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#vector-search

3 resources and 1 guide tagged #vector-search on ChangeGamer.

• Embeddings and Vector Search for Agents · Reference
  How to pick an embedding model, understand distance metrics, choose an ANN index type, and operate a vector store reliably in agent retrieval pipelines.
• Reranking for RAG: Cross-Encoders, LLM Rerankers, and Hosted APIs · Guide
  Second-stage retrieval step that re-scores bi-encoder candidates with full query-document attention, boosting precision without sacrificing recall; covers cross-encoder, LLM, and late-interaction reranking, hosted APIs, sizing heuristics, and evaluation.
• Hybrid Search for RAG: BM25 + Dense Retrieval and Fusion · Guide
  How to combine lexical (BM25/SPLADE) and dense vector retrieval with Reciprocal Rank Fusion for higher first-stage recall in RAG pipelines — with the RRF formula, a sparse-method comparison table, and verified DB support.

Guides

• How to Choose an Embedding Model for RAG (and Version It Like a Schema) · guide
  Embedding selection as an operations problem: the criteria that dominate total cost of ownership, the never-mix-spaces invariant, reindex migrations with dual indexes, and where quantization fits once cost shows up.