463 lines
17 KiB
Python
463 lines
17 KiB
Python
from typing import Any, Awaitable, Callable, Dict, Optional, Sequence
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from llama_index.bridge.pydantic import Field
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from llama_index.callbacks import CallbackManager
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from llama_index.constants import DEFAULT_TEMPERATURE
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from llama_index.core.llms.types import (
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ChatMessage,
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ChatResponse,
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ChatResponseAsyncGen,
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ChatResponseGen,
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CompletionResponse,
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CompletionResponseAsyncGen,
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CompletionResponseGen,
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LLMMetadata,
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)
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from llama_index.llms.base import llm_chat_callback, llm_completion_callback
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from llama_index.llms.generic_utils import (
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achat_to_completion_decorator,
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acompletion_to_chat_decorator,
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astream_chat_to_completion_decorator,
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astream_completion_to_chat_decorator,
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chat_to_completion_decorator,
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completion_to_chat_decorator,
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stream_chat_to_completion_decorator,
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stream_completion_to_chat_decorator,
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)
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from llama_index.llms.litellm_utils import (
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acompletion_with_retry,
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completion_with_retry,
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from_litellm_message,
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is_function_calling_model,
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openai_modelname_to_contextsize,
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to_openai_message_dicts,
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validate_litellm_api_key,
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)
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from llama_index.llms.llm import LLM
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from llama_index.types import BaseOutputParser, PydanticProgramMode
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DEFAULT_LITELLM_MODEL = "gpt-3.5-turbo"
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class LiteLLM(LLM):
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model: str = Field(
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default=DEFAULT_LITELLM_MODEL,
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description=(
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"The LiteLLM model to use. "
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"For complete list of providers https://docs.litellm.ai/docs/providers"
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),
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)
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temperature: float = Field(
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default=DEFAULT_TEMPERATURE,
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description="The temperature to use during generation.",
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gte=0.0,
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lte=1.0,
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)
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max_tokens: Optional[int] = Field(
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description="The maximum number of tokens to generate.",
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gt=0,
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)
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additional_kwargs: Dict[str, Any] = Field(
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default_factory=dict,
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description="Additional kwargs for the LLM API.",
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# for all inputs https://docs.litellm.ai/docs/completion/input
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)
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max_retries: int = Field(
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default=10, description="The maximum number of API retries."
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)
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def __init__(
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self,
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model: str = DEFAULT_LITELLM_MODEL,
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temperature: float = DEFAULT_TEMPERATURE,
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max_tokens: Optional[int] = None,
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additional_kwargs: Optional[Dict[str, Any]] = None,
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max_retries: int = 10,
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api_key: Optional[str] = None,
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api_type: Optional[str] = None,
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api_base: Optional[str] = None,
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callback_manager: Optional[CallbackManager] = None,
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system_prompt: Optional[str] = None,
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messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
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completion_to_prompt: Optional[Callable[[str], str]] = None,
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pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
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output_parser: Optional[BaseOutputParser] = None,
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**kwargs: Any,
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) -> None:
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if "custom_llm_provider" in kwargs:
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if (
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kwargs["custom_llm_provider"] != "ollama"
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and kwargs["custom_llm_provider"] != "vllm"
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): # don't check keys for local models
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validate_litellm_api_key(api_key, api_type)
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else: # by default assume it's a hosted endpoint
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validate_litellm_api_key(api_key, api_type)
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additional_kwargs = additional_kwargs or {}
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if api_key is not None:
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additional_kwargs["api_key"] = api_key
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if api_type is not None:
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additional_kwargs["api_type"] = api_type
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if api_base is not None:
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additional_kwargs["api_base"] = api_base
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super().__init__(
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model=model,
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temperature=temperature,
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max_tokens=max_tokens,
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additional_kwargs=additional_kwargs,
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max_retries=max_retries,
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callback_manager=callback_manager,
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system_prompt=system_prompt,
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messages_to_prompt=messages_to_prompt,
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completion_to_prompt=completion_to_prompt,
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pydantic_program_mode=pydantic_program_mode,
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output_parser=output_parser,
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**kwargs,
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)
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def _get_model_name(self) -> str:
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model_name = self.model
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if "ft-" in model_name: # legacy fine-tuning
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model_name = model_name.split(":")[0]
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elif model_name.startswith("ft:"):
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model_name = model_name.split(":")[1]
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return model_name
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@classmethod
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def class_name(cls) -> str:
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return "litellm_llm"
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@property
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def metadata(self) -> LLMMetadata:
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return LLMMetadata(
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context_window=openai_modelname_to_contextsize(self._get_model_name()),
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num_output=self.max_tokens or -1,
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is_chat_model=True,
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is_function_calling_model=is_function_calling_model(self._get_model_name()),
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model_name=self.model,
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)
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@llm_chat_callback()
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def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
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if self._is_chat_model:
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chat_fn = self._chat
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else:
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chat_fn = completion_to_chat_decorator(self._complete)
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return chat_fn(messages, **kwargs)
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@llm_chat_callback()
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def stream_chat(
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self, messages: Sequence[ChatMessage], **kwargs: Any
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) -> ChatResponseGen:
