The Neo4j LLM Knowledge Graph Builder processes text into a graph in eight steps: (1) Uploaded sources are stored as Document nodes in the graph. (2) Document types are loaded with LangChain loaders. (3) The content is split into Chunks. (4) Chunks are stored in the graph and connect to the document and to each other for advanced RAG patterns. (5) Highly similar chunks are connected with a SIMILAR relationship (with a weight attribute) to form a k-nearest neighbors graph. (6) Embeddings are computed and stored in the chunks and vector index. (7) The llm-graph-transformer or diffbot-graph-transformer extracts entities and relationships from the text. (8) Entities and their relationships are stored in the graph and connect to the originating chunks.

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  How to Improve Multi-Hop Reasoning With Knowledge Graphs and ...

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• How to Improve Multi-Hop Reasoning With Knowledge Graphs and ... neo4j.com Neo4j via serper

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• Neo4j LLM Knowledge Graph Builder concept