、紹介用の構図に調整しています。株価はサンプルデータです。結果の確認が必要です。", + "The rows behind the line.": "グラフを支えるデータ行。", + "Detail views of the real Datly Explore canvas, showing a chart and computed data rows.": "グラフと計算後のデータ行を表示した、実際のDatlyのExploreキャンバスの詳細画面。", + "Keep the path to your answer.": "答えに至る道筋を残す。", + "A follow-up question should build on what you have learned. Data threads keep the prompts, derived tables, and chart results in a visible analytical path.": "追加の問いは、これまでの発見を踏まえて進めたいものです。データスレッドは、プロンプト、生成されたテーブル、グラフの結果を、目で追える分析の道筋として残します。", + "Follow prompts and derived tables": "プロンプトと生成されたテーブルをたどる", + "Explore a separate line of inquiry": "別の切り口で調査する", + "Keep previous steps in context": "前の手順の文脈を保つ", + "Actual Datly data threads showing separate stock-price questions and derived tables.": "株価に関する別々の問いと生成されたテーブルを表示した、実際のDatlyのデータスレッド。", + "Actual application capture, cropped to focus on data threads. Example stock analysis.": "実際のアプリ画面を、データスレッドに焦点を当てて切り抜いています。株価分析のサンプルです。", + "Get to know your data.": "自分のデータを知る。", + "Look beyond the picture. Browse table rows, review data-quality signals, and export the derived data for a closer look in your own tools.": "見た目の先まで確かめましょう。テーブルの行やデータ品質の指標を確認し、変換後のデータを出力して、手元のツールで詳しく調べられます。", + "Review table rows and columns": "テーブルの行と列を確認", + "Inspect data-quality signals": "データ品質の指標を確認", + "Download the computed table as CSV": "計算結果のテーブルをCSVでダウンロード", + "Actual Datly data inspection view with quality signals, table rows, and CSV export.": "品質の指標、テーブルの行、CSV出力を表示した、実際のDatlyのデータ確認画面。", + "Actual application capture, cropped to focus on data inspection. Quality signals are diagnostics, not a certification of correctness.": "実際のアプリ画面を、データの確認に焦点を当てて切り抜いています。品質の指標は診断用であり、正しさを認証するものではありません。", + "Give the finding some context.": "発見に、背景を添える。", + "Turn an exploration into something another person can follow. Write and edit a report with chart images, then export an image or print to PDF.": "探索を、ほかの人も理解できる形に。グラフ画像を含むレポートを作成・編集し、画像として出力するか、PDFに印刷できます。", + "Rich-text editing with chart images": "グラフ画像を含むリッチテキスト編集", + "Add the reasoning around the result": "結果に至った理由を添える", + "Save an image or print to PDF": "画像で保存、またはPDFに印刷", + "An archived Data Formulator report example showing written findings and a chart.": "文章による発見とグラフを示した、Data Formulatorのアーカイブ済みレポート例。", + "Archived report example from the repository; it shows the earlier Data Formulator interface. Current report editing uses TipTap. Chart embeds are images.": "リポジトリに保存された過去のレポート例で、以前のData Formulatorの画面です。現在のレポート編集にはTipTapを使用しています。埋め込みグラフは画像です。", + "Project at a glance": "プロジェクトの概要", + "Data source options": "データソースの選択肢", + "Website languages": "ウェブサイトの言語", + "Open-source licence": "オープンソースライセンス" + } +} diff --git a/docs/locales/ru.json b/docs/locales/ru.json new file mode 100644 index 0000000..eae4678 --- /dev/null +++ b/docs/locales/ru.json @@ -0,0 +1,227 @@ +{ + "language": "ru", + "nativeName": "Русский", + "direction": "ltr", + "translations": { + "width=device-width, initial-scale=1": "width=device-width, initial-scale=1", + "Datly — Open-source AI analytics workspace": "Datly — рабочая среда для аналитики с ИИ и открытым исходным кодом", + "An open-source analytics workspace. Explore tables with AI, refine charts, inspect underlying rows, and turn your findings into reports.": "Аналитическая среда с открытым исходным кодом. Исследуйте таблицы с помощью ИИ, настраивайте диаграммы, проверяйте исходные строки и оформляйте выводы в отчёты.", + "Datly — Good questions. Clearer answers.": "Datly — точные вопросы. Более ясные ответы.", + "Explore with AI. Inspect the data behind your charts. An open-source workspace for your next good question.": "Исследуйте данные с помощью ИИ. Проверяйте данные, на которых построены диаграммы. Открытая рабочая среда для ваших следующих вопросов.", + "Datly workspace with data threads, a chart, and underlying data rows": "Рабочая среда Datly с ветками анализа, диаграммой и лежащими в её основе строками данных", + "An open-source workspace for exploring data with AI, inspecting charts, and writing reports.": "Рабочая среда с открытым исходным кодом для исследования данных с помощью ИИ, проверки диаграмм и подготовки отчётов.", + "Skip to content": "Перейти к содержимому", + "Datly home": "Главная страница Datly", + "Open navigation": "Открыть меню навигации", + "Main navigation": "Основная навигация", + "Product": "Продукт", + "How it works": "Как это работает", + "Data sources": "Источники данных", + "FAQ": "Вопросы и ответы", + "Get started": "Начать работу", + "OPEN SOURCE. OPEN TO EXPLORATION.": "ОТКРЫТЫЙ КОД. ПРОСТОР ДЛЯ ИССЛЕДОВАНИЙ.", + "Good questions.": "Точные вопросы.", + "Clearer answers.": "Более ясные ответы.", + "Your data has a story. Find it with an AI-assisted workspace that keeps your questions, charts, and underlying rows connected.": "В ваших данных есть история. Найдите её в рабочей среде с поддержкой ИИ, где вопросы, диаграммы и лежащие в их основе строки данных связаны между собой.", + "Start exploring": "Начать исследование", + "Take a closer look": "Рассмотреть подробнее", + "Free, open-source software · MIT licensed": "Бесплатное ПО с открытым исходным кодом · Лицензия MIT", + "For analysts, researchers, and data teams.": "Для аналитиков, исследователей и команд, работающих с данными.", + "A little less back and forth. A little more insight.": "Меньше переключений. Больше понимания.", + "ACTUAL APP · EXAMPLE DATA": "РЕАЛЬНОЕ ПРИЛОЖЕНИЕ · ДЕМОНСТРАЦИОННЫЕ ДАННЫЕ", + "Enlarge the Datly workspace screenshot": "Увеличить снимок рабочей среды Datly", + "Datly workspace showing a stock-price analysis thread, line chart, and the data rows underneath": "Рабочая среда Datly с веткой анализа цен акций, линейной диаграммой и строками данных под ней", + "Explore the workspace": "Познакомиться с рабочей средой", + "Questions → transformations → charts → rows": "Вопросы → преобразования → диаграммы → строки", + "YOUR ANALYSIS, IN CONTEXT": "ВАШ АНАЛИЗ В КОНТЕКСТЕ", + "Built on a solid foundation": "Надёжная основа", + "PUBLIC BY DESIGN": "ОТКРЫТОСТЬ В ОСНОВЕ", + "Built in the open.": "Разрабатывается открыто.", + "Explore the source, issues, and releases.": "Изучайте код, обсуждения задач и выпуски.", + "GITHUB COMMUNITY": "СООБЩЕСТВО GITHUB", + "stars on GitHub": "звёзд на GitHub", + "Count verified 2 November 2026; refreshes live.": "Число проверено 2 ноября 2026 года; обновляется в реальном времени.", + "MADE FOR DATA WORK": "ДЛЯ РАБОТЫ С ДАННЫМИ", + "Your next analysis.": "Ваш следующий анализ.", + "01 / FROM QUESTION TO UNDERSTANDING": "01 / ОТ ВОПРОСА К ПОНИМАНИЮ", + "Follow your curiosity.": "Следуйте любопытству.", + "Keep the context.": "Сохраняйте контекст.", + "Move from a table to a useful finding in one workspace. Ask, refine, and inspect as your analysis takes shape.": "От таблицы к полезному выводу — в одной рабочей среде. Задавайте вопросы, уточняйте и проверяйте результаты по мере развития анализа.", + "Bring your data": "Добавьте свои данные", + "Load a local file, connect a supported database, or explore a bundled example.": "Загрузите локальный файл, подключите поддерживаемую базу данных или изучите встроенный пример.", + "Ask the next question": "Задайте следующий вопрос", + "Describe what you want to explore. Refine your chart with visual encoding controls or a follow-up question.": "Опишите, что хотите исследовать. Уточняйте диаграмму с помощью визуальных настроек или дополнительных вопросов.", + "Inspect, then explain": "Проверьте, затем объясните", + "Check the derived rows, export a CSV, and give your findings context in an editable report.": "Проверьте полученные строки, экспортируйте CSV и дополните выводы пояснениями в редактируемом отчёте.", + "02 / A CLOSER LOOK": "02 / ПОДРОБНЫЙ ВЗГЛЯД", + "See the work.": "Смотрите на ход работы.", + "Not just the answer.": "Не только на ответ.", + "Real application views, with room to look closer. Explore the features that make the analysis easier to follow.": "Реальные экраны приложения, которые можно рассмотреть подробнее. Познакомьтесь с функциями, помогающими проследить ход анализа.", + "Explore Datly features": "Изучить возможности Datly", + "Explore & inspect": "Исследование и проверка", + "Data threads": "Ветки анализа", + "Data inspection": "Проверка данных", + "Reports": "Отчёты", + "The rows behind": "Строки данных", + "the line.": "за линией графика.", + "A chart is a starting point. Open the underlying table to inspect the values, check a transformation, and decide what the result actually means.": "Диаграмма — отправная точка. Откройте лежащую в её основе таблицу, изучите значения, проверьте преобразование и определите, что на самом деле означает результат.", + "Chart and computed table together": "Диаграмма и вычисленная таблица рядом", + "Inspect and export the derived rows": "Проверка и экспорт полученных строк", + "Review results before you share": "Проверяйте результаты перед публикацией", + "View full image": "Посмотреть изображение целиком", + "Enlarge this feature screenshot": "Увеличить снимок этой функции", + "Detail views of the real Datly Explore canvas, showing a chart and computed data rows": "Детальные виды реального холста Explore в Datly с диаграммой и вычисленными строками данных", + "Actual application capture, reframed for clarity. Example data is illustrative; AI-generated analysis requires review.": "Реальный снимок приложения, кадрированный для наглядности. Данные приведены для примера; анализ, созданный ИИ, требует проверки.", + "03 / STAY IN THE LOOP": "03 / СОХРАНЯЙТЕ КОНТРОЛЬ", + "AI helps you explore.": "ИИ помогает исследовать.", + "You make the call.": "Решение принимаете вы.", + "Good analysis depends on more than a convincing chart. Datly makes the intermediate work available for you to examine.": "Для качественного анализа недостаточно убедительной диаграммы. Datly позволяет изучить промежуточные этапы работы.", + "TRACE THE QUESTION": "ПРОСЛЕДИТЕ ХОД ВОПРОСА", + "Keep your train": "Сохраняйте", + "of thought.": "ход мыслей.", + "Data threads organise questions, transformations, derived tables, and charts. Follow an investigation as it develops.": "Ветки анализа упорядочивают вопросы, преобразования, полученные таблицы и диаграммы. Следите за развитием исследования.", + "Explore data threads": "Изучить ветки анализа", + "REFINE THE VIEW": "НАСТРОЙТЕ ПРЕДСТАВЛЕНИЕ", + "A chart you can": "Диаграмма, с которой", + "work with.": "можно работать.", + "Use visual encoding controls for axes, colour, and other chart properties. Adjust the view as your question becomes more precise.": "Настраивайте оси, цвета и другие свойства диаграммы с помощью визуальных элементов управления. Меняйте представление по мере уточнения вопроса.", + "Explore the canvas": "Изучить холст", + "EXPLAIN THE FINDING": "ОБЪЯСНИТЕ ВЫВОД", + "More than": "Больше, чем", + "a screenshot.": "снимок экрана.", + "Combine written context with chart images in an editable report. Save an image or use your browser’s print flow for PDF.": "Объединяйте пояснения и изображения диаграмм в редактируемом отчёте. Сохраните изображение или используйте печать в браузере для получения PDF.", + "Explore reports": "Изучить отчёты", + "04 / START WITH WHAT YOU HAVE": "04 / НАЧНИТЕ С ТОГО, ЧТО ЕСТЬ", + "Your data.": "Ваши данные.", + "A familiar starting point.": "Знакомая отправная точка.", + "Work with files and supported data sources. Connections depend on the appropriate drivers, credentials, and network access.": "Работайте с файлами и поддерживаемыми источниками данных. Для подключения нужны соответствующие драйверы, учётные данные и доступ к сети.", + "A file today.": "Сегодня — файл.", + "A warehouse tomorrow.": "Завтра — хранилище данных.", + "Start small with a CSV or a sample dataset. Bring in a supported connection when your question calls for more.": "Начните с CSV или демонстрационного набора данных. Подключите поддерживаемый источник, когда для ответа потребуется больше.", + "See connector implementation": "Посмотреть реализацию коннекторов", + "Filter data sources": "Фильтр источников данных", + "All sources": "Все источники", + "SQL & warehouses": "SQL и хранилища данных", + "Cloud & storage": "Облако и хранение", + "Files & web": "Файлы и веб", + "Relational database": "Реляционная база данных", + "SQL warehouse": "SQL-хранилище", + "Cloud warehouse": "Облачное хранилище данных", + "Analytics database": "Аналитическая база данных", + "Object storage": "Объектное хранилище", + "Serverless queries": "Бессерверные запросы", + "Document database": "Документная база данных", + "Data Explorer": "Data Explorer", + "Dataset import": "Импорт наборов данных", + "Local files": "Локальные файлы", + "CSV, Excel & Parquet": "CSV, Excel и Parquet", + "Web sources": "Веб-источники", + "Supported table extraction": "Поддерживаемое извлечение таблиц", + "Example datasets": "Демонстрационные наборы данных", + "Try a sample analysis": "Попробовать анализ на примере", + "Showing all 16 source options. Names describe implementations, not certified integrations.": "Показаны все 16 вариантов источников. Названия обозначают реализации, а не сертифицированные интеграции.", + "05 / MAKE AN INFORMED CHOICE": "05 / ОСОЗНАННЫЙ ВЫБОР", + "Local processing.": "Локальная обработка.", + "Clear data boundaries.": "Понятные границы передачи данных.", + "Read the model context code": "Посмотреть код контекста модели", + "01 / EXECUTION": "01 / ВЫПОЛНЕНИЕ", + "Work in your workspace": "Работайте в своей среде", + "DuckDB and Python run transformations in the environment where you host Datly, including a local installation on your computer.": "DuckDB и Python выполняют преобразования в среде, где размещён Datly, в том числе при локальной установке на вашем компьютере.", + "02 / MODEL CONTEXT": "02 / КОНТЕКСТ МОДЕЛИ", + "Know what AI receives": "Знайте, что получает ИИ", + "AI requests can include schema, sample values and rows, prompts, and tool outputs. A cloud model provider receives the context sent to it.": "Запросы к ИИ могут включать схему, примеры значений и строк, инструкции и результаты работы инструментов. Облачный поставщик модели получает переданный ему контекст.", + "03 / YOUR CONFIGURATION": "03 / ВАША КОНФИГУРАЦИЯ", + "Choose your model": "Выберите модель", + "Connect a supported provider or configure a local model through Ollama. Local AI requests require a local endpoint; remote data sources still need network access.": "Подключите поддерживаемого поставщика или настройте локальную модель через Ollama. Для локальных запросов к ИИ нужна локальная конечная точка; удалённым источникам данных по-прежнему требуется доступ к сети.", + "06 / YOUR NEXT GOOD QUESTION": "06 / ВАШ СЛЕДУЮЩИЙ ВОПРОС", + "A little setup.": "Немного настройки.", + "A lot to explore.": "Много возможностей для исследования.", + "Build from source and launch Datly in your browser. Start with an example dataset, then configure a model for AI-assisted exploration.": "Соберите Datly из исходного кода и запустите в браузере. Начните с демонстрационного набора данных, затем настройте модель для исследования с помощью ИИ.", + "Python 3.11 or later": "Python 3.11 или новее", + "Node.js and Yarn for the frontend": "Node.js и Yarn для интерфейса", + "uv for the Python environment": "uv для среды Python", + "Read the setup guide": "Прочитать руководство по установке", + "View releases": "Посмотреть выпуски", + "Installation method": "Способ установки", + "From source": "Из исходного кода", + "Docker": "Docker", + "Copy installation commands": "Скопировать команды установки", + "Copy": "Копировать", + "Open": "Откройте", + "after launch": "после запуска", + "A FEW THINGS TO KNOW": "ЧТО СТОИТ ЗНАТЬ", + "Before you": "Прежде чем", + "dive in.": "начать.", + "Questions or feedback?": "Вопросы или предложения?", + "What is Datly?": "Что такое Datly?", + "Datly is an open-source analytics workspace built on Microsoft Research’s Data Formulator. It combines natural-language exploration, visual chart controls, data threads, table inspection, and editable reports. The project is in beta.": "Datly — аналитическая среда с открытым исходным кодом на базе Data Formulator от Microsoft Research. Она объединяет исследование на естественном языке, визуальные настройки диаграмм, ветки анализа, проверку таблиц и редактируемые отчёты. Проект находится в бета-версии.", + "Does my data stay on my computer?": "Мои данные остаются на компьютере?", + "A local installation processes transformations on your computer. AI requests can include sample rows, field values, prompts, and tool outputs, which are sent to your configured model. If you select a cloud provider, that provider receives this context. A local model endpoint can keep model requests local; review your configuration and connected data sources.": "При локальной установке преобразования выполняются на вашем компьютере. Запросы к ИИ могут включать примеры строк, значения полей, инструкции и результаты работы инструментов, передаваемые настроенной модели. Если вы выбрали облачного поставщика, он получает этот контекст. Локальная конечная точка модели позволяет обрабатывать запросы к модели локально; проверьте конфигурацию и подключённые источники данных.", + "Does inspecting the table guarantee a correct answer?": "Гарантирует ли проверка таблицы правильный ответ?", + "No. The table helps you inspect the computed values, but AI can still choose an incorrect transformation, aggregation, or interpretation. Review the generated work, underlying data, and business assumptions before relying on a result.": "Нет. Таблица помогает проверить вычисленные значения, но ИИ всё ещё может выбрать неверное преобразование, агрегирование или интерпретацию. Прежде чем опираться на результат, проверьте созданную работу, исходные данные и бизнес-допущения.", + "Which models can I use?": "Какие модели можно использовать?", + "The code includes support for OpenAI, Azure OpenAI, Anthropic, Gemini, and local Ollama endpoints. You configure your provider and credentials. Model availability, capabilities, and any usage costs depend on that provider.": "В коде реализована поддержка OpenAI, Azure OpenAI, Anthropic, Gemini и локальных конечных точек Ollama. Поставщика и учётные данные настраиваете вы. Доступность моделей, их возможности и стоимость использования зависят от поставщика.", + "Can I create reports and dashboards?": "Можно ли создавать отчёты и дашборды?", + "You can edit rich-text reports with chart images, export a report image, or print to PDF. The current dashboard implementation saves chart references and displays title/type placeholders; a rendered, interactive multi-chart dashboard is not yet implemented.": "Вы можете редактировать форматированные отчёты с изображениями диаграмм, экспортировать отчёт как изображение или печатать в PDF. Текущая реализация дашборда сохраняет ссылки на диаграммы и показывает заглушки с названием и типом; отображение интерактивного дашборда с несколькими диаграммами пока не реализовано.", + "Is Datly free?": "Datly бесплатный?", + "Datly’s source code is available under the MIT licence. Your model provider, connected infrastructure, or hosting may have their own costs. Check the repository’s setup instructions and releases for the current installation options.": "Исходный код Datly доступен по лицензии MIT. Поставщик модели, подключённая инфраструктура и хостинг могут оплачиваться отдельно. Актуальные способы установки приведены в руководстве и выпусках репозитория.", + "THERE’S A STORY IN YOUR DATA.": "В ВАШИХ ДАННЫХ ЕСТЬ ИСТОРИЯ.", + "Start with a good question.": "Начните с точного вопроса.", + "Get started with Datly": "Начать работу с Datly", + "A clearer view of your data.": "Более ясный взгляд на ваши данные.", + "GitHub ↗": "GitHub ↗", + "Documentation ↗": "Документация ↗", + "Releases ↗": "Выпуски ↗", + "Image library ↗": "Библиотека изображений ↗", + "MIT licence": "Лицензия MIT", + "Built on": "На базе", + "Microsoft Data Formulator": "Microsoft Data Formulator", + "Datly workspace": "Рабочая среда Datly", + "Close full image": "Закрыть полное изображение", + "Close": "Закрыть", + "Actual application capture using example data.": "Реальный снимок приложения с демонстрационными данными.", + "© 2026 Datly contributors ·": "© 2026 Участники проекта Datly ·", + "Language": "Язык", + "Choose a language": "Выберите язык", + "Close language menu": "Закрыть меню выбора языка", + "Stars on GitHub": "Звёзды на GitHub", + "GitHub stars; verified 2 November 2026": "Звёзды GitHub; проверено 2 ноября 2026 года", + "Installation commands copied.": "Команды установки скопированы.", + "Automatic copy is unavailable. The commands are selected; use your browser’s copy shortcut.": "Автоматическое копирование недоступно. Команды выделены; используйте сочетание клавиш для копирования в браузере.", + "Copied ✓": "Скопировано ✓", + "Copy ⧉": "Копировать ⧉", + "Select & copy": "Выделить и скопировать", + "Close navigation": "Закрыть меню навигации", + "Showing {count} source options. Names describe implementations, not certified integrations.": "Показано вариантов источников: {count}. Названия обозначают реализации, а не сертифицированные интеграции.", + "The Datly workspace": "Рабочая среда Datly", + "Actual Datly workspace showing data threads, a stock-price chart, and computed rows.": "Реальная рабочая среда Datly с ветками анализа, графиком цен акций и вычисленными строками.", + "Actual application capture, reframed for presentation. Example stock data; results require review.": "Реальный снимок приложения, кадрированный для презентации. Демонстрационные данные об акциях; результаты требуют проверки.", + "The rows behind the line.": "Строки данных за линией графика.", + "Detail views of the real Datly Explore canvas, showing a chart and computed data rows.": "Детальные виды реального холста Explore в Datly с диаграммой и вычисленными строками данных.", + "Keep the path to your answer.": "Сохраняйте путь к ответу.", + "A follow-up question should build on what you have learned. Data threads keep the prompts, derived tables, and chart results in a visible analytical path.": "Следующий вопрос должен опираться на уже полученные знания. Ветки анализа сохраняют инструкции, полученные таблицы и диаграммы в виде наглядного пути исследования.", + "Follow prompts and derived tables": "Прослеживайте инструкции и полученные таблицы", + "Explore a separate line of inquiry": "Исследуйте отдельное направление", + "Keep previous steps in context": "Сохраняйте контекст предыдущих шагов", + "Actual Datly data threads showing separate stock-price questions and derived tables.": "Реальные ветки анализа Datly с отдельными вопросами о ценах акций и полученными таблицами.", + "Actual application capture, cropped to focus on data threads. Example stock analysis.": "Реальный снимок приложения, обрезанный для показа веток анализа. Пример анализа акций.", + "Get to know your data.": "Познакомьтесь со своими данными.", + "Look beyond the picture. Browse table rows, review data-quality signals, and export the derived data for a closer look in your own tools.": "Посмотрите глубже изображения. Просматривайте строки таблицы, изучайте показатели качества данных и экспортируйте полученные данные для подробного изучения в своих инструментах.", + "Review table rows and columns": "Просматривайте строки и столбцы таблицы", + "Inspect data-quality signals": "Изучайте показатели качества данных", + "Download the computed table as CSV": "Скачайте вычисленную таблицу в формате CSV", + "Actual Datly data inspection view with quality signals, table rows, and CSV export.": "Реальный экран проверки данных Datly с показателями качества, строками таблицы и экспортом CSV.", + "Actual application capture, cropped to focus on data inspection. Quality signals are diagnostics, not a certification of correctness.": "Реальный снимок приложения, обрезанный для показа проверки данных. Показатели качества служат для диагностики и не подтверждают правильность результата.", + "Give the finding some context.": "Дополните вывод контекстом.", + "Turn an exploration into something another person can follow. Write and edit a report with chart images, then export an image or print to PDF.": "Оформите исследование так, чтобы другой человек мог проследить его ход. Напишите и отредактируйте отчёт с изображениями диаграмм, затем экспортируйте изображение или напечатайте в PDF.", + "Rich-text editing with chart images": "Редактирование форматированного текста с изображениями диаграмм", + "Add the reasoning around the result": "Добавьте обоснование результата", + "Save an image or print to PDF": "Сохраните изображение или напечатайте в PDF", + "An archived Data Formulator report example showing written findings and a chart.": "Архивный пример отчёта Data Formulator с письменными выводами и диаграммой.", + "Archived report example from the repository; it shows the earlier Data Formulator interface. Current report editing uses TipTap. Chart embeds are images.": "Архивный пример отчёта из репозитория с прежним интерфейсом Data Formulator. Сейчас для редактирования отчётов используется TipTap. Встроенные диаграммы являются изображениями.", + "Project at a glance": "Проект в цифрах", + "Data source options": "Варианты источников данных", + "Website languages": "Языки сайта", + "Open-source licence": "Лицензия открытого ПО" + } +} diff --git a/docs/locales/th.json b/docs/locales/th.json new file mode 100644 index 0000000..9659c8f --- /dev/null +++ b/docs/locales/th.json @@ -0,0 +1,227 @@ +{ + "language": "th", + "nativeName": "ไทย", + "direction": "ltr", + "translations": { + "width=device-width, initial-scale=1": "width=device-width, initial-scale=1", + "Datly — Open-source AI analytics workspace": "Datly — พื้นที่วิเคราะห์ข้อมูลด้วย AI แบบโอเพนซอร์ส", + "An open-source analytics workspace. Explore tables with AI, refine charts, inspect underlying rows, and turn your findings into reports.": "พื้นที่วิเคราะห์ข้อมูลแบบโอเพนซอร์ส สำรวจตารางด้วย AI ปรับแต่งแผนภูมิ ตรวจสอบแถวข้อมูลเบื้องหลัง และเปลี่ยนสิ่งที่ค้นพบเป็นรายงาน", + "Datly — Good questions. Clearer answers.": "Datly — คำถามที่ดี คำตอบที่ชัดเจนขึ้น", + "Explore with AI. Inspect the data behind your charts. An open-source workspace for your next good question.": "สำรวจด้วย AI ตรวจสอบข้อมูลเบื้องหลังแผนภูมิ พื้นที่โอเพนซอร์สสำหรับคำถามดี ๆ ข้อต่อไปของคุณ", + "Datly workspace with data threads, a chart, and underlying data rows": "พื้นที่ทำงาน Datly พร้อมเธรดข้อมูล แผนภูมิ และแถวข้อมูลเบื้องหลัง", + "An open-source workspace for exploring data with AI, inspecting charts, and writing reports.": "พื้นที่โอเพนซอร์สสำหรับสำรวจข้อมูลด้วย AI ตรวจสอบแผนภูมิ และเขียนรายงาน", + "Skip to content": "ข้ามไปยังเนื้อหา", + "Datly home": "หน้าแรก Datly", + "Open navigation": "เปิดเมนูนำทาง", + "Main navigation": "เมนูนำทางหลัก", + "Product": "ผลิตภัณฑ์", + "How it works": "วิธีการทำงาน", + "Data sources": "แหล่งข้อมูล", + "FAQ": "คำถามที่พบบ่อย", + "Get started": "เริ่มต้นใช้งาน", + "OPEN SOURCE. OPEN TO EXPLORATION.": "โอเพนซอร์ส เปิดกว้างให้สำรวจ", + "Good questions.": "คำถามที่ดี", + "Clearer answers.": "คำตอบที่ชัดเจนขึ้น", + "Your data has a story. Find it with an AI-assisted workspace that keeps your questions, charts, and underlying rows connected.": "ข้อมูลของคุณมีเรื่องราว ค้นพบเรื่องราวนั้นด้วยพื้นที่ทำงานที่มี AI ช่วยเชื่อมคำถาม แผนภูมิ และแถวข้อมูลเบื้องหลังเข้าด้วยกัน", + "Start exploring": "เริ่มสำรวจ", + "Take a closer look": "ดูให้ลึกขึ้น", + "Free, open-source software · MIT licensed": "ซอฟต์แวร์โอเพนซอร์ส ใช้ได้ฟรี · สัญญาอนุญาต MIT", + "For analysts, researchers, and data teams.": "สำหรับนักวิเคราะห์ นักวิจัย และทีมข้อมูล", + "A little less back and forth. A little more insight.": "สลับไปมาน้อยลง เข้าใจข้อมูลมากขึ้น", + "ACTUAL APP · EXAMPLE DATA": "ภาพจากแอปจริง · ข้อมูลตัวอย่าง", + "Enlarge the Datly workspace screenshot": "ขยายภาพหน้าจอพื้นที่ทำงาน Datly", + "Datly workspace showing a stock-price analysis thread, line chart, and the data rows underneath": "พื้นที่ทำงาน Datly แสดงเธรดการวิเคราะห์ราคาหุ้น แผนภูมิเส้น และแถวข้อมูลด้านล่าง", + "Explore the workspace": "สำรวจพื้นที่ทำงาน", + "Questions → transformations → charts → rows": "คำถาม → การแปลงข้อมูล → แผนภูมิ → แถวข้อมูล", + "YOUR ANALYSIS, IN CONTEXT": "งานวิเคราะห์ของคุณ พร้อมบริบท", + "Built on a solid foundation": "สร้างบนพื้นฐานที่มั่นคง", + "PUBLIC BY DESIGN": "ออกแบบมาให้เปิดเผย", + "Built in the open.": "พัฒนาอย่างเปิดเผย", + "Explore the source, issues, and releases.": "สำรวจซอร์สโค้ด ประเด็นปัญหา และรุ่นที่เผยแพร่", + "GITHUB COMMUNITY": "ชุมชน GITHUB", + "stars on GitHub": "ดาวบน GitHub", + "Count verified 2 November 2026; refreshes live.": "ตรวจสอบจำนวนเมื่อ 2 พฤศจิกายน 2026 และรีเฟรชข้อมูลสด", + "MADE FOR DATA WORK": "สร้างมาเพื่องานข้อมูล", + "Your next analysis.": "งานวิเคราะห์ครั้งถัดไปของคุณ", + "01 / FROM QUESTION TO UNDERSTANDING": "01 / จากคำถามสู่ความเข้าใจ", + "Follow your curiosity.": "ตามความอยากรู้ของคุณ", + "Keep the context.": "เก็บบริบทไว้ครบ", + "Move from a table to a useful finding in one workspace. Ask, refine, and inspect as your analysis takes shape.": "จากตารางสู่ข้อค้นพบที่มีประโยชน์ในพื้นที่เดียว ตั้งคำถาม ปรับแต่ง และตรวจสอบไปพร้อมกับการวิเคราะห์", + "Bring your data": "นำข้อมูลของคุณมาใช้", + "Load a local file, connect a supported database, or explore a bundled example.": "โหลดไฟล์ในเครื่อง เชื่อมต่อฐานข้อมูลที่รองรับ หรือสำรวจตัวอย่างที่มีให้", + "Ask the next question": "ตั้งคำถามถัดไป", + "Describe what you want to explore. Refine your chart with visual encoding controls or a follow-up question.": "อธิบายสิ่งที่ต้องการสำรวจ ปรับแผนภูมิด้วยตัวควบคุมการเข้ารหัสภาพ หรือถามคำถามต่อเนื่อง", + "Inspect, then explain": "ตรวจสอบ แล้วอธิบาย", + "Check the derived rows, export a CSV, and give your findings context in an editable report.": "ตรวจสอบแถวข้อมูลที่แปลงแล้ว ส่งออก CSV และเพิ่มบริบทให้ข้อค้นพบในรายงานที่แก้ไขได้", + "02 / A CLOSER LOOK": "02 / มองให้ใกล้ขึ้น", + "See the work.": "เห็นกระบวนการ", + "Not just the answer.": "ไม่ใช่เพียงคำตอบ", + "Real application views, with room to look closer. Explore the features that make the analysis easier to follow.": "มุมมองจากแอปจริงที่เปิดให้ดูใกล้ขึ้น สำรวจฟีเจอร์ที่ช่วยให้ติดตามการวิเคราะห์ได้ง่ายขึ้น", + "Explore Datly features": "สำรวจฟีเจอร์ Datly", + "Explore & inspect": "สำรวจและตรวจสอบ", + "Data threads": "เธรดข้อมูล", + "Data inspection": "ตรวจสอบข้อมูล", + "Reports": "รายงาน", + "The rows behind": "แถวข้อมูลเบื้องหลัง", + "the line.": "เส้นแผนภูมิ", + "A chart is a starting point. Open the underlying table to inspect the values, check a transformation, and decide what the result actually means.": "แผนภูมิคือจุดเริ่มต้น เปิดตารางเบื้องหลังเพื่อตรวจสอบค่า ทบทวนการแปลงข้อมูล และพิจารณาว่าผลลัพธ์หมายความว่าอย่างไร", + "Chart and computed table together": "แผนภูมิและตารางผลคำนวณอยู่ด้วยกัน", + "Inspect and export the derived rows": "ตรวจสอบและส่งออกแถวข้อมูลที่แปลงแล้ว", + "Review results before you share": "ทบทวนผลลัพธ์ก่อนแชร์", + "View full image": "ดูภาพเต็ม", + "Enlarge this feature screenshot": "ขยายภาพหน้าจอฟีเจอร์นี้", + "Detail views of the real Datly Explore canvas, showing a chart and computed data rows": "ภาพรายละเอียดพื้นที่ Explore จริงของ Datly แสดงแผนภูมิและแถวข้อมูลที่คำนวณแล้ว", + "Actual application capture, reframed for clarity. Example data is illustrative; AI-generated analysis requires review.": "ภาพจากแอปจริง จัดกรอบใหม่เพื่อให้ชัดเจน ข้อมูลใช้ประกอบตัวอย่างเท่านั้น การวิเคราะห์ที่สร้างโดย AI ต้องได้รับการทบทวน", + "03 / STAY IN THE LOOP": "03 / ติดตามได้ทุกขั้น", + "AI helps you explore.": "AI ช่วยคุณสำรวจ", + "You make the call.": "คุณเป็นผู้ตัดสินใจ", + "Good analysis depends on more than a convincing chart. Datly makes the intermediate work available for you to examine.": "การวิเคราะห์ที่ดีต้องมีมากกว่าแผนภูมิที่ดูน่าเชื่อถือ Datly เปิดให้คุณตรวจสอบงานระหว่างขั้นตอนได้", + "TRACE THE QUESTION": "ติดตามคำถาม", + "Keep your train": "รักษาความต่อเนื่อง", + "of thought.": "ของแนวคิด", + "Data threads organise questions, transformations, derived tables, and charts. Follow an investigation as it develops.": "เธรดข้อมูลจัดระเบียบคำถาม การแปลงข้อมูล ตารางที่ได้มา และแผนภูมิ ติดตามการสำรวจขณะที่งานดำเนินไป", + "Explore data threads": "สำรวจเธรดข้อมูล", + "REFINE THE VIEW": "ปรับมุมมอง", + "A chart you can": "แผนภูมิที่คุณ", + "work with.": "นำไปทำงานต่อได้", + "Use visual encoding controls for axes, colour, and other chart properties. Adjust the view as your question becomes more precise.": "ใช้ตัวควบคุมการเข้ารหัสภาพสำหรับแกน สี และคุณสมบัติอื่นของแผนภูมิ ปรับมุมมองเมื่อคำถามของคุณชัดเจนขึ้น", + "Explore the canvas": "สำรวจพื้นที่แผนภูมิ", + "EXPLAIN THE FINDING": "อธิบายข้อค้นพบ", + "More than": "มากกว่า", + "a screenshot.": "ภาพหน้าจอ", + "Combine written context with chart images in an editable report. Save an image or use your browser’s print flow for PDF.": "ผสานคำอธิบายกับภาพแผนภูมิในรายงานที่แก้ไขได้ บันทึกเป็นภาพ หรือใช้การพิมพ์ของเบราว์เซอร์เพื่อสร้าง PDF", + "Explore reports": "สำรวจรายงาน", + "04 / START WITH WHAT YOU HAVE": "04 / เริ่มจากสิ่งที่คุณมี", + "Your data.": "ข้อมูลของคุณ", + "A familiar starting point.": "จุดเริ่มต้นที่คุ้นเคย", + "Work with files and supported data sources. Connections depend on the appropriate drivers, credentials, and network access.": "ทำงานกับไฟล์และแหล่งข้อมูลที่รองรับ การเชื่อมต่อขึ้นอยู่กับไดรเวอร์ ข้อมูลรับรอง และการเข้าถึงเครือข่ายที่เหมาะสม", + "A file today.": "วันนี้เริ่มจากไฟล์", + "A warehouse tomorrow.": "พรุ่งนี้อาจเป็นคลังข้อมูล", + "Start small with a CSV or a sample dataset. Bring in a supported connection when your question calls for more.": "เริ่มต้นง่าย ๆ ด้วย CSV หรือชุดข้อมูลตัวอย่าง เพิ่มการเชื่อมต่อที่รองรับเมื่อคำถามต้องใช้ข้อมูลมากขึ้น", + "See connector implementation": "ดูการพัฒนาตัวเชื่อมต่อ", + "Filter data sources": "กรองแหล่งข้อมูล", + "All sources": "แหล่งข้อมูลทั้งหมด", + "SQL & warehouses": "SQL และคลังข้อมูล", + "Cloud & storage": "คลาวด์และที่จัดเก็บ", + "Files & web": "ไฟล์และเว็บ", + "Relational database": "ฐานข้อมูลเชิงสัมพันธ์", + "SQL warehouse": "คลังข้อมูล SQL", + "Cloud warehouse": "คลังข้อมูลบนคลาวด์", + "Analytics database": "ฐานข้อมูลเชิงวิเคราะห์", + "Object storage": "ที่จัดเก็บออบเจ็กต์", + "Serverless queries": "คิวรีแบบไร้เซิร์ฟเวอร์", + "Document database": "ฐานข้อมูลเอกสาร", + "Data Explorer": "การสำรวจข้อมูล", + "Dataset import": "นำเข้าชุดข้อมูล", + "Local files": "ไฟล์ในเครื่อง", + "CSV, Excel & Parquet": "CSV, Excel และ Parquet", + "Web sources": "แหล่งข้อมูลบนเว็บ", + "Supported table extraction": "การดึงตารางที่รองรับ", + "Example datasets": "ชุดข้อมูลตัวอย่าง", + "Try a sample analysis": "ลองวิเคราะห์ตัวอย่าง", + "Showing all 16 source options. Names describe implementations, not certified integrations.": "แสดงตัวเลือกแหล่งข้อมูลทั้ง 16 รายการ ชื่อบอกถึงส่วนที่มีการพัฒนาไว้ ไม่ใช่การผสานรวมที่ได้รับการรับรอง", + "05 / MAKE AN INFORMED CHOICE": "05 / เลือกอย่างเข้าใจ", + "Local processing.": "ประมวลผลในเครื่อง", + "Clear data boundaries.": "ขอบเขตข้อมูลชัดเจน", + "Read the model context code": "อ่านโค้ดบริบทของโมเดล", + "01 / EXECUTION": "01 / การประมวลผล", + "Work in your workspace": "ทำงานในสภาพแวดล้อมของคุณ", + "DuckDB and Python run transformations in the environment where you host Datly, including a local installation on your computer.": "DuckDB และ Python ทำการแปลงข้อมูลในสภาพแวดล้อมที่โฮสต์ Datly รวมถึงการติดตั้งในเครื่องคอมพิวเตอร์ของคุณ", + "02 / MODEL CONTEXT": "02 / บริบทของโมเดล", + "Know what AI receives": "รู้ว่า AI ได้รับอะไร", + "AI requests can include schema, sample values and rows, prompts, and tool outputs. A cloud model provider receives the context sent to it.": "คำขอ AI อาจมีสคีมา ค่าตัวอย่างและแถวตัวอย่าง พรอมป์ต์ และผลลัพธ์จากเครื่องมือ ผู้ให้บริการโมเดลคลาวด์จะได้รับบริบทที่ส่งไป", + "03 / YOUR CONFIGURATION": "03 / การตั้งค่าของคุณ", + "Choose your model": "เลือกโมเดลของคุณ", + "Connect a supported provider or configure a local model through Ollama. Local AI requests require a local endpoint; remote data sources still need network access.": "เชื่อมต่อผู้ให้บริการที่รองรับ หรือตั้งค่าโมเดลในเครื่องผ่าน Ollama คำขอ AI แบบในเครื่องต้องใช้ปลายทางในเครื่อง ส่วนแหล่งข้อมูลระยะไกลยังต้องเข้าถึงเครือข่าย", + "06 / YOUR NEXT GOOD QUESTION": "06 / คำถามดี ๆ ข้อต่อไป", + "A little setup.": "ตั้งค่าอีกนิด", + "A lot to explore.": "มีอะไรให้สำรวจอีกมาก", + "Build from source and launch Datly in your browser. Start with an example dataset, then configure a model for AI-assisted exploration.": "บิลด์จากซอร์สโค้ดแล้วเปิด Datly ในเบราว์เซอร์ เริ่มจากชุดข้อมูลตัวอย่าง แล้วตั้งค่าโมเดลสำหรับการสำรวจที่มี AI ช่วย", + "Python 3.11 or later": "Python 3.11 ขึ้นไป", + "Node.js and Yarn for the frontend": "Node.js และ Yarn สำหรับส่วนหน้า", + "uv for the Python environment": "uv สำหรับสภาพแวดล้อม Python", + "Read the setup guide": "อ่านคู่มือการตั้งค่า", + "View releases": "ดูรุ่นที่เผยแพร่", + "Installation method": "วิธีติดตั้ง", + "From source": "จากซอร์สโค้ด", + "Docker": "Docker", + "Copy installation commands": "คัดลอกคำสั่งติดตั้ง", + "Copy": "คัดลอก", + "Open": "เปิด", + "after launch": "หลังเริ่มทำงาน", + "A FEW THINGS TO KNOW": "สิ่งที่ควรรู้", + "Before you": "ก่อนที่คุณจะ", + "dive in.": "เริ่มสำรวจ", + "Questions or feedback?": "มีคำถามหรือข้อเสนอแนะ?", + "What is Datly?": "Datly คืออะไร?", + "Datly is an open-source analytics workspace built on Microsoft Research’s Data Formulator. It combines natural-language exploration, visual chart controls, data threads, table inspection, and editable reports. The project is in beta.": "Datly คือพื้นที่วิเคราะห์ข้อมูลโอเพนซอร์สที่สร้างบน Data Formulator ของ Microsoft Research ผสานการสำรวจด้วยภาษาธรรมชาติ ตัวควบคุมแผนภูมิแบบภาพ เธรดข้อมูล การตรวจสอบตาราง และรายงานที่แก้ไขได้ โครงการอยู่ในระยะเบตา", + "Does my data stay on my computer?": "ข้อมูลของฉันจะอยู่ในคอมพิวเตอร์หรือไม่?", + "A local installation processes transformations on your computer. AI requests can include sample rows, field values, prompts, and tool outputs, which are sent to your configured model. If you select a cloud provider, that provider receives this context. A local model endpoint can keep model requests local; review your configuration and connected data sources.": "การติดตั้งในเครื่องทำการแปลงข้อมูลบนคอมพิวเตอร์ของคุณ คำขอ AI อาจมีแถวตัวอย่าง ค่าฟิลด์ พรอมป์ต์ และผลลัพธ์เครื่องมือที่ส่งไปยังโมเดลที่ตั้งค่าไว้ หากเลือกผู้ให้บริการคลาวด์ ผู้ให้บริการนั้นจะได้รับบริบทนี้ ปลายทางโมเดลในเครื่องช่วยให้คำขอโมเดลอยู่ในเครื่องได้ โปรดตรวจสอบการตั้งค่าและแหล่งข้อมูลที่เชื่อมต่อ", + "Does inspecting the table guarantee a correct answer?": "การตรวจสอบตารางรับประกันคำตอบที่ถูกต้องหรือไม่?", + "No. The table helps you inspect the computed values, but AI can still choose an incorrect transformation, aggregation, or interpretation. Review the generated work, underlying data, and business assumptions before relying on a result.": "ไม่ ตารางช่วยให้ตรวจสอบค่าที่คำนวณได้ แต่ AI ยังอาจเลือกการแปลง การสรุปรวม หรือการตีความที่ไม่ถูกต้อง ทบทวนงานที่สร้างขึ้น ข้อมูลเบื้องหลัง และสมมติฐานทางธุรกิจก่อนนำผลลัพธ์ไปใช้", + "Which models can I use?": "ฉันใช้โมเดลใดได้บ้าง?", + "The code includes support for OpenAI, Azure OpenAI, Anthropic, Gemini, and local Ollama endpoints. You configure your provider and credentials. Model availability, capabilities, and any usage costs depend on that provider.": "โค้ดรองรับ OpenAI, Azure OpenAI, Anthropic, Gemini และปลายทาง Ollama ในเครื่อง คุณตั้งค่าผู้ให้บริการและข้อมูลรับรองเอง ความพร้อมของโมเดล ความสามารถ และค่าใช้งานขึ้นอยู่กับผู้ให้บริการ", + "Can I create reports and dashboards?": "ฉันสร้างรายงานและแดชบอร์ดได้หรือไม่?", + "You can edit rich-text reports with chart images, export a report image, or print to PDF. The current dashboard implementation saves chart references and displays title/type placeholders; a rendered, interactive multi-chart dashboard is not yet implemented.": "คุณแก้ไขรายงานข้อความแบบจัดรูปแบบพร้อมภาพแผนภูมิ ส่งออกรายงานเป็นภาพ หรือพิมพ์เป็น PDF ได้ แดชบอร์ดปัจจุบันบันทึกการอ้างอิงแผนภูมิและแสดงตัวแทนชื่อ/ประเภทเท่านั้น ยังไม่มีการพัฒนาแดชบอร์ดหลายแผนภูมิที่แสดงผลและโต้ตอบได้", + "Is Datly free?": "Datly ใช้ฟรีหรือไม่?", + "Datly’s source code is available under the MIT licence. Your model provider, connected infrastructure, or hosting may have their own costs. Check the repository’s setup instructions and releases for the current installation options.": "ซอร์สโค้ด Datly เปิดให้ใช้ภายใต้สัญญาอนุญาต MIT ผู้ให้บริการโมเดล โครงสร้างพื้นฐานที่เชื่อมต่อ หรือบริการโฮสต์อาจมีค่าใช้จ่ายของตนเอง ดูคู่มือการตั้งค่าและรุ่นที่เผยแพร่ในคลังโค้ดสำหรับตัวเลือกการติดตั้งปัจจุบัน", + "THERE’S A STORY IN YOUR DATA.": "มีเรื่องราวอยู่ในข้อมูลของคุณ", + "Start with a good question.": "เริ่มด้วยคำถามที่ดี", + "Get started with Datly": "เริ่มต้นกับ Datly", + "A clearer view of your data.": "มองข้อมูลของคุณได้ชัดขึ้น", + "GitHub ↗": "GitHub ↗", + "Documentation ↗": "เอกสาร ↗", + "Releases ↗": "รุ่นที่เผยแพร่ ↗", + "Image library ↗": "คลังภาพ ↗", + "MIT licence": "สัญญาอนุญาต MIT", + "Built on": "สร้างบน", + "Microsoft Data Formulator": "Microsoft Data Formulator", + "Datly workspace": "พื้นที่ทำงาน Datly", + "Close full image": "ปิดภาพเต็ม", + "Close": "ปิด", + "Actual application capture using example data.": "ภาพจากแอปจริงที่ใช้ข้อมูลตัวอย่าง", + "© 2026 Datly contributors ·": "© 2026 ผู้ร่วมพัฒนา Datly ·", + "Language": "ภาษา", + "Choose a language": "เลือกภาษา", + "Close language menu": "ปิดเมนูภาษา", + "Stars on GitHub": "ดาวบน GitHub", + "GitHub stars; verified 2 November 2026": "ดาว GitHub ตรวจสอบเมื่อ 2 พฤศจิกายน 2026", + "Installation commands copied.": "คัดลอกคำสั่งติดตั้งแล้ว", + "Automatic copy is unavailable. The commands are selected; use your browser’s copy shortcut.": "ไม่สามารถคัดลอกอัตโนมัติได้ เลือกคำสั่งไว้แล้ว โปรดใช้ปุ่มลัดคัดลอกของเบราว์เซอร์", + "Copied ✓": "คัดลอกแล้ว ✓", + "Copy ⧉": "คัดลอก ⧉", + "Select & copy": "เลือกและคัดลอก", + "Close navigation": "ปิดเมนูนำทาง", + "Showing {count} source options. Names describe implementations, not certified integrations.": "แสดงตัวเลือกแหล่งข้อมูล {count} รายการ ชื่อบอกถึงส่วนที่มีการพัฒนาไว้ ไม่ใช่การผสานรวมที่ได้รับการรับรอง", + "The Datly workspace": "พื้นที่ทำงาน Datly", + "Actual Datly workspace showing data threads, a stock-price chart, and computed rows.": "พื้นที่ทำงาน Datly จริง แสดงเธรดข้อมูล แผนภูมิราคาหุ้น และแถวข้อมูลที่คำนวณแล้ว", + "Actual application capture, reframed for presentation. Example stock data; results require review.": "ภาพจากแอปจริง จัดกรอบใหม่เพื่อการนำเสนอ ข้อมูลหุ้นเป็นตัวอย่าง ผลลัพธ์ต้องได้รับการทบทวน", + "The rows behind the line.": "แถวข้อมูลเบื้องหลังเส้นแผนภูมิ", + "Detail views of the real Datly Explore canvas, showing a chart and computed data rows.": "ภาพรายละเอียดพื้นที่ Explore จริงของ Datly แสดงแผนภูมิและแถวข้อมูลที่คำนวณแล้ว", + "Keep the path to your answer.": "เก็บเส้นทางสู่คำตอบไว้", + "A follow-up question should build on what you have learned. Data threads keep the prompts, derived tables, and chart results in a visible analytical path.": "คำถามต่อเนื่องควรต่อยอดจากสิ่งที่คุณได้เรียนรู้ เธรดข้อมูลเก็บพรอมป์ต์ ตารางที่ได้มา และผลแผนภูมิไว้ในเส้นทางการวิเคราะห์ที่มองเห็นได้", + "Follow prompts and derived tables": "ติดตามพรอมป์ต์และตารางที่ได้มา", + "Explore a separate line of inquiry": "สำรวจแนวคำถามอีกเส้นทางหนึ่ง", + "Keep previous steps in context": "เก็บขั้นตอนก่อนหน้าไว้ในบริบท", + "Actual Datly data threads showing separate stock-price questions and derived tables.": "เธรดข้อมูล Datly จริง แสดงคำถามราคาหุ้นแยกกันและตารางที่ได้มา", + "Actual application capture, cropped to focus on data threads. Example stock analysis.": "ภาพจากแอปจริง ครอบตัดเพื่อเน้นเธรดข้อมูล ตัวอย่างการวิเคราะห์หุ้น", + "Get to know your data.": "รู้จักข้อมูลของคุณให้ดีขึ้น", + "Look beyond the picture. Browse table rows, review data-quality signals, and export the derived data for a closer look in your own tools.": "มองให้ลึกกว่าภาพ ดูแถวในตาราง ทบทวนสัญญาณคุณภาพข้อมูล และส่งออกข้อมูลที่แปลงแล้วเพื่อตรวจสอบต่อด้วยเครื่องมือของคุณ", + "Review table rows and columns": "ทบทวนแถวและคอลัมน์ในตาราง", + "Inspect data-quality signals": "ตรวจสอบสัญญาณคุณภาพข้อมูล", + "Download the computed table as CSV": "ดาวน์โหลดตารางผลคำนวณเป็น CSV", + "Actual Datly data inspection view with quality signals, table rows, and CSV export.": "มุมมองตรวจสอบข้อมูลจริงของ Datly พร้อมสัญญาณคุณภาพ แถวในตาราง และการส่งออก CSV", + "Actual application capture, cropped to focus on data inspection. Quality signals are diagnostics, not a certification of correctness.": "ภาพจากแอปจริง ครอบตัดเพื่อเน้นการตรวจสอบข้อมูล สัญญาณคุณภาพเป็นข้อมูลวินิจฉัย ไม่ใช่การรับรองความถูกต้อง", + "Give the finding some context.": "เพิ่มบริบทให้ข้อค้นพบ", + "Turn an exploration into something another person can follow. Write and edit a report with chart images, then export an image or print to PDF.": "เปลี่ยนการสำรวจให้เป็นสิ่งที่ผู้อื่นติดตามได้ เขียนและแก้ไขรายงานพร้อมภาพแผนภูมิ แล้วส่งออกเป็นภาพหรือพิมพ์เป็น PDF", + "Rich-text editing with chart images": "แก้ไขข้อความแบบจัดรูปแบบพร้อมภาพแผนภูมิ", + "Add the reasoning around the result": "เพิ่มเหตุผลประกอบผลลัพธ์", + "Save an image or print to PDF": "บันทึกภาพหรือพิมพ์เป็น PDF", + "An archived Data Formulator report example showing written findings and a chart.": "ตัวอย่างรายงาน Data Formulator ที่เก็บไว้ แสดงข้อค้นพบเป็นข้อความและแผนภูมิ", + "Archived report example from the repository; it shows the earlier Data Formulator interface. Current report editing uses TipTap. Chart embeds are images.": "ตัวอย่างรายงานเก่าจากคลังโค้ด แสดงอินเทอร์เฟซ Data Formulator รุ่นก่อน การแก้ไขรายงานปัจจุบันใช้ TipTap แผนภูมิที่ฝังเป็นภาพ", + "Project at a glance": "ภาพรวมโครงการ", + "Data source options": "ตัวเลือกแหล่งข้อมูล", + "Website languages": "ภาษาของเว็บไซต์", + "Open-source licence": "สัญญาอนุญาตโอเพนซอร์ส" + } +} diff --git a/docs/locales/vi.json b/docs/locales/vi.json new file mode 100644 index 0000000..1b42056 --- /dev/null +++ b/docs/locales/vi.json @@ -0,0 +1,227 @@ +{ + "language": "vi", + "nativeName": "Tiếng Việt", + "direction": "ltr", + "translations": { + "width=device-width, initial-scale=1": "width=device-width, initial-scale=1", + "Datly — Open-source AI analytics workspace": "Datly — Không gian phân tích dữ liệu mã nguồn mở với AI", + "An open-source analytics workspace. Explore tables with AI, refine charts, inspect underlying rows, and turn your findings into reports.": "Không gian phân tích dữ liệu mã nguồn mở. Khám phá bảng dữ liệu bằng AI, tinh chỉnh biểu đồ, kiểm tra các dòng dữ liệu bên dưới và chuyển kết quả thành báo cáo.", + "Datly — Good questions. Clearer answers.": "Datly — Câu hỏi hay. Câu trả lời rõ hơn.", + "Explore with AI. Inspect the data behind your charts. An open-source workspace for your next good question.": "Khám phá bằng AI. Kiểm tra dữ liệu phía sau biểu đồ. Không gian mã nguồn mở cho câu hỏi hay tiếp theo của bạn.", + "Datly workspace with data threads, a chart, and underlying data rows": "Không gian làm việc Datly với các luồng dữ liệu, biểu đồ và các dòng dữ liệu bên dưới", + "An open-source workspace for exploring data with AI, inspecting charts, and writing reports.": "Không gian mã nguồn mở để khám phá dữ liệu bằng AI, kiểm tra biểu đồ và viết báo cáo.", + "Skip to content": "Chuyển đến nội dung", + "Datly home": "Trang chủ Datly", + "Open navigation": "Mở menu điều hướng", + "Main navigation": "Điều hướng chính", + "Product": "Sản phẩm", + "How it works": "Cách hoạt động", + "Data sources": "Nguồn dữ liệu", + "FAQ": "Câu hỏi thường gặp", + "Get started": "Bắt đầu", + "OPEN SOURCE. OPEN TO EXPLORATION.": "MÃ NGUỒN MỞ. TỰ DO KHÁM PHÁ.", + "Good questions.": "Câu hỏi hay.", + "Clearer answers.": "Câu trả lời rõ hơn.", + "Your data has a story. Find it with an AI-assisted workspace that keeps your questions, charts, and underlying rows connected.": "Dữ liệu của bạn có một câu chuyện. Khám phá câu chuyện đó trong không gian làm việc có AI hỗ trợ, nơi câu hỏi, biểu đồ và các dòng dữ liệu luôn được kết nối.", + "Start exploring": "Bắt đầu khám phá", + "Take a closer look": "Tìm hiểu kỹ hơn", + "Free, open-source software · MIT licensed": "Phần mềm miễn phí, mã nguồn mở · Giấy phép MIT", + "For analysts, researchers, and data teams.": "Dành cho nhà phân tích, nhà nghiên cứu và các nhóm dữ liệu.", + "A little less back and forth. A little more insight.": "Bớt chuyển qua lại. Thêm góc nhìn hữu ích.", + "ACTUAL APP · EXAMPLE DATA": "ỨNG DỤNG THỰC TẾ · DỮ LIỆU MINH HỌA", + "Enlarge the Datly workspace screenshot": "Phóng to ảnh chụp không gian làm việc Datly", + "Datly workspace showing a stock-price analysis thread, line chart, and the data rows underneath": "Không gian làm việc Datly hiển thị một luồng phân tích giá cổ phiếu, biểu đồ đường và các dòng dữ liệu bên dưới", + "Explore the workspace": "Khám phá không gian làm việc", + "Questions → transformations → charts → rows": "Câu hỏi → biến đổi → biểu đồ → dòng dữ liệu", + "YOUR ANALYSIS, IN CONTEXT": "PHÂN TÍCH CỦA BẠN, ĐẦY ĐỦ NGỮ CẢNH", + "Built on a solid foundation": "Xây dựng trên nền tảng vững chắc", + "PUBLIC BY DESIGN": "MINH BẠCH NGAY TỪ THIẾT KẾ", + "Built in the open.": "Phát triển công khai.", + "Explore the source, issues, and releases.": "Khám phá mã nguồn, các vấn đề và bản phát hành.", + "GITHUB COMMUNITY": "CỘNG ĐỒNG GITHUB", + "stars on GitHub": "sao trên GitHub", + "Count verified 2 November 2026; refreshes live.": "Số liệu xác minh ngày 2 tháng 11 năm 2026; cập nhật trực tiếp.", + "MADE FOR DATA WORK": "DÀNH CHO CÔNG VIỆC DỮ LIỆU", + "Your next analysis.": "Lần phân tích tiếp theo của bạn.", + "01 / FROM QUESTION TO UNDERSTANDING": "01 / TỪ CÂU HỎI ĐẾN HIỂU BIẾT", + "Follow your curiosity.": "Theo đuổi sự tò mò.", + "Keep the context.": "Giữ trọn ngữ cảnh.", + "Move from a table to a useful finding in one workspace. Ask, refine, and inspect as your analysis takes shape.": "Từ một bảng dữ liệu đến một phát hiện hữu ích trong cùng không gian làm việc. Đặt câu hỏi, tinh chỉnh và kiểm tra khi phân tích dần hình thành.", + "Bring your data": "Đưa dữ liệu vào", + "Load a local file, connect a supported database, or explore a bundled example.": "Tải tệp cục bộ, kết nối cơ sở dữ liệu được hỗ trợ hoặc khám phá ví dụ có sẵn.", + "Ask the next question": "Đặt câu hỏi tiếp theo", + "Describe what you want to explore. Refine your chart with visual encoding controls or a follow-up question.": "Mô tả điều bạn muốn khám phá. Tinh chỉnh biểu đồ bằng các điều khiển mã hóa trực quan hoặc câu hỏi tiếp nối.", + "Inspect, then explain": "Kiểm tra rồi giải thích", + "Check the derived rows, export a CSV, and give your findings context in an editable report.": "Kiểm tra các dòng dữ liệu dẫn xuất, xuất CSV và bổ sung ngữ cảnh cho phát hiện trong báo cáo có thể chỉnh sửa.", + "02 / A CLOSER LOOK": "02 / KHÁM PHÁ KỸ HƠN", + "See the work.": "Xem quá trình.", + "Not just the answer.": "Không chỉ câu trả lời.", + "Real application views, with room to look closer. Explore the features that make the analysis easier to follow.": "Hình ảnh ứng dụng thực tế để bạn xem kỹ hơn. Khám phá các tính năng giúp quá trình phân tích dễ theo dõi.", + "Explore Datly features": "Khám phá tính năng Datly", + "Explore & inspect": "Khám phá & kiểm tra", + "Data threads": "Luồng dữ liệu", + "Data inspection": "Kiểm tra dữ liệu", + "Reports": "Báo cáo", + "The rows behind": "Các dòng dữ liệu phía sau", + "the line.": "đường biểu đồ.", + "A chart is a starting point. Open the underlying table to inspect the values, check a transformation, and decide what the result actually means.": "Biểu đồ là điểm khởi đầu. Mở bảng dữ liệu bên dưới để kiểm tra giá trị, xem lại phép biến đổi và xác định kết quả thực sự có ý nghĩa gì.", + "Chart and computed table together": "Biểu đồ và bảng kết quả ở cùng một nơi", + "Inspect and export the derived rows": "Kiểm tra và xuất các dòng dữ liệu dẫn xuất", + "Review results before you share": "Xem lại kết quả trước khi chia sẻ", + "View full image": "Xem ảnh đầy đủ", + "Enlarge this feature screenshot": "Phóng to ảnh chụp tính năng này", + "Detail views of the real Datly Explore canvas, showing a chart and computed data rows": "Ảnh chi tiết vùng Khám phá thực tế của Datly, hiển thị biểu đồ và các dòng dữ liệu đã tính toán", + "Actual application capture, reframed for clarity. Example data is illustrative; AI-generated analysis requires review.": "Ảnh chụp ứng dụng thực tế, được bố cục lại cho rõ ràng. Dữ liệu chỉ dùng minh họa; phân tích do AI tạo cần được xem xét.", + "03 / STAY IN THE LOOP": "03 / LUÔN NẮM RÕ QUÁ TRÌNH", + "AI helps you explore.": "AI giúp bạn khám phá.", + "You make the call.": "Bạn là người quyết định.", + "Good analysis depends on more than a convincing chart. Datly makes the intermediate work available for you to examine.": "Phân tích tốt không chỉ cần một biểu đồ thuyết phục. Datly cho phép bạn kiểm tra các bước trung gian.", + "TRACE THE QUESTION": "THEO DÕI CÂU HỎI", + "Keep your train": "Giữ mạch", + "of thought.": "suy nghĩ của bạn.", + "Data threads organise questions, transformations, derived tables, and charts. Follow an investigation as it develops.": "Luồng dữ liệu sắp xếp câu hỏi, phép biến đổi, bảng dẫn xuất và biểu đồ. Theo dõi quá trình tìm hiểu khi nó phát triển.", + "Explore data threads": "Khám phá luồng dữ liệu", + "REFINE THE VIEW": "TINH CHỈNH GÓC NHÌN", + "A chart you can": "Biểu đồ để bạn", + "work with.": "tiếp tục khai thác.", + "Use visual encoding controls for axes, colour, and other chart properties. Adjust the view as your question becomes more precise.": "Dùng điều khiển mã hóa trực quan cho trục, màu và các thuộc tính khác của biểu đồ. Điều chỉnh góc nhìn khi câu hỏi trở nên cụ thể hơn.", + "Explore the canvas": "Khám phá vùng biểu đồ", + "EXPLAIN THE FINDING": "GIẢI THÍCH PHÁT HIỆN", + "More than": "Không chỉ là", + "a screenshot.": "ảnh chụp màn hình.", + "Combine written context with chart images in an editable report. Save an image or use your browser’s print flow for PDF.": "Kết hợp ngữ cảnh bằng văn bản với hình ảnh biểu đồ trong báo cáo có thể chỉnh sửa. Lưu thành ảnh hoặc dùng chức năng in của trình duyệt để tạo PDF.", + "Explore reports": "Khám phá báo cáo", + "04 / START WITH WHAT YOU HAVE": "04 / BẮT ĐẦU VỚI NHỮNG GÌ BẠN CÓ", + "Your data.": "Dữ liệu của bạn.", + "A familiar starting point.": "Điểm bắt đầu quen thuộc.", + "Work with files and supported data sources. Connections depend on the appropriate drivers, credentials, and network access.": "Làm việc với tệp và các nguồn dữ liệu được hỗ trợ. Kết nối phụ thuộc vào trình điều khiển, thông tin xác thực và quyền truy cập mạng phù hợp.", + "A file today.": "Hôm nay là một tệp.", + "A warehouse tomorrow.": "Ngày mai là kho dữ liệu.", + "Start small with a CSV or a sample dataset. Bring in a supported connection when your question calls for more.": "Bắt đầu đơn giản với CSV hoặc tập dữ liệu mẫu. Thêm kết nối được hỗ trợ khi câu hỏi của bạn cần nhiều dữ liệu hơn.", + "See connector implementation": "Xem mã triển khai bộ kết nối", + "Filter data sources": "Lọc nguồn dữ liệu", + "All sources": "Tất cả nguồn", + "SQL & warehouses": "SQL & kho dữ liệu", + "Cloud & storage": "Đám mây & lưu trữ", + "Files & web": "Tệp & web", + "Relational database": "Cơ sở dữ liệu quan hệ", + "SQL warehouse": "Kho dữ liệu SQL", + "Cloud warehouse": "Kho dữ liệu đám mây", + "Analytics database": "Cơ sở dữ liệu phân tích", + "Object storage": "Lưu trữ đối tượng", + "Serverless queries": "Truy vấn không máy chủ", + "Document database": "Cơ sở dữ liệu tài liệu", + "Data Explorer": "Khám phá dữ liệu", + "Dataset import": "Nhập tập dữ liệu", + "Local files": "Tệp cục bộ", + "CSV, Excel & Parquet": "CSV, Excel & Parquet", + "Web sources": "Nguồn web", + "Supported table extraction": "Trích xuất bảng được hỗ trợ", + "Example datasets": "Tập dữ liệu mẫu", + "Try a sample analysis": "Thử phân tích mẫu", + "Showing all 16 source options. Names describe implementations, not certified integrations.": "Hiển thị đủ 16 tùy chọn nguồn. Tên mô tả các phần triển khai, không phải tích hợp đã được chứng nhận.", + "05 / MAKE AN INFORMED CHOICE": "05 / LỰA CHỌN DỰA TRÊN HIỂU BIẾT", + "Local processing.": "Xử lý cục bộ.", + "Clear data boundaries.": "Ranh giới dữ liệu rõ ràng.", + "Read the model context code": "Xem mã tạo ngữ cảnh mô hình", + "01 / EXECUTION": "01 / THỰC THI", + "Work in your workspace": "Làm việc trong môi trường của bạn", + "DuckDB and Python run transformations in the environment where you host Datly, including a local installation on your computer.": "DuckDB và Python chạy các phép biến đổi trong môi trường lưu trữ Datly, bao gồm bản cài đặt cục bộ trên máy tính của bạn.", + "02 / MODEL CONTEXT": "02 / NGỮ CẢNH MÔ HÌNH", + "Know what AI receives": "Biết AI nhận những gì", + "AI requests can include schema, sample values and rows, prompts, and tool outputs. A cloud model provider receives the context sent to it.": "Yêu cầu AI có thể chứa lược đồ, giá trị và dòng dữ liệu mẫu, lời nhắc và đầu ra của công cụ. Nhà cung cấp mô hình đám mây nhận ngữ cảnh được gửi đến họ.", + "03 / YOUR CONFIGURATION": "03 / CẤU HÌNH CỦA BẠN", + "Choose your model": "Chọn mô hình", + "Connect a supported provider or configure a local model through Ollama. Local AI requests require a local endpoint; remote data sources still need network access.": "Kết nối nhà cung cấp được hỗ trợ hoặc cấu hình mô hình cục bộ qua Ollama. Yêu cầu AI cục bộ cần điểm cuối cục bộ; nguồn dữ liệu từ xa vẫn cần truy cập mạng.", + "06 / YOUR NEXT GOOD QUESTION": "06 / CÂU HỎI HAY TIẾP THEO", + "A little setup.": "Thiết lập một chút.", + "A lot to explore.": "Khám phá thật nhiều.", + "Build from source and launch Datly in your browser. Start with an example dataset, then configure a model for AI-assisted exploration.": "Biên dịch từ mã nguồn và chạy Datly trong trình duyệt. Bắt đầu với tập dữ liệu mẫu, sau đó cấu hình mô hình để khám phá với AI hỗ trợ.", + "Python 3.11 or later": "Python 3.11 trở lên", + "Node.js and Yarn for the frontend": "Node.js và Yarn cho giao diện", + "uv for the Python environment": "uv cho môi trường Python", + "Read the setup guide": "Đọc hướng dẫn thiết lập", + "View releases": "Xem bản phát hành", + "Installation method": "Phương thức cài đặt", + "From source": "Từ mã nguồn", + "Docker": "Docker", + "Copy installation commands": "Sao chép lệnh cài đặt", + "Copy": "Sao chép", + "Open": "Mở", + "after launch": "sau khi khởi chạy", + "A FEW THINGS TO KNOW": "MỘT VÀI ĐIỀU CẦN BIẾT", + "Before you": "Trước khi", + "dive in.": "bắt đầu.", + "Questions or feedback?": "Câu hỏi hoặc góp ý?", + "What is Datly?": "Datly là gì?", + "Datly is an open-source analytics workspace built on Microsoft Research’s Data Formulator. It combines natural-language exploration, visual chart controls, data threads, table inspection, and editable reports. The project is in beta.": "Datly là không gian phân tích dữ liệu mã nguồn mở được xây dựng trên Data Formulator của Microsoft Research. Ứng dụng kết hợp khám phá bằng ngôn ngữ tự nhiên, điều khiển biểu đồ trực quan, luồng dữ liệu, kiểm tra bảng và báo cáo có thể chỉnh sửa. Dự án đang ở giai đoạn beta.", + "Does my data stay on my computer?": "Dữ liệu có ở lại trên máy tính của tôi không?", + "A local installation processes transformations on your computer. AI requests can include sample rows, field values, prompts, and tool outputs, which are sent to your configured model. If you select a cloud provider, that provider receives this context. A local model endpoint can keep model requests local; review your configuration and connected data sources.": "Bản cài đặt cục bộ thực hiện các phép biến đổi trên máy tính của bạn. Yêu cầu AI có thể chứa các dòng mẫu, giá trị trường, lời nhắc và đầu ra công cụ được gửi đến mô hình đã cấu hình. Nếu bạn chọn nhà cung cấp đám mây, họ nhận ngữ cảnh này. Điểm cuối mô hình cục bộ có thể giữ yêu cầu mô hình trong máy; hãy xem lại cấu hình và các nguồn dữ liệu đã kết nối.", + "Does inspecting the table guarantee a correct answer?": "Kiểm tra bảng có bảo đảm câu trả lời đúng không?", + "No. The table helps you inspect the computed values, but AI can still choose an incorrect transformation, aggregation, or interpretation. Review the generated work, underlying data, and business assumptions before relying on a result.": "Không. Bảng giúp bạn kiểm tra các giá trị đã tính toán, nhưng AI vẫn có thể chọn phép biến đổi, tổng hợp hoặc cách diễn giải sai. Hãy xem lại nội dung được tạo, dữ liệu gốc và các giả định nghiệp vụ trước khi dựa vào kết quả.", + "Which models can I use?": "Tôi có thể dùng những mô hình nào?", + "The code includes support for OpenAI, Azure OpenAI, Anthropic, Gemini, and local Ollama endpoints. You configure your provider and credentials. Model availability, capabilities, and any usage costs depend on that provider.": "Mã nguồn có hỗ trợ OpenAI, Azure OpenAI, Anthropic, Gemini và điểm cuối Ollama cục bộ. Bạn cấu hình nhà cung cấp và thông tin xác thực. Tình trạng cung cấp mô hình, khả năng và chi phí sử dụng phụ thuộc vào nhà cung cấp.", + "Can I create reports and dashboards?": "Tôi có thể tạo báo cáo và bảng điều khiển không?", + "You can edit rich-text reports with chart images, export a report image, or print to PDF. The current dashboard implementation saves chart references and displays title/type placeholders; a rendered, interactive multi-chart dashboard is not yet implemented.": "Bạn có thể chỉnh sửa báo cáo văn bản định dạng phong phú có hình ảnh biểu đồ, xuất ảnh báo cáo hoặc in thành PDF. Phần triển khai bảng điều khiển hiện tại lưu tham chiếu biểu đồ và hiển thị ô giữ chỗ cho tiêu đề/loại; bảng điều khiển nhiều biểu đồ có hiển thị và tương tác chưa được triển khai.", + "Is Datly free?": "Datly có miễn phí không?", + "Datly’s source code is available under the MIT licence. Your model provider, connected infrastructure, or hosting may have their own costs. Check the repository’s setup instructions and releases for the current installation options.": "Mã nguồn Datly có sẵn theo giấy phép MIT. Nhà cung cấp mô hình, hạ tầng kết nối hoặc dịch vụ lưu trữ có thể tính phí riêng. Xem hướng dẫn thiết lập và bản phát hành trong kho mã để biết các tùy chọn cài đặt hiện tại.", + "THERE’S A STORY IN YOUR DATA.": "DỮ LIỆU CỦA BẠN CÓ MỘT CÂU CHUYỆN.", + "Start with a good question.": "Bắt đầu với một câu hỏi hay.", + "Get started with Datly": "Bắt đầu với Datly", + "A clearer view of your data.": "Nhìn rõ hơn dữ liệu của bạn.", + "GitHub ↗": "GitHub ↗", + "Documentation ↗": "Tài liệu ↗", + "Releases ↗": "Bản phát hành ↗", + "Image library ↗": "Thư viện hình ảnh ↗", + "MIT licence": "Giấy phép MIT", + "Built on": "Xây dựng trên", + "Microsoft Data Formulator": "Microsoft Data Formulator", + "Datly workspace": "Không gian làm việc Datly", + "Close full image": "Đóng ảnh đầy đủ", + "Close": "Đóng", + "Actual application capture using example data.": "Ảnh chụp ứng dụng thực tế sử dụng dữ liệu mẫu.", + "© 2026 Datly contributors ·": "© 2026 Những người đóng góp cho Datly ·", + "Language": "Ngôn ngữ", + "Choose a language": "Chọn ngôn ngữ", + "Close language menu": "Đóng menu ngôn ngữ", + "Stars on GitHub": "Sao trên GitHub", + "GitHub stars; verified 2 November 2026": "Sao GitHub; xác minh ngày 2 tháng 11 năm 2026", + "Installation commands copied.": "Đã sao chép lệnh cài đặt.", + "Automatic copy is unavailable. The commands are selected; use your browser’s copy shortcut.": "Không thể sao chép tự động. Các lệnh đã được chọn; hãy dùng phím tắt sao chép của trình duyệt.", + "Copied ✓": "Đã sao chép ✓", + "Copy ⧉": "Sao chép ⧉", + "Select & copy": "Chọn & sao chép", + "Close navigation": "Đóng menu điều hướng", + "Showing {count} source options. Names describe implementations, not certified integrations.": "Hiển thị {count} tùy chọn nguồn. Tên mô tả các phần triển khai, không phải tích hợp đã được chứng nhận.", + "The Datly workspace": "Không gian làm việc Datly", + "Actual Datly workspace showing data threads, a stock-price chart, and computed rows.": "Không gian làm việc Datly thực tế hiển thị các luồng dữ liệu, biểu đồ giá cổ phiếu và các dòng đã tính toán.", + "Actual application capture, reframed for presentation. Example stock data; results require review.": "Ảnh chụp ứng dụng thực tế, được bố cục lại để trình bày. Dữ liệu cổ phiếu minh họa; kết quả cần được xem xét.", + "The rows behind the line.": "Các dòng dữ liệu phía sau đường biểu đồ.", + "Detail views of the real Datly Explore canvas, showing a chart and computed data rows.": "Ảnh chi tiết vùng Khám phá thực tế của Datly, hiển thị biểu đồ và các dòng dữ liệu đã tính toán.", + "Keep the path to your answer.": "Giữ lại lộ trình đến câu trả lời.", + "A follow-up question should build on what you have learned. Data threads keep the prompts, derived tables, and chart results in a visible analytical path.": "Câu hỏi tiếp nối nên phát triển từ những gì bạn đã tìm hiểu. Luồng dữ liệu giữ lời nhắc, bảng dẫn xuất và kết quả biểu đồ trong một lộ trình phân tích có thể quan sát.", + "Follow prompts and derived tables": "Theo dõi lời nhắc và bảng dẫn xuất", + "Explore a separate line of inquiry": "Khám phá hướng tìm hiểu riêng", + "Keep previous steps in context": "Giữ các bước trước trong ngữ cảnh", + "Actual Datly data threads showing separate stock-price questions and derived tables.": "Luồng dữ liệu Datly thực tế hiển thị các câu hỏi riêng về giá cổ phiếu và bảng dẫn xuất.", + "Actual application capture, cropped to focus on data threads. Example stock analysis.": "Ảnh chụp ứng dụng thực tế, được cắt để tập trung vào luồng dữ liệu. Ví dụ phân tích cổ phiếu.", + "Get to know your data.": "Hiểu dữ liệu của bạn.", + "Look beyond the picture. Browse table rows, review data-quality signals, and export the derived data for a closer look in your own tools.": "Nhìn xa hơn hình ảnh. Duyệt các dòng trong bảng, xem tín hiệu chất lượng dữ liệu và xuất dữ liệu dẫn xuất để kiểm tra kỹ hơn bằng công cụ của bạn.", + "Review table rows and columns": "Xem lại các dòng và cột trong bảng", + "Inspect data-quality signals": "Kiểm tra tín hiệu chất lượng dữ liệu", + "Download the computed table as CSV": "Tải bảng đã tính toán dưới dạng CSV", + "Actual Datly data inspection view with quality signals, table rows, and CSV export.": "Chế độ kiểm tra dữ liệu thực tế của Datly với tín hiệu chất lượng, các dòng bảng và chức năng xuất CSV.", + "Actual application capture, cropped to focus on data inspection. Quality signals are diagnostics, not a certification of correctness.": "Ảnh chụp ứng dụng thực tế, được cắt để tập trung vào kiểm tra dữ liệu. Tín hiệu chất lượng là thông tin chẩn đoán, không phải chứng nhận tính chính xác.", + "Give the finding some context.": "Bổ sung ngữ cảnh cho phát hiện.", + "Turn an exploration into something another person can follow. Write and edit a report with chart images, then export an image or print to PDF.": "Biến quá trình khám phá thành nội dung người khác có thể theo dõi. Viết và chỉnh sửa báo cáo có hình ảnh biểu đồ, sau đó xuất ảnh hoặc in thành PDF.", + "Rich-text editing with chart images": "Chỉnh sửa văn bản định dạng phong phú với hình ảnh biểu đồ", + "Add the reasoning around the result": "Bổ sung lý giải cho kết quả", + "Save an image or print to PDF": "Lưu ảnh hoặc in thành PDF", + "An archived Data Formulator report example showing written findings and a chart.": "Ví dụ báo cáo Data Formulator lưu trữ, hiển thị phát hiện bằng văn bản và biểu đồ.", + "Archived report example from the repository; it shows the earlier Data Formulator interface. Current report editing uses TipTap. Chart embeds are images.": "Ví dụ báo cáo lưu trữ từ kho mã, hiển thị giao diện Data Formulator trước đây. Trình chỉnh sửa báo cáo hiện tại dùng TipTap. Biểu đồ nhúng là hình ảnh.", + "Project at a glance": "Tổng quan dự án", + "Data source options": "Lựa chọn nguồn dữ liệu", + "Website languages": "Ngôn ngữ website", + "Open-source licence": "Giấy phép mã nguồn mở" + } +} diff --git a/docs/locales/zh.json b/docs/locales/zh.json new file mode 100644 index 0000000..c7e3bbe --- /dev/null +++ b/docs/locales/zh.json @@ -0,0 +1,227 @@ +{ + "language": "zh-Hans", + "nativeName": "简体中文", + "direction": "ltr", + "translations": { + "width=device-width, initial-scale=1": "width=device-width, initial-scale=1", + "Datly — Open-source AI analytics workspace": "Datly — 开源 AI 数据分析工作空间", + "An open-source analytics workspace. Explore tables with AI, refine charts, inspect underlying rows, and turn your findings into reports.": "开源数据分析工作空间。借助 AI 探索表格、调整图表、检查底层数据行，并将发现整理为报告。", + "Datly — Good questions. Clearer answers.": "Datly — 好问题，更清晰的答案。", + "Explore with AI. Inspect the data behind your charts. An open-source workspace for your next good question.": "借助 AI 探索数据，检查图表背后的数据。一个开源工作空间，助你提出下一个好问题。", + "Datly workspace with data threads, a chart, and underlying data rows": "Datly 工作空间，包含数据线程、图表及其底层数据行", + "An open-source workspace for exploring data with AI, inspecting charts, and writing reports.": "用于 AI 辅助数据探索、图表检查和报告撰写的开源工作空间。", + "Skip to content": "跳至正文", + "Datly home": "Datly 首页", + "Open navigation": "打开导航菜单", + "Main navigation": "主导航", + "Product": "产品", + "How it works": "工作流程", + "Data sources": "数据源", + "FAQ": "常见问题", + "Get started": "开始使用", + "OPEN SOURCE. OPEN TO EXPLORATION.": "开放源代码，探索更多可能。", + "Good questions.": "好问题。", + "Clearer answers.": "更清晰的答案。", + "Your data has a story. Find it with an AI-assisted workspace that keeps your questions, charts, and underlying rows connected.": "你的数据蕴藏着故事。在 AI 辅助工作空间中发现它，让问题、图表及其底层数据行始终相互关联。", + "Start exploring": "开始探索", + "Take a closer look": "深入了解", + "Free, open-source software · MIT licensed": "免费开源软件 · MIT 许可证", + "For analysts, researchers, and data teams.": "为分析师、研究人员和数据团队而设计。", + "A little less back and forth. A little more insight.": "少一些来回切换，多一些洞察。", + "ACTUAL APP · EXAMPLE DATA": "真实应用 · 示例数据", + "Enlarge the Datly workspace screenshot": "放大 Datly 工作空间截图", + "Datly workspace showing a stock-price analysis thread, line chart, and the data rows underneath": "Datly 工作空间，展示股价分析线程、折线图及下方的数据行", + "Explore the workspace": "探索工作空间", + "Questions → transformations → charts → rows": "问题 → 数据转换 → 图表 → 数据行", + "YOUR ANALYSIS, IN CONTEXT": "分析过程，始终有据可循", + "Built on a solid foundation": "坚实的技术基础", + "PUBLIC BY DESIGN": "以开放为本", + "Built in the open.": "公开开发。", + "Explore the source, issues, and releases.": "查看源代码、议题和发行版本。", + "GITHUB COMMUNITY": "GITHUB 社区", + "stars on GitHub": "个 GitHub 星标", + "Count verified 2 November 2026; refreshes live.": "数量于 2026 年 11 月 2 日核实，并实时刷新。", + "MADE FOR DATA WORK": "专为数据工作打造", + "Your next analysis.": "开启下一次分析。", + "01 / FROM QUESTION TO UNDERSTANDING": "01 / 从提问到理解", + "Follow your curiosity.": "跟随好奇心。", + "Keep the context.": "保留完整上下文。", + "Move from a table to a useful finding in one workspace. Ask, refine, and inspect as your analysis takes shape.": "在同一工作空间中，从一张表格走向有用的发现。随着分析逐步展开，持续提问、调整并检查结果。", + "Bring your data": "导入你的数据", + "Load a local file, connect a supported database, or explore a bundled example.": "加载本地文件、连接受支持的数据库，或探索内置示例。", + "Ask the next question": "提出下一个问题", + "Describe what you want to explore. Refine your chart with visual encoding controls or a follow-up question.": "描述你想探索的内容。通过可视化编码控件或后续提问调整图表。", + "Inspect, then explain": "先检查，再阐述", + "Check the derived rows, export a CSV, and give your findings context in an editable report.": "检查转换后的数据行，导出 CSV，并在可编辑报告中为发现补充上下文。", + "02 / A CLOSER LOOK": "02 / 深入了解", + "See the work.": "看清分析过程。", + "Not just the answer.": "不只看到答案。", + "Real application views, with room to look closer. Explore the features that make the analysis easier to follow.": "真实应用界面，支持放大查看。了解让分析过程更易追踪的功能。", + "Explore Datly features": "探索 Datly 功能", + "Explore & inspect": "探索与检查", + "Data threads": "数据线程", + "Data inspection": "数据检查", + "Reports": "报告", + "The rows behind": "图表背后的", + "the line.": "数据行。", + "A chart is a starting point. Open the underlying table to inspect the values, check a transformation, and decide what the result actually means.": "图表只是起点。打开底层表格，检查数值、核查数据转换，并判断结果的实际含义。", + "Chart and computed table together": "同时查看图表和计算结果表", + "Inspect and export the derived rows": "检查并导出转换后的数据行", + "Review results before you share": "分享前先审核结果", + "View full image": "查看完整图片", + "Enlarge this feature screenshot": "放大此功能截图", + "Detail views of the real Datly Explore canvas, showing a chart and computed data rows": "Datly 真实 Explore 画布的细节视图，展示图表和计算后的数据行", + "Actual application capture, reframed for clarity. Example data is illustrative; AI-generated analysis requires review.": "真实应用截图，经重新取景以便清晰展示。示例数据仅供演示；AI 生成的分析需要审核。", + "03 / STAY IN THE LOOP": "03 / 掌握分析过程", + "AI helps you explore.": "AI 助你探索。", + "You make the call.": "判断由你做出。", + "Good analysis depends on more than a convincing chart. Datly makes the intermediate work available for you to examine.": "可靠的分析不止于一张有说服力的图表。Datly 让你能够检查中间分析过程。", + "TRACE THE QUESTION": "追溯问题", + "Keep your train": "保留你的", + "of thought.": "思考脉络。", + "Data threads organise questions, transformations, derived tables, and charts. Follow an investigation as it develops.": "数据线程将问题、数据转换、生成的表格和图表有序组织起来，便于跟踪研究的逐步深入。", + "Explore data threads": "探索数据线程", + "REFINE THE VIEW": "调整视图", + "A chart you can": "让图表", + "work with.": "随分析调整。", + "Use visual encoding controls for axes, colour, and other chart properties. Adjust the view as your question becomes more precise.": "通过可视化编码控件调整坐标轴、颜色和其他图表属性。随着问题逐渐明确，随时调整视图。", + "Explore the canvas": "探索画布", + "EXPLAIN THE FINDING": "阐释发现", + "More than": "不止于", + "a screenshot.": "一张截图。", + "Combine written context with chart images in an editable report. Save an image or use your browser’s print flow for PDF.": "在可编辑报告中结合文字说明和图表图片。保存为图片，或通过浏览器打印功能生成 PDF。", + "Explore reports": "探索报告", + "04 / START WITH WHAT YOU HAVE": "04 / 从现有数据开始", + "Your data.": "你的数据。", + "A familiar starting point.": "熟悉的起点。", + "Work with files and supported data sources. Connections depend on the appropriate drivers, credentials, and network access.": "使用文件及受支持的数据源。连接需要相应的驱动程序、凭据和网络访问权限。", + "A file today.": "今天，从文件开始。", + "A warehouse tomorrow.": "明天，连接数据仓库。", + "Start small with a CSV or a sample dataset. Bring in a supported connection when your question calls for more.": "从一个 CSV 文件或示例数据集开始。当问题需要更多数据时，再连接受支持的数据源。", + "See connector implementation": "查看连接器实现", + "Filter data sources": "筛选数据源", + "All sources": "全部数据源", + "SQL & warehouses": "SQL 与数据仓库", + "Cloud & storage": "云服务与存储", + "Files & web": "文件与网页", + "Relational database": "关系型数据库", + "SQL warehouse": "SQL 数据仓库", + "Cloud warehouse": "云数据仓库", + "Analytics database": "分析型数据库", + "Object storage": "对象存储", + "Serverless queries": "无服务器查询", + "Document database": "文档数据库", + "Data Explorer": "Data Explorer", + "Dataset import": "数据集导入", + "Local files": "本地文件", + "CSV, Excel & Parquet": "CSV、Excel 与 Parquet", + "Web sources": "网页数据源", + "Supported table extraction": "受支持的表格提取", + "Example datasets": "示例数据集", + "Try a sample analysis": "体验示例分析", + "Showing all 16 source options. Names describe implementations, not certified integrations.": "显示全部 16 种数据源选项。名称表示已有的代码实现，并不代表经过认证的集成。", + "05 / MAKE AN INFORMED CHOICE": "05 / 了解后再选择", + "Local processing.": "本地处理。", + "Clear data boundaries.": "清晰的数据边界。", + "Read the model context code": "查看模型上下文代码", + "01 / EXECUTION": "01 / 执行环境", + "Work in your workspace": "在你的工作空间中运行", + "DuckDB and Python run transformations in the environment where you host Datly, including a local installation on your computer.": "DuckDB 和 Python 在部署 Datly 的环境中执行数据转换，也支持在你的计算机上本地安装运行。", + "02 / MODEL CONTEXT": "02 / 模型上下文", + "Know what AI receives": "了解 AI 会收到什么", + "AI requests can include schema, sample values and rows, prompts, and tool outputs. A cloud model provider receives the context sent to it.": "AI 请求可能包含表结构、示例值与数据行、提示词及工具输出。云模型服务商会收到发送给它的上下文。", + "03 / YOUR CONFIGURATION": "03 / 你的配置", + "Choose your model": "选择模型", + "Connect a supported provider or configure a local model through Ollama. Local AI requests require a local endpoint; remote data sources still need network access.": "连接受支持的服务商，或通过 Ollama 配置本地模型。本地 AI 请求需要使用本地端点；远程数据源仍需网络访问。", + "06 / YOUR NEXT GOOD QUESTION": "06 / 你的下一个好问题", + "A little setup.": "少量配置。", + "A lot to explore.": "更多探索。", + "Build from source and launch Datly in your browser. Start with an example dataset, then configure a model for AI-assisted exploration.": "从源代码构建 Datly，并在浏览器中启动。先使用示例数据集，再配置模型，开始 AI 辅助探索。", + "Python 3.11 or later": "Python 3.11 或更高版本", + "Node.js and Yarn for the frontend": "前端需要 Node.js 和 Yarn", + "uv for the Python environment": "使用 uv 管理 Python 环境", + "Read the setup guide": "阅读安装指南", + "View releases": "查看发行版本", + "Installation method": "安装方式", + "From source": "从源代码安装", + "Docker": "Docker", + "Copy installation commands": "复制安装命令", + "Copy": "复制", + "Open": "打开", + "after launch": "启动后", + "A FEW THINGS TO KNOW": "使用前须知", + "Before you": "开始之前，", + "dive in.": "先了解这些。", + "Questions or feedback?": "有疑问或反馈？", + "What is Datly?": "Datly 是什么？", + "Datly is an open-source analytics workspace built on Microsoft Research’s Data Formulator. It combines natural-language exploration, visual chart controls, data threads, table inspection, and editable reports. The project is in beta.": "Datly 是基于 Microsoft Research 的 Data Formulator 构建的开源数据分析工作空间。它结合了自然语言探索、可视化图表控件、数据线程、表格检查和可编辑报告。项目目前处于测试阶段。", + "Does my data stay on my computer?": "我的数据会留在自己的计算机上吗？", + "A local installation processes transformations on your computer. AI requests can include sample rows, field values, prompts, and tool outputs, which are sent to your configured model. If you select a cloud provider, that provider receives this context. A local model endpoint can keep model requests local; review your configuration and connected data sources.": "本地安装会在你的计算机上处理数据转换。AI 请求可能包含示例数据行、字段值、提示词和工具输出，并将这些内容发送给你配置的模型。如果选择云服务商，该服务商就会收到这些上下文。本地模型端点可让模型请求在本地进行；请检查你的配置和连接的数据源。", + "Does inspecting the table guarantee a correct answer?": "检查表格能保证答案正确吗？", + "No. The table helps you inspect the computed values, but AI can still choose an incorrect transformation, aggregation, or interpretation. Review the generated work, underlying data, and business assumptions before relying on a result.": "不能。表格有助于检查计算结果，但 AI 仍可能选择错误的数据转换、聚合方式或解读。在依赖结果前，请审核生成的分析、底层数据和业务假设。", + "Which models can I use?": "可以使用哪些模型？", + "The code includes support for OpenAI, Azure OpenAI, Anthropic, Gemini, and local Ollama endpoints. You configure your provider and credentials. Model availability, capabilities, and any usage costs depend on that provider.": "代码包含对 OpenAI、Azure OpenAI、Anthropic、Gemini 和本地 Ollama 端点的支持。你需要配置服务商和凭据。模型的可用性、能力及使用费用取决于相应服务商。", + "Can I create reports and dashboards?": "可以创建报告和仪表板吗？", + "You can edit rich-text reports with chart images, export a report image, or print to PDF. The current dashboard implementation saves chart references and displays title/type placeholders; a rendered, interactive multi-chart dashboard is not yet implemented.": "你可以编辑包含图表图片的富文本报告，将报告导出为图片，或打印为 PDF。目前的仪表板实现会保存图表引用，并显示标题和类型占位内容；尚未实现可渲染图表的交互式多图表仪表板。", + "Is Datly free?": "Datly 免费吗？", + "Datly’s source code is available under the MIT licence. Your model provider, connected infrastructure, or hosting may have their own costs. Check the repository’s setup instructions and releases for the current installation options.": "Datly 源代码按 MIT 许可证提供。模型服务商、所连接的基础设施或托管服务可能另行收费。请查看仓库中的安装说明和发行版本，了解当前的安装方式。", + "THERE’S A STORY IN YOUR DATA.": "你的数据蕴藏着故事。", + "Start with a good question.": "从一个好问题开始。", + "Get started with Datly": "开始使用 Datly", + "A clearer view of your data.": "更清晰地看懂你的数据。", + "GitHub ↗": "GitHub ↗", + "Documentation ↗": "文档 ↗", + "Releases ↗": "发行版本 ↗", + "Image library ↗": "图片库 ↗", + "MIT licence": "MIT 许可证", + "Built on": "基于", + "Microsoft Data Formulator": "Microsoft Data Formulator", + "Datly workspace": "Datly 工作空间", + "Close full image": "关闭完整图片", + "Close": "关闭", + "Actual application capture using example data.": "使用示例数据的真实应用截图。", + "© 2026 Datly contributors ·": "© 2026 Datly 贡献者 ·", + "Language": "语言", + "Choose a language": "选择语言", + "Close language menu": "关闭语言菜单", + "Stars on GitHub": "GitHub 星标", + "GitHub stars; verified 2 November 2026": "GitHub 星标；于 2026 年 11 月 2 日核实", + "Installation commands copied.": "安装命令已复制。", + "Automatic copy is unavailable. The commands are selected; use your browser’s copy shortcut.": "无法自动复制。命令已选中，请使用浏览器的复制快捷键。", + "Copied ✓": "已复制 ✓", + "Copy ⧉": "复制 ⧉", + "Select & copy": "选择并复制", + "Close navigation": "关闭导航菜单", + "Showing {count} source options. Names describe implementations, not certified integrations.": "显示 {count} 种数据源选项。名称表示已有的代码实现，并不代表经过认证的集成。", + "The Datly workspace": "Datly 工作空间", + "Actual Datly workspace showing data threads, a stock-price chart, and computed rows.": "真实的 Datly 工作空间，展示数据线程、股价图表和计算后的数据行。", + "Actual application capture, reframed for presentation. Example stock data; results require review.": "真实应用截图，经重新取景以便展示。使用示例股票数据；结果需要审核。", + "The rows behind the line.": "图表背后的数据行。", + "Detail views of the real Datly Explore canvas, showing a chart and computed data rows.": "Datly 真实 Explore 画布的细节视图，展示图表和计算后的数据行。", + "Keep the path to your answer.": "保留通往答案的路径。", + "A follow-up question should build on what you have learned. Data threads keep the prompts, derived tables, and chart results in a visible analytical path.": "后续问题应建立在已有发现之上。数据线程将提示词、生成的表格和图表结果保留在清晰可见的分析路径中。", + "Follow prompts and derived tables": "追踪提示词和生成的表格", + "Explore a separate line of inquiry": "探索独立的研究方向", + "Keep previous steps in context": "保留前序步骤的上下文", + "Actual Datly data threads showing separate stock-price questions and derived tables.": "Datly 真实的数据线程，展示不同的股价问题和生成的表格。", + "Actual application capture, cropped to focus on data threads. Example stock analysis.": "真实应用截图，经裁剪以突出数据线程。示例股票分析。", + "Get to know your data.": "深入了解你的数据。", + "Look beyond the picture. Browse table rows, review data-quality signals, and export the derived data for a closer look in your own tools.": "不止看图。浏览表格数据行、查看数据质量信号，并导出转换后的数据，在你自己的工具中进一步检查。", + "Review table rows and columns": "查看表格行和列", + "Inspect data-quality signals": "检查数据质量信号", + "Download the computed table as CSV": "将计算结果表下载为 CSV", + "Actual Datly data inspection view with quality signals, table rows, and CSV export.": "Datly 真实的数据检查界面，包含质量信号、表格数据行和 CSV 导出功能。", + "Actual application capture, cropped to focus on data inspection. Quality signals are diagnostics, not a certification of correctness.": "真实应用截图，经裁剪以突出数据检查。质量信号仅用于诊断，并不构成正确性认证。", + "Give the finding some context.": "为发现补充上下文。", + "Turn an exploration into something another person can follow. Write and edit a report with chart images, then export an image or print to PDF.": "将探索过程整理成他人能够理解的内容。撰写并编辑包含图表图片的报告，再导出为图片或打印为 PDF。", + "Rich-text editing with chart images": "包含图表图片的富文本编辑", + "Add the reasoning around the result": "补充结果背后的推理", + "Save an image or print to PDF": "保存为图片或打印为 PDF", + "An archived Data Formulator report example showing written findings and a chart.": "存档的 Data Formulator 报告示例，展示文字结论和图表。", + "Archived report example from the repository; it shows the earlier Data Formulator interface. Current report editing uses TipTap. Chart embeds are images.": "仓库中存档的报告示例，展示早期的 Data Formulator 界面。当前报告编辑使用 TipTap。嵌入的图表为图片。", + "Project at a glance": "项目概览", + "Data source options": "数据源选项", + "Website languages": "网站语言", + "Open-source licence": "开源许可证" + } +} diff --git a/docs/ru/index.html b/docs/ru/index.html new file mode 100644 index 0000000..2a7513c --- /dev/null +++ b/docs/ru/index.html @@ -0,0 +1,95 @@ + + + +Datly — рабочая среда для аналитики с ИИ и открытым исходным кодом + + + + + + + + + + + + + + + + + + + + + +
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ОТКРЫТЫЙ КОД. ПРОСТОР ДЛЯ ИССЛЕДОВАНИЙ.
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Точные вопросы.
Более ясные ответы.

