--- title: "Pinecone" description: "Pinecone vector database integration for semantic caching in Bifrost." icon: "database" --- ## Pinecone [Pinecone](https://www.pinecone.io/) is a managed vector database service designed for machine learning applications, offering both serverless and pod-based deployment options. ### Key Features - **Managed Service**: Fully managed with no infrastructure to maintain - **Serverless Option**: Pay-per-use pricing with automatic scaling - **High Performance**: Optimized for low-latency vector search - **Metadata Filtering**: Advanced filtering on vector metadata - **Namespaces**: Organize vectors into separate namespaces within an index ### Setup & Installation **Pinecone Cloud:** - Sign up at [pinecone.io](https://www.pinecone.io/) - Create a new index with the desired dimensions - Get your API key and index host URL from the console **Local Development (Pinecone Local):** ```bash docker run -d \ --name pinecone-local \ -p 5081:5081 \ ghcr.io/pinecone-io/pinecone-index:latest ``` ### Configuration Options ```go vectorConfig := &vectorstore.Config{ Enabled: true, Type: vectorstore.VectorStoreTypePinecone, Config: vectorstore.PineconeConfig{ APIKey: "your-pinecone-api-key", IndexHost: "your-index-host.svc.environment.pinecone.io", }, } store, err := vectorstore.NewVectorStore(context.Background(), vectorConfig, logger) ``` **Cloud Setup:** ```json { "vector_store": { "enabled": true, "type": "pinecone", "config": { "api_key": "your-pinecone-api-key", "index_host": "your-index-host.svc.environment.pinecone.io" } } } ``` **Local Development:** ```json { "vector_store": { "enabled": true, "type": "pinecone", "config": { "api_key": "pclocal", "index_host": "localhost:5081" } } } ``` For local development with Pinecone Local, any API key value works (e.g., "pclocal"). The index host should point to localhost:5081 by default. Pinecone requires all IDs to be unique strings. Namespaces are created automatically when you first upsert vectors. For the VectorStore interface API and usage examples, see [Vector Store Architecture](/architecture/framework/vector-store). For semantic caching setup, see [Semantic Caching](/features/semantic-caching).