chore: initial public snapshot for github upload
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"""
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OpenAI-like chat completion handler
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For handling OpenAI-like chat completions, like IBM WatsonX, etc.
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"""
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import json
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from typing import Any, Callable, Optional, Union
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import httpx
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import litellm
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from litellm import LlmProviders
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from litellm.llms.bedrock.chat.invoke_handler import MockResponseIterator
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from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
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from litellm.llms.databricks.streaming_utils import ModelResponseIterator
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from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig
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from litellm.llms.openai.openai import OpenAIConfig
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from litellm.types.utils import CustomStreamingDecoder, ModelResponse
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from litellm.utils import CustomStreamWrapper, ProviderConfigManager
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from ..common_utils import OpenAILikeBase, OpenAILikeError
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from .transformation import OpenAILikeChatConfig
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async def make_call(
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client: Optional[AsyncHTTPHandler],
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api_base: str,
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headers: dict,
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data: str,
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model: str,
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messages: list,
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logging_obj,
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streaming_decoder: Optional[CustomStreamingDecoder] = None,
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fake_stream: bool = False,
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):
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if client is None:
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client = litellm.module_level_aclient
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response = await client.post(
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api_base, headers=headers, data=data, stream=not fake_stream
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)
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if streaming_decoder is not None:
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completion_stream: Any = streaming_decoder.aiter_bytes(
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response.aiter_bytes(chunk_size=1024)
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)
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elif fake_stream:
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model_response = ModelResponse(**response.json())
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completion_stream = MockResponseIterator(model_response=model_response)
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else:
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completion_stream = ModelResponseIterator(
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streaming_response=response.aiter_lines(), sync_stream=False
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)
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# LOGGING
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logging_obj.post_call(
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input=messages,
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api_key="",
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original_response=completion_stream, # Pass the completion stream for logging
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additional_args={"complete_input_dict": data},
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)
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return completion_stream
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def make_sync_call(
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client: Optional[HTTPHandler],
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api_base: str,
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headers: dict,
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data: str,
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model: str,
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messages: list,
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logging_obj,
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streaming_decoder: Optional[CustomStreamingDecoder] = None,
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fake_stream: bool = False,
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timeout: Optional[Union[float, httpx.Timeout]] = None,
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):
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if client is None:
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client = litellm.module_level_client # Create a new client if none provided
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response = client.post(
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api_base, headers=headers, data=data, stream=not fake_stream, timeout=timeout
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)
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if response.status_code != 200:
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raise OpenAILikeError(status_code=response.status_code, message=response.read())
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if streaming_decoder is not None:
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completion_stream = streaming_decoder.iter_bytes(
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response.iter_bytes(chunk_size=1024)
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)
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elif fake_stream:
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model_response = ModelResponse(**response.json())
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completion_stream = MockResponseIterator(model_response=model_response)
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else:
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completion_stream = ModelResponseIterator(
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streaming_response=response.iter_lines(), sync_stream=True
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)
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# LOGGING
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logging_obj.post_call(
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input=messages,
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api_key="",
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original_response="first stream response received",
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additional_args={"complete_input_dict": data},
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)
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return completion_stream
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class OpenAILikeChatHandler(OpenAILikeBase):
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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async def acompletion_stream_function(
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self,
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model: str,
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messages: list,
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custom_llm_provider: str,
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api_base: 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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stream,
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data: dict,
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optional_params=None,
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litellm_params=None,
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logger_fn=None,
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headers={},
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client: Optional[AsyncHTTPHandler] = None,
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streaming_decoder: Optional[CustomStreamingDecoder] = None,
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fake_stream: bool = False,
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) -> CustomStreamWrapper:
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data["stream"] = True
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completion_stream = await make_call(
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client=client,
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api_base=api_base,
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headers=headers,
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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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streaming_decoder=streaming_decoder,
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)
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streamwrapper = CustomStreamWrapper(
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completion_stream=completion_stream,
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model=model,
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custom_llm_provider=custom_llm_provider,
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logging_obj=logging_obj,
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)
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return streamwrapper
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async def acompletion_function(
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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_prompt_dict: dict,
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model_response: ModelResponse,
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custom_llm_provider: str,
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print_verbose: Callable,
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client: Optional[AsyncHTTPHandler],
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encoding,
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api_key,
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logging_obj,
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stream,
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data: dict,
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base_model: Optional[str],
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optional_params: dict,
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litellm_params=None,
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logger_fn=None,
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headers={},
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timeout: Optional[Union[float, httpx.Timeout]] = None,
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json_mode: bool = False,
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) -> ModelResponse:
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if timeout is None:
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timeout = httpx.Timeout(timeout=600.0, connect=5.0)
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if client is None:
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client = litellm.module_level_aclient
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try:
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response = await client.post(
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api_base, headers=headers, data=json.dumps(data), timeout=timeout
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)
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response.raise_for_status()
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except httpx.HTTPStatusError as e:
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raise OpenAILikeError(
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status_code=e.response.status_code,
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message=e.response.text,
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)
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except httpx.TimeoutException:
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raise OpenAILikeError(status_code=408, message="Timeout error occurred.")
