Install datasight¶
datasight is distributed as a Python CLI on PyPI. The recommended
installer is uv, which installs datasight
as a global tool without managing a virtual environment. If you already
have a Python toolchain you prefer, pip works too.
This includes DuckDB, SQLite, PostgreSQL, Anthropic, OpenAI, GitHub Models, and Ollama support.
Configure an LLM provider¶
Run datasight config init to create ~/.config/datasight/.env, then add
your API key there — every project on this machine will pick it up.
Alternatively, paste the key into the project .env or export it in your
shell. Pick one of:
For Azure OpenAI or a corporate gateway, also set OPENAI_BASE_URL.
GITHUB_TOKEN is a GitHub token with access to GitHub Models — it is
not the same as a git push credential. Pick one source:
- GitHub CLI — if you already use
gh, rungh auth tokenand paste the output. This is the quickest path. - Fine-grained PAT — create one at
github.com/settings/personal-access-tokens
with the
Models: readaccount permission. No repository access is needed. Classic PATs do not grant Models access.
Install Ollama, pull a tool-calling model, then:
qwen2.5:7b is a good starting point for CLI queries (datasight ask).
For the web UI with visualizations, qwen2.5:14b handles the more
complex agent interactions better. For the best experience overall,
consider GitHub Models (free
tier) — only use Ollama when cost or data-security requirements demand
keeping inference local.
See the Configuration reference for every supported variable.
PNG chart export
datasight ask --chart-format png needs the optional export extra.
Reinstall with the extra — for example,
uv tool install "datasight[export]" or
pip install --user "datasight[export]".
The web UI does not need it.