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if self._is_chat_model:
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stream_chat_fn = self._stream_chat
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else:
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stream_chat_fn = stream_completion_to_chat_decorator(self._stream_complete)
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return stream_chat_fn(messages, **kwargs)
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@llm_completion_callback()
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def complete(
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self, prompt: str, formatted: bool = False, **kwargs: Any
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) -> CompletionResponse:
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# litellm assumes all llms are chat llms
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if self._is_chat_model:
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complete_fn = chat_to_completion_decorator(self._chat)
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else:
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complete_fn = self._complete
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return complete_fn(prompt, **kwargs)
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@llm_completion_callback()
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def stream_complete(
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self, prompt: str, formatted: bool = False, **kwargs: Any
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) -> CompletionResponseGen:
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if self._is_chat_model:
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stream_complete_fn = stream_chat_to_completion_decorator(self._stream_chat)
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else:
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stream_complete_fn = self._stream_complete
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return stream_complete_fn(prompt, **kwargs)
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@property
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def _is_chat_model(self) -> bool:
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# litellm assumes all llms are chat llms
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return True
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@property
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def _model_kwargs(self) -> Dict[str, Any]:
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base_kwargs = {
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"model": self.model,
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"temperature": self.temperature,
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"max_tokens": self.max_tokens,
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}
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return {
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**base_kwargs,
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**self.additional_kwargs,
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}
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def _get_all_kwargs(self, **kwargs: Any) -> Dict[str, Any]:
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return {
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**self._model_kwargs,
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**kwargs,
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}
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def _chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
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if not self._is_chat_model:
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raise ValueError("This model is not a chat model.")
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message_dicts = to_openai_message_dicts(messages)
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all_kwargs = self._get_all_kwargs(**kwargs)
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if "max_tokens" in all_kwargs and all_kwargs["max_tokens"] is None:
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all_kwargs.pop(
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"max_tokens"
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) # don't send max_tokens == None, this throws errors for Non OpenAI providers
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response = completion_with_retry(
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is_chat_model=self._is_chat_model,
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max_retries=self.max_retries,
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messages=message_dicts,
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stream=False,
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**all_kwargs,
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)
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message_dict = response["choices"][0]["message"]
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message = from_litellm_message(message_dict)
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return ChatResponse(
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message=message,
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raw=response,
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additional_kwargs=self._get_response_token_counts(response),
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)
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def _stream_chat(
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self, messages: Sequence[ChatMessage], **kwargs: Any
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) -> ChatResponseGen:
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if not self._is_chat_model:
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raise ValueError("This model is not a chat model.")
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message_dicts = to_openai_message_dicts(messages)
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all_kwargs = self._get_all_kwargs(**kwargs)
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if "max_tokens" in all_kwargs and all_kwargs["max_tokens"] is None:
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all_kwargs.pop(
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"max_tokens"
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) # don't send max_tokens == None, this throws errors for Non OpenAI providers
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def gen() -> ChatResponseGen:
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content = ""
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function_call: Optional[dict] = None
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for response in completion_with_retry(
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is_chat_model=self._is_chat_model,
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max_retries=self.max_retries,
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messages=message_dicts,
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stream=True,
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**all_kwargs,
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):
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delta = response["choices"][0]["delta"]
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role = delta.get("role", "assistant")
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content_delta = delta.get("content", "") or ""
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content += content_delta
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function_call_delta = delta.get("function_call", None)
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if function_call_delta is not None:
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if function_call is None:
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function_call = function_call_delta
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## ensure we do not add a blank function call
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if function_call.get("function_name", "") is None:
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del function_call["function_name"]
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else:
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function_call["arguments"] += function_call_delta["arguments"]
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additional_kwargs = {}
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if function_call is not None:
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additional_kwargs["function_call"] = function_call
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yield ChatResponse(
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message=ChatMessage(
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role=role,
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content=content,
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additional_kwargs=additional_kwargs,
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),
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delta=content_delta,
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raw=response,
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additional_kwargs=self._get_response_token_counts(response),
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)
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return gen()
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def _complete(self, prompt: str, **kwargs: Any) -> CompletionResponse:
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raise NotImplementedError("litellm assumes all llms are chat llms.")
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def _stream_complete(self, prompt: str, **kwargs: Any) -> CompletionResponseGen:
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raise NotImplementedError("litellm assumes all llms are chat llms.")
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def _get_max_token_for_prompt(self, prompt: str) -> int:
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try:
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import tiktoken
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except ImportError:
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raise ImportError(
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"Please install tiktoken to use the max_tokens=None feature."
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)
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context_window = self.metadata.context_window
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try:
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encoding = tiktoken.encoding_for_model(self._get_model_name())
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except KeyError:
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encoding = encoding = tiktoken.get_encoding(
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"cl100k_base"
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) # default to using cl10k_base
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tokens = encoding.encode(prompt)
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max_token = context_window - len(tokens)
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if max_token <= 0:
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raise ValueError(
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f"The prompt is too long for the model. "
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f"Please use a prompt that is less than {context_window} tokens."
