Pinecone

Use Pinecone as a fully managed vector database in Mem0 with serverless deployment and namespace-based multi-tenancy.

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.

New: Pinecone integration now supports custom namespaces! Use the namespace parameter to logically separate data within the same index. This is especially useful for multi-tenant or multi-user 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

Python

import os
from mem0 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
            "namespace": "my-namespace", # Optional: specify a namespace for multi-tenancy
            "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 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"})

import { Memory } from 'mem0ai/oss';

// Set OPENAI_API_KEY and PINECONE_API_KEY in your environment
const config = {
  vectorStore: {
    provider: 'pinecone',
    config: {
      collectionName: 'testing',
      embeddingModelDims: 1536, // Matches OpenAI's text-embedding-3-small
      namespace: 'my-namespace', // Optional: specify a namespace for multi-tenancy
      serverlessConfig: {
        cloud: 'aws', // 'aws' | 'gcp' | 'azure'
        region: 'us-east-1',
      },
      metric: 'cosine',
    },
  },
};

const memory = new Memory(config);
const messages = [
    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
    {"role": "assistant", "content": "How about 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."}
]
await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });

Config

Here are the parameters available for configuring Pinecone:

Python

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
namespace	Namespace for the collection, useful for multi-tenancy.	None

TypeScript

Parameter	Description	Default Value
collectionName	Name of the index/collection	Required
embeddingModelDims	Dimensions of the embedding model (must match your chosen embedding model)	1536
client	Existing Pinecone client instance	undefined
apiKey	API key for Pinecone	Environment variable: PINECONE_API_KEY
serverlessConfig	Configuration for serverless deployment (cloud, region)	undefined
podConfig	Configuration for pod-based deployment (environment, podType, pods, replicas, shards)	undefined
metric	Distance metric for vector similarity (cosine, dotproduct, euclidean)	"cosine"
batchSize	Batch size for insert operations	100
namespace	Namespace for the collection, useful for multi-tenancy.	undefined
extraParams	Extra parameters spread into the Pinecone createIndex call	{}

Important: You must choose either serverless_config or pod_config for your deployment, but not both.

Serverless Config Example

Python

config = {
    "vector_store": {
        "provider": "pinecone",
        "config": {
            "collection_name": "memory_index",
            "embedding_model_dims": 1536,  # For OpenAI's text-embedding-3-small
            "namespace": "my-namespace",  # Optional: custom namespace
            "serverless_config": {
                "cloud": "aws",  # or "gcp" or "azure"
                "region": "us-east-1"  # Choose appropriate region
            }
        }
    }
}

const config = {
  vectorStore: {
    provider: 'pinecone',
    config: {
      collectionName: 'memory_index',
      embeddingModelDims: 1536, // For OpenAI's text-embedding-3-small
      namespace: 'my-namespace', // Optional: custom namespace
      serverlessConfig: {
        cloud: 'aws', // 'gcp' | 'azure'
        region: 'us-east-1', // Choose appropriate region
      },
    },
  },
};

Pod Config Example

Python

config = {
    "vector_store": {
        "provider": "pinecone",
        "config": {
            "collection_name": "memory_index",
            "embedding_model_dims": 1536,  # For OpenAI's text-embedding-ada-002
            "namespace": "my-namespace",  # Optional: custom namespace
            "pod_config": {
                "environment": "gcp-starter",
                "replicas": 1,
                "pod_type": "starter"
            }
        }
    }
}

const config = {
  vectorStore: {
    provider: 'pinecone',
    config: {
      collectionName: 'memory_index',
      embeddingModelDims: 1536, // For OpenAI's text-embedding-ada-002
      namespace: 'my-namespace', // Optional: custom namespace
      podConfig: {
        environment: 'gcp-starter',
        replicas: 1,
        podType: 'starter',
      },
    },
  },
};