Memory Service

This MCP implementation, built with Python, provides a versatile framework for integrating with ChromaDB, a vector database for AI applications. It utilizes sentence transformers for efficient text embedding and tokenization, enabling advanced natural language processing capabilities. The implementation supports websocket connections, allowing for real-time data exchange and updates. By combining these technologies, it offers a robust solution for building AI-powered applications that require semantic search, document similarity comparisons, and content recommendation systems. This implementation is particularly well-suited for use cases such as intelligent document retrieval, content categorization, and personalized information delivery across various domains.