
Most common position in a pipeline	After a ChatPromptBuilder
Mandatory init variables	"api_key": The Azure OpenAI API key. Can be set with `AZURE_OPENAI_API_KEY` env var. "azure_ad_token": Microsoft Entra ID token. Can be set with `AZURE_OPENAI_AD_TOKEN` env var.
Mandatory run variables	“messages”: A list of `ChatMessage` objects representing the chat
Output variables	“replies”: A list of alternative replies of the LLM to the input chat
API reference	Generators
GitHub link	https://github.com/deepset-ai/haystack/blob/main/haystack/components/generators/chat/azure.py

Overview

AzureOpenAIChatGenerator supports OpenAI models deployed through Azure services. To see the list of supported models, head over to Azure documentation. 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.

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:

client = AzureOpenAIChatGenerator(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>")

📘
We recommend using environment variables instead of initialization parameters.

Then, the component needs a list of ChatMessage objects to operate. ChatMessage is a data class that contains a message, a role (who generated the message, such as user, assistant, system, function), and optional metadata. See the usage section for an example.

You can pass any chat completion parameters that are valid for the openai.ChatCompletion.create method directly to AzureOpenAIChatGenerator using the generation_kwargs parameter, both at initialization and to run() method. For more details on the supported parameters, refer to the Azure documentation.

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

Streaming

You can stream output as it’s generated. Pass a callback to streaming_callback. Use the built-in print_streaming_chunk to print text tokens and tool events (tool calls and tool results).

from haystack.components.generators.utils import print_streaming_chunk

# Configure any `Generator` or `ChatGenerator` with a streaming callback
component = SomeGeneratorOrChatGenerator(streaming_callback=print_streaming_chunk)

# If this is a `ChatGenerator`, pass a list of messages:
# from haystack.dataclasses import ChatMessage
# component.run([ChatMessage.from_user("Your question here")])

# If this is a (non-chat) `Generator`, pass a prompt:
# component.run({"prompt": "Your prompt here"})

📘
Streaming works only with a single response. If a provider supports multiple candidates, set n=1.

See our Streaming Support docs to learn more how StreamingChunk works and how to write a custom callback.

Give preference to print_streaming_chunk by default. Write a custom callback only if you need a specific transport (for example, SSE/WebSocket) or custom UI formatting.

Usage

On its own

Basic usage:

from haystack.dataclasses import ChatMessage
from haystack.components.generators.chat import AzureOpenAIChatGenerator
client = AzureOpenAIChatGenerator()
response = client.run(
	  [ChatMessage.from_user("What's Natural Language Processing? Be brief.")]
)
print(response)

With streaming:

from haystack.dataclasses import ChatMessage
from haystack.components.generators.chat import AzureOpenAIChatGenerator
client = AzureOpenAIChatGenerator(streaming_callback=lambda chunk: print(chunk.content, end="", flush=True))
response = client.run(
	  [ChatMessage.from_user("What's Natural Language Processing? Be brief.")]
)
print(response)

In a pipeline

from haystack.components.builders import ChatPromptBuilder
from haystack.components.generators.chat import AzureOpenAIChatGenerator
from haystack.dataclasses import ChatMessage
from haystack import Pipeline

# no parameter init, we don't use any runtime template variables
prompt_builder = ChatPromptBuilder()
llm = AzureOpenAIChatGenerator()

pipe = Pipeline()
pipe.add_component("prompt_builder", prompt_builder)
pipe.add_component("llm", llm)
pipe.connect("prompt_builder.prompt", "llm.messages")
location = "Berlin"
messages = [ChatMessage.from_system("Always respond in German even if some input data is in other languages."),
            ChatMessage.from_user("Tell me about {{location}}")]
pipe.run(data={"prompt_builder": {"template_variables":{"location": location}, "template": messages}})