The GraphRAG module integrated in the project breaks through the fragment retrieval limitations of traditional RAG and builds a knowledge graph of document entities to realize deep semantic understanding. The system automatically identifies named entities (e.g., people/organizations/locations) in the uploaded document processing stage and builds a co-occurring relationship network to form a semantic graph with hundreds of nodes. When dealing with complex queries such as "the relevance of the board members of company A to project B", the graph analysis can improve the accuracy by 60% compared with the plain text retrieval.
The technical implementation adopts Neo4j graph database to store ternary relationships, with GNN algorithm for relational reasoning. Specific application scenarios include contract clause association analysis, academic paper citation network mining and so on. Test data show that GraphRAG can improve the recognition of implicit relationships by 3 times for long documents with more than 50 pages.
This answer comes from the articleDeepSeek-RAG-Chatbot: a locally running DeepSeek RAG chatbotThe
