Vector Databases

The retrieval layer underneath every RAG system we ship.

Retrieval quality is where most RAG systems actually fail, long before the model does — so we treat the vector index as infrastructure that needs its own evaluation set, not a drop-in component. Whether that is a dedicated vector database or pgvector inside Postgres depends on corpus size and query patterns, not a default preference.

Where we use it

Semantic search and retrieval-augmented generation
Building evaluation sets before choosing an embedding model or index, not after
Reranking pipelines layered on top of vector similarity search

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