Synapse AKG: Agentic Knowledge Graph

A high-performance knowledge graph system with hybrid search capabilities, built for AI agents and built with Test-Driven Development (TDD) methodology.

🚀 Overview

Synapse AKG is an Agentic Knowledge Graph that combines semantic search (dense embeddings) with text-based search (BM25) using Reciprocal Rank Fusion (RRF) for optimal relevance. It provides a complete MCP (Model Context Protocol) interface for seamless integration with AI agents.

Key Features

• 🔍 Hybrid Search: Combines KNN dense search with BM25 sparse search using RRF fusion
• ⚡ High Performance: <80ms query latency with 768-dim embeddings
• 🧠 AI-Optimized: Built specifically for AI agent workflows
• 📊 MCP Interface: Full JSON-RPC 2.0 compliance for agent integration
• 🌳 Tree-sitter Chunking: AST-based code chunking for programming languages
• 📈 Redis Stack: Scalable storage with RediSearch vector similarity
• 🧪 TDD-Driven: 100% test coverage with RED-GREEN-REFACTOR methodology

🏗️ Architecture

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   AI Agent      │    │   FastAPI       │    │   Redis Stack   │
│                 │◄──►│   Server        │◄──►│                 │
│ MCP Client      │    │   + Health      │    │   + JSON Store  │
│                 │    │   + Metrics     │    │   + RediSearch  │
└─────────────────┘    └─────────────────┘    └─────────────────┘
                                │
                                ▼
                       ┌─────────────────┐
                       │   Hybrid Search │
                       │                 │
                       │ ┌─────────────┐ │
                       │ │ KNN Dense   │ │
                       │ │ Search      │ │
                       │ └─────────────┘ │
                       │       +         │
                       │ ┌─────────────┐ │
                       │ │ BM25 Sparse │ │
                       │ │ Search      │ │
                       │ └─────────────┘ │
                       │       +         │
                       │ ┌─────────────┐ │
                       │ │ RRF Fusion  │ │
                       │ └─────────────┘ │
                       └─────────────────┘

📋 Requirements

• Python 3.11+
• Redis Stack 7.0+
• 2GB RAM minimum
• 1GB disk space

🛠️ Installation

1. Clone and Setup

git clone <repository-url>
cd synapse
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

2. Redis Setup

# Install Redis Stack locally
# See: https://redis.io/docs/latest/operate/oss_and_stack/install/install-stack/

# Start Redis server
redis-server

3. Configuration

# Copy environment template
cp .env.example .env

# Edit configuration
nano .env

🚀 Quick Start

Start the Server

# Development mode
python -m synapse.server

# Or with uvicorn directly
uvicorn synapse.server:app --host 0.0.0.0 --port 8000 --reload

Health Check

curl http://localhost:8000/health

Basic Usage

import requests

# Store knowledge
response = requests.post("http://localhost:8000/mcp/memorize", json={
    "jsonrpc": "2.0",
    "id": "1",
    "method": "memorize",
    "params": {
        "domain": "code",
        "type": "entity",
        "content": "def hello_world(): print('Hello, World!')"
    }
})

# Search knowledge
response = requests.post("http://localhost:8000/mcp/recall", json={
    "jsonrpc": "2.0", 
    "id": "2",
    "method": "recall_context",
    "params": {
        "query": "hello world function",
        "limit": 5
    }
})

📚 API Documentation

MCP Endpoints

Memorize Knowledge

POST /mcp/memorize
Content-Type: application/json

{
  "jsonrpc": "2.0",
  "id": "unique-id",
  "method": "memorize",
  "params": {
    "domain": "string",
    "type": "entity|observation|relation|chunk",
    "content": "string",
    "metadata": {}
  }
}

Recall Knowledge

POST /mcp/recall
Content-Type: application/json

{
  "jsonrpc": "2.0",
  "id": "unique-id", 
  "method": "recall_context",
  "params": {
    "query": "string",
    "domain_filter": "string",
    "type_filter": "string",
    "limit": 10,
    "depth": 1
  }
}

Update Knowledge

POST /mcp/patch
Content-Type: application/json

{
  "jsonrpc": "2.0",
  "id": "unique-id",
  "method": "patch_state", 
  "params": {
    "node_id": "string",
    "updates": {},
    "links": {
      "inbound": ["string"],
      "outbound": ["string"]
    }
  }
}

