Rag Vault

🚀 RAG Vault

RAG Vault gives AI coding assistants fast access to your private documents such as API specs, research papers, and internal docs. Indexing and search run locally, and your data stays on your machine unless you explicitly ingest content from a remote URL.

🚀 Quick Start

RAG Vault offers a straightforward setup process. With just one command to run and minimal setup requirements, it provides privacy by default. Before adding MCP config, follow these steps:

1. Install Node.js 20 or newer.
2. Pick a documents directory and set BASE_DIR to that path.
3. Make sure your AI tool process can read BASE_DIR.
4. Restart your AI tool after editing config.

For Cursor

Add the following to ~/.cursor/mcp.json:

{
  "mcpServers": {
    "local-rag": {
      "type": "stdio",
      "command": "npx",
      "args": ["-y", "github:RobThePCGuy/rag-vault"],
      "env": {
        "BASE_DIR": "/path/to/your/documents"
      }
    }
  }
}

Replace /path/to/your/documents with your real absolute path.

For Claude Code

Add to .mcp.json in your project directory:

{
  "mcpServers": {
    "local-rag": {
      "type": "stdio",
      "command": "npx",
      "args": ["-y", "github:RobThePCGuy/rag-vault"],
      "env": {
        "BASE_DIR": "./documents",
        "DB_PATH": "./documents/.rag-db",
        "CACHE_DIR": "./.cache",
        "RAG_EMBEDDING_DEVICE": "cpu",
        "RAG_HYBRID_WEIGHT": "0.6",
        "RAG_GROUPING": "related"
      }
    }
  }
}

Or add inline via CLI:

claude mcp add local-rag --scope user --env BASE_DIR=/path/to/your/documents -- npx -y github:RobThePCGuy/rag-vault

For Codex

Add to ~/.codex/config.toml:

[mcp_servers.local-rag]
command = "npx"
args = ["-y", "github:RobThePCGuy/rag-vault"]

[mcp_servers.local-rag.env]
BASE_DIR = "/path/to/your/documents"

Install Skills (Optional)

For enhanced AI guidance on query formulation and result interpretation, install the RAG Vault skills:

# Claude Code (project-level - recommended for team projects)
npx github:RobThePCGuy/rag-vault skills install --claude-code

# Claude Code (user-level - available in all projects)
npx github:RobThePCGuy/rag-vault skills install --claude-code --global

# Codex (user-level)
npx github:RobThePCGuy/rag-vault skills install --codex

# Custom location
npx github:RobThePCGuy/rag-vault skills install --path /your/custom/path

Skills teach Claude best practices for:

• Query formulation and expansion strategies
• Score interpretation (< 0.3 = good match, > 0.5 = skip)
• When to use ingest_file vs ingest_data
• HTML ingestion and URL handling

Restart your AI tool, and start talking:

You: "Ingest api-spec.pdf"
AI:  Successfully ingested api-spec.pdf (47 chunks)

You: "How does authentication work?"
AI:  Based on section 3.2, authentication uses OAuth 2.0 with JWT tokens...

✨ Features

Solving Common Pain Points

Security Features

RAG Vault includes security features for production deployment:

• API Authentication: Optional API key via RAG_API_KEY
• Rate Limiting: Configurable request throttling
• CORS Control: Restrict allowed origins
• Security Headers: Helmet.js protection

See SECURITY.md for complete documentation.

📦 Installation

The installation process is simple. You can use the following commands based on your needs:

• For MCP mode: npx github:RobThePCGuy/rag-vault
• For web mode: npx github:RobThePCGuy/rag-vault web

💻 Usage Examples

Search Your Codebase Documentation

You: "Ingest all the markdown files in /docs"
AI:  Ingested 23 files (847 chunks total)

You: "What's the retry policy for failed API calls?"
AI:  According to error-handling.md, failed requests retry 3 times
     with exponential backoff: 1s, 2s, 4s...

Index Web Documentation

You: "Fetch https://docs.example.com/api and ingest the HTML"
AI:  Ingested "docs.example.com/api" (156 chunks)

You: "What rate limits apply to the /users endpoint?"
AI:  The API limits /users to 100 requests per minute per API key...

Build a Personal Knowledge Base

You: "Ingest my research papers folder"
AI:  Ingested 12 PDFs (2,341 chunks)

You: "What do recent studies say about transformer attention mechanisms?"
AI:  Based on attention-mechanisms-2024.pdf, the key finding is...

