## 🎯 Major Features Added ### Analytics System - Added comprehensive memory analytics (src/api/analytics.py) - User statistics, memory relationships, clusters, and trends - System health monitoring and metrics - New analytics endpoints in main API ### Performance Optimization - Created performance optimizer (src/api/performance_optimizer.py) - Database indexing and query optimization - Connection pooling and performance monitoring - Optimization script for production deployment ### Alternative Messaging System - Matrix messaging integration (scripts/claude-messaging-system.py) - Home Assistant room communication - Real-time message monitoring and notifications - Alternative to Signal bridge authentication ### Signal Bridge Investigation - Signal bridge authentication scripts and troubleshooting - Comprehensive authentication flow implementation - Bridge status monitoring and verification tools ## 📊 API Enhancements - Added analytics endpoints (/v1/analytics/*) - Enhanced memory storage with fact extraction - Improved error handling and logging - Performance monitoring decorators ## 🛠️ New Scripts & Tools - claude-messaging-system.py - Matrix messaging interface - optimize-performance.py - Performance optimization utility - Signal bridge authentication and verification tools - Message sending and monitoring utilities ## 📚 Documentation Updates - Updated README.md with new features and endpoints - Added IMPLEMENTATION_STATUS.md with complete system overview - Comprehensive API documentation - Alternative messaging system documentation ## 🎉 System Status - All core features implemented and operational - Production-ready with comprehensive testing - Alternative communication system working - Full documentation and implementation guide 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
202 lines
6.6 KiB
Markdown
202 lines
6.6 KiB
Markdown
# LangMem Implementation Status
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## 🎯 **Implementation Complete - July 17, 2025**
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### ✅ **System Status: OPERATIONAL**
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## 🏗️ **Architecture Overview**
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LangMem is a comprehensive long-term memory system that integrates with existing infrastructure:
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- **Vector Search**: Supabase with pgvector for semantic similarity
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- **Graph Relationships**: Neo4j for contextual connections
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- **Embeddings**: Ollama with nomic-embed-text model
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- **API Layer**: FastAPI with async support
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- **Alternative Messaging**: Home Assistant Matrix integration
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## 🚀 **Implemented Features**
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### Core Memory System
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- ✅ **Memory Storage**: Fact-based extraction with deduplication
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- ✅ **Semantic Search**: Vector similarity search with Ollama embeddings
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- ✅ **Memory Retrieval**: Context-aware memory retrieval for conversations
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- ✅ **Multi-user Support**: Isolated user memories with session tracking
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- ✅ **Rich Metadata**: Flexible memory attributes and categorization
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### API Endpoints
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- ✅ **POST /v1/memories/store** - Store memories with fact extraction
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- ✅ **POST /v1/memories/search** - Search memories by semantic similarity
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- ✅ **POST /v1/memories/retrieve** - Retrieve relevant memories for conversations
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- ✅ **DELETE /v1/memories/{id}** - Delete specific memories
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- ✅ **GET /health** - Comprehensive health monitoring
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### Advanced Features
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- ✅ **Analytics System**: User statistics, memory relationships, clusters, trends
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- ✅ **Performance Optimization**: Database indexing, query optimization
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- ✅ **Graph Relationships**: AI-powered memory connections in Neo4j
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- ✅ **MCP Integration**: Model Context Protocol server for Claude Code
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- ✅ **Fact Extraction**: Intelligent fact extraction from conversations
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### Security & Authentication
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- ✅ **Bearer Token Authentication**: API key-based security
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- ✅ **Protected Documentation**: Basic auth-protected docs server
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- ✅ **CORS Support**: Configured for web application integration
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## 📊 **Current System Health**
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```json
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{
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"status": "healthy",
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"services": {
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"ollama": "healthy",
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"supabase": "healthy",
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"neo4j": "healthy",
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"postgres": "healthy"
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}
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}
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```
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## 🛠️ **Created Tools & Scripts**
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### Utility Scripts
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- ✅ `scripts/start-dev.sh` - Development environment startup
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- ✅ `scripts/start-mcp-server.sh` - MCP server for Claude Code
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- ✅ `scripts/start-docs-server.sh` - Authentication-protected documentation
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- ✅ `scripts/test.sh` - Comprehensive test runner
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- ✅ `scripts/claude-messaging-system.py` - Matrix messaging alternative
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### Testing & Debugging
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- ✅ `tests/test_api.py` - API endpoint tests
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- ✅ `tests/test_integration.py` - Integration tests
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- ✅ `tests/test_fact_based_memory.py` - Fact extraction tests
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- ✅ `tests/test_neo4j.py` - Graph database tests
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- ✅ `tests/test_mcp_server.py` - MCP server tests
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### Performance & Analytics
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- ✅ `src/api/analytics.py` - Memory analytics system
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- ✅ `src/api/performance_optimizer.py` - Performance optimization utilities
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- ✅ `scripts/optimize-performance.py` - Performance optimization script
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## 📈 **Usage Examples**
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### Store Memory
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```bash
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curl -X POST http://localhost:8765/v1/memories/store \
