Graph RAG by Example

Graph-based retrieval-augmented generation (RAG) can yield better results than traditional vector search in certain scenarios, especially when dealing with highly interlinked sources such as technical documents or Web pages. LangChain and a vector database like Astra DB can simplify adding this technique to your GenAI data stack, allowing developers to get started with just a few lines of code. RAG takes information from a GenAI query and supplements it with domain-specific context that's both relevant and current, using a graph structure and graph traversal to compensate for potential limitations of vector search. Knowledge graphs can be built automatically without requiring extensive manual definition and tweaking, making them a versatile tool for searching a wide variety of content. With LangChain and Astra DB, developers can generate a content-centric graph that stores information in a hybrid graph/vector format, allowing for both similarity (vector search) and graph traversal search methods. This approach enables the creation of highly performant GenAI apps with high relevancy and low latency.

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