Search Exact Technical Terms

RAG Vault's hybrid search catches both meaning and exact matches:

You: "Search for ERR_CONNECTION_REFUSED"
AI:  Found 3 results mentioning ERR_CONNECTION_REFUSED:
     1. troubleshooting.md - "When you see ERR_CONNECTION_REFUSED..."
     2. network-errors.pdf - "Common causes include..."

📚 Documentation

Web Interface

RAG Vault includes a full-featured web UI for managing your documents without the command line.

Launch the Web UI

npx github:RobThePCGuy/rag-vault web

Open http://localhost:3000 in your browser.

What You Can Do

• Upload documents: Drag and drop PDF, DOCX, Markdown, TXT, JSON, JSONL, and NDJSON files
• Search instantly: Type queries and see results with relevance scores
• Preview content: Click any result to see the full chunk in context
• Manage files: View all indexed documents and delete what you do not need
• Switch databases: Create and switch between multiple knowledge bases
• Monitor status: See document counts, memory usage, and search mode
• Export/Import settings: Back up and restore your vault configuration
• Theme preferences: Switch between light, dark, or system theme
• Folder browser: Navigate directories to select documents

REST API

The web server exposes a REST API for programmatic access. Set RAG_API_KEY to require authentication:

# With authentication (when RAG_API_KEY is set)
curl -X POST "http://localhost:3000/api/v1/search" \
  -H "Authorization: Bearer your-api-key" \
  -H "Content-Type: application/json" \
  -d '{"query": "authentication", "limit": 5}'

# Search documents (no auth required if RAG_API_KEY is not set)
curl -X POST "http://localhost:3000/api/v1/search" \
  -H "Content-Type: application/json" \
  -d '{"query": "authentication", "limit": 5}'

# List all files
curl "http://localhost:3000/api/v1/files"

# Upload a document
curl -X POST "http://localhost:3000/api/v1/files/upload" \
  -F "file=@spec.pdf"

# Delete a file
curl -X DELETE "http://localhost:3000/api/v1/files" \
  -H "Content-Type: application/json" \
  -d '{"filePath": "/path/to/spec.pdf"}'

# Get system status
curl "http://localhost:3000/api/v1/status"

# Health check (for load balancers)
curl "http://localhost:3000/api/v1/health"

Reader API Endpoints

For programmatic document reading and cross-document discovery:

# Get all chunks for a document (ordered by index)
curl "http://localhost:3000/api/v1/documents/chunks?filePath=/path/to/doc.pdf"

# Find related chunks for cross-document discovery
curl "http://localhost:3000/api/v1/chunks/related?filePath=/path/to/doc.pdf&chunkIndex=0&limit=5"

# Batch request for multiple chunks (efficient for UIs)
curl -X POST "http://localhost:3000/api/v1/chunks/batch-related" \
  -H "Content-Type: application/json" \
  -d '{"chunks": [{"filePath": "/path/to/doc.pdf", "chunkIndex": 0}], "limit": 3}'

Remote Mode

RAG Vault can also run as an HTTP server for remote MCP clients like Claude.ai, Claude Desktop, or any client supporting Streamable HTTP or SSE transports.

# Start remote server (default port 3001)
npx github:RobThePCGuy/rag-vault --remote

# Custom port
npx github:RobThePCGuy/rag-vault --remote --port 8080

Stdio mode is unchanged -- omit --remote and everything works as before with Cursor, Claude Code, and Codex.

Connecting from Claude Desktop

Add to your Claude Desktop config:

{
  "mcpServers": {
    "rag-vault-remote": {
      "type": "url",
      "url": "http://localhost:3001/mcp"
    }
  }
}

Or via Claude Code CLI:

claude mcp add --transport http rag-vault http://localhost:3001/mcp

Connecting from Claude.ai

For Claude.ai (Pro/Max/Team/Enterprise), add as a custom connector with URL https://your-host:3001/mcp. For local development, expose your server with a tunnel:

cloudflared tunnel --url http://localhost:3001

Set RAG_API_KEY for authentication when exposing remotely. The server supports both Streamable HTTP (/mcp) and legacy SSE (/sse) transports, plus a health check at /health.