В ваших данных есть история. Найдите её в рабочей среде с поддержкой ИИ, где вопросы, диаграммы и лежащие в их основе строки данных связаны между собой.

Начать исследование Рассмотреть подробнее

Бесплатное ПО с открытым исходным кодом · Лицензия MIT

Для аналитиков, исследователей и команд, работающих с данными.

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Разрабатывается открыто.Число проверено 2 ноября 2026 года; обновляется в реальном времени.
249звёзд на GitHub16Варианты источников данных12Языки сайтаMITЛицензия открытого ПО
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Меньше переключений. Больше понимания.РЕАЛЬНОЕ ПРИЛОЖЕНИЕ · ДЕМОНСТРАЦИОННЫЕ ДАННЫЕ
Вопросы → преобразования → диаграммы → строкиВАШ АНАЛИЗ В КОНТЕКСТЕ
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Надёжная основа
DuckDBPythonVega-LiteReactTipTap
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01 / ОТ ВОПРОСА К ПОНИМАНИЮ

Следуйте любопытству.
Сохраняйте контекст.

От таблицы к полезному выводу — в одной рабочей среде. Задавайте вопросы, уточняйте и проверяйте результаты по мере развития анализа.

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01

Добавьте свои данные

Загрузите локальный файл, подключите поддерживаемую базу данных или изучите встроенный пример.

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02

Задайте следующий вопрос

Опишите, что хотите исследовать. Уточняйте диаграмму с помощью визуальных настроек или дополнительных вопросов.

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03

Проверьте, затем объясните

Проверьте полученные строки, экспортируйте CSV и дополните выводы пояснениями в редактируемом отчёте.

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02 / ПОДРОБНЫЙ ВЗГЛЯД

Смотрите на ход работы.
Не только на ответ.

Реальные экраны приложения, которые можно рассмотреть подробнее. Познакомьтесь с функциями, помогающими проследить ход анализа.

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Строки данных
за линией графика.

Диаграмма — отправная точка. Откройте лежащую в её основе таблицу, изучите значения, проверьте преобразование и определите, что на самом деле означает результат.

  • Диаграмма и вычисленная таблица рядом
  • Проверка и экспорт полученных строк
  • Проверяйте результаты перед публикацией

Реальный снимок приложения, кадрированный для наглядности. Данные приведены для примера; анализ, созданный ИИ, требует проверки.

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03 / СОХРАНЯЙТЕ КОНТРОЛЬ

ИИ помогает исследовать.
Решение принимаете вы.

Для качественного анализа недостаточно убедительной диаграммы. Datly позволяет изучить промежуточные этапы работы.

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ПРОСЛЕДИТЕ ХОД ВОПРОСА

Сохраняйте
ход мыслей.

Ветки анализа упорядочивают вопросы, преобразования, полученные таблицы и диаграммы. Следите за развитием исследования.

Изучить ветки анализа
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НАСТРОЙТЕ ПРЕДСТАВЛЕНИЕ

Диаграмма, с которой
можно работать.

Настраивайте оси, цвета и другие свойства диаграммы с помощью визуальных элементов управления. Меняйте представление по мере уточнения вопроса.

Изучить холст
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ОБЪЯСНИТЕ ВЫВОД

Больше, чем
снимок экрана.

Объединяйте пояснения и изображения диаграмм в редактируемом отчёте. Сохраните изображение или используйте печать в браузере для получения PDF.

Изучить отчёты
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04 / НАЧНИТЕ С ТОГО, ЧТО ЕСТЬ

Ваши данные.
Знакомая отправная точка.

Работайте с файлами и поддерживаемыми источниками данных. Для подключения нужны соответствующие драйверы, учётные данные и доступ к сети.

Сегодня — файл.
Завтра — хранилище данных.

Начните с CSV или демонстрационного набора данных. Подключите поддерживаемый источник, когда для ответа потребуется больше.

Посмотреть реализацию коннекторов
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  • Pg
    PostgreSQLРеляционная база данных
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  • Db
    DatabricksSQL-хранилище
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  • Bq
    BigQueryОблачное хранилище данных
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  • Ch
    ClickHouseАналитическая база данных
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  • My
    MySQLРеляционная база данных
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  • Ms
    SQL ServerРеляционная база данных
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  • S3
    Amazon S3Объектное хранилище
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  • Az
    Azure BlobОбъектное хранилище
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  • At
    AthenaБессерверные запросы
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  • Mo
    MongoDBДокументная база данных
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  • Co
    Cosmos DBДокументная база данных
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  • Ku
    Azure KustoData Explorer
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  • Su
    SupersetИмпорт наборов данных
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  • ↥
    Локальные файлыCSV, Excel и Parquet
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  • Wb
    Веб-источникиПоддерживаемое извлечение таблиц
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  • Ex
    Демонстрационные наборы данныхПопробовать анализ на примере
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Показаны все 16 вариантов источников. Названия обозначают реализации, а не сертифицированные интеграции.

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05 / ОСОЗНАННЫЙ ВЫБОР

Локальная обработка.
Понятные границы передачи данных.

Посмотреть код контекста модели
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01 / ВЫПОЛНЕНИЕ

Работайте в своей среде

DuckDB и Python выполняют преобразования в среде, где размещён Datly, в том числе при локальной установке на вашем компьютере.

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02 / КОНТЕКСТ МОДЕЛИ

Знайте, что получает ИИ

Запросы к ИИ могут включать схему, примеры значений и строк, инструкции и результаты работы инструментов. Облачный поставщик модели получает переданный ему контекст.

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03 / ВАША КОНФИГУРАЦИЯ

Выберите модель

Подключите поддерживаемого поставщика или настройте локальную модель через Ollama. Для локальных запросов к ИИ нужна локальная конечная точка; удалённым источникам данных по-прежнему требуется доступ к сети.

+
+

06 / ВАШ СЛЕДУЮЩИЙ ВОПРОС

Немного настройки.
Много возможностей для исследования.

Соберите Datly из исходного кода и запустите в браузере. Начните с демонстрационного набора данных, затем настройте модель для исследования с помощью ИИ.

  • Python 3.11 или новее
  • Node.js и Yarn для интерфейса
  • uv для среды Python
Прочитать руководство по установке Посмотреть выпуски 
# Get the project
+git clone https://github.com/arpanjot-coding/Datly.git
+cd Datly
+
+# Build the interface
+yarn
+yarn build
+
+# Install Python dependencies and launch
+uv sync
+uv run data_formulator
Откройте localhost:5567 после запуска

+

ЧТО СТОИТ ЗНАТЬ

Прежде чем
начать.

Вопросы или предложения?
+
Что такое Datly?

Datly — аналитическая среда с открытым исходным кодом на базе Data Formulator от Microsoft Research. Она объединяет исследование на естественном языке, визуальные настройки диаграмм, ветки анализа, проверку таблиц и редактируемые отчёты. Проект находится в бета-версии.

+
Мои данные остаются на компьютере?

При локальной установке преобразования выполняются на вашем компьютере. Запросы к ИИ могут включать примеры строк, значения полей, инструкции и результаты работы инструментов, передаваемые настроенной модели. Если вы выбрали облачного поставщика, он получает этот контекст. Локальная конечная точка модели позволяет обрабатывать запросы к модели локально; проверьте конфигурацию и подключённые источники данных.

+
Гарантирует ли проверка таблицы правильный ответ?

Нет. Таблица помогает проверить вычисленные значения, но ИИ всё ещё может выбрать неверное преобразование, агрегирование или интерпретацию. Прежде чем опираться на результат, проверьте созданную работу, исходные данные и бизнес-допущения.

+
Какие модели можно использовать?

В коде реализована поддержка OpenAI, Azure OpenAI, Anthropic, Gemini и локальных конечных точек Ollama. Поставщика и учётные данные настраиваете вы. Доступность моделей, их возможности и стоимость использования зависят от поставщика.

+
Можно ли создавать отчёты и дашборды?

Вы можете редактировать форматированные отчёты с изображениями диаграмм, экспортировать отчёт как изображение или печатать в PDF. Текущая реализация дашборда сохраняет ссылки на диаграммы и показывает заглушки с названием и типом; отображение интерактивного дашборда с несколькими диаграммами пока не реализовано.

+
Datly бесплатный?

Исходный код Datly доступен по лицензии MIT. Поставщик модели, подключённая инфраструктура и хостинг могут оплачиваться отдельно. Актуальные способы установки приведены в руководстве и выпусках репозитория.

+
+

В ВАШИХ ДАННЫХ ЕСТЬ ИСТОРИЯ.

Начните с точного вопроса.

Начать работу с Datly
+
+ +

Рабочая среда Datly

Реальный снимок приложения с демонстрационными данными.

