Chat Analysis
Integrates vector embeddings and knowledge graphs to enable advanced chat analysis tasks like topic modeling, sentiment analysis,
This MCP server for chat analysis, developed by Robin L. M. Cheung, integrates vector embeddings and knowledge graphs to provide advanced chat data processing capabilities. Built with Python, it leverages Neo4j for graph storage, Qdrant for vector search, and sentence transformers for embedding generation. The implementation stands out by combining semantic similarity search with graph-based relationship analysis, enabling more nuanced understanding of chat conversations. By exposing these capabilities through standardized MCP endpoints, it allows AI systems to perform complex chat analysis tasks such as topic modeling, sentiment analysis, and user behavior tracking. This server is particularly useful for applications in customer support analytics, social media monitoring, or building intelligent chatbots that can learn from and adapt to conversation patterns.
Source
Repository: https://github.com/rebots-online/mcp-chat-analysis-server
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