# MCP Local RAG [](https://github.com/damoqiongqiu/mcp-local-rag) [](https://www.npmjs.com/package/@damoqiongqiu/mcp-local-rag) [](https://opensource.org/licenses/MIT) [](https://www.typescriptlang.org/) [](https://registry.modelcontextprotocol.io/) > 🍴 Forked from [shinpr/mcp-local-rag](https://github.com/shinpr/mcp-local-rag) — original work by [Shinsuke Kagawa](https://github.com/shinpr) > > Local code intelligence engine for AI coding assistants. AST-level semantic chunking + keyword boost for pinpointing functions, classes, and APIs — fully private, zero setup. 📖 [中文文档](README_CN.md) --- ## Table of Contents 1. [Features](#1-features) 2. [Quick Start](#2-quick-start) - 2.1 [Configure Your AI Coding Tool](#21-configure-your-ai-coding-tool) - 2.2 [CLI Quick Start](#22-cli-quick-start) - 2.3 [First-Time Project Indexing](#23-first-time-project-indexing) 3. [Core Concepts](#3-core-concepts) - 3.1 [Dual-Strategy Chunking](#31-dual-strategy-chunking) - 3.2 [Hybrid Search](#32-hybrid-search) - 3.3 [Security Boundary](#33-security-boundary) 4. [MCP Tool Reference](#4-mcp-tool-reference) - 4.1 [Ingest Tools](#41-ingest-tools) - 4.2 [Search Tools](#42-search-tools) - 4.3 [Management Tools](#43-management-tools) - 4.4 [Code Intelligence](#44-code-intelligence) - 4.5 [System Tools](#45-system-tools) 5. [CLI](#5-cli) - 5.1 [Basic Commands](#51-basic-commands) - 5.2 [CLI Configuration](#52-cli-configuration) 6. [Network & Models](#6-network--models) - 6.1 [Mirror Auto-Detection](#61-mirror-auto-detection) - 6.2 [Model Selection](#62-model-selection) - 6.3 [File Watching](#63-file-watching) 7. [Search Tuning](#7-search-tuning) 8. [Performance Tuning](#8-performance-tuning) - 8.1 [Quantization Precision](#81-quantization-precision-rag_dtype) - 8.2 [Execution Device](#82-execution-device-rag_device) - 8.3 [Minimum Chunk Length](#83-minimum-chunk-length-chunk_min_length) - 8.4 [Recommended Configurations](#84-recommended-configurations) 9. [Configuration Reference](#9-configuration-reference) 10. [Troubleshooting](#10-troubleshooting) 11. [Development](#11-development) --- ## 1. Features - **Smart dual-strategy chunking** — AST-level code chunking via tree-sitter (splits at function/class/method boundaries, injects scope chain + imports). Semantic chunking for documents (splits by meaning, not character count). - **Semantic search + keyword boost** — Vector search first, then keyword matching boosts exact terms. `useEffect`, error codes, class names rank higher — not just semantically guessed. - **15 MCP tools** — Ingest, search, manage, code intelligence, and system ops in one server. - **AST code intelligence** — `find_definition` and `find_references` for IDE-level code navigation, powered by tree-sitter metadata captured at ingest time. - **Three-tier mirror auto-fallback** — `huggingface.co → hf-mirror.com → modelscope.cn`, zero config for users in mainland China. - **Runs entirely locally** — No API keys, no cloud, no data leaving your machine. Works offline after the first model download. - **Zero-friction setup** — One `npx` command. No Docker, Python, or servers to manage. --- ## 2. Quick Start Set `BASE_DIR` to the folder you want to search (`BASE_DIRS` for multiple roots — see [Configuration](#8-configuration-reference)). ### 2.1 Configure Your AI Coding Tool **Cursor** — `~/.cursor/mcp.json`: ```json { "mcpServers": { "local-rag": { "command": "npx", "args": ["-y", "@damoqiongqiu/mcp-local-rag"], "env": { "BASE_DIR": "/path/to/your/project" } } } } ``` **Claude