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code retrieval and MCP server for multi-language repositories built on @abhinav2203/codeflow-core.","maintainers":[{"name":"omnyx_industries","email":"abhinavnehra2203@gmail.com"}],"readme":"# CodeRag\n\nCodeRag is a standalone npm package that gives coding agents targeted retrieval over a codebase. It uses `@abhinav2203/codeflow-core` with tree-sitter for multi-language repo analysis, stores node documents in LanceDB, traverses graph edges for surrounding context, and can optionally ask a local LLM server to turn the retrieved context into an answer.\n\n**Supported languages:** TypeScript, JavaScript, Go, Python, C, C++, Rust.\n\n## What ships in this repo\n\n- Library API for indexing and querying a repo\n- CLI for local setup, indexing, querying, and git hook installation\n- MCP server exposing `query`, `lookup`, `explain`, `impact`, and `status`\n- Easy-to-swap interfaces for graph providers and LLM transports\n\n## Install\n\n```bash\nnpm install @abhinav2203/coderag\n```\n\n## Quick start\n\n1. Create a config file in the target repo:\n\n```json\n{\n  \"repoPath\": \".\",\n  \"storageRoot\": \".coderag\",\n  \"retrieval\": {\n    \"topK\": 6,\n    \"rerankK\": 3\n  },\n  \"traversal\": {\n    \"defaultDepth\": 1,\n    \"maxDepth\": 3\n  },\n  \"llm\": {\n    \"enabled\": false,\n    \"transport\": \"openai-compatible\",\n    \"baseUrl\": \"http://127.0.0.1:1234/v1\",\n    \"model\": \"your-local-model\"\n  }\n}\n```\n\n2. Initialize the index:\n\n```bash\nnpx coderag init\n```\n\n3. Query the repo:\n\n```bash\nnpx coderag query \"where is auth handled?\"\n```\n\n4. Run the MCP server:\n\n```bash\nnpx coderag serve-mcp\n```\n\n## Configuration\n\nCodeRag loads configuration in this order:\n\n1. Explicit `--config` path\n2. `coderag.config.json`\n3. `.coderag.json`\n4. `.env` values from the current working directory\n5. Environment overrides\n\nSupported environment overrides:\n\n- `CODERAG_REPO_PATH`\n- `CODERAG_STORAGE_ROOT`\n- `CODERAG_EMBEDDING_PROVIDER`\n- `CODERAG_EMBEDDING_DIMENSIONS`\n- `CODERAG_ONNX_MODEL_DIR`\n- `CODERAG_GEMINI_MODEL`\n- `CODERAG_GEMINI_API_KEY`\n- `CODERAG_GEMINI_AI_KEY`\n- `CODERAG_EMBEDDING_TIMEOUT_MS`\n- `CODERAG_TOP_K`\n- `CODERAG_RERANK_K`\n- `CODERAG_MAX_CONTEXT_CHARS`\n- `CODERAG_DEFAULT_DEPTH`\n- `CODERAG_MAX_DEPTH`\n- `CODERAG_LOCK_TIMEOUT_MS`\n- `CODERAG_LOCK_POLL_MS`\n- `CODERAG_LOCK_STALE_MS`\n- `CODERAG_SERVICE_HOST`\n- `CODERAG_SERVICE_PORT`\n- `CODERAG_SERVICE_API_KEY`\n- `CODERAG_LLM_ENABLED`\n- `CODERAG_LLM_TRANSPORT`\n- `CODERAG_LLM_BASE_URL`\n- `CODERAG_LLM_MODEL`\n- `CODERAG_LLM_API_KEY`\n- `CODERAG_LLM_TIMEOUT_MS`\n- `CODERAG_CUSTOM_HTTP_FORMAT`\n- `CODERAG_LLM_HEADERS`\n\nWhen `embedding.provider` is `gemini`, CodeRag defaults to `models/gemini-embedding-2` and requests 768-dimensional vectors explicitly so the stored embedding fingerprint matches the vectors written to LanceDB. It accepts either `CODERAG_GEMINI_API_KEY` or the compatibility alias `CODERAG_GEMINI_AI_KEY`.\n\nWhen `embedding.provider` is `onnx`, CodeRag uses `Xenova/gte-small` (384-dim, ~33MB) running locally via `@xenova/transformers`. No API key or external server needed. The model must be downloaded to `<onnxModelDir>/Xenova/gte-small/` (default `.coderag-models/models/Xenova/gte-small/`).\n\n```bash\n# Download the ONNX embedding model (~33MB)\npython3 -c \"\nfrom huggingface_hub import snapshot_download\nsnapshot_download('Xenova/gte-small', local_dir='.coderag-models/models',\n                  allow_patterns=['onnx/model_quantized.onnx', 'config.json',\n                                  'tokenizer.json', 'tokenizer_config.json',\n                                  'special_tokens_map.json'])\n\"\n\n# Then set embedding.provider to \"onnx\" in your config and run coderag init\n```\n\n## Local LLM integration\n\nCodeRag does not require a hosted model. The default documented path is any local or self-hosted model server that exposes an OpenAI-compatible HTTP API on a port.\n\n### OpenAI-compatible server\n\nPoint CodeRag at a server that exposes `/v1/chat/completions` and streams tokens over SSE.