How to Implement Hybrid Search (Keyword + Semantic) in RAG
Learn why combining keyword search (BM25) and semantic search (embeddings) improves retrieval accuracy in RAG.
How to Implement Hybrid Search (Keyword + Semantic) in RAG
While semantic vector search is powerful, it has blind spots. It struggles with exact matching of keywords, product codes, or acronyms. Hybrid Search solves this by running both Keyword Search (using BM25 algorithms) and Semantic Search (using vector embeddings) in parallel, merging and reranking the results for optimal accuracy.
Hybrid Search Pipeline
- Dense Search: Computes vector similarity to find conceptual meanings.
- Sparse Search: Matches keyword frequencies to find exact terms (BM25).
- Reciprocal Rank Fusion (RRF): Algorithm that combines rankings from both search outputs.
- Reranking (Cross-Encoders): Uses secondary models to evaluate retrieved chunks for relevance.
- Implementation: Supported natively in databases like Pinecone, pgvector, and Elasticsearch.
Engineering Deep Dive
Building production-grade systems in this domain requires moving past superficial setups. You must manage performance metrics, handle error boundaries, optimize resource utilization, and scale infrastructure to support concurrent requests. The Namaste AI course focuses heavily on these engineering paradigms, giving you the skills to design, debug, and deploy enterprise-level AI applications.
Sparse retrieval matches exact words (like TF-IDF). Dense retrieval matches vector meanings (embeddings).
Running a high-quality model on the top retrieved results to re-order them based on relevance before sending them to the LLM.
Because it combines the conceptual understanding of embeddings with the precision of keyword matching for codes and terms.
It requires maintaining both a keyword index and a vector index, which can increase storage and indexing times.
You can integrate pre-built reranking APIs like Cohere Rerank or BGE-Reranker into your retrieval pipelines.
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