ai-llm-search-retrieval

Hybrid search combines BM25 lexical matching with dense vector search using Reciprocal Rank Fusion to balance exact-match precision and semantic understanding, delivering higher relevance across diverse query types without sacrificing either capability.

HNSW-IF and multi-vector HNSW indexing enable approximate nearest neighbor search on massive datasets with sub-linear query time, supporting billion-scale document collections while maintaining efficient memory and latency characteristics.

Multi-stage ranking architectures using cross-encoder reranking and LLM-based query rewriting progressively refine search results, identifying top-k candidates at each stage to boost final result quality without full dataset re-scoring.

Hybrid search excels when your queries require exact phrase matching, domain-specific terminology, or catalog lookups alongside semantic similarity, whereas pure vector search alone struggles with precise lexical requirements or numerical identifiers.

Yes—BM25 statistical ranking and rule-based multi-stage architectures deliver strong relevance gains without ML dependencies, though cross-encoder reranking and LLM-based query rewriting introduce learning-based refinement for specialized use cases.

Hybrid search requires tokenized text for BM25 indexing and dense vector embeddings for HNSW indexing; prepare documents with consistent field schemas, generate embeddings via embedding models, and structure metadata for both lexical and semantic lookups.