Using GraphRAG+LangChain+Ollama: LLama 3.1 Runs Integrated Knowledge Graph and Vector Database (Neo4j)

Learn how to use LLama 3.1 for GraphRAG operations in 50 lines of code. Integrate knowledge graphs and vector databases with Neo4j and LangChain.

I'll show you how to use LLama 3.1, a locally run model, to perform GraphRAG operations in just 50 lines of code.

First, what is GraphRAG?

GraphRAG performs retrieval-augmented generation by considering relationships between entities and documents, focusing on nodes and relationships.

One GraphRAG architecture integrates knowledge graphs with vector databases: this method uses knowledge graphs and vector databases to gather relevant information.

A knowledge graph can capture relationships between vector chunks, including document hierarchies.

It provides structured entity information near chunks retrieved from vector searches, enriching prompts with valuable additional context.

This enriched prompt is input into the LLM for processing, and the LLM generates a response.

Finally, the generated answer is returned to the user. This architecture is useful for customer support, semantic search, and personalized recommendations.