chore: initial public snapshot for github upload
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"""
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Azure Anthropic handler - reuses AnthropicChatCompletion logic with Azure authentication
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"""
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import copy
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import json
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from typing import TYPE_CHECKING, Callable, Union
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import httpx
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from litellm.llms.anthropic.chat.handler import AnthropicChatCompletion
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from litellm.llms.custom_httpx.http_handler import (
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AsyncHTTPHandler,
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HTTPHandler,
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)
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from litellm.types.utils import ModelResponse
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from litellm.utils import CustomStreamWrapper
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from .transformation import AzureAnthropicConfig
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if TYPE_CHECKING:
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pass
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class AzureAnthropicChatCompletion(AnthropicChatCompletion):
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"""
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Azure Anthropic chat completion handler.
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Reuses all Anthropic logic but with Azure authentication.
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"""
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def __init__(self) -> None:
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super().__init__()
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def completion(
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self,
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model: str,
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messages: list,
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api_base: str,
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custom_llm_provider: str,
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custom_prompt_dict: dict,
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model_response: ModelResponse,
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print_verbose: Callable,
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encoding,
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api_key,
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logging_obj,
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optional_params: dict,
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timeout: Union[float, httpx.Timeout],
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litellm_params: dict,
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acompletion=None,
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logger_fn=None,
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headers={},
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client=None,
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):
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"""
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Completion method that uses Azure authentication instead of Anthropic's x-api-key.
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All other logic is the same as AnthropicChatCompletion.
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"""
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optional_params = copy.deepcopy(optional_params)
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stream = optional_params.pop("stream", None)
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json_mode: bool = optional_params.pop("json_mode", False)
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is_vertex_request: bool = optional_params.pop("is_vertex_request", False)
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_is_function_call = False
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messages = copy.deepcopy(messages)
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# Use AzureAnthropicConfig for both azure_anthropic and azure_ai Claude models
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config = AzureAnthropicConfig()
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headers = config.validate_environment(
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api_key=api_key,
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headers=headers,
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model=model,
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messages=messages,
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optional_params={**optional_params, "is_vertex_request": is_vertex_request},
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litellm_params=litellm_params,
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)
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data = config.transform_request(
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model=model,
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messages=messages,
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optional_params=optional_params,
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litellm_params=litellm_params,
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headers=headers,
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)
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## LOGGING
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logging_obj.pre_call(
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input=messages,
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api_key=api_key,
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additional_args={
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"complete_input_dict": data,
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"api_base": api_base,
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"headers": headers,
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},
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)
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print_verbose(f"_is_function_call: {_is_function_call}")
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if acompletion is True:
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if (
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stream is True
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): # if function call - fake the streaming (need complete blocks for output parsing in openai format)
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print_verbose("makes async azure anthropic streaming POST request")
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data["stream"] = stream
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return self.acompletion_stream_function(
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model=model,
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messages=messages,
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data=data,
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api_base=api_base,
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custom_prompt_dict=custom_prompt_dict,
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model_response=model_response,
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print_verbose=print_verbose,
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encoding=encoding,
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api_key=api_key,
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logging_obj=logging_obj,
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optional_params=optional_params,
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stream=stream,
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_is_function_call=_is_function_call,
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json_mode=json_mode,
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litellm_params=litellm_params,
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logger_fn=logger_fn,
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headers=headers,
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timeout=timeout,
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client=(
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client
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if client is not None and isinstance(client, AsyncHTTPHandler)
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else None
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),
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)
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else:
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return self.acompletion_function(
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model=model,
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messages=messages,
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data=data,
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api_base=api_base,
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custom_prompt_dict=custom_prompt_dict,
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model_response=model_response,
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print_verbose=print_verbose,
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encoding=encoding,
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api_key=api_key,
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provider_config=config,
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logging_obj=logging_obj,
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optional_params=optional_params,
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stream=stream,
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_is_function_call=_is_function_call,
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litellm_params=litellm_params,
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logger_fn=logger_fn,
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headers=headers,
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client=client,
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json_mode=json_mode,
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timeout=timeout,
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)
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else:
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## COMPLETION CALL
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if (
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stream is True
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): # if function call - fake the streaming (need complete blocks for output parsing in openai format)
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data["stream"] = stream
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# Import the make_sync_call from parent
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from litellm.llms.anthropic.chat.handler import make_sync_call
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completion_stream, response_headers = make_sync_call(
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client=client,
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api_base=api_base,
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headers=headers, # type: ignore
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data=json.dumps(data),
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model=model,
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messages=messages,
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logging_obj=logging_obj,
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timeout=timeout,
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json_mode=json_mode,
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)
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from litellm.llms.anthropic.common_utils import (
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process_anthropic_headers,
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)
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return CustomStreamWrapper(
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completion_stream=completion_stream,
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model=model,
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custom_llm_provider="azure_ai",
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logging_obj=logging_obj,
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_response_headers=process_anthropic_headers(response_headers),
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)
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else:
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if client is None or not isinstance(client, HTTPHandler):
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from litellm.llms.custom_httpx.http_handler import _get_httpx_client
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client = _get_httpx_client(params={"timeout": timeout})
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else:
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client = client
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try:
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response = client.post(
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api_base,
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headers=headers,
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data=json.dumps(data),
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timeout=timeout,
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)
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except Exception as e:
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from litellm.llms.anthropic.common_utils import AnthropicError
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status_code = getattr(e, "status_code", 500)
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error_headers = getattr(e, "headers", None)
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error_text = getattr(e, "text", str(e))
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error_response = getattr(e, "response", None)
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if error_headers is None and error_response:
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error_headers = getattr(error_response, "headers", None)
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if error_response and hasattr(error_response, "text"):
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error_text = getattr(error_response, "text", error_text)
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raise AnthropicError(
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message=error_text,
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status_code=status_code,
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headers=error_headers,
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)
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return config.transform_response(
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model=model,
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raw_response=response,
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model_response=model_response,
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logging_obj=logging_obj,
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api_key=api_key,
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request_data=data,
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messages=messages,
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optional_params=optional_params,
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litellm_params=litellm_params,
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encoding=encoding,
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json_mode=json_mode,
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)
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