---
title: "AzureOpenAIGenerator"
id: azureopenaigenerator
slug: "/azureopenaigenerator"
description: "This component enables text generation using OpenAI's large language models (LLMs) through Azure services."
---

# AzureOpenAIGenerator

This component enables text generation using OpenAI's large language models (LLMs) through Azure services.

<div className="key-value-table">

|  |  |
| --- | --- |
| **Most common position in a pipeline** | After a [`PromptBuilder`](../builders/promptbuilder.mdx) |
| **Mandatory init variables** | `api_key`: The Azure OpenAI API key. Can be set with `AZURE_OPENAI_API_KEY` env var.  <br /> <br />`azure_ad_token`: Microsoft Entra ID token. Can be set with `AZURE_OPENAI_AD_TOKEN` env var. |
| **Mandatory run variables** | `prompt`: A string containing the prompt for the LLM |
| **Output variables** | `replies`: A list of strings with all the replies generated by the LLM  <br /> <br />`meta`: A list of dictionaries with the metadata associated with each reply, such as token count, finish reason, and so on |
| **API reference** | [Generators](/reference/generators-api) |
| **GitHub link** | https://github.com/deepset-ai/haystack/blob/main/haystack/components/generators/azure.py |
| **Package name** | `haystack-ai` |

</div>

## Overview

`AzureOpenAIGenerator` supports OpenAI models deployed through Azure services. To see the list of supported models, head over to Azure [documentation](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models?source=recommendations). The default model used with the component is `gpt-4o-mini`.

To work with Azure components, you will need an Azure OpenAI API key, as well as an Azure OpenAI Endpoint. You can learn more about them in Azure [documentation](https://learn.microsoft.com/en-us/azure/ai-services/openai/reference).

The component uses `AZURE_OPENAI_API_KEY` and `AZURE_OPENAI_AD_TOKEN` environment variables by default. Otherwise, you can pass `api_key` and `azure_ad_token` at initialization:

```python
client = AzureOpenAIGenerator(
    azure_endpoint="<Your Azure endpoint e.g. `https://your-company.azure.openai.com/>",
    api_key=Secret.from_token("<your-api-key>"),
    azure_deployment="<a model name>",
)
```

:::info
We recommend using environment variables instead of initialization parameters.
:::

Then, the component needs a prompt to operate, but you can pass any text generation parameters valid for the `openai.ChatCompletion.create` method directly to this component using the `generation_kwargs` parameter, both at initialization and to `run()` method. For more details on the supported parameters, refer to the [Azure documentation](https://learn.microsoft.com/en-us/azure/ai-services/openai/reference).

You can also specify a model for this component through the `azure_deployment` init parameter.

### Streaming

`AzureOpenAIGenerator` supports streaming the tokens from the LLM directly in output. To do so, pass a function to the `streaming_callback` init parameter. Note that streaming the tokens is only compatible with generating a single response, so `n` must be set to 1 for streaming to work.

:::info
This component is designed for text generation, not for chat. If you want to use LLMs for chat, use [`AzureOpenAIChatGenerator`](azureopenaichatgenerator.mdx) instead.
:::

## Usage

### On its own

Basic usage:

```python
from haystack.components.generators import AzureOpenAIGenerator
client = AzureOpenAIGenerator()
response = client.run("What's Natural Language Processing? Be brief.")
print(response)

>> {'replies': ['Natural Language Processing (NLP) is a branch of artificial intelligence that focuses on
>> the interaction between computers and human language. It involves enabling computers to understand, interpret,
>> and respond to natural human language in a way that is both meaningful and useful.'], 'meta': [{'model':
>> 'gpt-4o-mini', 'index': 0, 'finish_reason': 'stop', 'usage': {'prompt_tokens': 16,
>> 'completion_tokens': 49, 'total_tokens': 65}}]}

```

With streaming:

```python
from haystack.components.generators import AzureOpenAIGenerator

client = AzureOpenAIGenerator(streaming_callback=lambda chunk: print(chunk.content, end="", flush=True))
response = client.run("What's Natural Language Processing? Be brief.")
print(response)

>>> Natural Language Processing (NLP) is a branch of artificial
	intelligence that focuses on the interaction between computers and human
  language. It involves enabling computers to understand, interpret,and respond
  to natural human language in a way that is both meaningful and useful.
>>> {'replies': ['Natural Language Processing (NLP) is a branch of artificial
	intelligence that focuses on the interaction between computers and human
  language. It involves enabling computers to understand, interpret,and respond
  to natural human language in a way that is both meaningful and useful.'],
  'meta': [{'model': 'gpt-4o-mini', 'index': 0, 'finish_reason':
  'stop', 'usage': {'prompt_tokens': 16, 'completion_tokens': 49,
  'total_tokens': 65}}]}

```

### In a Pipeline

```python
from haystack import Pipeline
from haystack.components.retrievers.in_memory import InMemoryBM25Retriever
from haystack.components.builders.prompt_builder import PromptBuilder
from haystack.components.generators import AzureOpenAIGenerator
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack import Document

docstore = InMemoryDocumentStore()
docstore.write_documents(
    [
        Document(content="Rome is the capital of Italy"),
        Document(content="Paris is the capital of France"),
    ],
)

query = "What is the capital of France?"

template = """
Given the following information, answer the question.

Context:
{% for document in documents %}
    {{ document.content }}
{% endfor %}

Question: {{ query }}?
"""
pipe = Pipeline()

pipe.add_component("retriever", InMemoryBM25Retriever(document_store=docstore))
pipe.add_component("prompt_builder", PromptBuilder(template=template))
pipe.add_component("llm", AzureOpenAIGenerator())
pipe.connect("retriever", "prompt_builder.documents")
pipe.connect("prompt_builder", "llm")

res = pipe.run({"prompt_builder": {"query": query}, "retriever": {"query": query}})

print(res)
```
