Vector search is a critical component of generative AI tooling because of how retrieval augmented generation (RAG) like FLARE helps LLMs incorporate up-to-date, customized information while avoiding hallucinations. At the same time, vector search is a feature, not a product — you need to query vectors as they relate to the rest of your data, not in isolation, and you shouldn’t need to build a pipeline to sync the rest of your data with a vector store to do that.

2023 has seen an explosion in vector search products and projects, making selecting among them a serious effort.  As you research the options, you’ll need to consider the following hard problems and the different approaches to solving them. Here, I’ll walk you through these challenges and describe how DataStax tackled them for our implementation of vector search for DataStax Astra DB and Apache Cassandra.

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