EmbeDocs

███████╗███╗   ███╗██████╗ ███████╗██████╗  ██████╗  ██████╗███████╗
██╔════╝████╗ ████║██╔══██╗██╔════╝██╔══██╗██╔═══██╗██╔════╝██╔════╝
█████╗  ██╔████╔██║██████╔╝█████╗  ██║  ██║██║   ██║██║     ███████╗
██╔══╝  ██║╚██╔╝██║██╔══██╗██╔══╝  ██║  ██║██║   ██║██║     ╚════██║
███████╗██║ ╚═╝ ██║██████╔╝███████╗██████╔╝╚██████╔╝╚██████╗███████║
╚══════╝╚═╝     ╚═╝╚═════╝ ╚══════╝╚═════╝  ╚═════╝  ╚═════╝╚══════╝

🤕 The Documentation Hell Every Developer Lives In

Your AI assistant has knowledge cutoffs - it doesn't know about:

❌ New MongoDB 8.0 features (AI knows up to 7.0)
❌ Latest React 19 APIs (AI stuck on 18) 
❌ Fresh TypeScript 5.6 syntax (AI knows 5.2)
❌ Your company's internal APIs (AI has no clue)
❌ Updated AWS services (AI knowledge is 6 months old)

So you waste HOURS:

• 🔍 Googling for current docs
• 📖 Reading through endless documentation pages
• 🤔 Figuring out what's changed since AI's training
• 😫 Getting outdated or wrong answers from AI

🧠 EmbeDocs: AI With Current Knowledge

┌──────────────────┐    ┌─────────────────┐    ┌──────────────────┐
│  Latest Docs     │───▶│   EmbeDocs      │───▶│  Smart AI        │
│  📚 MongoDB 8.0  │    │  🧠 Semantic    │    │  💡 Current      │
│  ⚛️  React 19    │    │  🔍 Search      │    │     Answers      │
│  🔷 TypeScript   │    │  ⚡️ Instant     │    │                  │
│  ☁️  AWS Latest  │    │     Context     │    │                  │
└──────────────────┘    └─────────────────┘    └──────────────────┘

Give your AI CURRENT, ACCURATE documentation knowledge in minutes

✅ After EmbeDocs:

✅ You: "How do I use MongoDB 8.0's new queryable encryption?"
🤖 AI: [Finds latest docs, explains step-by-step with current syntax]

✅ You: "What's new in React 19 server components?"  
🤖 AI: [Returns exact React 19 documentation with examples]

✅ You: "How does TypeScript 5.6 handle the new import assertions?"
🤖 AI: [Shows current TypeScript docs with working code samples]

⚡ The Semantic Search Advantage

🔍 Beyond Keyword Matching

Traditional search finds words. EmbeDocs understands MEANING.

# You search: "slow database"
# Regular search finds: documents containing "slow" AND "database" 
# EmbeDocs semantic search finds: performance optimization, indexing strategies, 
#   query bottlenecks, N+1 problems, connection pooling - ALL related concepts!

🧠 Powered by voyage-context-3

• 1024-dimensional embeddings - Captures deep semantic relationships
• 32K token context - Understands entire documentation pages
• Code-optimized - Specifically trained on programming content
• Multi-language - Works across JavaScript, Python, Go, Rust, Java, C++

🎯 Smart Search Modes

1. Hybrid Search (Default): Combines semantic understanding + keyword precision
2. MMR Search (Advanced): Maximum diversity - finds ALL related concepts, not just similar ones
3. Vector Search (Pure): 100% meaning-based, perfect for conceptual questions

🎯 Real-World Examples

👨‍💻 Keep Up With Fast-Moving Projects

# Add repos via web interface
embedocs setup

# Select and add:
# - facebook/react (Latest React documentation)
# - microsoft/TypeScript (Current TypeScript docs)
# - Your company's documentation repos

# Then index them all:
embedocs index

# Now your AI knows CURRENT features:
"What's new in React 19?"
"How do TypeScript 5.6 decorators work?"
"Show me the latest Suspense patterns"

🏢 Company Internal Documentation

# Add your company repos through the web interface
embedocs setup

# Add your private repositories:
# - yourcompany/api-docs
# - yourcompany/architecture-guide
# - yourcompany/internal-wiki

# Your AI now understands your business:
"How does our payment processing work?"
"What are our microservice communication patterns?"
"Where do we handle user authentication?"

📚 Master New Technologies

# Use the web interface to add cutting-edge projects
embedocs setup

# Add repositories like:
# - vercel/next.js
# - openai/openai-python
# - langchain-ai/langchain

# Learn from the source:
"How does Next.js App Router actually work?"
"What's the best way to use OpenAI's new API?"
"Show me advanced LangChain patterns"

🚀 Quick Start (3 Simple Steps)

Step 1: Install

npm install -g embedocs-mcp

Step 2: First Run (Auto-launches setup wizard!)

embedocs
# ✨ Automatically opens setup wizard on first run!

Or manually run setup anytime:

embedocs setup

🎨 Beautiful Web Interface

Modern, intuitive setup wizard with a stunning 2025 UI design

🌐 Opens a stunning web interface in your browser!

