Code Graph Knowledge System

Enterprise knowledge management platform with Neo4j graph database, multi-interface architecture (MCP/Web/REST), and intelligent code analysis for modern software development teams.

Overview

Code Graph Knowledge System is a production-ready platform that transforms code repositories and development documentation into a queryable knowledge graph. Built on Neo4j's graph database technology and powered by large language models, the system provides three distinct interfaces for different use cases: MCP protocol for AI assistants, Web UI for human users, and REST API for programmatic access.

The platform combines vector search, graph traversal, and LLM-driven analysis to deliver intelligent code intelligence capabilities including repository analysis, dependency mapping, impact assessment, and automated documentation generation.

Core Capabilities

Multi-Interface Architecture

MCP Protocol (Port 8000) - Model Context Protocol server for AI assistant integration

• Direct integration with Claude Desktop, Cursor, and other MCP-compatible tools

• 25+ specialized tools for code analysis and knowledge management

• Real-time task monitoring via Server-Sent Events

• Supports stdio and SSE transport modes

Web UI (Port 8080) - Browser-based interface for team collaboration

• Real-time task monitoring dashboard

• Repository ingestion and management

• Metrics visualization with interactive charts

• Built with React 18, TypeScript, and shadcn/ui components

REST API (Ports 8000, 8080) - HTTP endpoints for system integration

• Document ingestion and knowledge querying

• Task management and monitoring

• Prometheus metrics export

• OpenAPI/Swagger documentation

Knowledge Graph Engine

Code Intelligence - Graph-based code analysis without requiring LLMs

• Repository structure mapping and dependency tracking

• Function and class relationship analysis

• Impact analysis for code changes

• Context pack generation for AI assistants

• Support for 15+ programming languages

Memory Store - Project knowledge tracking with temporal awareness

• Fact, decision, pattern, and insight recording

• Memory evolution with superseding relationships

• Automatic extraction from conversations, commits, and code

• Vector search with embedding-based retrieval

Knowledge RAG - Document processing with hybrid search

• Multi-format document ingestion (Markdown, PDF, code files)

• Neo4j native vector indexing

• Hybrid search combining vector similarity and graph traversal

• Configurable chunking and embedding strategies

SQL Schema Parser - Database schema analysis with business domain classification

• Multi-dialect support (Oracle, MySQL, PostgreSQL, SQL Server)

• Configurable business domain templates (Insurance, E-commerce, Banking, Healthcare)

• Automated relationship detection and documentation generation

• Integration with knowledge graph for cross-referencing

Technology Stack

Backend Infrastructure

• FastAPI - High-performance async web framework

• Neo4j 5.x - Graph database with native vector indexing

• Python 3.13+ - Modern Python with type hints

• Uvicorn - ASGI server with WebSocket support

AI and ML Integration

• LlamaIndex - Document processing and retrieval pipeline

• Multiple LLM providers (Ollama, OpenAI, Gemini, OpenRouter)

• Flexible embedding models (HuggingFace, Ollama, OpenAI)

• Model Context Protocol (MCP) for AI assistant integration

Frontend Technology

• React 18 - Modern UI library with concurrent features

• TypeScript - Type-safe development

• TanStack Router - Type-safe routing

• shadcn/ui - Accessible component library

• Vite - Fast build tooling

Quick Start

Prerequisites

• Python 3.13 or higher

• Neo4j 5.0 or higher

• Docker (optional, for containerized deployment)

• Node.js 18+ (for frontend development)

Querying Knowledge

# Query the knowledge base
response = httpx.post("http://localhost:8000/api/v1/knowledge/query", json={
    "question": "How does the authentication system work?",
    "mode": "hybrid",  # or "graph_only", "vector_only"
    "use_tools": False,
    "top_k": 5
})

# Search similar documents
response = httpx.post("http://localhost:8000/api/v1/knowledge/search", json={
    "query": "user authentication",
    "top_k": 10
})

Installation

Clone the repository and install dependencies:

git clone https://github.com/royisme/codebase-rag.git
cd codebase-rag
pip install -r requirements.txt
# or using uv (recommended)
uv pip install -e .

Configure environment variables:

cp env.example .env
# Edit .env with your Neo4j credentials and LLM provider settings

Start Neo4j database:

docker run --name neo4j-code-graph \
  -p 7474:7474 -p 7687:7687 \
  -e NEO4J_AUTH=neo4j/password \
  -e NEO4J_PLUGINS='["apoc"]' \
  neo4j:5.15

Running the System

Complete System (MCP + Web UI + REST API)

python start.py

Access points:

• MCP SSE Service: http://localhost:8000/sse

• Web UI: http://localhost:8080

• REST API Documentation: http://localhost:8080/docs

• Prometheus Metrics: http://localhost:8080/metrics

MCP Server Only

python start_mcp.py

Docker Deployment

Three deployment modes available:

Minimal Mode - Code Graph only (no LLM required)

make docker-minimal

Standard Mode - Code Graph + Memory Store (embedding model required)

make docker-standard

Full Mode - All features (LLM + embedding required)

make docker-full

Usage Examples

MCP Integration

Configure in Claude Desktop or compatible MCP client:

{
  "mcpServers": {
    "code-graph": {
      "command": "python",
      "args": ["/path/to/start_mcp.py"],
      "cwd": "/path/to/codebase-rag"
    }
  }
}

Available MCP tools include:

• code_graph_ingest_repo - Ingest code repository

• code_graph_related - Find related code elements

• code_graph_impact - Analyze change impact

• query_knowledge - Query knowledge base

• add_memory - Store project knowledge

• extract_from_conversation - Extract insights from chat

• watch_task - Monitor task progress

REST API

Ingest a repository:

curl -X POST http://localhost:8080/api/v1/repositories/ingest \
  -H "Content-Type: application/json" \
  -d '{
    "url": "https://github.com/user/repo.git",
    "mode": "incremental",
    "languages": ["python", "typescript"]
  }'

