Azure AI Search

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Usage

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": "<your-azure-ai-search-service-name>",
            "api_key": "<your-api-key>",
            "collection_name": "mem0", 
            "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"})

​

Using binary compression for large vector collections

config = {
    "vector_store": {
        "provider": "azure_ai_search",
        "config": {
            "service_name": "<your-azure-ai-search-service-name>",
            "api_key": "<your-api-key>",
            "collection_name": "mem0", 
            "embedding_model_dims": 1536,
            "compression_type": "binary",
            "use_float16": True  # Use half precision for storage efficiency
        }
    }
}

​

Using hybrid search

config = {
    "vector_store": {
        "provider": "azure_ai_search",
        "config": {
            "service_name": "<your-azure-ai-search-service-name>",
            "api_key": "<your-api-key>",
            "collection_name": "mem0", 
            "embedding_model_dims": 1536,
            "hybrid_search": True,
            "vector_filter_mode": "postFilter"
        }
    }
}

​

Using Azure Identity for Authentication

1. Environment Credential: Azure client ID, secret, tenant ID, or certificate in environment variables for service principal authentication.
2. Workload Identity Credential: Utilizes Azure Workload Identity (relevant for Kubernetes and Azure workloads).
3. Managed Identity Credential: Authenticates as a Managed Identity (for apps/services hosted in Azure with Managed Identity enabled), this is the most secure production credential.
4. Shared Token Cache Credential / Visual Studio Credential (Windows only): Uses cached credentials from Visual Studio sign-ins (and sometimes VS Code if SSO is enabled).
5. Azure CLI Credential: Uses the currently logged-in user from the Azure CLI (az login), this is the most common development credential.
6. Azure PowerShell Credential: Uses the identity from Azure PowerShell (Connect-AzAccount).
7. Azure Developer CLI Credential: Uses the session from Azure Developer CLI (azd auth login).

1. In the Azure Portal, navigate to your Azure AI Search service.
2. In the left menu, select Settings > Keys.
3. Change the authentication setting to Role-based access control, or Both if you need API key compatibility. The default is “Key-based authentication”—you must switch it to use Azure roles.
4. Go to Access Control (IAM):
   • In the Azure Portal, select your Search service.
   • Click Access Control (IAM) on the left.
5. Add a Role Assignment:
   • Click Add > Add role assignment.
6. Choose Role:
   • Mem0 requires the Search Index Data Contributor and Search Service Contributor role.
7. Choose Member
   • To assign to a User, Group, Service Principle or Managed Identity:
     • For production it is recommended to use a service principal or managed identity.
       • For a service principal: select User, group, or service principal and search for the service principal.
       • For a managed identity: select Managed identity and choose the managed identity.
     • For development, you can assign the role to a user account.
       • For development: select *User, group, or service principal and pick a Azure Entra ID account (the same used with az login).
8. Complete the Assignment:
   • Click Review + Assign.

config = {
    "vector_store": {
        "provider": "azure_ai_search",
        "config": {
            "service_name": "<your-azure-ai-search-service-name>",
            "collection_name": "mem0", 
            "embedding_model_dims": 1536,
            "compression_type": "binary",
            "use_float16": True  # Use half precision for storage efficiency
        }
    }
}

​

Environment Variables to set to use Azure Identity Credential:

• For an Environment Credential, you will need to setup a Service Principal and set the following environment variables:
  • AZURE_TENANT_ID: Your Azure Active Directory tenant ID.
  • AZURE_CLIENT_ID: The client ID of your service principal or managed identity.
  • AZURE_CLIENT_SECRET: The client secret of your service principal.
• For a User-Assigned Managed Identity, you will need to set the following environment variable:
  • AZURE_CLIENT_ID: The client ID of the user-assigned managed identity.
• For a System-Assigned Managed Identity, no additional environment variables are needed.

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Developer logins to use for a Azure Identity Credential:

• For an Azure CLI Credential, you need to have the Azure CLI installed and logged in with az login.
• For an Azure PowerShell Credential, you need to have the Azure PowerShell module installed and logged in with Connect-AzAccount.
• For an Azure Developer CLI Credential, you need to have the Azure Developer CLI installed and logged in with azd auth login.

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Configuration Parameters

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Notes on Configuration Options

• compression_type:
  • none: No compression, uses full vector precision
  • scalar: Scalar quantization with reasonable balance of speed and accuracy
  • binary: Binary quantization for maximum compression with some accuracy trade-off
• vector_filter_mode:
  • preFilter: Applies filters before vector search (faster)
  • postFilter: Applies filters after vector search (may provide better relevance)
• use_float16: Using half precision (float16) reduces storage requirements but may slightly impact accuracy. Useful for very large vector collections.
• Filterable Fields: The implementation automatically extracts user_id, run_id, and agent_id fields from payloads for filtering.