Graph retrieval-augmented generation (GraphRAG) combines structured graph data with unstructured text, leveraging strengths of both to enhance context and depth in information retrieval.
Creating a knowledge graph is challenging, but Large Language Models (LLMs) can automate significant parts of the process, analyzing text to identify entities and relationships.
The recent integration of a graph construction module into LangChain is a step towards simplifying knowledge graph creation, streamlining the modeling process for users.
Setting up a Neo4j instance is essential for this process, with options for both cloud and local implementations to accommodate various user needs.
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