haystack/docs-website/reference_versioned_docs/version-2.18/integrations-api/aimlapi.md

---
title: "AIMLAPI"
id: integrations-aimlapi
description: "AIMLAPI integration for Haystack"
slug: "/integrations-aimlapi"
---

<a id="haystack_integrations.components.generators.aimlapi.chat.chat_generator"></a>

## Module haystack\_integrations.components.generators.aimlapi.chat.chat\_generator

<a id="haystack_integrations.components.generators.aimlapi.chat.chat_generator.AIMLAPIChatGenerator"></a>

### AIMLAPIChatGenerator

Enables text generation using AIMLAPI generative models.
For supported models, see AIMLAPI documentation.

Users can pass any text generation parameters valid for the AIMLAPI chat completion API
directly to this component using the `generation_kwargs` parameter in `__init__` or the `generation_kwargs`
parameter in `run` method.

Key Features and Compatibility:
- **Primary Compatibility**: Designed to work seamlessly with the AIMLAPI chat completion endpoint.
- **Streaming Support**: Supports streaming responses from the AIMLAPI chat completion endpoint.
- **Customizability**: Supports all parameters supported by the AIMLAPI chat completion endpoint.

This component uses the ChatMessage format for structuring both input and output,
ensuring coherent and contextually relevant responses in chat-based text generation scenarios.
Details on the ChatMessage format can be found in the
[Haystack docs](https://docs.haystack.deepset.ai/docs/chatmessage)

For more details on the parameters supported by the AIMLAPI API, refer to the
AIMLAPI documentation.

Usage example:
```python
from haystack_integrations.components.generators.aimlapi import AIMLAPIChatGenerator
from haystack.dataclasses import ChatMessage

messages = [ChatMessage.from_user("What's Natural Language Processing?")]

client = AIMLAPIChatGenerator(model="openai/gpt-5-chat-latest")
response = client.run(messages)
print(response)

>>{'replies': [ChatMessage(_content='Natural Language Processing (NLP) is a branch of artificial intelligence
>>that focuses on enabling computers to understand, interpret, and generate human language in a way that is
>>meaningful and useful.', _role=<ChatRole.ASSISTANT: 'assistant'>, _name=None,
>>_meta={'model': 'openai/gpt-5-chat-latest', 'index': 0, 'finish_reason': 'stop',
>>'usage': {'prompt_tokens': 15, 'completion_tokens': 36, 'total_tokens': 51}})]}
```

<a id="haystack_integrations.components.generators.aimlapi.chat.chat_generator.AIMLAPIChatGenerator.__init__"></a>

#### AIMLAPIChatGenerator.\_\_init\_\_

```python
def __init__(*,
             api_key: Secret = Secret.from_env_var("AIMLAPI_API_KEY"),
             model: str = "openai/gpt-5-chat-latest",
             streaming_callback: Optional[StreamingCallbackT] = None,
             api_base_url: Optional[str] = "https://api.aimlapi.com/v1",
             generation_kwargs: Optional[Dict[str, Any]] = None,
             tools: Optional[Union[List[Tool], Toolset]] = None,
             timeout: Optional[float] = None,
             extra_headers: Optional[Dict[str, Any]] = None,
             max_retries: Optional[int] = None,
             http_client_kwargs: Optional[Dict[str, Any]] = None)
```

Creates an instance of AIMLAPIChatGenerator. Unless specified otherwise,

the default model is `openai/gpt-5-chat-latest`.

**Arguments**:

- `api_key`: The AIMLAPI API key.
- `model`: The name of the AIMLAPI chat completion model to use.
- `streaming_callback`: A callback function that is called when a new token is received from the stream.
The callback function accepts StreamingChunk as an argument.
- `api_base_url`: The AIMLAPI API Base url.
For more details, see AIMLAPI documentation.
- `generation_kwargs`: Other parameters to use for the model. These parameters are all sent directly to
the AIMLAPI endpoint. See AIMLAPI API docs for more details.
Some of the supported parameters:
- `max_tokens`: The maximum number of tokens the output text can have.
- `temperature`: What sampling temperature to use. Higher values mean the model will take more risks.
    Try 0.9 for more creative applications and 0 (argmax sampling) for ones with a well-defined answer.
- `top_p`: An alternative to sampling with temperature, called nucleus sampling, where the model
    considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens
    comprising the top 10% probability mass are considered.
- `stream`: Whether to stream back partial progress. If set, tokens will be sent as data-only server-sent
    events as they become available, with the stream terminated by a data: [DONE] message.
- `safe_prompt`: Whether to inject a safety prompt before all conversations.
- `random_seed`: The seed to use for random sampling.
- `tools`: A list of tools or a Toolset for which the model can prepare calls. This parameter can accept either a
list of `Tool` objects or a `Toolset` instance.
- `timeout`: The timeout for the AIMLAPI API call.
- `extra_headers`: Additional HTTP headers to include in requests to the AIMLAPI API.
- `max_retries`: Maximum number of retries to contact AIMLAPI after an internal error.
If not set, it defaults to either the `AIMLAPI_MAX_RETRIES` environment variable, or set to 5.
- `http_client_kwargs`: A dictionary of keyword arguments to configure a custom `httpx.Client`or `httpx.AsyncClient`.
For more information, see the [HTTPX documentation](https://www.python-httpx.org/api/`client`).

