mirror of
https://github.com/BerriAI/litellm.git
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c7f14e936a
* remove unused imports * fix AmazonConverseConfig * fix test * fix import * ruff check fixes * test fixes * fix testing * fix imports
289 lines
12 KiB
Python
289 lines
12 KiB
Python
from typing import TYPE_CHECKING, Any, List, Optional, Union
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from httpx._models import Headers, Response
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import litellm
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from litellm.litellm_core_utils.prompt_templates.factory import (
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convert_to_azure_openai_messages,
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)
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from litellm.llms.base_llm.chat.transformation import BaseLLMException
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from litellm.types.utils import ModelResponse
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from ....exceptions import UnsupportedParamsError
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from ....types.llms.openai import (
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AllMessageValues,
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ChatCompletionToolChoiceFunctionParam,
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ChatCompletionToolChoiceObjectParam,
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ChatCompletionToolParam,
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ChatCompletionToolParamFunctionChunk,
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)
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from ...base_llm.chat.transformation import BaseConfig
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from ..common_utils import AzureOpenAIError
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if TYPE_CHECKING:
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from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
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LoggingClass = LiteLLMLoggingObj
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else:
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LoggingClass = Any
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class AzureOpenAIConfig(BaseConfig):
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"""
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Reference: https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#chat-completions
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The class `AzureOpenAIConfig` provides configuration for the OpenAI's Chat API interface, for use with Azure. Below are the parameters::
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- `frequency_penalty` (number or null): Defaults to 0. Allows a value between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, thereby minimizing repetition.
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- `function_call` (string or object): This optional parameter controls how the model calls functions.
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- `functions` (array): An optional parameter. It is a list of functions for which the model may generate JSON inputs.
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- `logit_bias` (map): This optional parameter modifies the likelihood of specified tokens appearing in the completion.
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- `max_tokens` (integer or null): This optional parameter helps to set the maximum number of tokens to generate in the chat completion.
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- `n` (integer or null): This optional parameter helps to set how many chat completion choices to generate for each input message.
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- `presence_penalty` (number or null): Defaults to 0. It penalizes new tokens based on if they appear in the text so far, hence increasing the model's likelihood to talk about new topics.
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- `stop` (string / array / null): Specifies up to 4 sequences where the API will stop generating further tokens.
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- `temperature` (number or null): Defines the sampling temperature to use, varying between 0 and 2.
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- `top_p` (number or null): An alternative to sampling with temperature, used for nucleus sampling.
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"""
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def __init__(
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self,
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frequency_penalty: Optional[int] = None,
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function_call: Optional[Union[str, dict]] = None,
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functions: Optional[list] = None,
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logit_bias: Optional[dict] = None,
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max_tokens: Optional[int] = None,
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n: Optional[int] = None,
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presence_penalty: Optional[int] = None,
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stop: Optional[Union[str, list]] = None,
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temperature: Optional[int] = None,
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top_p: Optional[int] = None,
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) -> None:
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locals_ = locals().copy()
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for key, value in locals_.items():
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if key != "self" and value is not None:
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setattr(self.__class__, key, value)
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@classmethod
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def get_config(cls):
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return super().get_config()
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def get_supported_openai_params(self, model: str) -> List[str]:
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return [
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"temperature",
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"n",
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"stream",
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"stream_options",
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"stop",
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"max_tokens",
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"max_completion_tokens",
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"tools",
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"tool_choice",
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"presence_penalty",
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"frequency_penalty",
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"logit_bias",
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"user",
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"function_call",
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"functions",
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"tools",
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"tool_choice",
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"top_p",
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"logprobs",
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"top_logprobs",
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"response_format",
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"seed",
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"extra_headers",
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"parallel_tool_calls",
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]
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def map_openai_params(
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self,
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non_default_params: dict,
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optional_params: dict,
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model: str,
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drop_params: bool,
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api_version: str = "",
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) -> dict:
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supported_openai_params = self.get_supported_openai_params(model)
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api_version_times = api_version.split("-")
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api_version_year = api_version_times[0]
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api_version_month = api_version_times[1]
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api_version_day = api_version_times[2]
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for param, value in non_default_params.items():
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if param == "tool_choice":
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"""
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This parameter requires API version 2023-12-01-preview or later
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tool_choice='required' is not supported as of 2024-05-01-preview
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"""
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## check if api version supports this param ##
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if (
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api_version_year < "2023"
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or (api_version_year == "2023" and api_version_month < "12")
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or (
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api_version_year == "2023"
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and api_version_month == "12"
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and api_version_day < "01"
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)
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):
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if litellm.drop_params is True or (
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drop_params is not None and drop_params is True
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):
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pass
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else:
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raise UnsupportedParamsError(
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status_code=400,
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message=f"""Azure does not support 'tool_choice', for api_version={api_version}. Bump your API version to '2023-12-01-preview' or later. This parameter requires 'api_version="2023-12-01-preview"' or later. Azure API Reference: https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#chat-completions""",
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)
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elif value == "required" and (
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api_version_year == "2024" and api_version_month <= "05"
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): ## check if tool_choice value is supported ##
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if litellm.drop_params is True or (
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drop_params is not None and drop_params is True
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):
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pass
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else:
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raise UnsupportedParamsError(
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status_code=400,
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message=f"Azure does not support '{value}' as a {param} param, for api_version={api_version}. To drop 'tool_choice=required' for calls with this Azure API version, set `litellm.drop_params=True` or for proxy:\n\n`litellm_settings:\n drop_params: true`\nAzure API Reference: https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#chat-completions",
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)
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else:
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optional_params["tool_choice"] = value
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elif param == "response_format" and isinstance(value, dict):
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json_schema: Optional[dict] = None
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schema_name: str = ""
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if "response_schema" in value:
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json_schema = value["response_schema"]
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schema_name = "json_tool_call"
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elif "json_schema" in value:
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json_schema = value["json_schema"]["schema"]
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schema_name = value["json_schema"]["name"]
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"""
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Follow similar approach to anthropic - translate to a single tool call.
