This post explores the use of knowledge graphs for RAG, using DataStax Astra DB for storage. The code for the examples is in this notebook using some prototype
Retrieval augmented generation refers to a variety of techniques for retrieving information and using it to provide contextual information for generative AI. The most common form operates on text chunks and involves: Extracting the text from the original documents . Chunking the text to specific sizes based on document structure and semantics. Storing chunks in a vector database keyed by an embedding of the chunk.
Then, the knowledge graph is traversed to retrieve all relationships within a given distance of those starting points. The default traversal depth is 3. The retrieved relationships and the original question are used to create a prompt and context for the LLM to answer the question. Extracting Entities From the Question As with the extraction of the knowledge graph, extracting the entities in a question can be done using a special model or an LLM with a specific prompt.
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