Code**: ```bash claude mcp add local-rag --scope user --env BASE_DIR=/path/to/your/project -- npx -y @damoqiongqiu/mcp-local-rag ``` **Codex** — `~/.codex/config.toml`: ```toml [mcp_servers.local-rag] command = "npx" args = ["-y", "@damoqiongqiu/mcp-local-rag"] [mcp_servers.local-rag.env] BASE_DIR = "/path/to/your/project" ``` **WorkBuddy** — Settings → Custom Connectors → Add: ```json { "mcpServers": { "local-rag": { "command": "npx", "args": ["-y", "@damoqiongqiu/mcp-local-rag"], "env": { "BASE_DIR": "/path/to/your/project" } } } } ``` > ⚠️ WorkBuddy: you MUST click "Trust" in the Custom Connectors list after adding, otherwise the server is silently blocked. ### 2.2 CLI Quick Start No MCP needed — run directly from the terminal: ```bash npx @damoqiongqiu/mcp-local-rag ingest ./src/ npx @damoqiongqiu/mcp-local-rag query "auth middleware" npx @damoqiongqiu/mcp-local-rag status ``` That's it. No Docker, Python, or server setup. ### 2.3 First-Time Project Indexing ``` You: "Index the src directory of this project" Assistant: Successfully ingested 156 files (2,847 chunks created) You: "Where's the middleware that handles API rate limiting?" Assistant: src/middleware/rateLimiter.ts — useRateLimiter(), lines 42–89 You: "How is the database connection pool configured?" Assistant: src/config/database.ts — createPool() default max: 20, idle: 5 ``` --- ## 3. Core Concepts ### 3.1 Dual-Strategy Chunking Chunking strategy is chosen per file type: - **Code files** (50+ languages) — `CodeChunker` parses source via tree-sitter AST, splits at structural boundaries (functions, classes, methods). Each chunk's `contextualizedText` includes its scope chain and import context for precise semantic search. - **Documents** (PDF/DOCX/TXT/MD/HTML) — `SemanticChunker` splits into sentences, groups by embedding similarity to find natural topic boundaries. Markdown code blocks remain intact — never split mid-block. ### 3.2 Hybrid Search Search = semantic similarity + keyword boost (`RAG_HYBRID_WEIGHT`, default 0.6): 1. Query vectorization → semantic search finds most relevant chunks 2. Quality filters apply (distance threshold, grouping) 3. Keyword matching boosts exact-term rankings Exact identifiers like `useEffect` are never buried by semantic approximations. ### 3.3 Security Boundary Only files under `BASE_DIR` / `BASE_DIRS` are accessible for ingest, list, delete, or read-neighbor operations. Symlinks resolved outside roots are rejected. Sibling-prefix paths (e.g., `/foo/barista` when root is `/foo/bar`) are also blocked — prevents path traversal attacks. --- ## 4. MCP Tool Reference 15 tools organized into 5 categories. ### 4.1 Ingest Tools | # | Tool | Purpose | Example | |---|------|---------|---------| | 1 | `ingest_file` | Single file (PDF/DOCX/TXT/MD/code) | `"Ingest ./docs/api-spec.pdf"` | | 2 | `ingest_data` | In-memory text/HTML | `"Fetch this page and ingest the HTML"` | | 3 | `ingest_directory` | Bulk directory ingest | `"Ingest everything under ./src"` | **`ingest_file`** supports 50+ code languages. PDFs support an optional visual mode — a local VLM generates captions for figure pages, making visual content searchable. Two profiles available: | Profile | Model | Cache | Suited for | |---------|------|------|------------| | `fast` (default) | SmolVLM-256M | ~250 MB | Light visual indexing | | `quality` | Qwen2.5-VL-3B-ONNX | ~2.9 GB | Figures with in-image text | ```bash # CLI npx @damoqiongqiu/mcp-local-rag ingest ./spec.pdf --visual --visual-quality quality # MCP "Ingest ./spec.pdf with visual: true, visualQuality: 'quality'" ``` **`ingest_data`** runs Readability → Markdown → index. Perfect for web content fetched by your AI assistant. Re-ingesting replaces old versions automatically. **`ingest_directory`** scans recursively, respects `.gitignore`, shows real-time progress via MCP notifications. ### 4.2 Search Tools | # | Tool | Purpose | Key Parameters | |---|------|---------|----------------| | 4 | `query_documents` | Hybrid search (semantic + keyword) | `query`, `limit`, `scope`, `highlightContext`, `fromTimestamp` | | 5 | `read_chunk_neighbors` | Expand context around results | `filePath`, `chunkIndex`, `before`, `after` | **`query_documents`** — `scope` accepts a single path prefix or list, restricting results to that subtree. `highlightContext` returns snippets around matched terms. `fromTimestamp` / `untilTimestamp` enable time-range filtering. **`read_chunk_neighbors`** — defaults to 2 chunks before and after (like `grep -C 2`), max 50 each. Response includes the target chunk marked `isTarget: true`. ### 4.3 Management Tools | # | Tool | Purpose | |---|------|---------| | 6 | `list_files` | List files with ingestion status (`ingested: true/false`) | | 7 | `delete_file` | Delete by file path or source URL | | 8 | `status` | Index stats: docs, chunks, memory, search mode | `list_files` supports `scope` filtering with the same prefix-match semantics as search. In large directories, scope accelerates the scan by skipping out-of-scope subtrees. ### 4.4 Code Intelligence | # | Tool | Purpose | Input | |---|------|---------|-------| | 9 | `find_definition` | Locate symbol definition (file, line range, scope) | Exact symbol name | | 10 | `find_references` | Find all references (import + text mention) | Symbol name | Both tools depend on AST metadata (imports, entities, scope chains) extracted by tree-sitter at ingest time. **Only works for code files ingested with `CodeChunker`** — files ingested before v0.18.7 lack this metadata and require `reindex_all` to rebuild. **`find_references`** uses a two-phase strategy: (1) exact match in `codeMeta.imports` → (2) FTS full-text search for the symbol name. Results are deduplicated by (filePath, chunkIndex), with import references listed first. ### 4.5 System Tools | # | Tool | Purpose | |---|------|---------| | 11 | `config` | Runtime hot read/write config — no restart needed | | 12 | `dedup_check` | SHA256 + Jaccard similarity to detect duplicate files | | 13 | `export_index` | Export entire index as JSON (backup or migration) | | 14 | `reindex_all` | Full re-chunk + re-embed (after model change) | | 15 | `reindex_stale` | Re-ingest only files modified on disk (incremental sync) | **`config`** hot-swaps `hybridWeight`, `modelName`, `cacheDir`, `baseDir`/`baseDirs`, etc. Switching models auto-disposes the old Embedder and initializes the new one — note: changing models alters the embedding space and requires `reindex_all`. **`dedup_check`** is especially useful in monorepos — `spot` ↔ `futures` mirror code is typically flagged with similarity 1.0. --- ## 5. CLI ### 5.1 Basic Commands ```bash # Ingest npx @damoqiongqiu/mcp-local-rag ingest ./src/ # Search (with scope) npx @damoqiongqiu/mcp-local-rag query "auth middleware" npx @damoqiongqiu/mcp-local-rag query "auth" --scope /docs/api # Context expansion npx @damoqiongqiu/mcp-local-rag read-neighbors --file-path /abs/path.md --chunk-index 5 # Management npx @damoqiongqiu/mcp-local-rag list --scope /docs/api npx @damoqiongqiu/mcp-local-rag