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)
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return max_token
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def _get_response_token_counts(self, raw_response: Any) -> dict:
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"""Get the token usage reported by the response."""
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if not isinstance(raw_response, dict):
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return {}
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usage = raw_response.get("usage", {})
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return {
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"prompt_tokens": usage.get("prompt_tokens", 0),
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"completion_tokens": usage.get("completion_tokens", 0),
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"total_tokens": usage.get("total_tokens", 0),
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}
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# ===== Async Endpoints =====
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@llm_chat_callback()
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async def achat(
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self,
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messages: Sequence[ChatMessage],
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**kwargs: Any,
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) -> ChatResponse:
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achat_fn: Callable[..., Awaitable[ChatResponse]]
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if self._is_chat_model:
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achat_fn = self._achat
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else:
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achat_fn = acompletion_to_chat_decorator(self._acomplete)
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return await achat_fn(messages, **kwargs)
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@llm_chat_callback()
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async def astream_chat(
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self,
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messages: Sequence[ChatMessage],
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**kwargs: Any,
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) -> ChatResponseAsyncGen:
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astream_chat_fn: Callable[..., Awaitable[ChatResponseAsyncGen]]
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if self._is_chat_model:
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astream_chat_fn = self._astream_chat
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else:
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astream_chat_fn = astream_completion_to_chat_decorator(
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self._astream_complete
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)
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return await astream_chat_fn(messages, **kwargs)
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@llm_completion_callback()
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async def acomplete(
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self, prompt: str, formatted: bool = False, **kwargs: Any
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) -> CompletionResponse:
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if self._is_chat_model:
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acomplete_fn = achat_to_completion_decorator(self._achat)
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else:
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acomplete_fn = self._acomplete
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return await acomplete_fn(prompt, **kwargs)
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@llm_completion_callback()
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async def astream_complete(
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self, prompt: str, formatted: bool = False, **kwargs: Any
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) -> CompletionResponseAsyncGen:
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if self._is_chat_model:
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astream_complete_fn = astream_chat_to_completion_decorator(
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self._astream_chat
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)
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else:
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astream_complete_fn = self._astream_complete
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return await astream_complete_fn(prompt, **kwargs)
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async def _achat(
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self, messages: Sequence[ChatMessage], **kwargs: Any
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) -> ChatResponse:
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if not self._is_chat_model:
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raise ValueError("This model is not a chat model.")
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message_dicts = to_openai_message_dicts(messages)
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all_kwargs = self._get_all_kwargs(**kwargs)
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response = await acompletion_with_retry(
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is_chat_model=self._is_chat_model,
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max_retries=self.max_retries,
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messages=message_dicts,
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stream=False,
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**all_kwargs,
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)
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message_dict = response["choices"][0]["message"]
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message = from_litellm_message(message_dict)
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return ChatResponse(
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message=message,
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raw=response,
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additional_kwargs=self._get_response_token_counts(response),
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)
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async def _astream_chat(
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self, messages: Sequence[ChatMessage], **kwargs: Any
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) -> ChatResponseAsyncGen:
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if not self._is_chat_model:
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raise ValueError("This model is not a chat model.")
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message_dicts = to_openai_message_dicts(messages)
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all_kwargs = self._get_all_kwargs(**kwargs)
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async def gen() -> ChatResponseAsyncGen:
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content = ""
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function_call: Optional[dict] = None
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async for response in await acompletion_with_retry(
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is_chat_model=self._is_chat_model,
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max_retries=self.max_retries,
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messages=message_dicts,
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stream=True,
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**all_kwargs,
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):
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delta = response["choices"][0]["delta"]
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role = delta.get("role", "assistant")
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content_delta = delta.get("content", "") or ""
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content += content_delta
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function_call_delta = delta.get("function_call", None)
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if function_call_delta is not None:
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if function_call is None:
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function_call = function_call_delta
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## ensure we do not add a blank function call
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if function_call.get("function_name", "") is None:
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del function_call["function_name"]
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else:
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function_call["arguments"] += function_call_delta["arguments"]
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additional_kwargs = {}
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if function_call is not None:
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additional_kwargs["function_call"] = function_call
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yield ChatResponse(
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message=ChatMessage(
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role=role,
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content=content,
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additional_kwargs=additional_kwargs,
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),
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delta=content_delta,
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raw=response,
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additional_kwargs=self._get_response_token_counts(response),
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)
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return gen()
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async def _acomplete(self, prompt: str, **kwargs: Any) -> CompletionResponse:
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raise NotImplementedError("litellm assumes all llms are chat llms.")
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async def _astream_complete(
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self, prompt: str, **kwargs: Any
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) -> CompletionResponseAsyncGen:
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raise NotImplementedError("litellm assumes all llms are chat llms.")
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