---
title: "OpenSearch"
description: "Use OpenSearch as a vector database in Mem0 with k-NN search support via AWS OpenSearch Service serverless collections."
---
[OpenSearch](https://opensearch.org/) is an enterprise-grade search and observability suite that brings order to unstructured data at scale. OpenSearch supports k-NN (k-Nearest Neighbors) and allows you to store and retrieve high-dimensional vector embeddings efficiently.

### Installation

OpenSearch support requires additional dependencies. Install them with:

```bash
pip install opensearch-py
```

### Prerequisites

Before using OpenSearch with Mem0, you need to set up a collection in AWS OpenSearch Service.

#### AWS OpenSearch Service
You can create a collection through the AWS Console:
- Navigate to [OpenSearch Service Console](https://console.aws.amazon.com/aos/home)
- Click "Create collection"
- Select "Serverless collection" and then enable "Vector search" capabilities
- Once created, note the endpoint URL (host) for your configuration


### Usage

```python
import os
from mem0 import Memory
import boto3
from opensearchpy import OpenSearch, RequestsHttpConnection, AWSV4SignerAuth

# For AWS OpenSearch Service with IAM authentication
region = 'us-west-2'
service = 'aoss'
credentials = boto3.Session().get_credentials()
auth = AWSV4SignerAuth(credentials, region, service)

config = {
    "vector_store": {
        "provider": "opensearch",
        "config": {
            "collection_name": "mem0",
            "host": "your-domain.us-west-2.aoss.amazonaws.com",
            "port": 443,
            "http_auth": auth,
            "embedding_model_dims": 1024,
            "connection_class": RequestsHttpConnection,
            "pool_maxsize": 20,
            "use_ssl": True,
            "verify_certs": True
        }
    }
}
```

### Add Memories

```python
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"})
```

### Search Memories

```python
results = m.search("What kind of movies does Alice like?", filters={"user_id": "alice"})
```

### Features

- Fast and Efficient Vector Search
- Can be deployed on-premises, in containers, or on cloud platforms like AWS OpenSearch Service
- Multiple authentication and security methods (Basic Authentication, API Keys, LDAP, SAML, and OpenID Connect)
- Automatic index creation with optimized mappings for vector search
- Memory optimization through disk-based vector search and quantization
- Real-time analytics and observability