 + + diff --git a/docs/site.js b/docs/site.js new file mode 100644 index 0000000..be51345 --- /dev/null +++ b/docs/site.js @@ -0,0 +1,78 @@ +const localeCopy=JSON.parse(document.querySelector('#locale-copy')?.textContent||'{}'); +const t=text=>localeCopy.translations?.[text]??text; +const siteRoot=new URL('.',document.querySelector('script[src$="site.js"]').src); +const numberFormat=new Intl.NumberFormat(localeCopy.language||'en'); +const features = { +workspace:{title:'The Datly workspace',image:'product-workspace',alt:'Actual Datly workspace showing data threads, a stock-price chart, and computed rows.',caption:'Actual application capture, reframed for presentation. Example stock data; results require review.'}, +explore:{title:'The rows behind the line.',description:'A chart is a starting point. 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Names describe implementations, not certified integrations.').replace('{count}',numberFormat.format(count)); +})); +const commands={ +source:'# Get the project\ngit clone https://github.com/arpanjot-coding/Datly.git\ncd Datly\n\n# Build the interface\nyarn\nyarn build\n\n# Install Python dependencies and launch\nuv sync\nuv run data_formulator', +docker:'# Requires Docker with Docker Compose\ngit clone https://github.com/arpanjot-coding/Datly.git\ncd Datly\n\n# Copy .env.template to .env before launch\n# macOS / Linux:\ncp .env.template .env\n# PowerShell: Copy-Item .env.template .env\n\n# Build and start\ndocker compose up --build' +}; +let activeSetup='source';const setupTabs=[...document.querySelectorAll('[data-setup]')]; +function selectSetup(key){activeSetup=key;setupTabs.forEach(tab=>{const selected=tab.dataset.setup===key;tab.setAttribute('aria-selected',String(selected));tab.tabIndex=selected?0:-1;});document.querySelector('#setup-code code').textContent=commands[key];document.querySelector('#setup-code').setAttribute('aria-labelledby','setup-tab-'+key);} +setupTabs.forEach(tab=>tab.addEventListener('click',()=>selectSetup(tab.dataset.setup))); +tabKeyboard(setupTabs,tab=>selectSetup(tab.dataset.setup)); +document.querySelector('#copy-code').addEventListener('click',async()=>{ + const button=document.querySelector('#copy-code'); + try{await navigator.clipboard.writeText(commands[activeSetup]);button.textContent=t("Copied ✓");document.querySelector('#copy-status').textContent=t("Installation commands copied.");setTimeout(()=>{button.textContent=t("Copy ⧉");},2500);} + catch{document.querySelector('#setup-code').focus();const selection=window.getSelection(),range=document.createRange();range.selectNodeContents(document.querySelector('#setup-code code'));selection.removeAllRanges();selection.addRange(range);button.textContent=t("Select & copy");document.querySelector('#copy-status').textContent=t("Automatic copy is unavailable. The commands are selected; use your browser’s copy shortcut.");} +}); +const dialog=document.querySelector('.image-dialog'); +document.querySelectorAll('.image-trigger').forEach(button=>button.addEventListener('click',()=>{ + const feature=features[button.dataset.image];if(!feature)return; + document.querySelector('#dialog-title').textContent=feature.title; + const image=document.querySelector('#dialog-image');image.src=new URL('images/'+feature.image+'.webp',siteRoot).href;image.alt=feature.alt; + document.querySelector('#dialog-caption').textContent=feature.caption;dialog.showModal(); +})); +document.querySelector('#close-dialog').addEventListener('click',()=>dialog.close()); +dialog.addEventListener('click',event=>{if(event.target===dialog){const bounds=dialog.getBoundingClientRect();if(event.clientXbounds.right||event.clientYbounds.bottom)dialog.close();}}); + + + +const languageMenu=document.querySelector('.language-menu'); 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document.querySelectorAll('[data-github-stars]').forEach(node=>{node.textContent=numberFormat.format(data.stargazers_count);node.title=t('Stars on GitHub');}); + }catch{/* Keep the dated, verified count when GitHub is unavailable or rate-limited. */} +} +refreshGitHubStars(); diff --git a/docs/site.webmanifest b/docs/site.webmanifest index fb863b9..0f766a9 100644 --- a/docs/site.webmanifest +++ b/docs/site.webmanifest @@ -2,10 +2,10 @@ "name": "Datly — AI Data Visualization IDE", "short_name": "Datly", "description": "Local-first AI data visualization IDE and exploratory analytics workbench powered by DuckDB and Vega-Lite.", - "start_url": "/Datly/", + "start_url": "./", "display": "standalone", - "background_color": "#07090E", - "theme_color": "#3B82F6", + "background_color": "#f8f9f4", + "theme_color": "#206b4e", "icons": [ { "src": "images/logo-128.png", diff --git a/docs/sitemap.xml b/docs/sitemap.xml index 249361d..f51c686 100644 --- a/docs/sitemap.xml +++ b/docs/sitemap.xml @@ -1,12 +1,15 @@ - - - https://arpanjot-coding.github.io/Datly/ - 2026-11-01T07:44:00+00:00 - weekly - 1.0 - + + https://arpanjot-coding.github.io/Datly/2026-11-02 + https://arpanjot-coding.github.io/Datly/ru/2026-11-02 + https://arpanjot-coding.github.io/Datly/zh/2026-11-02 + https://arpanjot-coding.github.io/Datly/ja/2026-11-02 + https://arpanjot-coding.github.io/Datly/vi/2026-11-02 + https://arpanjot-coding.github.io/Datly/th/2026-11-02 + https://arpanjot-coding.github.io/Datly/hi/2026-11-02 + https://arpanjot-coding.github.io/Datly/ar/2026-11-02 + https://arpanjot-coding.github.io/Datly/fr/2026-11-02 + https://arpanjot-coding.github.io/Datly/de/2026-11-02 + https://arpanjot-coding.github.io/Datly/es/2026-11-02 + https://arpanjot-coding.github.io/Datly/it/2026-11-02 diff --git a/docs/styles.css b/docs/styles.css new file mode 100644 index 0000000..dd0fc02 --- /dev/null +++ b/docs/styles.css @@ -0,0 +1,69 @@ 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div>span{display:block;color:#65755c;font-size:9px}.source-status{font-size:9px;line-height:1.8;margin-top:20px;color:#637458} +.privacy-top{display:flex;justify-content:space-between;align-items:flex-end;gap:40px}.privacy-top .text-link{font-size:10px}.privacy-grid{display:grid;grid-template-columns:repeat(3,1fr);gap:35px;background:#eef2e8;border:1px solid #dae3d2;border-radius:7px;padding:33px;margin-top:38px}.privacy-number{font-family:var(--mono);font-size:8px;letter-spacing:.08em;color:#607650}.privacy-grid h3{font-size:20px;margin:22px 0 13px}.privacy-grid p{font-size:11px;line-height:1.9} +.setup-section{background:#e8eee1;border-block:1px solid #d8e2ce}.setup-layout{display:grid;grid-template-columns:.85fr 1.15fr;gap:95px;align-items:center}.setup-copy>p:not(.eyebrow){max-width:340px;font-size:13px;line-height:1.9;margin-top:22px}.requirements{padding:0;margin:24px 0;list-style:none;font-size:11px;color:#64715c}.requirements li{padding-block:3px}.requirements 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#a9b9a0;border-radius:8px;color:var(--ink);background:var(--paper);overflow:auto;box-shadow:0 30px 150px #001d1490}.image-dialog::backdrop{background:#0b221bcc;backdrop-filter:blur(5px)}.dialog-top{display:flex;align-items:center;justify-content:space-between;gap:20px;padding:4px 6px 15px}.dialog-top h2{font:600 14px var(--sans);letter-spacing:0}.dialog-top button{font-size:12px;display:flex;align-items:center;gap:15px;padding:5px 8px}.dialog-top button span{font-size:24px}.image-dialog>img{width:100%;height:auto;border-radius:4px}.image-dialog>p{font-size:10px;margin:13px 5px 0} +@media(min-width:1600px){.container{max-width:1320px}.hero{padding-top:80px}} +@media(max-width:1100px){.container{width:calc(100% - 64px)}.site-header 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+.nav-github{display:inline-flex;align-items:center;gap:6px;white-space:nowrap}.github-star-icon{color:#857229;font-size:12px} +.site-header nav{margin-inline-start:auto;gap:23px}.nav-inner>.wordmark{margin-inline-end:20px} +.language-menu{position:relative;margin-inline-start:20px;font-size:10px;flex-shrink:0}.language-menu summary{list-style:none;cursor:pointer;display:flex;align-items:center;gap:7px;padding:10px 4px}.language-menu summary::-webkit-details-marker{display:none}.language-menu summary>span:first-child{font-size:17px}.language-options{position:absolute;inset-inline-end:0;top:42px;width:196px;max-height:65vh;overflow:auto;background:#fffef9;border:1px solid #d2ddc9;border-radius:6px;box-shadow:0 15px 40px #173d2a20;padding:12px;z-index:30}.language-options p{font-size:9px;padding:4px 8px 9px;border-bottom:1px solid var(--line);margin-bottom:5px}.language-options a{display:flex;align-items:center;justify-content:space-between;font-size:12px;padding:6px 8px;border-radius:3px}.language-options a:hover,.language-options a[aria-current=page]{background:#e9f0e1} +.project-proof{display:grid;grid-template-columns:repeat(3,1fr);padding-block:30px;border-bottom:1px solid var(--line)}.project-proof>a,.project-proof>div{display:flex;flex-direction:column;gap:7px;padding-inline:32px;border-inline-start:1px solid var(--line)}.project-proof>:first-child{padding-inline-start:0;border:0}.project-proof>*>span{font-size:10px;color:#687960}.project-proof .proof-kicker{font:8px var(--mono);letter-spacing:.1em}.project-proof strong{font-size:21px;letter-spacing:-.03em;font-weight:500}.project-proof .star-verification{font-size:8px} +html[dir=rtl] body{text-align:start}html[dir=rtl] .terminal,html[dir=rtl] pre{direction:ltr;text-align:left}html[dir=rtl] .feature-copy{padding:20px 0 20px 12px}html[dir=rtl] .steps article+article{border-left:0;border-right:1px solid var(--line);padding-right:38px;padding-left:20px}html[dir=rtl] .feature-copy li:before{margin-right:0;margin-left:10px}html[dir=rtl] .requirements li:before{margin-right:0;margin-left:10px} +html:lang(zh-Hans) h1,html:lang(ja) h1,html:lang(th) h1,html:lang(hi) h1,html:lang(ar) h1{font-size:clamp(47px,5.5vw,76px);line-height:1.25;letter-spacing:-.025em}html:lang(zh-Hans) em,html:lang(ja) em,html:lang(th) em,html:lang(hi) em,html:lang(ar) em{font-family:var(--sans);font-style:normal;color:#387c54}html:lang(zh-Hans),html:lang(ja),html:lang(th),html:lang(hi),html:lang(ar){letter-spacing:0}html:lang(ar) .eyebrow,html:lang(th) .eyebrow,html:lang(hi) .eyebrow{letter-spacing:.01em}html:lang(ar) body{font-family:"Segoe UI",Tahoma,Arial,sans-serif}html:lang(hi) body{font-family:"Nirmala UI","Segoe UI",sans-serif}html:lang(th) body{font-family:"Leelawadee UI","Segoe UI",sans-serif} +@media(max-width:1150px){.site-header nav{gap:15px;font-size:11px}.language-menu{margin-inline-start:14px}.nav-github{margin-left:0}} +@media(max-width:960px){.site-header{height:72px}.menu-toggle{display:grid;gap:6px;padding:12px 4px;margin-inline-start:auto}.menu-toggle span{width:23px;height:1px;background:var(--ink)}.site-header nav{display:none;position:absolute;left:0;right:0;top:71px;background:var(--paper);padding:25px 32px;border-bottom:1px solid var(--line);align-items:stretch;gap:18px}.site-header nav.is-open{display:flex;flex-direction:column}.site-header nav .button{align-self:flex-start}.language-menu{margin-inline-start:18px}.project-proof>*>span{font-size:9px}.project-proof strong{font-size:19px}.project-proof>a,.project-proof>div{padding-inline:20px}} +@media(max-width:520px){.audience-line{font-size:10px}.language-menu{font-size:9px;margin-inline-start:16px}.language-menu summary{gap:5px}.language-options{width:190px}.project-proof{grid-template-columns:1fr;gap:19px;padding-block:25px}.project-proof>a,.project-proof>div,.project-proof>:first-child{padding:0;border:0}.project-proof>*>span{font-size:10px}.project-proof .proof-kicker{font-size:7px}.project-proof strong{font-size:21px}.project-proof .star-verification{font-size:8px}html:lang(zh-Hans) h1,html:lang(ja) h1,html:lang(th) h1,html:lang(hi) h1,html:lang(ar) h1{font-size:clamp(34px,10vw,49px)}html[dir=rtl] .steps article,html[dir=rtl] .steps article+article{border-right:0;padding:25px 30px 27px 55px}.terminal pre{font-size:10px}} + + +html:not([lang=en]) .hero-copy{grid-template-columns:1.5fr 1fr;gap:45px}html:not([lang=en]) h1{font-size:clamp(44px,4.8vw,66px);line-height:1.15}.hero-actions{flex-wrap:wrap;gap:16px}.hero-actions .button,.hero-actions .text-link{flex-shrink:0;white-space:nowrap} +@media(max-width:800px){html:not([lang=en]) .hero-copy{grid-template-columns:1fr;gap:24px}} +@media(max-width:520px){html:not([lang=en]) h1{font-size:clamp(32px,9.5vw,45px)}} + +/* Put verified project achievements in the first screen, before app imagery. */ +.hero-copy { padding-bottom: 30px; } +.hero-stats { margin-bottom: 30px; border-block: 1px solid #d3dfca; } +.hero-stats-heading { display: flex; justify-content: space-between; gap: 20px; padding-top: 15px; color: #456241; font-size: 11px; } +.stat-verification { font-size: 9px; color: #66765c; } +.hero-stats-grid { display: grid; grid-template-columns: repeat(4, minmax(0, 1fr)); padding-block: 18px 24px; } +.hero-stat { display: flex; flex-direction: column; gap: 5px; padding-inline: 30px; border-inline-start: 1px solid #d3dfca; } +.hero-stat:first-child { padding-inline-start: 0; border: 0; } +.hero-stat strong { display: flex; align-items: center; gap: 13px; font-size: 48px; line-height: 1.15; font-weight: 550; letter-spacing: -.055em; font-variant-numeric: tabular-nums; } +.hero-stat-stars strong { color: #206b4e; } +.hero-stat .stat-star { color: #8d7a36; font-size: 23px; } +.hero-stat .stat-label { font-size: 12px; color: #53674b; line-height: 1.5; } +.hero-stat:hover .stat-label { color: #206b4e; text-decoration: underline; text-underline-offset: 4px; } +@media (max-width: 800px) { + .hero-stats-heading { font-size: 10px; } + .stat-verification { max-width: 210px; text-align: end; font-size: 8px; } + .hero-stat { padding-inline: 18px; } + .hero-stat strong { font-size: 39px; } + .hero-stat .stat-label { font-size: 10px; } +} +@media (max-width: 520px) { + .hero-copy { padding-bottom: 25px; } + .hero-stats { margin-bottom: 24px; } + .hero-stats-heading { padding-top: 13px; gap: 16px; } + .hero-stats-grid { grid-template-columns: repeat(2, minmax(0, 1fr)); gap: 20px 0; padding-block: 16px 20px; } + .hero-stat { padding-inline: 20px 8px; } + .hero-stat:nth-child(odd) { padding-inline-start: 0; border: 0; } + .hero-stat strong { font-size: 35px; } + .hero-stat .stat-label { font-size: 11px; } + .hero-stat .stat-star { font-size: 18px; } + .stat-verification { max-width: 168px; font-size: 7px; line-height: 1.6; } +} diff --git a/docs/th/index.html b/docs/th/index.html new file mode 100644 index 0000000..e271d32 --- /dev/null +++ b/docs/th/index.html @@ -0,0 +1,95 @@ + + + +Datly — พื้นที่วิเคราะห์ข้อมูลด้วย AI แบบโอเพนซอร์ส + + + + + + + + + + + + + + + + + + + + + +
DatlyBETA
TH

ภาษา

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โอเพนซอร์ส เปิดกว้างให้สำรวจ
+

คำถามที่ดี
คำตอบที่ชัดเจนขึ้น

ข้อมูลของคุณมีเรื่องราว ค้นพบเรื่องราวนั้นด้วยพื้นที่ทำงานที่มี AI ช่วยเชื่อมคำถาม แผนภูมิ และแถวข้อมูลเบื้องหลังเข้าด้วยกัน

เริ่มสำรวจ ดูให้ลึกขึ้น

ซอฟต์แวร์โอเพนซอร์ส ใช้ได้ฟรี · สัญญาอนุญาต MIT

สำหรับนักวิเคราะห์ นักวิจัย และทีมข้อมูล

+
พัฒนาอย่างเปิดเผยตรวจสอบจำนวนเมื่อ 2 พฤศจิกายน 2026 และรีเฟรชข้อมูลสด
249ดาวบน GitHub16ตัวเลือกแหล่งข้อมูล12ภาษาของเว็บไซต์MITสัญญาอนุญาตโอเพนซอร์ส
+
สลับไปมาน้อยลง เข้าใจข้อมูลมากขึ้นภาพจากแอปจริง · ข้อมูลตัวอย่าง
คำถาม → การแปลงข้อมูล → แผนภูมิ → แถวข้อมูลงานวิเคราะห์ของคุณ พร้อมบริบท
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สร้างบนพื้นฐานที่มั่นคง
DuckDBPythonVega-LiteReactTipTap
+

01 / จากคำถามสู่ความเข้าใจ

ตามความอยากรู้ของคุณ
เก็บบริบทไว้ครบ

จากตารางสู่ข้อค้นพบที่มีประโยชน์ในพื้นที่เดียว ตั้งคำถาม ปรับแต่ง และตรวจสอบไปพร้อมกับการวิเคราะห์

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01

นำข้อมูลของคุณมาใช้

โหลดไฟล์ในเครื่อง เชื่อมต่อฐานข้อมูลที่รองรับ หรือสำรวจตัวอย่างที่มีให้

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02

ตั้งคำถามถัดไป

อธิบายสิ่งที่ต้องการสำรวจ ปรับแผนภูมิด้วยตัวควบคุมการเข้ารหัสภาพ หรือถามคำถามต่อเนื่อง

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03

ตรวจสอบ แล้วอธิบาย

ตรวจสอบแถวข้อมูลที่แปลงแล้ว ส่งออก CSV และเพิ่มบริบทให้ข้อค้นพบในรายงานที่แก้ไขได้

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02 / มองให้ใกล้ขึ้น

เห็นกระบวนการ
ไม่ใช่เพียงคำตอบ

มุมมองจากแอปจริงที่เปิดให้ดูใกล้ขึ้น สำรวจฟีเจอร์ที่ช่วยให้ติดตามการวิเคราะห์ได้ง่ายขึ้น

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แถวข้อมูลเบื้องหลัง
เส้นแผนภูมิ

แผนภูมิคือจุดเริ่มต้น เปิดตารางเบื้องหลังเพื่อตรวจสอบค่า ทบทวนการแปลงข้อมูล และพิจารณาว่าผลลัพธ์หมายความว่าอย่างไร

  • แผนภูมิและตารางผลคำนวณอยู่ด้วยกัน
  • ตรวจสอบและส่งออกแถวข้อมูลที่แปลงแล้ว
  • ทบทวนผลลัพธ์ก่อนแชร์

ภาพจากแอปจริง จัดกรอบใหม่เพื่อให้ชัดเจน ข้อมูลใช้ประกอบตัวอย่างเท่านั้น การวิเคราะห์ที่สร้างโดย AI ต้องได้รับการทบทวน

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03 / ติดตามได้ทุกขั้น

AI ช่วยคุณสำรวจ
คุณเป็นผู้ตัดสินใจ

การวิเคราะห์ที่ดีต้องมีมากกว่าแผนภูมิที่ดูน่าเชื่อถือ Datly เปิดให้คุณตรวจสอบงานระหว่างขั้นตอนได้

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ติดตามคำถาม

รักษาความต่อเนื่อง
ของแนวคิด

เธรดข้อมูลจัดระเบียบคำถาม การแปลงข้อมูล ตารางที่ได้มา และแผนภูมิ ติดตามการสำรวจขณะที่งานดำเนินไป

สำรวจเธรดข้อมูล
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ปรับมุมมอง

แผนภูมิที่คุณ
นำไปทำงานต่อได้

ใช้ตัวควบคุมการเข้ารหัสภาพสำหรับแกน สี และคุณสมบัติอื่นของแผนภูมิ ปรับมุมมองเมื่อคำถามของคุณชัดเจนขึ้น

สำรวจพื้นที่แผนภูมิ
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อธิบายข้อค้นพบ

มากกว่า
ภาพหน้าจอ

ผสานคำอธิบายกับภาพแผนภูมิในรายงานที่แก้ไขได้ บันทึกเป็นภาพ หรือใช้การพิมพ์ของเบราว์เซอร์เพื่อสร้าง PDF

สำรวจรายงาน
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04 / เริ่มจากสิ่งที่คุณมี

ข้อมูลของคุณ
จุดเริ่มต้นที่คุ้นเคย

ทำงานกับไฟล์และแหล่งข้อมูลที่รองรับ การเชื่อมต่อขึ้นอยู่กับไดรเวอร์ ข้อมูลรับรอง และการเข้าถึงเครือข่ายที่เหมาะสม

วันนี้เริ่มจากไฟล์
พรุ่งนี้อาจเป็นคลังข้อมูล

เริ่มต้นง่าย ๆ ด้วย CSV หรือชุดข้อมูลตัวอย่าง เพิ่มการเชื่อมต่อที่รองรับเมื่อคำถามต้องใช้ข้อมูลมากขึ้น

ดูการพัฒนาตัวเชื่อมต่อ
    +
  • Pg
    PostgreSQLฐานข้อมูลเชิงสัมพันธ์
    • +
  • Db
    Databricksคลังข้อมูล SQL
    • +
  • Bq
    BigQueryคลังข้อมูลบนคลาวด์
    • +
  • Ch
    ClickHouseฐานข้อมูลเชิงวิเคราะห์
    • +
  • My
    MySQLฐานข้อมูลเชิงสัมพันธ์
    • +
  • Ms
    SQL Serverฐานข้อมูลเชิงสัมพันธ์
    • +
  • S3
    Amazon S3ที่จัดเก็บออบเจ็กต์
    • +
  • Az
    Azure Blobที่จัดเก็บออบเจ็กต์
    • +
  • At
    Athenaคิวรีแบบไร้เซิร์ฟเวอร์
    • +
  • Mo
    MongoDBฐานข้อมูลเอกสาร
    • +
  • Co
    Cosmos DBฐานข้อมูลเอกสาร
    • +
  • Ku
    Azure Kustoการสำรวจข้อมูล
    • +
  • Su
    Supersetนำเข้าชุดข้อมูล
    • +
  • ↥
    ไฟล์ในเครื่องCSV, Excel และ Parquet
    • +
  • Wb
    แหล่งข้อมูลบนเว็บการดึงตารางที่รองรับ
    • +
  • Ex
    ชุดข้อมูลตัวอย่างลองวิเคราะห์ตัวอย่าง
    • +

แสดงตัวเลือกแหล่งข้อมูลทั้ง 16 รายการ ชื่อบอกถึงส่วนที่มีการพัฒนาไว้ ไม่ใช่การผสานรวมที่ได้รับการรับรอง

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05 / เลือกอย่างเข้าใจ

ประมวลผลในเครื่อง
ขอบเขตข้อมูลชัดเจน

อ่านโค้ดบริบทของโมเดล
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01 / การประมวลผล

ทำงานในสภาพแวดล้อมของคุณ

DuckDB และ Python ทำการแปลงข้อมูลในสภาพแวดล้อมที่โฮสต์ Datly รวมถึงการติดตั้งในเครื่องคอมพิวเตอร์ของคุณ

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02 / บริบทของโมเดล

รู้ว่า AI ได้รับอะไร

คำขอ AI อาจมีสคีมา ค่าตัวอย่างและแถวตัวอย่าง พรอมป์ต์ และผลลัพธ์จากเครื่องมือ ผู้ให้บริการโมเดลคลาวด์จะได้รับบริบทที่ส่งไป

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03 / การตั้งค่าของคุณ

เลือกโมเดลของคุณ

เชื่อมต่อผู้ให้บริการที่รองรับ หรือตั้งค่าโมเดลในเครื่องผ่าน Ollama คำขอ AI แบบในเครื่องต้องใช้ปลายทางในเครื่อง ส่วนแหล่งข้อมูลระยะไกลยังต้องเข้าถึงเครือข่าย

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06 / คำถามดี ๆ ข้อต่อไป

ตั้งค่าอีกนิด
มีอะไรให้สำรวจอีกมาก

บิลด์จากซอร์สโค้ดแล้วเปิด Datly ในเบราว์เซอร์ เริ่มจากชุดข้อมูลตัวอย่าง แล้วตั้งค่าโมเดลสำหรับการสำรวจที่มี AI ช่วย

  • Python 3.11 ขึ้นไป
  • Node.js และ Yarn สำหรับส่วนหน้า
  • uv สำหรับสภาพแวดล้อม Python
อ่านคู่มือการตั้งค่า ดูรุ่นที่เผยแพร่ 
# Get the project
+git clone https://github.com/arpanjot-coding/Datly.git
+cd Datly
+
+# Build the interface
+yarn
+yarn build
+
+# Install Python dependencies and launch
+uv sync
+uv run data_formulator
เปิด localhost:5567 หลังเริ่มทำงาน

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สิ่งที่ควรรู้

ก่อนที่คุณจะ
เริ่มสำรวจ

มีคำถามหรือข้อเสนอแนะ?
+
Datly คืออะไร?

Datly คือพื้นที่วิเคราะห์ข้อมูลโอเพนซอร์สที่สร้างบน Data Formulator ของ Microsoft Research ผสานการสำรวจด้วยภาษาธรรมชาติ ตัวควบคุมแผนภูมิแบบภาพ เธรดข้อมูล การตรวจสอบตาราง และรายงานที่แก้ไขได้ โครงการอยู่ในระยะเบตา

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ข้อมูลของฉันจะอยู่ในคอมพิวเตอร์หรือไม่?

การติดตั้งในเครื่องทำการแปลงข้อมูลบนคอมพิวเตอร์ของคุณ คำขอ AI อาจมีแถวตัวอย่าง ค่าฟิลด์ พรอมป์ต์ และผลลัพธ์เครื่องมือที่ส่งไปยังโมเดลที่ตั้งค่าไว้ หากเลือกผู้ให้บริการคลาวด์ ผู้ให้บริการนั้นจะได้รับบริบทนี้ ปลายทางโมเดลในเครื่องช่วยให้คำขอโมเดลอยู่ในเครื่องได้ โปรดตรวจสอบการตั้งค่าและแหล่งข้อมูลที่เชื่อมต่อ

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การตรวจสอบตารางรับประกันคำตอบที่ถูกต้องหรือไม่?

ไม่ ตารางช่วยให้ตรวจสอบค่าที่คำนวณได้ แต่ AI ยังอาจเลือกการแปลง การสรุปรวม หรือการตีความที่ไม่ถูกต้อง ทบทวนงานที่สร้างขึ้น ข้อมูลเบื้องหลัง และสมมติฐานทางธุรกิจก่อนนำผลลัพธ์ไปใช้

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ฉันใช้โมเดลใดได้บ้าง?

โค้ดรองรับ OpenAI, Azure OpenAI, Anthropic, Gemini และปลายทาง Ollama ในเครื่อง คุณตั้งค่าผู้ให้บริการและข้อมูลรับรองเอง ความพร้อมของโมเดล ความสามารถ และค่าใช้งานขึ้นอยู่กับผู้ให้บริการ

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ฉันสร้างรายงานและแดชบอร์ดได้หรือไม่?

คุณแก้ไขรายงานข้อความแบบจัดรูปแบบพร้อมภาพแผนภูมิ ส่งออกรายงานเป็นภาพ หรือพิมพ์เป็น PDF ได้ แดชบอร์ดปัจจุบันบันทึกการอ้างอิงแผนภูมิและแสดงตัวแทนชื่อ/ประเภทเท่านั้น ยังไม่มีการพัฒนาแดชบอร์ดหลายแผนภูมิที่แสดงผลและโต้ตอบได้

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Datly ใช้ฟรีหรือไม่?

ซอร์สโค้ด Datly เปิดให้ใช้ภายใต้สัญญาอนุญาต MIT ผู้ให้บริการโมเดล โครงสร้างพื้นฐานที่เชื่อมต่อ หรือบริการโฮสต์อาจมีค่าใช้จ่ายของตนเอง ดูคู่มือการตั้งค่าและรุ่นที่เผยแพร่ในคลังโค้ดสำหรับตัวเลือกการติดตั้งปัจจุบัน

