🔎 CodeRAG

A standalone, local-first semantic code-search engine for large and custom codebases.

CodeRAG indexes a whole codebase into a hybrid (vector + keyword) search index and answers questions like "where is retry/backoff handled?" with the exact functions, classes, and files that matter — ranked by meaning, not just string match.

It runs entirely on your machine with no API key (a local ONNX embedding model is the default), keeps its index up to date as you edit, and is built to stay fast on large codebases. Use it from the CLI, embed it as a Python library, self-host it as an HTTP service, or browse with the web UI.

Built for the cases off-the-shelf IDE assistants don't cover well: a codebase that's too big, too private, or too custom — or a search/RAG capability you want to own and embed in your own tools.

✨ Highlights

• Local-first, zero-key. Default embeddings run locally via fastembed (ONNX, no PyTorch). OpenAI is optional.
• Symbol-aware chunking. Indexes functions, classes, and methods (Python via ast; JS/TS/Go/Rust/Java via tree-sitter), not crude fixed-size blocks — so results point at real code units with file:line citations.
• Hybrid retrieval. Dense vector search + BM25 keyword search, fused with Reciprocal Rank Fusion. Great at both "what does this mean" and exact-identifier lookups.
• Incremental & live. Content-hashed indexing only re-embeds files that changed; a debounced watcher keeps the index current as you code. No duplicate or stale vectors.
• Built to scale. Exact Flat search for small repos, automatic switch to approximate IVF past a threshold so it stays fast at 100k+ chunks.
• Four surfaces, one engine. CLI · Python library · HTTP/REST · Streamlit UI — all thin wrappers over the same CodeRAG object.

🚀 Quick start

pip install -e .            # core engine (local embeddings included)
# optional extras:
pip install -e ".[server]"  # HTTP/REST API
pip install -e ".[ui]"      # Streamlit web UI
pip install -e ".[openai]"  # OpenAI embeddings / LLM answers

Index a codebase and search it — no configuration, no API key:

coderag index --watched-dir /path/to/your/repo
coderag search "where are duplicate vectors removed on file change" --watched-dir /path/to/your/repo

1. coderag/indexer.py:141 (Indexer._index_file)  [method, sim=0.70]
   def _index_file(self, item): removed = 0; existing = self.store.get_file(item.rel) …
2. coderag/indexer.py:1  [window, sim=0.74]
   """Incremental indexing orchestration. ...the critical correctness property…"""

By default the index lives in ./.coderag/. Set CODERAG_WATCHED_DIR / CODERAG_STORE_DIR (or copy example.env to .env) to avoid repeating flags.

🧑‍💻 The four surfaces

CLI

coderag index [PATH] [--full]     # build / incrementally update the index
coderag search "QUERY" [-k 8]     # hybrid search; add --json or --answer
coderag watch                     # index, then keep it live as files change
coderag serve --port 8000         # run the HTTP API  (needs [server])
coderag ui                        # launch the web UI (needs [ui])
coderag status                    # index stats (files, chunks, model, index type)

Python library

from coderag import CodeRAG, Config

cr = CodeRAG(Config.from_env(watched_dir="/path/to/repo"))
cr.index()

for hit in cr.search("how is the FAISS index persisted?"):
    print(f"{hit.location}  {hit.symbol}  (sim={hit.similarity:.2f})")
    print(hit.text)

HTTP / REST (coderag serve)

curl "http://127.0.0.1:8000/search?q=token%20validation&k=5"
curl -X POST http://127.0.0.1:8000/index -d '{"full": false}' -H 'content-type: application/json'
curl "http://127.0.0.1:8000/status"
curl "http://127.0.0.1:8000/file?path=coderag/api.py&start_line=1&end_line=40"

Self-host it once and point any number of custom apps or teammates at a big shared codebase.

Web UI (coderag ui)

Streamlit app: search box, retrieved chunks with path:line citations and similarity scores, a one-click Reindex button, and an optional streamed LLM answer (when an OpenAI key is configured).

🏗️ How it works

graph LR
    A[Source files] --> B[Symbol-aware chunking<br/>ast / tree-sitter]
    B --> C[Embeddings<br/>fastembed · OpenAI]
    C --> D[(SQLite store<br/>chunks + vectors + FTS5)]
    D --> E[FAISS index<br/>Flat → IVF]
    Q[Query] --> F[Dense + BM25]
    E --> F
    D --> F
    F --> G[Reciprocal Rank Fusion]
    G --> H[Ranked hits<br/>path:line + score]

• SQLite is the source of truth (chunk text, line ranges, symbols, content hashes, and the raw vectors). The FAISS index is a rebuildable cache — it can always be reconstructed from SQLite, so switching models or index types never corrupts your data.
• Each file's content is hashed; unchanged files are skipped on re-index. A changed file's old chunks are removed from both the store and the vector index before new ones are added — so editing never accumulates stale or duplicate vectors.

⚙️ Configuration

Everything is configurable via CODERAG_* environment variables or a .env file (see example.env). Common ones:

🧩 Supported languages

Symbol-aware (function/class/method level): Python, JavaScript, TypeScript/TSX, Go, Rust, Java. Many other languages and docs (C/C++, Ruby, PHP, Markdown, YAML, …) are indexed with a line-window fallback, so they remain searchable.

🛠️ Development

python -m venv venv && source venv/bin/activate
pip install -e ".[dev,server,openai]"

pytest -m "not integration"     # fast, offline (uses a deterministic fake embedder)
pytest -m integration           # exercises the real local model (downloads once)
black --check . && isort --check-only . && flake8 coderag tests && mypy coderag

See DEVELOPMENT.md and AGENTS.md for architecture and contribution details.

📄 License

Apache License 2.0 — see LICENSE.

🙏 Acknowledgments

FAISS · fastembed · tree-sitter · FastAPI · Streamlit · watchdog

⭐ If CodeRAG helps you, please give it a star!