Source Code to Knowledge Base Generator with MCP Server

Convert any source code repository into a searchable knowledge base with automatic chunking, embedding generation, and intelligent search capabilities. Now with MCP (Model Context Protocol) support for Claude Code and Cursor integration!

📦 Now available on npm! Install with: npm install -g @vezlo/src-to-kb

Available Commands

After installation, you'll have access to these commands:

• src-to-kb - Generate knowledge base from source code
• src-to-kb-search - Search the knowledge base
• src-to-kb-api - Start REST API server with Swagger docs
• src-to-kb-mcp - Start MCP server for IDE integration
• src-to-kb-mcp-install - Auto-configure Claude Code/Cursor

Features

• 📁 Multi-language Support: JavaScript, TypeScript, Python, Java, C++, Go, Rust, and more
• 🎯 Answer Modes: Three modes for different users - End User (simple), Developer (technical), Copilot (code-focused)
• 🌐 REST API: Full-featured API with Swagger documentation for integration with external services
• 🔍 Smart Chunking: Intelligent code splitting with configurable overlap
• 🧹 Code Cleaning: Optional comment removal and whitespace normalization
• 🔢 Embeddings: Optional OpenAI embeddings for semantic search
• 📊 Statistics: Comprehensive analysis of your codebase
• 🚀 Fast Processing: Efficient file scanning and processing
• 💾 Structured Storage: Organized JSON output for easy integration
• 🤖 MCP Server: Direct integration with Claude Code, Cursor, and other MCP-compatible tools
• 💡 AI-Powered Search: Uses OpenAI GPT-5 (latest reasoning model) for intelligent query understanding and helpful answers
• 🔐 API Authentication: Optional API key authentication for secure access
• 🌐 External Server Integration: Send code to external servers for processing and search via REST API

Quick Start

🚀 For Existing Projects (Next.js, React, etc.)

# Install globally
npm install -g @vezlo/src-to-kb

# Generate KB from your project
src-to-kb ./my-nextjs-app --output ./my-kb

# Start API server
src-to-kb-api

# Search your codebase
src-to-kb-search search "How does routing work?" --mode developer

That's it! Your codebase is now searchable with AI assistance.

External Server Integration 🌐

🚀 Try It Now with Public Demo Server

Experience external server integration immediately with our production-ready assistant-server:

# Generate knowledge base using assistant-server
USE_EXTERNAL_KB=true EXTERNAL_KB_URL=https://your-assistant-server.com/api/knowledge/items src-to-kb ./your-repo

# Search using assistant-server
USE_EXTERNAL_KB=true EXTERNAL_KB_URL=https://your-assistant-server.com/api/search src-to-kb-search search "how does authentication work?"

Assistant Server: vezlo/assistant-server - Production-ready Node.js/TypeScript API server with vector search and Docker deployment

🏢 Enterprise Setup

For production deployments or custom servers:

📖 Complete Guide: External Server Setup Guide

1. Basic Usage

Process your repository with default settings:

# If installed globally via npm
src-to-kb /path/to/your/repo

# Or using the script directly
node kb-generator.js /path/to/your/repo

2. With Custom Output Directory

src-to-kb /path/to/your/repo --output ./my-knowledge-base

3. With OpenAI Embeddings

# Set your OpenAI API key
export OPENAI_API_KEY=your-api-key-here

# Generate with embeddings
src-to-kb /path/to/your/repo --embeddings

4. Search with Answer Modes

Choose the right answer mode for your needs:

# First generate a knowledge base
src-to-kb ./your-project --output ./project-kb

# Search with different modes:

# End User Mode - Simple, non-technical answers
src-to-kb-search search "how do I reset password?" --kb ./project-kb --mode enduser

# Developer Mode - Technical details and architecture (default)
src-to-kb-search search "authentication flow" --kb ./project-kb --mode developer