<a id="haystack_integrations.components.generators.aimlapi.chat.chat_generator.AIMLAPIChatGenerator.to_dict"></a>

#### AIMLAPIChatGenerator.to\_dict

```python
def to_dict() -> Dict[str, Any]
```

Serialize this component to a dictionary.

**Returns**:

The serialized component as a dictionary.

<a id="haystack_integrations.components.generators.aimlapi.chat.chat_generator.AIMLAPIChatGenerator.from_dict"></a>

#### AIMLAPIChatGenerator.from\_dict

```python
@classmethod
def from_dict(cls, data: dict[str, Any]) -> "OpenAIChatGenerator"
```

Deserialize this component from a dictionary.

**Arguments**:

- `data`: The dictionary representation of this component.

**Returns**:

The deserialized component instance.

<a id="haystack_integrations.components.generators.aimlapi.chat.chat_generator.AIMLAPIChatGenerator.run"></a>

#### AIMLAPIChatGenerator.run

```python
@component.output_types(replies=list[ChatMessage])
def run(messages: list[ChatMessage],
        streaming_callback: Optional[StreamingCallbackT] = None,
        generation_kwargs: Optional[dict[str, Any]] = None,
        *,
        tools: Optional[ToolsType] = None,
        tools_strict: Optional[bool] = None)
```

Invokes chat completion based on the provided messages and generation parameters.

**Arguments**:

- `messages`: A list of ChatMessage instances representing the input messages.
- `streaming_callback`: A callback function that is called when a new token is received from the stream.
- `generation_kwargs`: Additional keyword arguments for text generation. These parameters will
override the parameters passed during component initialization.
For details on OpenAI API parameters, see [OpenAI documentation](https://platform.openai.com/docs/api-reference/chat/create).
- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
If set, it will override the `tools` parameter provided during initialization.
- `tools_strict`: Whether to enable strict schema adherence for tool calls. If set to `True`, the model will follow exactly
the schema provided in the `parameters` field of the tool definition, but this may increase latency.
If set, it will override the `tools_strict` parameter set during component initialization.

**Returns**:

A dictionary with the following key:
- `replies`: A list containing the generated responses as ChatMessage instances.

<a id="haystack_integrations.components.generators.aimlapi.chat.chat_generator.AIMLAPIChatGenerator.run_async"></a>

#### AIMLAPIChatGenerator.run\_async

```python
@component.output_types(replies=list[ChatMessage])
async def run_async(messages: list[ChatMessage],
                    streaming_callback: Optional[StreamingCallbackT] = None,
                    generation_kwargs: Optional[dict[str, Any]] = None,
                    *,
                    tools: Optional[ToolsType] = None,
                    tools_strict: Optional[bool] = None)
```

Asynchronously invokes chat completion based on the provided messages and generation parameters.

This is the asynchronous version of the `run` method. It has the same parameters and return values
but can be used with `await` in async code.

**Arguments**:

- `messages`: A list of ChatMessage instances representing the input messages.
- `streaming_callback`: A callback function that is called when a new token is received from the stream.
Must be a coroutine.
- `generation_kwargs`: Additional keyword arguments for text generation. These parameters will
override the parameters passed during component initialization.
For details on OpenAI API parameters, see [OpenAI documentation](https://platform.openai.com/docs/api-reference/chat/create).
- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
If set, it will override the `tools` parameter provided during initialization.
- `tools_strict`: Whether to enable strict schema adherence for tool calls. If set to `True`, the model will follow exactly
the schema provided in the `parameters` field of the tool definition, but this may increase latency.
If set, it will override the `tools_strict` parameter set during component initialization.

**Returns**:

A dictionary with the following key:
- `replies`: A list containing the generated responses as ChatMessage instances.