> ## 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.

# OpenSearch

> 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 an additional client library. Install the one for your SDK:

<CodeGroup>
  ```bash Python theme={null}
  pip install opensearch-py
  ```

  ```bash TypeScript theme={null}
  npm install @opensearch-project/opensearch
  ```
</CodeGroup>

### 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

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

  ```typescript TypeScript theme={null}
  import { Memory } from 'mem0ai/oss';

  // Basic self-hosted OpenSearch. For AWS OpenSearch Serverless, build an
  // @opensearch-project/opensearch Client with AwsSigv4Signer and pass it as
  // `client` instead of host/port/user/password.
  const config = {
    vectorStore: {
      provider: 'opensearch',
      config: {
        collectionName: 'mem0',
        embeddingModelDims: 1024,
        host: 'localhost',
        port: 9200,
        user: 'admin',
        password: 'admin',
        useSSL: false,
        verifyCerts: false,
      },
    },
  };

  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>

### Configuration Options

<Tabs>
  <Tab title="Python">
    | Parameter | Type | Default | Description |
    | - | - | - | - |
    | `collection_name` | string | required | Name of the OpenSearch index |
    | `host` | string | required | OpenSearch endpoint URL |
    | `port` | int | 9200 | Port number |
    | `http_auth` | object | None | Authentication credentials (e.g., AWSV4SignerAuth) |
    | `embedding_model_dims` | int | 1536 | Dimension of embedding vectors |
    | `use_ssl` | bool | False | Enable SSL/TLS connection |
    | `verify_certs` | bool | False | Verify SSL certificates |
    | `auto_refresh` | bool | False | Automatically refresh index after insert. OpenSearch refreshes every \~1 second by default, so this is rarely needed. |
  </Tab>

  <Tab title="TypeScript">
    | Parameter | Type | Default | Description |
    | - | - | - | - |
    | `collectionName` | string | required | Name of the OpenSearch index |
    | `embeddingModelDims` | number | 1536 | Dimension of embedding vectors |
    | `host` | string | `localhost` | OpenSearch endpoint host |
    | `port` | number | 9200 | Port number |
    | `httpAuth` | object | None | Authentication credentials, an object or `[user, password]` tuple |
    | `user` | string | None | Username for basic auth (used together with `password`) |
    | `password` | string | None | Password for basic auth (used together with `user`) |
    | `useSSL` | boolean | false | Enable SSL/TLS connection |
    | `verifyCerts` | boolean | false | Verify SSL certificates |
    | `autoRefresh` | boolean | false | Refresh the index after each write so new memories are searchable immediately. Not supported on AWS Serverless. |
    | `client` | object | None | Preconfigured OpenSearch client, e.g. one built with AwsSigv4Signer for AWS auth |
  </Tab>
</Tabs>

<Note>
  The defaults above match a local OpenSearch instance. The AWS OpenSearch Serverless
  example earlier on this page intentionally overrides them with `port=443`, `use_ssl=True`,
  and `verify_certs=True`, which are required when connecting to a Serverless collection.
</Note>

<Note>
  For **AWS OpenSearch Serverless**, keep `auto_refresh=False` (the default).
  The `indices.refresh()` API is not supported on Serverless collections.
</Note>

### Add Memories

```python theme={null}
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 theme={null}
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


This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.