RagAgent/README.md

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:

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.