## Amazon S3 Vectors

Amazon S3 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:

Python

```
pip install boto3
```

TypeScript

```
npm install @aws-sdk/client-s3vectors
```

### 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).

Python

```
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

```
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" } });
```

### 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:

```
{
  "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.