Zaid
110 lines
4.0 KiB
Plaintext
110 lines
4.0 KiB
Plaintext
---
|
|
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
|
|
}
|
|
}
|
|
}
|
|
```
|
|
|
|
### Configuration Options
|
|
|
|
| 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. |
|
|
|
|
<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
|
|
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
|