hybrid-retrieval

Pure vector search fails for exact keyword queries in RAG because it prioritizes semantic similarity, often missing exact terms. Hybrid retrieval solves this by combining dense vector embeddings with sparse BM25 keyword matching to ensure precise terminology is captured.

To combine semantic and keyword search for technical documentation, implement a hybrid retrieval strategy. This approach integrates dense vector embeddings for semantic context with sparse BM25 retrieval to accurately match specific error codes and precise terminology.

Yes, you can use Reciprocal Rank Fusion (RRF) to merge dense and sparse search results. RRF is a supported fusion technique that effectively combines the rankings from semantic vector embeddings and BM25 keyword retrieval to optimize RAG accuracy.

The best way to improve RAG accuracy for precise terminology queries is applying hybrid search with optional reranking. This combines dense and sparse retrieval to capture both semantic context and exact keyword matches, then reranks results to enhance overall relevance.

You should use hybrid retrieval instead of semantic search alone when queries depend on exact terms, such as troubleshooting specific error codes in technical documentation. It ensures documents containing crucial keywords are prioritized alongside semantically related content.

Yes, hybrid search supports optional reranking for enhanced result relevance. After combining dense vector embeddings and sparse BM25 keyword retrieval, you can apply reranking to further optimize the accuracy of documents retrieved for RAG systems.