status npx @damoqiongqiu/mcp-local-rag delete ./docs/old.pdf npx @damoqiongqiu/mcp-local-rag delete --source "https://..." ``` `query`, `read-neighbors`, `list`, `status`, `delete` emit JSON to stdout (pipe to `jq`). `ingest` emits progress to stderr. Global options (`--db-path`, `--cache-dir`, `--model-name`) go before the subcommand: ```bash npx @damoqiongqiu/mcp-local-rag --help ``` > ⚠️ The CLI does NOT read your MCP client config (`mcp.json`, etc.). Configure via flags or environment variables. ### 5.2 CLI Configuration **Flags** — global options before, subcommand options after: ```bash npx @damoqiongqiu/mcp-local-rag --db-path ./my-db query "auth" --base-dir ./docs ``` `--base-dir` is repeatable on `ingest` and `list`: ```bash npx @damoqiongqiu/mcp-local-rag ingest --base-dir ./docs --base-dir ./specs ./docs/readme.md ``` **Environment variables**: ```bash export DB_PATH=./my-db export BASE_DIR=./docs npx @damoqiongqiu/mcp-local-rag query "auth" ``` For multiple roots, use `BASE_DIRS` (JSON array): ```bash export BASE_DIRS='["/Users/me/work","/Users/me/specs"]' ``` Precedence: CLI flags > environment variables > defaults. --- ## 6. Network & Models ### 6.1 Mirror Auto-Detection huggingface.co is inaccessible from mainland China. Built-in three-tier mirror chain with automatic fallback: ``` huggingface.co → hf-mirror.com → modelscope.cn ``` At startup, each mirror is HEAD-probed (3s timeout). The first reachable mirror with a complete API is selected: - **With proxy** (`HTTPS_PROXY`) → direct to huggingface.co - **No proxy** → auto-switch to hf-mirror.com - **hf-mirror API unavailable** → fallback to modelscope.cn **No manual `HF_ENDPOINT` required.** For manual control: | Env Var | Effect | |---------|--------| | `HF_AUTO_MIRROR=false` | Disable auto-detection, use huggingface.co only | | `HF_ENDPOINT=` | Force a specific mirror, skip auto-detection | > v0.18.5+ uses `setGlobalDispatcher(ProxyAgent)` — all Node.js 22 network requests go through the proxy. ### 6.2 Model Selection 6 embedding models with alias resolution via model-registry: | Model | Alias | Size | Dims | |-------|-------|------|------| | `Xenova/all-MiniLM-L6-v2` (default) | `mini` | ~90 MB | 384 | | `Xenova/all-MiniLM-L12-v2` | — | ~120 MB | 384 | | `Xenova/bge-small-en-v1.5` | `bge-small` | ~130 MB | 384 | | `Xenova/all-mpnet-base-v2` | `mpnet` | ~420 MB | 768 | | `Xenova/bge-base-en-v1.5` | — | ~420 MB | 768 | | `Xenova/multi-qa-mpnet-base-dot-v1` | `multi-qa` | ~420 MB | 768 | **Guidance**: code repos → default model + high keyword boost; multilingual → consider `embeddinggemma-300m`; scientific papers → consider `allenai-specter`. **RAG_DTYPE** controls ONNX precision (`fp32` / `fp16` / `q8`). Default `fp32`; use `q8` when memory-constrained. ⚠️ Changing models or dtype requires deleting `DB_PATH` and re-indexing. ### 6.3 File Watching Set `RAG_WATCH=true` — the server starts recursive `fs.watch` on baseDirs (500ms debounce): - File creation/modification → auto `ingest_file` - File deletion → auto `delete_file` Ideal for actively changing projects. --- ## 7. Search Tuning | Variable | Default | Description | |----------|---------|-------------| | `RAG_HYBRID_WEIGHT` | `0.6` | Keyword boost: 0 = semantic only, 1 = keyword only | | `RAG_GROUPING` | unset | `similar` = top group only, `related` = top 2 groups | | `RAG_MAX_DISTANCE` | unset | Filter low-relevance results (e.g., `0.5`) | | `RAG_MAX_FILES` | unset | Limit results to top N files | **Code-focused tuning (recommended default):** ```json { "RAG_HYBRID_WEIGHT": "0.7", "RAG_GROUPING": "similar" } ``` **Document-focused tuning:** ```json { "RAG_HYBRID_WEIGHT": "0.4", "RAG_GROUPING": "related" } ``` Keyword boost is applied *after* semantic filtering — improves precision without introducing noise. --- ## 8. Performance Tuning Beyond search accuracy, inference performance is also configurable. All optimizations are environment variables — no code changes required. ### 8.1 Quantization Precision (`RAG_DTYPE`) Controls ONNX model inference precision. For `all-MiniLM-L6-v2`, three levels are available: | Value | Model Size | Speed | Memory | Precision Loss | Best For | |-------|-----------|-------|--------|---------------|----------| | `fp32` (default) | ~90 MB | baseline | ~80 MB | none | First use, maximum accuracy | | `fp16` | ~45 MB | 20-30% faster | ~45 MB | negligible | **Recommended** for daily use | | `q8` | ~45 MB | 30-50% faster | ~45 MB | minor | Low memory, large projects | ```json "env": { "RAG_DTYPE": "fp16", "BASE_DIR": "..." } ``` ⚠️ Changing dtype **requires index rebuild** — embedding spaces are incompatible. **Verify it works:** After restart, call `status` via MCP and check the `dtype` field. Should match your setting (e.g., `"fp16"`). **If it fails:** Startup throws `EmbeddingError` with a list of supported dtypes. Common cause: the model doesn't provide the `q8` variant — switch to `fp16`. ### 8.2 Execution Device (`RAG_DEVICE`) Controls which ONNX Runtime backend to use: | Value | Backend | Notes | |-------|---------|-------| | `cpu` (default) | CPU | Most stable, no extra dependencies | | `webgpu` | GPU (WebGPU) | ⚠️ Experimental: M1/M2 Mac uses Metal, NVIDIA uses Vulkan | ```json "env": { "RAG_DEVICE": "webgpu", "RAG_DTYPE": "fp16", "BASE_DIR": "..." } ``` ⚠️ Changing device changes the embedding space — requires index rebuild. **Stacks with `RAG_DTYPE`** — `fp16 + webgpu` gives both model-size reduction and GPU speedup. **Verify it works:** MCP startup log should show `Loading model on device "webgpu"`. `status` should show `device: "webgpu"`. **If it fails:** - `Unsupported device` at startup → WebGPU unavailable in your environment, revert to `"cpu"` - Starts successfully but inference crashes → likely an ONNX WebGPU backend bug, revert to `"cpu"` - Just delete the `RAG_DEVICE` line to fall back — other config is untouched ### 8.3 Minimum Chunk Length (`CHUNK_MIN_LENGTH`) Filters out chunks shorter than this value during ingest. Default `50` keeps nearly everything; `200` drops 30-40% of noise fragments. ```json "env": { "CHUNK_MIN_LENGTH": "200", "BASE_DIR": "..." } ``` ⚠️ Blunt instrument — short but important code (e.g., config constants) may also be discarded. Requires index rebuild. Sweet spot: `100-200`. ### 8.4 Recommended Configurations | Scenario | Config | |----------|--------| | Daily development | `RAG_DTYPE=fp16` | | Large project + M1/M2 Mac | `RAG_DTYPE=fp16, RAG_DEVICE=webgpu` | | Memory-constrained | `RAG_DTYPE=q8` | All changes require `reindex_all` (MCP) or re-running `ingest` (CLI). If something breaks, delete the failing env line to revert to defaults. --- ## 9. Configuration Reference MCP server: environment variables only (via your MCP client's `env` block). CLI: environment variables + equivalent flags (flags take precedence). | Env Var | CLI Flag | Default | Description | |---------|----------|---------|-------------| | `BASE_DIR` | `--base-dir` (repeatable) | `cwd` | Document root (security boundary) | | `BASE_DIRS` | — | unset | JSON array