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 + + diff --git a/docs/translations.js b/docs/translations.js deleted file mode 100644 index ee67521..0000000 --- a/docs/translations.js +++ /dev/null @@ -1,236 +0,0 @@ -/** - * Datly Multi-Language Translation Corpus (24 Languages) - * Automatically maps visitor country to language by IP and browser settings. - */ - -window.DATLY_LANGUAGES = { - "en": { - "name": "English", - "native": "English", - "flag": "🇺🇸", - "dir": "ltr" - }, - "es": { - "name": "Spanish", - "native": "Español", - "flag": "🇪🇸", - "dir": "ltr" - }, - "de": { - "name": "German", - "native": "Deutsch", - "flag": "🇩🇪", - "dir": "ltr" - }, - "it": { - "name": "Italian", - "native": "Italiano", - "flag": "🇮🇹", - "dir": "ltr" - }, - "fr": { - "name": "French", - "native": "Français", - "flag": "🇫🇷", - "dir": "ltr" - }, - "pt": { - "name": "Portuguese", - "native": "Português", - "flag": "🇧🇷", - "dir": "ltr" - }, - "ru": { - "name": "Russian", - "native": "Русский", - "flag": "🇷🇺", - "dir": "ltr" - }, - "zh": { - "name": "Chinese (Simplified)", - "native": "简体中文", - "flag": "🇨🇳", - "dir": "ltr" - }, - "zh-TW": { - "name": "Chinese (Traditional)", - "native": "繁體中文", - "flag": "🇹🇼", - "dir": "ltr" - }, - "ja": { - "name": "Japanese", - "native": "日本語", - "flag": "🇯🇵", - "dir": "ltr" - }, - "ko": { - "name": "Korean", - "native": "한국어", - "flag": "🇰🇷", - "dir": "ltr" - }, - "ar": { - "name": "Arabic", - "native": "العربية", - "flag": "🇸🇦", - "dir": "rtl" - }, - "hi": { - "name": "Hindi", - "native": "हिन्दी", - "flag": "🇮🇳", - "dir": "ltr" - }, - "th": { - "name": "Thai", - "native": "ไทย", - "flag": "🇹🇭", - "dir": "ltr" - }, - "fil": { - "name": "Filipino", - "native": "Filipino", - "flag": "🇵🇭", - "dir": "ltr" - }, - "vi": { - "name": "Vietnamese", - "native": "Tiếng Việt", - "flag": "🇻🇳", - "dir": "ltr" - }, - "id": { - "name": "Indonesian", - "native": "Bahasa Indonesia", - "flag": "🇮🇩", - "dir": "ltr" - }, - "tr": { - "name": "Turkish", - "native": "Türkçe", - "flag": "🇹🇷", - "dir": "ltr" - }, - "nl": { - "name": "Dutch", - "native": "Nederlands", - "flag": "🇳🇱", - "dir": "ltr" - }, - "pl": { - "name": "Polish", - "native": "Polski", - "flag": "🇵🇱", - "dir": "ltr" - }, - "sv": { - "name": "Swedish", - "native": "Svenska", - "flag": "🇸🇪", - "dir": "ltr" - }, - "uk": { - "name": "Ukrainian", - "native": "Українська", - "flag": "🇺🇦", - "dir": "ltr" - }, - "el": { - "name": "Greek", - "native": "Ελληνικά", - "flag": "🇬🇷", - "dir": "ltr" - }, - "he": { - "name": "Hebrew", - "native": "עברית", - "flag": "🇮🇱", - "dir": "rtl" - } -}; - -window.DATLY_COUNTRY_MAP = { - "ES": "es", - "MX": "es", - "AR": "es", - "CO": "es", - "CL": "es", - "PE": "es", - "VE": "es", - "EC": "es", - "GT": "es", - "CU": "es", - "BO": "es", - "DO": "es", - "HN": "es", - "PY": "es", - "SV": "es", - "NI": "es", - "CR": "es", - "PA": "es", - "UY": "es", - "DE": "de", - "AT": "de", - "CH": "de", - "LI": "de", - "IT": "it", - "SM": "it", - "VA": "it", - "FR": "fr", - "BE": "fr", - "MC": "fr", - "SN": "fr", - "CI": "fr", - "BR": "pt", - "PT": "pt", - "AO": "pt", - "MZ": "pt", - "RU": "ru", - "BY": "ru", - "KZ": "ru", - "KG": "ru", - "CN": "zh", - "SG": "zh", - "TW": "zh-TW", - "HK": "zh-TW", - "MO": "zh-TW", - "JP": "ja", - "KR": "ko", - "SA": "ar", - "AE": "ar", - "EG": "ar", - "IQ": "ar", - "MA": "ar", - "DZ": "ar", - "SD": "ar", - "QA": "ar", - "KW": "ar", - "OM": "ar", - "JO": "ar", - "LB": "ar", - "TN": "ar", - "BH": "ar", - "IN": "hi", - "TH": "th", - "PH": "fil", - "VN": "vi", - "ID": "id", - "TR": "tr", - "NL": "nl", - "PL": "pl", - "SE": "sv", - "UA": "uk", - "GR": "el", - "CY": "el", - "IL": "he", - "US": "en", - "GB": "en", - "CA": "en", - "AU": "en", - "NZ": "en", - "IE": "en", - "ZA": "en" -}; - -window.DATLY_TRANSLATIONS = {"en": {"nav_features": "Features", "nav_canvas": "Ground Truth", "nav_connectors": "Connectors (15+)", "nav_comparison": "Compare", "nav_quickstart": "Quickstart", "nav_faq": "FAQ", "nav_star": "Star", "nav_quickstart_btn": "Quickstart", "hero_badge": "Next-Generation AI Data IDE & Analytics Workbench", "hero_title_1": "Talk to your data.", "hero_title_2": "Verify every single row.", "hero_desc": "Datly is the open-source, local-first analytics IDE where natural language transforms enterprise tables into interactive charts, and the computed data rows stay right on screen under the line. Zero cloud data exfiltration. Zero hallucinations.", "hero_btn_quickstart": "Get Started in 1 Command", "hero_btn_sub": "uv run data_formulator", "hero_btn_github": "View on GitHub", "hero_btn_desktop": "Desktop App", "trust_local": "100% Local Processing (DuckDB + In-Memory)", "trust_connectors": "15+ Enterprise Connectors", "trust_privacy": "Zero Data Exfiltration", "trust_models": "Multi-Model Agnostic (Claude, GPT, Ollama)", "ide_status": "DuckDB In-Memory (Local)", "ide_tab_ide": "Full Analytics IDE", "ide_tab_explore": "Explore Ground-Truth Canvas", "ide_tab_connectors": "15+ Data Connectors", "ide_tab_dashboards": "Multi-Chart Dashboards", "ide_tab_reports": "TipTap Narrative Reports", "pillars_kicker": "01 / ARCHITECTURAL PILLARS", "pillars_title": "Engineering Rigor Meets Fluid Exploration", "pillars_desc": "Built from the ground up to solve the state corruption of notebooks and the rigidity of legacy BI.", "p1_title": "Non-Destructive Data Threads", "p1_desc": "Each analytical inquiry forms an isolated thread branch (DAG). Follow-up questions build on intermediate derived tables without mutating baseline datasets or overwriting state.", "p1_tag": "Lineage State Machine", "p2_title": "Ground-Truth Data Canvas", "p2_desc": "Say goodbye to AI hallucinations. Every visualization is drawn from a physical derived table that sits directly underneath the chart. Inspect every cell and download verified CSVs.", "p2_tag": "Zero Phantom Metrics", "p3_title": "DuckDB In-Memory Engine", "p3_desc": "Lightning-fast vectorized query execution powered by DuckDB, Pandas, and PyArrow. Run complex aggregations, window functions, and joins across millions of rows locally in milliseconds.", "p3_tag": "Vectorized Analytics", "p4_title": "Concept Encoding Shelves", "p4_desc": "Drag and drop data fields into intuitive visual encoding shelves: X, Y, Color, Size, Facet, and Repeat. Bi-directional sync between natural language prompts and Vega-Lite specs.", "p4_tag": "Grammar of Graphics", "p5_title": "TipTap Narrative Reports", "p5_desc": "Transform raw exploratory charts into executive-ready communication artifacts. A rich WYSIWYG editor allows you to interweave narrative commentary with live embedded visual cards.", "p5_tag": "Executive Publishing", "p6_title": "Local-First Zero Exfiltration", "p6_desc": "Enterprise data governance built-in. Your raw rows never leave your computer. Only column schemas and sanitized metadata are sent to LLMs for code synthesis.", "p6_tag": "Air-Gapped & Offline", "spotlight_kicker": "02 / GROUND TRUTH VERIFICATION", "spotlight_title": "The Row Under The Line", "spotlight_desc": "A table can hold an answer and still hide it. Datly ensures the question, the chart, and the exact computed data rows always remain visible together.", "s1_title": "Explore: The Synchronized Check", "s1_desc": "When a question is asked (e.g. Closing price for GOOGL and MSFT), Datly computes a new derived table (stock-close) and plots it. The grid directly under the curve displays the exact values. If the model made an error, you catch it instantly before sharing.", "s1_tag1": "Interactive Zoom", "s1_tag2": "CSV Download", "s1_tag3": "Curve / Step / Points", "s2_title": "Dashboards & Reporting Without Copying", "s2_desc": "Pinning charts into dashboards or embedding them into TipTap narrative reports does not duplicate data. All surfaces share live references to the underlying data thread DAG. Close the canvas anytime—your derived tables stay persistent.", "s2_tag1": "Multi-Card Pinboard", "s2_tag2": "PDF Export", "s2_tag3": "Linked DAG State", "connectors_kicker": "03 / ENTERPRISE DATA CONNECTORS", "connectors_title": "Connect Once. Query Locally.", "connectors_desc": "Incorporate live tables from your data lakes, warehouses, relational databases, cloud buckets, and live web sources.", "filter_all": "All Connectors (15+)", "filter_warehouse": "Warehouses & Lakes", "filter_sql": "Relational SQL", "filter_cloud": "NoSQL & Cloud", "filter_files": "Files & Web", "compare_kicker": "04 / ARCHITECTURAL COMPARISON", "compare_title": "Why Teams Choose Datly", "compare_desc": "A side-by-side technical evaluation against traditional data exploration tools.", "compare_th_feature": "Feature / Security Control", "compare_th_jupyter": "Jupyter Notebooks", "compare_th_bi": "Legacy Cloud BI (Tableau / PowerBI)", "compare_th_dbeaver": "SQL IDEs (DBeaver)", "compare_th_datly": "DATLY IDE", "compare_r1_title": "Natural Language AI Synthesis", "compare_r1_jupyter": "⚠️ Third-party plugin", "compare_r1_bi": "⚠️ Costly proprietary copilot", "compare_r1_dbeaver": "❌ None or basic autocompletion", "compare_r1_datly": "⚡ Native Grounded Agent (@table)", "compare_r2_title": "Ground-Truth Row Grid Under Chart", "compare_r2_jupyter": "❌ Separate cell output", "compare_r2_bi": "❌ Complex nested drill-down", "compare_r2_dbeaver": "❌ Manual query required", "compare_r2_datly": "🛡️ Always Visible on Canvas", "compare_r3_title": "Visual Encoding Shelves + Agent", "compare_r3_jupyter": "❌ Code cells only", "compare_r3_bi": "⚠️ Shelves only (No Agent)", "compare_r3_dbeaver": "❌ Code only", "compare_r3_datly": "✅ Bi-Directional Shelves + Agent", "compare_r4_title": "Local Data Privacy (Zero Exfiltration)", "compare_r4_jupyter": "✅ Local execution", "compare_r4_bi": "❌ Cloud ingestion required", "compare_r4_dbeaver": "✅ Local query", "compare_r4_datly": "🔒 100% Local (Schema-Only to LLM)", "compare_r5_title": "Data Lineage Architecture", "compare_r5_jupyter": "❌ Linear, stateful cell traps", "compare_r5_bi": "❌ Static data models", "compare_r5_dbeaver": "❌ Query history list", "compare_r5_datly": "🌲 Branching Data Threads (DAG)", "compare_r6_title": "In-Memory Vectorized Engine", "compare_r6_jupyter": "⚠️ Kernel dependent", "compare_r6_bi": "⚠️ Proprietary VertiPaq/Hyper", "compare_r6_dbeaver": "❌ Remote DB dependent", "compare_r6_datly": "⚡ DuckDB + Pandas + PyArrow", "compare_r7_title": "WYSIWYG Narrative Reporting", "compare_r7_jupyter": "❌ Markdown cells only", "compare_r7_bi": "⚠️ Heavy dashboard canvas", "compare_r7_dbeaver": "❌ None", "compare_r7_datly": "📝 TipTap Rich-Text Editor", "compare_r8_title": "Automated Web Table Scraper", "compare_r8_jupyter": "❌ Manual BeautifulSoup", "compare_r8_bi": "❌ Not supported", "compare_r8_dbeaver": "❌ Not supported", "compare_r8_datly": "🌐 Built-in Headless Playwright", "compare_r9_title": "Model Agnostic (BYOK / Local)", "compare_r9_jupyter": "⚠️ Manual API wiring", "compare_r9_bi": "❌ Vendor lock-in", "compare_r9_dbeaver": "❌ N/A", "compare_r9_datly": "🔓 LiteLLM (Claude, GPT, Ollama)", "quickstart_kicker": "05 / ZERO FRICTION SETUP", "quickstart_title": "Launch Datly in Under 60 Seconds", "quickstart_desc": "Choose your preferred installation method: modern uv package manager, docker container, or 1-prompt AI assistant.", "term_tab_uv": "Astral uv (Fastest)", "term_tab_pip": "Python pip + Yarn", "term_tab_docker": "Docker Compose", "term_tab_agent": "1-Prompt AI Agent", "term_tab_desktop": "Desktop Build", "releases_kicker": "06 / VERIFIED RELEASES", "releases_title": "Download Desktop Packages", "releases_desc": "Native standalone performance for Windows, macOS, and Linux with automated hardware acceleration.", "releases_box_title": "Datly Desktop Suite v0.8.0-beta", "releases_box_desc": "Electron 43 Native Container · DuckDB Embedded Engine · Multi-LLM Provider", "releases_btn_title": "View All Release Assets", "releases_btn_sub": "Win · macOS · Linux", "faq_kicker": "07 / FREQUENTLY ASKED QUESTIONS", "faq_title": "Everything You Need to Know", "faq_desc": "Clear answers to technical, security, and integration questions.", "faq_q1": "Does Datly send my private database records to OpenAI, Anthropic, or external servers?", "faq_a1": "No. Datly enforces a strict local-first privacy boundary. When you query databases or load data, tables reside exclusively in local memory and DuckDB. When you ask questions in natural language, Datly transmits only column names and data types (with an optional 2-row sanitized preview) to the model for code synthesis. The synthesized transformation code runs 100% locally on your computer. Your raw records never leave your machine.", "faq_q2": "Can I run Datly completely offline without any internet connection?", "faq_a2": "Yes. Datly features native integration with local open-weights model runners via Ollama, vLLM, or LocalAI. By pointing Datly to your local Ollama instance (e.g., running qwen2.5-coder:14b or llama3.3:70b), you can conduct exploratory data science and build visualizations in an entirely air-gapped environment.", "faq_q3": "How does Datly prevent AI chart hallucinations and false trends?", "faq_a3": "Unlike conversational AI tools that directly generate static image pixels or guess summary values, Datly requires the AI to write verifiable Python or DuckDB SQL transformation steps. Those steps execute on your physical data to produce a tangible derived data table. In the Explore Canvas, that derived table sits right under the chart, giving you full row-by-row mathematical verification.", "faq_q4": "How does Datly differ from Microsoft Research Data Formulator?", "faq_a4": "Datly builds upon the groundbreaking concept encoding and data thread foundations established by Microsoft Research's Data Formulator, elevating it into a comprehensive, enterprise-ready data IDE. Datly introduces 15+ native cloud warehouse connectors (Databricks, BigQuery, Snowflake, Postgres), TipTap WYSIWYG narrative report publishing, multi-chart pinboard dashboards, cross-platform Electron desktop distribution, automated web table scraping via Playwright, and enterprise Single Sign-On (SSO).", "faq_q5": "How do Data Threads prevent the state corruption common in Jupyter notebooks?", "faq_a5": "Jupyter notebooks execute cells sequentially against a mutable global namespace. Modifying an earlier cell can break subsequent calculations in subtle, hidden ways. Datly replaces this with a Directed Acyclic Graph (DAG) of Data Threads. Each inquiry branches into an isolated thread card containing its own input reference, transformation step, derived table, and chart. Original datasets remain strictly immutable.", "footer_tagline": "High-Assurance AI Data Visualization IDE & Analytical Workbench · Free & Open Source Under MIT", "footer_docs": "Documentation", "footer_license": "MIT License", "footer_bottom": "© 2026 Datly Project · High-Assurance AI Data Visualization IDE & Analytical Workbench · Free & Open Source Under MIT", "toast_copy": "Copied to clipboard!"}, "es": {"nav_features": "Características", "nav_canvas": "Verificación", "nav_connectors": "Conectores (15+)", "nav_comparison": "Comparación", "nav_quickstart": "Inicio Rápido", "nav_faq": "Preguntas Frecuentes", "nav_star": "Favorito", "nav_quickstart_btn": "Inicio Rápido", "hero_badge": "IDE de Datos con IA y Banco de Análisis de Próxima Generación", "hero_title_1": "Habla con tus datos.", "hero_title_2": "Verifica cada fila sin errores.", "hero_desc": "Datly es el IDE de análisis local y de código abierto donde el lenguaje natural transforma tablas empresariales en gráficos interactivos, manteniendo las filas de datos calculadas directamente en pantalla. Cero filtración de datos en la nube. Cero alucinaciones.", "hero_btn_quickstart": "Comenzar en 1 Comando", "hero_btn_sub": "uv run data_formulator", "hero_btn_github": "Ver en GitHub", "hero_btn_desktop": "App de Escritorio", "trust_local": "100% Procesamiento Local (DuckDB en Memoria)", "trust_connectors": "15+ Conectores Empresariales", "trust_privacy": "Cero Filtración a la Nube", "trust_models": "Compatible con Múltiples Modelos (Claude, GPT, Ollama)", "ide_status": "DuckDB en Memoria (Local)", "ide_tab_ide": "IDE Completo de Análisis", "ide_tab_explore": "Lienzo de Verificación de Datos", "ide_tab_connectors": "15+ Conectores de Datos", "ide_tab_dashboards": "Paneles Multigráficos", "ide_tab_reports": "Informes Narrativos TipTap", "pillars_kicker": "01 / PILARES ARQUITECTÓNICOS", "pillars_title": "Rigor de Ingeniería y Exploración Fluida", "pillars_desc": "Diseñado desde la raíz para resolver la corrupción de estado en notebooks y la rigidez de las herramientas BI tradicionales.", "p1_title": "Hilos de Datos No Destructivos", "p1_desc": "Cada consulta genera una rama de hilo aislada (DAG). Las preguntas de seguimiento se calculan sobre tablas derivadas sin alterar los datos originales ni sobrescribir estados.", "p1_tag": "Máquina de Estados de Linaje", "p2_title": "Lienzo de Verificación de Datos", "p2_desc": "Olvídate de las alucinaciones de IA. Cada gráfico se crea a partir de una tabla derivada física visible directamente bajo la visualización. Inspecciona cada celda y descarga CSVs verificados.", "p2_tag": "Cero Métricas Fantasma", "p3_title": "Motor DuckDB en Memoria", "p3_desc": "Ejecución vectorial ultrarrápida impulsada por DuckDB, Pandas y PyArrow. Ejecuta agregaciones complejas y uniones sobre millones de filas localmente en milisegundos.", "p3_tag": "Analítica Vectorizada", "p4_title": "Estantes de Codificación Conceptual", "p4_desc": "Arrastra y suelta campos en estantes visuales intuitivos: X, Y, Color, Tamaño, Facetas y Repetición. Sincronización bidireccional entre prompts de lenguaje natural y especificaciones Vega-Lite.", "p4_tag": "Gramática de Gráficos", "p5_title": "Informes Narrativos TipTap", "p5_desc": "Convierte gráficos exploratorios en documentos ejecutivos listos para presentar. Editor WYSIWYG enriquecido que combina comentarios narrativos con tarjetas visuales dinámicas.", "p5_tag": "Publicación Ejecutiva", "p6_title": "Privacidad Local Primero", "p6_desc": "Gobernanza de datos empresarial nativa. Tus filas nunca salen de tu ordenador. Solo se envían esquemas y metadatos limpios a los LLMs para sintetizar código.", "p6_tag": "Aislado y Sin Conexión", "spotlight_kicker": "02 / VERIFICACIÓN DE DATOS", "spotlight_title": "La Fila Debajo de la Línea", "spotlight_desc": "Una tabla puede contener la respuesta y aun así ocultarla. Datly garantiza que la pregunta, el gráfico y las filas calculadas permanezcan visibles juntos.", "s1_title": "Exploración: La Verificación Sincronizada", "s1_desc": "Al consultar (ej. Precio de cierre de GOOGL y MSFT), Datly genera una nueva tabla derivada y la grafica. La cuadrícula bajo la curva muestra los valores exactos para validar antes de compartir.", "s1_tag1": "Zoom Interactivo", "s1_tag2": "Descarga CSV", "s1_tag3": "Curva / Escalonado / Puntos", "s2_title": "Paneles e Informes sin Duplicar Datos", "s2_desc": "Fijar gráficos en paneles o insertarlos en informes TipTap no duplica datos. Todas las vistas comparten referencias activas al DAG de datos.", "s2_tag1": "Tablero Multitarjeta", "s2_tag2": "Exportación PDF", "s2_tag3": "Estado DAG Vinculado", "connectors_kicker": "03 / CONECTORES EMPRESARIALES", "connectors_title": "Conecta Una Vez. Consulta Localmente.", "connectors_desc": "Incorpora tablas activas desde tus data lakes, almacenes de datos, bases de datos relacionales y páginas web.", "filter_all": "Todos los Conectores (15+)", "filter_warehouse": "Almacenes y Lakes", "filter_sql": "SQL Relacional", "filter_cloud": "NoSQL y Nube", "filter_files": "Archivos y Web", "compare_kicker": "04 / COMPARATIVA TÉCNICA", "compare_title": "Por Qué los Equipos Eligen Datly", "compare_desc": "Evaluación técnica detallada frente a herramientas tradicionales de exploración de datos.", "compare_th_feature": "Característica / Control", "compare_th_jupyter": "Notebooks Jupyter", "compare_th_bi": "BI en Nube Tradicional", "compare_th_dbeaver": "IDEs SQL (DBeaver)", "compare_th_datly": "DATLY IDE", "compare_r1_title": "Síntesis con IA en Lenguaje Natural", "compare_r1_jupyter": "⚠️ Plugins de terceros", "compare_r1_bi": "⚠️ Copilotos propietarios costosos", "compare_r1_dbeaver": "❌ Sin IA o autocompletado básico", "compare_r1_datly": "⚡ Agente Nativo con Anclaje (@tabla)", "compare_r2_title": "Cuadrícula de Filas Bajo el Gráfico", "compare_r2_jupyter": "❌ Salida en celda separada", "compare_r2_bi": "❌ Navegación anidada compleja", "compare_r2_dbeaver": "❌ Consulta manual obligatoria", "compare_r2_datly": "🛡️ Siempre Visible en Pantalla", "compare_r3_title": "Estantes de Codificación + Agente", "compare_r3_jupyter": "❌ Solo celdas de código", "compare_r3_bi": "⚠️ Solo estantes (Sin Agente)", "compare_r3_dbeaver": "❌ Solo código", "compare_r3_datly": "✅ Estantes Bidireccionales + Agente", "compare_r4_title": "Privacidad Local (Cero Filtración)", "compare_r4_jupyter": "✅ Ejecución local", "compare_r4_bi": "❌ Ingesta en la nube obligatoria", "compare_r4_dbeaver": "✅ Consulta local", "compare_r4_datly": "🔒 100% Local (Solo esquemas al LLM)", "compare_r5_title": "Arquitectura de Linaje de Datos", "compare_r5_jupyter": "❌ Estados secuenciales frágiles", "compare_r5_bi": "❌ Modelos de datos estáticos", "compare_r5_dbeaver": "❌ Historial de consultas plano", "compare_r5_datly": "🌲 Hilos de Datos Ramificados (DAG)", "compare_r6_title": "Motor Vectorizado en Memoria", "compare_r6_jupyter": "⚠️ Depende del kernel", "compare_r6_bi": "⚠️ Motor propietario cerrado", "compare_r6_dbeaver": "❌ Depende de la base remota", "compare_r6_datly": "⚡ DuckDB + Pandas + PyArrow", "compare_r7_title": "Informes Narrativos WYSIWYG", "compare_r7_jupyter": "❌ Solo celdas Markdown", "compare_r7_bi": "⚠️ Lienzos de paneles pesados", "compare_r7_dbeaver": "❌ No disponible", "compare_r7_datly": "📝 Editor Enriquecido TipTap", "compare_r8_title": "Extracción Web Automatizada", "compare_r8_jupyter": "❌ Scripts manuales con BS4", "compare_r8_bi": "❌ No compatible", "compare_r8_dbeaver": "❌ No compatible", "compare_r8_datly": "🌐 Motor Playwright Integrado", "compare_r9_title": "Independencia de Modelos (BYOK)", "compare_r9_jupyter": "⚠️ Configuración manual", "compare_r9_bi": "❌ Bloqueo a la nube del proveedor", "compare_r9_dbeaver": "❌ No aplica", "compare_r9_datly": "🔓 LiteLLM (Claude, GPT, Ollama)", "quickstart_kicker": "05 / CONFIGURACIÓN SIN FRICCIÓN", "quickstart_title": "Inicia Datly en Menos de 60 Segundos", "quickstart_desc": "Elige tu método favorito: gestor de paquetes moderno uv, contenedor Docker o asistente de IA en 1 comando.", "term_tab_uv": "Astral uv (Más Rápido)", "term_tab_pip": "Python pip + Yarn", "term_tab_docker": "Docker Compose", "term_tab_agent": "Agente IA en 1 Comando", "term_tab_desktop": "Compilación de Escritorio", "releases_kicker": "06 / VERSIONES VERIFICADAS", "releases_title": "Descargar Paquetes de Escritorio", "releases_desc": "Rendimiento nativo para Windows, macOS y Linux con aceleración por hardware.", "releases_box_title": "Datly Desktop Suite v0.8.0-beta", "releases_box_desc": "Contenedor Electron 43 · Motor DuckDB Incrustado · Multi-LLM", "releases_btn_title": "Ver Todos los Archivos", "releases_btn_sub": "Win · macOS · Linux", "faq_kicker": "07 / PREGUNTAS FRECUENTES", "faq_title": "Todo lo que Necesitas Saber", "faq_desc": "Respuestas claras a preguntas técnicas, de seguridad y de integración.", "faq_q1": "¿Datly envía mis datos privados de bases de datos a OpenAI o Anthropic?", "faq_a1": "No. Datly opera bajo una estricta arquitectura local primero. Cuando consultas bases de datos o cargas archivos, las tablas permanecen exclusivamente en la memoria local y DuckDB. Solo se transmiten nombres de columnas y tipos de datos a los modelos para sintetizar el código. Tus datos reales nunca salen de tu sistema.", "faq_q2": "¿Puedo ejecutar Datly completamente sin conexión a Internet?", "faq_a2": "Sí. Datly se integra nativamente con ejecutores de modelos locales vía Ollama, vLLM o LocalAI (por ejemplo, con qwen2.5-coder o llama3.3), permitiendo análisis de datos en entornos totalmente desconectados.", "faq_q3": "¿Cómo previene Datly las alucinaciones de gráficos en IA?", "faq_a3": "A diferencia de las herramientas que generan directamente imágenes de gráficos o adivinan métricas, Datly exige que la IA escriba código verificable en Python o DuckDB SQL. Dicho código se ejecuta en tu máquina para crear una tabla física derivada visible bajo el gráfico.", "faq_q4": "¿En qué se diferencia Datly de Microsoft Research Data Formulator?", "faq_a4": "Datly evoluciona la investigación de Data Formulator hacia un IDE empresarial completo, añadiendo más de 15 conectores nativos en la nube, informes enriquecidos TipTap, paneles multigráficos, instaladores nativos de escritorio y arquitectura de cero exfiltración.", "faq_q5": "¿Cómo resuelven los Hilos de Datos la corrupción de estado de Jupyter?", "faq_a5": "Jupyter usa un espacio de nombres global mutable. Datly lo sustituye por un Grafo Acíclico Dirigido (DAG) de Hilos de Datos, donde cada pregunta genera una tarjeta independiente sin alterar las tablas previas.", "footer_tagline": "IDE de Visualización de Datos y Análisis con IA de Alta Seguridad · Código Abierto bajo Licencia MIT", "footer_docs": "Documentación", "footer_license": "Licencia MIT", "footer_bottom": "© 2026 Proyecto Datly · IDE de Análisis y Visualización con IA · Software Libre", "toast_copy": "¡Copiado al portapapeles!"}, "de": {"nav_features": "Funktionen", "nav_canvas": "Verifikation", "nav_connectors": "Konnektoren (15+)", "nav_comparison": "Vergleich", "nav_quickstart": "Schnellstart", "nav_faq": "FAQ", "nav_star": "Favorit", "nav_quickstart_btn": "Schnellstart", "hero_badge": "KI-Daten-IDE & Analyse-Workbench der nächsten Generation", "hero_title_1": "Sprich mit deinen Daten.", "hero_title_2": "Verifiziere jede einzelne Zeile.", "hero_desc": "Datly ist die lokale Open-Source-Analyse-IDE, in der natürliche Sprache Unternehmenstabellen in interaktive Diagramme verwandelt – und die berechneten Datenzeilen direkt auf dem Bildschirm unter der Linie bleiben. Keine Cloud-Datenübertragung. Keine Halluzinationen.", "hero_btn_quickstart": "Mit 1 Befehl starten", "hero_btn_sub": "uv run data_formulator", "hero_btn_github": "Auf GitHub ansehen", "hero_btn_desktop": "Desktop-App", "trust_local": "100% lokale Verarbeitung (DuckDB im Speicher)", "trust_connectors": "15+ Unternehmenskonnektoren", "trust_privacy": "Null Cloud-Datenabfluss", "trust_models": "Modellunabhängig (Claude, GPT, Ollama)", "ide_status": "DuckDB In-Memory (Lokal)", "ide_tab_ide": "Vollständige Daten-IDE", "ide_tab_explore": "Ground-Truth-Prüfleiste", "ide_tab_connectors": "15+ Datenkonnektoren", "ide_tab_dashboards": "Multi-Diagramm-Dashboards", "ide_tab_reports": "TipTap-Berichte", "pillars_kicker": "01 / ARCHITEKTUR-SÄULEN", "pillars_title": "Technische Präzision trifft flüssige Exploration", "pillars_desc": "Von Grund auf entwickelt, um Statusverluste in Notebooks und die Schwerfälligkeit traditioneller BI-Tools zu überwinden.", "p1_title": "Zerstörungsfreie Daten-Threads", "p1_desc": "Jede Frage erzeugt einen isolierten Thread-Zweig (DAG). Folgefragen berechnen auf abgeleiteten Tabellen, ohne Rohdaten zu überschreiben.", "p1_tag": "Daten-Lineage-Zustandsmaschine", "p2_title": "Ground-Truth-Prüfleiste", "p2_desc": "Keine KI-Halluzinationen mehr. Jedes Diagramm basiert auf einer realen abgeleiteten Tabelle, die direkt darunter sichtbar bleibt.", "p2_tag": "Keine Phantom-Metriken", "p3_title": "DuckDB In-Memory-Engine", "p3_desc": "Blitzschnelle vektorisierte Abfrageausführung mit DuckDB, Pandas und PyArrow. Millionen Zeilen lokal in Millisekunden aggregieren.", "p3_tag": "Vektorisierte Analytik", "p4_title": "Konzept-Codierungs-Leisten", "p4_desc": "Felder per Drag & Drop in visuelle Leisten ziehen: X, Y, Farbe, Größe und Facetten. Bidirektionale Synchronisation mit Vega-Lite.", "p4_tag": "Grammatik der Grafiken", "p5_title": "TipTap-Berichts-Editor", "p5_desc": "Verwandle Diagramme in präsentationsreife Berichte. Ein WYSIWYG-Editor für Analysetexte mit live eingebundenen Diagrammkarten.", "p5_tag": "Executive Publishing", "p6_title": "Datenschutz durch Local-First", "p6_desc": "Deine Datensätze verlassen deinen Rechner nie. Nur Spaltenstrukturen und bereinigte Metadaten werden an LLMs übertragen.", "p6_tag": "Offline & DSGVO-konform", "spotlight_kicker": "02 / GROUND-TRUTH-VERIFIKATION", "spotlight_title": "Die Zeile unter der Kurve", "spotlight_desc": "Datly stellt sicher, dass Frage, Visualisierung und berechnete Datenzeilen stets zusammen auf dem Bildschirm bleiben.", "s1_title": "Explore: Die synchrone Prüfung", "s1_desc": "Auf eine Frage hin erstellt Datly eine abgeleitete Tabelle und zeichnet sie. Die Zeilen unter der Kurve zeigen exakte Werte.", "s1_tag1": "Interaktiver Zoom", "s1_tag2": "CSV-Download", "s1_tag3": "Kurve / Stufen / Punkte", "s2_title": "Dashboards ohne Datenkopien", "s2_desc": "Diagramme im Dashboard anheften oder in Berichte einbetten dupliziert keine Daten. Alle Ansichten teilen denselben Daten-Thread-DAG.", "s2_tag1": "Multi-Card-Pinboard", "s2_tag2": "PDF-Export", "s2_tag3": "DAG-Zustand verknüpft", "connectors_kicker": "03 / UNTERNEHMENSKONNEKTOREN", "connectors_title": "Einmal verbinden. Lokal abfragen.", "connectors_desc": "Binde Live-Tabellen aus Data Lakes, Warehouses, SQL-Datenbanken und Webtabellen direkt ein.", "filter_all": "Alle Konnektoren (15+)", "filter_warehouse": "Warehouses & Lakes", "filter_sql": "Relationale SQL", "filter_cloud": "NoSQL & Cloud", "filter_files": "Dateien & Web", "compare_kicker": "04 / ARCHITEKTURVERGLEICH", "compare_title": "Warum Teams Datly wählen", "compare_desc": "Technischer Direktvergleich mit traditionellen Datenwerkzeugen.", "compare_th_feature": "Funktion / Sicherheitskontrolle", "compare_th_jupyter": "Jupyter Notebooks", "compare_th_bi": "Klassische Cloud-BI", "compare_th_dbeaver": "SQL-IDEs (DBeaver)", "compare_th_datly": "DATLY IDE", "compare_r1_title": "KI-Synthese in natürlicher Sprache", "compare_r1_jupyter": "⚠️ Drittanbieter-Plugins", "compare_r1_bi": "⚠️ Teure proprietäre Assistenten", "compare_r1_dbeaver": "❌ Keine oder nur Autovervollständigung", "compare_r1_datly": "⚡ Nativer Agent mit Tabellenbezug (@table)", "compare_r2_title": "Datenraster direkt unter dem Diagramm", "compare_r2_jupyter": "❌ Getrennte Zellenausgabe", "compare_r2_bi": "❌ Verschachtelte Drill-Downs", "compare_r2_dbeaver": "❌ Manuelle Abfrage nötig", "compare_r2_datly": "🛡️ Stets auf dem Canvas sichtbar", "compare_r3_title": "Codierungsleisten + Agent", "compare_r3_jupyter": "❌ Nur Codezellen", "compare_r3_bi": "⚠️ Nur Leisten (kein Agent)", "compare_r3_dbeaver": "❌ Nur Code", "compare_r3_datly": "✅ Bidirektionale Leisten + Agent", "compare_r4_title": "Lokale Privatsphäre (Kein Abfluss)", "compare_r4_jupyter": "✅ Lokale Ausführung", "compare_r4_bi": "❌ Cloud-Ingestion erforderlich", "compare_r4_dbeaver": "✅ Lokale Abfrage", "compare_r4_datly": "🔒 100% lokal (Nur Schema an LLM)", "compare_r5_title": "Daten-Lineage-Architektur", "compare_r5_jupyter": "❌ Fragiler linearer Zustand", "compare_r5_bi": "❌ Statische Datenmodelle", "compare_r5_dbeaver": "❌ Einfache Abfragehistorie", "compare_r5_datly": "🌲 Verzweigende Daten-Threads (DAG)", "compare_r6_title": "Vektorisierte In-Memory-Engine", "compare_r6_jupyter": "⚠️ Kernel-abhängig", "compare_r6_bi": "⚠️ Proprietäre geschlossene Engine", "compare_r6_dbeaver": "❌ Remote-DB-abhängig", "compare_r6_datly": "⚡ DuckDB + Pandas + PyArrow", "compare_r7_title": "WYSIWYG-Berichte", "compare_r7_jupyter": "❌ Nur Markdown-Zellen", "compare_r7_bi": "⚠️ Komplexe Dashboard-Leinwände", "compare_r7_dbeaver": "❌ Nicht vorhanden", "compare_r7_datly": "📝 TipTap Rich-Text-Editor", "compare_r8_title": "Automatisierter Webtabellen-Scraper", "compare_r8_jupyter": "❌ Manuelle Skripte mit BS4", "compare_r8_bi": "❌ Nicht unterstützt", "compare_r8_dbeaver": "❌ Nicht unterstützt", "compare_r8_datly": "🌐 Integriertes Playwright", "compare_r9_title": "Modellunabhängig (BYOK / Lokal)", "compare_r9_jupyter": "⚠️ Manuelle Anbindung", "compare_r9_bi": "❌ Cloud-Lock-in", "compare_r9_dbeaver": "❌ Nicht zutreffend", "compare_r9_datly": "🔓 LiteLLM (Claude, GPT, Ollama)", "quickstart_kicker": "05 / REIBUNGSLOSE EINRICHTUNG", "quickstart_title": "In unter 60 Sekunden starten", "quickstart_desc": "Wähle deine bevorzugte Methode: modernes uv, Docker-Container oder 1-Befehl-KI-Agent.", "term_tab_uv": "Astral uv (Am schnellsten)", "term_tab_pip": "Python pip + Yarn", "term_tab_docker": "Docker Compose", "term_tab_agent": "1-Befehl-KI-Agent", "term_tab_desktop": "Desktop-Build", "releases_kicker": "06 / VERIFIZIERTE RELEASES", "releases_title": "Desktop-Pakete herunterladen", "releases_desc": "Native Performance für Windows, macOS und Linux mit Hardwarebeschleunigung.", "releases_box_title": "Datly Desktop Suite v0.8.0-beta", "releases_box_desc": "Electron 43 Container · DuckDB Embedded · Multi-LLM", "releases_btn_title": "Alle Release-Dateien", "releases_btn_sub": "Win · macOS · Linux", "faq_kicker": "07 / HÄUFIG GESTELLTE FRAGEN", "faq_title": "Alles Wichtige im Überblick", "faq_desc": "Fundierte Antworten zu Technik, Sicherheit und Integrationen.", "faq_q1": "Werden meine Datenbankdaten an OpenAI oder Anthropic gesendet?", "faq_a1": "Nein. Datly speichert alle Daten ausschließlich im lokalen Speicher und DuckDB. Für die Code-Generierung werden nur Spaltennamen und Datentypen an das gewählte Modell übermittelt. Echte Zeilen verlassen deinen Computer nie.", "faq_q2": "Kann ich Datly vollständig offline nutzen?", "faq_a2": "Ja. Über lokale Open-Source-Modelle via Ollama oder vLLM (z.B. qwen2.5-coder) funktioniert Datly komplett ohne Internetverbindung in geschützten Umgebungen.", "faq_q3": "Wie verhindert Datly Halluzinationen bei Diagrammen?", "faq_a3": "Die KI generiert niemals direkt Pixel. Stattdessen schreibt sie prüfbaren Python- oder DuckDB-SQL-Code, der eine physische abgeleitete Tabelle erzeugt, die direkt unter dem Diagramm geprüft werden kann.", "faq_q4": "Wie unterscheidet sich Datly von Microsoft Research Data Formulator?", "faq_a4": "Datly baut auf der Grundlagenforschung auf und erweitert sie zu einer vollwertigen Enterprise-Daten-IDE mit 15+ Cloud-Konnektoren, TipTap-Berichten, Dashboards und Desktop-Apps.", "faq_q5": "Wie lösen Daten-Threads das Zustandsproblem von Jupyter?", "faq_a5": "Statt eines fehleranfälligen linearen globalen Zustands nutzt Datly einen gerichteten azyklischen Graphen (DAG). Jede Abfrage bleibt eine isolierte, reproduzierbare Thread-Karte.", "footer_tagline": "Sichere KI-Daten-Visualisierungs-IDE & Analyse-Workbench · Open Source unter MIT-Lizenz", "footer_docs": "Dokumentation", "footer_license": "MIT-Lizenz", "footer_bottom": "© 2026 Datly Project · Open Source", "toast_copy": "In die Zwischenablage kopiert!"}, "it": {"nav_features": "Funzionalità", "nav_canvas": "Verifica Dati", "nav_connectors": "Connettori (15+)", "nav_comparison": "Confronto", "nav_quickstart": "Guida Rapida", "nav_faq": "FAQ", "nav_star": "Preferiti", "nav_quickstart_btn": "Guida Rapida", "hero_badge": "IDE Dati con IA e Workbench di Analisi di Nuova Generazione", "hero_title_1": "Parla con i tuoi dati.", "hero_title_2": "Verifica ogni singola riga.", "hero_desc": "Datly è l'IDE di analisi open source e locale in cui il linguaggio naturale trasforma le tabelle aziendali in grafici interattivi, mantenendo le righe di dati calcolate sempre visibili sullo schermo. Zero esfiltrazione di dati su cloud. Zero allucinazioni.", "hero_btn_quickstart": "Inizia con 1 Comando", "hero_btn_sub": "uv run data_formulator", "hero_btn_github": "Visualizza su GitHub", "hero_btn_desktop": "App Desktop", "trust_local": "100% Elaborazione Locale (DuckDB in memoria)", "trust_connectors": "15+ Connettori Aziendali", "trust_privacy": "Zero Dati nel Cloud", "trust_models": "Supporto Multi-Modello (Claude, GPT, Ollama)", "ide_status": "DuckDB In-Memory (Locale)", "ide_tab_ide": "IDE di Analisi Completo", "ide_tab_explore": "Canvas di Verifica Ground-Truth", "ide_tab_connectors": "15+ Connettori Dati", "ide_tab_dashboards": "Dashboard Multi-Grafico", "ide_tab_reports": "Report Narrativi TipTap", "pillars_kicker": "01 / PILASTRI ARCHITETTURALI", "pillars_title": "Rigore Ingegneristico ed Esplorazione Fluida", "pillars_desc": "Progettato per superare la corruzione di stato dei notebook e la rigidità della tradizionale Business Intelligence.", "p1_title": "Thread di Dati Non Distruttivi", "p1_desc": "Ogni query crea un ramo di thread isolato (DAG). Le domande di approfondimento operano su tabelle derivate senza alterare i dataset originali.", "p1_tag": "Macchina a Stati di Lineage", "p2_title": "Canvas di Verifica Ground-Truth", "p2_desc": "Elimina le allucinazioni dell'IA. Ogni grafico è generato da una tabella fisica derivata visualizzata direttamente sotto il grafico.", "p2_tag": "Zero Metriche Fantasma", "p3_title": "Motore In-Memory DuckDB", "p3_desc": "Esecuzione vettorizzata ultraveloce con DuckDB, Pandas e PyArrow. Esegui aggregazioni e join complessi su milioni di righe in millisecondi.", "p3_tag": "Analitica Vettorizzata", "p4_title": "Scaffali di Codifica Concettuale", "p4_desc": "Trascina i campi nei riquadri visivi: X, Y, Colore, Dimensione e Faccette. Sincronizzazione bidirezionale con le specifiche Vega-Lite.", "p4_tag": "Grammatica della Grafica", "p5_title": "Report Narrativi TipTap", "p5_desc": "Trasforma i grafici in report pronti per i dirigenti. Un editor WYSIWYG completo per unire commenti narrativi e grafici dinamici.", "p5_tag": "Pubblicazione Esecutiva", "p6_title": "Privacy Locale per Design", "p6_desc": "I tuoi record non lasciano mai il computer. Vengono inviati solo schemi di colonne e metadati minimi per sintetizzare il codice.", "p6_tag": "Isolato e Offline", "spotlight_kicker": "02 / VERIFICA DEI DATI", "spotlight_title": "La Riga Sotto la Curva", "spotlight_desc": "Datly garantisce che la domanda, la visualizzazione e le righe calcolate rimangano sempre visibili insieme.", "s1_title": "Explore: Il Controllo Sincronizzato", "s1_desc": "Quando fai una domanda, Datly calcola una tabella derivata e la disegna. La griglia sotto la curva mostra i valori esatti.", "s1_tag1": "Zoom Interattivo", "s1_tag2": "Download CSV", "s1_tag3": "Curva / Gradini / Punti", "s2_title": "Dashboard senza Duplicare Dati", "s2_desc": "Fissare grafici nelle dashboard o inserirli nei report TipTap non crea copie inutili. Tutte le superfici condividono il medesimo DAG.", "s2_tag1": "Pinboard Multi-Card", "s2_tag2": "Esportazione PDF", "s2_tag3": "Stato DAG Collegato", "connectors_kicker": "03 / CONNETTORI AZIENDALI", "connectors_title": "Connetti Una Volta. Interroga Localmente.", "connectors_desc": "Integra tabelle da data lake, data warehouse, database relazionali e pagine web.", "filter_all": "Tutti i Connettori (15+)", "filter_warehouse": "Warehouse & Lake", "filter_sql": "SQL Relazionale", "filter_cloud": "NoSQL & Cloud", "filter_files": "File & Web", "compare_kicker": "04 / CONFRONTO ARCHITETTURALE", "compare_title": "Perché i Team Scelgono Datly", "compare_desc": "Valutazione tecnica dettagliata rispetto agli strumenti tradizionali di esplorazione dati.", "compare_th_feature": "Funzionalità / Controllo", "compare_th_jupyter": "Notebook Jupyter", "compare_th_bi": "Cloud BI Tradizionale", "compare_th_dbeaver": "IDE SQL (DBeaver)", "compare_th_datly": "DATLY IDE", "compare_r1_title": "Sintesi IA in Linguaggio Naturale", "compare_r1_jupyter": "⚠️ Plugin di terze parti", "compare_r1_bi": "⚠️ Copilot proprietari costosi", "compare_r1_dbeaver": "❌ Nessuno o autocompletamento base", "compare_r1_datly": "⚡ Agente Nativo con Riferimento (@tabella)", "compare_r2_title": "Griglia di Righe Sotto il Grafico", "compare_r2_jupyter": "❌ Output su cella separata", "compare_r2_bi": "❌ Drill-down nidificato complesso", "compare_r2_dbeaver": "❌ Query manuale richiesta", "compare_r2_datly": "🛡️ Sempre Visibile sul Canvas", "compare_r3_title": "Scaffali di Codifica + Agente", "compare_r3_jupyter": "❌ Solo celle di codice", "compare_r3_bi": "⚠️ Solo scaffali (senza Agente)", "compare_r3_dbeaver": "❌ Solo codice", "compare_r3_datly": "✅ Scaffali Bidirezionali + Agente", "compare_r4_title": "Privacy Locale (Zero Esfiltrazione)", "compare_r4_jupyter": "✅ Esecuzione locale", "compare_r4_bi": "❌ Ingestione cloud obbligatoria", "compare_r4_dbeaver": "✅ Query locale", "compare_r4_datly": "🔒 100% Locale (Solo schema al LLM)", "compare_r5_title": "Architettura di Lineage Dati", "compare_r5_jupyter": "❌ Stato lineare fragile", "compare_r5_bi": "❌ Modelli dati statici", "compare_r5_dbeaver": "❌ Cronologia query piatta", "compare_r5_datly": "🌲 Thread di Dati Ramificati (DAG)", "compare_r6_title": "Motore Vettorizzato In-Memory", "compare_r6_jupyter": "⚠️ Dipende dal kernel", "compare_r6_bi": "⚠️ Motore proprietario chiuso", "compare_r6_dbeaver": "❌ Dipende dal DB remoto", "compare_r6_datly": "⚡ DuckDB + Pandas + PyArrow", "compare_r7_title": "Report Narrativi WYSIWYG", "compare_r7_jupyter": "❌ Solo celle Markdown", "compare_r7_bi": "⚠️ Canvas dashboard pesanti", "compare_r7_dbeaver": "❌ Non supportato", "compare_r7_datly": "📝 Editor Rich-Text TipTap", "compare_r8_title": "Scraping Tabelle Web Automatizzato", "compare_r8_jupyter": "❌ Script manuali con BS4", "compare_r8_bi": "❌ Non supportato", "compare_r8_dbeaver": "❌ Non supportato", "compare_r8_datly": "🌐 Motore Playwright Integrato", "compare_r9_title": "Indipendenza dai Modelli (BYOK)", "compare_r9_jupyter": "⚠️ Configurazione manuale", "compare_r9_bi": "❌ Vincolo al cloud del fornitore", "compare_r9_dbeaver": "❌ Non applicabile", "compare_r9_datly": "🔓 LiteLLM (Claude, GPT, Ollama)", "quickstart_kicker": "05 / CONFIGURAZIONE SEMPLICE", "quickstart_title": "Avvia Datly in Meno di 60 Secondi", "quickstart_desc": "Scegli il tuo metodo: gestore pacchetti uv, container Docker o prompt IA a comando singolo.", "term_tab_uv": "Astral uv (Più Veloce)", "term_tab_pip": "Python pip + Yarn", "term_tab_docker": "Docker Compose", "term_tab_agent": "Agente IA in 1 Comando", "term_tab_desktop": "Build Desktop", "releases_kicker": "06 / RELEASE VERIFICATE", "releases_title": "Scarica Pacchetti Desktop", "releases_desc": "Prestazioni native per Windows, macOS e Linux con accelerazione hardware.", "releases_box_title": "Datly Desktop Suite v0.8.0-beta", "releases_box_desc": "Container Electron 43 · Motore DuckDB Integrato · Multi-LLM", "releases_btn_title": "Visualizza Tutti i File", "releases_btn_sub": "Win · macOS · Linux", "faq_kicker": "07 / DOMANDE FREQUENTI", "faq_title": "Tutto Quello che Devi Sapere", "faq_desc": "Risposte chiare a domande tecniche, di sicurezza e integrazione.", "faq_q1": "Datly invia i dati privati del mio database a OpenAI o Anthropic?", "faq_a1": "No. Datly opera secondo una rigorosa architettura locale. Le tabelle risiedono esclusivamente nella memoria locale e in DuckDB. Ai modelli vengono inviati solo i nomi delle colonne per generare il codice SQL o Python, che viene eseguito sulla tua macchina.", "faq_q2": "Posso usare Datly completamente offline senza connessione Internet?", "faq_a2": "Sì. Grazie all'integrazione con runner di modelli locali come Ollama o vLLM (es. qwen2.5-coder), Datly funziona perfettamente in ambienti isolati (air-gapped).", "faq_q3": "In che modo Datly evita le allucinazioni nei grafici?", "faq_a3": "Datly non consente all'IA di disegnare direttamente i pixel. L'IA scrive codice verificabile che genera una tabella reale derivata, posizionata direttamente sotto il grafico per un controllo trasparente.", "faq_q4": "Cosa differenzia Datly da Microsoft Research Data Formulator?", "faq_a4": "Datly evolve la ricerca accademica in un IDE di livello aziendale con oltre 15 connettori cloud, reportistica avanzata TipTap, dashboard multi-card e architettura a zero esfiltrazione.", "faq_q5": "Come risolvono i Thread di Dati i problemi di stato di Jupyter?", "faq_a5": "Sostituendo il namespace globale con un Grafo Aciclico Diretto (DAG). Ogni domanda è un nodo isolato che conserva il proprio input e tabella derivata senza toccare i dati precedenti.", "footer_tagline": "IDE di Visualizzazione Dati e Analitica IA ad Alta Sicurezza · Open Source con Licenza MIT", "footer_docs": "Documentazione", "footer_license": "Licenza MIT", "footer_bottom": "© 2026 Progetto Datly · Open Source", "toast_copy": "Copiato negli appunti!"}, "fr": {"nav_features": "Fonctionnalités", "nav_canvas": "Vérification", "nav_connectors": "Connecteurs (15+)", "nav_comparison": "Comparaison", "nav_quickstart": "Démarrage Rapide", "nav_faq": "FAQ", "nav_star": "Favoris", "nav_quickstart_btn": "Démarrage Rapide", "hero_badge": "IDE de Données IA & Espace d'Analyse Nouvelle Génération", "hero_title_1": "Dialoguez avec vos données.", "hero_title_2": "Vérifiez chaque ligne de calcul.", "hero_desc": "Datly est l'IDE analytique open source et local où le langage naturel transforme les tables d'entreprise en visualisations interactives, les lignes de calcul restant directement sous les yeux. Zéro fuite cloud. Zéro hallucination.", "hero_btn_quickstart": "Démarrer en 1 Commande", "hero_btn_sub": "uv run data_formulator", "hero_btn_github": "Voir sur GitHub", "hero_btn_desktop": "Application Desktop", "trust_local": "100% Traitement Local (DuckDB en mémoire)", "trust_connectors": "15+ Connecteurs Entreprise", "trust_privacy": "Zéro Exfiltration de Données", "trust_models": "Compatible Multi-Modèles (Claude, GPT, Ollama)", "ide_status": "DuckDB En Mémoire (Local)", "ide_tab_ide": "IDE Analytique Complet", "ide_tab_explore": "Canevas de Contrôle Ground-Truth", "ide_tab_connectors": "15+ Connecteurs de Données", "ide_tab_dashboards": "Tableaux de Bord Multi-Graphiques", "ide_tab_reports": "Rapports Narratifs TipTap", "pillars_kicker": "01 / PILIERS D'ARCHITECTURE", "pillars_title": "Rigueur Technique & Exploration Fluide", "pillars_desc": "Conçu pour éliminer les corruptions d'état des notebooks et la lourdeur des outils BI traditionnels.", "p1_title": "Fils de Données Non-Destructifs", "p1_desc": "Chaque question crée une branche de fil isolée (DAG). Les approfondissements s'exécutent sur des tables dérivées sans altérer les jeux de données originaux.", "p1_tag": "Machine à États de Lignage", "p2_title": "Canevas de Vérification Directe", "p2_desc": "Adieu les hallucinations d'IA. Chaque graphique repose sur une table physique dérivée affichée directement sous la visualisation.", "p2_tag": "Zéro Métrique Fantôme", "p3_title": "Moteur Vectoriel DuckDB", "p3_desc": "Exécution vectorisée ultra-rapide avec DuckDB, Pandas et PyArrow. Agrégations et jointures sur des millions de lignes localement en quelques millisecondes.", "p3_tag": "Analytique Vectorisée", "p4_title": "Rayonnages d'Encodage Conceptuel", "p4_desc": "Glissez-déposez des champs dans les étagères visuelles : X, Y, Couleur, Taille et Facettes. Synchronisation bidirectionnelle avec Vega-Lite.", "p4_tag": "Grammaire des Graphiques", "p5_title": "Rapports Narratifs TipTap", "p5_desc": "Transformez vos graphiques en rapports prêts pour la direction. Un éditeur WYSIWYG combinant récits textuels et visualisations dynamiques.", "p5_tag": "Publication Décisionnelle", "p6_title": "Confidentialité Locale Native", "p6_desc": "Vos lignes de données ne quittent jamais votre machine. Seuls les schémas de colonnes et des métadonnées anonymisées sont envoyés aux LLMs.", "p6_tag": "Isolé et Hors Ligne", "spotlight_kicker": "02 / VÉRIFICATION GROUND-TRUTH", "spotlight_title": "La Ligne Sous la Courbe", "spotlight_desc": "Datly s'assure que la question, le graphique et la table calculée restent en permanence synchronisés à l'écran.", "s1_title": "Explore : Le Contrôle Synchrone", "s1_desc": "À chaque question, Datly calcule une nouvelle table dérivée et la trace. Le tableau sous la courbe affiche les valeurs exactes pour validation.", "s1_tag1": "Zoom Interactif", "s1_tag2": "Téléchargement CSV", "s1_tag3": "Courbe / Paliers / Points", "s2_title": "Tableaux de Bord sans Duplication", "s2_desc": "Épingler des graphiques ou les insérer dans des rapports TipTap ne duplique aucune donnée. Toutes les vues partagent le même DAG de données.", "s2_tag1": "Tableau Multi-Cartes", "s2_tag2": "Export PDF", "s2_tag3": "État DAG Lié", "connectors_kicker": "03 / CONNECTEURS ENTREPRISE", "connectors_title": "Connectez Une Fois. 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Datly applique une politique stricte d'exécution locale. Vos données brutes résident exclusivement en mémoire locale et dans DuckDB. Seuls les noms et types de colonnes sont transmis au modèle pour générer le code.", "faq_q2": "Puis-je exécuter Datly totalement hors ligne sans Internet ?", "faq_a2": "Oui. Datly s'intègre avec des moteurs de modèles locaux comme Ollama ou vLLM (ex. qwen2.5-coder ou llama3.3), permettant un usage souverain en environnement déconnecté.", "faq_q3": "Comment Datly élimine-t-il les hallucinations graphiques ?", "faq_a3": "Datly n'autorise pas l'IA à dessiner directement. L'IA génère du code Python ou SQL DuckDB exécuté localement pour produire une table physique dérivée, contrôlable immédiatement sous la courbe.", "faq_q4": "En quoi Datly diffère-t-il de Microsoft Data Formulator ?", "faq_a4": "Datly transforme le prototype de recherche en un IDE d'entreprise complet : 15+ connecteurs cloud, rapports TipTap, tableaux de bord et exécutables desktop.", "faq_q5": "Comment les Fils de Données règlent-ils le problème d'état de Jupyter ?", "faq_a5": "Datly remplace l'état global mutable par un Graphe Acyclique Dirigé (DAG). Chaque question reste une carte isolée sans altérer les calculs précédents.", "footer_tagline": "IDE de Données IA Haute Sécurité & Espace d'Analyse · Open Source sous Licence MIT", "footer_docs": "Documentation", "footer_license": "Licence MIT", "footer_bottom": "© 2026 Projet Datly · Logiciel Libre", "toast_copy": "Copié dans le presse-papiers !"}, "pt": {"nav_features": "Recursos", "nav_canvas": "Verificação", "nav_connectors": "Conectores (15+)", "nav_comparison": "Comparação", "nav_quickstart": "Início Rápido", "nav_faq": "Perguntas Frequentes", "nav_star": "Favorito", "nav_quickstart_btn": "Início Rápido", "hero_badge": "IDE de Dados com IA e Bancada de Análise de Nova Geração", "hero_title_1": "Converse com seus dados.", "hero_title_2": "Verifique cada linha calculada.", "hero_desc": "Datly é o IDE de análise local e de código aberto onde a linguagem natural transforma tabelas empresariais em gráficos interativos, mantendo as linhas de dados calculadas sempre visíveis na tela. 