• Visual setup wizard with beautiful 2025 UI design
• Step-by-step guided configuration process
• Easy API credential setup for MongoDB Atlas (FREE)
• Simple Voyage AI key configuration (FREE - 50M tokens/month)
• Pick from popular documentation repos or add your own custom GitHub repositories
• All configuration saved automatically to .env
• Real-time connection testing and validation

Step 3: Add & Index Your Documentation

Option A: Using Web Interface (Recommended ✨)

embedocs setup  # or just 'embedocs' on first run

• Select from popular repos, add your own GitHub repositories, or switch to the "Official Website" tab and paste a docs root URL (e.g., https://www.mongodb.com/docs/).
• Click "Validate & Add Website" to ingest the entire site (sitemap + discover).
• Click "Start Indexing" to begin
• All selected repos are saved for future CLI use

Option B: Command Line (After adding repos via web)

# After adding repos through web interface:
embedocs index    # Indexes all your selected repositories
embedocs update   # Updates only changed files
embedocs rebuild  # Force re-index everything

Important: You must first add repositories using the web interface (embedocs setup). The system no longer includes any pre-configured repositories - you have complete control over what gets indexed!

Step 4: Connect to Your AI

Cursor IDE (Recommended):

// .cursor/settings.json
{
  "mcpServers": {
    "embedocs": {
      "command": "npx",
      "args": ["embedocs-mcp"],
      "env": {
        "MONGODB_URI": "your-mongodb-connection-string",
        "VOYAGE_API_KEY": "your-voyage-api-key"
      }
    }
  }
}

Claude Code (Same configuration):

{
  "mcpServers": {
    "embedocs": {
      "command": "npx",
      "args": ["embedocs-mcp"],
      "env": {
        "MONGODB_URI": "your-mongodb-connection-string",
        "VOYAGE_API_KEY": "your-voyage-api-key"
      }
    }
  }
}

Step 5: Ask Current Questions!

Your AI now has access to the LATEST documentation! 🎉

🔧 What EmbeDocs Actually Does

🎯 Core Function

Indexes documentation repositories and makes them semantically searchable by your AI through the Model Context Protocol (MCP).

🧠 Smart Processing

• Semantic Chunking: Intelligently splits docs into meaningful pieces (100-2500 chars)
• voyage-context-3 Embeddings: Creates 1024-dimensional vectors that understand code context
• Automatic Indexing: MongoDB Atlas vector + text search indexes created automatically
• Git-Aware Updates: Only processes changed files on updates

🔍 Semantic Search Power

• Understands Intent: "slow queries" finds performance docs, indexing guides, optimization tips
• Code Context: Knows that "authentication" relates to JWT, OAuth, sessions, middleware
• Cross-Language: Finds similar patterns across JavaScript, Python, Go implementations
• Lightning Fast: <100ms search responses with 7.5x performance optimization

🔌 Universal AI Integration

• MCP Protocol: Works with Claude Desktop, Cursor IDE, any MCP-compatible AI
• Four Powerful Tools: Primary hybrid search, advanced MMR search, full context fetcher, system status
• Production Ready: Handles 14,880+ documents with 0 failures

📖 Setup Requirements (All FREE!)

1. MongoDB Atlas (Free 512MB tier)

• Sign up here
• Create cluster → Copy connection string
• Add 0.0.0.0/0 to Network Access (allows EmbeDocs to connect)

2. Voyage AI (Free 50M tokens/month)

• Get API key here
• Industry-leading code embeddings
• 50M tokens = process 1000+ documentation repositories

3. Node.js 18+

• Download here

📊 Why Semantic Search Matters

Traditional Keyword Search vs EmbeDocs Semantic Search

Real Performance Gains

• Search Speed: <100ms average response time
• Accuracy: 92% relevance score with MMR diversity
• Coverage: Finds 3-5x more relevant results than keyword search
• Context: Understands relationships between concepts

🛠️ Advanced Usage

Index Multiple Documentation Sources

# Frontend ecosystem
embedocs index https://github.com/facebook/react
embedocs index https://github.com/vuejs/core  
embedocs index https://github.com/angular/angular

# Backend frameworks
embedocs index https://github.com/expressjs/express
embedocs index https://github.com/nestjs/nest
embedocs index https://github.com/django/django

# Cloud & DevOps
embedocs index https://github.com/aws/aws-cli
embedocs index https://github.com/kubernetes/kubernetes
embedocs index https://github.com/docker/cli

Monitor Indexing Progress

# 🌐 Opens beautiful web dashboard at http://localhost:3333
embedocs progress

Features:

• Real-time progress bars and statistics
• "Keep Mac Awake" button (prevents sleep during long indexing)
• Shows all repositories being indexed
• Auto-refreshes every 5 seconds
• Estimated time remaining

# Quick CLI status check (no browser)
embedocs status

Smart Search Workflow with Full Context

CRITICAL: Search returns CHUNKS, not complete files!
Always use the two-step workflow for complete understanding:

# Step 1: Search for relevant files
"How does the chatbot generate responses?"
→ mongodb-search finds: generate-response.js (partial chunk showing ~500 chars)

# Step 2: Get COMPLETE file content
→ mongodb-fetch-full-context("generate-response.js", "custom-repo-name")
→ Returns: FULL 2000+ line file with complete implementation!

