hybrid-search-implementation

Fusion methods for hybrid search include Reciprocal Rank Fusion (RRF), linear combination, and cross-encoder reranking. These techniques merge vector similarity scores with keyword search rankings to balance semantic relevance and exact term matching accuracy.

To combine vector and keyword search results using RRF, implement Reciprocal Rank Fusion to merge the ranked lists from both retrieval methods. This approach normalizes scores based on rank positions, ensuring balanced contributions from semantic and exact matches.

Yes, this hybrid search implementation works with Elasticsearch for keyword-based retrieval and sentence-transformers for generating vector embeddings. It also relies on asyncpg and numpy to manage database connections and perform numerical computations for result fusion.

Pure vector search misses specific domain vocabulary queries because semantic embeddings often generalize exact technical terms or product codes. Hybrid search overcomes this by combining vector similarity with keyword-based exact matching to ensure precise vocabulary retrieval.

Cross-encoder reranking for search fusion provides high accuracy but introduces latency limitations due to its pairwise scoring mechanism. While it effectively refines merged vector and keyword results, this computational overhead makes it less suitable for real-time or large-scale retrieval pipelines.