> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mem0.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Pinecone

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

[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

<CodeGroup>
  ```python Python theme={null}
  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 theme={null}
  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 theme={null}
  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 TypeScript theme={null}
  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 theme={null}
  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 TypeScript theme={null}
  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>