of roots, overrides `BASE_DIR` | | `DB_PATH` | `--db-path` | `./lancedb/` | Vector database path | | `CACHE_DIR` | `--cache-dir` | `./models/` | Model cache — recommend absolute path | | `MODEL_NAME` | `--model-name` | `all-MiniLM-L6-v2` | HuggingFace model ID | | `MAX_FILE_SIZE` | `--max-file-size` | 100 MB | Max file size in bytes | | `CHUNK_MIN_LENGTH` | `--chunk-min-length` | `50` | Min chunk length (1–10000 chars) | | `RAG_DEVICE` | — | `cpu` | ONNX execution device | | `RAG_DTYPE` | — | `fp32` | Quantization (`fp32`/`fp16`/`q8`) | | `HTTPS_PROXY` | — | unset | Model download proxy. v0.18.5+ globally effective | | `HF_ENDPOINT` | — | `huggingface.co` | Manual mirror override | | `HF_AUTO_MIRROR` | — | `true` | Auto-detection toggle | | `RAG_WATCH` | — | unset | File watching (`true`/`1`) | **Root resolution order**: CLI `--base-dir` > `BASE_DIRS` > `BASE_DIR` > `cwd`. `BASE_DIRS` and `BASE_DIR` are never merged. Only JSON array syntax supported for `BASE_DIRS` — delimiter syntax is intentionally rejected. --- ## 10. Troubleshooting

Model download failed

Symptoms: `fetch failed`, `status` shows `searchMode: fts` instead of `hybrid`. **Solutions:** 1. **Network restriction** (mainland China, etc.) — use proxy: ```json "env": { "HTTPS_PROXY": "http://127.0.0.1:7890" } ``` Set in your MCP client config, not the terminal. v0.18.5+ globally effective via `setGlobalDispatcher`. 2. **Auto-mirror fallback** (v0.18.2+, default) — three-tier probe. Usually works without any config. 3. **Manual override** — `HF_ENDPOINT=https://modelscope.cn` or [download models manually](https://huggingface.co/Xenova/all-MiniLM-L6-v2) into `CACHE_DIR`. 4. **npx cached old version** — clear and restart: ```bash rm -rf ~/.npm/_npx/ ```

MCP client doesn't see tools

1. Verify config file syntax 2. WorkBuddy users: confirm "Trust" button clicked 3. Restart client completely (Cmd+Q on macOS) 4. Test directly: `npx @damoqiongqiu/mcp-local-rag` should run without errors

Rebuilding the index

After switching models or when the database is corrupted: 1. Stop the MCP service 2. Delete `DB_PATH` directory (default `./lancedb/`) — safe, doesn't affect source files 3. Restart MCP → fresh database auto-created 4. Bulk re-ingest: ```bash npx @damoqiongqiu/mcp-local-rag ingest ./src/ ```

FAQ

- **Private?** Yes. After model download, nothing leaves your machine. - **Offline?** Yes, once models are cached. - **Supported formats?** 50+ code languages + PDF/DOCX/TXT/MD/HTML. No Excel, PPT, or images. - **GPU acceleration?** Opt-in via `RAG_DEVICE`. Support depends on your system, Node.js version, and the ONNX backend. - **Backup?** Copy the `DB_PATH` directory.

--- ## 11. Development ```bash git clone https://github.com/damoqiongqiu/mcp-local-rag.git cd mcp-local-rag pnpm install ``` ```bash pnpm test # All tests pnpm run type-check # TypeScript check pnpm run check:fix # Lint + format pnpm run check:all # Full CI pipeline ``` ```text src/ index.ts # Entry point server/ # MCP tool handlers cli/ # CLI subcommands parser/ # PDF/DOCX/TXT/MD/code parsing chunker/ # SemanticChunker + CodeChunker embedder/ # Transformers.js embeddings vectordb/ # LanceDB operations utils/ # Shared utilities (security, scan, scope) __tests__/ # Test suites ``` --- ## License MIT License. Free for personal and commercial use. ## Acknowledgments Built with [Model Context Protocol](https://modelcontextprotocol.io/) (Anthropic), [LanceDB](https://lancedb.com/), and [Transformers.js](https://huggingface.co/docs/transformers.js).