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what are embeeding models

what are embeeding models

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May 26, 2026

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Vector Databases

Vector databases store high‑dimensional embeddings and enable fast similarity search (e.g., nearest‑neighbor retrieval for a query q∈Rdq\in\mathbb{R}^d) instead of exact key lookups, powering RAG, semantic search, and recommendation systems.

  • Use Approximate Nearest Neighbor (ANN) indexes such as HNSW, IVF, and PQ to trade a small loss in recall for large gains in latency and scalability.
  • Combine vector search with metadata filtering, CRUD APIs, replication, and sharding to meet production AI requirements.
  • Retrieval pipelines ingest data, embed it, index vectors, encode queries, perform ANN search, apply filters, and optionally rerank results.
  • Choice of embedding model, distance metric (cosine, dot‑product, L2L_2), and index type determines recall‑latency‑memory trade‑offs.
  • Hybrid search (semantic + lexical) and reranking improve coverage and precision in RAG applications.
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