Pinecone is a fully managed vector database designed for machine learning applications, offering high performance vector search with low latency at scale. It’s particularly well-suited for semantic search, recommendation systems, and other AI-powered applications.
Note: Before configuring Pinecone, you need to select an embedding model (e.g., OpenAI, Cohere, or custom models) and ensure the embedding_model_dims in your config matches your chosen model’s dimensions. For example, OpenAI’s text-embedding-3-small uses 1536 dimensions.
Usage
import os
from zentry import Memory
os.environ["OPENAI_API_KEY"] = "sk-xx"
os.environ["PINECONE_API_KEY"] = "your-api-key"
# Example using serverless configuration
config = {
"vector_store": {
"provider": "pinecone",
"config": {
"collection_name": "testing",
"embedding_model_dims": 1536, # Matches OpenAI's text-embedding-3-small
"serverless_config": {
"cloud": "aws", # Choose between 'aws' or 'gcp' or 'azure'
"region": "us-east-1"
},
"metric": "cosine"
}
}
}
m = Memory.from_config(config)
messages = [
{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
{"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
{"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
]
m.add(messages, user_id="alice", metadata={"category": "movies"})
Config
Here are the parameters available for configuring Pinecone:
| Parameter | Description | Default Value |
|---|
collection_name | Name of the index/collection | Required |
embedding_model_dims | Dimensions of the embedding model (must match your chosen embedding model) | Required |
client | Existing Pinecone client instance | None |
api_key | API key for Pinecone | Environment variable: PINECONE_API_KEY |
environment | Pinecone environment | None |
serverless_config | Configuration for serverless deployment (AWS or GCP or Azure) | None |
pod_config | Configuration for pod-based deployment | None |
hybrid_search | Whether to enable hybrid search | False |
metric | Distance metric for vector similarity | "cosine" |
batch_size | Batch size for operations | 100 |
Important: You must choose either serverless_config or pod_config for your deployment, but not both.
Serverless Config Example
config = {
"vector_store": {
"provider": "pinecone",
"config": {
"collection_name": "memory_index",
"embedding_model_dims": 1536, # For OpenAI's text-embedding-3-small
"serverless_config": {
"cloud": "aws", # or "gcp" or "azure"
"region": "us-east-1" # Choose appropriate region
}
}
}
}
Pod Config Example
config = {
"vector_store": {
"provider": "pinecone",
"config": {
"collection_name": "memory_index",
"embedding_model_dims": 1536, # For OpenAI's text-embedding-ada-002
"pod_config": {
"environment": "gcp-starter",
"replicas": 1,
"pod_type": "starter"
}
}
}
}