LangChain Graph RAG

LangChain Graph RAG is a retrieval-augmented generation approach that combines knowledge graphs with LangChain’s framework to enhance information retrieval and response generation. This technique leverages graph databases to organize data as interconnected nodes and relationships, enabling more contextual and comprehensive retrieval compared to traditional vector-based methods. The GraphRetriever from the langchain-graph-retriever package provides functionality that merges unstructured similarity search on vectors with structured traversal of metadata properties. Graph RAG addresses limitations of standard RAG systems by capturing complex relationships between entities, enabling multi-hop reasoning, and providing richer context for language models. The approach involves constructing knowledge graphs from unstructured text, storing them in graph databases like Neo4j or Memgraph, and querying them using natural language. This allows for more accurate and contextually relevant responses in AI applications.