raglite-sqlite

Local-first retrieval augmented generation toolkit built on SQLite. Raglite bundles ingestion, chunking, hybrid BM25/vector search, Typer CLI workflows, and a FastAPI microservice. Everything runs on CPU and stores state in a single SQLite database.

Quickstart in 6 lines

python -m venv .venv && source .venv/bin/activate
pip install "raglite-sqlite[server]"
raglite init-db --db demo.db
raglite ingest --db demo.db --path demo/mini_corpus
raglite query --db demo.db --text "quick start guide" --k 5 --alpha 0.6
raglite serve --db demo.db --host 127.0.0.1 --port 8080

On Windows use \.venv\Scripts\activate for step two.

Installation

Raglite is published as raglite-sqlite on PyPI. Install the core toolkit with:

pip install raglite-sqlite

To enable the optional FastAPI server, install the server extra:

pip install "raglite-sqlite[server]"

Development dependencies (linters, type checkers, packaging utilities) can be installed with the dev extra:

pip install "raglite-sqlite[dev]"

Features

• Deterministic chunking and debug embeddings for air-gapped demos, with an easy upgrade path to SentenceTransformer models.
• Hybrid BM25 + cosine search with rerank option and automatic Python fallback when SQLite vector extensions are unavailable.
• Typer CLI (raglite), FastAPI server (raglite serve), and Python API for scripted use.
• Offline demo kit (demo/mini_corpus) with one-shot run scripts and proof artifacts.
• Tiny eval and benchmark scripts that run in seconds and provide reproducible metrics.

Demo & proof artifacts

Explore the bundled two-minute corpus and scripts inside demo/:

• demo/mini_corpus/: 12 varied documents (how-to, FAQ, articles, product docs).
• demo/run_demo.sh / demo/run_demo.ps1: create a virtual environment, install Raglite, ingest the demo corpus, run a sample query, and start the FastAPI server with curl hints.
• demo/eval_set.jsonl: 25 ground-truth query snippets for offline evaluation.

A placeholder animation (demo/demo.gif) can be replaced with your own screenshots once you run the scripts.

CLI highlights

• raglite self-test builds a temporary database from the demo corpus, runs three canned queries, prints titles/snippets, reports the active vector backend, and dumps stats.
• raglite stats now returns document, chunk, embedding counts plus backend, embedding model/dimensions, FTS status, and alpha.
• raglite benchmark / raglite eval invoke the new tiny scripts under scripts/.

Vector backends

Raglite automatically selects the most capable vector backend:

1. SQLite extension (sqlite-vec or sqlite-vss): if loadable, cosine similarity runs directly inside SQLite for best performance. Example load step:

   import sqlite3
   conn = sqlite3.connect("raglite.db")
   conn.enable_load_extension(True)
   conn.load_extension("sqlite_vec")
   conn.enable_load_extension(False)

2. Python fallback (default): BM25 prefilters the top 200 rows and cosine similarity is computed with NumPy arrays in Python. This works cross-platform with zero extra dependencies.
3. None: if the embeddings table is absent, vector search is skipped and BM25 answers requests alone.

raglite self-test, raglite stats, and the benchmark script print which path you are on. Expect the Python fallback to be a few milliseconds slower per query but fully portable.

Architecture

erDiagram
  documents ||--o{ chunks : contains
  chunks ||--o{ embeddings : has
  documents {
    int id
    text path
    text title
    text mime
    text meta_json
    datetime created_at
  }
  chunks {
    int id
    int document_id
    int chunk_idx
    text text
    int tokens
    text tags_json
  }
  embeddings {
    int id
    int chunk_id
    text model
    int dim
    blob embedding
  }

  %% Additional structure
  %% chunk_fts is an FTS5 virtual table with triggers syncing chunk text changes.

Loading

chunk_fts is a virtual FTS5 table kept in sync with the chunks table via insert/update triggers; see src/raglite/schema.sql for details.

Evaluations & benchmarks

• python scripts/eval_small.py compares BM25, Hybrid (α=0.6), and optional rerank on the bundled dataset. Hybrid matches BM25 on this micro-set by default and rerank (if installed) can further improve conversational queries.
• python scripts/bench_basic.py duplicates the demo corpus to ~2k chunks, reports indexing throughput, query latency (p50/p95) with and without vector fallback, database size, and backend selection.
• Both scripts support --tiny for <30s smoke runs and fall back to packaged data when installed from wheels.

Limitations & Guidance

• Designed for small/medium local corpora, not massive ANN workloads. For millions of vectors, adopt a dedicated vector database.
• Best with a single writer; readers work fine with WAL mode. Remember to back up both *.db and *.db-wal files.
• Increase α to ≥0.6 when keyword precision matters; lower it or enable rerank (with raglite-sqlite[rerank]) for conversational questions.
• Raglite is local-first—avoid ingesting secrets or PII. Use .ragliteignore patterns or preprocess files to filter sensitive content.

Tests & quality

Run the full suite that matches CI:

ruff check src tests
black --check src tests
mypy src
pytest -q
raglite self-test

License

MIT