Vector Search in Elasticsearch: From Keywords to Meaning - Building Semantic Search and RAG Pipelines

Short summary

Vector search finds documents by semantic meaning rather than keyword matching. Elasticsearch uses HNSW (Hierarchical Navigable Small World) for fast approximate nearest-neighbor search, offering speed/accuracy trade-offs through tuning parameters. The post covers production RAG pipelines: chunking documents, embedding with models like E5 or OpenAI, indexing in Elasticsearch, and retrieving context for LLM generation.

• •Vector search complements BM25 by understanding semantic similarity across synonyms and related concepts
• •HNSW algorithm balances speed and accuracy; tune m and ef_construction for graph quality, num_candidates for query-time recall
• •RAG pipeline: chunk → embed → index → query → retrieve → generate, with multiple embedding model options from ELSER to OpenAI

Generated with AI, which can make mistakes.