Add support for using Pinecone as an online store in Feast to enable low-latency feature serving backed by a vector database.

Motivation

Pinecone is widely used as a managed vector database for AI and retrieval-augmented generation (RAG) applications. As feature stores are increasingly used alongside vector search systems, a native Pinecone integration would enable users to:

• Store and retrieve embeddings as online features.
• Serve vector features directly from Pinecone.
• Reduce infrastructure complexity for AI workloads already using Pinecone.
• Support hybrid ML and GenAI use cases from a single feature management workflow.

Proposed Solution

Implement a Pinecone online store plugin that:

• Supports reading and writing feature values to Pinecone.
• Maps Feast entities to Pinecone namespaces or metadata.
• Supports configurable index names and namespaces.
• Provides batch materialization into Pinecone.
• Supports online feature retrieval with low latency.
• Includes configuration examples and documentation.

Example configuration:

online_store:
type: pinecone
api_key: ${PINECONE_API_KEY}
index_name: feast-online
namespace: default

Considerations

• Authentication using Pinecone API keys.
• Support for multiple namespaces.
• Metadata filtering where applicable.
• Error handling and retry logic.
• Compatibility with existing Feast OnlineStore abstractions.

Additional Context

This integration would benefit users building retrieval-augmented generation (RAG), semantic search, recommendation systems, and embedding-based ML pipelines that already rely on Pinecone for vector storage.