Vectoring in on Pinecone
Practical AI
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Navigating Vector Databases and RAG
This chapter explores the critical role of vector databases in managing embeddings for optimized searches, particularly in relation to retrieval-augmented generation (RAG) and large language models (LLMs). It discusses the essential features of RAG methods, such as metadata filters and namespaces, while showcasing their applications in enterprise scenarios. The chapter also addresses the complexities of implementing RAG systems, highlighting the need for organizations to assess their unique requirements and continuously monitor performance for best results.
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