# SPDX-FileCopyrightText: 2022-present deepset GmbH # # SPDX-License-Identifier: Apache-2.0 import os from typing import Any, Callable, Dict, Optional # pylint: disable=import-error from openai.lib.azure import AzureOpenAI from haystack import component, default_from_dict, default_to_dict, logging from haystack.components.generators.chat import OpenAIChatGenerator from haystack.dataclasses import StreamingChunk from haystack.utils import Secret, deserialize_callable, deserialize_secrets_inplace, serialize_callable logger = logging.getLogger(__name__) @component class AzureOpenAIChatGenerator(OpenAIChatGenerator): """ Generates text using OpenAI's models on Azure. It works with the gpt-4 and gpt-3.5-turbo - type models and supports streaming responses from OpenAI API. It uses [ChatMessage](https://docs.haystack.deepset.ai/docs/data-classes#chatmessage) format in input and output. You can customize how the text is generated by passing parameters to the OpenAI API. Use the `**generation_kwargs` argument when you initialize the component or when you run it. Any parameter that works with `openai.ChatCompletion.create` will work here too. For details on OpenAI API parameters, see [OpenAI documentation](https://platform.openai.com/docs/api-reference/chat). ### Usage example ```python from haystack.components.generators.chat import AzureOpenAIGenerator from haystack.dataclasses import ChatMessage from haystack.utils import Secret messages = [ChatMessage.from_user("What's Natural Language Processing?")] client = AzureOpenAIGenerator( azure_endpoint="", api_key=Secret.from_token(""), azure_deployment="") 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 useful.', role=, name=None, meta={'model': 'gpt-4o-mini', 'index': 0, 'finish_reason': 'stop', 'usage': {'prompt_tokens': 15, 'completion_tokens': 36, 'total_tokens': 51}})] } ``` """ # pylint: disable=super-init-not-called def __init__( self, azure_endpoint: Optional[str] = None, api_version: Optional[str] = "2023-05-15", azure_deployment: Optional[str] = "gpt-4o-mini", api_key: Optional[Secret] = Secret.from_env_var("AZURE_OPENAI_API_KEY", strict=False), azure_ad_token: Optional[Secret] = Secret.from_env_var("AZURE_OPENAI_AD_TOKEN", strict=False), organization: Optional[str] = None, streaming_callback: Optional[Callable[[StreamingChunk], None]] = None, timeout: Optional[float] = None, max_retries: Optional[int] = None, generation_kwargs: Optional[Dict[str, Any]] = None, default_headers: Optional[Dict[str, str]] = None, ): """ Initialize the Azure OpenAI Chat Generator component. :param azure_endpoint: The endpoint of the deployed model, for example `"https://example-resource.azure.openai.com/"`. :param api_version: The version of the API to use. Defaults to 2023-05-15. :param azure_deployment: The deployment of the model, usually the model name. :param api_key: The API key to use for authentication. :param azure_ad_token: [Azure Active Directory token](https://www.microsoft.com/en-us/security/business/identity-access/microsoft-entra-id). :param organization: Your organization ID, defaults to `None`. For help, see [Setting up your organization](https://platform.openai.com/docs/guides/production-best-practices/setting-up-your-organization). :param streaming_callback: A callback function called when a new token is received from the stream. It accepts [StreamingChunk](https://docs.haystack.deepset.ai/docs/data-classes#streamingchunk) as an argument. :param timeout: Timeout for OpenAI client calls. If not set, it defaults to either the `OPENAI_TIMEOUT` environment variable, or 30 seconds. :param max_retries: Maximum number of retries to contact OpenAI after an internal error. If not set, it defaults to either the `OPENAI_MAX_RETRIES` environment variable, or set to 5. :param generation_kwargs: Other parameters to use for the model. These parameters are sent directly to the OpenAI endpoint. For details, see [OpenAI documentation](https://platform.openai.com/docs/api-reference/chat). Some of the supported parameters: - `max_tokens`: The maximum number of tokens the output text can have. - `temperature`: The sampling temperature to use. Higher values mean the model takes more risks. Try 0.9 for more