Metadata-Version: 2.4
Name: ariadne-data
Version: 0.3.1
Summary: Governed metrics for autonomous agents: OKF context layer + OSI semantic layer behind one MCP facade
Requires-Python: >=3.12
Description-Content-Type: text/markdown
Requires-Dist: duckdb>=1.0
Requires-Dist: sqlglot>=25.0
Requires-Dist: openai>=1.40
Requires-Dist: pyyaml>=6.0
Provides-Extra: mcp
Requires-Dist: mcp<2,>=1.2; extra == "mcp"
Provides-Extra: documents
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# Ariadne

**Governed metrics for AI agents.** Ariadne gives agents a reviewed source of truth for business metrics: definitions are compiled and queried by Ariadne, while business policies and caveats travel with the answer.

Raw text-to-SQL asks an agent to guess tables, joins, filters, and definitions. Ariadne separates those responsibilities into two layers:

- **Semantic Layer (OSI):** reviewed metric definitions, compiled to SQL and executed read-only by Ariadne.
- **Context Layer (OKF):** business policies, source caveats, and terminology that explain how to interpret a result.

Ariadne is a Python package with embedded DuckDB. Use it locally through the CLI, connect an agent through optional MCP, or add the separately distributed Ariadne Agent. A human remains responsible for source approval and for admitting or promoting the reviewed catalog. Ariadne is not a BI dashboard or a high-concurrency metrics API.

## Documentation

The documentation follows [Diátaxis](https://diataxis.fr/):

- **[Tutorial](docs/tutorials/getting-started.md):** create a project and reach a first governed query.
- **[How-to guides](docs/README.md#how-to-guides):** complete specific tasks such as Git-backed authoring, private Docker MCP setup, or reading multiple S3 storages.
- **[Reference](docs/README.md#reference):** look up CLI commands, data-source configuration, and migration mappings.
- **[Explanation](docs/explanations/how-ariadne-works.md):** understand Ariadne's layers, governance gates, and authority boundaries.

The [documentation index](docs/README.md) lists all guides and references for **Ariadne 0.3.0**.

## Source development

Work on Ariadne itself happens from the package checkout, not inside a data project:

```bash
uv venv --python 3.12 .venv
uv pip install --python .venv/bin/python -e .
source .venv/bin/activate
# Bundled end-to-end demo: live configured LLM, human review, then a fresh query:
examples/retail-demo/demo.sh
```

For a candidate-wheel check, run `uv build`, install `dist/ariadne_data-*.whl`
into a fresh virtual environment, and exercise the walkthrough with the
installed console scripts rather than the editable install.
