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Merge 9e4071f83 into 2a64cd4e9

Pull Request Pull Request #9345: feat: add serialization to `State` / move `State` to utils

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haystack/components/generators/chat/azure.py

# SPDX-FileCopyrightText: 2022-present deepset GmbH <info@deepset.ai>
#
# SPDX-License-Identifier: Apache-2.0

import os
from typing import Any, Dict, List, Optional, Union

from openai.lib.azure import AsyncAzureADTokenProvider, AsyncAzureOpenAI, AzureADTokenProvider, AzureOpenAI

from haystack import component, default_from_dict, default_to_dict
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.dataclasses.streaming_chunk import StreamingCallbackT
from haystack.tools import (
    Tool,
    Toolset,
    _check_duplicate_tool_names,
    deserialize_tools_or_toolset_inplace,
    serialize_tools_or_toolset,
)
from haystack.utils import Secret, deserialize_callable, deserialize_secrets_inplace, serialize_callable
from haystack.utils.http_client import init_http_client


@component
class AzureOpenAIChatGenerator(OpenAIChatGenerator):
    """
    Generates text using OpenAI's models on Azure.

    It works with the gpt-4 - type models and supports streaming responses
    from OpenAI API. It uses [ChatMessage](https://docs.haystack.deepset.ai/docs/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 AzureOpenAIChatGenerator
    from haystack.dataclasses import ChatMessage
    from haystack.utils import Secret

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

    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="<this a model name, e.g. gpt-4o-mini>")
    response = client.run(messages)
    print(response)
    ```

    ```
    {'replies':
        [ChatMessage(_role=<ChatRole.ASSISTANT: 'assistant'>, _content=[TextContent(text=
        "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.")],
         _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
    # ruff: noqa: PLR0913
    def __init__(  # pylint: disable=too-many-positional-arguments
        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[StreamingCallbackT] = None,
        timeout: Optional[float] = None,
        max_retries: Optional[int] = None,
        generation_kwargs: Optional[Dict[str, Any]] = None,
        default_headers: Optional[Dict[str, str]] = None,
        tools: Optional[Union[List[Tool], Toolset]] = None,
        tools_strict: bool = False,
        *,
        azure_ad_token_provider: Optional[Union[AzureADTokenProvider, AsyncAzureADTokenProvider]] = None,
        http_client_kwargs: Optional[Dict[str, Any]] = 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.
        :param 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.
        :param 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.
        :param azure_ad_token_provider: A function that returns an Azure Active Directory token, will be invoked on
            every request.
        :param 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).
        """
        # 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 if timeout is not None else float(os.environ.get("OPENAI_TIMEOUT", "30.0"))
        self.max_retries = max_retries if max_retries is not None else int(os.environ.get("OPENAI_MAX_RETRIES", "5"))
        self.default_headers = default_headers or {}
        self.azure_ad_token_provider = azure_ad_token_provider
        self.http_client_kwargs = http_client_kwargs
        _check_duplicate_tool_names(list(tools or []))
        self.tools = tools
        self.tools_strict = tools_strict

        client_args: Dict[str, Any] = {
            "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,
            "azure_ad_token_provider": azure_ad_token_provider,
        }

        self.client = AzureOpenAI(
            http_client=init_http_client(self.http_client_kwargs, async_client=False), **client_args
        )
        self.async_client = AsyncAzureOpenAI(
            http_client=init_http_client(self.http_client_kwargs, async_client=True), **client_args
        )

    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
        azure_ad_token_provider_name = None
        if self.azure_ad_token_provider:
            azure_ad_token_provider_name = serialize_callable(self.azure_ad_token_provider)
        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,
            tools=serialize_tools_or_toolset(self.tools),
            tools_strict=self.tools_strict,
            azure_ad_token_provider=azure_ad_token_provider_name,
            http_client_kwargs=self.http_client_kwargs,
        )

    @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"])
        deserialize_tools_or_toolset_inplace(data["init_parameters"], key="tools")
        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)
        serialized_azure_ad_token_provider = init_params.get("azure_ad_token_provider")
        if serialized_azure_ad_token_provider:
            data["init_parameters"]["azure_ad_token_provider"] = deserialize_callable(
                serialized_azure_ad_token_provider
            )
        return default_from_dict(cls, data)