\n\n```json\n{\n  \"llm\": {\n    \"enabled\": true,\n    \"transport\": \"openai-compatible\",\n    \"baseUrl\": \"http://127.0.0.1:1234/v1\",\n    \"model\": \"qwen2.5-coder-14b-instruct\"\n  }\n}\n```\n\nCodeRag sends:\n\n- the user question\n- the assembled CodeRag context package\n- a system prompt that tells the model to answer only from retrieved code context\n\nCompatibility notes:\n\n- `baseUrl` may already include `/v1`; CodeRag preserves that path when calling `/chat/completions`.\n- If a provider rejects `system` role messages, CodeRag retries by folding the system prompt into the first user message.\n- Prompt assembly is compact and file-aware so small-context local models can still answer from retrieved code without receiving duplicated file bodies.\n\n### Custom HTTP server\n\nIf your local model server is not OpenAI-compatible, use `transport: \"custom-http\"`.\n\nRequest body:\n\n```json\n{\n  \"question\": \"where is auth handled?\",\n  \"model\": \"local-model\",\n  \"stream\": true,\n  \"context\": {\n    \"graphSummary\": \"...\"\n  },\n  \"messages\": [\n    { \"role\": \"system\", \"content\": \"...\" },\n    { \"role\": \"user\", \"content\": \"...\" }\n  ]\n}\n```\n\nSupported response formats:\n\n- `json`: `{ \"answer\": \"...\" }`\n- `ndjson`: one JSON object per line with `token` chunks and an optional final `answer`\n- `sse`: `data:` frames with `token` chunks and an optional final `answer`\n\nExample:\n\n```json\n{\n  \"llm\": {\n    \"enabled\": true,\n    \"transport\": \"custom-http\",\n    \"baseUrl\": \"http://127.0.0.1:8080\",\n    \"model\": \"local-model\",\n    \"customHttpFormat\": \"ndjson\"\n  }\n}\n```\n\n## Retrieval behavior\n\n- Indexing stores one generated markdown document per blueprint node.\n- Search uses deterministic local embeddings, source-span-aware lexical reranking, query expansion for operational terms, and a penalty for oversized catch-all nodes.\n- Page index retrieval reads full files from disk and caches them by `mtimeMs`.\n- Graph traversal expands both upstream and downstream neighbors up to the requested depth.\n- If no LLM is configured, `query` returns `answerMode: \"context-only\"` with the same context package.\n\n## CLI\n\n```bash\ncoderag init [--config path]\ncoderag index [--config path]\ncoderag reindex [--config path] [--full]\ncoderag query \"question\" [--config path] [--depth 2] [--json]\ncoderag serve-mcp [--config path]\ncoderag serve-http [--config path]\ncoderag doctor [--config path]\n```\n\n## Git hook\n\n`coderag init` installs a `post-commit` hook that triggers `coderag reindex` and preserves any pre-existing hook logic.\n\n## Production notes\n\n- TypeScript, JavaScript, Go, Python, C, C++, and Rust repos are supported.\n- Excluded directories: `node_modules`, `.git`, `.next`, `dist`, `build`, `target`, `__pycache__`, `vendor`, `.venv`, `artifacts`, `coverage`.\n- Call-site extraction is best effort for dynamic dispatch, reflection, or generated code. Missing call sites are returned as unresolved metadata, not guessed values.\n- The built-in `local-hash` embedding strategy is deterministic and zero-setup. The `onnx` provider runs `Xenova/gte-small` locally (384-dim, ~33MB) for semantic-quality embeddings without any API key. If you need cloud-quality embeddings, use the `gemini` provider.\n- `serve-http` exposes `/health`, `/ready`, `/metrics`, and `/v1/*` endpoints. `/ready` only reports ready once the index exists, contains documents, and matches the configured embedding fingerprint.\n- If you use Gemini embeddings, set `CODERAG_GEMINI_API_KEY` or `CODERAG_GEMINI_AI_KEY` before indexing. Changing `CODERAG_GEMINI_MODEL` requires a full reindex because the persisted embedding fingerprint includes the model name and dimensions.\n- Live E2E runs in this repo were verified against an OpenAI-compatible NVIDIA endpoint and against both the CodeRag and CodeFlow repositories.\n\n## Development\n\n```bash\nnpm install\nnpm run lint\nnpm run check\nnpm test\nnpm run build\n```\n","readmeFilename":"README.md"}