Integrating Neo4j with Generative AI

In this module, you will setup and use the Neo4j GraphRAG for Python package to:

• Create and configure a vector retrieverA component that searches a data source and returns the information relevant to a query. Often used to provide context for a language model..
• Perform semantic search using a vector indexA structure over a vector property. The database searches it to find the vectors nearest a given one, rather than comparing every vector stored..
• Build a GraphRAGRetrieval-augmented generation whose context comes from a knowledge graph, so the model can follow the relationships between facts. pipeline that uses retrievers to give context to an LLMA model trained on text to predict the next token, and so to generate language..
• Use graph traversalFollowing relationships from one node to the next to reach other parts of a graph. in combination with vector searchFinding the records whose vectors lie closest to a query vector. for enhanced retrieval.
• Implement text-to-Cypher retrieval for natural language querying.

In addition, you will explore popular GenAI frameworks for integrating with Neo4j, such as LangChain and LlamaIndex.

If you are ready, let's get going!