1	# SPDX-FileCopyrightText: 2022-present deepset GmbH <info@deepset.ai>
2	#
3	# SPDX-License-Identifier: Apache-2.0
4
5	import os	1✔
6	from typing import Any, Dict, List, Optional, Union	1✔
7
8	from openai.lib.azure import AsyncAzureADTokenProvider, AsyncAzureOpenAI, AzureADTokenProvider, AzureOpenAI	1✔
9
10	from haystack import component, default_from_dict, default_to_dict	1✔
11	from haystack.components.generators.chat import OpenAIChatGenerator	1✔
12	from haystack.dataclasses.streaming_chunk import StreamingCallbackT	1✔
13	from haystack.tools import (	1✔
14	Tool,
15	Toolset,
16	_check_duplicate_tool_names,
17	deserialize_tools_or_toolset_inplace,
18	serialize_tools_or_toolset,
19	)
20	from haystack.utils import Secret, deserialize_callable, deserialize_secrets_inplace, serialize_callable	1✔
21	from haystack.utils.http_client import init_http_client	1✔
22
23
24	@component	1✔
25	class AzureOpenAIChatGenerator(OpenAIChatGenerator):	1✔
26	"""
27	Generates text using OpenAI's models on Azure.
28
29	It works with the gpt-4 - type models and supports streaming responses
30	from OpenAI API. It uses [ChatMessage](https://docs.haystack.deepset.ai/docs/chatmessage)
31	format in input and output.
32
33	You can customize how the text is generated by passing parameters to the
34	OpenAI API. Use the `**generation_kwargs` argument when you initialize
35	the component or when you run it. Any parameter that works with
36	`openai.ChatCompletion.create` will work here too.
37
38	For details on OpenAI API parameters, see
39	[OpenAI documentation](https://platform.openai.com/docs/api-reference/chat).
40
41	### Usage example
42
43	```python
44	from haystack.components.generators.chat import AzureOpenAIChatGenerator
45	from haystack.dataclasses import ChatMessage
46	from haystack.utils import Secret
47
48	messages = [ChatMessage.from_user("What's Natural Language Processing?")]
49
50	client = AzureOpenAIChatGenerator(
51	azure_endpoint="<Your Azure endpoint e.g. `https://your-company.azure.openai.com/>",
52	api_key=Secret.from_token("<your-api-key>"),
53	azure_deployment="<this a model name, e.g. gpt-4o-mini>")
54	response = client.run(messages)
55	print(response)
56	```
57
58	```
59	{'replies':
60	[ChatMessage(_role=<ChatRole.ASSISTANT: 'assistant'>, _content=[TextContent(text=
61	"Natural Language Processing (NLP) is a branch of artificial intelligence that focuses on
62	enabling computers to understand, interpret, and generate human language in a way that is useful.")],
63	_name=None,
64	_meta={'model': 'gpt-4o-mini', 'index': 0, 'finish_reason': 'stop',
65	'usage': {'prompt_tokens': 15, 'completion_tokens': 36, 'total_tokens': 51}})]
66	}
67	```
68	"""
69
70	# pylint: disable=super-init-not-called
71	# ruff: noqa: PLR0913
72	def __init__( # pylint: disable=too-many-positional-arguments	1✔
73	self,
74	azure_endpoint: Optional[str] = None,
75	api_version: Optional[str] = "2023-05-15",
76	azure_deployment: Optional[str] = "gpt-4o-mini",
77	api_key: Optional[Secret] = Secret.from_env_var("AZURE_OPENAI_API_KEY", strict=False),
78	azure_ad_token: Optional[Secret] = Secret.from_env_var("AZURE_OPENAI_AD_TOKEN", strict=False),
79	organization: Optional[str] = None,
80	streaming_callback: Optional[StreamingCallbackT] = None,
81	timeout: Optional[float] = None,
82	max_retries: Optional[int] = None,
83	generation_kwargs: Optional[Dict[str, Any]] = None,
84	default_headers: Optional[Dict[str, str]] = None,
85	tools: Optional[Union[List[Tool], Toolset]] = None,
86	tools_strict: bool = False,
87	*,
88	azure_ad_token_provider: Optional[Union[AzureADTokenProvider, AsyncAzureADTokenProvider]] = None,
89	http_client_kwargs: Optional[Dict[str, Any]] = None,
90	):
91	"""
92	Initialize the Azure OpenAI Chat Generator component.
93
94	:param azure_endpoint: The endpoint of the deployed model, for example `"https://example-resource.azure.openai.com/"`.
95	:param api_version: The version of the API to use. Defaults to 2023-05-15.
96	:param azure_deployment: The deployment of the model, usually the model name.
97	:param api_key: The API key to use for authentication.
