> ## 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.

# Amazon S3 Vectors

> Use Amazon S3 Vectors as a cost-optimized vector storage service in Mem0 with AWS credential authentication.

[Amazon S3 Vectors](https://aws.amazon.com/s3/features/vectors/) is a purpose-built, cost-optimized vector storage and query service for semantic search and AI applications. It provides S3-level elasticity and durability with sub-second query performance.

### Installation

S3 Vectors support requires additional dependencies. Install them with:

<CodeGroup>
  ```bash Python theme={null}
  pip install boto3
  ```

  ```bash TypeScript theme={null}
  npm install @aws-sdk/client-s3vectors
  ```
</CodeGroup>

### Usage

To use Amazon S3 Vectors with Mem0, you need to have an AWS account and the necessary IAM permissions (`s3vectors:*`). Ensure your environment is configured with AWS credentials (e.g., via `~/.aws/credentials` or environment variables).

<CodeGroup>
  ```python Python theme={null}
  import os
  from mem0 import Memory

  # Ensure your AWS credentials are configured in your environment
  # e.g., by setting AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, and AWS_DEFAULT_REGION

  config = {
      "vector_store": {
          "provider": "s3_vectors",
          "config": {
              "vector_bucket_name": "my-mem0-vector-bucket",
              "collection_name": "my-memories-index",
              "embedding_model_dims": 1536,
              "distance_metric": "cosine",
              "region_name": "us-east-1"
          }
      }
  }

  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 movie? 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';

  // Ensure your AWS credentials are configured in your environment
  // e.g., by setting AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, and AWS_DEFAULT_REGION

  const config = {
    vectorStore: {
      provider: 's3_vectors',
      config: {
        vectorBucketName: 'my-mem0-vector-bucket',
        collectionName: 'my-memories-index',
        embeddingModelDims: 1536,
        distanceMetric: 'cosine',
        region: 'us-east-1',
      },
    },
  };

  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 a thriller movie? 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 Amazon S3 Vectors:

| Parameter              | Description                                                                      | Default Value                         |
| ---------------------- | -------------------------------------------------------------------------------- | ------------------------------------- |
| `vector_bucket_name`   | The name of the S3 Vector bucket to use. It will be created if it doesn't exist. | Required                              |
| `collection_name`      | The name of the vector index within the bucket.                                  | `mem0`                                |
| `embedding_model_dims` | Dimensions of the embedding model. Must match your embedder.                     | `1536`                                |
| `distance_metric`      | Distance metric for similarity search. Options: `cosine`, `euclidean`.           | `cosine`                              |
| `region_name`          | The AWS region where the bucket and index reside.                                | `None` (uses default from AWS config) |

### IAM Permissions

Your AWS identity (user or role) needs permissions to perform actions on S3 Vectors. A minimal policy would look like this:

```json theme={null}
{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": "s3vectors:*",
      "Resource": "*"
    }
  ]
}
```

For production, it is recommended to scope down the resource ARN to your specific buckets and indexes.