OpenSearch

Use OpenSearch as a vector database in Mem0 with k-NN search support via AWS OpenSearch Service serverless collections.

OpenSearch 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:

Python

pip install opensearch-py

npm install @opensearch-project/opensearch

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
• 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
        }
    }
}

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" } });

Configuration Options

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.

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

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.

For AWS OpenSearch Serverless, keep auto_refresh=False (the default). The indices.refresh() API is not supported on Serverless collections.

Add Memories

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

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