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#
# 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 import OpenAIGenerator
from haystack.dataclasses import StreamingChunk
from haystack.utils import Secret, deserialize_callable, deserialize_secrets_inplace, serialize_callable
logger = logging.getLogger(__name__)
@component
class AzureOpenAIGenerator(OpenAIGenerator):
"""
Generates text using OpenAI's large language models (LLMs).
It works with the gpt-4 and gpt-3.5-turbo family of models.
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 import AzureOpenAIGenerator
from haystack.utils import Secret
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="<this a model name, e.g. gpt-4o-mini>")
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}}]}
```
"""
# 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,
system_prompt: Optional[str] = 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 Generator.
: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 system_prompt: The system prompt to use for text generation. If not provided, the Generator
omits the system prompt and uses the default system prompt.
:param timeout: Timeout for AzureOpenAI client. If not set, it is inferred from the
`OPENAI_TIMEOUT` environment variable or set to 30.
:param max_retries: Maximum retries to establish contact with AzureOpenAI if it returns an internal error.
If not set, it is inferred from the `OPENAI_MAX_RETRIES` environment variable or set to 5.
:param generation_kwargs: Other parameters to use for the model, sent directly to
the OpenAI endpoint. See [OpenAI documentation](https://platform.openai.com/docs/api-reference/chat) for
more details.
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`: An alternative to sampling with temperature, called nucleus sampling, where the model
considers the results of the tokens with 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.system_prompt = system_prompt
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: str = 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,
system_prompt=self.system_prompt,
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,
timeout=self.timeout,
max_retries=self.max_retries,
default_headers=self.default_headers,
)
@classmethod
def from_dict(cls, data: Dict[str, Any]) -> "AzureOpenAIGenerator":
"""
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)
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