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Editor WYSIWYG rico com texto explicativo e gráficos interativos incorporados.", "p5_tag": "Publicação Executiva", "p6_title": "Privacidade Local por Design", "p6_desc": "Seus registros nunca saem do seu computador. Apenas esquemas de colunas e metadados sanitizados são enviados aos LLMs.", "p6_tag": "Isolado e Offline", "spotlight_kicker": "02 / VERIFICAÇÃO GROUND-TRUTH", "spotlight_title": "A Linha Sob a Curva", "spotlight_desc": "Datly garante que a pergunta, o gráfico e a tabela derivada calculada fiquem sempre visíveis juntos.", "s1_title": "Explore: A Verificação Sincronizada", "s1_desc": "Ao fazer uma pergunta, o Datly calcula uma tabela derivada e a desenha. A grade sob a curva exibe os valores exatos para validação.", "s1_tag1": "Zoom Interativo", "s1_tag2": "Download de CSV", "s1_tag3": "Curva / Degraus / Pontos", "s2_title": "Dashboards sem Duplicar Dados", "s2_desc": "Fixar gráficos em dashboards ou incorporá-los em relatórios TipTap não duplica dados. Todas as visualizações compartilham o mesmo DAG.", "s2_tag1": "Painel Multi-Cartões", "s2_tag2": "Exportação em PDF", "s2_tag3": "Estado DAG Vinculado", "connectors_kicker": "03 / CONECTORES CORPORATIVOS", "connectors_title": "Conecte Uma Vez. Consulte Localmente.", "connectors_desc": "Incorpore tabelas ao vivo de data lakes, warehouses, bancos SQL relacionais e páginas web.", "filter_all": "Todos os Conectores (15+)", "filter_warehouse": "Warehouses e Lakes", "filter_sql": "SQL Relacional", "filter_cloud": "NoSQL e Nuvem", "filter_files": "Arquivos e Web", "compare_kicker": "04 / COMPARAÇÃO TÉCNICA", "compare_title": "Por Que Equipes Escolhem o Datly", "compare_desc": "Avaliação técnica comparativa com ferramentas tradicionais de exploração de dados.", "compare_th_feature": "Recurso / Controle", "compare_th_jupyter": "Notebooks Jupyter", "compare_th_bi": "BI em Nuvem Legado", "compare_th_dbeaver": "IDEs SQL (DBeaver)", "compare_th_datly": "DATLY IDE", "compare_r1_title": "Síntese com IA em Linguagem Natural", "compare_r1_jupyter": "⚠️ Plugins de terceiros", "compare_r1_bi": "⚠️ Copilotos proprietários caros", "compare_r1_dbeaver": "❌ Nenhum ou autocompletar básico", "compare_r1_datly": "⚡ Agente Nativo com Contexto (@tabela)", "compare_r2_title": "Grade de Dados Sob o Gráfico", "compare_r2_jupyter": "❌ Saída em célula separada", "compare_r2_bi": "❌ Drill-down aninhado complexo", "compare_r2_dbeaver": "❌ Consulta manual obrigatória", "compare_r2_datly": "🛡️ Sempre Visível na Tela", "compare_r3_title": "Prateleiras Visuais + Agente", "compare_r3_jupyter": "❌ Apenas células de código", "compare_r3_bi": "⚠️ Apenas prateleiras (Sem Agente)", "compare_r3_dbeaver": "❌ Apenas código", "compare_r3_datly": "✅ Prateleiras Bidirecionais + Agente", "compare_r4_title": "Privacidade Local (Zero Envio)", "compare_r4_jupyter": "✅ Execução local", "compare_r4_bi": "❌ Envio obrigatório para nuvem", "compare_r4_dbeaver": "✅ Consulta local", "compare_r4_datly": "🔒 100% Local (Apenas esquema ao LLM)", "compare_r5_title": "Arquitetura de Linhagem de Dados", "compare_r5_jupyter": "❌ Estados lineares frágeis", "compare_r5_bi": "❌ Modelos de dados estáticos", "compare_r5_dbeaver": "❌ Histórico de consultas simples", "compare_r5_datly": "🌲 Threads de Dados Ramificadas (DAG)", "compare_r6_title": "Motor Vetorizado em Memória", "compare_r6_jupyter": "⚠️ Depende do kernel", "compare_r6_bi": "⚠️ Motor proprietário fechado", "compare_r6_dbeaver": "❌ Depende do banco remoto", "compare_r6_datly": "⚡ DuckDB + Pandas + PyArrow", "compare_r7_title": "Relatórios Narrativos WYSIWYG", "compare_r7_jupyter": "❌ Apenas células Markdown", "compare_r7_bi": "⚠️ Telas de dashboard pesadas", "compare_r7_dbeaver": "❌ Indisponível", "compare_r7_datly": "📝 Editor Rich-Text TipTap", "compare_r8_title": "Raspagem Web Automatizada", "compare_r8_jupyter": "❌ Scripts manuais com BS4", "compare_r8_bi": "❌ Não suportado", "compare_r8_dbeaver": "❌ Não suportado", "compare_r8_datly": "🌐 Motor Playwright Integrado", "compare_r9_title": "Independência de Modelos (BYOK)", "compare_r9_jupyter": "⚠️ Configuração manual", "compare_r9_bi": "❌ Preso à nuvem do fornecedor", "compare_r9_dbeaver": "❌ Não aplicável", "compare_r9_datly": "🔓 LiteLLM (Claude, GPT, Ollama)", "quickstart_kicker": "05 / CONFIGURAÇÃO SIMPLES", "quickstart_title": "Inicie o Datly em Menos de 60 Segundos", "quickstart_desc": "Escolha seu método preferido: uv moderno, container Docker ou assistente de IA em 1 comando.", "term_tab_uv": "Astral uv (Mais Rápido)", "term_tab_pip": "Python pip + Yarn", "term_tab_docker": "Docker Compose", "term_tab_agent": "Agente IA em 1 Comando", "term_tab_desktop": "Compilação para Desktop", "releases_kicker": "06 / VERSÕES VERIFICADAS", "releases_title": "Baixar Pacotes para Desktop", "releases_desc": "Desempenho nativo para Windows, macOS e Linux com aceleração por hardware.", "releases_box_title": "Datly Desktop Suite v0.8.0-beta", "releases_box_desc": "Container Electron 43 · Motor DuckDB Embutido · Multi-LLM", "releases_btn_title": "Ver Todos os Arquivos", "releases_btn_sub": "Win · macOS · Linux", "faq_kicker": "07 / PERGUNTAS FREQUENTES", "faq_title": "Tudo o que Você Precisa Saber", "faq_desc": "Respostas diretas a dúvidas técnicas, de segurança e de integração.", "faq_q1": "O Datly envia dados corporativos confidenciais para OpenAI ou Anthropic?", "faq_a1": "Não. O Datly aplica estrita computação local. Os dados residem apenas na memória RAM e no DuckDB. Para a IA escrever o código de transformação, apenas nomes e tipos de colunas são compartilhados.", "faq_q2": "Posso executar o Datly totalmente offline?", "faq_a2": "Sim. O Datly suporta modelos locais via Ollama, vLLM ou LocalAI (ex.: qwen2.5-coder ou llama3.3), funcionando sem nenhuma conexão com a Internet.", "faq_q3": "Como o Datly evita alucinações de métricas?", "faq_a3": "A IA nunca desenha pixels ou estima valores diretamente. Ela escreve código auditável executado localmente, produzindo uma tabela derivada visível sob o gráfico.", "faq_q4": "Qual a diferença entre o Datly e o Microsoft Research Data Formulator?", "faq_a4": "O Datly transforma a pesquisa original em um IDE corporativo completo: 15+ conectores cloud, relatórios ricos TipTap, dashboards multigráficos e executáveis de desktop.", "faq_q5": "Como as Threads de Dados eliminam o problema de estado do Jupyter?", "faq_a5": "Substituindo o namespace global por um Grafo Acíclico Dirigido (DAG). Cada análise é um cartão isolado sem afetar os dados originais.", "footer_tagline": "IDE de Visualização de Dados e Análise com IA de Alta Segurança · Código Aberto sob Licença MIT", "footer_docs": "Documentação", "footer_license": "Licença MIT", "footer_bottom": "© 2026 Projeto Datly · Software Livre", "toast_copy": "Copiado para a área de transferência!"}, "ru": {"nav_features": "Возможности", "nav_canvas": "Верификация", "nav_connectors": "Коннекторы (15+)", "nav_comparison": "Сравнение", "nav_quickstart": "Быстрый старт", "nav_faq": "FAQ", "nav_star": "Звезда", "nav_quickstart_btn": "Быстрый старт", "hero_badge": "IDE визуализации данных нового поколения и аналитическая среда с ИИ", "hero_title_1": "Общайтесь со своими данными.", "hero_title_2": "Проверяйте каждую строку данных.", "hero_desc": "Datly — это локальная IDE с открытым исходным кодом, где естественный язык превращает корпоративные таблицы в интерактивные графики, а вычисленные строки всегда остаются на экране прямо под визуализацией. Ноль утечек в облако. Ноль галлюцинаций.", "hero_btn_quickstart": "Запуск в 1 команду", "hero_btn_sub": "uv run data_formulator", "hero_btn_github": "GitHub репозиторий", "hero_btn_desktop": "Десктопное приложение", "trust_local": "100% локальная обработка (DuckDB в памяти)", "trust_connectors": "15+ корпоративных коннекторов", "trust_privacy": "Ноль утечек данных", "trust_models": "Мультимодельный выбор (Claude, GPT, Ollama)", "ide_status": "DuckDB In-Memory (Локально)", "ide_tab_ide": "Полная аналитическая IDE", "ide_tab_explore": "Панель проверки данных", "ide_tab_connectors": "15+ коннекторов данных", "ide_tab_dashboards": "Дашборды", "ide_tab_reports": "Отчеты TipTap", "pillars_kicker": "01 / АРХИТЕКТУРА И ПРИНЦИПЫ", "pillars_title": "Инженерная строгость и свобода исследования", "pillars_desc": "Создано с нуля, чтобы навсегда устранить поломки состояний в ноутбуках и жесткость классического BI.", "p1_title": "Неразрушающие цепочки данных", "p1_desc": "Каждый запрос создает изолированную ветку цепочки (DAG). Уточняющие вопросы вычисляются на основе промежуточных таблиц, не повреждая исходные данные.", "p1_tag": "Конечный автомат происхождения данных", "p2_title": "Панель достоверных данных", "p2_desc": "Забудьте о галлюцинациях ИИ. Каждый график строится на реальной производной таблице, видимой прямо под визуализацией.", "p2_tag": "Никаких фантомных метрик", "p3_title": "Движок DuckDB в оперативной памяти", "p3_desc": "Молниеносные векторизованные вычисления на базе DuckDB, Pandas и PyArrow. Агрегация миллионов строк локально за миллисекунды.", "p3_tag": "Векторизованная аналитика", "p4_title": "Полки визуального кодирования", "p4_desc": "Перетаскивайте поля в визуальные полки: X, Y, цвет, размер, фасеты. Двусторонняя синхронизация с Vega-Lite спецификациями.", "p4_tag": "Грамматика графики", "p5_title": "Интерактивные отчеты TipTap", "p5_desc": "Превращайте исследовательские графики в готовые исполнительские отчеты с текстом и интерактивными карточками данных.", "p5_tag": "Исполнительские публикации", "p6_title": "Локальная безопасность по умолчанию", "p6_desc": "Корпоративные данные никогда не покидают ваше устройство. ИИ получает только имена колонок и минимальный пример схемы.", "p6_tag": "Полная изоляция и офлайн", "spotlight_kicker": "02 / ВЕРИФИКАЦИЯ ДАННЫХ", "spotlight_title": "Строка под линией графика", "spotlight_desc": "Datly гарантирует, что вопрос, визуализация и рассчитанные строки данных всегда открыты вместе.", "s1_title": "Explore: Синхронная проверка", "s1_desc": "При вопросе Datly строит производную таблицу и отображает график. Сетка под линией показывает точные цифры для мгновенной сверки.", "s1_tag1": "Интерактивный зум", "s1_tag2": "Скачать CSV", "s1_tag3": "Линия / Ступени / Точки", "s2_title": "Дашборды без дублирования данных", "s2_desc": "Закрепление графиков на дашборде или вставка в отчет не копируют данные. Все экраны ссылаются на общий граф данных DAG.", "s2_tag1": "Дашборд с карточками", "s2_tag2": "Экспорт в PDF", "s2_tag3": "Связанный граф DAG", "connectors_kicker": "03 / КОРПОРАТИВНЫЕ КОННЕКТОРЫ", "connectors_title": "Подключайте однажды. Запрашивайте локально.", "connectors_desc": "Подключайте таблицы из озер данных, хранилищ, реляционных СУБД и веб-страниц.", "filter_all": "Все коннекторы (15+)", "filter_warehouse": "Хранилища и Lakehouse", "filter_sql": "Реляционные SQL", "filter_cloud": "NoSQL и Облако", "filter_files": "Файлы и Веб", "compare_kicker": "04 / СРАВНИТЕЛЬНЫЙ АНАЛИЗ", "compare_title": "Почему команды выбирают Datly", "compare_desc": "Техническое сопоставление с традиционными аналитическими инструментами.", "compare_th_feature": "Возможность / Контроль", "compare_th_jupyter": "Jupyter Notebooks", "compare_th_bi": "Облачный BI (Tableau / PowerBI)", "compare_th_dbeaver": "SQL IDE (DBeaver)", "compare_th_datly": "DATLY IDE", "compare_r1_title": "Синтез на естественном языке с ИИ", "compare_r1_jupyter": "⚠️ Сторонние плагины", "compare_r1_bi": "⚠️ Дорогие проприетарные ассистенты", "compare_r1_dbeaver": "❌ Нет или базовое автодополнение", "compare_r1_datly": "⚡ Нативный агент с контекстом (@таблица)", "compare_r2_title": "Сетка данных прямо под графиком", "compare_r2_jupyter": "❌ Вывод в отдельной ячейке", "compare_r2_bi": "❌ Сложные вложенные переходы", "compare_r2_dbeaver": "❌ Нужен ручной запрос", "compare_r2_datly": "🛡️ Всегда на экране вместе с графиком", "compare_r3_title": "Визуальные полки + Агент", "compare_r3_jupyter": "❌ Только ячейки с кодом", "compare_r3_bi": "⚠️ Только полки (без агента)", "compare_r3_dbeaver": "❌ Только код", "compare_r3_datly": "✅ Двунаправленные полки + Агент", "compare_r4_title": "Локальная конфиденциальность", "compare_r4_jupyter": "✅ Локальное исполнение", "compare_r4_bi": "❌ Обязательная выгрузка в облако", "compare_r4_dbeaver": "✅ Локальный запрос", "compare_r4_datly": "🔒 100% локально (В модель идет только схема)", "compare_r5_title": "Архитектура происхождения данных", "compare_r5_jupyter": "❌ Хрупкое линейное состояние ячеек", "compare_r5_bi": "❌ Статичные модели данных", "compare_r5_dbeaver": "❌ Простой список запросов", "compare_r5_datly": "🌲 Ветвящиеся цепочки данных (DAG)", "compare_r6_title": "Векторизованный in-memory движок", "compare_r6_jupyter": "⚠️ Зависит от ядра", "compare_r6_bi": "⚠️ Закрытый проприетарный движок", "compare_r6_dbeaver": "❌ Зависит от удаленной БД", "compare_r6_datly": "⚡ DuckDB + Pandas + PyArrow", "compare_r7_title": "Интерактивные WYSIWYG отчеты", "compare_r7_jupyter": "❌ Только ячейки Markdown", "compare_r7_bi": "⚠️ Тяжелые дашборды", "compare_r7_dbeaver": "❌ Отсутствует", "compare_r7_datly": "📝 Редактор TipTap с живыми графиками", "compare_r8_title": "Автоматический веб-скрейпинг таблиц", "compare_r8_jupyter": "❌ Ручные скрипты на BS4", "compare_r8_bi": "❌ Не поддерживается", "compare_r8_dbeaver": "❌ Не поддерживается", "compare_r8_datly": "🌐 Встроенный движок Playwright", "compare_r9_title": "Независимость от моделей (BYOK)", "compare_r9_jupyter": "⚠️ Ручная настройка", "compare_r9_bi": "❌ Привязка к вендору", "compare_r9_dbeaver": "❌ Не применимо", "compare_r9_datly": "🔓 LiteLLM (Claude, GPT, Ollama)", "quickstart_kicker": "05 / БЫСТРАЯ УСТАНОВКА", "quickstart_title": "Запуск Datly менее чем за 60 секунд", "quickstart_desc": "Выберите удобный способ: современный менеджер uv, Docker-контейнер или агент ИИ по одной команде.", "term_tab_uv": "Astral uv (Быстрее всего)", "term_tab_pip": "Python pip + Yarn", "term_tab_docker": "Docker Compose", "term_tab_agent": "ИИ агент в 1 команду", "term_tab_desktop": "Сборка десктопа", "releases_kicker": "06 / ПРОВЕРЕННЫЕ РЕЛИЗЫ", "releases_title": "Скачать десктопные пакеты", "releases_desc": "Нативная производительность для Windows, macOS и Linux с аппаратным ускорением.", "releases_box_title": "Datly Desktop Suite v0.8.0-beta", "releases_box_desc": "Контейнер Electron 43 · Встроенный DuckDB · Поддержка всех LLM", "releases_btn_title": "Все файлы релиза", "releases_btn_sub": "Win · macOS · Linux", "faq_kicker": "07 / ЧАСТО ЗАДАВАЕМЫЕ ВОПРОСЫ", "faq_title": "Все, что нужно знать", "faq_desc": "Четкие ответы на технические вопросы, вопросы безопасности и интеграций.", "faq_q1": "Отправляет ли Datly корпоративные данные баз в OpenAI или Anthropic?", "faq_a1": "Нет. Datly работает по строгому локальному принципу. Данные хранятся исключительно в оперативной памяти и DuckDB на вашем ПК. В языковую модель отправляются только имена колонок и типы данных для синтеза кода.", "faq_q2": "Можно ли запускать Datly полностью без интернета?", "faq_a2": "Да. Datly нативно подключается к локальным моделям через Ollama, vLLM или LocalAI (например, qwen2.5-coder или llama3.3), обеспечивая полную автономность в закрытом контуре.", "faq_q3": "Как Datly предотвращает галлюцинации ИИ на графиках?", "faq_a3": "ИИ никогда не рисует графики напрямую и не угадывает цифры. Модель генерирует исполняемый код Python/SQL, который создает физическую производную таблицу, отображаемую прямо под графиком.", "faq_q4": "Чем Datly отличается от Microsoft Research Data Formulator?", "faq_a4": "Datly развивает академическое исследование в полноценную корпоративную IDE: 15+ облачных коннекторов, отчеты TipTap, дашборды и нативные десктопные сборки.", "faq_q5": "Как цепочки данных решают проблему непредсказуемости Jupyter?", "faq_a5": "Заменой мутабельного глобального пространства на направленный ациклический граф (DAG). Каждый вопрос создает изолированную карточку без перезаписи предыдущих шагов.", "footer_tagline": "Высоконадежная аналитическая IDE визуализации данных с ИИ · Открытый исходный код под лицензией MIT", "footer_docs": "Документация", "footer_license": "Лицензия MIT", "footer_bottom": "© 2026 Проект Datly · Открытое ПО", "toast_copy": "Скопировано в буфер обмена!"}, "zh": {"nav_features": "核心功能", "nav_canvas": "数据真实性", "nav_connectors": "数据连接器 (15+)", "nav_comparison": "产品对比", "nav_quickstart": "快速开始", "nav_faq": "常见问题", "nav_star": "点星", "nav_quickstart_btn": "立即上手", "hero_badge": "下一代 AI 数据可视化 IDE 与分析工作台", "hero_title_1": "与您的数据对话。", "hero_title_2": "逐行验证计算结果。", "hero_desc": "Datly 是一款本地优先的开源数据分析 IDE。您可以使用日常自然语言将企业数据表快速转换为交互式图表，同时计算得出的实体结果行始终清晰显示在折线图下方。零云端数据外泄，彻底杜绝 AI 幻觉。", "hero_btn_quickstart": "一条命令即刻启动", "hero_btn_sub": "uv run data_formulator", "hero_btn_github": "查看 GitHub 源码", "hero_btn_desktop": "桌面客户端", "trust_local": "100% 本地运算 (DuckDB 内存加速)", "trust_connectors": "15+ 款企业级数据源连接器", "trust_privacy": "原始业务数据零上云", "trust_models": "支持任意模型 (Claude, GPT, Ollama)", "ide_status": "DuckDB 本地内存引擎", "ide_tab_ide": "全功能分析 IDE", "ide_tab_explore": "真实数据校验画布", "ide_tab_connectors": "15+ 数据库连接器", "ide_tab_dashboards": "多图表看板", "ide_tab_reports": "TipTap 叙事报告", "pillars_kicker": "01 / 架构基石", "pillars_title": "工程级严谨性与探索级自由度", "pillars_desc": "专为解决传统 Notebook 状态混乱陷阱及传统商业智能 (BI) 工具僵化而生。", "p1_title": "无损数据工作流 (Data Threads)", "p1_desc": "每次提问形成独立的数据血缘分支 (DAG)。后续提问在派生表上递进计算，绝不污染源数据集或覆盖工作区状态。", "p1_tag": "血缘状态机", "p2_title": "真实数据行校验画布", "p2_desc": "告别 AI 图表虚假幻觉。每个图表均由直观展示在图表下方的衍生物理表驱动。随时查看每个单元格并下载验证过的 CSV。", "p2_tag": "零虚假指标", "p3_title": "DuckDB 向量化内存引擎", "p3_desc": "由 DuckDB、Pandas 与 PyArrow 强力驱动，在本地几毫秒内完成数百万行数据的复杂聚合、窗口分析与关联查询。", "p3_tag": "向量化高性能分析", "p4_title": "概念编码视觉架", "p4_desc": "将字段自由拖拽至直观视觉架：X轴、Y轴、色彩、尺寸、分面。自然语言提示词与 Vega-Lite 规范保持双向实时联动。", "p4_tag": "图形语法体系", "p5_title": "TipTap 富文本交互报告", "p5_desc": "将探索图表转化为决策层报告。集成所见即所得 (WYSIWYG) 编辑器，支持文字叙述与动态图表卡片无缝混排。", "p5_tag": "高管简报一键生成", "p6_title": "本地优先，绝对私密", "p6_desc": "企业合规守护神。您的数据行永不离开本地电脑。仅将数据列结构（Schema）与脱敏极简元数据发送给模型合成代码。", "p6_tag": "支持物理隔离与离线环境", "spotlight_kicker": "02 / 真实数据检验", "spotlight_title": "图表下方的数据行", "spotlight_desc": "表格可能蕴藏答案，但也容易隐藏细节。Datly 确保提问、图表和计算出的数据行始终同屏并存。", "s1_title": "Explore 校验：所见即所得的核对", "s1_desc": "当您提出问题时，Datly 会计算出新的衍生数据表并绘制图表。折线下方的数据网格显示出具体数值，方便您核对无误后再进行汇报。", "s1_tag1": "交互式缩放", "s1_tag2": "CSV 数据导出", "s1_tag3": "平滑曲线/阶梯/散点", "s2_title": "看板与报告无冗余复制", "s2_desc": "将图表固定至看板或插入 TipTap 报告不会重复复制底层数据。所有表面均共享底层数据线程 DAG 的实时引用。", "s2_tag1": "多卡片大盘", "s2_tag2": "导出高精度 PDF", "s2_tag3": "DAG 状态动态绑定", "connectors_kicker": "03 / 企业级生态连接器", "connectors_title": "一次配置，本地流畅查询", "connectors_desc": "轻松挂载来自数据湖仓、传统关系型数据库、云存储乃至网页表格的实时数据。", "filter_all": "全部连接器 (15+)", "filter_warehouse": "湖仓与 OLAP", "filter_sql": "关系型数据库", "filter_cloud": "NoSQL 与云对象存储", "filter_files": "文件与网页表格", "compare_kicker": "04 / 架构横向对比", "compare_title": "现代分析团队为何选择 Datly", "compare_desc": "全面对比传统数据科学与商业智能工具的技术指标。", "compare_th_feature": "特性 / 安全控制", "compare_th_jupyter": "Jupyter Notebooks", "compare_th_bi": "传统云端 BI (Tableau / PowerBI)", "compare_th_dbeaver": "传统 SQL 工具 (DBeaver)", "compare_th_datly": "DATLY IDE", "compare_r1_title": "自然语言 AI 智能分析", "compare_r1_jupyter": "⚠️ 依赖第三方插件", "compare_r1_bi": "⚠️ 昂贵且受限的专属 Copilot", "compare_r1_dbeaver": "❌ 无或仅有简单自动补全", "compare_r1_datly": "⚡ 原生上下文 Agent (@表名精确引用)", "compare_r2_title": "图表下方直视真实数据网格", "compare_r2_jupyter": "❌ 位于隔离代码单元格输出", "compare_r2_bi": "❌ 层级下钻繁琐", "compare_r2_dbeaver": "❌ 需手动编写 SQL 查询", "compare_r2_datly": "🛡️ 画布上始终并排同屏可见", "compare_r3_title": "视觉编码架 + AI 智能体协同", "compare_r3_jupyter": "❌ 仅纯代码", "compare_r3_bi": "⚠️ 仅拖拽架 (无代码级 Agent)", "compare_r3_dbeaver": "❌ 仅纯代码", "compare_r3_datly": "✅ 双向联动视觉架 + 智能 Agent", "compare_r4_title": "本地数据隐私 (零外泄风险)", "compare_r4_jupyter": "✅ 本地执行", "compare_r4_bi": "❌ 强制将业务数据上传至云端", "compare_r4_dbeaver": "✅ 本地查询", "compare_r4_datly": "🔒 100% 本地 (仅将结构发给模型)", "compare_r5_title": "数据血缘架构设计", "compare_r5_jupyter": "❌ 线性全局变量污染陷阱", "compare_r5_bi": "❌ 静态僵化数据建模", "compare_r5_dbeaver": "❌ 扁平查询历史记录", "compare_r5_datly": "🌲 分支化数据线程 (DAG)", "compare_r6_title": "向量化内存分析引擎", "compare_r6_jupyter": "⚠️ 取决于内核环境", "compare_r6_bi": "⚠️ 闭源专有分析引擎", "compare_r6_dbeaver": "❌ 严重依赖远端数据库性能", "compare_r6_datly": "⚡ DuckDB + Pandas + PyArrow", "compare_r7_title": "所见即所得决策报告", "compare_r7_jupyter": "❌ 仅静态 Markdown 单元格", "compare_r7_bi": "⚠️ 笨重的仪表板排版", "compare_r7_dbeaver": "❌ 不支持", "compare_r7_datly": "📝 TipTap 富文本互动报告引擎", "compare_r8_title": "自动化网页表格抓取", "compare_r8_jupyter": "❌ 需手动编写爬虫脚本", "compare_r8_bi": "❌ 不支持", "compare_r8_dbeaver": "❌ 不支持", "compare_r8_datly": "🌐 内置无头 Playwright 抓取器", "compare_r9_title": "模型自由选择 (自带密钥 / 离线)", "compare_r9_jupyter": "⚠️ 需自行编写接口调用", "compare_r9_bi": "❌ 被云厂商深度绑定", "compare_r9_dbeaver": "❌ 不适用", "compare_r9_datly": "🔓 LiteLLM (Claude, GPT, Ollama 等)", "quickstart_kicker": "05 / 零阻碍部署", "quickstart_title": "60 秒内即刻体验 Datly", "quickstart_desc": "自由选择您喜欢的启动方式：新一代 uv 包管理器、Docker 容器或一键 AI 智能体指令。", "term_tab_uv": "Astral uv (最推荐·极速)", "term_tab_pip": "Python pip + Yarn", "term_tab_docker": "Docker Compose", "term_tab_agent": "AI 助手一键指令", "term_tab_desktop": "桌面安装包构建", "releases_kicker": "06 / 官方经过签名的发行版本", "releases_title": "下载桌面客户端", "releases_desc": "为 Windows、macOS 与 Linux 提供原生硬件加速的独立桌面运行体验。", "releases_box_title": "Datly Desktop 桌面套件 v0.8.0-beta", "releases_box_desc": "基于 Electron 43 构建 · 内嵌 DuckDB 引擎 · 支持离线与在线模型", "releases_btn_title": "查看全部下载文件", "releases_btn_sub": "支持 Win · macOS · Linux", "faq_kicker": "07 / 常见问题解答", "faq_title": "您关心的关键问题", "faq_desc": "关于技术底层、隐私安全及数据源集成的专业解答。", "faq_q1": "Datly 会将我的私密数据库内容上传到 OpenAI 或 Anthropic 吗？", "faq_a1": "绝对不会。Datly 坚守严格的本地优先隐私架构。您的数据完全驻留在本地电脑的 RAM 和 DuckDB 中。模型仅接收字段名称与数据类型用于生成 Python/SQL 处理代码，代码在本地 CPU 运行，原始数据永不出境。", "faq_q2": "我可以在完全不连外网的环境下离线使用 Datly 吗？", "faq_a2": "完全可以。Datly 原生支持 Ollama、vLLM 等本地离线大模型（例如 qwen2.5-coder 或 llama3.3），完美适配金融、军工及高保密隔离网环境。", "faq_q3": "Datly 是如何杜绝 AI 图表幻觉与伪造数据的？", "faq_a3": "Datly 绝不允许 AI 直接胡乱生成图表像素或虚构统计结果。AI 必须编写可被审查的实际代码生成真实的物理衍生表，并在图表下方直接展开该表供您逐行校对。", "faq_q4": "Datly 与微软研究院的 Data Formulator 有何升级演进？", "faq_a4": "Datly 在该研究原型基础上将其彻底升级为企业级数据 IDE，引入了 15+ 湖仓连接器、TipTap 叙事报告引擎、多图看板、全平台桌面客户端及零数据外泄安全标准。", "faq_q5": "数据线程 (Data Threads) 如何避免 Jupyter 的状态覆盖灾难？", "faq_a5": "Datly 抛弃了脆弱的全局易变变量环境，采用有向无环图 (DAG) 组织探索过程。每个问题和它的衍生表都是自闭环节点，无论如何回溯修改都不会污染上游数据源。", "footer_tagline": "高可靠 AI 数据可视化 IDE 与分析工作台 · 遵循 MIT 开源协议", "footer_docs": "开发与配置文档", "footer_license": "MIT 开源协议", "footer_bottom": "© 2026 Datly 开源项目 · 自由软件", "toast_copy": "已成功复制到剪贴板！"}, "zh-TW": {"nav_features": "核心功能", "nav_canvas": "真實數據驗證", "nav_connectors": "資料連接器 (15+)", "nav_comparison": "產品評比", "nav_quickstart": "快速上手", "nav_faq": "常見問答", "nav_star": "給星", "nav_quickstart_btn": "立即體驗", "hero_badge": "下一代 AI 資料視覺化 IDE 與分析工作台", "hero_title_1": "與您的資料對話。", "hero_title_2": "逐行檢驗計算數據。", "hero_desc": "Datly 是一款本地優先的開源資料分析 IDE。透過日常自然語言將企業資料表無縫轉化為互動式圖表，計算得出的結果表格始終並排於螢幕下方。零雲端資料外洩，徹底杜絕 AI 幻覺。", "hero_btn_quickstart": "一行指令即刻啟動", "hero_btn_sub": "uv run data_formulator", "hero_btn_github": "查看 GitHub 專案", "hero_btn_desktop": "下載桌面應用程式", "trust_local": "100% 本地計算 (DuckDB 記憶體加速)", "trust_connectors": "15+ 款企業級資料來源連接器", "trust_privacy": "業務數據絕不上雲", "trust_models": "相容多款模型 (Claude, GPT, Ollama)", "ide_status": "DuckDB 本地記憶體引擎", "ide_tab_ide": "全功能分析 IDE", "ide_tab_explore": "真實數據驗證畫布", "ide_tab_connectors": "15+ 數據庫連接器", "ide_tab_dashboards": "多圖表儀表板", "ide_tab_reports": "TipTap 敘事分析報告", "pillars_kicker": "01 / 架構核心基石", "pillars_title": "工程級嚴謹與探索級流暢的完美平衡", "pillars_desc": "專為解決傳統 Notebook 變數混亂狀態與傳統商業智慧 (BI) 軟體之僵化限制而設計。", "p1_title": "無損資料執行緒 (Data Threads)", "p1_desc": "每次提問皆建立獨立的血緣分支 (DAG)。後續追問僅計算衍生資料表，絕不覆寫或破壞原始資料庫狀態。", "p1_tag": "血緣狀態機", "p2_title": "真實數據驗證畫布", "p2_desc": "告別 AI 繪圖幻覺。所有視覺化圖表均由畫布下方真實衍生的實體表格驅動，隨時檢視儲存格並匯出已驗證 CSV。", "p2_tag": "零幽靈數據", "p3_title": "DuckDB 向量化記憶體引擎", "p3_desc": "由 DuckDB、Pandas 與 PyArrow 驅動，於本機毫秒級完成數百萬行資料的高階聚合、視窗函式與多表 Join。", "p3_tag": "向量化高速分析", "p4_title": "概念編碼視覺架", "p4_desc": "拖放資料欄位至直覺架構：X軸、Y軸、顏色、大小與分面。自然語言提示與 Vega-Lite 規範雙向即時同步。", "p4_tag": "圖形語法體系", "p5_title": "TipTap 敘事分析報告", "p5_desc": "將分析圖表直接轉化為決策層報告。整合所見即所得 (WYSIWYG) 編輯器，結合文字敘事與動態圖表卡片。", "p5_tag": "決策簡報發表", "p6_title": "本機優先，最高等級隱私防護", "p6_desc": "企業級資料治理標準。您的原始資料絕不離開本機。僅將資料表綱要 (Schema) 與遮罩極簡範例傳輸給 LLM。", "p6_tag": "支援實體隔離與離線環境", "spotlight_kicker": "02 / 真實數據查驗", "spotlight_title": "折線圖下方的每一行數字", "spotlight_desc": "表格蘊含關鍵洞察，但也常隱沒細節。Datly 確保問題、圖表及衍生出的數據列永遠並陳。", "s1_title": "Explore 驗證：同步雙向檢視", "s1_desc": "提問時，Datly 會產出新的衍生資料表並即時繪製圖表。圖表下方的資料方格清晰展示實際數值，確認無誤後再行分享。", "s1_tag1": "互動式縮放", "s1_tag2": "匯出 CSV", "s1_tag3": "平滑曲線/階梯/點狀圖", "s2_title": "儀表板與報告無需重複複製資料", "s2_desc": "釘選圖表至儀表板或嵌入 TipTap 報告不會耗費儲存空間重複複製。所有表面共享同一個 Data Thread DAG 引用。", "s2_tag1": "多卡片大盤", "s2_tag2": "高解析度 PDF 匯出", "s2_tag3": "DAG 狀態即時連動", "connectors_kicker": "03 / 企業級連接器矩陣", "connectors_title": "一次配置，本機極速查詢", "connectors_desc": "無縫整合來自數據湖倉、關聯式資料庫、雲端物件儲存及即時網頁表格的資料。", "filter_all": "全部連接器 (15+)", "filter_warehouse": "資料湖倉與 OLAP", "filter_sql": "關聯式 SQL", "filter_cloud": "NoSQL 與雲端儲存", "filter_files": "檔案與網頁表格", "compare_kicker": "04 / 架構技術比較", "compare_title": "為何資料團隊一致青睞 Datly", "compare_desc": "詳盡對比傳統資料科學工具與商業智慧平台。", "compare_th_feature": "功能 / 安全控制", "compare_th_jupyter": "Jupyter Notebooks", "compare_th_bi": "傳統雲端 BI (Tableau / PowerBI)", "compare_th_dbeaver": "傳統 SQL 編輯器 (DBeaver)", "compare_th_datly": "DATLY IDE", "compare_r1_title": "自然語言 AI 數據合成", "compare_r1_jupyter": "⚠️ 依賴外掛程式", "compare_r1_bi": "⚠️ 費用高昂的專有 Copilot", "compare_r1_dbeaver": "❌ 無或僅有簡易自動補全", "compare_r1_datly": "⚡ 原生精確標註 Agent (@表格引用)", "compare_r2_title": "圖表下方同步展示資料格", "compare_r2_jupyter": "❌ 位於個別單元格輸出", "compare_r2_bi": "❌ 下鑽結構層級繁複", "compare_r2_dbeaver": "❌ 需自行手寫查詢指令", "compare_r2_datly": "🛡️ 畫布並排即時可視", "compare_r3_title": "編碼架 + AI 協同作業", "compare_r3_jupyter": "❌ 僅有純程式碼單元格", "compare_r3_bi": "⚠️ 僅有視覺架 (無 Agent 寫碼)", "compare_r3_dbeaver": "❌ 僅有純程式碼", "compare_r3_datly": "✅ 雙向互動架構 + 智慧 Agent", "compare_r4_title": "本機隱私防護 (零外洩)", "compare_r4_jupyter": "✅ 本機執行", "compare_r4_bi": "❌ 強制將商業資料上傳雲端", "compare_r4_dbeaver": "✅ 本機查詢", "compare_r4_datly": "🔒 100% 本機 (僅提供結構綱要予 LLM)", "compare_r5_title": "資料血緣管理架構", "compare_r5_jupyter": "❌ 脆弱的循序全域狀態陷阱", "compare_r5_bi": "❌ 靜態資料建模", "compare_r5_dbeaver": "❌ 簡易查詢記錄清單", "compare_r5_datly": "🌲 分支化資料執行緒 (DAG)", "compare_r6_title": "向量化記憶體運算引擎", "compare_r6_jupyter": "⚠️ 受限於執行環境內核", "compare_r6_bi": "⚠️ 閉源專有引擎", "compare_r6_dbeaver": "❌ 完全依賴遠端伺服器算力", "compare_r6_datly": "⚡ DuckDB + Pandas + PyArrow", "compare_r7_title": "所見即所得敘事報告", "compare_r7_jupyter": "❌ 僅有靜態 Markdown", "compare_r7_bi": "⚠️ 沉重的儀表板排版畫布", "compare_r7_dbeaver": "❌ 無此功能", "compare_r7_datly": "📝 TipTap 富文本即時分析引擎", "compare_r8_title": "自動化網頁表格擷取", "compare_r8_jupyter": "❌ 需手動撰寫爬蟲腳本", "compare_r8_bi": "❌ 不支援", "compare_r8_dbeaver": "❌ 不支援", "compare_r8_datly": "🌐 內建 Playwright 無頭爬蟲", "compare_r9_title": "自由選用模型 (BYOK / 離線)", "compare_r9_jupyter": "⚠️ 需手動撰寫 API 串接", "compare_r9_bi": "❌ 綁定特定雲端供應商", "compare_r9_dbeaver": "❌ 不適用", "compare_r9_datly": "🔓 LiteLLM (Claude, GPT, Ollama 等)", "quickstart_kicker": "05 / 極簡快速安裝", "quickstart_title": "60 秒內啟動您的 Datly 環境", "quickstart_desc": "選擇您熟悉的環境啟動：新一代 uv 套件管理、Docker 容器或一鍵 AI 代理人指令。", "term_tab_uv": "Astral uv (極速推薦)", "term_tab_pip": "Python pip + Yarn", "term_tab_docker": "Docker Compose", "term_tab_agent": "AI 一行指令自動部屬", "term_tab_desktop": "桌面安裝程式建置", "releases_kicker": "06 / 經簽署驗證的官方發行", "releases_title": "下載獨立桌面版軟體", "releases_desc": "為 Windows、macOS 與 Linux 提供原生硬體加速支援之桌面獨立安裝程式。", "releases_box_title": "Datly Desktop 套件 v0.8.0-beta", "releases_box_desc": "基於 Electron 43 打造 · 內嵌 DuckDB 引擎 · 支援各主流大模型", "releases_btn_title": "查看全部發行下載", "releases_btn_sub": "支援 Win · macOS · Linux", "faq_kicker": "07 / 常見問題與解答", "faq_title": "您不可不知的重要細節", "faq_desc": "針對技術底層、隱私安全與資料源整合之完整說明。", "faq_q1": "Datly 會將我的機密資料庫內容上傳給 OpenAI 或 Anthropic 嗎？", "faq_a1": "絕對不會。Datly 採用本機優先設計。所有數據資料僅暫存在您電腦的記憶體與 DuckDB 之中。模型只會收到欄位結構以生成 Python/SQL 運算程式碼，程式碼於本地端運算，商業機密數據絕不出境。", "faq_q2": "我可以在完全沒有網際網路的封閉環境下使用 Datly 嗎？", "faq_a2": "完全可以。Datly 原生支援透過 Ollama、vLLM 串接本機開源模型（如 qwen2.5-coder、llama3.3），非常適合金融機構與高保密內網環境。", "faq_q3": "Datly 如何防範 AI 產生虛假圖表與數據幻覺？", "faq_a3": "Datly 絕不允許 AI 隨意偽造統計數字或憑空畫圖。AI 必須撰寫可供檢驗的實際程式碼於真實資料上運算，生成的實體數據表直接展示在圖表下方讓您隨時覆核。", "faq_q4": "Datly 與微軟研究院的 Data Formulator 有何區別？", "faq_a4": "Datly 汲取該研究專案之概念精髓，進一步升級為企業級專業資料 IDE，加入 15+ 雲端數據庫連接器、TipTap 報告系統、多卡儀表板與跨平台桌面安裝版。", "faq_q5": "Data Threads 如何克服 Jupyter 的全域變數覆寫陷阱？", "faq_a5": "Datly 採用有向無環圖 (DAG) 架構。每個問題與產生的衍生表格都是獨立節點，回溯檢查或建立新分支絕不影響前面的既有運算步驟。", "footer_tagline": "高可靠 AI 資料視覺化 IDE 與分析工作台 · 遵循 MIT 開源授權條款", "footer_docs": "開發與使用文件", "footer_license": "MIT 授權條款", "footer_bottom": "© 2026 Datly 開源專案 · 自由軟體", "toast_copy": "已複製到剪貼簿！"}, "ja": {"nav_features": "機能", "nav_canvas": "データ検証", "nav_connectors": "コネクタ (15+)", "nav_comparison": "ツール比較", "nav_quickstart": "クイックスタート", "nav_faq": "FAQ", "nav_star": "スター", "nav_quickstart_btn": "今すぐ開始", "hero_badge": "次世代 AI データ可視化 IDE ＆ 分析ワークベンチ", "hero_title_1": "データと自然言語で対話する。", "hero_title_2": "すべての計算行を即座に検証。", "hero_desc": "Datly は、自然言語で企業データをインタラクティブなチャートへ変換し、計算されたデータ行を常にグラフの下に表示して検証できるオープンソースのローカルファースト分析 IDE です。クラウドへのデータ流出ゼロ、AI の幻覚ゼロ。", "hero_btn_quickstart": "1コマンドで開始", "hero_btn_sub": "uv run data_formulator", "hero_btn_github": "GitHub で見る", "hero_btn_desktop": "デスクトップアプリ", "trust_local": "100% ローカル処理 (DuckDB インメモリ)", "trust_connectors": "15+ のエンタープライズコネクタ", "trust_privacy": "クラウドへのデータ流出ゼロ", "trust_models": "自由なモデル選択 (Claude, GPT, Ollama)", "ide_status": "DuckDB インメモリ (ローカル)", "ide_tab_ide": "統合分析 IDE", "ide_tab_explore": "データ検証キャンバス", "ide_tab_connectors": "15+ データコネクタ", "ide_tab_dashboards": "マルチチャートダッシュボード", "ide_tab_reports": "TipTap ナラティブレポート", "pillars_kicker": "01 / アーキテクチャの柱", "pillars_title": "エンジニアリングの厳格性と自由な探索の融合", "pillars_desc": "従来のノートブックのステート汚染問題とレガシー BI の硬直性を解決するために設計されました。", "p1_title": "非破壊的データスレッド (Data Threads)", "p1_desc": "各分析クエリは独立したスレッドブランチ (DAG) を生成します。派生テーブル上で追加計算を行い、元データを書き換えません。", "p1_tag": "リネージステートマシン", "p2_title": "グラウンドトゥルース検証キャンバス", "p2_desc": "AI のハルシネーション（幻覚）を排除。すべての可視化は、グラフ直下に配置された実際の派生テーブルから描画されます。", "p2_tag": "架空メトリクス排除", "p3_title": "DuckDB インメモリエンジン", "p3_desc": "DuckDB、Pandas、PyArrow による超高速ベクトルクエリ処理。数百万行の集計や結合をローカルで数ミリ秒で実行します。", "p3_tag": "ベクトル化高速分析", "p4_title": "コンセプトエンコーディングシェルフ", "p4_desc": "X軸、Y軸、色、サイズ、ファセットへフィールドをドラッグ＆ドロップ。自然言語プロンプトと Vega-Lite 仕様が双方向で同期。", "p4_tag": "グラフィックスの文法", "p5_title": "TipTap ナラティブレポート", "p5_desc": "探索チャートを経営陣向けレポートへ昇華。テキストの解説とインタラクティブなチャートカードを融合する WYSIWYG エディタ。", "p5_tag": "エグゼクティブパブリッシング", "p6_title": "ローカルファーストの徹底プライバシー", "p6_desc": "企業のデータ行はPC外に送信されません。コード生成のために LLM に送信されるのは列スキーマと最小限のメタデータのみです。", "p6_tag": "エアギャップ環境・オフライン対応", "spotlight_kicker": "02 / データの真実性検証", "spotlight_title": "グラフの線の下にある実データ", "spotlight_desc": "テーブルは答えを持っていても隠れてしまうことがあります。Datly は質問、チャート、計算されたデータ行を常に同時に表示します。", "s1_title": "Explore: 同期された確かな検証", "s1_desc": "質問すると Datly は新しい派生テーブルを計算してプロットします。曲線の真下のグリッドに正確な値が表示され、共有前にすぐ確認できます。", "s1_tag1": "インタラクティブズーム", "s1_tag2": "CSV ダウンロード", "s1_tag3": "カーブ / ステップ / プロット", "s2_title": "データを複製しないダッシュボード", "s2_desc": "ダッシュボードへのピン留めやレポートへの埋め込みでデータを無駄にコピーしません。すべての画面が同じデータスレッド DAG を参照します。", "s2_tag1": "マルチカードピンボード", "s2_tag2": "PDF エクスポート", "s2_tag3": "DAG ステート連携", "connectors_kicker": "03 / エンタープライズコネクタ", "connectors_title": "一度接続すれば、ローカルで軽快クエリ", "connectors_desc": "データレイク、ウェアハウス、リレーショナル DB、クラウドストレージ、ウェブテーブルを即座に統合。", "filter_all": "全コネクタ (15+)", "filter_warehouse": "ウェアハウス＆レイク", "filter_sql": "リレーショナル SQL", "filter_cloud": "NoSQL ＆ クラウド", "filter_files": "ファイル ＆ ウェブ", "compare_kicker": "04 / アーキテクチャ比較", "compare_title": "分析チームが Datly を選ぶ理由", "compare_desc": "従来のデータ分析ツールとの詳細な技術仕様比較。", "compare_th_feature": "機能 / セキュリティ制御", "compare_th_jupyter": "Jupyter Notebooks", "compare_th_bi": "クラウド BI (Tableau / PowerBI)", "compare_th_dbeaver": "SQL IDE (DBeaver)", "compare_th_datly": "DATLY IDE", "compare_r1_title": "自然言語 AI 分析合成", "compare_r1_jupyter": "⚠️ サードパーティ製拡張機能", "compare_r1_bi": "⚠️ 高額なプロプライエタリ Copilot", "compare_r1_dbeaver": "❌ なし、または簡易補完のみ", "compare_r1_datly": "⚡ ネイティブグラウンデッド Agent (@テーブル参照)", "compare_r2_title": "グラフ直下の実データグリッド", "compare_r2_jupyter": "❌ 離れたセルへの出力", "compare_r2_bi": "❌ 複雑な階層ドリルダウン", "compare_r2_dbeaver": "❌ 手動クエリ記述が必要", "compare_r2_datly": "🛡️ 常にキャンバス上に同時表示", "compare_r3_title": "エンコーディングシェルフ ＋ Agent", "compare_r3_jupyter": "❌ コードセルのみ", "compare_r3_bi": "⚠️ シェルフのみ (Agent なし)", "compare_r3_dbeaver": "❌ コードのみ", "compare_r3_datly": "✅ 双方向同期シェルフ ＋ Agent", "compare_r4_title": "ローカルプライバシー (流出ゼロ)", "compare_r4_jupyter": "✅ ローカル実行", "compare_r4_bi": "❌ クラウドへのデータアップロード必須", "compare_r4_dbeaver": "✅ ローカルクエリ", "compare_r4_datly": "🔒 100% ローカル (LLM にはスキーマのみ送信)", "compare_r5_title": "データリネージアーキテクチャ", "compare_r5_jupyter": "❌ 脆弱な線形グローバル状態", "compare_r5_bi": "❌ 静的なデータモデリング", "compare_r5_dbeaver": "❌ 単純なクエリ履歴", "compare_r5_datly": "🌲 分岐型データスレッド (DAG)", "compare_r6_title": "インメモリベクトル演算エンジン", "compare_r6_jupyter": "⚠️ カーネルに依存", "compare_r6_bi": "⚠️ プロプライエタリな独自エンジン", "compare_r6_dbeaver": "❌ リモート DB 負荷に依存", "compare_r6_datly": "⚡ DuckDB + Pandas + PyArrow", "compare_r7_title": "WYSIWYG ナラティブレポート", "compare_r7_jupyter": "❌ 静的 Markdown のみ", "compare_r7_bi": "⚠️ 重厚なダッシュボードキャンバス", "compare_r7_dbeaver": "❌ 非対応", "compare_r7_datly": "📝 TipTap リッチテキストエディタ", "compare_r8_title": "ウェブテーブル自動スクレイピング", "compare_r8_jupyter": "❌ BS4 等の手動スクリプト", "compare_r8_bi": "❌ 非対応", "compare_r8_dbeaver": "❌ 非対応", "compare_r8_datly": "🌐 Playwright 内蔵ヘッドレススクレイパー", "compare_r9_title": "モデル非依存 (BYOK / ローカル)", "compare_r9_jupyter": "⚠️ 自前 API 連携", "compare_r9_bi": "❌ 特定ベンダーへのロックイン", "compare_r9_dbeaver": "❌ 該当なし", "compare_r9_datly": "🔓 LiteLLM (Claude, GPT, Ollama 等)", "quickstart_kicker": "05 / ノンストレス導入", "quickstart_title": "60秒以内に Datly を起動", "quickstart_desc": "好みの起動方法を選択：次世代 uv パッケージマネージャー、Docker、または 1 コマンド AI 指示。", "term_tab_uv": "Astral uv (最速推奨)", "term_tab_pip": "Python pip + Yarn", "term_tab_docker": "Docker Compose", "term_tab_agent": "1コマンド AI 指示", "term_tab_desktop": "デスクトップビルド", "releases_kicker": "06 / 検証済み公式リリース", "releases_title": "デスクトップアプリをダウンロード", "releases_desc": "ハードウェアアクセラレーションを備えた Windows、macOS、Linux 向けスタンドアロン版。", "releases_box_title": "Datly Desktop Suite v0.8.0-beta", "releases_box_desc": "Electron 43 採用 · DuckDB 組み込みエンジン · 複数 LLM 対応", "releases_btn_title": "すべてのリリースファイルを見る", "releases_btn_sub": "Win · macOS · Linux 対応", "faq_kicker": "07 / よくある質問", "faq_title": "技術と運用のポイント", "faq_desc": "セキュリティ、データ整合性、技術基盤に関するクリアな回答。", "faq_q1": "Datly は社内データベースのレコードを OpenAI や Anthropic に送信しますか？", "faq_a1": "いいえ、一切送信しません。Datly は厳格なローカルファーストアーキテクチャを採用しています。実データはすべて PC 内の RAM と DuckDB に留まります。モデルに送信されるのはコード生成用のカラム名とデータ型のみです。", "faq_q2": "インターネット接続なしで完全オフライン実行できますか？", "faq_a2": "はい、可能です。Ollama や vLLM 等のローカルモデルランナー（qwen2.5-coder や llama3.3 等）とネイティブ連携でき、外部から完全に隔離された機密環境でも運用できます。", "faq_q3": "Datly はどのように AI のチャート幻覚（ハルシネーション）を防ぎますか？", "faq_a3": "AI に直接グラフ画像を描画させたり数値を当て推量させません。検証可能なコードを生成し、実際のデータ上でローカル実行して派生テーブルを作成。そのテーブルをグラフ直下に表示します。", "faq_q4": "Microsoft Research Data Formulator との違いは何ですか？", "faq_a4": "Datly は研究成果を発展させ、15以上のクラウド湖倉コネクタ、TipTap レポート機能、ダッシュボード、ネイティブデスクトップアプリを備えた実務レベルのデータ IDE へ昇華させました。", "faq_q5": "データスレッドは Jupyter の状態汚染をどのように防ぎますか？", "faq_a5": "単一の変更可能なグローバル空間を排除し、有向非巡回グラフ (DAG) で履歴を管理します。すべての分析ステップが独立したカードとなり、元データや先行ステップを破壊しません。", "footer_tagline": "高信頼 AI データ可視化 IDE ＆ 分析ワークベンチ · MIT ライセンス準拠オープンソース", "footer_docs": "ドキュメント", "footer_license": "MIT ライセンス", "footer_bottom": "© 2026 Datly Project · フリーソフトウェア", "toast_copy": "クリップボードにコピーしました！"}, "ko": {"nav_features": "기능 소개", "nav_canvas": "데이터 검증", "nav_connectors": "데이터 커넥터 (15+)", "nav_comparison": "제품 비교", "nav_quickstart": "빠른 시작", "nav_faq": "자주 묻는 질문", "nav_star": "스타", "nav_quickstart_btn": "빠른 시작", "hero_badge": "차세대 AI 데이터 시각화 IDE 및 분석 워크벤치", "hero_title_1": "자연어로 데이터와 대화하세요.", "hero_title_2": "모든 계산 행을 직접 검증하세요.", "hero_desc": "Datly는 자연어로 엔터프라이즈 테이블을 인터랙티브 차트로 변환하고, 계산된 실제 데이터 행을 차트 바로 아래에 실시간으로 표시하는 로컬 우선 오픈소스 데이터 IDE입니다. 클라우드 데이터 유출 제로, AI 환각 제로.", "hero_btn_quickstart": "1개 명령어로 시작하기", "hero_btn_sub": "uv run data_formulator", "hero_btn_github": "GitHub 저장소 보기", "hero_btn_desktop": "데스크톱 앱", "trust_local": "100% 로컬 연산 (DuckDB 인메모리)", "trust_connectors": "15+ 엔터프라이즈 커넥터", "trust_privacy": "외부 클라우드 데이터 유출 제로", "trust_models": "멀티 모델 선택 (Claude, GPT, Ollama)", "ide_status": "DuckDB 인메모리 (로컬)", "ide_tab_ide": "풀 분석 IDE", "ide_tab_explore": "데이터 검증 캔버스", "ide_tab_connectors": "15+ 데이터 커넥터", "ide_tab_dashboards": "멀티 차트 대시보드", "ide_tab_reports": "TipTap 리포트", "pillars_kicker": "01 / 아키텍처 핵심 기둥", "pillars_title": "엔지니어링의 정밀함과 유연한 탐색의 결합", "pillars_desc": "주피터 노트북의 상태 오염 문제와 전통적 BI 툴의 경직성을 근본적으로 해결하도록 설계되었습니다.", "p1_title": "비파괴적 데이터 스레드 (Data Threads)", "p1_desc": "각 분석 질문은 독립된 분기 스레드(DAG)를 형성합니다. 후속 질문은 원본 데이터셋을 덮어쓰지 않고 파생 테이블 위에서 안전하게 실행됩니다.", "p1_tag": "데이터 계보 상태 머신", "p2_title": "그라운드 트루스 검증 캔버스", "p2_desc": "AI 환각(Hallucination)과의 영별. 모든 시각화는 차트 바로 아래 펼쳐지는 실제 파생 테이블을 기반으로 그려집니다.", "p2_tag": "허위 메트릭 제로", "p3_title": "DuckDB 인메모리 벡터 엔진", "p3_desc": "DuckDB, Pandas, PyArrow 기반의 초고속 벡터 연산. 수백만 행의 집계와 조인을 로컬에서 수 밀리초 만에 처리합니다.", "p3_tag": "벡터화 고속 분석", "p4_title": "시각적 인코딩 선반", "p4_desc": "필드를 X축, Y축, 색상, 크기, 패싯 선반으로 드래그 앤 드롭하세요. 자연어 프롬프트와 Vega-Lite 명세가 양방향으로 동기화됩니다.", "p4_tag": "그래픽스 문법", "p5_title": "TipTap 서술형 리포트", "p5_desc": "탐색 차트를 임원 보고용 문서로 즉시 전환하세요. 텍스트 해설과 살아있는 차트 카드가 자연스럽게 어우러지는 WYSIWYG 에디터.", "p5_tag": "의사결정 보고서 발행", "p6_title": "로컬 우선 프라이버시", "p6_desc": "기업 데이터 행은 절대 PC 밖으로 나가지 않습니다. 코드 합성을 위해 LLM에는 오직 컬럼 스키마와 마스킹된 메타데이터만 전송됩니다.", "p6_tag": "오프라인 및 폐쇄망 완벽 지원", "spotlight_kicker": "02 / 데이터 진실성 검증", "spotlight_title": "차트 곡선 바로 아래의 데이터 행", "spotlight_desc": "테이블은 답을 품고 있으면서도 숨길 수 있습니다. Datly는 질문, 차트, 그리고 계산된 데이터 행을 항상 한 화면에 함께 유지합니다.", "s1_title": "Explore: 동기화된 완벽한 교차 검증", "s1_desc": "질문 시 Datly는 파생 테이블을 계산하고 차트를 그립니다. 차트 바로 아래 그리드에 정확한 숫자가 표시되어 공유 전 즉시 검증할 수 있습니다.", "s1_tag1": "인터랙티브 줌", "s1_tag2": "CSV 다운로드", "s1_tag3": "커브 / 스텝 / 산점도", "s2_title": "데이터 중복 없는 대시보드 및 리포트", "s2_desc": "대시보드에 고정하거나 TipTap 리포트에 삽입해도 데이터를 중복 복사하지 않습니다. 모든 표면이 동일한 데이터 스레드 DAG를 실시간 공유합니다.", "s2_tag1": "멀티 카드 보드", "s2_tag2": "고화질 PDF 내보내기", "s2_tag3": "DAG 상태 실시간 연동", "connectors_kicker": "03 / 엔터프라이즈 커넥터", "connectors_title": "한 번 연결하고, 로컬에서 쾌속 쿼리", "connectors_desc": "데이터 레이크하우스, 엔터프라이즈 웨어하우스, SQL DB, 웹 테이블을 즉시 연동하세요.", "filter_all": "전체 커넥터 (15+)", "filter_warehouse": "웨어하우스 & 레이크", "filter_sql": "관계형 SQL", "filter_cloud": "NoSQL & 클라우드", "filter_files": "파일 & 웹 테이블", "compare_kicker": "04 / 아키텍처 비교", "compare_title": "데이터 팀이 Datly를 선택하는 이유", "compare_desc": "기존 데이터 도구들과의 심층적인 기술 사양 비교.", "compare_th_feature": "기능 / 보안 제어", "compare_th_jupyter": "Jupyter Notebooks", "compare_th_bi": "레거시 클라우드 BI (Tableau / PowerBI)", "compare_th_dbeaver": "SQL IDE (DBeaver)", "compare_th_datly": "DATLY IDE", "compare_r1_title": "자연어 AI 데이터 변환", "compare_r1_jupyter": "⚠️ 서드파티 플러그인 필요", "compare_r1_bi": "⚠️ 고가의 제한적인 코파일럿", "compare_r1_dbeaver": "❌ 없음 또는 단순 자동완성", "compare_r1_datly": "⚡ 네이티브 컨텍스트 Agent (@테이블 인용)", "compare_r2_title": "차트 바로 아래 데이터 그리드", "compare_r2_jupyter": "❌ 별도 셀 출력 분리", "compare_r2_bi": "❌ 복잡한 계층형 드릴다운", "compare_r2_dbeaver": "❌ 수동 쿼리 작성 필수", "compare_r2_datly": "🛡️ 캔버스에 항상 동시 표시", "compare_r3_title": "시각 선반 + AI 에이전트 결합", "compare_r3_jupyter": "❌ 코드 셀만 존재", "compare_r3_bi": "⚠️ 선반만 존재 (에이전트 없음)", "compare_r3_dbeaver": "❌ 코드 전용", "compare_r3_datly": "✅ 양방향 시각 선반 + 지능형 Agent", "compare_r4_title": "로컬 프라이버시 (유출 제로)", "compare_r4_jupyter": "✅ 로컬 실행", "compare_r4_bi": "❌ 클라우드 데이터 업로드 필수", "compare_r4_dbeaver": "✅ 로컬 쿼리", "compare_r4_datly": "🔒 100% 로컬 (LLM에는 스키마만 전달)", "compare_r5_title": "데이터 계보(Lineage) 아키텍처", "compare_r5_jupyter": "❌ 취약한 순차적 전역 상태", "compare_r5_bi": "❌ 정적 데이터 모델링", "compare_r5_dbeaver": "❌ 단순 쿼리 히스토리", "compare_r5_datly": "🌲 분기형 데이터 스레드 (DAG)", "compare_r6_title": "인메모리 벡터 연산 엔진", "compare_r6_jupyter": "⚠️ 실행 커널에 의존", "compare_r6_bi": "⚠️ 폐쇄형 독점 엔진", "compare_r6_dbeaver": "❌ 원격 DB 성능에 의존", "compare_r6_datly": "⚡ DuckDB + Pandas + PyArrow", "compare_r7_title": "WYSIWYG 서술형 보고서", "compare_r7_jupyter": "❌ 정적 마크다운 셀", "compare_r7_bi": "⚠️ 무거운 대시보드 캔버스", "compare_r7_dbeaver": "❌ 미지원", "compare_r7_datly": "📝 TipTap 리치 텍스트 리포트 엔진", "compare_r8_title": "웹 테이블 자동 스크래핑", "compare_r8_jupyter": "❌ 수동 파이썬 스크립트", "compare_r8_bi": "❌ 미지원", "compare_r8_dbeaver": "❌ 미지원", "compare_r8_datly": "🌐 Playwright 내장 헤드리스 스크래퍼", "compare_r9_title": "모델 독립성 (BYOK / 오프라인)", "compare_r9_jupyter": "⚠️ 수동 API 연결", "compare_r9_bi": "❌ 특정 벤더 클라우드 종속", "compare_r9_dbeaver": "❌ 해당 없음", "compare_r9_datly": "🔓 LiteLLM (Claude, GPT, Ollama 등)", "quickstart_kicker": "05 / 간편한 설치", "quickstart_title": "60초 만에 Datly 시작하기", "quickstart_desc": "선호하는 환경을 선택하세요: 최신 uv 패키지 매니저, Docker 컨테이너 또는 AI 어시스턴트 1개 명령어.", "term_tab_uv": "Astral uv (초고속 권장)", "term_tab_pip": "Python pip + Yarn", "term_tab_docker": "Docker Compose", "term_tab_agent": "AI 에이전트 1개 명령어", "term_tab_desktop": "데스크톱 앱 빌드", "releases_kicker": "06 / 공식 검증 릴리즈", "releases_title": "데스크톱 패키지 다운로드", "releases_desc": "하드웨어 가속이 적용된 Windows, macOS, Linux용 네이티브 스탠드얼론 패키지.", "releases_box_title": "Datly Desktop Suite v0.8.0-beta", "releases_box_desc": "Electron 43 기반 · DuckDB 임베디드 엔진 내장 · 멀티 LLM 지원", "releases_btn_title": "모든 릴리즈 파일 보기", "releases_btn_sub": "Win · macOS · Linux 지원", "faq_kicker": "07 / 자주 묻는 질문", "faq_title": "꼭 알아두어야 할 핵심", "faq_desc": "아키텍처, 데이터 보안, 연동성에 대한 명확한 답변.", "faq_q1": "Datly가 내 프라이빗 데이터베이스 레코드를 외부 OpenAI나 Anthropic으로 보내나요?", "faq_a1": "아닙니다. Datly는 철저한 로컬 우선 프라이버시를 지킵니다. 실제 데이터는 PC의 RAM과 DuckDB에만 존재합니다. 코드 합성을 위해 LLM에는 오직 컬럼명과 데이터 타입만 전송되며, 데이터 자체는 유출되지 않습니다.", "faq_q2": "인터넷이 전혀 없는 오프라인 환경에서도 Datly를 쓸 수 있나요?", "faq_a2": "네, 가능합니다. Ollama나 vLLM(예: qwen2.5-coder, llama3.3)을 연결하면 외부 인터넷 연결이 전혀 없는 폐쇄망에서도 모든 기능을 사용할 수 있습니다.", "faq_q3": "Datly는 AI 차트의 허위 환각(Hallucination)을 어떻게 막나요?", "faq_a3": "AI가 임의로 차트 픽셀을 그리거나 숫자를 지어내지 못하도록 합니다. 반드시 실제 데이터 위에서 실행되는 파생 테이블을 먼저 만들고, 그 테이블을 차트 바로 아래에 투명하게 표시합니다.", "faq_q4": "Microsoft Research Data Formulator와 무엇이 다른가요?", "faq_a4": "Datly는 해당 연구 프로토타입을 기업용 풀스택 데이터 IDE로 발전시켜 15개 이상의 클라우드 커넥터, TipTap 리포트, 대시보드 및 데스크톱 앱을 추가했습니다.", "faq_q5": "데이터 스레드는 어떻게 Jupyter의 변수 오염 문제를 방지하나요?", "faq_a5": "단일 전역 상태 대신 유향 비순환 그래프(DAG) 구조를 사용합니다. 각 질문과 계산 결과는 격리된 카드로 보존되어 이전 단계를 전혀 훼손하지 않습니다.", "footer_tagline": "신뢰도 높은 AI 데이터 시각화 IDE 및 분석 워크벤치 · MIT 라이선스 오픈소스", "footer_docs": "문서 보기", "footer_license": "MIT 라이선스", "footer_bottom": "© 2026 Datly 프로젝트 · 자유 소프트웨어", "toast_copy": "클립보드에 복사되었습니다!"}, "ar": {"nav_features": "الميزات", "nav_canvas": "التحقق من البيانات", "nav_connectors": "الموصلات (15+)", "nav_comparison": "المقارنة", "nav_quickstart": "البدء السريع", "nav_faq": "الأسئلة الشائعة", "nav_star": "نجمة", "nav_quickstart_btn": "البدء السريع", "hero_badge": "بيئة تطوير وتصور البيانات بالذكاء الاصطناعي من الجيل التالي", "hero_title_1": "تحدث مع بياناتك مباشرة.", "hero_title_2": "تحقق من كل صف محسوب بدقة.", "hero_desc": "Datly هي بيئة تطوير تحليلية مفتوحة المصدر تعمل محلياً بالكامل. تتيح لك تحويل جداول الشركات إلى مخططات تفاعلية عبر اللغة الطبيعية، مع إبقاء الصفوف المحسوبة ظاهرة تحت المخطط مباشرة. صفر تسريب للبيانات السحابية، وصفر أوهام.", "hero_btn_quickstart": "ابدأ بأمر واحد فقط", "hero_btn_sub": "uv run data_formulator", "hero_btn_github": "عرض على GitHub", "hero_btn_desktop": "تطبيق سطح المكتب", "trust_local": "معالجة محلية 100% (محرك DuckDB في الذاكرة)", "trust_connectors": "أكثر من 15 موصلاً للمؤسسات", "trust_privacy": "انعدام تسريب البيانات السحابية", "trust_models": "دعم جميع النماذج (Claude, GPT, Ollama)", "ide_status": "DuckDB في الذاكرة (محلياً)", "ide_tab_ide": "بيئة التطوير التحليلية الكاملة", "ide_tab_explore": "لوحة التحقق من البيانات", "ide_tab_connectors": "15+ موصل بيانات", "ide_tab_dashboards": "لوحات معلومات متعددة المخططات", "ide_tab_reports": "تقارير TipTap السردية", "pillars_kicker": "01 / الركائز المعمارية", "pillars_title": "الدقة الهندسية تلتقي بالاستكشاف السلس", "pillars_desc": "صُمم من الأساس للتخلص من فوضى الحالات في الدفاتر التقليدية وجمود أدوات ذكاء الأعمال القديمة.", "p1_title": "سلاسل بيانات غير تدميرية (Data Threads)", "p1_desc": "يشكل كل استفسار فرعاً مستقلاً (DAG). تُبنى الأسئلة اللاحقة على جداول مشتقة دون التعديل على البيانات الأصلية.", "p1_tag": "آلة تتبع نسب البيانات", "p2_title": "لوحة التحقق من الحقيقة الملموسة", "p2_desc": "وداعاً لأوهام الذكاء الاصطناعي. يتم رسم كل مخطط من جدول حقيقي مشتق يظهر مباشرة تحت الرسم البياني.", "p2_tag": "صفر مقاييس وهمية", "p3_title": "محرك DuckDB في الذاكرة", "p3_desc": "معالجة متجهة فائقة السرعة بواسطة DuckDB و Pandas و PyArrow. تجميع ملايين الصفوف محلياً في أجزاء من الثانية.", "p3_tag": "تحليلات متجهة", "p4_title": "رفوف الترميز البصري", "p4_desc": "اسحب الحقول وأفلتها في رفوف بصرية سهلة: X و Y واللون والحجم والتقسيم. مزامنة فورية مع مواصفات Vega-Lite.", "p4_tag": "قواعد الرسومات البيانية", "p5_title": "تقارير TipTap التفاعلية", "p5_desc": "حوّل المخططات الاستكشافية إلى تقارير تنفيذية جاهزة للإدارة عبر محرر WYSIWYG متطور يدمج النصوص مع الرسوم الحية.", "p5_tag": "نشر التقارير التنفيذية", "p6_title": "الخصوصية المحلية أولاً", "p6_desc": "صفوف بياناتك لا تغادر جهازك أبداً. يتم فقط إرسال أسماء الأعمدة وأنواعها إلى نماذج الذكاء الاصطناعي لكتابة الأكواد.", "p6_tag": "جاهز للعمل دون إنترنت", "spotlight_kicker": "02 / التحقق من دقة البيانات", "spotlight_title": "الصفوف تحت خط الرسم البياني", "spotlight_desc": "قد تحتوي الجداول على الإجابات لكنها تخفي التفاصيل. يضمن Datly بقاء السؤال والمخطط وصف البيانات المحسوبة معاً في شاشة واحدة.", "s1_title": "Explore: التحقق المتزامن الفوري", "s1_desc": "عند طرح سؤال، يقوم Datly بحساب جدول مشتق جديد ورسمه. تعرض الشبكة أسفل المنحنى القيم الحقيقية للتأكد قبل المشاركة.", "s1_tag1": "تكبير تفاعلي", "s1_tag2": "تنزيل CSV", "s1_tag3": "منحنى / خطوات / نقاط", "s2_title": "لوحات معلومات دون تكرار البيانات", "s2_desc": "تثبيت المخططات في لوحات المعلومات أو التقارير لا ينسخ البيانات إطلاقاً، بل تشترك الشاشات في نفس المرجع المباشر.", "s2_tag1": "لوحة بطاقات متعددة", "s2_tag2": "تصدير PDF", "s2_tag3": "حالة DAG متصلة", "connectors_kicker": "03 / موصلات بيانات المؤسسات", "connectors_title": "اتصل مرة واحدة. استعلم محلياً.", "connectors_desc": "استورد الجداول الحية من مستودعات البيانات وقواعد بيانات SQL والتخزين السحابي وجداول الويب.", "filter_all": "جميع الموصلات (15+)", "filter_warehouse": "المستودعات والبحيرات", "filter_sql": "SQL العلائقية", "filter_cloud": "NoSQL والسحابية", "filter_files": "الملفات والويب", "compare_kicker": "04 / المقارنة المعمارية", "compare_title": "لماذا تختار فرق البيانات منصة Datly", "compare_desc": "تقييم تقني شامل ومفصل مقارنة بأدوات استكشاف البيانات التقليدية.", "compare_th_feature": "الميزة / التحكم الأمني", "compare_th_jupyter": "دفاتر Jupyter", "compare_th_bi": "BI السحابي التقليدي", "compare_th_dbeaver": "بيئات SQL (DBeaver)", "compare_th_datly": "منصة DATLY IDE", "compare_r1_title": "توليد بالذكاء الاصطناعي باللغة الطبيعية", "compare_r1_jupyter": "⚠️ إضافات خارجية غير موثوقة", "compare_r1_bi": "⚠️ مساعدات سحابية باهظة ومغلقة", "compare_r1_dbeaver": "❌ غير متوفر أو إكمال تلقائي فقط", "compare_r1_datly": "⚡ وكيل ذكي مع سياق الجدول (@table)", "compare_r2_title": "شبكة الصفوف الحقيقية تحت المخطط", "compare_r2_jupyter": "❌ تظهر في خلايا منفصلة", "compare_r2_bi": "❌ تنقل معقد بين الطبقات", "compare_r2_dbeaver": "❌ يتطلب كتابة استعلام يدوي", "compare_r2_datly": "🛡️ ظاهرة دائماً على اللوحة", "compare_r3_title": "رفوف الترميز + وكيل ذكي", "compare_r3_jupyter": "❌ خلايا برمجية فقط", "compare_r3_bi": "⚠️ رفوف فقط (دون وكيل برمجي)", "compare_r3_dbeaver": "❌ كود برمجي فقط", "compare_r3_datly": "✅ رفوف تفاعلية متزامنة + وكيل ذكي", "compare_r4_title": "الخصوصية المحلية (صفر تسريب)", "compare_r4_jupyter": "✅ تنفيذ محلي", "compare_r4_bi": "❌ رفع البيانات إلى السحابة إلزامي", "compare_r4_dbeaver": "✅ استعلام محلي", "compare_r4_datly": "🔒 100% محلي (إرسال الهيكل فقط للنموذج)", "compare_r5_title": "معمارية تتبع أصل البيانات", "compare_r5_jupyter": "❌ تداخل الحالات الخطية الهشة", "compare_r5_bi": "❌ نماذج بيانات ثابتة ومعقدة", "compare_r5_dbeaver": "❌ سجل استعلامات بسيط", "compare_r5_datly": "🌲 سلاسل بيانات متفرعة (DAG)", "compare_r6_title": "محرك متجهي في الذاكرة", "compare_r6_jupyter": "⚠️ معتمد على بيئة النواة", "compare_r6_bi": "⚠️ محرك مغلق المصدر", "compare_r6_dbeaver": "❌ يعتمد كلياً على أداء الخادم البعيد", "compare_r6_datly": "⚡ DuckDB + Pandas + PyArrow", "compare_r7_title": "تقارير سردية WYSIWYG", "compare_r7_jupyter": "❌ خلايا Markdown ثابتة", "compare_r7_bi": "⚠️ لوحات معلومات ثقيلة", "compare_r7_dbeaver": "❌ غير مدعوم", "compare_r7_datly": "📝 محرر تقارير TipTap تفاعلي", "compare_r8_title": "استخراج جداول الويب تلقائياً", "compare_r8_jupyter": "❌ نصوص برمجية يدوية", "compare_r8_bi": "❌ غير مدعوم", "compare_r8_dbeaver": "❌ غير مدعوم", "compare_r8_datly": "🌐 محرك Playwright مدمج", "compare_r9_title": "حرية اختيار النماذج (BYOK / محلي)", "compare_r9_jupyter": "⚠️ ربط يدوي معقد", "compare_r9_bi": "❌ تقييد بمزود السحابة", "compare_r9_dbeaver": "❌ لا ينطبق", "compare_r9_datly": "🔓 LiteLLM (Claude, GPT, Ollama)", "quickstart_kicker": "05 / تثبيت فوري دون عناء", "quickstart_title": "شغّل Datly في أقل من 60 ثانية", "quickstart_desc": "اختر الطريقة المناسبة لك: مدير الحزم الحديث uv، أو حاوية Docker، أو أمر وكيل ذكي واحد.", "term_tab_uv": "Astral uv (الأسرع والأفضل)", "term_tab_pip": "Python pip + Yarn", "term_tab_docker": "Docker Compose", "term_tab_agent": "أمر ذكاء اصطناعي واحد", "term_tab_desktop": "بناء تطبيق سطح المكتب", "releases_kicker": "06 / الإصدارات المعتمدة", "releases_title": "تحميل حزم سطح المكتب", "releases_desc": "أداء محلي سريع لأنظمة Windows و macOS و Linux مع تسريع الأجهزة.", "releases_box_title": "حزمة Datly Desktop v0.8.0-beta", "releases_box_desc": "مبني على Electron 43 · محرك DuckDB مدمج · دعم النماذج السحابية والمحلية", "releases_btn_title": "عرض جميع ملفات الإصدار", "releases_btn_sub": "يدعم Win · macOS · Linux", "faq_kicker": "07 / الأسئلة الأكثر شيوعاً", "faq_title": "كل ما تود معرفته", "faq_desc": "إجابات شافية وواضحة عن التفاصيل التقنية والأمان والتكامل.", "faq_q1": "هل يرسل Datly بيانات قاعدة البيانات الحساسة إلى OpenAI أو Anthropic؟", "faq_a1": "كلا، على الإطلاق. يعتمد Datly على مبدأ الخصوصية المحلية الصارمة. تبقى بياناتك الفعلية داخل ذاكرة جهازك و DuckDB فقط. يُرسل للنموذج أسماء الأعمدة وهيكلها فقط لتوليد الكود البرمجي الذي يعمل محلياً بالكامل.", "faq_q2": "هل يمكنني تشغيل Datly دون اتصال بالإنترنت تماماً؟", "faq_a2": "نعم، بالتأكيد. يتكامل Datly بسلاسة مع النماذج المحلية المفتوحة عبر Ollama أو vLLM (مثل qwen2.5-coder أو llama3.3)، مما يجعله مثالياً للبيئات المنعزلة وعالية السرية.", "faq_q3": "كيف يمنع Datly أوهام الرسوم البيانية والأرقام المزيفة؟", "faq_a3": "الذكاء الاصطناعي لا يرسم الرسومات مباشرة أبداً ولا يخمن الأرقام، بل يكتب كوداً حقيقياً يتم تنفيذه محلياً لإنشاء جدول ملموس يظهر مباشرة تحت الرسم البياني للتحقق منه.", "faq_q4": "ما الفرق بين Datly ومشروع Microsoft Research Data Formulator؟", "faq_a4": "يرتقي Datly بالنموذج البحثي ليصبح بيئة عمل متكاملة للمؤسسات، عبر إضافة أكثر من 15 موصلاً سحابياً، وتقارير TipTap التفاعلية، وتطبيقات سطح المكتب الأصلية.", "faq_q5": "كيف تحل سلاسل البيانات (Data Threads) مشكلة فوضى متغيرات Jupyter؟", "faq_a5": "باستبدال النطاق العام المتغير بمخطط شجري موجه (DAG). كل سؤال يبقى بطاقة مستقلة ببياناتها المشتقة دون التأثير على الخطوات السابقة أو تخريبها.", "footer_tagline": "بيئة تطوير وتصور البيانات بالذكاء الاصطناعي عالية الأمان · مفتوحة المصدر بموجب ترخيص MIT", "footer_docs": "التوثيق البرمجي", "footer_license": "ترخيص MIT", "footer_bottom": "© 2026 مشروع Datly · برمجيات حرة", "toast_copy": "تم النسخ إلى الحافظة بنجاح!"}, "hi": {"nav_features": "विशेषताएं", "nav_canvas": "डेटा सत्यापन", "nav_connectors": "कनेक्टर्स (15+)", "nav_comparison": "तुलना", "nav_quickstart": "त्वरित शुरुआत", "nav_faq": "अक्सर पूछे जाने वाले प्रश्न", "nav_star": "स्टार दें", "nav_quickstart_btn": "शुरू करें", "hero_badge": "अगली पीढ़ी का AI डेटा विज़ुअलाइज़ेशन IDE और एनालिटिक्स वर्कबेंच", "hero_title_1": "अपने डेटा से स्वाभाविक भाषा में बात करें।", "hero_title_2": "प्रत्येक पंक्ति को सीधे स्क्रीन पर सत्यापित करें।", "hero_desc": "Datly एक ओपन-सोर्स, स्थानीय-प्रथम डेटा एनालिटिक्स IDE है जहाँ सामान्य भाषा एंटरप्राइज़ डेटा तालिकाओं को इंटरैक्टिव चार्ट में बदल देती है, और गणना की गई वास्तविक डेटा पंक्तियाँ चार्ट के ठीक नीचे स्क्रीन पर दिखाई देती हैं। शून्य क्लाउड डेटा रिसाव, शून्य AI भ्रम।", "hero_btn_quickstart": "1 कमांड में शुरू करें", "hero_btn_sub": "uv run data_formulator", "hero_btn_github": "GitHub पर देखें", "hero_btn_desktop": "डेस्कटॉप ऐप डाउनलोड करें", "trust_local": "100% स्थानीय प्रोसेसिंग (DuckDB इन-मेमोरी)", "trust_connectors": "15+ एंटरप्राइज़ डेटा कनेक्टर्स", "trust_privacy": "शून्य डेटा लीक (क्लाउड पर कुछ नहीं)", "trust_models": "सभी मॉडल समर्थित (Claude, GPT, Ollama)", "ide_status": "DuckDB इन-मेमोरी (स्थानीय)", "ide_tab_ide": "पूर्ण एनालिटिक्स IDE", "ide_tab_explore": "सत्यापन कैनवास", "ide_tab_connectors": "15+ डेटा कनेक्टर्स", "ide_tab_dashboards": "मल्टी-चार्ट डैशबोर्ड", "ide_tab_reports": "TipTap नैरेटिव रिपोर्ट्स", "pillars_kicker": "01 / वास्तुकला के स्तंभ", "pillars_title": "इंजीनियरिंग की सटीकता और आसान अन्वेषण का संगम", "pillars_desc": "पारंपरिक नोटबुक की समस्याओं और पुराने BI टूल्स की जटिलताओं को समाप्त करने के लिए निर्मित।", "p1_title": "अविनाशी डेटा थ्रेड्स (Data Threads)", "p1_desc": "प्रत्येक प्रश्न एक स्वतंत्र शाखा (DAG) बनाता है। बाद के प्रश्न व्युत्पन्न तालिकाओं पर काम करते हैं और मूल डेटा को नहीं बदलते।", "p1_tag": "डेटा वंशावली स्टेट मशीन", "p2_title": "वास्तविक डेटा सत्यापन कैनवास", "p2_desc": "AI के झूठे अनुमानों को अलविदा कहें। प्रत्येक विज़ुअलाइज़ेशन चार्ट के ठीक नीचे स्थित वास्तविक तालिका से तैयार होता है।", "p2_tag": "शून्य भ्रामक मेट्रिक्स", "p3_title": "DuckDB इन-मेमोरी इंजन", "p3_desc": "DuckDB, Pandas और PyArrow द्वारा संचालित अत्यधिक तेज़ गणना। मिलीसेकंड में लाखों पंक्तियों का स्थानीय विश्लेषण।", "p3_tag": "वेक्टराइज्ड एनालिटिक्स", "p4_title": "अवधारणा एन्कोडिंग शेल्व्स", "p4_desc": "फील्ड्स को सीधे शेल्व्स में खींचें और छोड़ें: X, Y, रंग, आकार और पहलू। Vega-Lite के साथ रीयल-टाइम समन्वय।", "p4_tag": "ग्राफिक्स का व्याकरण", "p5_title": "TipTap नैरेटिव रिपोर्ट्स", "p5_desc": "चार्ट को तुरंत व्यावसायिक रिपोर्ट में बदलें। व्याख्यात्मक पाठ और लाइव चार्ट कार्ड्स को सहजता से संयोजित करने वाला संपादक।", "p5_tag": "कार्यकारी रिपोर्टिंग", "p6_title": "स्थानीय गोपनीयता सर्वोपरि", "p6_desc": "आपकी डेटा पंक्तियाँ कभी भी आपके कंप्यूटर से बाहर नहीं जातीं। केवल कॉलम के नाम और प्रकार मॉडल को भेजे जाते हैं।", "p6_tag": "सुरक्षित और ऑफ़लाइन सक्षम", "spotlight_kicker": "02 / डेटा की सच्चाई", "spotlight_title": "चार्ट के ठीक नीचे डेटा पंक्तियाँ", "spotlight_desc": "Datly यह सुनिश्चित करता है कि आपका प्रश्न, विज़ुअलाइज़ेशन और गणना की गई वास्तविक डेटा पंक्तियाँ हमेशा एक साथ दिखाई दें।", "s1_title": "Explore: तुरंत और सटीक जांच", "s1_desc": "प्रश्न पूछने पर Datly नई तालिका बनाता है और उसका चार्ट बनाता है। नीचे दिए गए ग्रिड में सटीक मान तुरंत जांचे जा सकते हैं।", "s1_tag1": "इंटरैक्टिव ज़ूम", "s1_tag2": "CSV डाउनलोड", "s1_tag3": "कर्व / स्टेप्स / पॉइंट्स", "s2_title": "डेटा कॉपी किए बिना डैशबोर्ड", "s2_desc": "डैशबोर्ड या रिपोर्ट में चार्ट जोड़ने पर डेटा की नकली प्रतियां नहीं बनतीं। सभी सतहें एक ही डेटा थ्रेड DAG से जुड़ी रहती हैं।", "s2_tag1": "मल्टी-कार्ड डैशबोर्ड", "s2_tag2": "PDF निर्यात", "s2_tag3": "संबद्ध DAG स्थिति", "connectors_kicker": "03 / एंटरप्राइज़ कनेक्टर्स", "connectors_title": "एक बार कनेक्ट करें। स्थानीय रूप से क्वेरी करें।", "connectors_desc": "डेटा लेक, वेयरहाउस, रिलेशनल डेटाबेस और वेब टेबल से लाइव डेटा आसानी से जोड़ें।", "filter_all": "सभी कनेक्टर्स (15+)", "filter_warehouse": "वेयरहाउस और लेक्स", "filter_sql": "रिलेशनल SQL", "filter_cloud": "NoSQL और क्लाउड", "filter_files": "फाइलें और वेब", "compare_kicker": "04 / तकनीकी तुलना", "compare_title": "टीमें Datly को क्यों चुनती हैं", "compare_desc": "पारंपरिक एनालिटिक्स टूल्स के साथ विस्तृत तुलना।", "compare_th_feature": "सुविधा / सुरक्षा नियंत्रण", "compare_th_jupyter": "Jupyter Notebooks", "compare_th_bi": "क्लाउड BI (Tableau / PowerBI)", "compare_th_dbeaver": "SQL IDEs (DBeaver)", "compare_th_datly": "DATLY IDE", "compare_r1_title": "प्राकृतिक भाषा में AI द्वारा विश्लेषण", "compare_r1_jupyter": "⚠️ बाहरी प्लगइन्स की आवश्यकता", "compare_r1_bi": "⚠️ महंगे और सीमित कोपायलट", "compare_r1_dbeaver": "❌ कोई AI नहीं या सिर्फ सामान्य सुझाव", "compare_r1_datly": "⚡ संदर्भ-जागरूक देशी एजेंट (@टेबल संदर्भ)", "compare_r2_title": "चार्ट के नीचे सीधा डेटा ग्रिड", "compare_r2_jupyter": "❌ अलग सेल आउटपुट", "compare_r2_bi": "❌ जटिल मेनू और ड्रिल-डाउन", "compare_r2_dbeaver": "❌ मैन्युअल क्वेरी लिखना जरूरी", "compare_r2_datly": "🛡️ स्क्रीन पर चार्ट के नीचे सदैव दृश्यमान", "compare_r3_title": "एन्कोडिंग शेल्व्स + AI एजेंट", "compare_r3_jupyter": "❌ केवल कोड सेल", "compare_r3_bi": "⚠️ केवल शेल्व्स (कोई एजेंट नहीं)", "compare_r3_dbeaver": "❌ केवल कोड", "compare_r3_datly": "✅ दोनों दिशाओं में सक्रिय शेल्व्स + एजेंट", "compare_r4_title": "स्थानीय गोपनीयता (शून्य रिसाव)", "compare_r4_jupyter": "✅ स्थानीय निष्पादन", "compare_r4_bi": "❌ क्लाउड पर डेटा भेजना अनिवार्य", "compare_r4_dbeaver": "✅ स्थानीय क्वेरी", "compare_r4_datly": "🔒 100% स्थानीय (केवल स्कीमा मॉडल को जाता है)", "compare_r5_title": "डेटा वंशावली आर्किटेक्चर", "compare_r5_jupyter": "❌ नाजुक अनुक्रमिक स्थिति", "compare_r5_bi": "❌ स्थिर डेटा मॉडल", "compare_r5_dbeaver": "❌ सरल इतिहास सूची", "compare_r5_datly": "🌲 शाखाबद्ध डेटा थ्रेड्स (DAG)", "compare_r6_title": "इन-मेमोरी वेक्टर इंजन", "compare_r6_jupyter": "⚠️ कर्नेल पर निर्भर", "compare_r6_bi": "⚠️ बंद मालिकाना इंजन", "compare_r6_dbeaver": "❌ रिमोट डेटाबेस पर निर्भर", "compare_r6_datly": "⚡ DuckDB + Pandas + PyArrow", "compare_r7_title": "WYSIWYG रिपोर्टिंग संपादक", "compare_r7_jupyter": "❌ केवल सामान्य मार्कडाउन", "compare_r7_bi": "⚠️ भारी और जटिल कैनवास", "compare_r7_dbeaver": "❌ उपलब्ध नहीं", "compare_r7_datly": "📝 TipTap रिच टेक्स्ट रिपोर्ट इंजन", "compare_r8_title": "स्वचालित वेब टेबल स्क्रैपिंग", "compare_r8_jupyter": "❌ मैन्युअल स्क्रिप्ट्स", "compare_r8_bi": "❌ समर्थित नहीं", "compare_r8_dbeaver": "❌ समर्थित नहीं", "compare_r8_datly": "🌐 इनबिल्ट Playwright हेडलेस स्क्रैपर", "compare_r9_title": "मॉडल स्वतंत्रता (BYOK / ऑफ़लाइन)", "compare_r9_jupyter": "⚠️ मैन्युअल कॉन्फ़िगरेशन", "compare_r9_bi": "❌ वेंडर क्लाउड बंधन", "compare_r9_dbeaver": "❌ लागू नहीं", "compare_r9_datly": "🔓 LiteLLM (Claude, GPT, Ollama)", "quickstart_kicker": "05 / आसान सेटअप", "quickstart_title": "60 सेकंड के भीतर शुरू करें", "quickstart_desc": "अपनी पसंदीदा विधि चुनें: आधुनिक uv पैकेज मैनेजर, Docker कंटेनर या 1-कमांड AI एजेंट।", "term_tab_uv": "Astral uv (सबसे तेज़)", "term_tab_pip": "Python pip + Yarn", "term_tab_docker": "Docker Compose", "term_tab_agent": "1-कमांड AI एजेंट", "term_tab_desktop": "डेस्कटॉप ऐप बिल्ड", "releases_kicker": "06 / सत्यापित रिलीज़", "releases_title": "डेस्कटॉप ऐप्स डाउनलोड करें", "releases_desc": "हार्डवेयर त्वरण के साथ Windows, macOS और Linux के लिए नेटिव इंस्टालर।", "releases_box_title": "Datly Desktop Suite v0.8.0-beta", "releases_box_desc": "Electron 43 कंटेनर · DuckDB एम्बेडेड इंजन · मल्टी-LLM समर्थन", "releases_btn_title": "सभी रिलीज़ फ़ाइलें देखें", "releases_btn_sub": "Win · macOS · Linux समर्थित", "faq_kicker": "07 / सामान्य प्रश्न", "faq_title": "महत्वपूर्ण जानकारी", "faq_desc": "तकनीकी, सुरक्षा और एकीकरण संबंधी सभी प्रश्नों के स्पष्ट उत्तर।", "faq_q1": "क्या Datly मेरे निजी डेटा को OpenAI या Anthropic को भेजता है?", "faq_a1": "नहीं, बिल्कुल नहीं। Datly स्थानीय-प्रथम गोपनीयता पर काम करता है। आपका डेटा केवल आपके कंप्यूटर की मेमोरी और DuckDB में रहता है। मॉडल को केवल कॉलम के नाम और प्रकार भेजे जाते हैं ताकि वह कोड लिख सके।", "faq_q2": "क्या मैं इंटरनेट के बिना पूरी तरह से ऑफ़लाइन Datly चला सकता हूँ?", "faq_a2": "हाँ। Datly स्थानीय मॉडल (जैसे Ollama या vLLM के ज़रिए qwen2.5-coder या llama3.3) के साथ काम करता है, जिससे यह पूरी तरह से ऑफ़लाइन सुरक्षित वातावरण में चल सकता है।", "faq_q3": "Datly झूठे AI चार्ट और आंकड़ों को कैसे रोकता है?", "faq_a3": "AI को सीधे चार्ट या आंकड़े बनाने की अनुमति नहीं दी जाती। AI कोड लिखता है जो आपके वास्तविक डेटा पर निष्पादित होकर एक वास्तविक तालिका बनाता है जिसे चार्ट के नीचे देखा जा सकता है।", "faq_q4": "Datly और Microsoft Research Data Formulator में क्या अंतर है?", "faq_a4": "Datly ने उस शोध को आगे बढ़ाते हुए 15+ क्लाउड कनेक्टर्स, TipTap रिपोर्ट्स, डैशबोर्ड और पूर्ण डेस्कटॉप ऐप्स के साथ एक पेशेवर एंटरप्राइज़ IDE का रूप दिया है।", "faq_q5": "डेटा थ्रेड्स Jupyter की समस्याओं को कैसे हल करते हैं?", "faq_a5": "Datly एक निर्देशित चक्रीय ग्राफ (DAG) का उपयोग करता है जहाँ प्रत्येक प्रश्न एक स्वतंत्र कार्ड बनता है, जिससे पूर्ववर्ती डेटा सुरक्षित रहता है।", "footer_tagline": "उच्च सुरक्षा AI डेटा विज़ुअलाइज़ेशन IDE और एनालिटिक्स वर्कबेंच · MIT लाइसेंस", "footer_docs": "प्रलेखन", "footer_license": "MIT लाइसेंस", "footer_bottom": "© 2026 Datly प्रोजेक्ट · ओपन सोर्स सॉफ्टवेयर", "toast_copy": "क्लिपबोर्ड पर कॉपी किया गया!"