# Copilot Mode - Code examples and implementation patterns
src-to-kb-search search "implement user login" --kb ./project-kb --mode copilot

# View available modes
src-to-kb-search modes

Answer Modes 🎯

The search tool adapts its responses based on who's asking:

Mode	For	Description	Example Use Case
enduser	Non-technical users	Simple explanations without technical jargon, focuses on features and capabilities	Product managers, business stakeholders asking about features
developer	Software developers	Full technical details including architecture, dependencies, and implementation details	Engineers understanding codebase structure and design patterns
copilot	Coding assistance	Code examples, snippets, and implementation patterns ready to use	Developers looking for code to copy/adapt for their implementation

Mode Examples

# CEO asks: "What payment methods do we support?"
src-to-kb-search search "payment methods" --mode enduser
# Returns: Simple list of supported payment options

# Developer asks: "How is payment processing implemented?"
src-to-kb-search search "payment processing" --mode developer
# Returns: Technical details about payment gateway integration, API endpoints, error handling

# Developer needs: "Show me payment integration code"
src-to-kb-search search "payment integration" --mode copilot
# Returns: Actual code snippets for payment implementation

How Modes Work

• Filtering: Each mode filters results differently (e.g., end users don't see test files)
• AI Prompts: Custom prompts guide AI to give appropriate responses
• Formatting: Answers are formatted based on the audience (code blocks for developers, plain text for end users)
• Context: Technical depth is adjusted (high for developers, low for end users)

Installation

Option 1: Install from npm (Recommended) ✅

# Install globally from npm registry
npm install -g @vezlo/src-to-kb

# Now use the commands anywhere on your system
src-to-kb /path/to/repo                    # Generate knowledge base
src-to-kb-search search "your query"       # Search knowledge base
src-to-kb-mcp                              # Start MCP server for Claude/Cursor

Option 2: Use with npx (No Installation)

# Run directly without installing
npx @vezlo/src-to-kb /path/to/repo
npx @vezlo/src-to-kb-search search "your query"
npx @vezlo/src-to-kb-mcp

Option 3: Install in a Project

# Add as a project dependency
npm install @vezlo/src-to-kb

# Use with npx in your project
npx src-to-kb /path/to/repo

Option 4: Clone from GitHub (For Development)

# Clone the repository
git clone https://github.com/vezlo/src-to-kb.git
cd src-to-kb

# Install dependencies
npm install

# Run directly
node kb-generator.js /path/to/repo

CLI Options

Usage: node kb-generator.js <repository-path> [options]

Options:
  --output, -o        Output directory (default: ./knowledge-base)
  --chunk-size        Chunk size in characters (default: 1000)
  --chunk-overlap     Overlap between chunks (default: 200)
  --max-file-size     Maximum file size in MB (default: 10)
  --embeddings        Generate OpenAI embeddings (requires OPENAI_API_KEY)
  --no-comments       Exclude comments from code
  --exclude           Additional paths to exclude (comma-separated)
  --extensions        File extensions to include (comma-separated)

Complete Example Workflow

# 1. Generate knowledge base from your frontend code
src-to-kb ./frontend/ --output ./frontend-kb

# 2. Different users asking different questions:

# Product Manager asks about features
src-to-kb-search search "password reset feature" --kb ./frontend-kb --mode enduser

# Developer investigates technical implementation
src-to-kb-search search "authentication flow" --kb ./frontend-kb --mode developer

# Developer needs code examples
src-to-kb-search search "login component implementation" --kb ./frontend-kb --mode copilot

