Understanding Hybrid Search and its benefits in combining keyword and vector search for better results.
Feature | Keyword Search | Vector Search | Hybrid Search |
---|---|---|---|
Based On | Lexical matching (BM25, TF-IDF) | Embedding similarity (cosine, dot) | Both |
Strength | Exact matches, relevance | Contextual meaning | Balanced relevance + meaning |
Weakness | No semantic understanding | Misses exact keywords | Slightly heavier in compute |
Example Match | ”chicken soup” = chicken soup | ”chicken soup” = hot broth with chicken | Both literal and related concepts |
Best Use Case | Legal docs, structured data | Chatbots, Q&A, semantic search | Multimodal, real-world messy user queries |
Database | Hybrid Search Support |
---|---|
pgvector | ✅ Yes |
milvus | ✅ Yes |
lancedb | ✅ Yes |
qdrantdb | ✅ Yes |
weaviate | ✅ Yes |
mongodb | ✅ Yes (Atlas Vector Search) |
pgvector