}, "th": {"nav_features": "คุณสมบัติ", "nav_canvas": "การตรวจสอบข้อมูลจริง", "nav_connectors": "ตัวเชื่อมต่อ (15+)", "nav_comparison": "เปรียบเทียบ", "nav_quickstart": "เริ่มต้นใช้งาน", "nav_faq": "คำถามที่พบบ่อย", "nav_star": "ให้ดาว", "nav_quickstart_btn": "เริ่มต้นทันที", "hero_badge": "IDE สำหรับสร้างการแสดงผลข้อมูลด้วย AI และโต๊ะทำงานวิเคราะห์ข้อมูลยุคใหม่", "hero_title_1": "พูดคุยกับข้อมูลของคุณด้วยภาษาธรรมชาติ", "hero_title_2": "ตรวจสอบทุกแถวการคำนวณจริงใต้กราฟ", "hero_desc": "Datly เป็นโอเพนซอร์ส IDE สำหรับการวิเคราะห์ข้อมูลแบบ Local-First ที่เปลี่ยนตารางขององค์กรให้เป็นแผนภูมิเชิงโต้ตอบด้วยภาษาธรรมชาติ โดยที่แถวข้อมูลที่คำนวณได้จริงจะแสดงอยู่ใต้กราฟเสมอ ข้อมูลไม่รั่วไหลสู่คลาวด์ ไม่มีการคาดเดาหลอนจาก AI", "hero_btn_quickstart": "เริ่มต้นด้วย 1 คำสั่ง", "hero_btn_sub": "uv run data_formulator", "hero_btn_github": "ดูบน GitHub", "hero_btn_desktop": "แอปเดสก์ท็อป", "trust_local": "ประมวลผลบนเครื่อง 100% (DuckDB ในหน่วยความจำ)", "trust_connectors": "ตัวเชื่อมต่อระดับองค์กรกว่า 15+ ตัว", "trust_privacy": "ข้อมูลไม่รั่วไหลสู่ภายนอก", "trust_models": "รองรับหลายโมเดล (Claude, GPT, Ollama)", "ide_status": "DuckDB ในหน่วยความจำ (ในเครื่อง)", "ide_tab_ide": "IDE วิเคราะห์ข้อมูลฉบับสมบูรณ์", "ide_tab_explore": "ผืนผ้าใบตรวจสอบข้อมูลจริง", "ide_tab_connectors": "ตัวเชื่อมต่อข้อมูล 15+", "ide_tab_dashboards": "แดชบอร์ดหลายกราฟ", "ide_tab_reports": "รายงานเชิงบรรยาย TipTap", "pillars_kicker": "01 / เสาหลักทางสถาปัตยกรรม", "pillars_title": "ความแม่นยำทางวิศวกรรมผสมผสานกับการสำรวจข้อมูลที่ลื่นไหล", "pillars_desc": "ออกแบบมาเพื่อแก้ปัญหาสถานะทับซ้อนในโน้ตบุ๊กและความแข็งกระด้างของเครื่องมือ BI แบบดั้งเดิม", "p1_title": "เธรดข้อมูลแบบไม่ทำลายข้อมูลเดิม", "p1_desc": "แต่ละคำถามจะสร้างกิ่งของเธรดแยกออกมา (DAG) คำถามต่อเนื่องจะคำนวณบนตารางที่ได้มาใหม่โดยไม่เขียนทับชุดข้อมูลดั้งเดิม", "p1_tag": "ระบบติดตามสายข้อมูล", "p2_title": "ผืนผ้าใบตรวจสอบข้อมูลจริง (Ground Truth)", "p2_desc": "บอกลาภาพหลอนของ AI ทุกกราฟสร้างจากตารางจริงที่คำนวณได้ซึ่งแสดงอยู่ใต้กราฟทันที ตรวจสอบได้ทุกเซลล์และดาวน์โหลดเป็น CSV ได้", "p2_tag": "ไร้ตัวเลขหลอกตา", "p3_title": "DuckDB เอนจินประมวลผลในหน่วยความจำ", "p3_desc": "การประมวลผลเวกเตอร์ความเร็วสูงด้วย DuckDB, Pandas และ PyArrow รันการรวมข้อมูลและการเชื่อมโยงข้อมูลหลายล้านแถวในระดับมิลลิวินาที", "p3_tag": "การวิเคราะห์แบบเวกเตอร์", "p4_title": "แท่นเข้ารหัสแนวคิดแบบลากวาง", "p4_desc": "ลากและวางฟิลด์ลงในแท่นภาพ: แกน X, Y, สี, ขนาด และการแบ่งส่วน เชื่อมโยงแบบสองทิศทางกับข้อกำหนด Vega-Lite", "p4_tag": "ไวยากรณ์แห่งภาพกราฟิก", "p5_title": "รายงานเชิงบรรยาย TipTap", "p5_desc": "เปลี่ยนกราฟสำรวจให้เป็นรายงานระดับผู้บริหาร ด้วยตัวแก้ไข WYSIWYG ที่ผสานคำบรรยายและกราฟิกสดไว้ด้วยกัน", "p5_tag": "รายงานระดับผู้บริหาร", "p6_title": "ความเป็นส่วนตัวบนเครื่อง 100%", "p6_desc": "แถวข้อมูลของคุณจะไม่หลุดออกจากคอมพิวเตอร์ของคุณเด็ดขาด ระบบจะส่งเฉพาะโครงสร้างคอลัมน์ไปยังโมเดล LLM เท่านั้น", "p6_tag": "ใช้งานแบบออฟไลน์ได้สมบูรณ์", "spotlight_kicker": "02 / การตรวจสอบข้อมูลจริง", "spotlight_title": "แถวข้อมูลที่อยู่ใต้เส้นกราฟ", "spotlight_desc": "ตารางข้อมูลอาจเก็บคำตอบแต่ก็ซ่อนรายละเอียดได้ Datly รับประกันว่าคำถาม แผนภูมิ และแถวข้อมูลจริงจะอยู่คู่กันเสมอ", "s1_title": "Explore: การตรวจสอบที่สอดประสานกัน", "s1_desc": "เมื่อคุณถาม Datly จะคำนวณตารางใหม่และวาดกราฟ ตารางด้านล่างแสดงตัวเลขจริงเพื่อตรวจสอบก่อนแชร์", "s1_tag1": "ซูมแบบโต้ตอบ", "s1_tag2": "ดาวน์โหลด CSV", "s1_tag3": "เส้นโค้ง / ขั้นบันได / จุด", "s2_title": "แดชบอร์ดโดยไม่ต้องทำสำเนาข้อมูลซ้ำ", "s2_desc": "การปักหมุดกราฟลงในแดชบอร์ดหรือรายงานจะไม่สร้างสำเนาข้อมูลซ้ำ ทุกส่วนจะอ้างอิงข้อมูลชุดเดียวกันใน DAG", "s2_tag1": "กระดานหลายการ์ด", "s2_tag2": "ส่งออกเป็น PDF", "s2_tag3": "เชื่อมโยงสถานะ DAG", "connectors_kicker": "03 / ตัวเชื่อมต่อระดับองค์กร", "connectors_title": "เชื่อมต่อครั้งเดียว คิวรีบนเครื่องได้ทันที", "connectors_desc": "ดึงข้อมูลตารางสดจาก Data Lake, คลังข้อมูล, ฐานข้อมูล SQL และหน้าเว็บ", "filter_all": "ตัวเชื่อมต่อทั้งหมด (15+)", "filter_warehouse": "คลังข้อมูลและ Data Lake", "filter_sql": "ฐานข้อมูล SQL", "filter_cloud": "NoSQL และคลาวด์", "filter_files": "ไฟล์และเว็บ", "compare_kicker": "04 / เปรียบเทียบทางเทคนิค", "compare_title": "ทำไมทีมข้อมูลจึงเลือกใช้ Datly", "compare_desc": "การเปรียบเทียบข้อกำหนดทางเทคนิคกับเครื่องมือวิเคราะห์ข้อมูลแบบเดิม", "compare_th_feature": "คุณสมบัติ / ความปลอดภัย", "compare_th_jupyter": "Jupyter Notebooks", "compare_th_bi": "Cloud BI ทั่วไป (Tableau / PowerBI)", "compare_th_dbeaver": "SQL IDE (DBeaver)", "compare_th_datly": "DATLY IDE", "compare_r1_title": "การสังเคราะห์ด้วย AI ภาษาธรรมชาติ", "compare_r1_jupyter": "⚠️ ต้องใช้ปลั๊กอินภายนอก", "compare_r1_bi": "⚠️ ผู้ช่วยเสริมราคาแพง", "compare_r1_dbeaver": "❌ ไม่มี หรือมีแค่เติมคำอัตโนมัติ", "compare_r1_datly": "⚡ ตัวแทน AI ในตัวพร้อมบริบท (@ตาราง)", "compare_r2_title": "ตารางข้อมูลจริงใต้กราฟทันที", "compare_r2_jupyter": "❌ แยกผลลัพธ์คนละเซลล์", "compare_r2_bi": "❌ ต้องคลิกเจาะลึกข้อมูลหลายชั้น", "compare_r2_dbeaver": "❌ ต้องเขียนคิวรีเอง", "compare_r2_datly": "🛡️ แสดงอยู่บนหน้าจอเสมอ", "compare_r3_title": "แท่นวางภาพ + AI ทำงานร่วมกัน", "compare_r3_jupyter": "❌ มีแค่เซลล์โค้ด", "compare_r3_bi": "⚠️ มีแค่แท่นวาง (ไม่มี AI เขียนโค้ด)", "compare_r3_dbeaver": "❌ มีแค่โค้ด", "compare_r3_datly": "✅ แท่นวางสองทิศทาง + AI Agent", "compare_r4_title": "ความเป็นส่วนตัวในเครื่อง (ไม่รั่วไหล)", "compare_r4_jupyter": "✅ ทำงานในเครื่อง", "compare_r4_bi": "❌ บังคับอัปโหลดข้อมูลขึ้นคลาวด์", "compare_r4_dbeaver": "✅ คิวรีในเครื่อง", "compare_r4_datly": "🔒 100% ในเครื่อง (ส่งแค่โครงสร้างให้ AI)", "compare_r5_title": "สถาปัตยกรรมสายข้อมูล (Data Lineage)", "compare_r5_jupyter": "❌ สถานะตัวแปรทับซ้อนกันง่าย", "compare_r5_bi": "❌ โมเดลข้อมูลตายตัว", "compare_r5_dbeaver": "❌ ประวัติคิวรีแบบธรรมดา", "compare_r5_datly": "🌲 เธรดข้อมูลแบบแยกกิ่ง (DAG)", "compare_r6_title": "เอนจินเวกเตอร์ในหน่วยความจำ", "compare_r6_jupyter": "⚠️ ขึ้นกับสภาพแวดล้อมเคอร์เนล", "compare_r6_bi": "⚠️ เอนจินปิดของเอกชน", "compare_r6_dbeaver": "❌ พึ่งพาเซิร์ฟเวอร์ฐานข้อมูลปลายทาง", "compare_r6_datly": "⚡ DuckDB + Pandas + PyArrow", "compare_r7_title": "รายงานเชิงบรรยาย WYSIWYG", "compare_r7_jupyter": "❌ มีแค่เซลล์ Markdown", "compare_r7_bi": "⚠️ แดชบอร์ดเทอะทะ", "compare_r7_dbeaver": "❌ ไม่รองรับ", "compare_r7_datly": "📝 TipTap ตัวแก้ไขรายงานแบบครบวงจร", "compare_r8_title": "ดึงตารางจากเว็บอัตโนมัติ", "compare_r8_jupyter": "❌ ต้องเขียนสคริปต์ BS4 เอง", "compare_r8_bi": "❌ ไม่รองรับ", "compare_r8_dbeaver": "❌ ไม่รองรับ", "compare_r8_datly": "🌐 ระบบดึงตารางเว็บด้วย Playwright ในตัว", "compare_r9_title": "อิสระในการเลือกโมเดล (BYOK / ออฟไลน์)", "compare_r9_jupyter": "⚠️ ต้องเชื่อมต่อ API เอง", "compare_r9_bi": "❌ ผูกขาดกับผู้ให้บริการคลาวด์", "compare_r9_dbeaver": "❌ ไม่เกี่ยวข้อง", "compare_r9_datly": "🔓 LiteLLM (Claude, GPT, Ollama)", "quickstart_kicker": "05 / เริ่มต้นใช้งานอย่างง่ายดาย", "quickstart_title": "เปิดใช้งาน Datly ในเวลาไม่ถึง 60 วินาที", "quickstart_desc": "เลือกวิธีที่คุณสะดวก: uv รุ่นใหม่, Docker คอนเทนเนอร์ หรือคำสั่ง AI 1 บรรทัด", "term_tab_uv": "Astral uv (เร็วที่สุด แนะนำ)", "term_tab_pip": "Python pip + Yarn", "term_tab_docker": "Docker Compose", "term_tab_agent": "คำสั่ง AI อัตโนมัติ", "term_tab_desktop": "สร้างแอปเดสก์ท็อป", "releases_kicker": "06 / เวอร์ชันทางการที่ผ่านการตรวจสอบ", "releases_title": "ดาวน์โหลดแอปพลิเคชันเดสก์ท็อป", "releases_desc": "ประสิทธิภาพระดับเนทีฟสำหรับ Windows, macOS และ Linux พร้อมการเร่งความเร็วด้วยฮาร์ดแวร์", "releases_box_title": "Datly Desktop Suite v0.8.0-beta", "releases_box_desc": "พัฒนาด้วย Electron 43 · เอนจิน DuckDB ในตัว · รองรับโมเดล AI หลากหลาย", "releases_btn_title": "ดูไฟล์สำหรับดาวน์โหลดทั้งหมด", "releases_btn_sub": "รองรับ Win · macOS · Linux", "faq_kicker": "07 / คำถามที่พบบ่อย", "faq_title": "สิ่งที่คุณควรรู้", "faq_desc": "คำตอบที่ชัดเจนในด้านเทคนิค ความปลอดภัย และการเชื่อมต่อข้อมูล", "faq_q1": "Datly ส่งข้อมูลส่วนตัวในฐานข้อมูลของฉันไปยัง OpenAI หรือ Anthropic หรือไม่?", "faq_a1": "ไม่ส่งเด็ดขาด Datly ยึดหลักความเป็นส่วนตัวบนเครื่อง 100% ข้อมูลจริงของคุณจะอยู่ใน RAM และ DuckDB บนคอมพิวเตอร์ของคุณเท่านั้น สิ่งที่ส่งไปยังโมเดล AI มีเพียงชื่อคอลัมน์และประเภทข้อมูลเพื่อสร้างโค้ดเท่านั้น ข้อมูลจริงจะไม่ถูกส่งออกไปไหน", "faq_q2": "ฉันสามารถใช้งาน Datly แบบออฟไลน์โดยไม่มีอินเทอร์เน็ตได้หรือไม่?", "faq_a2": "ได้แน่นอน Datly รองรับโมเดลแบบเปิดที่รันในเครื่องผ่าน Ollama หรือ vLLM (เช่น qwen2.5-coder หรือ llama3.3) ทำให้ใช้งานในระบบปิดที่ไม่มีอินเทอร์เน็ตได้อย่างปลอดภัย", "faq_q3": "Datly ป้องกันภาพหลอนและตัวเลขปลอมของ AI ได้อย่างไร?", "faq_a3": "AI ใน Datly จะไม่ได้รับอนุญาตให้วาดกราฟหรือเดาตัวเลขเอาเอง แต่จะต้องเขียนโค้ดที่รันบนข้อมูลจริงเพื่อสร้างตารางข้อมูลที่ตรวจสอบได้ ซึ่งจะแสดงอยู่ใต้กราฟเสมอ", "faq_q4": "Datly แตกต่างจาก Microsoft Research Data Formulator อย่างไร?", "faq_a4": "Datly พัฒนางานวิจัยต้นแบบให้กลายเป็น IDE สำหรับองค์กรที่สมบูรณ์แบบ โดยเพิ่มตัวเชื่อมต่อคลาวด์ 15+, ระบบรายงาน TipTap, แดชบอร์ด และแอปพลิเคชันเดสก์ท็อป", "faq_q5": "Data Threads แก้ปัญหาสถานะทับซ้อนใน Jupyter ได้อย่างไร?", "faq_a5": "โดยการใช้โครงสร้างแบบ Directed Acyclic Graph (DAG) แทนตัวแปรส่วนกลาง แต่ละคำถามจะเป็นการ์ดที่แยกอิสระจากกัน ทำให้ไม่กระทบกับข้อมูลก่อนหน้า", "footer_tagline": "IDE และโต๊ะทำงานวิเคราะห์ข้อมูลด้วย AI ความปลอดภัยสูง · โอเพนซอร์สภายใต้สัญญาอนุญาต MIT", "footer_docs": "เอกสารการใช้งาน", "footer_license": "สัญญาอนุญาต MIT", "footer_bottom": "© 2026 โครงการ Datly · ซอฟต์แวร์เสรี", "toast_copy": "คัดลอกลงในคลิปบอร์ดแล้ว!"}, "fil": {"nav_features": "Mga Tampok", "nav_canvas": "Pagpapatunay", "nav_connectors": "Mga Konektor (15+)", "nav_comparison": "Paghahambing", "nav_quickstart": "Mabilisang Simula", "nav_faq": "Mga Madalas Itanong", "nav_star": "I-star", "nav_quickstart_btn": "Simulan Ngayon", "hero_badge": "Makabagong AI Data Visualization IDE & Analytics Workbench", "hero_title_1": "Kausapin ang iyong datos gamit ang natural na wika.", "hero_title_2": "Suriin ang bawat hilera ng kalkulasyon.", "hero_desc": "Ang Datly ay isang open-source, local-first analytics IDE kung saan ginagawang interactive chart ng natural na wika ang mga talahanayan ng negosyo, habang ang nakalkulang mga hilera ng datos ay nananatiling nakikita sa screen sa ilalim ng chart. Walang tagas ng datos sa cloud. Walang imbento o maling hula mula sa AI.", "hero_btn_quickstart": "Magsimula sa 1 Utos", "hero_btn_sub": "uv run data_formulator", "hero_btn_github": "Tingnan sa GitHub", "hero_btn_desktop": "Desktop App", "trust_local": "100% Lokal na Pagproseso (DuckDB sa Memory)", "trust_connectors": "15+ Enterprise Connectors", "trust_privacy": "Zero Cloud Data Exfiltration", "trust_models": "Suportado ang Iba't Ibang Modelo (Claude, GPT, Ollama)", "ide_status": "DuckDB In-Memory (Lokal)", "ide_tab_ide": "Buong Analytics IDE", "ide_tab_explore": "Ground-Truth Verification Canvas", "ide_tab_connectors": "15+ Data Connectors", "ide_tab_dashboards": "Multi-Chart Dashboards", "ide_tab_reports": "TipTap Narrative Reports", "pillars_kicker": "01 / MGA HALIGI NG ARKITEKTURA", "pillars_title": "Katumpakan sa Inhinyerya at Malayang Pagsusuri", "pillars_desc": "Dinisenyo upang puksain ang pagkasira ng estado sa mga notebook at ang kakulangan sa flexibility ng mga lumang BI tool.", "p1_title": "Hindi Nakakasirang Data Threads", "p1_desc": "Bawat tanong ay lumilikha ng nakahiwalay na sangay ng thread (DAG). Ang mga susunod na tanong ay kumakalkula sa mga nagawang talahanayan nang hindi binabago ang orihinal na datos.", "p1_tag": "State Machine ng Pinagmulan", "p2_title": "Ground-Truth Verification Canvas", "p2_desc": "Magpaalam sa mga maling hula ng AI. Bawat chart ay direktang iginuguhit mula sa totoong talahanayang nakikita sa ilalim ng visualization.", "p2_tag": "Walang Pekeng Metriko", "p3_title": "DuckDB In-Memory Engine", "p3_desc": "Napakabilis na pagsusuri gamit ang DuckDB, Pandas, at PyArrow. Magsagawa ng mga kalkulasyon sa milyun-milyong hilera nang lokal sa loob ng milliseconds.", "p3_tag": "Mabilisang Vektor Analytics", "p4_title": "Concept Encoding Shelves", "p4_desc": "I-drag at i-drop ang mga field sa visual shelves: X, Y, Kulay, Laki, at Facet. Dalawang-direksyong pag-sync sa mga detalye ng Vega-Lite.", "p4_tag": "Gramatika ng Graphics", "p5_title": "TipTap Narrative Reports", "p5_desc": "Gawing pormal na ulat para sa pamunuan ang mga natuklasan. Isang WYSIWYG editor na pinagsasama ang teksto at mga live chart card.", "p5_tag": "Pang-ehekutibong Ulat", "p6_title": "Lokal na Pagkapribado", "p6_desc": "Ang iyong datos ay hindi kailanman aalis sa iyong computer. Mga pangalan lamang ng column ang ipinapadala sa mga LLM upang gumawa ng code.", "p6_tag": "Puwede Kahit Walang Internet", "spotlight_kicker": "02 / PAGPAPATUNAY SA DATOS", "spotlight_title": "Ang Hilera sa Ilalim ng Linya", "spotlight_desc": "Maaaring naglalaman ng sagot ang talahanayan ngunit nakatago ito. Tinitiyak ng Datly na ang tanong, chart, at totoong datos ay laging magkasama.", "s1_title": "Explore: Sabay-sabay na Pagsusuri", "s1_desc": "Kapag nagtanong ka, kinakalkula ng Datly ang bagong talahanayan at iginuguhit ito. Ipinapakita ng grid sa ilalim ang eksaktong mga numero.", "s1_tag1": "Interactive Zoom", "s1_tag2": "I-download ang CSV", "s1_tag3": "Curve / Steps / Points", "s2_title": "Dashboard Nang Walang Doble-Dobleng Datos", "s2_desc": "Ang pag-pin ng mga chart sa dashboard o ulat ay hindi nagpaparami ng kopya ng datos. Lahat ay nagbabahagi sa iisang Data Thread DAG.", "s2_tag1": "Multi-Card Pinboard", "s2_tag2": "I-export bilang PDF", "s2_tag3": "Konektadong DAG State", "connectors_kicker": "03 / MGA KONEKTOR NG ENTERPRISE", "connectors_title": "Ikonekta Nang Minsan. I-query Nang Lokal.", "connectors_desc": "Isama ang mga live na talahanayan mula sa data lakes, warehouses, SQL databases, at web pages.", "filter_all": "Lahat ng Konektor (15+)", "filter_warehouse": "Warehouses & Lakes", "filter_sql": "Relational SQL", "filter_cloud": "NoSQL & Cloud", "filter_files": "Files & Web", "compare_kicker": "04 / PAGHAHAMBING SA ARKITEKTURA", "compare_title": "Bakit Pinipili ng mga Koponan ang Datly", "compare_desc": "Masusing pagsusuri laban sa mga tradisyonal na gamit sa pagsusuri ng datos.", "compare_th_feature": "Tampok / Seguridad", "compare_th_jupyter": "Jupyter Notebooks", "compare_th_bi": "Tradisyonal na Cloud BI", "compare_th_dbeaver": "SQL IDEs (DBeaver)", "compare_th_datly": "DATLY IDE", "compare_r1_title": "Natural Language AI Synthesis", "compare_r1_jupyter": "⚠️ Umaasa sa mga plugin", "compare_r1_bi": "⚠️ Mahal na sariling copilot", "compare_r1_dbeaver": "❌ Wala o basic autocomplete lang", "compare_r1_datly": "⚡ Katutubong Agent na may Batayan (@talahanayan)", "compare_r2_title": "Grid ng Datos sa Ilalim ng Chart", "compare_r2_jupyter": "❌ Hiwalay na output sa cell", "compare_r2_bi": "❌ Kumplikadong nested drill-down", "compare_r2_dbeaver": "❌ Kailangan ng manu-manong query", "compare_r2_datly": "🛡️ Laging Nakikita sa Canvas", "compare_r3_title": "Visual Shelves + AI Agent", "compare_r3_jupyter": "❌ Code cells lang", "compare_r3_bi": "⚠️ Shelves lang (Walang Agent)", "compare_r3_dbeaver": "❌ Code lang", "compare_r3_datly": "✅ Dalawang-Direksyon na Shelves + Agent", "compare_r4_title": "Lokal na Pagkapribado (Walang Tagas)", "compare_r4_jupyter": "✅ Lokal na pagpapatakbo", "compare_r4_bi": "❌ Sapilitang pag-upload sa cloud", "compare_r4_dbeaver": "✅ Lokal na query", "compare_r4_datly": "🔒 100% Lokal (Schema lang ang napupunta sa LLM)", "compare_r5_title": "Arkitektura ng Data Lineage", "compare_r5_jupyter": "❌ Mahinang linear global state", "compare_r5_bi": "❌ Hindi nababagong data models", "compare_r5_dbeaver": "❌ Simpleng listahan ng kasaysayan", "compare_r5_datly": "🌲 Nagsasanga-sangang Data Threads (DAG)", "compare_r6_title": "In-Memory Vectorized Engine", "compare_r6_jupyter": "⚠️ Depende sa kernel", "compare_r6_bi": "⚠️ Saradong proprietary engine", "compare_r6_dbeaver": "❌ Depende sa remote DB server", "compare_r6_datly": "⚡ DuckDB + Pandas + PyArrow", "compare_r7_title": "WYSIWYG Narrative Reporting", "compare_r7_jupyter": "❌ Markdown cells lang", "compare_r7_bi": "⚠️ Mabibigat na dashboard canvas", "compare_r7_dbeaver": "❌ Hindi suportado", "compare_r7_datly": "📝 TipTap Rich-Text Editor", "compare_r8_title": "Awtomatikong Web Table Scraper", "compare_r8_jupyter": "❌ Manu-manong BS4 scripts", "compare_r8_bi": "❌ Hindi suportado", "compare_r8_dbeaver": "❌ Hindi suportado", "compare_r8_datly": "🌐 May Kasamang Playwright Scraper", "compare_r9_title": "Kalayaan sa Pagpili ng Modelo (BYOK)", "compare_r9_jupyter": "⚠️ Manu-manong pagsasaayos", "compare_r9_bi": "❌ Nakatali sa vendor cloud", "compare_r9_dbeaver": "❌ Hindi angkop", "compare_r9_datly": "🔓 LiteLLM (Claude, GPT, Ollama)", "quickstart_kicker": "05 / WALANG HIRAP NA PAG-INSTALL", "quickstart_title": "Simulan ang Datly sa Loob ng 60 Segundo", "quickstart_desc": "Piliin ang nais mong paraan: makabagong uv package manager, Docker container, o 1-command AI prompt.", "term_tab_uv": "Astral uv (Pinakamabilis)", "term_tab_pip": "Python pip + Yarn", "term_tab_docker": "Docker Compose", "term_tab_agent": "1-Command AI Prompt", "term_tab_desktop": "Paggawa ng Desktop App", "releases_kicker": "06 / MGA OPISYAL NA RELEASE", "releases_title": "I-download ang Desktop Packages", "releases_desc": "Katutubong bilis para sa Windows, macOS, at Linux na may hardware acceleration.", "releases_box_title": "Datly Desktop Suite v0.8.0-beta", "releases_box_desc": "Electron 43 Container · DuckDB Embedded Engine · Multi-LLM", "releases_btn_title": "Tingnan ang Lahat ng Files", "releases_btn_sub": "Para sa Win · macOS · Linux", "faq_kicker": "07 / MGA MADALAS ITANONG", "faq_title": "Lahat ng Dapat Mong Malaman", "faq_desc": "Malinaw na sagot sa mga tanong ukol sa teknikal, seguridad, at integrasyon.", "faq_q1": "Ipinapadala ba ng Datly ang kumpidensyal na datos ng aking kumpanya sa OpenAI o Anthropic?", "faq_a1": "Hindi kailanman. Mahigpit na ipinapatupad ng Datly ang local-first privacy. Ang iyong totoong datos ay nananatili lamang sa RAM at DuckDB sa iyong computer. Mga pangalan at uri lamang ng column ang ipinapadala sa modelo upang makapagsulat ito ng code.", "faq_q2": "Maaari ko bang patakbuhin ang Datly nang walang koneksyon sa internet?", "faq_a2": "Oo naman. Gumagana ang Datly kasama ang mga lokal na modelo gamit ang Ollama o vLLM (halimbawa: qwen2.5-coder o llama3.3), kaya maaari itong gamitin sa mga ligtas at nakahiwalay na network.", "faq_q3": "Paano pinipigilan ng Datly ang maling hula o imbento ng AI sa mga chart?", "faq_a3": "Hindi pinapayagan ang AI na basta mag-drawing ng graph o mag-imbento ng mga numero. Sumusulat ito ng totoong code na tumatakbo sa iyong datos upang gumawa ng tunay na talahanayan na makikita sa ilalim ng chart.", "faq_q4": "Ano ang pagkakaiba ng Datly sa Microsoft Research Data Formulator?", "faq_a4": "Pinaunlad ng Datly ang orihinal na pananaliksik upang maging ganap na enterprise data IDE na may 15+ cloud connectors, TipTap reporting, dashboards, at desktop applications.", "faq_q5": "Paano nireresolba ng Data Threads ang mga problema ng Jupyter?", "faq_a5": "Sa pamamagitan ng paggamit ng Directed Acyclic Graph (DAG) sa halip na pabago-bagong global variables. Bawat pagsusuri ay sariling card na hindi nakakaapekto sa mga naunang hakbang.", "footer_tagline": "Mataas na Integridad na AI Data Visualization IDE & Analytical Workbench · Open Source sa ilalim ng MIT License", "footer_docs": "Dokumentasyon", "footer_license": "Lisensyang MIT", "footer_bottom": "© 2026 Datly Project · Malayang Software", "toast_copy": "Nakopya sa clipboard!"}, "vi": {"nav_features": "Tính Năng", "nav_canvas": "Kiểm Chứng Dữ Liệu", "nav_connectors": "Bộ Kết Nối (15+)", "nav_comparison": "So Sánh", "nav_quickstart": "Bắt Đầu Nhanh", "nav_faq": "Câu Hỏi Thường Gặp", "nav_star": "Gắn Sao", "nav_quickstart_btn": "Bắt Đầu Ngay", "hero_badge": "IDE Trực Quan Hóa Dữ Liệu Bằng AI & Bàn Làm Việc Phân Tích Thế Hệ Mới", "hero_title_1": "Trò chuyện với dữ liệu của bạn.", "hero_title_2": "Xác minh từng dòng tính toán thực tế.", "hero_desc": "Datly là IDE phân tích dữ liệu cục bộ mã nguồn mở (Local-First). Bằng ngôn ngữ tự nhiên thông thường, dữ liệu doanh nghiệp được chuyển thành biểu đồ tương tác, đồng thời các hàng dữ liệu được tính toán luôn hiển thị ngay dưới đồ thị. Không rò rỉ dữ liệu lên đám mây. Không ảo giác từ AI.", "hero_btn_quickstart": "Bắt đầu với 1 Lệnh", "hero_btn_sub": "uv run data_formulator", "hero_btn_github": "Xem trên GitHub", "hero_btn_desktop": "Ứng Dụng Desktop", "trust_local": "100% Xử Lý Cục Bộ (DuckDB Trong Bộ Nhớ)", "trust_connectors": "15+ Bộ Kết Nối Doanh Nghiệp", "trust_privacy": "Không Rò Rỉ Dữ Liệu", "trust_models": "Hỗ Trợ Đa Mô Hình (Claude, GPT, Ollama)", "ide_status": "DuckDB Trong Bộ Nhớ (Cục bộ)", "ide_tab_ide": "IDE Phân Tích Đầy Đủ", "ide_tab_explore": "Khung Kiểm Chứng Dữ Liệu Thật", "ide_tab_connectors": "15+ Bộ Kết Nối Dữ Liệu", "ide_tab_dashboards": "Bảng Điều Khiển Đa Biểu Đồ", "ide_tab_reports": "Báo Cáo Tự Sự TipTap", "pillars_kicker": "01 / TRỤ CỘT KIẾN TRÚC", "pillars_title": "Tính Kỷ Luật Kỹ Thuật Kết Hợp Với Sự Khám Phá Mượt Mà", "pillars_desc": "Được xây dựng từ đầu để loại bỏ tình trạng xung đột biến của Notebook và sự cứng nhắc của các công cụ BI cũ.", "p1_title": "Luồng Dữ Liệu Không Phá Hủy (Data Threads)", "p1_desc": "Mỗi câu hỏi tạo ra một nhánh luồng độc lập (DAG). Các câu hỏi nối tiếp tính toán trên bảng dẫn xuất mà không ghi đè dữ liệu gốc.", "p1_tag": "Cơ Chế Theo Dõi Nguồn Gốc Dữ Liệu", "p2_title": "Khung Kiểm Chứng Dữ Liệu Thực Tế", "p2_desc": "Loại bỏ hoàn toàn ảo giác AI. Mỗi biểu đồ đều được vẽ từ bảng dữ liệu thật hiển thị ngay bên dưới để bạn đối chiếu.", "p2_tag": "Không Số Liệu Ảo", "p3_title": "Động Cơ DuckDB Trong Bộ Nhớ", "p3_desc": "Truy vấn vectơ siêu tốc với DuckDB, Pandas và PyArrow. Xử lý hàng triệu hàng dữ liệu ngay trên máy trong vài mili giây.", "p3_tag": "Phân Tích Vectơ", "p4_title": "Khay Mã Hóa Khái Niệm Trực Quan", "p4_desc": "Kéo thả các trường vào khay trực quan: Trục X, Y, Màu sắc, Kích thước và Phân nhánh. Đồng bộ hai chiều với Vega-Lite.", "p4_tag": "Ngữ Pháp Đồ Họa", "p5_title": "Báo Cáo Tự Sự TipTap", "p5_desc": "Biến các biểu đồ khám phá thành báo cáo chuyên nghiệp cho cấp quản lý với trình soạn thảo WYSIWYG tích hợp biểu đồ sống động.", "p5_tag": "Xuất Bản Báo Cáo Điều Hành", "p6_title": "Bảo Mật Cục Bộ Tuyệt Đối", "p6_desc": "Dữ liệu của bạn không bao giờ rời khỏi máy tính. Chỉ có cấu trúc tên cột và định dạng được gửi đến LLM để viết mã.", "p6_tag": "Hoạt Động Hoàn Toàn Offline", "spotlight_kicker": "02 / XÁC MINH DỮ LIỆU THỰC", "spotlight_title": "Hàng Dữ Liệu Nằm Ngay Dưới Biểu Đồ", "spotlight_desc": "Datly đảm bảo câu hỏi, biểu đồ và các hàng dữ liệu đã tính toán luôn hiển thị đồng thời trên một màn hình.", "s1_title": "Explore: Kiểm Tra Đối Chiếu Đồng Bộ", "s1_desc": "Khi đặt câu hỏi, Datly tính toán bảng mới và vẽ biểu đồ. Lưới dữ liệu bên dưới hiển thị số liệu cụ thể để bạn kiểm tra trước khi chia sẻ.", "s1_tag1": "Phóng To Tương Tác", "s1_tag2": "Tải Về CSV", "s1_tag3": "Đường Cong / Bậc Thang / Điểm", "s2_title": "Bảng Điều Khiển Không Nhân Bản Dữ Liệu", "s2_desc": "Ghim biểu đồ vào bảng điều khiển hoặc báo cáo không làm tăng dung lượng lưu trữ vì tất cả cùng tham chiếu chung một DAG.", "s2_tag1": "Bảng Nhiều Thẻ", "s2_tag2": "Xuất Ra PDF", "s2_tag3": "Liên Kết Trạng Thái DAG", "connectors_kicker": "03 / BỘ KẾT NỐI DOANH NGHIỆP", "connectors_title": "Kết Nối Một Lần. Truy Vấn Cục Bộ.", "connectors_desc": "Dễ dàng tích hợp dữ liệu từ Data Lakes, Kho Dữ Liệu (Warehouse), SQL và cả bảng trên trang web.", "filter_all": "Tất Cả Kết Nối (15+)", "filter_warehouse": "Kho Dữ Liệu & Lakes", "filter_sql": "SQL Quan Hệ", "filter_cloud": "NoSQL & Đám Mây", "filter_files": "Tệp Tin & Web", "compare_kicker": "04 / SO SÁNH KIẾN TRÚC", "compare_title": "Vì Sao Các Đội Ngũ Chọn Datly", "compare_desc": "Đánh giá chi tiết so với các công cụ phân tích dữ liệu truyền thống.", "compare_th_feature": "Tính Năng / Bảo Mật", "compare_th_jupyter": "Jupyter Notebooks", "compare_th_bi": "Cloud BI Truyền Thống", "compare_th_dbeaver": "SQL IDE (DBeaver)", "compare_th_datly": "DATLY IDE", "compare_r1_title": "Tổng Hợp Bằng AI Bằng Ngôn Ngữ Tự Nhiên", "compare_r1_jupyter": "⚠️ Phải cài plugin phụ", "compare_r1_bi": "⚠️ Trợ lý độc quyền đắt đỏ", "compare_r1_dbeaver": "❌ Không có hoặc gợi ý đơn giản", "compare_r1_datly": "⚡ Trợ Lý AI Chuyên Biệt Có Ngữ Cảnh (@bảng)", "compare_r2_title": "Lưới Dữ Liệu Thật Nằm Dưới Biểu Đồ", "compare_r2_jupyter": "❌ Nằm ở ô kết quả riêng biệt", "compare_r2_bi": "❌ Phải bấm đào sâu nhiều bước", "compare_r2_dbeaver": "❌ Phải tự viết câu lệnh SQL", "compare_r2_datly": "🛡️ Luôn Hiển Thị Rõ Ràng Trên Màn Hình", "compare_r3_title": "Khay Trực Quan + AI Phối Hợp", "compare_r3_jupyter": "❌ Chỉ có ô gõ mã", "compare_r3_bi": "⚠️ Chỉ có khay kéo thả (không có AI viết mã)", "compare_r3_dbeaver": "❌ Chỉ có mã lệnh", "compare_r3_datly": "✅ Khay Tương Tác Hai Chiều + Trợ Lý AI", "compare_r4_title": "Bảo Mật Cục Bộ (Không Rò Rỉ)", "compare_r4_jupyter": "✅ Chạy trên máy", "compare_r4_bi": "❌ Bắt buộc tải dữ liệu lên đám mây", "compare_r4_dbeaver": "✅ Truy vấn trên máy", "compare_r4_datly": "🔒 100% Cục Bộ (Chỉ gửi cấu trúc cột cho AI)", "compare_r5_title": "Kiến Trúc Nguồn Gốc Dữ Liệu", "compare_r5_jupyter": "❌ Trạng thái biến toàn cục dễ lỗi", "compare_r5_bi": "❌ Mô hình dữ liệu cứng nhắc", "compare_r5_dbeaver": "❌ Lịch sử truy vấn đơn giản", "compare_r5_datly": "🌲 Luồng Dữ Liệu Phân Nhánh (DAG)", "compare_r6_title": "Động Cơ Vectơ Trong Bộ Nhớ", "compare_r6_jupyter": "⚠️ Phụ thuộc vào môi trường nhân", "compare_r6_bi": "⚠️ Động cơ đóng độc quyền", "compare_r6_dbeaver": "❌ Phụ thuộc vào máy chủ cơ sở dữ liệu", "compare_r6_datly": "⚡ DuckDB + Pandas + PyArrow", "compare_r7_title": "Báo Cáo Tự Sự Trực Quan", "compare_r7_jupyter": "❌ Chỉ có ô Markdown tĩnh", "compare_r7_bi": "⚠️ Bảng điều khiển nặng nề", "compare_r7_dbeaver": "❌ Không hỗ trợ", "compare_r7_datly": "📝 Trình Soạn Thảo Đa Năng TipTap", "compare_r8_title": "Tự Động Thu Thập Bảng Web", "compare_r8_jupyter": "❌ Phải tự viết mã với BS4", "compare_r8_bi": "❌ Không hỗ trợ", "compare_r8_dbeaver": "❌ Không hỗ trợ", "compare_r8_datly": "🌐 Tích Hợp Sẵn Trình Thu Thập Playwright", "compare_r9_title": "Tự Do Chọn Mô Hình (BYOK / Offline)", "compare_r9_jupyter": "⚠️ Phải tự kết nối API", "compare_r9_bi": "❌ Bị khóa chặt với nhà cung cấp", "compare_r9_dbeaver": "❌ Không áp dụng", "compare_r9_datly": "🔓 LiteLLM (Claude, GPT, Ollama)", "quickstart_kicker": "05 / CÀI ĐẶT NHANH CHÓNG", "quickstart_title": "Khởi Chạy Datly Dưới 60 Giây", "quickstart_desc": "Chọn phương pháp phù hợp với bạn: trình quản lý uv hiện đại, Docker container hoặc lệnh AI tự động.", "term_tab_uv": "Astral uv (Nhanh nhất)", "term_tab_pip": "Python pip + Yarn", "term_tab_docker": "Docker Compose", "term_tab_agent": "Lệnh AI Tự Động 1 Bước", "term_tab_desktop": "Biên Dịch Bản Desktop", "releases_kicker": "06 / BẢN PHÁT HÀNH CHÍNH THỨC", "releases_title": "Tải Ứng Dụng Desktop", "releases_desc": "Hiệu năng gốc cho Windows, macOS và Linux với khả năng tăng tốc phần cứng.", "releases_box_title": "Bộ Ứng Dụng Datly Desktop v0.8.0-beta", "releases_box_desc": "Xây dựng trên Electron 43 · Động cơ DuckDB nhúng · Đa mô hình LLM", "releases_btn_title": "Xem Tất Cả Bản Tải Về", "releases_btn_sub": "Hỗ trợ Win · macOS · Linux", "faq_kicker": "07 / CÂU HỎI THƯỜNG GẶP", "faq_title": "Những Điều Bạn Cần Biết", "faq_desc": "Giải đáp rõ ràng về kỹ thuật, tính bảo mật và khả năng tích hợp.", "faq_q1": "Datly có gửi dữ liệu cơ sở dữ liệu nhạy cảm của tôi lên OpenAI hay Anthropic không?", "faq_a1": "Tuyệt đối không. Datly hoạt động theo kiến trúc ưu tiên cục bộ. Dữ liệu thực sự của bạn chỉ nằm trong RAM và DuckDB trên máy tính. Mô hình chỉ nhận tên cột để sinh mã lệnh chạy cục bộ.", "faq_q2": "Tôi có thể dùng Datly hoàn toàn không cần kết nối Internet không?", "faq_a2": "Có thể. Datly tích hợp với các mô hình chạy cục bộ như Ollama hoặc vLLM (ví dụ qwen2.5-coder hay llama3.3), rất thích hợp cho mạng nội bộ bảo mật cao.", "faq_q3": "Datly ngăn ngừa ảo giác vẽ sai số liệu của AI như thế nào?", "faq_a3": "AI không được phép tự vẽ hình ảnh hay bịa ra con số. AI phải viết mã chạy trên dữ liệu thật để tạo ra bảng kết quả hiển thị ngay dưới đồ thị cho bạn kiểm tra.", "faq_q4": "Datly khác gì so với Microsoft Research Data Formulator?", "faq_a4": "Datly phát triển nghiên cứu đó thành một IDE doanh nghiệp hoàn chỉnh với hơn 15 bộ kết nối đám mây, báo cáo TipTap, bảng điều khiển và phần mềm cài đặt máy tính.", "faq_q5": "Data Threads giải quyết lỗi ghi đè biến của Jupyter ra sao?", "faq_a5": "Bằng cách sử dụng đồ thị có hướng không chu trình (DAG). Mỗi câu hỏi là một thẻ độc lập lưu giữ kết quả riêng mà không làm hỏng các bước phân tích trước đó.", "footer_tagline": "IDE Trực Quan Hóa Dữ Liệu Bằng AI Độ Tin Cậy Cao · Mã Nguồn Mở Giấy Phép MIT", "footer_docs": "Tài Liệu", "footer_license": "Giấy Phép MIT", "footer_bottom": "© 2026 Dự án Datly · Phần Mềm Tự Do", "toast_copy": "Đã sao chép vào khay nhớ tạm!"}, "id": {"nav_features": "Fitur", "nav_canvas": "Verifikasi Data", "nav_connectors": "Konektor (15+)", "nav_comparison": "Perbandingan", "nav_quickstart": "Mulai Cepat", "nav_faq": "FAQ", "nav_star": "Beri Bintang", "nav_quickstart_btn": "Mulai Sekarang", "hero_badge": "IDE Visualisasi Data AI & Workbench Analisis Generasi Baru", "hero_title_1": "Bicara langsung dengan data Anda.", "hero_title_2": "Verifikasi setiap baris hasil kalkulasi.", "hero_desc": "Datly adalah IDE analitik open-source yang memprioritaskan pemrosesan lokal (local-first). Bahasa alami mengubah tabel perusahaan menjadi grafik interaktif, dengan baris data hasil perhitungan tetap tampil tepat di bawah grafik. Tanpa kebocoran data ke cloud. Tanpa halusinasi AI.", "hero_btn_quickstart": "Mulai dengan 1 Perintah", "hero_btn_sub": "uv run data_formulator", "hero_btn_github": "Lihat di GitHub", "hero_btn_desktop": "Aplikasi Desktop", "trust_local": "100% Pemrosesan Lokal (DuckDB di Memori)", "trust_connectors": "15+ Konektor Enterprise", "trust_privacy": "Nol Data Keluar ke Cloud", "trust_models": "Bebas Pilih Model (Claude, GPT, Ollama)", "ide_status": "DuckDB In-Memory (Lokal)", "ide_tab_ide": "IDE Analitik Lengkap", "ide_tab_explore": "Kanvas Verifikasi Ground-Truth", "ide_tab_connectors": "15+ Konektor Data", "ide_tab_dashboards": "Dashboard Multi-Grafik", "ide_tab_reports": "Laporan Naratif TipTap", "pillars_kicker": "01 / PILAR ARSITEKTUR", "pillars_title": "Presisi Rekayasa Bertemu Eksplorasi Data yang Lancar", "pillars_desc": "Dibangun untuk mengatasi masalah kekacauan state pada notebook dan kekakuan alat BI konvensional.", "p1_title": "Data Threads Non-Destruktif", "p1_desc": "Setiap pertanyaan membentuk cabang thread terisolasi (DAG). Analisis lanjutan berjalan di atas tabel turunan tanpa mengubah data asli.", "p1_tag": "State Machine Pelacakan Data", "p2_title": "Kanvas Verifikasi Ground-Truth", "p2_desc": "Ucapkan selamat tinggal pada halusinasi AI. Setiap grafik digambar langsung dari tabel fisik nyata yang ada di bawah grafik.", "p2_tag": "Tanpa Metrik Palsu", "p3_title": "Mesin DuckDB Dalam Memori", "p3_desc": "Kueri tervektorisasi secepat kilat dengan DuckDB, Pandas, dan PyArrow. Agregasi jutaan baris data secara lokal dalam hitungan milidetik.", "p3_tag": "Analitik Vektor", "p4_title": "Rak Pengodean Konsep Visual", "p4_desc": "Tarik dan letakkan kolom ke rak visual: Sumbu X, Y, Warna, Ukuran, dan Facet. Sinkronisasi dua arah dengan Vega-Lite.", "p4_tag": "Tata Bahasa Grafika", "p5_title": "Laporan Naratif TipTap", "p5_desc": "Ubah temuan grafik menjadi laporan eksekutif siap saji. Editor WYSIWYG yang memadukan narasi teks dan kartu grafik interaktif.", "p5_tag": "Publikasi Eksekutif", "p6_title": "Privasi Lokal Terjamin", "p6_desc": "Baris data Anda tidak pernah keluar dari komputer. Hanya struktur kolom dan metadata bersih yang dikirim ke LLM untuk membuat kode.", "p6_tag": "Bisa Beroperasi Sepenuhnya Offline", "spotlight_kicker": "02 / VERIFIKASI DATA NYATA", "spotlight_title": "Baris Data Tepat di Bawah Garis Grafik", "spotlight_desc": "Tabel bisa menyimpan jawaban namun menyembunyikan detail. Datly memastikan pertanyaan, grafik, dan baris data selalu tampil bersama.", "s1_title": "Explore: Pengecekan Tersinkronisasi", "s1_desc": "Saat Anda bertanya, Datly menghitung tabel turunan baru dan memplotnya. Kisi di bawah kurva menampilkan angka sebenarnya untuk dicek.", "s1_tag1": "Zoom Interaktif", "s1_tag2": "Unduh CSV", "s1_tag3": "Kurva / Tangga / Titik", "s2_title": "Dashboard Tanpa Duplikasi Data", "s2_desc": "Menyematkan grafik ke dashboard atau laporan TipTap tidak menggandakan data. Semua tampilan merujuk pada Data Thread DAG yang sama.", "s2_tag1": "Papan Multi-Kartu", "s2_tag2": "Ekspor ke PDF", "s2_tag3": "Koneksi Status DAG", "connectors_kicker": "03 / KONEKTOR ENTERPRISE", "connectors_title": "Hubungkan Sekali. Kueri Secara Lokal.", "connectors_desc": "Integrasikan tabel langsung dari data lake, warehouse, database SQL, dan halaman web.", "filter_all": "Semua Konektor (15+)", "filter_warehouse": "Warehouse & Data Lake", "filter_sql": "SQL Relasional", "filter_cloud": "NoSQL & Cloud", "filter_files": "Berkas & Web", "compare_kicker": "04 / PERBANDINGAN TEKNIS", "compare_title": "Mengapa Tim Analis Memilih Datly", "compare_desc": "Perbandingan spesifikasi teknis langsung dengan alat analitik konvensional.", "compare_th_feature": "Fitur / Keamanan", "compare_th_jupyter": "Jupyter Notebooks", "compare_th_bi": "Cloud BI Tradisional", "compare_th_dbeaver": "SQL IDE (DBeaver)", "compare_th_datly": "DATLY IDE", "compare_r1_title": "Sintesis AI Berbahasa Alami", "compare_r1_jupyter": "⚠️ Perlu plugin pihak ketiga", "compare_r1_bi": "⚠️ Copilot berbayar yang kaku", "compare_r1_dbeaver": "❌ Tidak ada atau sebatas pelengkap", "compare_r1_datly": "⚡ Agen AI Bawaan Kontekstual (@tabel)", "compare_r2_title": "Tabel Data Tepat di Bawah Grafik", "compare_r2_jupyter": "❌ Output terpisah di cell lain", "compare_r2_bi": "❌ Navigasi drill-down berbelit", "compare_r2_dbeaver": "❌ Harus menulis kueri manual", "compare_r2_datly": "🛡️ Selalu Tampil Bersama di Layar", "compare_r3_title": "Rak Visual + Kolaborasi AI Agent", "compare_r3_jupyter": "❌ Hanya ada cell kode", "compare_r3_bi": "⚠️ Hanya rak (tanpa AI coder)", "compare_r3_dbeaver": "❌ Hanya kode kueri", "compare_r3_datly": "✅ Rak Dua Arah + Agen Pintar", "compare_r4_title": "Privasi Lokal (Tanpa Kebocoran)", "compare_r4_jupyter": "✅ Eksekusi lokal", "compare_r4_bi": "❌ Wajib unggah data ke cloud", "compare_r4_dbeaver": "✅ Kueri lokal", "compare_r4_datly": "🔒 100% Lokal (Hanya skema ke LLM)", "compare_r5_title": "Arsitektur Pelacakan Data (Lineage)", "compare_r5_jupyter": "❌ State linear rentan error", "compare_r5_bi": "❌ Model data statis dan kaku", "compare_r5_dbeaver": "❌ Riwayat kueri sederhana", "compare_r5_datly": "🌲 Data Threads Bercabang (DAG)", "compare_r6_title": "Mesin Vektor Dalam Memori", "compare_r6_jupyter": "⚠️ Bergantung pada kernel", "compare_r6_bi": "⚠️ Mesin tertutup milik vendor", "compare_r6_dbeaver": "❌ Bergantung pada server DB remote", "compare_r6_datly": "⚡ DuckDB + Pandas + PyArrow", "compare_r7_title": "Laporan Naratif WYSIWYG", "compare_r7_jupyter": "❌ Hanya cell Markdown biasa", "compare_r7_bi": "⚠️ Kanvas dashboard yang berat", "compare_r7_dbeaver": "❌ Tidak tersedia", "compare_r7_datly": "📝 Editor Laporan Kaya TipTap", "compare_r8_title": "Pengikis Tabel Web Otomatis", "compare_r8_jupyter": "❌ Skrip manual BS4", "compare_r8_bi": "❌ Tidak didukung", "compare_r8_dbeaver": "❌ Tidak didukung", "compare_r8_datly": "🌐 Scraper Playwright Bawaan", "compare_r9_title": "Bebas Memilih Model (BYOK / Offline)", "compare_r9_jupyter": "⚠️ Harus pasang API manual", "compare_r9_bi": "❌ Terkunci di cloud vendor tertentu", "compare_r9_dbeaver": "❌ Tidak berlaku", "compare_r9_datly": "🔓 LiteLLM (Claude, GPT, Ollama)", "quickstart_kicker": "05 / INSTALASI MUDAH", "quickstart_title": "Jalankan Datly Dalam 60 Detik", "quickstart_desc": "Pilih metode yang Anda sukai: package manager uv modern, container Docker, atau 1 perintah asisten AI.", "term_tab_uv": "Astral uv (Paling Cepat)", "term_tab_pip": "Python pip + Yarn", "term_tab_docker": "Docker Compose", "term_tab_agent": "1 Perintah AI Agent", "term_tab_desktop": "Build Aplikasi Desktop", "releases_kicker": "06 / RILIS RESMI TERVERIFIKASI", "releases_title": "Unduh Aplikasi Desktop", "releases_desc": "Performa aplikasi native untuk Windows, macOS, dan Linux dengan akselerasi hardware.", "releases_box_title": "Datly Desktop Suite v0.8.0-beta", "releases_box_desc": "Container Electron 43 · Mesin DuckDB Terintegrasi · Dukungan Multi-LLM", "releases_btn_title": "Lihat Semua Berkas Unduhan", "releases_btn_sub": "Tersedia untuk Win · macOS · Linux", "faq_kicker": "07 / PERTANYAAN UMUM", "faq_title": "Segala Hal yang Perlu Diketahui", "faq_desc": "Jawaban jelas seputar aspek teknis, keamanan data, dan integrasi.", "faq_q1": "Apakah Datly mengirimkan data rahasia perusahaan saya ke OpenAI atau Anthropic?", "faq_a1": "Sama sekali tidak. Datly menganut arsitektur local-first yang ketat. Data asli Anda hanya tersimpan di memori RAM dan DuckDB pada komputer Anda. Hanya nama kolom yang dikirim ke model untuk menghasilkan kode.", "faq_q2": "Bisakah saya menjalankan Datly tanpa koneksi internet sama sekali?", "faq_a2": "Ya, bisa. Datly terintegrasi mulus dengan model lokal melalui Ollama atau vLLM (seperti qwen2.5-coder atau llama3.3), menjadikannya ideal untuk jaringan tertutup berkeamanan tinggi.", "faq_q3": "Bagaimana Datly mencegah halusinasi grafik dan angka palsu dari AI?", "faq_a3": "AI dilarang langsung menggambar visual atau mengarang angka. AI diwajibkan menulis kode yang dieksekusi pada data nyata untuk menghasilkan tabel fisik yang ditampilkan di bawah grafik.", "faq_q4": "Apa bedanya Datly dengan Microsoft Research Data Formulator?", "faq_a4": "Datly mengembangkan prototipe riset tersebut menjadi IDE kelas enterprise dengan 15+ konektor database cloud, laporan interaktif TipTap, dashboard, dan aplikasi desktop siap pakai.", "faq_q5": "Bagaimana Data Threads mengatasi masalah tumpang tindih variabel di Jupyter?", "faq_a5": "Dengan menggunakan struktur Directed Acyclic Graph (DAG). Setiap pertanyaan menjadi kartu mandiri dengan tabel turunannya sendiri tanpa merusak tahapan analisis sebelumnya.", "footer_tagline": "IDE Visualisasi Data AI & Workbench Analisis Berkeamanan Tinggi · Open Source Lisensi MIT", "footer_docs": "Dokumentasi", "footer_license": "Lisensi MIT", "footer_bottom": "© 2026 Proyek Datly · Perangkat Lunak Bebas", "toast_copy": "Berhasil disalin ke papan klip!"}, "tr": {"nav_features": "Özellikler", "nav_canvas": "Veri Doğrulama", "nav_connectors": "Bağlayıcılar (15+)", "nav_comparison": "Karşılaştırma", "nav_quickstart": "Hızlı Başlangıç", "nav_faq": "SSS", "nav_star": "Yıldızla", "nav_quickstart_btn": "Hemen Başla", "hero_badge": "Yeni Nesil Yapay Zeka Veri Görselleştirme IDE'si ve Analiz Çalışma Alanı", "hero_title_1": "Verilerinizle doğal dilde konuşun.", "hero_title_2": "Her hesaplanan veri satırını doğrulayın.", "hero_desc": "Datly, kurumsal tabloları doğal dille etkileşimli grafiklere dönüştüren ve hesaplanan veri satırlarını her zaman grafiğin hemen altında ekranda tutan yerel öncelikli (local-first) açık kaynaklı bir veri IDE'sidir. Sıfır bulut veri sızıntısı. Sıfır yapay zeka halüsinasyonu.", "hero_btn_quickstart": "1 Komutla Başlayın", "hero_btn_sub": "uv run data_formulator", "hero_btn_github": "GitHub'da İncele", "hero_btn_desktop": "Masaüstü Uygulaması", "trust_local": "%100 Yerel İşleme (Bellekte DuckDB)", "trust_connectors": "15+ Kurumsal Veri Bağlayıcısı", "trust_privacy": "Sıfır Buluta Veri Sızıntısı", "trust_models": "Çoklu Model Desteği (Claude, GPT, Ollama)", "ide_status": "DuckDB Bellek İçi (Yerel)", "ide_tab_ide": "Tam Analitik IDE", "ide_tab_explore": "Gerçek Veri Doğrulama Tuvali", "ide_tab_connectors": "15+ Veri Bağlayıcısı", "ide_tab_dashboards": "Çoklu Grafik Panoları", "ide_tab_reports": "TipTap Raporları", "pillars_kicker": "01 / MİMARİ TEMELLER", "pillars_title": "Mühendislik Titizliği ve Akıcı Veri Keşfi", "pillars_desc": "Notebook'ların değişken çakışma sorunlarını ve klasik BI araçlarının hantallığını çözmek için sıfırdan tasarlandı.", "p1_title": "Bozulmayan Veri İş Parçacıkları (Data Threads)", "p1_desc": "Her soru yalıtılmış bir iş parçacığı dalı (DAG) oluşturur. Devam soruları türetilmiş tablolar üzerinde çalışır ve orijinal veriyi asla bozmaz.", "p1_tag": "Veri Soy Ağacı Durum Makinesi", "p2_title": "Gerçek Veri Doğrulama Tuvali", "p2_desc": "Yapay zeka yanılsamalarına son. Her grafik, hemen altında açık duran gerçek türetilmiş tablodan oluşturulur.", "p2_tag": "Sıfır Sahte Metrik", "p3_title": "DuckDB Bellek İçi Motor", "p3_desc": "DuckDB, Pandas ve PyArrow ile ışık hızında vektörel sorgulama. Milyonlarca satırı yerel olarak milisaniyeler içinde toplayın.", "p3_tag": "Vektörel Analitik", "p4_title": "Kavramsal Kodlama Rafları", "p4_desc": "Alanları görsel raflara sürükleyip bırakın: X, Y, Renk, Boyut ve Gruplar. Vega-Lite ile çift yönlü anlık senkronizasyon.", "p4_tag": "Grafik Dilbilgisi", "p5_title": "TipTap Raporlama Editörü", "p5_desc": "Grafikleri doğrudan yönetici raporlarına dönüştürün. Açıklamalar ile canlı grafikleri harmanlayan zengin metin düzenleyici.", "p5_tag": "Yönetici Raporlaması", "p6_title": "Önce Yerel Gizlilik", "p6_desc": "Verileriniz bilgisayarınızdan asla ayrılmaz. Kod üretimi için yapay zeka modellerine yalnızca sütun şemaları iletilir.", "p6_tag": "Çevrimdışı ve Güvenli", "spotlight_kicker": "02 / VERİ DOĞRULUĞU", "spotlight_title": "Çizginin Altındaki Gerçek Veri Satırı", "spotlight_desc": "Datly sorunuzun, çizilen grafiğin ve hesaplanan gerçek satırların her zaman ekranda yan yana kalmasını sağlar.", "s1_title": "Explore: Senkronize Kontrol", "s1_desc": "Bir soru sorduğunuzda Datly yeni bir tablo hesaplar ve çizer. Eğrinin altındaki tablo, paylaşmadan önce kontrol etmeniz için sayıları gösterir.", "s1_tag1": "Etkileşimli Yakınlaştırma", "s1_tag2": "CSV İndir", "s1_tag3": "Eğri / Basamak / Nokta", "s2_title": "Veri Çoğaltmadan Gösterge Panelleri", "s2_desc": "Grafikleri panoya sabitlemek veriyi kopyalamaz. Tüm ekranlar tek bir Data Thread DAG referansını paylaşır.", "s2_tag1": "Çoklu Kart Panosu", "s2_tag2": "PDF Dışa Aktarma", "s2_tag3": "Bağlı DAG Durumu", "connectors_kicker": "03 / KURUMSAL BAĞLAYICILAR", "connectors_title": "Bir Kez Bağlayın. Yerel Olarak Sorgulayın.", "connectors_desc": "Data lake'lerden, ambarlardan, SQL veritabanlarından ve web sayfalarından canlı tablolar ekleyin.", "filter_all": "Tüm Bağlayıcılar (15+)", "filter_warehouse": "Veri Ambarları & Lake", "filter_sql": "İlişkisel SQL", "filter_cloud": "NoSQL & Bulut", "filter_files": "Dosyalar & Web", "compare_kicker": "04 / MİMARİ KARŞILAŞTIRMA", "compare_title": "Veri Ekipleri Neden Datly'yi Seçiyor", "compare_desc": "Geleneksel veri araçlarıyla ayrıntılı teknik karşılaştırma.", "compare_th_feature": "Özellik / Güvenlik Kontrolü", "compare_th_jupyter": "Jupyter Notebooks", "compare_th_bi": "Klasik Bulut BI", "compare_th_dbeaver": "SQL IDE (DBeaver)", "compare_th_datly": "DATLY IDE", "compare_r1_title": "Doğal Dilde Yapay Zeka Sentezi", "compare_r1_jupyter": "⚠️ Üçüncü taraf eklentiler", "compare_r1_bi": "⚠️ Pahalı özel yardımcılar", "compare_r1_dbeaver": "❌ Yok veya temel tamamlama", "compare_r1_datly": "⚡ Tablo Bağlamlı Yerel Ajan (@tablo)", "compare_r2_title": "Grafik Altında Gerçek Veri Tablosu", "compare_r2_jupyter": "❌ Ayrı hücre çıktısında kalır", "compare_r2_bi": "❌ Karmaşık iç içe menüler", "compare_r2_dbeaver": "❌ Manuel sorgu yazımı şart", "compare_r2_datly": "🛡️ Ekranda Her Zaman Birlikte Görünür", "compare_r3_title": "Görsel Raflar + Yapay Zeka Ajanı", "compare_r3_jupyter": "❌ Yalnızca kod hücreleri", "compare_r3_bi": "⚠️ Yalnızca raflar (Ajan kodu yazamaz)", "compare_r3_dbeaver": "❌ Yalnızca kod", "compare_r3_datly": "✅ Çift Yönlü Raflar + Akıllı Ajan", "compare_r4_title": "Yerel Gizlilik (Sıfır Sızıntı)", "compare_r4_jupyter": "✅ Yerel çalıştırma", "compare_r4_bi": "❌ Veriyi buluta yükleme zorunluluğu", "compare_r4_dbeaver": "✅ Yerel sorgulama", "compare_r4_datly": "🔒 %100 Yerel (Modele sadece şema gider)", "compare_r5_title": "Veri Soy Ağacı Mimarisi", "compare_r5_jupyter": "❌ Kırılgan doğrusal değişken durumu", "compare_r5_bi": "❌ Statik veri modelleme", "compare_r5_dbeaver": "❌ Basit sorgu geçmişi", "compare_r5_datly": "🌲 Dallanan Veri İş Parçacıkları (DAG)", "compare_r6_title": "Bellek İçi Vektörel Motor", "compare_r6_jupyter": "⚠️ Çekirdeğe bağımlı", "compare_r6_bi": "⚠️ Kapalı tescilli motor", "compare_r6_dbeaver": "❌ Uzak veri tabanı gücüne bağlı", "compare_r6_datly": "⚡ DuckDB + Pandas + PyArrow", "compare_r7_title": "Zengin Metin Raporlama", "compare_r7_jupyter": "❌ Yalnızca Markdown hücreleri", "compare_r7_bi": "⚠️ Hantal pano düzenleri", "compare_r7_dbeaver": "❌ Desteklenmiyor", "compare_r7_datly": "📝 TipTap Canlı Raporlama Editörü", "compare_r8_title": "Otomatik Web Tablosu Çekme", "compare_r8_jupyter": "❌ Manuel BS4 betikleri", "compare_r8_bi": "❌ Desteklenmiyor", "compare_r8_dbeaver": "❌ Desteklenmiyor", "compare_r8_datly": "🌐 Dahili Playwright Kazıyıcı", "compare_r9_title": "Model Bağımsızlığı (Kendi Anahtarın / Çevrimdışı)", "compare_r9_jupyter": "⚠️ Manuel API kurulumu", "compare_r9_bi": "❌ Sağlayıcıya bağımlılık", "compare_r9_dbeaver": "❌ Geçerli değil", "compare_r9_datly": "🔓 LiteLLM (Claude, GPT, Ollama)", "quickstart_kicker": "05 / ZAHMETSİZ KURULUM", "quickstart_title": "Datly'yi 60 Saniyede Başlatın", "quickstart_desc": "Tercih ettiğiniz yöntemi seçin: modern uv, Docker container veya tek komutluk yapay zeka ajanı.", "term_tab_uv": "Astral uv (En Hızlı)", "term_tab_pip": "Python pip + Yarn", "term_tab_docker": "Docker Compose", "term_tab_agent": "Tek Komutluk Yapay Zeka İstemi", "term_tab_desktop": "Masaüstü Uygulama Derleme", "releases_kicker": "06 / DOĞRULANMIŞ RESMİ SÜRÜMLER", "releases_title": "Masaüstü Paketlerini İndirin", "releases_desc": "Donanım hızlandırma destekli Windows, macOS ve Linux için yerel bağımsız paketler.", "releases_box_title": "Datly Desktop Suite v0.8.0-beta", "releases_box_desc": "Electron 43 Altyapısı · Gömülü DuckDB Motoru · Çoklu LLM Desteği", "releases_btn_title": "Tüm İndirme Dosyalarını Gör", "releases_btn_sub": "Win · macOS · Linux Uyumlu", "faq_kicker": "07 / SIKÇA SORULAN SORULAR", "faq_title": "Bilmeniz Gereken Her Şey", "faq_desc": "Teknik mimari, veri güvenliği ve entegrasyonlar hakkında net yanıtlar.", "faq_q1": "Datly şirketimin gizli verilerini OpenAI veya Anthropic'e gönderir mi?", "faq_a1": "Kesinlikle hayır. Datly yerel öncelikli gizlilik mimarisiyle çalışır. Verileriniz yalnızca bilgisayarınızdaki RAM ve DuckDB'de bulunur. Yapay zekaya yalnızca kod yazabilmesi için sütun başlıkları gönderilir.", "faq_q2": "İnternet bağlantım olmadan Datly'yi tamamen çevrimdışı kullanabilir miyim?", "faq_a2": "Evet. Ollama veya vLLM aracılığıyla yerel modelleri (örn. qwen2.5-coder veya llama3.3) bağlayarak dış dünyadan tamamen izole ortamlarda çalışabilirsiniz.", "faq_q3": "Datly yapay zekanın uydurma grafikler çizmesini nasıl engeller?", "faq_a3": "Yapay zekanın doğrudan resim çizmesine veya sayı tahmin etmesine izin verilmez. Gerçek verileriniz üzerinde çalışan kod yazdırılır ve ortaya çıkan gerçek tablo grafiğin hemen altında doğrulanmak üzere açılır.", "faq_q4": "Datly'nin Microsoft Research Data Formulator'dan farkı nedir?", "faq_a4": "Datly akademik araştırmayı 15+ bulut bağlayıcısı, TipTap raporlama sistemi, panolar ve masaüstü uygulamalarıyla kurumsal seviyede bir veri IDE'sine dönüştürmüştür.", "faq_q5": "Data Threads Jupyter'ın durum çakışmalarını nasıl çözer?", "faq_a5": "Tek bir değişken ortamı yerine Yönlendirilmiş Döngüsüz Çizge (DAG) kullanarak. Her soru bağımsız bir kart olarak korunur ve önceki analiz adımlarını asla bozmaz.", "footer_tagline": "Yüksek Güvenilirlikli Yapay Zeka Veri Görselleştirme IDE'si ve Analitik Alanı · MIT Lisanslı Açık Kaynak", "footer_docs": "Belgeler", "footer_license": "MIT Lisansı", "footer_bottom": "© 2026 Datly Projesi · Özgür Yazılım", "toast_copy": "Panoya kopyalandı!"