Hybrid Search

Why It Matters

Pure semantic search sometimes fails on exact matches. If a user searches for “error code E-4521,” semantic search might return documents about error handling in general rather than the specific error code. Traditional keyword search would find the exact match immediately.

Conversely, keyword search misses semantic relationships. Searching “how to make my API faster” won’t find your document titled “Latency Optimization Techniques” unless those exact words appear.

Hybrid search gives you both. It’s especially valuable for enterprise search, technical documentation, and any domain with specific terminology, product names, or codes that users need to find exactly.

For AI engineers building RAG systems, hybrid search often improves retrieval quality without the complexity of query expansion or advanced prompting techniques. It’s one of the highest-impact, lowest-effort improvements you can make.

Implementation Basics

Hybrid search runs two parallel retrieval paths and merges the results:

1. Semantic Path (Dense Retrieval) Convert the query to an embedding and find similar vectors using cosine similarity. This captures conceptual matches.

2. Keyword Path (Sparse Retrieval) Use BM25 or similar algorithm to score documents by keyword overlap. This catches exact matches and specific terminology.

3. Fusion Combine the two result sets. Common approaches:

• Reciprocal Rank Fusion (RRF): Score based on rank position in each list. Simple and effective.
• Linear combination: Weighted sum of normalized scores (e.g., 0.7 × semantic + 0.3 × keyword).
• Learned fusion: Train a model to combine scores, though this adds complexity.

Implementation options:

• Weaviate, Qdrant: Native hybrid search support, just configure both indexes.
• Pinecone + Elasticsearch: Run two systems and merge in your application code.
• Supabase: pgvector for semantic search plus full-text search on the same database.

Tuning the balance: Start with equal weights and adjust based on your data. Technical documentation often benefits from higher keyword weight; conversational queries favor semantic. Test with real user queries to find the right balance.

The overhead of hybrid search is minimal since you’re adding one more retrieval step. The quality improvement is often significant, especially for domains with specialized vocabulary.