Defined term
Vector store
A database optimized for similarity search over embeddings.
A vector store indexes embeddings using algorithms like HNSW or IVF for sub-linear nearest-neighbor search. Choices include managed services (Pinecone, Weaviate Cloud), open-source (Qdrant, Weaviate, Milvus), and Postgres extensions (pgvector). The right choice depends on scale, latency target, hybrid search needs, and operational constraints.
When it matters
When you have 10,000+ documents to retrieve over and your query volume justifies dedicated infrastructure. Below that, pgvector in your existing Postgres often beats specialized vector DBs.
Real example
A 4M-vector store on Pinecone for a legal-research workflow: 99.9% uptime, P99 query latency 45ms, 12 metadata filters per query (jurisdiction, date, topic, doc-type). Reranking on top-100 results before final answer generation.
KPIs to watch
Query latency P95 (<100ms target), retrieval recall@10 (>0.85), index refresh cycle time (<4h for daily ingest).
Related terms
Embeddings
Numerical vectors that represent the meaning of a text, image, or other piece of content.
RAG (Retrieval-Augmented Generation)
Generation grounded in retrieved source documents rather than the model's parametric memory alone.
Semantic search
Search by meaning, not by keyword overlap.
AI-native PR stack
The instrumented tooling an AI-native PR team runs for media research, pitch drafting, monitoring, and measurement — with humans owning relationships and final messaging.
See it in action
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