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except Exception as e:
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raise OpenAILikeError(status_code=500, message=str(e))
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return OpenAILikeChatConfig._transform_response(
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model=model,
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response=response,
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model_response=model_response,
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stream=stream,
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logging_obj=logging_obj,
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optional_params=optional_params,
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api_key=api_key,
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data=data,
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messages=messages,
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print_verbose=print_verbose,
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encoding=encoding,
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json_mode=json_mode,
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custom_llm_provider=custom_llm_provider,
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base_model=base_model,
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)
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def completion(
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self,
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*,
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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: Optional[str],
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logging_obj,
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optional_params: dict,
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acompletion=None,
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litellm_params: dict = {},
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logger_fn=None,
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headers: Optional[dict] = None,
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timeout: Optional[Union[float, httpx.Timeout]] = None,
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client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
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custom_endpoint: Optional[bool] = None,
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streaming_decoder: Optional[
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CustomStreamingDecoder
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] = None, # if openai-compatible api needs custom stream decoder - e.g. sagemaker
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fake_stream: bool = False,
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):
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custom_endpoint = custom_endpoint or optional_params.pop(
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"custom_endpoint", None
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)
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base_model: Optional[str] = optional_params.pop("base_model", None)
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api_base, headers = self._validate_environment(
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api_base=api_base,
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api_key=api_key,
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endpoint_type="chat_completions",
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custom_endpoint=custom_endpoint,
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headers=headers,
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)
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stream: bool = optional_params.pop("stream", None) or False
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extra_body = optional_params.pop("extra_body", {})
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json_mode = optional_params.pop("json_mode", None)
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optional_params.pop("max_retries", None)
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if not fake_stream:
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optional_params["stream"] = stream
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if messages is not None and custom_llm_provider is not None:
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provider_config = ProviderConfigManager.get_provider_chat_config(
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model=model, provider=LlmProviders(custom_llm_provider)
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)
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if isinstance(provider_config, OpenAIGPTConfig) or isinstance(
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provider_config, OpenAIConfig
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):
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messages = provider_config._transform_messages(
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messages=messages, model=model
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)
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data = {
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"model": model,
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"messages": messages,
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**optional_params,
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**extra_body,
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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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if acompletion is True:
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if client is None or not isinstance(client, AsyncHTTPHandler):
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client = None
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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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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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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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custom_llm_provider=custom_llm_provider,
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streaming_decoder=streaming_decoder,
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fake_stream=fake_stream,
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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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custom_llm_provider=custom_llm_provider,
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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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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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base_model=base_model,
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client=client,
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json_mode=json_mode,
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)
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else:
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## COMPLETION CALL
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if stream is True:
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completion_stream = make_sync_call(
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client=(
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client
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if client is not None and isinstance(client, HTTPHandler)
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else None
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),
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api_base=api_base,
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headers=headers,
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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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streaming_decoder=streaming_decoder,
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fake_stream=fake_stream,
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timeout=timeout,
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)
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# completion_stream.__iter__()
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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=custom_llm_provider,
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logging_obj=logging_obj,
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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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client = HTTPHandler(timeout=timeout) # type: ignore
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try:
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response = client.post(
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url=api_base, headers=headers, data=json.dumps(data)
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)
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response.raise_for_status()
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except httpx.HTTPStatusError as e:
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raise OpenAILikeError(
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status_code=e.response.status_code,
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message=e.response.text,
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)
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except httpx.TimeoutException:
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raise OpenAILikeError(
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status_code=408, message="Timeout error occurred."
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)
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except Exception as e:
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raise OpenAILikeError(status_code=500, message=str(e))
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return OpenAILikeChatConfig._transform_response(
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model=model,
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response=response,
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model_response=model_response,
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stream=stream,
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logging_obj=logging_obj,
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optional_params=optional_params,
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api_key=api_key,
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data=data,
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messages=messages,
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print_verbose=print_verbose,
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encoding=encoding,
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json_mode=json_mode,
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custom_llm_provider=custom_llm_provider,
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base_model=base_model,
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)
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