When implementing a RAG pipeline, how much do the indexing structure of the vector database, similarity metrics, and batch query performance impact the retrieval stage? In the absence of a clear business scenario, which metrics should be prioritized to evaluate the suitability of the vector store? Does anyone have recommended evaluation methods or practical experience to share?
In Retrieval-Augmented Generation (RAG) systems, how does the choice of vector database impact retrieval quality and response speed? Additionally, how can adaptability to different embedding models be evaluated?
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In the absence of business scenario constraints, when evaluating vector databases, should we prioritize query latency or recall accuracy? For different embedding models, is there a unified metric or benchmark to measure their adaptability to a specific vector database?