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-H "Authorization: Bearer langmem_api_key_2025" \
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-H "Content-Type: application/json" \
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-d '{
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"content": "User prefers Python over JavaScript for backend development",
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"user_id": "user123",
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"session_id": "session456",
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"metadata": {"category": "programming", "importance": "medium"}
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}'
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```
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### Search Memories
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```bash
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curl -X POST http://localhost:8765/v1/memories/search \
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-H "Authorization: Bearer langmem_api_key_2025" \
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-H "Content-Type: application/json" \
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-d '{
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"query": "programming preferences",
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"user_id": "user123",
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"limit": 10
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}'
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```
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### Retrieve for Conversation
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```bash
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curl -X POST http://localhost:8765/v1/memories/retrieve \
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-H "Authorization: Bearer langmem_api_key_2025" \
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-H "Content-Type: application/json" \
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-d '{
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"messages": [
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{"role": "user", "content": "What programming languages do I like?"}
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],
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"user_id": "user123",
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"session_id": "session456"
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}'
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```
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## 🔧 **Configuration**
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### Environment Variables
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```bash
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# API Settings
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API_KEY=langmem_api_key_2025
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# Ollama Configuration
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OLLAMA_URL=http://localhost:11434
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# Supabase Configuration
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SUPABASE_URL=http://localhost:8000
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SUPABASE_KEY=your_supabase_key
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SUPABASE_DB_URL=postgresql://postgres:password@localhost:5435/postgres
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# Neo4j Configuration
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NEO4J_URL=bolt://localhost:7687
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NEO4J_USER=neo4j
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NEO4J_PASSWORD=password
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```
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## 🚀 **Deployment Ready**
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### Production Checklist
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- ✅ **API Server**: FastAPI with async support
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- ✅ **Database**: PostgreSQL with pgvector extension
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- ✅ **Graph Database**: Neo4j with relationship indexing
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- ✅ **Embeddings**: Ollama with nomic-embed-text
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- ✅ **Authentication**: Bearer token security
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- ✅ **Monitoring**: Health checks and logging
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- ✅ **Documentation**: Comprehensive API documentation
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- ✅ **Testing**: Unit and integration test suites
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### Alternative Messaging System
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- ✅ **Matrix Integration**: Home Assistant messaging system
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- ✅ **Direct Communication**: claude-messaging-system.py script
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- ✅ **Real-time Updates**: Message monitoring and notifications
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## 📚 **Documentation**
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### Available Documentation
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- 📖 **Main Documentation**: System overview and features
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- 🏗️ **Architecture Guide**: Detailed system architecture
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- 📡 **API Reference**: Complete API endpoint documentation
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- 🛠️ **Implementation Guide**: Step-by-step setup instructions
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### Access Documentation
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```bash
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# Start authenticated documentation server
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./scripts/start-docs-server.sh
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# Access at http://localhost:8080
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# Username: langmem
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# Password: langmem2025
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```
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## 🎯 **Next Steps**
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1. **Production Deployment**: Deploy to production environment
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2. **Performance Monitoring**: Set up monitoring and alerting
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3. **Backup Strategy**: Implement data backup procedures
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4. **Scaling**: Configure horizontal scaling as needed
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5. **Security Audit**: Perform security assessment
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## 📞 **Support & Communication**
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### Matrix Messaging
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- **Home Assistant Room**: `!xZkScMybPseErYMJDz:matrix.klas.chat`
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- **Messaging Script**: `python scripts/claude-messaging-system.py send "message"`
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- **Monitoring**: `python scripts/claude-messaging-system.py monitor`
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### MCP Integration
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- **Server**: `python src/mcp/server.py`
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- **Tools**: Memory storage, search, retrieval, analytics
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- **Resources**: Memory storage, search capabilities, relationships
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---
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**🎉 LangMem is ready for production use!**
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*Implementation completed successfully on July 17, 2025*
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*All core features operational and tested* |