Query knowledge base:

curl -X POST http://localhost:8080/api/v1/knowledge/query \
  -H "Content-Type: application/json" \
  -d '{
    "question": "How does authentication work in this codebase?",
    "mode": "hybrid",
    "top_k": 5
  }'

Monitor tasks:

curl http://localhost:8080/api/v1/tasks?status=processing

Web UI

Navigate to http://localhost:8080 to access:

• Dashboard - System health and quick actions

• Tasks - Real-time task monitoring with progress indicators

• Repositories - Repository management and ingestion

• Metrics - System performance and usage metrics

Configuration

Key environment variables:

# Server Ports
MCP_PORT=8000              # MCP SSE service
WEB_UI_PORT=8080           # Web UI and REST API

# Neo4j Configuration
NEO4J_URI=bolt://localhost:7687
NEO4J_USER=neo4j
NEO4J_PASSWORD=password
NEO4J_DATABASE=neo4j

# LLM Provider (ollama, openai, gemini, openrouter)
LLM_PROVIDER=ollama
OLLAMA_HOST=http://localhost:11434
OLLAMA_MODEL=llama3.2

# Embedding Provider (ollama, openai, gemini, openrouter)
EMBEDDING_PROVIDER=ollama
OLLAMA_EMBEDDING_MODEL=nomic-embed-text

# Processing Configuration
CHUNK_SIZE=512
CHUNK_OVERLAP=50
TOP_K=5
VECTOR_DIMENSION=384

For complete configuration options, see Configuration Guide.

Architecture

Dual-Server Design

The system employs a dual-server architecture optimized for different access patterns:

Port 8000 (Primary) - MCP SSE Service

• Server-Sent Events endpoint for real-time communication

• Optimized for AI assistant integration

• Handles long-running task monitoring

• WebSocket support for bidirectional communication

Port 8080 (Secondary) - Web UI + REST API

• React-based monitoring interface

• RESTful API for external integrations

• Prometheus metrics endpoint

• Static file serving for frontend

Both servers share the same backend services and Neo4j database, ensuring consistency across all interfaces.

Component Architecture

┌─────────────────────────────────────────────────────────┐
│                   Client Interfaces                      │
├──────────────┬──────────────┬──────────────────────────┤
│  MCP Client  │   Web UI     │      REST API            │
│  (AI Tools)  │  (Browser)   │   (External Systems)     │
└──────┬───────┴──────┬───────┴──────────┬───────────────┘
       │              │                  │
       └──────────────┼──────────────────┘
                      │
       ┌──────────────▼──────────────┐
       │     FastAPI Application      │
       ├──────────────┬──────────────┤
       │   Services   │  Task Queue  │
       └──────┬───────┴──────┬───────┘
              │              │
       ┌──────▼──────┐  ┌───▼────┐
       │   Neo4j     │  │  LLM   │
       │  Database   │  │Provider│
       └─────────────┘  └────────┘

Development

Project Structure

codebase-rag/
├── src/codebase_rag/
│   ├── api/                    # FastAPI routes
│   ├── core/                   # Application core
│   ├── services/               # Business logic
│   │   ├── code_ingestor.py    # Code repository processing
│   │   ├── graph_service.py    # Graph operations
│   │   ├── memory_store.py     # Project memory management
│   │   ├── neo4j_knowledge_service.py  # Knowledge base
│   │   ├── task_queue.py       # Async task processing
│   │   └── sql/                # SQL parsing services
│   └── mcp/                    # MCP protocol handlers
├── frontend/                   # React Web UI
│   ├── src/
│   │   ├── components/         # UI components
│   │   ├── routes/             # Page routes
│   │   └── lib/                # API client
│   └── package.json
├── tests/                      # Test suite
├── docs/                       # Documentation
└── scripts/                    # Utility scripts

Running Tests

# Backend tests
pytest tests/ -v

# Frontend tests
cd frontend && npm test

# Integration tests (requires Neo4j)
pytest tests/ -m integration

# Coverage report
pytest tests/ --cov=src --cov-report=html

Code Quality

# Format code
black .
isort .

# Linting
ruff check .
ruff check . --fix

# Type checking
mypy src/

Frontend Development

cd frontend
npm install
npm run dev        # Start dev server at http://localhost:3000
npm run build      # Build for production
npm run lint       # Check for issues
npm test           # Run tests

Deployment

Production Deployment

See Docker Deployment Guide for production deployment configurations including:

• Multi-stage Docker builds

• Environment-specific configurations

• Scaling and load balancing

• Security best practices

• Monitoring and logging setup

System Requirements

Minimum Configuration

• CPU: 2 cores

• RAM: 4 GB

• Storage: 10 GB

Recommended Configuration

• CPU: 4+ cores

• RAM: 8+ GB

• Storage: 50+ GB SSD

• Network: 100 Mbps+

Documentation

Complete documentation available at https://vantagecraft.dev/docs/code-graph

Key Documentation Sections

• Quick Start Guide - Get up and running in 5 minutes

• Architecture Overview - System design and components

• MCP Integration - AI assistant integration

• REST API Reference - Complete API documentation

• Deployment Guide - Production deployment

• Development Guide - Contributing and development

Community and Support

• Documentation: Complete Documentation

• Neo4j Guide: README_Neo4j.md

• Issues: GitHub Issues

• Discussions: GitHub Discussions

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

Built with excellent open source technologies:

• Neo4j - Graph database platform

• LlamaIndex - Data framework for LLM applications

• FastAPI - Modern web framework for Python

• React - Library for building user interfaces

• Model Context Protocol - AI assistant integration standard