Hybrid Search with Sparse-Dense Vectors
Learn how Pinecone combines dense semantic vectors with sparse keyword vectors to deliver hybrid search that captures both meaning and exact terms.
The Limits of Dense-Only Search
Dense vectors capture meaning but can miss exact terms like product codes, names, or rare keywords. A user searching 'error E4012' may get semantically related but wrong results.
Hybrid search fixes this by adding keyword matching.
Dense vs Sparse Vectors
Two vector kinds:
- Dense — a few hundred floats encoding semantic meaning
- Sparse — mostly zeros, with weights only for present terms (like keyword scores)
Sparse vectors behave like classic keyword search.
All lessons in this course
- Filtering with Metadata
- Managing Namespaces
- Real-time Updates & Deletions
- Hybrid Search with Sparse-Dense Vectors