This tool aims to significantly enhance information retrieval efficiency within enterprises by providing an Agent-facing Model Context Protocol (MCP) for document search. By leveraging the GraphRAG framework, it extracts user-relevant information from scattered documents, representing entities, relationships, and claims as a knowledge graph. This approach allows other agents to connect to a centralized document retrieval service, simplifying integration, improving scalability of intelligent agents, and delivering more structured, context-aware, and explainable responses than traditional RAG pipelines.
주요 기능
011 GitHub stars
02Centralized document retrieval service for intelligent agents
03Semantic search and graph traversal for relevant information extraction
04Retrieval-Augmented Generation (RAG) using large language models
05Graph-based knowledge representation for enhanced context
06Caching mechanisms for improved efficiency in pipeline stages
사용 사례
01Extracting specific, user-relevant information from diverse enterprise documents for agent consumption
02Integrating and scaling intelligent agents across an enterprise by providing a unified document search API
03Generating accurate and context-rich answers grounded in a structured knowledge graph for complex queries
