---
title: "OpenAIChatGenerator"
id: openaichatgenerator
slug: "/openaichatgenerator"
description: "`OpenAIChatGenerator` enables chat completion using OpenAI’s large language models (LLMs)."
---

# OpenAIChatGenerator

`OpenAIChatGenerator` enables chat completion using OpenAI’s large language models (LLMs).

|                                        |                                                                                                      |
| :------------------------------------- | :--------------------------------------------------------------------------------------------------- |
| **Most common position in a pipeline** | After a [ChatPromptBuilder](../builders/chatpromptbuilder.mdx)                                                 |
| **Mandatory init variables**           | "api_key": An OpenAI API key. Can be set with `OPENAI_API_KEY` env var.                              |
| **Mandatory run variables**            | “messages”: A list of [`ChatMessage`](../../concepts/data-classes/chatmessage.mdx)  objects representing the chat |
| **Output variables**                   | “replies”: A list of alternative replies of the LLM to the input chat                                |
| **API reference**                      | [Generators](/reference/generators-api)                                                                     |
| **GitHub link**                        | https://github.com/deepset-ai/haystack/blob/main/haystack/components/generators/chat/openai.py     |

## Overview

`OpenAIChatGenerator` supports OpenAI models starting from gpt-3.5-turbo and later (gpt-4, gpt-4-turbo, and so on).

`OpenAIChatGenerator` needs an OpenAI key to work. It uses an ` OPENAI_API_KEY` environment variable by default. Otherwise, you can pass an API key at initialization with `api_key`:

```python
generator = OpenAIChatGenerator(model="gpt-4o-mini")
```

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](#usage) section for an example.

You can pass any chat completion parameters valid for the `openai.ChatCompletion.create` method directly to `OpenAIChatGenerator` using the `generation_kwargs` parameter, both at initialization and to `run()` method. For more details on the parameters supported by the OpenAI API, refer to the [OpenAI documentation](https://platform.openai.com/docs/api-reference/chat).

`OpenAIChatGenerator` can support custom deployments of your OpenAI models through the `api_base_url` init parameter.

### Structured Output

`OpenAIChatGenerator` supports structured output generation, allowing you to receive responses in a predictable format. You can use Pydantic models or JSON schemas to define the structure of the output through the `response_format` parameter in `generation_kwargs`.

This is useful when you need to extract structured data from text or generate responses that match a specific format.

```python
from pydantic import BaseModel
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.dataclasses import ChatMessage

class NobelPrizeInfo(BaseModel):
    recipient_name: str
    award_year: int
    category: str
    achievement_description: str
    nationality: str

client = OpenAIChatGenerator(
    model="gpt-4o-2024-08-06",
    generation_kwargs={"response_format": NobelPrizeInfo}
)

response = client.run(messages=[
    ChatMessage.from_user(
        "In 2021, American scientist David Julius received the Nobel Prize in"
        " Physiology or Medicine for his groundbreaking discoveries on how the human body"
        " senses temperature and touch."
    )
])
print(response["replies"][0].text)

```

:::note
Model Compatibility and Limitations

- Pydantic models and JSON schemas are supported for latest models starting from `gpt-4o-2024-08-06`.
- Older models only support basic JSON mode through `{"type": "json_object"}`. For details, see [OpenAI JSON mode documentation](https://platform.openai.com/docs/guides/structured-outputs#json-mode).
- Streaming limitation: When using streaming with structured outputs, you must provide a JSON schema instead of a Pydantic model for `response_format`.
- For complete information, check the [OpenAI Structured Outputs documentation](https://platform.openai.com/docs/guides/structured-outputs).
:::

### 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).

```python
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"})
```

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

:::

See our [Streaming Support](guides-to-generators/choosing-the-right-generator.mdx#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:

```python
from haystack.dataclasses import ChatMessage
from haystack.components.generators.chat import OpenAIChatGenerator

client = OpenAIChatGenerator()
response = client.run(
	  [ChatMessage.from_user("What's Natural Language Processing? Be brief.")]
)
print(response)

```

With streaming:

```python
from haystack.dataclasses import ChatMessage
from haystack.components.generators.chat import OpenAIChatGenerator

client = OpenAIChatGenerator(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)

```

With multimodal inputs:

```python
from haystack.dataclasses import ChatMessage, ImageContent
from haystack.components.generators.chat import OpenAIChatGenerator

llm = OpenAIChatGenerator(model="gpt-4o-mini")

image = ImageContent.from_file_path("apple.jpg", detail="low")
user_message = ChatMessage.from_user(content_parts=[
	"What does the image show? Max 5 words.",
	image
	])

response = llm.run([user_message])["replies"][0].text
print(response)

```

### In a Pipeline

```python
from haystack.components.builders import ChatPromptBuilder
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.dataclasses import ChatMessage
from haystack import Pipeline
from haystack.utils import Secret

## no parameter init, we don't use any runtime template variables
prompt_builder = ChatPromptBuilder()
llm = OpenAIChatGenerator(api_key=Secret.from_env_var("OPENAI_API_KEY"), model="gpt-4o-mini")

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}})

```

## Additional References

:notebook: Tutorial: [Building a Chat Application with Function Calling](https://haystack.deepset.ai/tutorials/40_building_chat_application_with_function_calling)

🧑‍🍳 Cookbook: [Function Calling with OpenAIChatGenerator](https://haystack.deepset.ai/cookbook/function_calling_with_openaichatgenerator)