98	:param azure_ad_token: [Azure Active Directory token](https://www.microsoft.com/en-us/security/business/identity-access/microsoft-entra-id).
99	:param organization: Your organization ID, defaults to `None`. For help, see
100	[Setting up your organization](https://platform.openai.com/docs/guides/production-best-practices/setting-up-your-organization).
101	:param streaming_callback: A callback function called when a new token is received from the stream.
102	It accepts [StreamingChunk](https://docs.haystack.deepset.ai/docs/data-classes#streamingchunk)
103	as an argument.
104	:param timeout: Timeout for OpenAI client calls. If not set, it defaults to either the
105	`OPENAI_TIMEOUT` environment variable, or 30 seconds.
106	:param max_retries: Maximum number of retries to contact OpenAI after an internal error.
107	If not set, it defaults to either the `OPENAI_MAX_RETRIES` environment variable, or set to 5.
108	:param generation_kwargs: Other parameters to use for the model. These parameters are sent directly to
109	the OpenAI endpoint. For details, see [OpenAI documentation](https://platform.openai.com/docs/api-reference/chat).
110	Some of the supported parameters:
111	- `max_tokens`: The maximum number of tokens the output text can have.
112	- `temperature`: The sampling temperature to use. Higher values mean the model takes more risks.
113	Try 0.9 for more creative applications and 0 (argmax sampling) for ones with a well-defined answer.
114	- `top_p`: Nucleus sampling is an alternative to sampling with temperature, where the model considers
115	tokens with a top_p probability mass. For example, 0.1 means only the tokens comprising
116	the top 10% probability mass are considered.
117	- `n`: The number of completions to generate for each prompt. For example, with 3 prompts and n=2,
118	the LLM will generate two completions per prompt, resulting in 6 completions total.
119	- `stop`: One or more sequences after which the LLM should stop generating tokens.
120	- `presence_penalty`: The penalty applied if a token is already present.
121	Higher values make the model less likely to repeat the token.
122	- `frequency_penalty`: Penalty applied if a token has already been generated.
123	Higher values make the model less likely to repeat the token.
124	- `logit_bias`: Adds a logit bias to specific tokens. The keys of the dictionary are tokens, and the
125	values are the bias to add to that token.
126	:param default_headers: Default headers to use for the AzureOpenAI client.
127	:param tools:
128	A list of tools or a Toolset for which the model can prepare calls. This parameter can accept either a
129	list of `Tool` objects or a `Toolset` instance.
130	:param tools_strict:
131	Whether to enable strict schema adherence for tool calls. If set to `True`, the model will follow exactly
132	the schema provided in the `parameters` field of the tool definition, but this may increase latency.
133	:param azure_ad_token_provider: A function that returns an Azure Active Directory token, will be invoked on
134	every request.
135	:param http_client_kwargs:
136	A dictionary of keyword arguments to configure a custom `httpx.Client`or `httpx.AsyncClient`.
137	For more information, see the [HTTPX documentation](https://www.python-httpx.org/api/#client).
138	"""
139	# We intentionally do not call super().__init__ here because we only need to instantiate the client to interact
140	# with the API.
141
142	# Why is this here?
143	# AzureOpenAI init is forcing us to use an init method that takes either base_url or azure_endpoint as not
144	# None init parameters. This way we accommodate the use case where env var AZURE_OPENAI_ENDPOINT is set instead
145	# of passing it as a parameter.
146	azure_endpoint = azure_endpoint or os.environ.get("AZURE_OPENAI_ENDPOINT")	1✔
147	if not azure_endpoint:	1✔
148	raise ValueError("Please provide an Azure endpoint or set the environment variable AZURE_OPENAI_ENDPOINT.")	×
149
150	if api_key is None and azure_ad_token is None:	1✔
151	raise ValueError("Please provide an API key or an Azure Active Directory token.")	×
152
153	# The check above makes mypy incorrectly infer that api_key is never None,