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When using tools in this way: - https://docs.anthropic.com/en/docs/build-with-claude/tool-use#json-mode
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- You usually want to provide a single tool
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- You should set tool_choice (see Forcing tool use) to instruct the model to explicitly use that tool
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- Remember that the model will pass the input to the tool, so the name of the tool and description should be from the model’s perspective.
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"""
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if json_schema is not None and (
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(api_version_year <= "2024" and api_version_month < "08")
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or "gpt-4o" not in model
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): # azure api version "2024-08-01-preview" onwards supports 'json_schema' only for gpt-4o
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_tool_choice = ChatCompletionToolChoiceObjectParam(
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type="function",
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function=ChatCompletionToolChoiceFunctionParam(
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name=schema_name
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),
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)
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_tool = ChatCompletionToolParam(
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type="function",
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function=ChatCompletionToolParamFunctionChunk(
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name=schema_name, parameters=json_schema
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),
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)
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optional_params["tools"] = [_tool]
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optional_params["tool_choice"] = _tool_choice
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optional_params["json_mode"] = True
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else:
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optional_params["response_format"] = value
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elif param in supported_openai_params:
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optional_params[param] = value
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return optional_params
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def transform_request(
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self,
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model: str,
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messages: List[AllMessageValues],
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optional_params: dict,
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litellm_params: dict,
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headers: dict,
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) -> dict:
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messages = convert_to_azure_openai_messages(messages)
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return {
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"model": model,
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"messages": messages,
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**optional_params,
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}
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def transform_response(
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self,
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model: str,
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raw_response: Response,
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model_response: ModelResponse,
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logging_obj: LoggingClass,
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request_data: dict,
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messages: List[AllMessageValues],
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optional_params: dict,
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litellm_params: dict,
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encoding: Any,
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api_key: Optional[str] = None,
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json_mode: Optional[bool] = None,
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) -> ModelResponse:
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raise NotImplementedError(
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"Azure OpenAI handler.py has custom logic for transforming response, as it uses the OpenAI SDK."
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)
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def get_mapped_special_auth_params(self) -> dict:
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return {"token": "azure_ad_token"}
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def map_special_auth_params(self, non_default_params: dict, optional_params: dict):
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for param, value in non_default_params.items():
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if param == "token":
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optional_params["azure_ad_token"] = value
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return optional_params
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def get_eu_regions(self) -> List[str]:
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"""
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Source: https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models#gpt-4-and-gpt-4-turbo-model-availability
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"""
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return ["europe", "sweden", "switzerland", "france", "uk"]
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def get_us_regions(self) -> List[str]:
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"""
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Source: https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models#gpt-4-and-gpt-4-turbo-model-availability
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"""
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return [
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"us",
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"eastus",
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"eastus2",
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"eastus2euap",
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"eastus3",
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"southcentralus",
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"westus",
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"westus2",
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"westus3",
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"westus4",
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]
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def get_error_class(
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self, error_message: str, status_code: int, headers: Union[dict, Headers]
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) -> BaseLLMException:
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return AzureOpenAIError(
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message=error_message, status_code=status_code, headers=headers
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)
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def validate_environment(
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self,
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headers: dict,
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model: str,
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messages: List[AllMessageValues],
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optional_params: dict,
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api_key: Optional[str] = None,
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) -> dict:
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raise NotImplementedError(
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"Azure OpenAI has custom logic for validating environment, as it uses the OpenAI SDK."
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)
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