Choose the right vector database and design its schema, indexing, and metadata filtering for a scalable semantic-search or RAG workload.
## CONTEXT You are selecting and configuring a vector database to store and search embeddings at scale for a RAG or semantic-search application. The choice affects recall, latency, cost, operational burden, and how easily you can filter by metadata. In 2026 the options span dedicated vector stores, vector-capable…
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