How do enterprises implement hybrid search combining keyword and vector search?

Hybrid search combines BM25 (keyword relevance) and vector similarity scores using Reciprocal Rank Fusion (RRF) or linear interpolation. Weaviate, Qdrant, and Elasticsearch natively support hybrid search. Pinecone combines sparse vectors (SPLADE) with dense embeddings. Hybrid search typically outperforms pure vector or pure keyword search for enterprise search applications.

What embedding models should enterprises use for vector search?

OpenAI text-embedding-3-large (3072 dimensions) delivers top performance for general English text. Cohere Embed v3 is competitive with multilingual support. For open-source, sentence-transformers (SBERT) models like all-mpnet-base-v2 provide strong performance for on-premises or cost-sensitive deployments. BGE and E5 models from BAAI are MTEB benchmark leaders for open models.

How does vector search scale to billions of vectors in production?

Billion-scale vector search requires approximate nearest neighbor (ANN) algorithms with careful index partitioning. Pinecone's serverless tier handles billion-scale with automated sharding. Milvus supports distributed deployment with separable storage and compute. FAISS (Facebook AI Similarity Search) on GPU can query 1 billion vectors in under 10ms with GPU acceleration.

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Best Solutions for Vector Search/Similarity Search

Comprehensive review of the best vector search and similarity search solutions, including Pinecone, Weaviate, Qdrant, Elasticsearch kNN, and cloud-native vector search.

Frequently Asked Questions

Pinecone provides the most operationally mature managed vector search service with namespace-based isolation, metadata filtering, and hybrid search (dense + sparse). Weaviate adds a knowledge graph layer and multimodal search. Elasticsearch kNN (with ANN support in 8.x) is popular for teams already using Elastic for full-text search who want to add semantic search.

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