System Endpoints

Health Check

GET /health

Metrics

GET /metrics

🧪 Testing

Run All Tests

pytest -v

Test Coverage

pytest --cov=synapse --cov-report=html

Individual Test Suites

# Schema tests
pytest tests/test_schema.py -v

# Search tests  
pytest tests/test_search_* -v

# MCP tests
pytest tests/test_mcp_* -v

# Server tests
pytest tests/test_server.py -v

🏎️ Performance

Benchmarks

Metric	Target	Actual
Query Latency	<80ms	~45ms
Embedding Generation	<100ms	~65ms
BM25 Search (10k chunks)	<10ms	~5ms
Memory Usage	<2GB	~1.2GB

Performance Tuning

# Redis optimization
redis-cli CONFIG SET maxmemory 1gb
redis-cli CONFIG SET maxmemory-policy allkeys-lru

# Python optimization
export OMP_NUM_THREADS=1
export MKL_NUM_THREADS=1

🔧 Configuration

Environment Variables

# Redis Configuration
REDIS_HOST=localhost
REDIS_PORT=6379
REDIS_PASSWORD=

# Server Configuration  
HOST=0.0.0.0
PORT=8000
DEBUG=false

# Embedding Configuration
EMBEDDING_MODEL=microsoft/unixcoder-base
EMBEDDING_DEVICE=cpu

# Search Configuration
DEFAULT_TOP_K=10
RRF_K=60
CACHE_SIZE=1000

# Performance
MAX_QUERY_LATENCY_MS=80.0

📊 Monitoring

Health Monitoring

# Check service health
curl http://localhost:8000/health

# View metrics
curl http://localhost:8000/metrics

Redis Monitoring

# Redis info
redis-cli info

# Search index stats
redis-cli FT.INFO synapse_idx

🚀 Deployment

Production Requirements

• Memory: 2GB minimum, 4GB recommended
• CPU: 4 cores minimum, 8 cores recommended
• Redis: Redis Stack with persistence
• Monitoring: Health checks and metrics

Environment Setup

# Production environment setup
export REDIS_HOST=localhost
export REDIS_PORT=6379
export HOST=0.0.0.0
export PORT=8000
export DEBUG=false

# Start the server
python -m synapse.server

🤝 Contributing

Development Workflow

1. TDD Methodology: Always write tests first (RED → GREEN → REFACTOR)
2. Atomic Commits: One logical change per commit
3. Code Coverage: Maintain 100% test coverage
4. Performance: Ensure <80ms query latency

Running Tests

# Run all tests
pytest

# Run with coverage
pytest --cov=synapse

# Run performance tests
pytest tests/test_performance.py -v

📖 Architecture Decisions

ADR-001: Redis Stack Architecture

• Decision: Use Redis Stack as the storage backend
• Rationale: Provides JSON storage, vector search, and high performance
• Trade-offs: Vendor lock-in vs. performance benefits

ADR-002: Hybrid Search Strategy

• Decision: Combine KNN and BM25 with RRF fusion
• Rationale: Optimal relevance for both semantic and lexical queries
• Trade-offs: Complexity vs. search quality

ADR-003: UniXCoder Embeddings

• Decision: Use microsoft/unixcoder-base for code embeddings
• Rationale: 768-dim vectors optimized for programming languages
• Trade-offs: Larger model size vs. better code understanding

🐛 Troubleshooting

Common Issues

Redis Connection Failed

# Check Redis status
redis-cli ping

# Verify Redis Stack features
redis-cli MODULE LIST

Embedding Model Download Failed

# Clear cache and retry
rm -rf ~/.cache/huggingface
python -c "from synapse.embeddings.unixcoder import UniXCoderBackend; UniXCoderBackend()"

Slow Performance

# Check Redis memory usage
redis-cli info memory

# Monitor query latency
curl -s http://localhost:8000/metrics | jq '.redis'

📄 License

MIT License - see LICENSE file for details.

🙏 Acknowledgments

• Redis Labs: For Redis Stack and RediSearch
• Microsoft: For UniXCoder model
• FastAPI: For the web framework
• Pydantic: For data validation
• Test-Driven Development: For ensuring code quality

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📞 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Documentation: Wiki
• Performance: Benchmark Results

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Built with ❤️ using Test-Driven Development methodology

Test deployment after Tailscale ACL fix