What Is Hybrid Search in RAG?
A retrieval strategy that combines semantic (vector) search with keyword (full-text) search and merges the results, capturing both meaning and exact matches.
Vector search finds passages that mean the same thing as the query; keyword search finds passages that contain the same strings. Each fails where the other excels: vectors miss exact error codes and product names, keywords miss paraphrases and synonyms.
Hybrid search runs both and merges the ranked lists — commonly with reciprocal rank fusion (RRF), which rewards documents that rank well in either list without needing to calibrate incomparable scores against each other.
For technical documentation, hybrid retrieval is close to mandatory: users search both ways, often in the same query ("ERR_4012 after rotating keys").
How Kelu uses this
Every Kelu retrieval — chat grounding, the Search API, the widget's search mode, MCP's search tool — runs pgvector cosine similarity and PostgreSQL full-text ranking in parallel, then fuses them with RRF. Source-group filters and document visibility are applied at query time.
Related terms
Semantic Search
Search that ranks results by meaning similarity between the query and documents, using embeddings, rather than by matching keywords.
Read definitionEmbeddings
Numeric vector representations of text in which semantically similar passages end up close together, enabling search by meaning rather than exact words.
Read definitionReciprocal Rank Fusion (RRF)
A way to merge ranked result lists into one by scoring each item on its position in each list, not its raw score, so different search methods can be combined.
Read definitionReranking
A second pass in which a stronger model re-scores the top search results against the question, so the passages that actually answer it rise to the top.
Read definitionVector Database
A data store optimized for saving embedding vectors and finding the nearest neighbors of a query vector quickly, at scale.
Read definitionRAG (Retrieval-Augmented Generation)
An AI architecture that retrieves relevant documents from a trusted knowledge source and passes them to a language model, so answers are grounded in real content instead of the model's training data.
Read definition
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