# 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. (Optional) Run Postgres and the app via Docker (clone the repo first):
   - `git clone git@git.lesha.spb.ru:alex/RagAgent.git && cd RagAgent`
   - `docker compose up -d` — starts Postgres and the RAG app in one network `rag_net`; app connects to DB at host `postgres`.
   - On first start (empty DB), scripts in `docker/postgres-init/` run automatically (extension + tables). To disable, comment out the init volume in `docker-compose.yml`.
   - Default DSN inside the app: `postgresql://rag:rag_secret@postgres:5432/rag`. Override with `POSTGRES_*` and `RAG_REPO_PATH` (path to your knowledge-base repo, mounted into the app container).
   - Run commands: `docker compose run --rm app index --story my-branch`, `docker compose run --rm app ask "Question?"`.
2. Configure environment variables:
   - `RAG_REPO_PATH` — path to git repo with text files
   - `RAG_DB_DSN` — Postgres DSN (e.g. `postgresql://rag:rag_secret@localhost:5432/rag`)
   - `RAG_EMBEDDINGS_DIM` — embedding vector dimension (e.g. `1536`)
3. Create DB schema (only if not using Docker, or if init was disabled):
   - `python scripts/create_db.py` (or `psql "$RAG_DB_DSN" -f scripts/schema.sql`)
4. 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`
5. 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).

## Embeddings (GigaChat)

If `GIGACHAT_CREDENTIALS` is set (e.g. in `.env` for local runs), embeddings use GigaChat API; otherwise the stub client is used. Optional env: `GIGACHAT_EMBEDDINGS_MODEL` (default `Embeddings`), `GIGACHAT_VERIFY_SSL` (`true`/`false`). Ensure `RAG_EMBEDDINGS_DIM` matches the model output (see GigaChat docs).

## Notes

- LLM client is still a stub; replace it in `src/rag_agent/agent/pipeline.py` for real answers.
- This project requires Postgres with the `pgvector` extension.