154	# which propagates the incorrect type.
155	self.api_key = api_key # type: ignore	1✔
156	self.azure_ad_token = azure_ad_token	1✔
157	self.generation_kwargs = generation_kwargs or {}	1✔
158	self.streaming_callback = streaming_callback	1✔
159	self.api_version = api_version	1✔
160	self.azure_endpoint = azure_endpoint	1✔
161	self.azure_deployment = azure_deployment	1✔
162	self.organization = organization	1✔
163	self.model = azure_deployment or "gpt-4o-mini"	1✔
164	self.timeout = timeout if timeout is not None else float(os.environ.get("OPENAI_TIMEOUT", "30.0"))	1✔
165	self.max_retries = max_retries if max_retries is not None else int(os.environ.get("OPENAI_MAX_RETRIES", "5"))	1✔
166	self.default_headers = default_headers or {}	1✔
167	self.azure_ad_token_provider = azure_ad_token_provider	1✔
168	self.http_client_kwargs = http_client_kwargs	1✔
169	_check_duplicate_tool_names(list(tools or []))	1✔
170	self.tools = tools	1✔
171	self.tools_strict = tools_strict	1✔
172
173	client_args: Dict[str, Any] = {	1✔
174	"api_version": api_version,
175	"azure_endpoint": azure_endpoint,
176	"azure_deployment": azure_deployment,
177	"api_key": api_key.resolve_value() if api_key is not None else None,
178	"azure_ad_token": azure_ad_token.resolve_value() if azure_ad_token is not None else None,
179	"organization": organization,
180	"timeout": self.timeout,
181	"max_retries": self.max_retries,
182	"default_headers": self.default_headers,
183	"azure_ad_token_provider": azure_ad_token_provider,
184	}
185
186	self.client = AzureOpenAI(	1✔
187	http_client=init_http_client(self.http_client_kwargs, async_client=False), **client_args
188	)
189	self.async_client = AsyncAzureOpenAI(	1✔
190	http_client=init_http_client(self.http_client_kwargs, async_client=True), **client_args
191	)
192
193	def to_dict(self) -> Dict[str, Any]:	1✔
194	"""
195	Serialize this component to a dictionary.
196
197	:returns:
198	The serialized component as a dictionary.
199	"""
200	callback_name = serialize_callable(self.streaming_callback) if self.streaming_callback else None	1✔
201	azure_ad_token_provider_name = None	1✔
202	if self.azure_ad_token_provider:	1✔
203	azure_ad_token_provider_name = serialize_callable(self.azure_ad_token_provider)	1✔
204	return default_to_dict(	1✔
205	self,
206	azure_endpoint=self.azure_endpoint,
207	azure_deployment=self.azure_deployment,
208	organization=self.organization,
209	api_version=self.api_version,
210	streaming_callback=callback_name,
211	generation_kwargs=self.generation_kwargs,
212	timeout=self.timeout,
213	max_retries=self.max_retries,
214	api_key=self.api_key.to_dict() if self.api_key is not None else None,
215	azure_ad_token=self.azure_ad_token.to_dict() if self.azure_ad_token is not None else None,
216	default_headers=self.default_headers,
217	tools=serialize_tools_or_toolset(self.tools),
218	tools_strict=self.tools_strict,
219	azure_ad_token_provider=azure_ad_token_provider_name,
220	http_client_kwargs=self.http_client_kwargs,
221	)
222
223	@classmethod	1✔
224	def from_dict(cls, data: Dict[str, Any]) -> "AzureOpenAIChatGenerator":	1✔
225	"""
226	Deserialize this component from a dictionary.
227
228	:param data: The dictionary representation of this component.
229	:returns:
230	The deserialized component instance.
231	"""
232	deserialize_secrets_inplace(data["init_parameters"], keys=["api_key", "azure_ad_token"])	1✔
233	deserialize_tools_or_toolset_inplace(data["init_parameters"], key="tools")	1✔
234	init_params = data.get("init_parameters", {})	1✔
235	serialized_callback_handler = init_params.get("streaming_callback")	1✔
236	if serialized_callback_handler:	1✔
237	data["init_parameters"]["streaming_callback"] = deserialize_callable(serialized_callback_handler)	×
238	serialized_azure_ad_token_provider = init_params.get("azure_ad_token_provider")	1✔
239	if serialized_azure_ad_token_provider:	1✔
240	data["init_parameters"]["azure_ad_token_provider"] = deserialize_callable(	×
241	serialized_azure_ad_token_provider
242	)
243	return default_from_dict(cls, data)	1✔

deepset-ai / haystack / 15049844454

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