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Mini RAG (Done Properly)

A small, honest retrieval-augmented generation system built as a comparison lab: upload text, ask questions, and see four retrievers race head-to-head with real latency, real recall, and real receipts.

Most portfolio RAGs ship as "look, it answers questions." This one ships as "look at the tradeoffs." The whole point is to make the cost of retrieval choices visible.

What it does

  • Upload .txt / .md files (or seed the Paul Graham essay corpus).
  • Ask a question. The system runs the same query through four retrievers in parallel:
    1. Naive cosine — numpy loop over every chunk (exact, O(n))
    2. FAISS Flat — exact, SIMD-accelerated
    3. FAISS IVF — approximate nearest neighbour, tunable nlist / nprobe
    4. Hybrid (BM25 + dense, RRF fusion) — sparse + dense reranked
  • Each retriever reports latency, retrieved chunks, and similarity scores.
  • A latency chart accumulates per-query points across methods. Slide the corpus size from 100 → 50 000 chunks and watch the crossover point where naive collapses and FAISS pulls away.
  • Every answer cites its sources. No hidden chunks, no vibes.

Why it isn't boring

Three things separate this from a weekend tutorial:

  1. Comparison is the product, not a footnote — the homepage is a side-by-side benchmark.
  2. An eval harness (make eval) reports Recall@5, Recall@10, MRR, and p50/p95 latency against a hand-written gold set. Numbers, not vibes.
  3. Local-only embeddings (bge-small-en-v1.5) — no API key required to demo retrieval. Anthropic key is only needed for answer synthesis.

Quickstart

cp .env.example .env
# (optional) put your ANTHROPIC_API_KEY *or* OPENROUTER_API_KEY in .env

make bootstrap-uv   # one-time: installs uv (Astral's Python manager)
make install        # uv sync for API, pnpm install for web
make seed           # loads the eval corpus as the starter document
make dev-api        # FastAPI on :8000  (one terminal)
make dev-web        # Next.js on :3000  (another terminal)

Open http://localhost:3000.

No python3-venv or system pip needed. uv ships its own Python and manages apps/api/.venv for you. Every Python target uses uv run ….

Or with Docker:

make docker-build
make docker-up

Eval

make eval

Runs all retrievers against apps/api/src/eval/gold.jsonl. Writes JSON to eval_results/ and prints a comparison table.

Architecture

apps/
  api/           FastAPI + SQLite + FAISS + sentence-transformers
    src/
      api/         thin routers
      services/    ingestion, retrieval, answer, benchmark
      retrievers/  naive | faiss_flat | faiss_ivf | bm25 | hybrid
      chunking/    recursive char split with overlap
      embeddings/  bge-small wrapper
      repositories/ document, chunk, eval_run
      eval/        harness + gold set
      core/        config, logger, errors, db
    tests/         mirrors src/
  web/           Next.js + Tailwind + shadcn
    app/         comparison lab + eval results page
    components/  upload, query box, answer columns, latency chart
    lib/api/     repository pattern, no raw fetch in components

Tradeoffs called out

  • bge-small over MiniLM — slightly slower, meaningfully better retrieval.
  • FAISS IVF over HNSW — IVF has the cleaner nlist / nprobe story to explain in a portfolio piece.
  • Synthetic corpus expansion for the slider — chunks are duplicated and lightly perturbed to hit 50 k for the crossover demo. Honest about it in the UI.
  • No reranker in v1 — adds a model download and complicates the comparison story. Easy to bolt on as v2.

Branching

See rules.md. All work goes through feat/* branches into dev. dev is always green.

License

MIT.