# 3. Get statistics about the codebase
src-to-kb-search stats --kb ./frontend-kb

# 4. List all TypeScript files
src-to-kb-search type TypeScript --kb ./frontend-kb

# 5. View available answer modes
src-to-kb-search modes

More Examples

Process Any Repository

# Using npm package
src-to-kb /path/to/repo --output ./repo-kb --embeddings

# Or with npx
npx @vezlo/src-to-kb /path/to/repo --output ./repo-kb --embeddings

Process Only JavaScript and TypeScript Files

src-to-kb /path/to/repo --extensions .js,.ts,.jsx,.tsx

Exclude Test and Build Directories

src-to-kb /path/to/repo --exclude tests,build,dist,coverage

Large Repositories with Custom Chunking

src-to-kb /path/to/large-repo \
  --chunk-size 2000 \
  --chunk-overlap 400 \
  --max-file-size 20

Testing

Run the included test suite to verify functionality:

# Run comprehensive tests
node test.js

# This will:
# 1. Create a test repository with sample files
# 2. Process it into a knowledge base
# 3. Verify the output structure
# 4. Test chunking on large files
# 5. Verify language detection

REST API Server

The Source-to-KB REST API provides programmatic access to all functionality with comprehensive Swagger documentation.

Starting the API Server

# Start with defaults (port 3000, no authentication)
src-to-kb-api

# With custom port and API key
PORT=8080 API_KEY=your-secret-key src-to-kb-api

# With all options
PORT=8080 API_KEY=secret OPENAI_API_KEY=sk-... src-to-kb-api

API Documentation

Once started, visit: http://localhost:3000/api/v1/docs for interactive Swagger UI

Key Endpoints

• POST /api/v1/knowledge-bases - Create new knowledge base
• POST /api/v1/search - Search with mode selection
• GET /api/v1/modes - List available answer modes
• GET /api/v1/statistics/{id} - Get KB statistics
• POST /api/v1/process-file - Process single file

Example API Usage

// Create knowledge base
const response = await fetch('http://localhost:3000/api/v1/knowledge-bases', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
    'X-API-Key': 'your-api-key'
  },
  body: JSON.stringify({
    name: 'My Project',
    sourcePath: '/path/to/project',
    options: { chunkSize: 1500 }
  })
});

// Search with mode
const searchResponse = await fetch('http://localhost:3000/api/v1/search', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
    'X-API-Key': 'your-api-key'
  },
  body: JSON.stringify({
    query: 'authentication',
    knowledgeBaseId: 'abc123',
    mode: 'developer'
  })
});

For complete API documentation, see API_DOCUMENTATION.md

MCP Server for Claude Code

Quick Setup 🚀

# 1. Install the package globally
npm install -g @vezlo/src-to-kb

# 2. Find your global npm installation path
npm list -g @vezlo/src-to-kb --depth=0

# 3. Add to Claude Code (replace the path with your npm global path)
# For macOS/Linux with nvm:
claude mcp add src-to-kb -- node ~/.nvm/versions/node/v22.6.0/lib/node_modules/@vezlo/src-to-kb/mcp-server.mjs

# For macOS/Linux without nvm:
claude mcp add src-to-kb -- node /usr/local/lib/node_modules/@vezlo/src-to-kb/mcp-server.mjs

# For Windows:
claude mcp add src-to-kb -- node %APPDATA%\npm\node_modules\@vezlo\src-to-kb\mcp-server.mjs

# With OpenAI API key for embeddings:
claude mcp add src-to-kb --env OPENAI_API_KEY=your-key -- node [your-path]/mcp-server.mjs

Alternative: Using npx (if the above doesn't work)

# Try with npx (may not work on all systems)
claude mcp add src-to-kb -- npx -y @vezlo/src-to-kb src-to-kb-mcp

Managing the MCP Server

# Check if installed
claude mcp list

# Remove if needed
claude mcp remove src-to-kb

# Get server details
claude mcp get src-to-kb

After Installation

1. Restart Claude Code completely
2. Test by asking Claude:
   • "Generate a knowledge base for this project"
   • "Search for authentication implementations"
   • "What languages does this codebase use?"
   • "Find files similar to config.js"

See MCP_SETUP.md for manual setup and MCP_TOOLS_GUIDE.md for detailed tool documentation.

