datasight 0.8.1

AI-powered data exploration with natural language

datasight

Status: early and evolving. This project is in active development and the code is changing rapidly — APIs, CLI flags, and behavior may shift between commits. Feedback and bug reports from users are very welcome; please open an issue on GitHub.

AI-powered data exploration with natural language.

datasight connects an AI agent to your database and provides a web UI where you can ask questions in natural language. The agent writes SQL, runs queries, and generates interactive Plotly visualizations.

Supports DuckDB, PostgreSQL, SQLite, and Flight SQL databases. Also queries local CSV, Parquet, and Excel (.xlsx) files directly — no database setup required. Supports Anthropic Claude (default), GitHub Models (open source), and Ollama (local) as LLM backends.

Quick start

uv tool install datasight

# Create a new project
mkdir my-project && cd my-project
datasight init

# Edit .env with your API key and database path
# Edit schema_description.md to describe your data
# Edit queries.yaml with example questions

# Run the web UI
datasight run

Open http://localhost:8084 and start asking questions. By default, the web UI binds to 127.0.0.1; use --host to expose TCP intentionally, or --unix-socket /path/to/datasight.sock for SSH-forwarded socket workflows on HPC systems.

Explore CSV, Parquet, or Excel files with no setup

# Launch the web UI with no project, then paste a file or directory path
# into "Explore Files" to create views automatically
datasight run

# Or inspect a file from the command line (schema, row count, column stats)
datasight inspect generation.parquet

# Or build a persistent project from CSV/Parquet inputs
datasight generate generation.csv plants.csv --db-path grid.duckdb

Or ask from the command line without starting a server:

datasight ask "What are the top 10 records?"
datasight ask "Show trends by year" --chart-format html -o chart.html
datasight profile
datasight quality --format markdown -o quality.md
datasight ask --file questions.txt --output-dir batch-output

Features

• Natural language queries — ask questions in English, get SQL + results
• Interactive charts — Plotly visualizations with chart-type switching
• Multiple databases — DuckDB, PostgreSQL, SQLite, and Flight SQL
• Query files directly — point at a local CSV, Parquet, or Excel file (or directory) and start asking questions; datasight creates DuckDB views (or one table per Excel sheet) on the fly
• Headless CLI — datasight ask runs queries without a web server
• Deterministic CLI workflows — profile, quality, dimension, trend, and recipe commands that do not require an LLM
• Schema browser — sidebar with tables, columns, and example queries
• Schema auto-discovery — tables, columns, and types detected automatically
• Domain context — describe your data in Markdown for better AI understanding
• Example queries — seed the AI with question/SQL pairs
• Reusable prompt recipes — project-specific analysis prompts derived from the schema
• Multi-chart dashboard — pin results, filter cards, and configure layouts
• Session export — share a session as a self-contained HTML page, or export it as a runnable Python script (editable SQL constants, embedded Plotly specs, hardcoded DB path with --db / --output-dir overrides) that re-runs the analysis without datasight or the AI in the loop
• Keyboard shortcuts — ? to see all shortcuts, / to focus input
• Streaming responses — real-time SSE streaming from the LLM

Architecture

datasight pairs a FastAPI backend with a Svelte 5 + TypeScript + Tailwind CSS frontend built with Vite. It supports multiple LLM backends — Anthropic (default), OpenAI, GitHub Models, and Ollama — selectable via LLM_PROVIDER in .env.

datasight run / datasight ask / datasight profile / datasight quality
  → LLM provider (Anthropic / OpenAI / GitHub Models / Ollama)
    → DuckDB / PostgreSQL / SQLite / Flight SQL (or CSV/Parquet via DuckDB views)
    → Plotly chart generator
  → Web UI (SSE streaming) or CLI output

Documentation

uv sync --extra dev
. .venv/bin/activate
zensical serve
zensical build
python scripts/generate_cli_reference.py

Development Tests

# Build frontend assets for FastAPI serving after a clean checkout
bash scripts/build-frontend.sh

# Python test suite
pytest

# CI-safe Python test suite, excluding tests that need local Ollama
pytest -m "not integration"

# Frontend unit tests (Vitest)
cd frontend && npm test

# Frontend E2E tests (Playwright, requires datasight run)
cd frontend && npm run test:e2e

# Rebuild frontend for FastAPI serving after frontend changes
bash scripts/build-frontend.sh

Generated web assets under src/datasight/web/static/ and src/datasight/web/templates/index.html are not checked in. Run bash scripts/build-frontend.sh before using datasight run from a clean checkout when you want FastAPI to serve the production UI.

Ollama-backed CLI tests are marked integration because they require a running local Ollama server with the qwen2.5:7b model available. CI runs pytest -m "not integration"; run pytest -m integration locally when you want to exercise the live LLM path.

Software Record

datasight is developed under NLR Software Record SWR-26-045.