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Quickstart

From install to a cited answer in about five minutes, using the document_qa recipe — a ready-to-run project you point at your own documents. It ships a filled-in schema, extractor, and pipeline, so there's no code to write for a first answer. For the full command surface and customization, see the usage guide.

1. Install

uv add 'reigner[openai,ingestion]'

openai pulls the SDK for the recipe's default ingestion model; ingestion pulls the document loaders. Prefer Claude or Gemini? Swap the extra (anthropic / gemini) and change the model line in step 4.

2. Scaffold a project

$ reigner init myqa --recipe document_qa
✓ Scaffolded myqa/ (document_qa recipe mode)
$ cd myqa

3. Add your API key

cp .env.example .env
# edit .env → OPENAI_API_KEY=sk-...

Reigner auto-loads this .env from the project root — no export needed.

4. Drop in a corpus

Put a document or two in library/raw/. For a throwaway first run, one tiny markdown file is enough:

<!-- library/raw/faq.md -->
# Orbit FAQ

## Pricing
Orbit is $8 per user per month, billed annually.

The recipe's default ingestion model is openai:gpt-5.5. To use another provider, edit the model line in extractors/my_extractor.py.

5. Compile the corpus

$ reigner ingest
# representative output
✓ loaded 1 document
✓ extracted 1 entity → library/artifacts/
✓ built BM25 index → search-index/documents.json

Want to see the plan first without spending tokens? reigner ingest --dry-run lists the file → loader mapping and exits.

6. Ask a question

$ reigner chat
› What does Orbit cost?
# representative output
  ✓ Retrieved  2 calls · 6.1k tok · $0.004 · 1.8s

  Sources
    [1] faq.md  section=pricing

  ╭─ Answer ──────────────────────────────╮
  │  Orbit is $8 per user per month. [1]   │
  ╰────────────────────────────────────────╯

The answer is grounded in a retrieved source and cited inline as [1]. Swap in your own documents and re-run ingest — new files compile, unchanged ones are skipped.

Where to go next