Searching the Knowledge Base

Answer Modes

The search tool supports three different answer modes to tailor responses based on your needs:

• enduser: Simplified answers for non-technical users, focusing on features and capabilities
• developer: Detailed technical answers including architecture and implementation details (default)
• copilot: Code-focused answers with examples and patterns for implementation

# Examples with different modes
src-to-kb-search search "how to use API?" --mode enduser      # Simple explanation
src-to-kb-search search "authentication flow" --mode developer # Technical details
src-to-kb-search search "login implementation" --mode copilot  # Code examples

# List available modes
src-to-kb-search modes

AI-Powered Search (with OpenAI)

When OPENAI_API_KEY is set, searches use GPT-5 (OpenAI's latest reasoning model) for intelligent answers:

# Set your OpenAI API key
export OPENAI_API_KEY=your-api-key-here

# Get intelligent, context-aware answers with mode selection
src-to-kb-search search "how does authentication work?" --kb ./project-kb --mode developer
src-to-kb-search search "where is password reset?" --kb ./project-kb --mode enduser

Basic Search (without OpenAI)

Without an API key, the tool provides basic keyword search:

# Basic search with pattern matching
src-to-kb-search search "authentication" --kb ./project-kb

# Find all JavaScript files
src-to-kb-search type JavaScript --kb ./project-kb

# Show statistics
src-to-kb-search stats --kb ./project-kb

# Find similar files
src-to-kb-search similar src/index.js --kb ./project-kb

Search Options

# Specify knowledge base path
src-to-kb-search search "query" --kb ./my-knowledge-base

# Select answer mode
src-to-kb-search search "query" --mode enduser|developer|copilot

# Show detailed evidence
src-to-kb-search search "query" --verbose

# Get raw search results (old format)
src-to-kb-search search "query" --raw

Output Structure

The generator creates the following directory structure:

knowledge-base/
├── documents/      # Document metadata (without content)
│   ├── doc_xxx.json
│   └── ...
├── chunks/         # Document chunks for searching
│   ├── doc_xxx.json
│   └── ...
├── embeddings/     # OpenAI embeddings (if enabled)
│   ├── doc_xxx.json
│   └── ...
└── metadata/       # Summary and statistics
    └── summary.json

Document Format

Each document contains:

{
  "id": "doc_1234567890_abc123",
  "path": "/full/path/to/file.js",
  "relativePath": "src/file.js",
  "fileName": "file.js",
  "extension": ".js",
  "size": 2048,
  "checksum": "sha256-hash",
  "metadata": {
    "createdAt": "2024-01-01T00:00:00.000Z",
    "modifiedAt": "2024-01-01T00:00:00.000Z",
    "lines": 100,
    "language": "JavaScript",
    "type": "code"
  }
}

Chunk Format

Each chunk contains:

{
  "id": "doc_xxx_chunk_0",
  "index": 0,
  "content": "chunk content here...",
  "startLine": 1,
  "endLine": 25,
  "size": 1000
}

🔧 Integration with Existing Projects

Next.js / React Integration

Transform your frontend codebase into a searchable knowledge base with AI-powered assistance:

Quick Setup

# 1. Generate knowledge base from your project
src-to-kb /path/to/nextjs-app --output ./nextjs-kb

# 2. Start the API server
src-to-kb-api

# 3. Query your codebase
curl -X POST http://localhost:3000/api/v1/search \
  -H "Content-Type: application/json" \
  -d '{"query": "How is authentication implemented?", "knowledgeBaseId": "your-kb-id", "mode": "developer"}'

