## Documentation Index

Fetch the complete documentation index at: [/llms.txt](https://docs.mem0.ai/llms.txt)

Use this file to discover all available pages before exploring further.

[Pinecone](https://www.pinecone.io/) 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

TypeScript

```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
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:

| 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`                            |

| 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

TypeScript

```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
      },
    },
  },
};
```

#### Pod Config Example

Python

TypeScript

```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',
      },
    },
  },
};
```