creative applications and 0 (argmax sampling) for ones with a well-defined answer. - `top_p`: Nucleus sampling is an alternative to sampling with temperature, where the model considers tokens with a top_p probability mass. For example, 0.1 means only the tokens comprising the top 10% probability mass are considered. - `n`: The number of completions to generate for each prompt. For example, with 3 prompts and n=2, the LLM will generate two completions per prompt, resulting in 6 completions total. - `stop`: One or more sequences after which the LLM should stop generating tokens. - `presence_penalty`: The penalty applied if a token is already present. Higher values make the model less likely to repeat the token. - `frequency_penalty`: Penalty applied if a token has already been generated. Higher values make the model less likely to repeat the token. - `logit_bias`: Adds a logit bias to specific tokens. The keys of the dictionary are tokens, and the values are the bias to add to that token. :param default_headers: Default headers to use for the AzureOpenAI client. """ # We intentionally do not call super().__init__ here because we only need to instantiate the client to interact # with the API. # Why is this here? # AzureOpenAI init is forcing us to use an init method that takes either base_url or azure_endpoint as not # None init parameters. This way we accommodate the use case where env var AZURE_OPENAI_ENDPOINT is set instead # of passing it as a parameter. azure_endpoint = azure_endpoint or os.environ.get("AZURE_OPENAI_ENDPOINT") if not azure_endpoint: raise ValueError("Please provide an Azure endpoint or set the environment variable AZURE_OPENAI_ENDPOINT.") if api_key is None and azure_ad_token is None: raise ValueError("Please provide an API key or an Azure Active Directory token.") # The check above makes mypy incorrectly infer that api_key is never None, # which propagates the incorrect type. self.api_key = api_key # type: ignore self.azure_ad_token = azure_ad_token self.generation_kwargs = generation_kwargs or {} self.streaming_callback = streaming_callback self.api_version = api_version self.azure_endpoint = azure_endpoint self.azure_deployment = azure_deployment self.organization = organization self.model = azure_deployment or "gpt-4o-mini" self.timeout = timeout or float(os.environ.get("OPENAI_TIMEOUT", 30.0)) self.max_retries = max_retries or int(os.environ.get("OPENAI_MAX_RETRIES", 5)) self.default_headers = default_headers or {} self.client = AzureOpenAI( api_version=api_version, azure_endpoint=azure_endpoint, azure_deployment=azure_deployment, api_key=api_key.resolve_value() if api_key is not None else None, azure_ad_token=azure_ad_token.resolve_value() if azure_ad_token is not None else None, organization=organization, timeout=self.timeout, max_retries=self.max_retries, default_headers=self.default_headers, ) def to_dict(self) -> Dict[str, Any]: """ Serialize this component to a dictionary. :returns: The serialized component as a dictionary. """ callback_name = serialize_callable(self.streaming_callback) if self.streaming_callback else None return default_to_dict( self, azure_endpoint=self.azure_endpoint, azure_deployment=self.azure_deployment, organization=self.organization, api_version=self.api_version, streaming_callback=callback_name, generation_kwargs=self.generation_kwargs, timeout=self.timeout, max_retries=self.max_retries, api_key=self.api_key.to_dict() if self.api_key is not None else None, azure_ad_token=self.azure_ad_token.to_dict() if self.azure_ad_token is not None else None, default_headers=self.default_headers, ) @classmethod def from_dict(cls, data: Dict[str, Any]) -> "AzureOpenAIChatGenerator": """ Deserialize this component from a dictionary. :param data: The dictionary representation of this component. :returns: The deserialized component instance. """ deserialize_secrets_inplace(data["init_parameters"], keys=["api_key", "azure_ad_token"]) init_params = data.get("init_parameters", {}) serialized_callback_handler = init_params.get("streaming_callback") if serialized_callback_handler: data["init_parameters"]["streaming_callback"] = deserialize_callable(serialized_callback_handler) return default_from_dict(cls, data)