OpenSearch - Mem0 — ContextQMD

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 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
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