[Azure AI Search](https://learn.microsoft.com/en-us/azure/search/search-what-is-azure-search/) (formerly known as "Azure Cognitive Search") provides secure information retrieval at scale over user-owned content in traditional and generative AI search applications.

### Usage

```python
import os
from mem0 import Memory

os.environ["OPENAI_API_KEY"] = "sk-xx"   #this key is used for embedding purpose

config = {
    "vector_store": {
        "provider": "azure_ai_search",
        "config": {
            "service_name": "ai-search-test",
            "api_key": "*****",
            "collection_name": "mem0", 
            "embedding_model_dims": 1536 ,
            "use_compression": False
        }
    }
}

m = Memory.from_config(config)
m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
```

### Config

Let's see the available parameters for the `qdrant` config:
service_name (str): Azure Cognitive Search service name.  
| Parameter | Description | Default Value |
| --- | --- | --- |
| `service_name` | Azure AI Search service name | `None` |
| `api_key` | API key of the Azure AI Search service | `None` |
| `collection_name` | The name of the collection/index to store the vectors, it will be created automatically if not exist | `mem0` |
| `embedding_model_dims` | Dimensions of the embedding model | `1536` |
| `use_compression` | Use scalar quantization vector compression | False |