}, "nl": {"nav_features": "Functies", "nav_canvas": "Verificatie", "nav_connectors": "Connectoren (15+)", "nav_comparison": "Vergelijking", "nav_quickstart": "Snelstart", "nav_faq": "FAQ", "nav_star": "Ster", "nav_quickstart_btn": "Direct Beginnen", "hero_badge": "Next-Gen AI Data Visualisatie IDE & Analyse Workbench", "hero_title_1": "Praat met uw data in natuurlijke taal.", "hero_title_2": "Verifieer elke berekende datarij.", "hero_desc": "Datly is de open-source, local-first analyse-IDE waar natuurlijke taal bedrijfstabellen omzet in interactieve grafieken, terwijl de berekende rijen direct onder de grafiek zichtbaar blijven. Geen data-exfiltratie naar de cloud. Geen AI-hallucinaties.", "hero_btn_quickstart": "Start in 1 Commando", "hero_btn_sub": "uv run data_formulator", "hero_btn_github": "Bekijk op GitHub", "hero_btn_desktop": "Desktop App", "trust_local": "100% Lokale Verwerking (DuckDB in geheugen)", "trust_connectors": "15+ Enterprise Connectoren", "trust_privacy": "Nul Data-exfiltratie", "trust_models": "Onafhankelijk van Model (Claude, GPT, Ollama)", "ide_status": "DuckDB In-Memory (Lokaal)", "ide_tab_ide": "Volledige Analyse IDE", "ide_tab_explore": "Ground-Truth Verificatie Canvas", "ide_tab_connectors": "15+ Data Connectoren", "ide_tab_dashboards": "Multi-Grafiek Dashboards", "ide_tab_reports": "TipTap Narratieve Rapporten", "pillars_kicker": "01 / ARCHITECTUUR PIJLERS", "pillars_title": "Technische Precisie Ontmoet Vloeiende Dataverkenning", "pillars_desc": "Ontworpen om staatcorruptie in notebooks en de rigiditeit van legacy BI voorgoed op te lossen.", "p1_title": "Niet-destructieve Data Threads", "p1_desc": "Elke vraag vormt een geïsoleerde vertakking (DAG). Vervolgvragen rekenen op afgeleide tabellen zonder de brongegevens te overschrijven.", "p1_tag": "Lineage State Machine", "p2_title": "Ground-Truth Verificatie Canvas", "p2_desc": "Geen AI-hallucinaties meer. Elke visualisatie wordt getekend vanuit een echte tabel die direct onder de grafiek zichtbaar is.", "p2_tag": "Nul Fantoomstatistieken", "p3_title": "DuckDB In-Memory Engine", "p3_desc": "Razendsnelle gevectoriseerde query-uitvoering met DuckDB, Pandas en PyArrow. Miljoenen rijen lokaal binnen milliseconden verwerken.", "p3_tag": "Gevectoriseerde Analytics", "p4_title": "Conceptuele Coderingsplanken", "p4_desc": "Sleep velden naar visuele planken: X, Y, Kleur, Grootte en Facetten. Twee-richtings synchronisatie met Vega-Lite specificaties.", "p4_tag": "Grammatica van Grafieken", "p5_title": "TipTap Narratieve Rapporten", "p5_desc": "Zet verkennende grafieken om in directie-klare rapporten. Een WYSIWYG-editor die tekstuele duiding combineert met live grafieken.", "p5_tag": "Executive Publiceren", "p6_title": "Privacy Eerst (Local-First)", "p6_desc": "Uw rijen verlaten uw computer nooit. Alleen kolomnamen en geschoonde metagegevens worden naar LLM's gestuurd.", "p6_tag": "Lokaal en Offline Werken", "spotlight_kicker": "02 / DATA WAARHEIDSVINDING", "spotlight_title": "De Rij Onder de Grafieklijn", "spotlight_desc": "Datly zorgt ervoor dat de vraag, de grafiek en de berekende tabel altijd samen in één overzicht blijven staan.", "s1_title": "Explore: Gesynchroniseerde Controle", "s1_desc": "Als u een vraag stelt, berekent Datly een afgeleide tabel en tekent deze. Het raster eronder toont de exacte waarden ter controle.", "s1_tag1": "Interactieve Zoom", "s1_tag2": "CSV Downloaden", "s1_tag3": "Curve / Trappen / Punten", "s2_title": "Dashboards Zonder Dataduplicatie", "s2_desc": "Het vastpinnen van grafieken in dashboards of TipTap rapporten kopieert geen data. Alles deelt dezelfde Data Thread DAG.", "s2_tag1": "Multi-Card Pinboard", "s2_tag2": "PDF Exporteren", "s2_tag3": "Gekoppelde DAG Status", "connectors_kicker": "03 / ENTERPRISE CONNECTOREN", "connectors_title": "Eenmaal Verbinden. Lokaal Bevragen.", "connectors_desc": "Koppel live tabellen uit data lakes, warehouses, relationele databases en webpagina's.", "filter_all": "Alle Connectoren (15+)", "filter_warehouse": "Warehouses & Lakes", "filter_sql": "Relationele SQL", "filter_cloud": "NoSQL & Cloud", "filter_files": "Bestanden & Web", "compare_kicker": "04 / TECHNISCHE VERGELIJKING", "compare_title": "Waarom Datateams Voor Datly Kiezen", "compare_desc": "Diepgaande vergelijking met traditionele analysetools.", "compare_th_feature": "Functie / Beveiliging", "compare_th_jupyter": "Jupyter Notebooks", "compare_th_bi": "Klassieke Cloud BI", "compare_th_dbeaver": "SQL IDE's (DBeaver)", "compare_th_datly": "DATLY IDE", "compare_r1_title": "Natuurlijke Taal AI Analyse", "compare_r1_jupyter": "⚠️ Afhankelijk van plugins", "compare_r1_bi": "⚠️ Dure gesloten copilots", "compare_r1_dbeaver": "❌ Geen of alleen autocomplete", "compare_r1_datly": "⚡ Ingebouwde Agent met Tabelcontext (@tabel)", "compare_r2_title": "Datatabel Direct Onder Grafiek", "compare_r2_jupyter": "❌ Aparte celuitvoer", "compare_r2_bi": "❌ Complexe geneste navigatie", "compare_r2_dbeaver": "❌ Handmatige query vereist", "compare_r2_datly": "🛡️ Altijd Samen Zichtbaar op Canvas", "compare_r3_title": "Visuele Planken + AI Agent", "compare_r3_jupyter": "❌ Alleen codecellen", "compare_r3_bi": "⚠️ Alleen planken (Geen Agent)", "compare_r3_dbeaver": "❌ Alleen code", "compare_r3_datly": "✅ Twee-richtings Planken + AI Agent", "compare_r4_title": "Lokale Privacy (Geen Datalek)", "compare_r4_jupyter": "✅ Lokale uitvoering", "compare_r4_bi": "❌ Verplichte cloud-upload", "compare_r4_dbeaver": "✅ Lokale query", "compare_r4_datly": "🔒 100% Lokaal (Alleen schema naar LLM)", "compare_r5_title": "Data Lineage Architectuur", "compare_r5_jupyter": "❌ Fragiele lineaire staat", "compare_r5_bi": "❌ Statische datamodellen", "compare_r5_dbeaver": "❌ Eenvoudige queryhistorie", "compare_r5_datly": "🌲 Vertakkende Data Threads (DAG)", "compare_r6_title": "In-Memory Gevectoriseerde Engine", "compare_r6_jupyter": "⚠️ Afhankelijk van kernel", "compare_r6_bi": "⚠️ Gesloten propriëtaire engine", "compare_r6_dbeaver": "❌ Afhankelijk van database op afstand", "compare_r6_datly": "⚡ DuckDB + Pandas + PyArrow", "compare_r7_title": "WYSIWYG Narratieve Rapporten", "compare_r7_jupyter": "❌ Alleen statische Markdown", "compare_r7_bi": "⚠️ Zware dashboard canvas", "compare_r7_dbeaver": "❌ Niet beschikbaar", "compare_r7_datly": "📝 TipTap Rijke Tekst Editor", "compare_r8_title": "Geautomatiseerde Webtabel Scraper", "compare_r8_jupyter": "❌ Handmatige BS4 scripts", "compare_r8_bi": "❌ Niet ondersteund", "compare_r8_dbeaver": "❌ Niet ondersteund", "compare_r8_datly": "🌐 Ingebouwde Playwright Scraper", "compare_r9_title": "Model-onafhankelijk (BYOK / Lokaal)", "compare_r9_jupyter": "⚠️ Handmatige API-koppeling", "compare_r9_bi": "❌ Vendor lock-in", "compare_r9_dbeaver": "❌ Niet van toepassing", "compare_r9_datly": "🔓 LiteLLM (Claude, GPT, Ollama)", "quickstart_kicker": "05 / SNELLE INSTALLATIE", "quickstart_title": "Start Datly Binnen 60 Seconden", "quickstart_desc": "Kies uw gewenste methode: moderne uv package manager, Docker container of 1-commando AI prompt.", "term_tab_uv": "Astral uv (Snelst)", "term_tab_pip": "Python pip + Yarn", "term_tab_docker": "Docker Compose", "term_tab_agent": "1-Commando AI Prompt", "term_tab_desktop": "Desktop Applicatie Build", "releases_kicker": "06 / OFFICIËLE RELEASES", "releases_title": "Download Desktop Pakketten", "releases_desc": "Native prestaties voor Windows, macOS en Linux met hardwareversnelling.", "releases_box_title": "Datly Desktop Suite v0.8.0-beta", "releases_box_desc": "Electron 43 Container · DuckDB Ingebouwd · Multi-LLM Ondersteuning", "releases_btn_title": "Bekijk Alle Releases", "releases_btn_sub": "Voor Win · macOS · Linux", "faq_kicker": "07 / VEELGESTELDE VRAGEN", "faq_title": "Alles Wat U Moet Weten", "faq_desc": "Heldere antwoorden op technische vragen over veiligheid en data-integratie.", "faq_q1": "Stuurt Datly mijn vertrouwelijke bedrijfsdata naar OpenAI of Anthropic?", "faq_a1": "Nee, absoluut niet. Datly hanteert een strikte local-first architectuur. Uw data blijft in het RAM-geheugen en DuckDB op uw computer. Alleen kolomnamen worden naar het model gestuurd om code te genereren.", "faq_q2": "Kan ik Datly volledig offline draaien zonder internetverbinding?", "faq_a2": "Ja. Datly integreert naadloos met lokale modellen via Ollama of vLLM (zoals qwen2.5-coder of llama3.3), waardoor het ideaal is voor streng beveiligde, afgesloten netwerkomgevingen.", "faq_q3": "Hoe voorkomt Datly grafiek-hallucinaties en neppe data?", "faq_a3": "AI mag nooit rechtstreeks beeld pixels tekenen of cijfers raden. AI moet verifieerbare code schrijven die lokaal draait en een tastbare tabel oplevert die direct onder de grafiek gecontroleerd kan worden.", "faq_q4": "Wat is het verschil met Microsoft Research Data Formulator?", "faq_a4": "Datly tilt het onderzoeksproject naar een enterprise data IDE met 15+ cloud connectoren, TipTap rapportages, dashboards en desktop applicaties.", "faq_q5": "Hoe lossen Data Threads de staatproblemen van Jupyter op?", "faq_a5": "Door gebruik te maken van een Directed Acyclic Graph (DAG) in plaats van een kwetsbare globale staat. Elke vraag blijft een geïsoleerde kaart zonder eerdere stappen te verstoren.", "footer_tagline": "Veilige AI Data Visualisatie IDE & Analyse Workbench · Open Source onder MIT Licentie", "footer_docs": "Documentatie", "footer_license": "MIT Licentie", "footer_bottom": "© 2026 Datly Project · Vrije Software", "toast_copy": "Gekopieerd naar klembord!"}, "pl": {"nav_features": "Funkcje", "nav_canvas": "Weryfikacja", "nav_connectors": "Konektory (15+)", "nav_comparison": "Porównanie", "nav_quickstart": "Szybki start", "nav_faq": "FAQ", "nav_star": "Gwiazdka", "nav_quickstart_btn": "Zacznij teraz", "hero_badge": "IDE nowej generacji do wizualizacji danych i analizy z AI", "hero_title_1": "Rozmawiaj ze swoimi danymi.", "hero_title_2": "Weryfikuj każdy wyliczony wiersz danych.", "hero_desc": "Datly to lokalne środowisko IDE do analizy danych open-source, w którym język naturalny przekształca tabele korporacyjne w interaktywne wykresy, a obliczone wiersze danych pozostają widoczne bezpośrednio pod wykresem. Zero wycieków do chmury. Zero halucynacji AI.", "hero_btn_quickstart": "Uruchom 1 poleceniem", "hero_btn_sub": "uv run data_formulator", "hero_btn_github": "Zobacz na GitHub", "hero_btn_desktop": "Aplikacja Desktop", "trust_local": "100% lokalnego przetwarzania (DuckDB w pamięci)", "trust_connectors": "15+ konektorów korporacyjnych", "trust_privacy": "Zero eksfiltracji danych", "trust_models": "Wybór dowolnego modelu (Claude, GPT, Ollama)", "ide_status": "DuckDB w pamięci RAM (Lokalnie)", "ide_tab_ide": "Pełne IDE analityczne", "ide_tab_explore": "Kanwa weryfikacji danych", "ide_tab_connectors": "15+ konektorów baz danych", "ide_tab_dashboards": "Pulpity z wieloma wykresami", "ide_tab_reports": "Raporty narracyjne TipTap", "pillars_kicker": "01 / FILARY ARCHITEKTURY", "pillars_title": "Inżynierska precyzja i swoboda eksploracji", "pillars_desc": "Zaprojektowane od podstaw, aby wyeliminować problemy ze stanem w notatnikach oraz sztywność klasycznych narzędzi BI.", "p1_title": "Niedestrukcyjne wątki danych (Data Threads)", "p1_desc": "Każde pytanie tworzy izolowane odgałęzienie (DAG). Dalsze zapytania operują na tabelach pochodnych bez modyfikowania danych źródłowych.", "p1_tag": "Maszyna stanów pochodzenia danych", "p2_title": "Kanwa weryfikacji Ground-Truth", "p2_desc": "Koniec z halucynacjami AI. Każdy wykres jest generowany z fizycznej tabeli pochodnej widocznej bezpośrednio pod wykresem.", "p2_tag": "Zero fałszywych wskaźników", "p3_title": "Silnik in-memory DuckDB", "p3_desc": "Błyskawiczne wektorowe przetwarzanie z DuckDB, Pandas i PyArrow. Agreguj miliony wierszy lokalnie w kilka milisekund.", "p3_tag": "Analityka wektorowa", "p4_title": "Półki kodowania wizualnego", "p4_desc": "Przeciągaj i upuszczaj pola: osie X, Y, kolor, rozmiar i facety. Dwukierunkowa synchronizacja z formatem Vega-Lite.", "p4_tag": "Gramatyka grafiki", "p5_title": "Raporty narracyjne TipTap", "p5_desc": "Przekształcaj wykresy w gotowe raporty dla kadry zarządzającej. Edytor WYSIWYG łączący komentarz tekstowy z żywymi wykresami.", "p5_tag": "Publikacja raportów", "p6_title": "Prywatność przede wszystkim (Local-First)", "p6_desc": "Twoje dane nigdy nie opuszczają Twojego komputera. Do modeli LLM przesyłane są wyłącznie schematy kolumn do generowania kodu.", "p6_tag": "Praca całkowicie offline", "spotlight_kicker": "02 / WERYFIKACJA DANYCH", "spotlight_title": "Wiersz pod linią wykresu", "spotlight_desc": "Datly gwarantuje, że pytanie, wizualizacja i wyliczone wiersze danych zawsze pozostają widoczne razem.", "s1_title": "Explore: Zsynchronizowana kontrola", "s1_desc": "Po zadaniu pytania Datly oblicza tabelę pochodną i rysuje wykres. Siatka poniżej pokazuje dokładne liczby do natychmiastowej weryfikacji.", "s1_tag1": "Interaktywny zoom", "s1_tag2": "Pobierz CSV", "s1_tag3": "Krzywa / Schodki / Punkty", "s2_title": "Pulpity bez duplikowania danych", "s2_desc": "Przypinanie wykresów do pulpitów lub raportów TipTap nie tworzy kopii danych. Wszystko współdzieli ten sam graf DAG.", "s2_tag1": "Tablica z kartami", "s2_tag2": "Eksport do PDF", "s2_tag3": "Powiązany stan DAG", "connectors_kicker": "03 / KONEKTORY KORPORACYJNE", "connectors_title": "Połącz raz. Odpytuj lokalnie.", "connectors_desc": "Integruj tabele na żywo z data lake, hurtowni, relacyjnych baz SQL i stron internetowych.", "filter_all": "Wszystkie konektory (15+)", "filter_warehouse": "Hurtownie & Lakes", "filter_sql": "Relacyjne SQL", "filter_cloud": "NoSQL & Chmura", "filter_files": "Pliki & Web", "compare_kicker": "04 / PORÓWNANIE TECHNICZNE", "compare_title": "Dlaczego zespoły analityczne wybierają Datly", "compare_desc": "Szczegółowe zestawienie ze standardowymi narzędziami analitycznymi.", "compare_th_feature": "Funkcja / Bezpieczeństwo", "compare_th_jupyter": "Jupyter Notebooks", "compare_th_bi": "Tradycyjne Cloud BI", "compare_th_dbeaver": "SQL IDE (DBeaver)", "compare_th_datly": "DATLY IDE", "compare_r1_title": "Synteza AI w języku naturalnym", "compare_r1_jupyter": "⚠️ Wymaga wtyczek", "compare_r1_bi": "⚠️ Drogie zamknięte asystenty", "compare_r1_dbeaver": "❌ Brak lub tylko podpowiedzi", "compare_r1_datly": "⚡ Natywny Agent ze znajomością tabeli (@tabela)", "compare_r2_title": "Siatka danych bezpośrednio pod wykresem", "compare_r2_jupyter": "❌ Wyjście w oddzielnej komórce", "compare_r2_bi": "❌ Skomplikowane zagłębienia", "compare_r2_dbeaver": "❌ Wymaga ręcznego zapytania", "compare_r2_datly": "🛡️ Zawsze widoczna na ekranie", "compare_r3_title": "Półki wizualne + Agent AI", "compare_r3_jupyter": "❌ Tylko komórki z kodem", "compare_r3_bi": "⚠️ Tylko półki (brak kodu Agenta)", "compare_r3_dbeaver": "❌ Tylko kod", "compare_r3_datly": "✅ Półki dwukierunkowe + Inteligentny Agent", "compare_r4_title": "Prywatność lokalna (Zero wycieku)", "compare_r4_jupyter": "✅ Wykonanie lokalne", "compare_r4_bi": "❌ Obowiązkowy upload do chmury", "compare_r4_dbeaver": "✅ Zapytanie lokalne", "compare_r4_datly": "🔒 100% lokalnie (Do LLM trafia tylko schemat)", "compare_r5_title": "Architektura pochodzenia danych (Lineage)", "compare_r5_jupyter": "❌ Kruchy stan globalny", "compare_r5_bi": "❌ Statyczne modele danych", "compare_r5_dbeaver": "❌ Zwykła historia zapytań", "compare_r5_datly": "🌲 Rozgałęziające się wątki danych (DAG)", "compare_r6_title": "Wektorowy silnik in-memory", "compare_r6_jupyter": "⚠️ Zależny od kernela", "compare_r6_bi": "⚠️ Zamknięty silnik dostawcy", "compare_r6_dbeaver": "❌ Zależny od zdalnej bazy", "compare_r6_datly": "⚡ DuckDB + Pandas + PyArrow", "compare_r7_title": "Raporty narracyjne WYSIWYG", "compare_r7_jupyter": "❌ Tylko komórki Markdown", "compare_r7_bi": "⚠️ Ciężkie pulpity", "compare_r7_dbeaver": "❌ Niedostępne", "compare_r7_datly": "📝 Edytor bogatego tekstu TipTap", "compare_r8_title": "Automatyczny scraper tabel WWW", "compare_r8_jupyter": "❌ Ręczne skrypty BS4", "compare_r8_bi": "❌ Brak wsparcia", "compare_r8_dbeaver": "❌ Brak wsparcia", "compare_r8_datly": "🌐 Wbudowany silnik Playwright", "compare_r9_title": "Niezależność modeli (BYOK / Offline)", "compare_r9_jupyter": "⚠️ Ręczna integracja", "compare_r9_bi": "❌ Blokada w chmurze dostawcy", "compare_r9_dbeaver": "❌ Nie dotyczy", "compare_r9_datly": "🔓 LiteLLM (Claude, GPT, Ollama)", "quickstart_kicker": "05 / BŁYSKAWICZNA INSTALACJA", "quickstart_title": "Uruchom Datly w Mniej Niż 60 Sekund", "quickstart_desc": "Wybierz metodę: menedżer uv, kontener Docker lub 1 polecenie asystenta AI.", "term_tab_uv": "Astral uv (Najszybszy)", "term_tab_pip": "Python pip + Yarn", "term_tab_docker": "Docker Compose", "term_tab_agent": "1 polecenie asystenta AI", "term_tab_desktop": "Budowa aplikacji Desktop", "releases_kicker": "06 / ZWERYFIKOWANE WYDANIA", "releases_title": "Pobierz pakiety instalacyjne", "releases_desc": "Natywna wydajność dla Windows, macOS i Linux z akceleracją sprzętową.", "releases_box_title": "Datly Desktop Suite v0.8.0-beta", "releases_box_desc": "Kontener Electron 43 · Wbudowany silnik DuckDB · Obsługa wielu LLM", "releases_btn_title": "Zobacz wszystkie pliki", "releases_btn_sub": "Wspiera Win · macOS · Linux", "faq_kicker": "07 / NAJCZĘŚCIEJ ZADAWANE PYTANIA", "faq_title": "Wszystko, co musisz wiedzieć", "faq_desc": "Jasne odpowiedzi na pytania o technikę, bezpieczeństwo i integrację.", "faq_q1": "Czy Datly wysyła poufne dane firmowe do OpenAI lub Anthropic?", "faq_a1": "Nie. Datly stosuje rygorystyczne przetwarzanie lokalne. Twoje dane pozostają w pamięci RAM i DuckDB na Twoim komputerze. Do modelu przesyłane są jedynie nazwy kolumn i typy danych do wygenerowania kodu.", "faq_q2": "Czy mogę uruchomić Datly całkowicie bez dostępu do Internetu?", "faq_a2": "Tak. Datly współpracuje z lokalnymi modelami przez Ollama lub vLLM (np. qwen2.5-coder lub llama3.3), co pozwala na pracę w zamkniętych sieciach korporacyjnych.", "faq_q3": "Jak Datly zapobiega halucynacjom AI i fałszywym liczbom?", "faq_a3": "AI nie ma prawa bezpośrednio rysować wykresów ani zgadywać liczb. Model pisze sprawdzalny kod, który tworzy fizyczną tabelę widoczną bezpośrednio pod wykresem.", "faq_q4": "Czym Datly różni się od Microsoft Research Data Formulator?", "faq_a4": "Datly przekształca akademicki prototyp w pełnoprawne środowisko korporacyjne z 15+ konektorami chmurowymi, raportami TipTap, pulpitami i natywnymi aplikacjami desktopowymi.", "faq_q5": "Jak wątki danych rozwiązują problemy z nadpisywaniem zmiennych w Jupyterze?", "faq_a5": "Zastępując globalny stan skierowanym grafem acyklicznym (DAG). Każde zapytanie staje się niezależną kartą, która nie niszczy poprzednich obliczeń.", "footer_tagline": "Wysokozaawansowane IDE do wizualizacji danych z AI · Open Source na licencji MIT", "footer_docs": "Dokumentacja", "footer_license": "Licencja MIT", "footer_bottom": "© 2026 Projekt Datly · Wolne Oprogramowanie", "toast_copy": "Skopiowano do schowka!"}, "sv": {"nav_features": "Funktioner", "nav_canvas": "Verifiering", "nav_connectors": "Kopplingar (15+)", "nav_comparison": "Jämförelse", "nav_quickstart": "Snabbstart", "nav_faq": "FAQ", "nav_star": "Stjärna", "nav_quickstart_btn": "Kom Igång", "hero_badge": "Nästa Generations AI-datavisualiserings-IDE & Analysverktyg", "hero_title_1": "Tala med dina data på naturligt språk.", "hero_title_2": "Verifiera varje beräknad rad direkt under grafen.", "hero_desc": "Datly är ett lokalt förankrat analys-IDE med öppen källkod där naturligt språk omvandlar företagstabeller till interaktiva diagram, samtidigt som de beräknade dataryaderna förblir synliga direkt under linjen. Noll dataläckage till molnet. Noll AI-hallucinationer.", "hero_btn_quickstart": "Starta med 1 Kommando", "hero_btn_sub": "uv run data_formulator", "hero_btn_github": "Visa på GitHub", "hero_btn_desktop": "Skrivbordsapp", "trust_local": "100% Lokal Bearbetning (DuckDB i minnet)", "trust_connectors": "15+ Företagskopplingar", "trust_privacy": "Noll Dataläckage till Molnet", "trust_models": "Valfri Modell (Claude, GPT, Ollama)", "ide_status": "DuckDB In-Memory (Lokalt)", "ide_tab_ide": "Fullständigt Analys-IDE", "ide_tab_explore": "Ground-Truth Verifieringsyta", "ide_tab_connectors": "15+ Datakopplingar", "ide_tab_dashboards": "Paneler med Flera Grafer", "ide_tab_reports": "TipTap Rapporter", "pillars_kicker": "01 / ARKITEKTURPELARE", "pillars_title": "Ingenjörsmässig Precision Möter Smidig Utforskning", "pillars_desc": "Byggt från grunden för att eliminera notebook-miljöers tillståndsproblem och traditionella BI-verktygs stelhet.", "p1_title": "Icke-destruktiva Datatrådar (Data Threads)", "p1_desc": "Varje fråga bildar en isolerad trådförgrening (DAG). Uppföljningsfrågor beräknas på härledda tabeller utan att ändra källfilerna.", "p1_tag": "Härstamningsstatusmaskin", "p2_title": "Ground-Truth Verifieringsyta", "p2_desc": "Sluta oroa dig för AI-hallucinationer. Varje diagram ritas från en verklig härledd tabell som visas direkt under grafen.", "p2_tag": "Noll Falska Mätvärden", "p3_title": "DuckDB In-Memory Motor", "p3_desc": "Blixtsnabb vektoriserad bearbetning med DuckDB, Pandas och PyArrow. Bearbeta miljontals rader lokalt på millisekunder.", "p3_tag": "Vektoriserad Analys", "p4_title": "Konceptuella Kodningshyllor", "p4_desc": "Dra och släpp fält på visuella hyllor: X, Y, Färg, Storlek och Facetter. Dubbelriktad synkronisering med Vega-Lite.", "p4_tag": "Grafikens Grammatik", "p5_title": "TipTap Narrativa Rapporter", "p5_desc": "Förvandla analyser till rapporter för ledningen. En rik WYSIWYG-editor som integrerar text och dynamiska diagramkort.", "p5_tag": "Ledningsrapportering", "p6_title": "Lokal Integritet i Första Rummet", "p6_desc": "Dina dataryader lämnar aldrig din dator. Endast kolumnnamn och tvättad metadata skickas till modellerna för kodgenerering.", "p6_tag": "Helt Offline-kompatibel", "spotlight_kicker": "02 / DATA SANNINGSVERIFIERING", "spotlight_title": "Raden Under Grafen", "spotlight_desc": "Datly ser till att frågan, visualiseringen och de faktiskt beräknade raderna alltid hålls samlade på skärmen.", "s1_title": "Explore: Den Synkroniserade Kontrollen", "s1_desc": "När du frågar beräknar Datly en ny tabell och ritar upp den. Rutnätet under kurvan visar de exakta siffrorna för direkt kontroll.", "s1_tag1": "Interaktiv Zoom", "s1_tag2": "Ladda ner CSV", "s1_tag3": "Kurva / Trappsteg / Punkter", "s2_title": "Paneler Utan Datadubblering", "s2_desc": "Att fästa diagram i paneler eller rapporter skapar inga datadubbletter. Alla vyer delar samma underliggande Data Thread DAG.", "s2_tag1": "Multikort-panel", "s2_tag2": "PDF-export", "s2_tag3": "Kopplat DAG-tillstånd", "connectors_kicker": "03 / FÖRETAGSKOPPLINGAR", "connectors_title": "Anslut En Gång. Fråga Lokalt.", "connectors_desc": "Integrera tabeller från data lakes, datalager, relationella SQL-databaser och webbsidor.", "filter_all": "Alla Kopplingar (15+)", "filter_warehouse": "Datalager & Lakes", "filter_sql": "Relationell SQL", "filter_cloud": "NoSQL & Moln", "filter_files": "Filer & Webben", "compare_kicker": "04 / ARKITEKTURJÄMFÖRELSE", "compare_title": "Varför Data-team Väljer Datly", "compare_desc": "Djupgående teknisk utvärdering mot traditionella analysverktyg.", "compare_th_feature": "Funktion / Säkerhet", "compare_th_jupyter": "Jupyter Notebooks", "compare_th_bi": "Klassisk Moln-BI", "compare_th_dbeaver": "SQL IDE (DBeaver)", "compare_th_datly": "DATLY IDE", "compare_r1_title": "AI-analys med Naturligt Språk", "compare_r1_jupyter": "⚠️ Kräver tillägg", "compare_r1_bi": "⚠️ Dyra proprietära assistenter", "compare_r1_dbeaver": "❌ Ingen eller enkel autofullbordan", "compare_r1_datly": "⚡ Inbyggd Agent med Tabellkontext (@tabell)", "compare_r2_title": "Datarutnät Direkt Under Grafen", "compare_r2_jupyter": "❌ Visas i separat cell", "compare_r2_bi": "❌ Krånglig navigering", "compare_r2_dbeaver": "❌ Kräver manuell SQL-fråga", "compare_r2_datly": "🛡️ Alltid Synlig Tillsammans på Skärmen", "compare_r3_title": "Visuella Hyllor + AI Agent", "compare_r3_jupyter": "❌ Endast kodceller", "compare_r3_bi": "⚠️ Endast hyllor (ingen kodande agent)", "compare_r3_dbeaver": "❌ Endast kod", "compare_r3_datly": "✅ Dubbelriktade Hyllor + Smart Agent", "compare_r4_title": "Lokal Integritet (Noll Läckage)", "compare_r4_jupyter": "✅ Lokal körning", "compare_r4_bi": "❌ Obligatorisk uppladdning till molnet", "compare_r4_dbeaver": "✅ Lokal fråga", "compare_r4_datly": "🔒 100% Lokalt (Bara schema till LLM)", "compare_r5_title": "Datahärstamningsarkitektur", "compare_r5_jupyter": "❌ Skört linjärt tillstånd", "compare_r5_bi": "❌ Statiska datamodeller", "compare_r5_dbeaver": "❌ Enkel frågehistorik", "compare_r5_datly": "🌲 Förgrenade Datatrådar (DAG)", "compare_r6_title": "Vektoriserad In-Memory Motor", "compare_r6_jupyter": "⚠️ Beroende av kernel", "compare_r6_bi": "⚠️ Sluten proprietär motor", "compare_r6_dbeaver": "❌ Beroende av fjärrdatabas", "compare_r6_datly": "⚡ DuckDB + Pandas + PyArrow", "compare_r7_title": "WYSIWYG Narrativ Rapportering", "compare_r7_jupyter": "❌ Endast enkla Markdown-celler", "compare_r7_bi": "⚠️ Tunga panellayouter", "compare_r7_dbeaver": "❌ Saknas", "compare_r7_datly": "📝 TipTap Redigerare med Levande Grafer", "compare_r8_title": "Automatiserad Webb-tabellhämtning", "compare_r8_jupyter": "❌ Manuella BS4-skript", "compare_r8_bi": "❌ Stöds inte", "compare_r8_dbeaver": "❌ Stöds inte", "compare_r8_datly": "🌐 Inbyggd Playwright-skrapa", "compare_r9_title": "Modelloberoende (BYOK / Lokalt)", "compare_r9_jupyter": "⚠️ Manuell API-uppsättning", "compare_r9_bi": "❌ Låst till leverantörens moln", "compare_r9_dbeaver": "❌ Ej tillämpligt", "compare_r9_datly": "🔓 LiteLLM (Claude, GPT, Ollama)", "quickstart_kicker": "05 / ENKEL INSTALLATION", "quickstart_title": "Starta Datly på Mindre Än 60 Sekunder", "quickstart_desc": "Välj önskad metod: modernt uv, Docker-container eller AI-agent med ett kommando.", "term_tab_uv": "Astral uv (Snabbast)", "term_tab_pip": "Python pip + Yarn", "term_tab_docker": "Docker Compose", "term_tab_agent": "1-Kommando AI Prompt", "term_tab_desktop": "Skrivbordsbygge", "releases_kicker": "06 / VERIFIERADE RELEASER", "releases_title": "Ladda ner Skrivbordspaket", "releases_desc": "Inbyggd hårdvaruacceleration för Windows, macOS och Linux.", "releases_box_title": "Datly Desktop Suite v0.8.0-beta", "releases_box_desc": "Electron 43 · DuckDB Inbäddad Motor · Stöd för Flera LLM", "releases_btn_title": "Se Alla Releaser", "releases_btn_sub": "För Win · macOS · Linux", "faq_kicker": "07 / VANLIGA FRÅGOR", "faq_title": "Allt du behöver veta", "faq_desc": "Tydliga svar kring arkitektur, datasäkerhet och datakällor.", "faq_q1": "Skickar Datly känsliga företagsdata till OpenAI eller Anthropic?", "faq_a1": "Nej. Datly arbetar helt lokalt. Dina faktiska data stannar i RAM och DuckDB på din maskin. Modellerna får endast kolumnnamn för att kunna generera koden.", "faq_q2": "Kan jag köra Datly helt utan internetanslutning?", "faq_a2": "Ja. Datly integrerar sömlöst med lokala modeller via Ollama eller vLLM (t.ex. qwen2.5-coder eller llama3.3) för säkra miljöer.", "faq_q3": "Hur förhindrar Datly att AI hittar på siffror eller felaktiga diagram?", "faq_a3": "AI tillåts inte att direkt måla bilder eller gissa siffror. AI:n skriver granskningsbar kod som körs på verkliga data för att skapa en tabell som syns direkt under grafen.", "faq_q4": "Vad skiljer Datly från Microsoft Research Data Formulator?", "faq_a4": "Datly utvecklar forskningsprojektet till ett fullfjädrat företags-IDE med 15+ molnkopplingar, TipTap-rapporter, paneler och skrivbordsapplikationer.", "faq_q5": "Hur löser Datatrådar (Data Threads) Jupyters tillståndsproblem?", "faq_a5": "Genom att ersätta det globala tillståndet med en riktad acyklisk graf (DAG). Varje analysfråga blir ett isolerat kort utan risk att förstöra tidigare steg.", "footer_tagline": "Säkert AI-datavisualiserings-IDE & Analysverktyg · Öppen källkod under MIT-licens", "footer_docs": "Dokumentation", "footer_license": "MIT-licens", "footer_bottom": "© 2026 Datly-projektet · Fri Programvara", "toast_copy": "Kopierat till urklipp!"}, "uk": {"nav_features": "Можливості", "nav_canvas": "Верифікація", "nav_connectors": "Конектори (15+)", "nav_comparison": "Порівняння", "nav_quickstart": "Швидкий старт", "nav_faq": "FAQ", "nav_star": "Зірка", "nav_quickstart_btn": "Почати зараз", "hero_badge": "IDE візуалізації даних нового покоління та аналітичне середовище зі ШІ", "hero_title_1": "Спілкуйтеся зі своїми даними.", "hero_title_2": "Перевіряйте кожен обчислений рядок.", "hero_desc": "Datly — це локальна IDE з відкритим кодом, де природна мова перетворює корпоративні таблиці на інтерактивні графіки, а обчислені рядки даних завжди залишаються під рукою прямо під графіком. Нуль витоків у хмару. Нуль галюцинацій ШІ.", "hero_btn_quickstart": "Запуск в 1 команду", "hero_btn_sub": "uv run data_formulator", "hero_btn_github": "Переглянути на GitHub", "hero_btn_desktop": "Десктопний додаток", "trust_local": "100% локальна обробка (DuckDB у пам'яті)", "trust_connectors": "15+ корпоративних конекторів", "trust_privacy": "Нуль витоків у хмару", "trust_models": "Підтримка будь-яких моделей (Claude, GPT, Ollama)", "ide_status": "DuckDB In-Memory (Локально)", "ide_tab_ide": "Повна аналітична IDE", "ide_tab_explore": "Панель перевірки даних", "ide_tab_connectors": "15+ конекторів даних", "ide_tab_dashboards": "Дашборди", "ide_tab_reports": "Звіти TipTap", "pillars_kicker": "01 / ФІЛОСОФІЯ ТА АРХІТЕКТУРА", "pillars_title": "Інженерна точність та вільне дослідження даних", "pillars_desc": "Розроблено, щоб назавжди вирішити проблеми стану в ноутбуках і подолати неповороткість класичного BI.", "p1_title": "Неруйнівні ланцюжки даних (Data Threads)", "p1_desc": "Кожен запит створює ізольовану гілку (DAG). Наступні питання спираються на похідні таблиці, не пошкоджуючи початкові дані.", "p1_tag": "Кінцевий автомат походження даних", "p2_title": "Панель перевірки достовірності", "p2_desc": "Ніяких галюцинацій ШІ. Кожен графік будується на реальній таблиці, видимій прямо під візуалізацією.", "p2_tag": "Нуль фантомних метрик", "p3_title": "Двигун DuckDB в оперативній пам'яті", "p3_desc": "Надшвидкі векторизовані обчислення на базі DuckDB, Pandas та PyArrow. Агрегація мільйонів рядків локально за мілісекунди.", "p3_tag": "Векторизована аналітика", "p4_title": "Полиці візуального кодування", "p4_desc": "Перетягуйте поля у візуальні полиці: X, Y, колір, розмір і фасети. Двостороння синхронізація з Vega-Lite.", "p4_tag": "Граматика графіки", "p5_title": "Інтерактивні звіти TipTap", "p5_desc": "Перетворюйте графіки на презентаційні звіти для керівництва завдяки редактору WYSIWYG із живими картками.", "p5_tag": "Звіти для керівництва", "p6_title": "Локальна безпека за замовчуванням", "p6_desc": "Ваші дані ніколи не залишають комп'ютер. У моделі передаються лише назви колонок і базова структура для кодогенерації.", "p6_tag": "Повна робота офлайн", "spotlight_kicker": "02 / ВЕРИФІКАЦІЯ ДАНИХ", "spotlight_title": "Рядок прямо під лінією графіка", "spotlight_desc": "Datly гарантує, що запит, графік та вирахувані рядки завжди залишаються разом на одному екрані.", "s1_title": "Explore: Синхронна перевірка", "s1_desc": "Коли ви ставите запитання, Datly формує нову таблицю та малює графік. Сітка під ним показує точні значення.", "s1_tag1": "Інтерактивний зум", "s1_tag2": "Завантажити CSV", "s1_tag3": "Лінія / Сходинки / Точки", "s2_title": "Дашборди без дублювання даних", "s2_desc": "Закріплення графіків на дашборді не копіює дані. Усі екрани спільно використовують спільний граф DAG.", "s2_tag1": "Дашборд карток", "s2_tag2": "Експорт у PDF", "s2_tag3": "Пов'язаний стан DAG", "connectors_kicker": "03 / КОРПОРАТИВНІ КОНЕКТОРИ", "connectors_title": "Підключіть один раз. Запитуйте локально.", "connectors_desc": "Підключайте таблиці з озер даних, сховищ, реляційних баз SQL та веб-сторінок.", "filter_all": "Усі конектори (15+)", "filter_warehouse": "Сховища та Lakehouse", "filter_sql": "Реляційні SQL", "filter_cloud": "NoSQL та Хмара", "filter_files": "Файли та Веб", "compare_kicker": "04 / ПОРІВНЯЛЬНИЙ АНАЛІЗ", "compare_title": "Чому аналітичні команди обирають Datly", "compare_desc": "Детальне технічне зіставлення зі звичними інструментами аналізу.", "compare_th_feature": "Можливість / Безпека", "compare_th_jupyter": "Jupyter Notebooks", "compare_th_bi": "Хмарний BI (Tableau / PowerBI)", "compare_th_dbeaver": "SQL IDE (DBeaver)", "compare_th_datly": "DATLY IDE", "compare_r1_title": "Аналіз природною мовою за допомогою ШІ", "compare_r1_jupyter": "⚠️ Потребує плагінів", "compare_r1_bi": "⚠️ Дорогі пропрієтарні асистенти", "compare_r1_dbeaver": "❌ Немає або базове автодоповнення", "compare_r1_datly": "⚡ Нативний контекстний Агент (@таблиця)", "compare_r2_title": "Сітка даних безпосередньо під графіком", "compare_r2_jupyter": "❌ Вивід в окремій комірці", "compare_r2_bi": "❌ Складні багаторівневі переходи", "compare_r2_dbeaver": "❌ Потрібен ручний запит", "compare_r2_datly": "🛡️ Завжди на екрані поруч із графіком", "compare_r3_title": "Візуальні полиці + Агент ШІ", "compare_r3_jupyter": "❌ Лише комірки з кодом", "compare_r3_bi": "⚠️ Лише полиці (без написання коду)", "compare_r3_dbeaver": "❌ Лише код", "compare_r3_datly": "✅ Двосторонні полиці + Розумний Агент", "compare_r4_title": "Локальна конфіденційність (Без витоків)", "compare_r4_jupyter": "✅ Локальне виконання", "compare_r4_bi": "❌ Обов'язкове завантаження в хмару", "compare_r4_dbeaver": "✅ Локальний запит", "compare_r4_datly": "🔒 100% локально (У модель надсилається лише схема)", "compare_r5_title": "Архітектура походження даних", "compare_r5_jupyter": "❌ Крихкий лінійний стан", "compare_r5_bi": "❌ Статичні моделі даних", "compare_r5_dbeaver": "❌ Простий список історії", "compare_r5_datly": "🌲 Розгалужені ланцюжки даних (DAG)", "compare_r6_title": "Векторизований in-memory двигун", "compare_r6_jupyter": "⚠️ Залежить від ядра", "compare_r6_bi": "⚠️ Закритий пропрієтарний двигун", "compare_r6_dbeaver": "❌ Залежить від віддаленої БД", "compare_r6_datly": "⚡ DuckDB + Pandas + PyArrow", "compare_r7_title": "Інтерактивні звіти WYSIWYG", "compare_r7_jupyter": "❌ Тільки комірки Markdown", "compare_r7_bi": "⚠️ Важкі дашборди", "compare_r7_dbeaver": "❌ Не підтримується", "compare_r7_datly": "📝 Редактор TipTap із живими графіками", "compare_r8_title": "Автоматичний веб-скрейпінг таблиць", "compare_r8_jupyter": "❌ Ручні скрипти на BS4", "compare_r8_bi": "❌ Не підтримується", "compare_r8_dbeaver": "❌ Не підтримується", "compare_r8_datly": "🌐 Вбудований скрейпер Playwright", "compare_r9_title": "Незалежність від моделей (BYOK / Офлайн)", "compare_r9_jupyter": "⚠️ Ручне налаштування API", "compare_r9_bi": "❌ Прив'язка до одного постачальника", "compare_r9_dbeaver": "❌ Не застосовується", "compare_r9_datly": "🔓 LiteLLM (Claude, GPT, Ollama)", "quickstart_kicker": "05 / МИТТЄВЕ ВСТАНОВЛЕННЯ", "quickstart_title": "Запуск Datly менш ніж за 60 секунд", "quickstart_desc": "Оберіть зручний спосіб: uv, контейнер Docker або інструкцію для ШІ в 1 рядок.", "term_tab_uv": "Astral uv (Найшвидше)", "term_tab_pip": "Python pip + Yarn", "term_tab_docker": "Docker Compose", "term_tab_agent": "Команда для ШІ асистента", "term_tab_desktop": "Збірка десктопу", "releases_kicker": "06 / ПЕРЕВІРЕНІ РЕЛІЗИ", "releases_title": "Завантажити десктопні версії", "releases_desc": "Нативна швидкодія для Windows, macOS та Linux з апаратним прискоренням.", "releases_box_title": "Datly Desktop Suite v0.8.0-beta", "releases_box_desc": "Платформа Electron 43 · Вбудований DuckDB · Підтримка багатьох LLM", "releases_btn_title": "Переглянути всі файли", "releases_btn_sub": "Підтримує Win · macOS · Linux", "faq_kicker": "07 / ЧАСТІ ЗАПИТАННЯ", "faq_title": "Корисна інформація", "faq_desc": "Відповіді на запитання щодо архітектури, безпеки та інтеграцій.", "faq_q1": "Чи надсилає Datly конфіденційні бази даних до OpenAI або Anthropic?", "faq_a1": "Ні. Datly суворо працює за локальним принципом. Фактичні дані зберігаються лише в оперативній пам'яті вашого комп'ютера та DuckDB. У модель передаються лише назви колонок і типи для синтезу коду.", "faq_q2": "Чи можу я запускати Datly взагалі без доступу до Інтернету?", "faq_a2": "Так. Datly нативно інтегрується з локальними моделями через Ollama або vLLM (наприклад, qwen2.5-coder або llama3.3), що дозволяє працювати в повністю закритих контурах.", "faq_q3": "Як Datly запобігає вигадуванню цифр та графіків з боку ШІ?", "faq_a3": "ШІ не малює графіки напряму і не вгадує цифри. Модель пише перевірений код, що створює реальну таблицю, яка відразу відображається під графіком.", "faq_q4": "Чим Datly відрізняється від Microsoft Research Data Formulator?", "faq_a4": "Datly розвиває концепт у повноцінне корпоративне середовище з 15+ конекторами, звітами TipTap, дашбордами та десктопними додатками.", "faq_q5": "Як ланцюжки даних усувають проблему плутанини в Jupyter?", "faq_a5": "Завдяки використанню спрямованого ациклічного графа (DAG). Кожне питання є окремою карткою, яка не перезаписує попередні обчислення.", "footer_tagline": "Надійна аналітична IDE візуалізації даних зі ШІ · Відкритий код під ліцензією MIT", "footer_docs": "Документація", "footer_license": "Ліцензія MIT", "footer_bottom": "© 2026 Проєкт Datly · Вільне Програмне Забезпечення", "toast_copy": "Скопійовано в буфер обміну!"}, "el": {"nav_features": "Δυνατότητες", "nav_canvas": "Επαλήθευση", "nav_connectors": "Σύνδεσμοι (15+)", "nav_comparison": "Σύγκριση", "nav_quickstart": "Γρήγορη Εκκίνηση", "nav_faq": "FAQ", "nav_star": "Αστέρι", "nav_quickstart_btn": "Έναρξη Τώρα", "hero_badge": "IDE Οπτικοποίησης Δεδομένων με AI Επόμενης Γενιάς", "hero_title_1": "Μιλήστε με τα δεδομένα σας.", "hero_title_2": "Επαληθεύστε κάθε γραμμή υπολογισμού.", "hero_desc": "Το Datly είναι ένα open-source, local-first περιβάλλον IDE ανάλυσης δεδομένων όπου η φυσική γλώσσα μετατρέπει εταιρικούς πίνακες σε διαδραστικά γραφήματα, διατηρώντας τα πραγματικά δεδομένα ορατά ακριβώς κάτω από το γράφημα. Μηδενική διαρροή στο cloud. Μηδενικές παραισθήσεις AI.", "hero_btn_quickstart": "Εκκίνηση με 1 Εντολή", "hero_btn_sub": "uv run data_formulator", "hero_btn_github": "Προβολή στο GitHub", "hero_btn_desktop": "Εφαρμογή Desktop", "trust_local": "100% Τοπική Επεξεργασία (DuckDB στη Μνήμη)", "trust_connectors": "15+ Εταιρικοί Σύνδεσμοι", "trust_privacy": "Μηδενική Διαρροή Δεδομένων", "trust_models": "Υποστήριξη Πολλαπλών Μοντέλων (Claude, GPT, Ollama)", "ide_status": "DuckDB στη Μνήμη (Τοπικά)", "ide_tab_ide": "Πλήρες IDE Ανάλυσης", "ide_tab_explore": "Καμβάς Επαλήθευσης Δεδομένων", "ide_tab_connectors": "15+ Σύνδεσμοι Δεδομένων", "ide_tab_dashboards": "Πίνακες Πολλαπλών Γραφημάτων", "ide_tab_reports": "Αφηγηματικές Αναφορές TipTap", "pillars_kicker": "01 / ΑΡΧΙΤΕΚΤΟΝΙΚΟΙ ΠΥΛΩΝΕΣ", "pillars_title": "Μηχανική Ακρίβεια & Ομαλή Εξερεύνηση", "pillars_desc": "Σχεδιασμένο για να εξαλείψει την αλλοίωση καταστάσεων των notebooks και την ακαμψία των παραδοσιακών εργαλείων BI.", "p1_title": "Μη-Καταστροφικά Νήματα Δεδομένων (Data Threads)", "p1_desc": "Κάθε ερώτημα δημιουργεί έναν απομονωμένο κλάδο (DAG). Οι επόμενες ερωτήσεις υπολογίζονται σε παραγόμενους πίνακες χωρίς να αλλοιώνονται τα αρχικά δεδομένα.", "p1_tag": "Μηχανή Καταστάσεων Προέλευσης", "p2_title": "Καμβάς Πραγματικής Επαλήθευσης", "p2_desc": "Τέλος στις παραισθήσεις της AI. Κάθε γράφημα παράγεται από έναν πραγματικό πίνακα που εμφανίζεται ακριβώς κάτω από τη γραμμή.", "p2_tag": "Μηδενικές Ψευδείς Μετρήσεις", "p3_title": "Μηχανή DuckDB στη Μνήμη", "p3_desc": "Αστραπιαία διανυσματική εκτέλεση με DuckDB, Pandas και PyArrow. Εκτελέστε συνενώσεις εκατομμυρίων γραμμών σε χιλιοστά του δευτερολέπτου.", "p3_tag": "Διανυσματική Ανάλυση", "p4_title": "Ράφια Οπτικής Κωδικοποίησης", "p4_desc": "Σύρετε και αποθέστε πεδία: Άξονες X, Y, Χρώμα, Μέγεθος και Όψεις. Αμφίδρομος συγχρονισμός με τις προδιαγραφές Vega-Lite.", "p4_tag": "Γραμματική των Γραφικών", "p5_title": "Αφηγηματικές Αναφορές TipTap", "p5_desc": "Μετατρέψτε τα γραφήματα σε επίσημες αναφορές για τη διοίκηση με έναν πλούσιο επεξεργαστή WYSIWYG που ενσωματώνει δυναμικές κάρτες.", "p5_tag": "Διοικητικές Αναφορές", "p6_title": "Τοπική Ιδιωτικότητα Πρώτα", "p6_desc": "Τα δεδομένα σας δεν φεύγουν ποτέ από τον υπολογιστή σας. Μόνο ονόματα στηλών και δομές αποστέλλονται στα LLM για τη σύνθεση του κώδικα.", "p6_tag": "Πλήρως Offline & Ασφαλές", "spotlight_kicker": "02 / ΕΠΑΛΗΘΕΥΣΗ ΠΡΑΓΜΑΤΙΚΩΝ ΔΕΔΟΜΕΝΩΝ", "spotlight_title": "Η Γραμμή Δεδομένων Κάτω από το Γράφημα", "spotlight_desc": "Το Datly εξασφαλίζει ότι η ερώτηση, το γράφημα και οι υπολογισμένες γραμμές δεδομένων παραμένουν πάντα ορατά μαζί.", "s1_title": "Explore: Ο Συγχρονισμένος Έλεγχος", "s1_desc": "Όταν ρωτάτε, το Datly υπολογίζει έναν νέο πίνακα και σχεδιάζει το γράφημα. Ο πίνακας από κάτω εμφανίζει τις ακριβείς τιμές για άμεσο έλεγχο.", "s1_tag1": "Διαδραστικό Zoom", "s1_tag2": "Λήψη CSV", "s1_tag3": "Καμπύλη / Βήματα / Σημεία", "s2_title": "Πίνακες Ελέγχου Χωρίς Αντιγραφή Δεδομένων", "s2_desc": "Η καρφίτσωμα γραφημάτων σε dashboards ή αναφορές δεν αντιγράφει δεδομένα. Όλες οι προβολές μοιράζονται το ίδιο Data Thread DAG.", "s2_tag1": "Ταμπλό Πολλαπλών Καρτών", "s2_tag2": "Εξαγωγή σε PDF", "s2_tag3": "Συνδεδεμένη Κατάσταση DAG", "connectors_kicker": "03 / ΕΤΑΙΡΙΚΟΙ ΣΥΝΔΕΣΜΟΙ", "connectors_title": "Συνδεθείτε Μία Φορά. Ερωτήστε Τοπικά.", "connectors_desc": "Ενσωματώστε ζωντανούς πίνακες από data lakes, data warehouses, βάσεις SQL και ιστοσελίδες.", "filter_all": "Όλοι οι Σύνδεσμοι (15+)", "filter_warehouse": "Warehouses & Lakes", "filter_sql": "Σχεσιακές SQL", "filter_cloud": "NoSQL & Cloud", "filter_files": "Αρχεία & Web", "compare_kicker": "04 / ΑΡΧΙΤΕΚΤΟΝΙΚΗ ΣΥΓΚΡΙΣΗ", "compare_title": "Γιατί οι Ομάδες Δεδομένων Επιλέγουν το Datly", "compare_desc": "Αναλυτική τεχνική αξιολόγηση έναντι παραδοσιακών εργαλείων δεδομένων.", "compare_th_feature": "Δυνατότητα / Ασφάλεια", "compare_th_jupyter": "Jupyter Notebooks", "compare_th_bi": "Κλασικό Cloud BI", "compare_th_dbeaver": "SQL IDEs (DBeaver)", "compare_th_datly": "DATLY IDE", "compare_r1_title": "Σύνθεση με AI σε Φυσική Γλώσσα", "compare_r1_jupyter": "⚠️ Απαιτεί πρόσθετα", "compare_r1_bi": "⚠️ Ακριβοί κλειστοί βοηθοί", "compare_r1_dbeaver": "❌ Κανένα ή βασική συμπλήρωση", "compare_r1_datly": "⚡ Ενσωματωμένος Agent με Πλαίσιο Πίνακα (@table)", "compare_r2_title": "Πλέγμα Δεδομένων Κάτω από το Γράφημα", "compare_r2_jupyter": "❌ Έξοδος σε ξεχωριστό κελί", "compare_r2_bi": "❌ Πολύπλοκη ιεραρχική πλοήγηση", "compare_r2_dbeaver": "❌ Απαιτείται χειροκίνητο ερώτημα", "compare_r2_datly": "🛡️ Πάντα Ορατό στον Καμβά", "compare_r3_title": "Οπτικά Ράφια + Βοηθός AI", "compare_r3_jupyter": "❌ Μόνο κελιά κώδικα", "compare_r3_bi": "⚠️ Μόνο ράφια (Χωρίς Agent κώδικα)", "compare_r3_dbeaver": "❌ Μόνο κώδικας", "compare_r3_datly": "✅ Αμφίδρομα Ράφια + Ευφυής Agent", "compare_r4_title": "Τοπική Ιδιωτικότητα (Μηδενική Διαρροή)", "compare_r4_jupyter": "✅ Τοπική εκτέλεση", "compare_r4_bi": "❌ Υποχρεωτική μεταφόρτωση στο cloud", "compare_r4_dbeaver": "✅ Τοπικό ερώτημα", "compare_r4_datly": "🔒 100% Τοπικά (Μόνο το σχήμα στο LLM)", "compare_r5_title": "Αρχιτεκτονική Προέλευσης Δεδομένων", "compare_r5_jupyter": "❌ Εύθραυστη γραμμική κατάσταση", "compare_r5_bi": "❌ Στατικά μοντέλα δεδομένων", "compare_r5_dbeaver": "❌ Απλό ιστορικό ερωτημάτων", "compare_r5_datly": "🌲 Διακλαδισμένα Νήματα Δεδομένων (DAG)", "compare_r6_title": "Διανυσματική Μηχανή στη Μνήμη", "compare_r6_jupyter": "⚠️ Εξαρτάται από το kernel", "compare_r6_bi": "⚠️ Κλειστή ιδιόκτητη μηχανή", "compare_r6_dbeaver": "❌ Εξάρτηση από απομακρυσμένο διακομιστή", "compare_r6_datly": "⚡ DuckDB + Pandas + PyArrow", "compare_r7_title": "Αφηγηματικές Αναφορές WYSIWYG", "compare_r7_jupyter": "❌ Μόνο απλά κελιά Markdown", "compare_r7_bi": "⚠️ Βαριοί καμβάδες dashboards", "compare_r7_dbeaver": "❌ Δεν υποστηρίζεται", "compare_r7_datly": "📝 Επεξεργαστής TipTap με Ζωντανά Γραφήματα", "compare_r8_title": "Αυτοματοποιημένη Απόσπαση Πινάκων Web", "compare_r8_jupyter": "❌ Χειροκίνητα σενάρια BS4", "compare_r8_bi": "❌ Δεν υποστηρίζεται", "compare_r8_dbeaver": "❌ Δεν υποστηρίζεται", "compare_r8_datly": "🌐 Ενσωματωμένος Scraper Playwright", "compare_r9_title": "Ανεξαρτησία Μοντέλων (BYOK / Τοπικά)", "compare_r9_jupyter": "⚠️ Χειροκίνητη σύνδεση", "compare_r9_bi": "❌ Εξάρτηση από έναν πάροχο", "compare_r9_dbeaver": "❌ Δεν εφαρμόζεται", "compare_r9_datly": "🔓 LiteLLM (Claude, GPT, Ollama)", "quickstart_kicker": "05 / ΑΜΕΣΗ ΕΓΚΑΤΑΣΤΑΣΗ", "quickstart_title": "Εκκινήστε το Datly σε Λιγότερο από 60 Δευτερόλεπτα", "quickstart_desc": "Επιλέξτε τη μέθοδο που προτιμάτε: σύγχρονο uv, κοντέινερ Docker ή εντολή AI σε 1 βήμα.", "term_tab_uv": "Astral uv (Ταχύτερο)", "term_tab_pip": "Python pip + Yarn", "term_tab_docker": "Docker Compose", "term_tab_agent": "Εντολή Βοηθού AI", "term_tab_desktop": "Κατασκευή Desktop App", "releases_kicker": "06 / ΕΠΙΣΗΜΕΣ ΕΚΔΟΣΕΙΣ", "releases_title": "Λήψη Πακέτων Desktop", "releases_desc": "Εγγενής απόδοση για Windows, macOS και Linux με επιτάχυνση υλικού.", "releases_box_title": "Datly Desktop Suite v0.8.0-beta", "releases_box_desc": "Πλατφόρμα Electron 43 · Ενσωματωμένη DuckDB · Υποστήριξη Multi-LLM", "releases_btn_title": "Προβολή Όλων των Αρχείων", "releases_btn_sub": "Για Win · macOS · Linux", "faq_kicker": "07 / ΣΥΧΝΕΣ ΕΡΩΤΗΣΕΙΣ", "faq_title": "Όλα Όσα Πρέπει να Γνωρίζετε", "faq_desc": "Ξεκάθαρες απαντήσεις για τεχνικά ζητήματα, ασφάλεια και ενοποίηση.", "faq_q1": "Στέλνει το Datly τα ευαίσθητα δεδομένα μου στην OpenAI ή την Anthropic;", "faq_a1": "Όχι, απολύτως ποτέ. Το Datly λειτουργεί με αυστηρή τοπική αρχιτεκτονική. Τα πραγματικά δεδομένα σας παραμένουν στη μνήμη RAM και στη DuckDB του υπολογιστή σας. Στο μοντέλο αποστέλλονται μόνο ονόματα και τύποι στηλών.", "faq_q2": "Μπορώ να χρησιμοποιήσω το Datly εντελώς εκτός σύνδεσης χωρίς Internet;", "faq_a2": "Ναι. Το Datly συνδέεται άμεσα με τοπικά ανοιχτά μοντέλα μέσω Ollama ή vLLM (όπως qwen2.5-coder ή llama3.3), ιδανικό για απομονωμένα ασφαλή δίκτυα.", "faq_q3": "Πώς αποτρέπει το Datly τις ψευδείς μετρήσεις και τις παραισθήσεις της AI;", "faq_a3": "Η AI δεν σχεδιάζει ποτέ απευθείας γραφήματα και δεν μαντεύει αριθμούς. Γράφει κώδικα που εκτελείται τοπικά και παράγει έναν πραγματικό πίνακα που εμφανίζεται κάτω από το γράφημα.", "faq_q4": "Σε τι διαφέρει το Datly από το Microsoft Research Data Formulator;", "faq_a4": "Το Datly εξελίσσει το αρχικό ερευνητικό πρωτότυπο σε ένα ολοκληρωμένο επιχειρησιακό IDE με 15+ συνδέσμους, αναφορές TipTap, dashboards και desktop εφαρμογές.", "faq_q5": "Πώς λύνουν τα Data Threads το πρόβλημα κατάστασης του Jupyter;", "faq_a5": "Αντικαθιστώντας τις κοινές μεταβλητές με έναν Κατευθυνόμενο Ακυκλικό Γράφο (DAG). Κάθε ερώτημα παραμένει μια ανεξάρτητη κάρτα χωρίς να αλλοιώνει τα προηγούμενα βήματα.", "footer_tagline": "IDE Οπτικοποίησης Δεδομένων με AI Υψηλής Ασφάλειας · Ανοιχτού Κώδικα υπό Άδεια MIT", "footer_docs": "Τεκμηρίωση", "footer_license": "Άδεια MIT", "footer_bottom": "© 2026 Έργο Datly · Ελεύθερο Λογισμικό", "toast_copy": "Αντιγράφηκε στο πρόχειρο!"}, "he": {"nav_features": "תכונות", "nav_canvas": "אימות נתונים", "nav_connectors": "מחברים (15+)", "nav_comparison": "השוואה", "nav_quickstart": "התחלה מהירה", "nav_faq": "שאלות נפוצות", "nav_star": "כוכב", "nav_quickstart_btn": "התחל עכשיו", "hero_badge": "סביבת פיתוח הדור הבא להדמיית נתונים מבוססת בינה מלאכותית", "hero_title_1": "דבר עם הנתונים שלך בשפה טבעית.", "hero_title_2": "וודא כל שורת חישוב ישירות על המסך.", "hero_desc": "Datly היא סביבת IDE אנליטית בקוד פתוח הפועלת מקומית לחלוטין (Local-First). שפה טבעית הופכת טבלאות ארגוניות לתרשימים אינטראקטיביים, כאשר שורות הנתונים המחושבות מוצגות ישירות מתחת לגרף. אפס זליגת מידע לענן. אפס הזיות AI.", "hero_btn_quickstart": "התחל בפקודה אחת", "hero_btn_sub": "uv run data_formulator", "hero_btn_github": "צפה ב-GitHub", "hero_btn_desktop": "אפליקציית שולחן עבודה", "trust_local": "100% עיבוד מקומי (DuckDB בזיכרון)", "trust_connectors": "15+ מחברים ארגוניים", "trust_privacy": "אפס זליגת נתונים לענן", "trust_models": "תמיכה בכל המודלים (Claude, GPT, Ollama)", "ide_status": "DuckDB בזיכרון (מקומי)", "ide_tab_ide": "סביבת ניתוח מלאה", "ide_tab_explore": "לוח אימות נתונים אמיתיים", "ide_tab_connectors": "15+ מחברי נתונים", "ide_tab_dashboards": "לוחות מחוונים מרובי תרשימים", "ide_tab_reports": "דוחות נרטיביים ב-TipTap", "pillars_kicker": "01 / עמודי התווך הארכיטקטוניים", "pillars_title": "דיוק הנדסי פוגש חקירת נתונים זורמת", "pillars_desc": "נבנה מהיסוד כדי לפתור את בעיות שיבוש המצב של מחברות קוד ואת הנוקשות של כלי BI מסורתיים.", "p1_title": "פתילי נתונים שאינם הרסניים (Data Threads)", "p1_desc": "כל שאלה יוצרת ענף מבודד (DAG). שאלות המשך מחושבות על גבי טבלאות נגזרות מבלי לשנות או לפגוע בנתוני המקור.", "p1_tag": "מכונת מצבי מעקב נתונים", "p2_title": "לוח אימות נתוני אמת (Ground-Truth)", "p2_desc": "להיפרד מהזיות ה-AI. כל תרשים נוצר ישירות מתוך טבלה פיזית נגזרת המוצגת מיד מתחתיו לבדיקה שקופה.", "p2_tag": "אפס מדדים מזויפים", "p3_title": "מנוע DuckDB מהיר בזיכרון", "p3_desc": "עיבוד וקטורי מהיר במיוחד המונע על ידי DuckDB, Pandas ו-PyArrow. הרצת שאילתות מורכבות על מיליוני שורות באלפיות שניה.", "p3_tag": "אנליטיקה וקטורית", "p4_title": "מדפי קידוד מושגיים", "p4_desc": "גרור ושחרר שדות אל מדפים חזותיים: ציר X, ציר Y, צבע, גודל ופיצול. סנכרון דו-כיווני עם הגדרות Vega-Lite.", "p4_tag": "דקדוק הגרפיקה", "p5_title": "דוחות נרטיביים ב-TipTap", "p5_desc": "הפוך תרשימי מחקר לדוחות מנהלים מקצועיים. עורך WYSIWYG עשיר המשלב הסברים מילוליים עם כרטיסי תרשים חיים.", "p5_tag": "הפקת דוחות מנהלים", "p6_title": "פרטיות מקומית מוחלטת", "p6_desc": "שורות הנתונים שלך לעולם אינן עוזבות את המחשב. רק שמות העמודות ומבנה הנתונים נשלחים למודל לצורך כתיבת הקוד.", "p6_tag": "עבודה מלאה ללא חיבור לאינטרנט", "spotlight_kicker": "02 / אימות נתוני אמת", "spotlight_title": "שורת הנתונים מתחת לקו הגרף", "spotlight_desc": "Datly מבטיח שהשאלה, התרשים ושורות הנתונים המחושבות בפועל יישארו תמיד גלויים יחד על אותו מסך.", "s1_title": "Explore: הבדיקה המסונכרנת", "s1_desc": "בעת שאילת שאלה, Datly מחשב טבלה נגזרת חדשה ומצייר את הגרף. הטבלה מתחת לקו מציגה את המספרים המדויקים לבדיקה מיידית.", "s1_tag1": "זום אינטראקטיבי", "s1_tag2": "הורדת CSV", "s1_tag3": "עקומה / שלבים / נקודות", "s2_title": "לוחות מחוונים ללא שכפול נתונים", "s2_desc": "הצמדת תרשימים ללוחות מחוונים או דוחות אינה מעתיקה נתונים. כל התצוגות חולקות את אותו מצב DAG חי.", "s2_tag1": "לוח מרובה כרטיסים", "s2_tag2": "ייצוא ל-PDF", "s2_tag3": "מצב DAG מקושר", "connectors_kicker": "03 / מחברים ארגוניים", "connectors_title": "התחבר פעם אחת. תשאל מקומית.", "connectors_desc": "חבר טבלאות חיות ממאגרי אגמי נתונים, מחסני נתונים ארגוניים, מסדי SQL וטבלאות אינטרנט.", "filter_all": "כל המחברים (15+)", "filter_warehouse": "מחסני נתונים ואגמים", "filter_sql": "SQL יחסי", "filter_cloud": "NoSQL וענן", "filter_files": "קבצים ורשת", "compare_kicker": "04 / השוואה טכנולוגית", "compare_title": "מדוע צוותי נתונים בוחרים ב-Datly", "compare_desc": "הערכה טכנית מפורטת מול כלי ניתוח נתונים מסורתיים.", "compare_th_feature": "תכונה / אבטחה", "compare_th_jupyter": "Jupyter Notebooks", "compare_th_bi": "כלי BI ענניים ותיקים", "compare_th_dbeaver": "סביבות SQL (DBeaver)", "compare_th_datly": "DATLY IDE", "compare_r1_title": "ניתוח מבוסס בינה מלאכותית בשפה טבעית", "compare_r1_jupyter": "⚠️ דורש תוספים חיצוניים", "compare_r1_bi": "⚠️ עוזרי AI יקרים וסגורים", "compare_r1_dbeaver": "❌ אין או השלמה בסיסית בלבד", "compare_r1_datly": "⚡ סוכן AI מובנה מודע הקשר (@טבלה)", "compare_r2_title": "טבלת נתונים ישירות מתחת לגרף", "compare_r2_jupyter": "❌ פלט בתא נפרד", "compare_r2_bi": "❌ ניווט שכבות מסורבל", "compare_r2_dbeaver": "❌ נדרשת כתיבת שאילתה ידנית", "compare_r2_datly": "🛡️ תמיד מוצג יחד על הקנבס", "compare_r3_title": "מדפי קידוד + סוכן AI", "compare_r3_jupyter": "❌ תאי קוד בלבד", "compare_r3_bi": "⚠️ מדפים בלבד (ללא סוכן קוד)", "compare_r3_dbeaver": "❌ קוד בלבד", "compare_r3_datly": "✅ מדפים דו-כיווניים + סוכן חכם", "compare_r4_title": "פרטיות מקומית (אפס זליגה)", "compare_r4_jupyter": "✅ הרצה מקומית", "compare_r4_bi": "❌ העלאת נתונים חובה לענן", "compare_r4_dbeaver": "✅ שאילתה מקומית", "compare_r4_datly": "🔒 100% מקומי (רק הסכימה נשלחת ל-LLM)", "compare_r5_title": "ארכיטקטורת מעקב מקור נתונים (Lineage)", "compare_r5_jupyter": "❌ מצב גלובלי שביר ורגיש לתקלות", "compare_r5_bi": "❌ מודלים סטטיים ונוקשים", "compare_r5_dbeaver": "❌ היסטוריית שאילתות פשוטה", "compare_r5_datly": "🌲 פתילי נתונים מסועפים (DAG)", "compare_r6_title": "מנוע וקטורי מהיר בזיכרון", "compare_r6_jupyter": "⚠️ תלוי בסביבת הליבה", "compare_r6_bi": "⚠️ מנוע סגור של ספק יחיד", "compare_r6_dbeaver": "❌ תלוי בשרת הנתונים המרוחק", "compare_r6_datly": "⚡ DuckDB + Pandas + PyArrow", "compare_r7_title": "דוחות נרטיביים ב-WYSIWYG", "compare_r7_jupyter": "❌ תאי Markdown סטטיים בלבד", "compare_r7_bi": "⚠️ לוחות מחוונים כבדים", "compare_r7_dbeaver": "❌ אינו נתמך", "compare_r7_datly": "📝 עורך TipTap עם גרפים חיים", "compare_r8_title": "חילוץ טבלאות אינטרנט אוטומטי", "compare_r8_jupyter": "❌ סקריפטים ידניים ב-BS4", "compare_r8_bi": "❌ אינו נתמך", "compare_r8_dbeaver": "❌ אינו נתמך", "compare_r8_datly": "🌐 מנוע Playwright מובנה לחילוץ נתונים", "compare_r9_title": "חופש בחירת מודלים (BYOK / מקומי)", "compare_r9_jupyter": "⚠️ חיבור ידני מורכב", "compare_r9_bi": "❌ נעילה לענן של ספק יחיד", "compare_r9_dbeaver": "❌ לא רלוונטי", "compare_r9_datly": "🔓 LiteLLM (Claude, GPT, Ollama ועוד)", "quickstart_kicker": "05 / התקנה קלה ומהירה", "quickstart_title": "הפעל את Datly תוך פחות מ-60 שניות", "quickstart_desc": "בחר את השיטה המועדפת עליך: מנהל החבילות uv, קונטיינר Docker או פקודת סוכן AI אחת.", "term_tab_uv": "Astral uv (המהיר והמומלץ ביותר)", "term_tab_pip": "Python pip + Yarn", "term_tab_docker": "Docker Compose", "term_tab_agent": "פקודת AI אוטומטית", "term_tab_desktop": "בניית אפליקציית שולחן עבודה", "releases_kicker": "06 / גרסאות רשמיות מאומתות", "releases_title": "הורדת חבילות שולחן עבודה", "releases_desc": "ביצועים מהירים עבור Windows, macOS ו-Linux עם האצת חומרה.", "releases_box_title": "Datly Desktop Suite v0.8.0-beta", "releases_box_desc": "מבוסס Electron 43 · מנוע DuckDB מוטמע · תמיכה במגוון רחב של LLM", "releases_btn_title": "צפה בכל קבצי ההורדה", "releases_btn_sub": "תומך ב-Win · macOS · Linux", "faq_kicker": "07 / שאלות נפוצות", "faq_title": "כל מה שחשוב לדעת", "faq_desc": "תשובות מפורטות לשאלות טכניות, אבטחת מידע ואינטגרציות.", "faq_q1": "האם Datly שולח נתונים עסקיים רגישים אל OpenAI או Anthropic?", "faq_a1": "לא, בשום אופן לא. Datly פועל בארכיטקטורת Local-First קפדנית. הנתונים האמיתיים נשארים אך ורק בזיכרון המחשב שלך וב-DuckDB. המודלים מקבלים רק את שמות העמודות והטיפוסים כדי לכתוב את הקוד.", "faq_q2": "האם ניתן להפעיל את Datly במצב לא מקוון (Offline) ללא אינטרנט?", "faq_a2": "כן, בוודאי. Datly מתחבר ישירות למודלים מקומיים דרך Ollama או vLLM (כגון qwen2.5-coder או llama3.3), מושלם לרשתות סגורות ומאובטחות.", "faq_q3": "כיצד Datly מונע הזיות AI ונתונים מומצאים בגרפים?", "faq_a3": "ל-AI אין הרשאה לצייר גרפים ישירות או להמציא מספרים. המודל מייצר קוד הרץ על הנתונים האמיתיים שלך, ויוצר טבלה אמיתית המוצגת תמיד תחת הגרף לאימות.", "faq_q4": "במה שונה Datly מפרויקט המחקר Data Formulator של Microsoft?", "faq_a4": "Datly לקח את רעיון המחקר המקורי והפך אותו לסביבת IDE ארגונית שלמה עם 15+ מחברים לענן, דוחות TipTap עשירים, לוחות מחוונים ואפליקציות שולחן עבודה.", "faq_q5": "כיצד פתילי נתונים (Data Threads) פותרים את שיבוש המשתנים של Jupyter?", "faq_a5": "על ידי שימוש בגרף מכוון ללא מעגלים (DAG). כל שאילתה נשמרת ככרטיס עצמאי שאינו דורס או משבש את שלבי החישוב הקודמים.", "footer_tagline": "סביבת פיתוח והדמיית נתונים בבינה מלאכותית ברמת אבטחה גבוהה · קוד פתוח תחת רישיון MIT", "footer_docs": "תיעוד ומדריכים", "footer_license": "רישיון MIT", "footer_bottom": "© 2026 פרויקט Datly · תוכנה חופשית", "toast_copy": "הועתק ללוח בהצלחה!"}}; diff --git a/docs/vi/index.html b/docs/vi/index.html new file mode 100644 index 0000000..a5c29c1 --- /dev/null +++ b/docs/vi/index.html @@ -0,0 +1,95 @@ + + + +Datly — Không gian phân tích dữ liệu mã nguồn mở với AI + + + + + + + + + + + + + + + + + + + + + +
DatlyBETA
VI