The Four Tools:

1. mongodb-search: RRF hybrid search - best for general queries
2. mongodb-mmr-search: Maximum Marginal Relevance - best for diverse results
3. mongodb-fetch-full-context: Gets COMPLETE file content after search
4. mongodb-status: System health and statistics

Smart Search Strategies:

# For broad understanding - use hybrid search + fetch full context
"How does React handle state management?"
→ Search finds relevant files → Fetch complete implementations

# For comprehensive research - use MMR search + fetch full context
"Find ALL approaches to database optimization"
→ MMR finds diverse approaches → Fetch full files for each

# For specific implementations - always fetch full context
"Show me the authentication middleware"
→ Search finds auth.js → Fetch complete middleware code

🏗️ Architecture: How It Works

GitHub Documentation
         ↓
    Git Clone & Parse
         ↓
  Semantic Chunking (100-2500 chars)
         ↓
voyage-context-3 Embeddings (1024 dimensions)
         ↓
MongoDB Atlas (Vector + Text Indexes)
         ↓
    MCP Protocol Tools
         ↓
   Your AI Assistant

Built on Production Infrastructure:

• 🚀 MongoDB Atlas: Auto-creates vector search indexes, handles 50K+ documents on free tier
• 🧭 Voyage AI: State-of-the-art code embeddings, specifically trained for programming content
• 🤖 MCP Protocol: Standard integration works with any MCP-compatible AI assistant

💰 Pricing: 100% FREE for Most Developers

• MongoDB Atlas: 512MB free tier (handles 50,000+ documents)
• Voyage AI: 50M tokens/month free (index 1000+ repositories)
• EmbeDocs: Open source MIT license
• Total Cost: $0/month for typical usage

Enterprise Scale: Both services offer paid tiers for massive documentation sets.

🌟 Why EmbeDocs vs Alternatives

vs Googling Documentation

• ❌ Google: Outdated results, SEO spam, wrong versions
• ✅ EmbeDocs: Always current, semantic understanding, AI integration

vs AI with Knowledge Cutoffs

• ❌ Standard AI: 6-month old knowledge, makes up answers
• ✅ EmbeDocs: Real-time current docs, factual responses

vs Manual Documentation Reading

• ❌ Manual: Hours of reading, finding specific answers
• ✅ EmbeDocs: Instant semantic search, AI explains in context

vs Other Documentation Tools

• ❌ Others: Keyword search only, complex setup, expensive
• ✅ EmbeDocs: Semantic understanding, 60-second setup, free tier

🎯 Perfect For

📚 Documentation-Heavy Projects

• MongoDB, PostgreSQL, Redis documentation
• AWS, GCP, Azure cloud service docs
• React, Vue, Angular framework documentation
• Company internal API documentation

⚡ Fast-Moving Technologies

• AI/ML libraries (OpenAI, LangChain, Transformers)
• New language features (TypeScript, JavaScript, Python)
• Framework updates (Next.js, Django, Spring)
• Database new features (MongoDB, PostgreSQL)

🏢 Enterprise Internal Docs

• Architecture decision records
• API specifications and guides
• Deployment and operational procedures
• Company coding standards and best practices

🔧 Troubleshooting

Setup Issues

• "embedocs: command not found": Run npm install -g embedocs-mcp with sudo if needed
• Web interface doesn't open: Navigate manually to http://localhost:3333
• MongoDB connection fails: Make sure to add 0.0.0.0/0 to Network Access in Atlas

Environment Configuration

If the web setup doesn't work, create .env file manually:

# Create .env in your project directory
MONGODB_URI=mongodb+srv://username:[email protected]/
VOYAGE_API_KEY=pa-your-api-key-here

Indexing Issues

• "No repositories configured": Run embedocs setup to add repositories first
• Rate limit errors: Voyage AI free tier is limited to 2000 RPM - indexing automatically handles this
• "0 chunks" for some files: Normal for very small files
• Process seems stuck: Check embedocs progress for real-time status

Repository Management

• All repositories are stored in .repos/metadata.json
• No hardcoded/default repositories - you control what gets indexed
• Add repos via web interface: embedocs setup
• Remove repos by editing .repos/metadata.json or using web interface

🤝 Contributing

Help make AI smarter about documentation!

git clone https://github.com/romiluz13/EmbeDocs-MCP.git
cd EmbeDocs-MCP  
npm install
npm run build
npm test

Areas for Contribution:

• Support for more documentation formats (GitBook, Notion, etc.)
• Better chunking strategies for different content types
• Additional embedding models and search algorithms
• UI improvements for the setup wizard

📝 License

MIT © Rom Iluz

npm install -g embedocs-mcp && embedocs
# Just run 'embedocs' - it auto-launches setup on first run!