66 lines
2.9 KiB
Plaintext
66 lines
2.9 KiB
Plaintext
[OpenSearch](https://opensearch.org/) is an open-source, 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>=2.8.0
|
||
```
|
||
|
||
### Usage
|
||
|
||
```python
|
||
import os
|
||
from mem0 import Memory
|
||
|
||
os.environ["OPENAI_API_KEY"] = "sk-xx"
|
||
|
||
config = {
|
||
"vector_store": {
|
||
"provider": "opensearch",
|
||
"config": {
|
||
"collection_name": "mem0",
|
||
"host": "localhost",
|
||
"port": 9200,
|
||
"embedding_model_dims": 1536
|
||
}
|
||
}
|
||
}
|
||
|
||
m = Memory.from_config(config)
|
||
messages = [
|
||
{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
|
||
{"role": "assistant", "content": "How about a 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"})
|
||
```
|
||
|
||
### Config
|
||
|
||
Let's see the available parameters for the `opensearch` config:
|
||
|
||
| Parameter | Description | Default Value |
|
||
| ---------------------- | -------------------------------------------------- | ------------- |
|
||
| `collection_name` | The name of the index to store the vectors | `mem0` |
|
||
| `embedding_model_dims` | Dimensions of the embedding model | `1536` |
|
||
| `host` | The host where the OpenSearch server is running | `localhost` |
|
||
| `port` | The port where the OpenSearch server is running | `9200` |
|
||
| `api_key` | API key for authentication | `None` |
|
||
| `user` | Username for basic authentication | `None` |
|
||
| `password` | Password for basic authentication | `None` |
|
||
| `verify_certs` | Whether to verify SSL certificates | `False` |
|
||
| `auto_create_index` | Whether to automatically create the index | `True` |
|
||
| `use_ssl` | Whether to use SSL for connection | `False` |
|
||
|
||
### 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
|