hybrid-search-implementation

Combine vector and keyword search using fusion techniques like Reciprocal Rank Fusion (RRF), linear combination, or cross-encoder reranking to capture both semantic meaning and exact term matches for improved recall.

Reciprocal Rank Fusion (RRF) blends vector and keyword search result rankings by scoring documents based on their reciprocal ranks in both lists, providing a robust fusion method without requiring score calibration.

Yes, Elasticsearch supports hybrid search by combining text matching with vector similarity, allowing you to fuse results using techniques like Reciprocal Rank Fusion or linear combination to enhance retrieval accuracy.

You can implement RAG hybrid search in PostgreSQL using Python with asyncpg and numpy, combining semantic vector similarity with exact keyword matching to ensure specific product codes or names are precisely captured.

Use cross-encoder reranking when you need to re-score and refine fused hybrid search results, applying a more precise model to the top documents to maximize final ranking accuracy in information retrieval systems.

Pure vector search focuses on semantic similarity and often misses exact keyword matches, making hybrid search necessary to ensure precise terms like product codes are accurately retrieved alongside contextually relevant documents.