Pinecone home page
Legacy
Search...
⌘K
Ask AI
Log In
Sign Up Free
Sign Up Free
Search...
Navigation
Hybrid search and sparse vectors
Hybrid search and sparse vectors
Guides
Reference
Examples
Integrations
Libraries
Troubleshooting
Releases
Forum
Support
Status
Account management
Downgrade your organization
Getting started
Overview
Quickstart
Authentication
Choosing index type and size
Examples
Organizations
Understanding organizations
Manage Billing
Manage Cost
Projects
Understanding projects
Manage projects
gcp-starter environment
Indexes
Understanding indexes
Manage indexes
Back up indexes
Scale indexes
Understanding collections
Data
Insert data
Manage datasets
Query data
Filtering with metadata
Using namespaces
Manage datasets
Hybrid search and sparse vectors
Hybrid search and sparse vectors
Understanding hybrid search
Sparse-dense vectors
Encoding sparse vectors
Operations
Moving to production
Performance tuning
Troubleshooting
Monitoring
Understanding multitenancy
Reference
Architecture
Security
Limits
On this page
Overview
Concepts
Tasks
Hybrid search and sparse vectors
Hybrid search and sparse vectors
Overview
This category contains concept topics and guides for tasks related to Pinecone hybrid search and sparse vectors.
This feature is in
public preview
. Test thoroughly before using it in production.
Concepts
Understanding hybrid search
describes how to combine semantic and keyword search search in Pinecone using sparse-dense vectors.
Sparse-dense vectors
describes the format of sparse-dense vectors used in Pinecone hybrid search.
Tasks
Encoding sparse-dense vectors
describes how to encode sparse vectors for upsert to your index.
Upserting sparse-dense vectors
describes how to upsert sparse-dense vectors to your index.
Querying sparse-dense vectors
describes how to query sparse-dense vectors.
Was this page helpful?
Yes
No
Creating and loading private datasets
Understanding hybrid search
Assistant
Responses are generated using AI and may contain mistakes.