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

<CodeGroup> ```python 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"})


```typescript TypeScript
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" } });

</CodeGroup>

Config

Here are the parameters available for configuring Pinecone:

<Tabs> <Tab title="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` | </Tab> <Tab title="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 | `{}` | </Tab> </Tabs>

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

Serverless Config Example

<CodeGroup> ```python 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 } } } } ```

typescript

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
      },
    },
  },
};

</CodeGroup>

Pod Config Example

<CodeGroup> ```python 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" } } } } ```

typescript

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',
      },
    },
  },
};

</CodeGroup>