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Hybrid Search

Definition

Hybrid search is a retrieval method that combines semantic search (vector/embedding-based) with lexical search (keyword/BM25-based) to find relevant information. By merging both approaches, hybrid search achieves higher accuracy than either method alone.

How it works

Semantic search excels at understanding meaning but can miss specific terms, product names, and exact phrases. Keyword search (BM25) excels at matching specific terms but misses paraphrased content. Hybrid search combines both:

  1. Semantic search: Finds content with similar meaning to the query
  2. BM25 keyword search: Finds content containing the exact terms
  3. Reciprocal Rank Fusion (RRF): Merges and re-ranks both result sets

This means searching for "Chatsy Pro plan pricing" would find documents about "Chatsy Pro subscription cost" (semantic) AND documents containing the exact term "Pro plan" (keyword). Neither search alone would find both.

Why it matters

For AI chatbots, retrieval accuracy directly determines answer quality. If the search step misses relevant content, the AI cannot generate a correct answer, no matter how good the language model is. Hybrid search is the current best practice for RAG systems because it maximizes recall without sacrificing precision.

How Chatsy uses it

Chatsy uses hybrid search as its core retrieval engine. Customer questions are searched against the knowledge base using both pgvector semantic search and PostgreSQL full-text search (BM25). Results are merged using Reciprocal Rank Fusion to provide the most relevant passages to the AI for answer generation.

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What else should I know about Hybrid Search?

Reciprocal Rank Fusion (RRF) merges and re-ranks results from both search methods

Key takeaways

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Example scenarios

  1. 01

    Product name + intent matching

    A customer asks "how do I set up Chatsy Pro webhooks?" Keyword search finds documents containing "Chatsy Pro" and "webhooks" (exact terms). Semantic search finds documents about "configuring event notifications" (meaning). Hybrid search returns both, ensuring the most relevant result ranks first.

  2. 02

    Technical jargon handling

    A developer asks about "CORS errors on the REST API." Keyword search catches the exact technical terms (CORS, REST API). Semantic search also finds related articles about "cross-origin request configuration" and "API access control." The combined results cover both exact and related content.

  3. 03

    Misspelled query recovery

    A customer types "refud polcy" (misspelled). Keyword search fails because no documents contain those misspellings. Semantic search still matches the query to the "Refund Policy" article because the embedding captures meaning despite typos. Hybrid search recovers from the keyword failure.

Key takeaways

  • Hybrid search combines semantic vector search with BM25 keyword search for maximum accuracy
  • Reciprocal Rank Fusion (RRF) merges and re-ranks results from both search methods
  • Test recall on your own questions: the gain over vector search alone depends on your content
  • Keyword search catches exact terms and product names that semantic search can miss
  • The additional latency is negligible (10-50ms) because both searches run in parallel

When it does not apply

  • You have very small content corpora where pure keyword search already covers the cases.
  • Your latency requirements demand a single index round-trip with no merge step.

Frequently asked questions

Yes, in most cases. Hybrid search usually finds more of the relevant passages than vector search alone, especially for queries containing specific terms, product names, or technical jargon that semantic search can miss.

The additional latency is negligible, typically 10-50 milliseconds. Both searches run in parallel and results are merged. The accuracy improvement far outweighs the minimal latency cost.

Always, if your platform supports it. Hybrid search is strictly better than vector-only search for customer support because support queries frequently contain specific product names, error codes, and technical terms that keyword search handles better than semantic search.

RRF is an algorithm that combines ranked result lists from multiple search methods. It scores each result based on its rank position in each list (1/rank), then sums the scores. Results that rank highly in both keyword and semantic search get the highest combined scores, surfacing the most relevant content.

Examples include modern Elasticsearch and OpenSearch with vector plugins enabled, Weaviate with BM25 plus vector retrieval, Vespa, and PostgreSQL using pgvector alongside built-in full-text search (the approach Chatsy uses). Each runs lexical and vector queries in parallel and fuses the results.

Semantic search uses only vector similarity to match by meaning. Hybrid search runs both semantic search and keyword search (BM25) in parallel and merges the results, typically with Reciprocal Rank Fusion. Hybrid wins when queries contain specific product names, error codes, or jargon that pure semantic search can blur.

Yes, in spirit. Google Search blends classic keyword and link-based ranking with neural retrieval models like BERT and MUM, plus knowledge graph signals. It is not exactly the BM25-plus-vector pattern used inside RAG systems, but it is conceptually a hybrid of lexical and semantic understanding.

Related terms

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Further reading

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