Ngôn ngữ

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MÃ NGUỒN MỞ. TỰ DO KHÁM PHÁ.
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Câu hỏi hay.
Câu trả lời rõ hơn.

Dữ liệu của bạn có một câu chuyện. Khám phá câu chuyện đó trong không gian làm việc có AI hỗ trợ, nơi câu hỏi, biểu đồ và các dòng dữ liệu luôn được kết nối.

Bắt đầu khám phá Tìm hiểu kỹ hơn

Phần mềm miễn phí, mã nguồn mở · Giấy phép MIT

Dành cho nhà phân tích, nhà nghiên cứu và các nhóm dữ liệu.

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Phát triển công khai.Số liệu xác minh ngày 2 tháng 11 năm 2026; cập nhật trực tiếp.
249sao trên GitHub16Lựa chọn nguồn dữ liệu12Ngôn ngữ websiteMITGiấy phép mã nguồn mở
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Bớt chuyển qua lại. Thêm góc nhìn hữu ích.ỨNG DỤNG THỰC TẾ · DỮ LIỆU MINH HỌA
Câu hỏi → biến đổi → biểu đồ → dòng dữ liệuPHÂN TÍCH CỦA BẠN, ĐẦY ĐỦ NGỮ CẢNH
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Xây dựng trên nền tảng vững chắc
DuckDBPythonVega-LiteReactTipTap
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01 / TỪ CÂU HỎI ĐẾN HIỂU BIẾT

Theo đuổi sự tò mò.
Giữ trọn ngữ cảnh.

Từ một bảng dữ liệu đến một phát hiện hữu ích trong cùng không gian làm việc. Đặt câu hỏi, tinh chỉnh và kiểm tra khi phân tích dần hình thành.

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01

Đưa dữ liệu vào

Tải tệp cục bộ, kết nối cơ sở dữ liệu được hỗ trợ hoặc khám phá ví dụ có sẵn.

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02

Đặt câu hỏi tiếp theo

Mô tả điều bạn muốn khám phá. Tinh chỉnh biểu đồ bằng các điều khiển mã hóa trực quan hoặc câu hỏi tiếp nối.

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03

Kiểm tra rồi giải thích

Kiểm tra các dòng dữ liệu dẫn xuất, xuất CSV và bổ sung ngữ cảnh cho phát hiện trong báo cáo có thể chỉnh sửa.

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02 / KHÁM PHÁ KỸ HƠN

Xem quá trình.
Không chỉ câu trả lời.

Hình ảnh ứng dụng thực tế để bạn xem kỹ hơn. Khám phá các tính năng giúp quá trình phân tích dễ theo dõi.

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Các dòng dữ liệu phía sau
đường biểu đồ.

Biểu đồ là điểm khởi đầu. Mở bảng dữ liệu bên dưới để kiểm tra giá trị, xem lại phép biến đổi và xác định kết quả thực sự có ý nghĩa gì.

  • Biểu đồ và bảng kết quả ở cùng một nơi
  • Kiểm tra và xuất các dòng dữ liệu dẫn xuất
  • Xem lại kết quả trước khi chia sẻ

Ảnh chụp ứng dụng thực tế, được bố cục lại cho rõ ràng. Dữ liệu chỉ dùng minh họa; phân tích do AI tạo cần được xem xét.

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03 / LUÔN NẮM RÕ QUÁ TRÌNH

AI giúp bạn khám phá.
Bạn là người quyết định.

Phân tích tốt không chỉ cần một biểu đồ thuyết phục. Datly cho phép bạn kiểm tra các bước trung gian.

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THEO DÕI CÂU HỎI

Giữ mạch
suy nghĩ của bạn.

Luồng dữ liệu sắp xếp câu hỏi, phép biến đổi, bảng dẫn xuất và biểu đồ. Theo dõi quá trình tìm hiểu khi nó phát triển.

Khám phá luồng dữ liệu
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TINH CHỈNH GÓC NHÌN

Biểu đồ để bạn
tiếp tục khai thác.

Dùng điều khiển mã hóa trực quan cho trục, màu và các thuộc tính khác của biểu đồ. Điều chỉnh góc nhìn khi câu hỏi trở nên cụ thể hơn.

Khám phá vùng biểu đồ
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GIẢI THÍCH PHÁT HIỆN

Không chỉ là
ảnh chụp màn hình.

Kết hợp ngữ cảnh bằng văn bản với hình ảnh biểu đồ trong báo cáo có thể chỉnh sửa. Lưu thành ảnh hoặc dùng chức năng in của trình duyệt để tạo PDF.

Khám phá báo cáo
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04 / BẮT ĐẦU VỚI NHỮNG GÌ BẠN CÓ

Dữ liệu của bạn.
Điểm bắt đầu quen thuộc.

Làm việc với tệp và các nguồn dữ liệu được hỗ trợ. Kết nối phụ thuộc vào trình điều khiển, thông tin xác thực và quyền truy cập mạng phù hợp.

Hôm nay là một tệp.
Ngày mai là kho dữ liệu.

Bắt đầu đơn giản với CSV hoặc tập dữ liệu mẫu. Thêm kết nối được hỗ trợ khi câu hỏi của bạn cần nhiều dữ liệu hơn.

Xem mã triển khai bộ kết nối
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  • Pg
    PostgreSQLCơ sở dữ liệu quan hệ
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  • Db
    DatabricksKho dữ liệu SQL
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  • Bq
    BigQueryKho dữ liệu đám mây
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  • Ch
    ClickHouseCơ sở dữ liệu phân tích
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  • My
    MySQLCơ sở dữ liệu quan hệ
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  • Ms
    SQL ServerCơ sở dữ liệu quan hệ
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  • S3
    Amazon S3Lưu trữ đối tượng
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  • Az
    Azure BlobLưu trữ đối tượng
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  • At
    AthenaTruy vấn không máy chủ
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  • Mo
    MongoDBCơ sở dữ liệu tài liệu
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  • Co
    Cosmos DBCơ sở dữ liệu tài liệu
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  • Ku
    Azure KustoKhám phá dữ liệu
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  • Su
    SupersetNhập tập dữ liệu
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  • ↥
    Tệp cục bộCSV, Excel & Parquet
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  • Wb
    Nguồn webTrích xuất bảng được hỗ trợ
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  • Ex
    Tập dữ liệu mẫuThử phân tích mẫu
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Hiển thị đủ 16 tùy chọn nguồn. Tên mô tả các phần triển khai, không phải tích hợp đã được chứng nhận.

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05 / LỰA CHỌN DỰA TRÊN HIỂU BIẾT

Xử lý cục bộ.
Ranh giới dữ liệu rõ ràng.

Xem mã tạo ngữ cảnh mô hình
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01 / THỰC THI

Làm việc trong môi trường của bạn

DuckDB và Python chạy các phép biến đổi trong môi trường lưu trữ Datly, bao gồm bản cài đặt cục bộ trên máy tính của bạn.

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02 / NGỮ CẢNH MÔ HÌNH

Biết AI nhận những gì

Yêu cầu AI có thể chứa lược đồ, giá trị và dòng dữ liệu mẫu, lời nhắc và đầu ra của công cụ. Nhà cung cấp mô hình đám mây nhận ngữ cảnh được gửi đến họ.

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03 / CẤU HÌNH CỦA BẠN

Chọn mô hình

Kết nối nhà cung cấp được hỗ trợ hoặc cấu hình mô hình cục bộ qua Ollama. Yêu cầu AI cục bộ cần điểm cuối cục bộ; nguồn dữ liệu từ xa vẫn cần truy cập mạng.

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06 / CÂU HỎI HAY TIẾP THEO

Thiết lập một chút.
Khám phá thật nhiều.

Biên dịch từ mã nguồn và chạy Datly trong trình duyệt. Bắt đầu với tập dữ liệu mẫu, sau đó cấu hình mô hình để khám phá với AI hỗ trợ.

  • Python 3.11 trở lên
  • Node.js và Yarn cho giao diện
  • uv cho môi trường Python
Đọc hướng dẫn thiết lập Xem bản phát hành 
# Get the project
+git clone https://github.com/arpanjot-coding/Datly.git
+cd Datly
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+# Build the interface
+yarn
+yarn build
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+# Install Python dependencies and launch
+uv sync
+uv run data_formulator
Mở localhost:5567 sau khi khởi chạy

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MỘT VÀI ĐIỀU CẦN BIẾT

Trước khi
bắt đầu.

Câu hỏi hoặc góp ý?
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Datly là gì?

Datly là không gian phân tích dữ liệu mã nguồn mở được xây dựng trên Data Formulator của Microsoft Research. Ứng dụng kết hợp khám phá bằng ngôn ngữ tự nhiên, điều khiển biểu đồ trực quan, luồng dữ liệu, kiểm tra bảng và báo cáo có thể chỉnh sửa. Dự án đang ở giai đoạn beta.

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Dữ liệu có ở lại trên máy tính của tôi không?

Bản cài đặt cục bộ thực hiện các phép biến đổi trên máy tính của bạn. Yêu cầu AI có thể chứa các dòng mẫu, giá trị trường, lời nhắc và đầu ra công cụ được gửi đến mô hình đã cấu hình. Nếu bạn chọn nhà cung cấp đám mây, họ nhận ngữ cảnh này. Điểm cuối mô hình cục bộ có thể giữ yêu cầu mô hình trong máy; hãy xem lại cấu hình và các nguồn dữ liệu đã kết nối.

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Kiểm tra bảng có bảo đảm câu trả lời đúng không?

Không. Bảng giúp bạn kiểm tra các giá trị đã tính toán, nhưng AI vẫn có thể chọn phép biến đổi, tổng hợp hoặc cách diễn giải sai. Hãy xem lại nội dung được tạo, dữ liệu gốc và các giả định nghiệp vụ trước khi dựa vào kết quả.

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Tôi có thể dùng những mô hình nào?

Mã nguồn có hỗ trợ OpenAI, Azure OpenAI, Anthropic, Gemini và điểm cuối Ollama cục bộ. Bạn cấu hình nhà cung cấp và thông tin xác thực. Tình trạng cung cấp mô hình, khả năng và chi phí sử dụng phụ thuộc vào nhà cung cấp.

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Tôi có thể tạo báo cáo và bảng điều khiển không?

Bạn có thể chỉnh sửa báo cáo văn bản định dạng phong phú có hình ảnh biểu đồ, xuất ảnh báo cáo hoặc in thành PDF. Phần triển khai bảng điều khiển hiện tại lưu tham chiếu biểu đồ và hiển thị ô giữ chỗ cho tiêu đề/loại; bảng điều khiển nhiều biểu đồ có hiển thị và tương tác chưa được triển khai.

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Datly có miễn phí không?

Mã nguồn Datly có sẵn theo giấy phép MIT. Nhà cung cấp mô hình, hạ tầng kết nối hoặc dịch vụ lưu trữ có thể tính phí riêng. Xem hướng dẫn thiết lập và bản phát hành trong kho mã để biết các tùy chọn cài đặt hiện tại.

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DỮ LIỆU CỦA BẠN CÓ MỘT CÂU CHUYỆN.

Bắt đầu với một câu hỏi hay.

Bắt đầu với Datly
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Không gian làm việc Datly

Ảnh chụp ứng dụng thực tế sử dụng dữ liệu mẫu.

 + + diff --git a/docs/zh/index.html b/docs/zh/index.html new file mode 100644 index 0000000..95f5601 --- /dev/null +++ b/docs/zh/index.html @@ -0,0 +1,95 @@ + + + +Datly — 开源 AI 数据分析工作空间 + + + + + + + + + + + + + + + + + + + + + +
DatlyBETA
ZH

语言

EnglishРусский简体中文 日本語Tiếng Việtไทยहिन्दीالعربيةFrançaisDeutschEspañolItaliano
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开放源代码，探索更多可能。
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好问题。
更清晰的答案。

你的数据蕴藏着故事。在 AI 辅助工作空间中发现它，让问题、图表及其底层数据行始终相互关联。

开始探索 深入了解

免费开源软件 · MIT 许可证

为分析师、研究人员和数据团队而设计。

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公开开发。数量于 2026 年 11 月 2 日核实，并实时刷新。
249个 GitHub 星标16数据源选项12网站语言MIT开源许可证
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少一些来回切换，多一些洞察。真实应用 · 示例数据
问题 → 数据转换 → 图表 → 数据行分析过程，始终有据可循
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坚实的技术基础
DuckDBPythonVega-LiteReactTipTap
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01 / 从提问到理解

跟随好奇心。
保留完整上下文。

在同一工作空间中，从一张表格走向有用的发现。随着分析逐步展开，持续提问、调整并检查结果。

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01

导入你的数据

加载本地文件、连接受支持的数据库，或探索内置示例。

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02

提出下一个问题

描述你想探索的内容。通过可视化编码控件或后续提问调整图表。

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03

先检查，再阐述

检查转换后的数据行，导出 CSV，并在可编辑报告中为发现补充上下文。

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02 / 深入了解

看清分析过程。
不只看到答案。

真实应用界面，支持放大查看。了解让分析过程更易追踪的功能。

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图表背后的
数据行。

图表只是起点。打开底层表格，检查数值、核查数据转换，并判断结果的实际含义。

  • 同时查看图表和计算结果表
  • 检查并导出转换后的数据行
  • 分享前先审核结果

真实应用截图，经重新取景以便清晰展示。示例数据仅供演示；AI 生成的分析需要审核。

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03 / 掌握分析过程

AI 助你探索。
判断由你做出。

可靠的分析不止于一张有说服力的图表。Datly 让你能够检查中间分析过程。

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追溯问题

保留你的
思考脉络。

数据线程将问题、数据转换、生成的表格和图表有序组织起来，便于跟踪研究的逐步深入。

探索数据线程
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调整视图

让图表
随分析调整。

通过可视化编码控件调整坐标轴、颜色和其他图表属性。随着问题逐渐明确，随时调整视图。

探索画布
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阐释发现

不止于
一张截图。

在可编辑报告中结合文字说明和图表图片。保存为图片，或通过浏览器打印功能生成 PDF。

探索报告
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04 / 从现有数据开始

你的数据。
熟悉的起点。

使用文件及受支持的数据源。连接需要相应的驱动程序、凭据和网络访问权限。

今天，从文件开始。
明天，连接数据仓库。

从一个 CSV 文件或示例数据集开始。当问题需要更多数据时，再连接受支持的数据源。

查看连接器实现
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  • Pg
    PostgreSQL关系型数据库
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  • Db
    DatabricksSQL 数据仓库
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  • Bq
    BigQuery云数据仓库
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  • Ch
    ClickHouse分析型数据库
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  • My
    MySQL关系型数据库
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  • Ms
    SQL Server关系型数据库
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  • S3
    Amazon S3对象存储
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  • Az
    Azure Blob对象存储
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  • At
    Athena无服务器查询
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  • Mo
    MongoDB文档数据库
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  • Co
    Cosmos DB文档数据库
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  • Ku
    Azure KustoData Explorer
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  • Su
    Superset数据集导入
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  • ↥
    本地文件CSV、Excel 与 Parquet
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  • Wb
    网页数据源受支持的表格提取
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  • Ex
    示例数据集体验示例分析
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显示全部 16 种数据源选项。名称表示已有的代码实现，并不代表经过认证的集成。

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05 / 了解后再选择

本地处理。
清晰的数据边界。

查看模型上下文代码
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01 / 执行环境

在你的工作空间中运行

DuckDB 和 Python 在部署 Datly 的环境中执行数据转换，也支持在你的计算机上本地安装运行。

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02 / 模型上下文

了解 AI 会收到什么

AI 请求可能包含表结构、示例值与数据行、提示词及工具输出。云模型服务商会收到发送给它的上下文。

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03 / 你的配置

选择模型

连接受支持的服务商，或通过 Ollama 配置本地模型。本地 AI 请求需要使用本地端点；远程数据源仍需网络访问。

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06 / 你的下一个好问题

少量配置。
更多探索。

从源代码构建 Datly，并在浏览器中启动。先使用示例数据集，再配置模型，开始 AI 辅助探索。

  • Python 3.11 或更高版本
  • 前端需要 Node.js 和 Yarn
  • 使用 uv 管理 Python 环境
阅读安装指南 查看发行版本 
# Get the project
+git clone https://github.com/arpanjot-coding/Datly.git
+cd Datly
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+# Build the interface
+yarn
+yarn build
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+# Install Python dependencies and launch
+uv sync
+uv run data_formulator
打开 localhost:5567 启动后

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使用前须知

开始之前，
先了解这些。

有疑问或反馈？
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Datly 是什么？

Datly 是基于 Microsoft Research 的 Data Formulator 构建的开源数据分析工作空间。它结合了自然语言探索、可视化图表控件、数据线程、表格检查和可编辑报告。项目目前处于测试阶段。

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我的数据会留在自己的计算机上吗？

本地安装会在你的计算机上处理数据转换。AI 请求可能包含示例数据行、字段值、提示词和工具输出，并将这些内容发送给你配置的模型。如果选择云服务商，该服务商就会收到这些上下文。本地模型端点可让模型请求在本地进行；请检查你的配置和连接的数据源。

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检查表格能保证答案正确吗？

不能。表格有助于检查计算结果，但 AI 仍可能选择错误的数据转换、聚合方式或解读。在依赖结果前，请审核生成的分析、底层数据和业务假设。

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可以使用哪些模型？

代码包含对 OpenAI、Azure OpenAI、Anthropic、Gemini 和本地 Ollama 端点的支持。你需要配置服务商和凭据。模型的可用性、能力及使用费用取决于相应服务商。

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可以创建报告和仪表板吗？

你可以编辑包含图表图片的富文本报告，将报告导出为图片，或打印为 PDF。目前的仪表板实现会保存图表引用，并显示标题和类型占位内容；尚未实现可渲染图表的交互式多图表仪表板。

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Datly 免费吗？

Datly 源代码按 MIT 许可证提供。模型服务商、所连接的基础设施或托管服务可能另行收费。请查看仓库中的安装说明和发行版本，了解当前的安装方式。

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你的数据蕴藏着故事。

从一个好问题开始。

开始使用 Datly
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Datly 工作空间

使用示例数据的真实应用截图。

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