Hybrid Search

Hybrid search is the combination of dense vector similarity search with sparse keyword search (typically BM25 or SPLADE), merging the strengths of both retrieval modes for higher overall quality. Dense vector search excels at semantic matching ("car" matches "automobile") but can miss exact-keyword matches important for proper nouns, product codes, and rare terms. Sparse keyword search excels at exact and near-exact matches but cannot bridge synonyms or paraphrases. Hybrid search blends results via fusion techniques like Reciprocal Rank Fusion (RRF), weighted score averaging, or learned reranking. Major vector databases including Pinecone, Weaviate, Qdrant, Vespa, Elasticsearch, and OpenSearch all support hybrid search natively. AI governance teams favor hybrid search for compliance-critical applications because the keyword component provides explainability — operators can point to specific terms that triggered retrieval — and the vector component provides recall on paraphrased queries. The BEIR and MTEB benchmarks consistently show hybrid approaches outperforming either method alone on most retrieval tasks. Hybrid search is now the recommended default in most production RAG architectures.

Hybrid search in Centralpoint: Centralpoint coordinates hybrid search across whatever vector backend supports it — Weaviate, Vespa, Pinecone, Elasticsearch — with per-skill tuning of fusion weights. The model-agnostic platform routes generation to OpenAI, Claude, Gemini, or LLAMA, meters tokens, keeps prompts local, and deploys hybrid-search chatbots through one line of JavaScript.


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