🔧 Technical Details

How It Works

Document → Parse → Chunk by meaning → Embed locally → Store in LanceDB
                         ↓
Query → Embed → Vector search → Keyword boost → Quality filter → Results

• Smart chunking: Splits by meaning, not character count. Keeps code blocks intact.
• Hybrid search: Vector similarity finds related content. Keyword boost ranks exact matches higher.
• Quality filtering: Groups results by relevance gaps instead of arbitrary top-K cutoffs.
• Local by default: Embeddings via Transformers.js. Storage via LanceDB. Network is only needed for initial model download or if you explicitly ingest remote URLs.
• MCP tools included: query_documents, ingest_file, ingest_data, delete_file, list_files, status, feedback_pin, feedback_dismiss, and feedback_stats.

Supported Formats

Configuration

Environment Variables

Windows users: RAG_EMBEDDING_DEVICE=auto attempts GPU providers (DirectML) which can fail if ONNX Runtime GPU binaries are not available. If you see embedding initialization errors, set RAG_EMBEDDING_DEVICE=cpu in your MCP config for reliable operation. See the GPU acceleration FAQ for details.

One-command override (no .env edit):

# MCP mode
npx github:RobThePCGuy/rag-vault --embedding-device cpu

# Web mode
npx github:RobThePCGuy/rag-vault web --embedding-device dml

# Explicitly force auto detection
npx github:RobThePCGuy/rag-vault --gpu-auto

Search Tuning

Security (optional)

Advanced

Copy for a complete configuration template.

For code-heavy content, try:

"env": {
  "RAG_HYBRID_WEIGHT": "0.8",
  "RAG_GROUPING": "similar"
}

📄 License

MIT: free for personal and commercial use.

Acknowledgments

Built with Model Context Protocol, LanceDB, and Transformers.js.

Started as a fork of mcp-local-rag by Shinsuke Kagawa. Now it’s its own thing. Huge credit to upstream contributors for the foundation, I’ve been iterating hard from there. Local-first dev tools, all the way.

Frequently Asked Questions

"env": {
  "RAG_EMBEDDING_DEVICE": "cpu"
}

"MODEL_NAME": "onnx-community/embeddinggemma-300m-ONNX"

Troubleshooting

Development

git clone https://github.com/RobThePCGuy/rag-vault.git
cd rag-vault
pnpm install
pnpm --prefix web-ui install

# Install local git hooks (recommended, even for solo dev)
pnpm hooks:install

# Fast local quality gate (backend + web-ui type/lint/format, deps, unused, build, unit tests)
pnpm check:all

# Unit tests only (no model download required)
pnpm test:unit

# Integration/E2E tests (requires model download/network)
pnpm test:integration

# Build
pnpm build

# Run MCP server locally (stdio)
pnpm dev

# Run MCP server locally (remote HTTP + SSE)
pnpm dev:remote

# Run web server locally
pnpm web:dev

# Release to npm (local, guarded)
pnpm release          # patch
pnpm release:minor
pnpm release:major
pnpm release:dry

Test Tiers

• pnpm test:unit: deterministic tests for local/CI quality checks, excluding model-download integration paths.
• pnpm test:integration: full integration and E2E workflows, including embedding model initialization.

Use RUN_EMBEDDING_INTEGRATION=1 to explicitly opt into network/model-dependent suites.

Release Strategy

• Releases are local and scripted via scripts/release-npm.sh.
• Supported bumps: patch, minor, major.
• The script runs dependency installs, pnpm check:all, and pnpm ui:build before touching version files.
• package.json and server.json versions are updated only after checks pass, and auto-restored if any later step fails.
• pnpm release:dry performs the full gate plus npm dry-run publish and always restores version files.

Project Structure

src/
├── bin/             # CLI subcommands (skills install)
├── chunker/         # Semantic text splitting
├── embedder/        # Transformers.js wrapper
├── errors/          # Error handling utilities
├── explainability/  # Keyword-based result explanations
├── flywheel/        # Feedback loop (pin/dismiss reranking)
├── parser/          # PDF, DOCX, HTML parsing
├── query/           # Advanced query syntax parser
├── server/          # MCP tool handlers + remote transport
├── utils/           # Config, file helpers, process handlers
├── vectordb/        # LanceDB + hybrid search
└── web/             # Express server + REST API

web-ui/              # React frontend (Vite + Tailwind)