# RAG Agent (Postgres)

Custom RAG agent that indexes text files from a git repository into Postgres
and answers queries using retrieval + LLM generation. Commits are tied to
**stories**; indexing and retrieval can be scoped by story.

## Quick start

1. Configure environment variables:
   - `RAG_REPO_PATH` — path to git repo with text files
   - `RAG_DB_DSN` — Postgres DSN (e.g. `postgresql://user:pass@localhost:5432/rag`)
   - `RAG_EMBEDDINGS_DIM` — embedding vector dimension (e.g. `1536`)
2. Create DB schema:
   - `python scripts/create_db.py` (or `psql "$RAG_DB_DSN" -f scripts/schema.sql`)
3. Index files for a story (e.g. branch name as story slug):
   - `rag-agent index --story my-branch --changed --base-ref HEAD~1 --head-ref HEAD`
4. Ask a question (optionally scoped to a story):
   - `rag-agent ask "What is covered?"`
   - `rag-agent ask "What is covered?" --story my-branch`

## Git hook (index on commit)

Install the post-commit hook so changed files are indexed after each commit:

```bash
cp scripts/post-commit .git/hooks/post-commit && chmod +x .git/hooks/post-commit
```

Story for the commit is taken from (in order): env `RAG_STORY`, file `.rag-story` in repo root (one line = slug), or current branch name.

## DB structure

- **stories** — story slug (e.g. branch name); documents and chunks are tied to a story.
- **documents** — path + version per story; unique `(story_id, path)`.
- **chunks** — text chunks with embeddings (pgvector); updated when documents are re-indexed.

Scripts: `scripts/create_db.py` (Python, uses `ensure_schema` and `RAG_*` env), `scripts/schema.sql` (raw SQL).

## Notes

- The default embedding/LLM clients are stubs. Replace them in
  `src/rag_agent/index/embeddings.py` and `src/rag_agent/agent/pipeline.py`.
- This project requires Postgres with the `pgvector` extension.