Hybrid Search: Enhancing Recall with Keyword and Vector Retrieval

Hybrid search is an approach that integrates both keyword-based retrieval and vector-based retrieval techniques to enhance the recall of search systems. Keyword search relies on matching query terms with indexed terms, which is efficient for exact matches but may miss relevant documents due to vocabulary mismatches. On the other hand, vector retrieval uses embeddings to capture semantic meanings, allowing the system to identify relevant documents even when there is no exact keyword match.

In a hybrid search system, queries are processed using both methods: keyword retrieval quickly identifies documents with exact matches, while vector retrieval uncovers documents with semantic relevance. This dual approach ensures that the search system can retrieve a broader set of relevant documents, improving the overall recall without significantly sacrificing precision.

Implementing a hybrid search system requires careful consideration of how to blend the results from both retrieval methods. Techniques such as result re-ranking, score normalization, and weighted combinations are often employed to provide a cohesive and relevant set of search results. By leveraging the strengths of both keyword and vector retrieval, hybrid search systems can offer more comprehensive and accurate search capabilities.