React Component Example

// components/CodeSearch.jsx
import { useState } from 'react';

export default function CodeSearch() {
  const [query, setQuery] = useState('');
  const [result, setResult] = useState(null);

  const search = async () => {
    const response = await fetch('http://localhost:3000/api/v1/search', {
      method: 'POST',
      headers: { 'Content-Type': 'application/json' },
      body: JSON.stringify({
        query,
        knowledgeBaseId: 'your-kb-id',
        mode: 'developer'
      })
    });
    const data = await response.json();
    setResult(data);
  };

  return (
    <div>
      <input
        value={query}
        onChange={(e) => setQuery(e.target.value)}
        placeholder="Ask about your codebase..."
      />
      <button onClick={search}>Search</button>
      {result && <div>{result.answer}</div>}
    </div>
  );
}

Integration Ideas

• 🎓 Onboarding Assistant: Help new developers understand your codebase
• 📖 In-App Documentation: Provide context-aware help within your application
• 🔍 Code Review Helper: Find similar patterns and best practices
• 🤖 Development Copilot: Get AI suggestions based on your existing code
• 📊 Code Analytics Dashboard: Visualize codebase statistics and complexity

CI/CD Integration

# GitHub Actions example
name: Update Knowledge Base
on: [push]
jobs:
  update-kb:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      - run: npm install -g @vezlo/src-to-kb
      - run: src-to-kb . --output ./kb
      # Upload KB as artifact or deploy to server

Enterprise Setup

For production environments:

# Start with authentication and custom port
API_KEY=secure-key PORT=8080 src-to-kb-api

# Use with Docker
docker run -p 3000:3000 -e API_KEY=secret vezlo/src-to-kb-api

Use Cases

1. Code Documentation: Generate searchable documentation from your codebase
2. AI Training: Prepare code for fine-tuning or RAG systems
3. Code Analysis: Analyze patterns and structure across large repositories
4. Knowledge Extraction: Extract domain knowledge from source code
5. Code Search: Build intelligent code search systems
6. IDE Integration: Use directly in Claude Code or Cursor for code understanding
7. Team Knowledge Sharing: Create searchable knowledge bases for team onboarding

Performance

• Processes ~1000 files/minute on average hardware
• Memory efficient - streams large files
• Parallel chunk processing
• Configurable file size limits

Supported Languages

• JavaScript (.js, .jsx)
• TypeScript (.ts, .tsx)
• Python (.py)
• Java (.java)
• C/C++ (.c, .cpp, .h, .hpp)
• C# (.cs)
• Go (.go)
• Rust (.rs)
• Ruby (.rb)
• PHP (.php)
• Swift (.swift)
• Kotlin (.kt)
• Scala (.scala)
• And many more...

Configuration Files

Also processes:

• JSON (.json)
• YAML (.yaml, .yml)
• XML (.xml)
• Markdown (.md)
• HTML/CSS (.html, .css, .scss)
• SQL (.sql)

Tips

1. Chunking Strategy:

   • Use smaller chunks (500-1000) for precise search
   • Use larger chunks (2000-3000) for more context

2. Overlap:

   • 10-20% overlap helps maintain context between chunks
   • Increase overlap for code with many dependencies

3. Exclusions:

   • Always exclude node_modules, vendor, dist directories
   • Consider excluding auto-generated files

4. File Size:

   • Default 10MB limit prevents processing of large binaries
   • Increase for legitimate large source files

Programmatic Usage

const { KnowledgeBaseGenerator } = require('./kb-generator');

async function generateKB() {
  const generator = new KnowledgeBaseGenerator({
    outputPath: './my-kb',
    chunkSize: 1500,
    generateEmbeddings: true,
    openaiApiKey: 'your-api-key'
  });

  generator.on('fileProcessed', (data) => {
    console.log(`Processed: ${data.file}`);
  });

  const result = await generator.processRepository('/path/to/repo');
  console.log(`Generated KB with ${result.documents.length} documents`);
}

generateKB();

License

This software is dual-licensed:

• Non-Commercial Use: Free under AGPL-3.0 license
• Commercial Use: Requires a commercial license - contact us for details

See LICENSE file for full details.

Contributing

Feel free to submit issues and enhancement requests!