> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mem0.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Azure OpenAI

> Configure Azure OpenAI as an embedding provider in Mem0 with API key, deployment, and endpoint settings.

To use Azure OpenAI embedding models, set the `EMBEDDING_AZURE_OPENAI_API_KEY`, `EMBEDDING_AZURE_DEPLOYMENT`, `EMBEDDING_AZURE_ENDPOINT` and `EMBEDDING_AZURE_API_VERSION` environment variables. You can obtain the Azure OpenAI API key from the Azure Portal.

### Usage

<CodeGroup>
  ```python Python theme={null}
  import os
  from mem0 import Memory

  os.environ["EMBEDDING_AZURE_OPENAI_API_KEY"] = "your-api-key"
  os.environ["EMBEDDING_AZURE_DEPLOYMENT"] = "your-deployment-name"
  os.environ["EMBEDDING_AZURE_ENDPOINT"] = "your-api-base-url"
  os.environ["EMBEDDING_AZURE_API_VERSION"] = "version-to-use"

  os.environ["OPENAI_API_KEY"] = "your_api_key" # For LLM


  config = {
      "embedder": {
          "provider": "azure_openai",
          "config": {
              "model": "text-embedding-3-large",
              "azure_kwargs": {
                    "api_version": "",
                    "azure_deployment": "",
                    "azure_endpoint": "",
                    "api_key": "",
                    "default_headers": {
                      "CustomHeader": "your-custom-header",
                    }
                }
          }
      }
  }

  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="john")
  ```

  ```typescript TypeScript theme={null}
  import { Memory } from 'mem0ai/oss';

  const config = {
      embedder: {
          provider: "azure_openai",
          config: {
              model: "text-embedding-3-large",
              modelProperties: {
                  endpoint: "your-api-base-url",
                  deployment: "your-deployment-name",
                  apiVersion: "version-to-use",
              }
          }
      }
  }

  const memory = new Memory(config);

  const 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."}
  ]

  await memory.add(messages, { userId: "john" });
  ```
</CodeGroup>

As an alternative to using an API key, the Azure Identity credential chain can be used to authenticate with [Azure OpenAI role-based security](https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/role-based-access-control).

<Note> If an API key is provided, it will be used for authentication over an Azure Identity </Note>

Below is a sample configuration for using Mem0 with Azure OpenAI and Azure Identity:

```python theme={null}
import os
from mem0 import Memory
# You can set the values directly in the config dictionary or use environment variables

os.environ["LLM_AZURE_DEPLOYMENT"] = "your-deployment-name"
os.environ["LLM_AZURE_ENDPOINT"] = "your-api-base-url"
os.environ["LLM_AZURE_API_VERSION"] = "version-to-use"

config = {
    "llm": {
        "provider": "azure_openai_structured",
        "config": {
            "model": "your-deployment-name",
            "temperature": 0.1,
            "max_tokens": 2000,
            "azure_kwargs": {
                  "azure_deployment": "<your-deployment-name>",
                  "api_version": "<version-to-use>",
                  "azure_endpoint": "<your-api-base-url>",
                  "default_headers": {
                    "CustomHeader": "your-custom-header",
                  }
              }
        }
    }
}
```

Refer to [Azure Identity troubleshooting tips](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/identity/azure-identity/TROUBLESHOOTING.md#troubleshoot-environmentcredential-authentication-issues) for setting up an Azure Identity credential.

### Config

Here are the parameters available for configuring Azure OpenAI embedder:

<Tabs>
  <Tab title="Python">
    | Parameter        | Description                            | Default Value            |
    | ---------------- | -------------------------------------- | ------------------------ |
    | `model`          | The name of the embedding model to use | `text-embedding-3-small` |
    | `embedding_dims` | Dimensions of the embedding model      | `1536`                   |
    | `azure_kwargs`   | The Azure OpenAI configs               | `config_keys`            |
  </Tab>

  <Tab title="TypeScript">
    | Parameter         | Description                                   | Default Value                |
    | ----------------- | --------------------------------------------- | ---------------------------- |
    | `model`           | The name of the embedding model to use        | `text-embedding-3-small`     |
    | `embeddingDims`   | Dimensions of the embedding model             | `1536`                       |
    | `apiKey`          | Azure OpenAI API key                          | `None`                       |
    | `modelProperties` | Object containing endpoint and other settings | `{ endpoint: "",...rest   }` |
  </Tab>
</Tabs>