Tool Use - AI Conversations Do You Need A Vector Database in 2026? (ft Arjun Patel)
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Feb 17, 2026 Arjun Patel, Senior Developer Advocate at Pinecone who builds vector search and RAG systems. He explores what vector stores enable and when they matter. He breaks down chunking strategies, dense vs sparse embeddings, reranking and metadata filtering. He demos Pinecone integrations and a no-code assistant for searching and citing documents.
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What Makes Embeddings Better
- Embedding quality depends on training objective, tokenization, and dataset distribution.
- Fine-tuned retrieval embeddings and domain data improve search relevance for specialized tasks.
Rerank Results For Higher Relevance
- Add a re-ranker after vector search to rescore query-document pairs for relevance.
- Use the vector DB to narrow candidates, then rerank the top results to improve final quality.
Filter And Partition Your Index
- Use metadata filters and namespaces to limit search scope before vector querying.
- Attach tags or partition per user so searches run only against relevant subsets of vectors.