Module Overview
In this module, you will learn:
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What Retrieval Augmented Generation (RAG) is and how it combines understanding user queries, retrieving relevant information, and generating responses using that information.
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How vectors and embeddings work, and how they can be used in RAG to find relevant information.
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How to use a vector index in Neo4j and when vector indexes are useful for finding context for Generative AI applications.
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What GraphRAG techniques are, and how they can be used to enhance information retrieval by combining vector search with graph traversal and relationships.
If you are ready, let’s get going!