Pinecone
Pinecone 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
- Create a new index with the desired dimensions
- Get your API key and index host URL from the console
Local Development (Pinecone Local):
docker run -d \
--name pinecone-local \
-p 5081:5081 \
ghcr.io/pinecone-io/pinecone-index:latest
Configuration Options
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:{
"vector_store": {
"enabled": true,
"type": "pinecone",
"config": {
"api_key": "your-pinecone-api-key",
"index_host": "your-index-host.svc.environment.pinecone.io"
}
}
}
{
"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.
