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Litellm merge pr (#7161)
* build: merge branch * test: fix openai naming * fix(main.py): fix openai renaming * style: ignore function length for config factory * fix(sagemaker/): fix routing logic * fix: fix imports * fix: fix override
This commit is contained in:
Krish Dholakia
GitHub
parent
d5aae81c6d
commit
350cfc36f7
88 changed files with 3617 additions and 4421 deletions
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@ -4,59 +4,42 @@ import time
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import traceback
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import types
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from enum import Enum
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from typing import Any, Callable, List, Optional
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from typing import Any, Callable, List, Optional, Union
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import requests # type: ignore
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from httpx._models import Headers
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import litellm
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from litellm.llms.base_llm.transformation import BaseLLMException
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from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig
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from litellm.utils import Choices, Message, ModelResponse, Usage
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class MaritalkError(Exception):
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def __init__(self, status_code, message):
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self.status_code = status_code
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self.message = message
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super().__init__(
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self.message
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) # Call the base class constructor with the parameters it needs
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class MaritalkError(BaseLLMException):
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def __init__(
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self,
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status_code: int,
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message: str,
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headers: Optional[Union[dict, Headers]] = None,
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):
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super().__init__(status_code=status_code, message=message, headers=headers)
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class MaritTalkConfig:
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"""
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The class `MaritTalkConfig` provides configuration for the MaritTalk's API interface. Here are the parameters:
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- `max_tokens` (integer): Maximum number of tokens the model will generate as part of the response. Default is 1.
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- `model` (string): The model used for conversation. Default is 'maritalk'.
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- `do_sample` (boolean): If set to True, the API will generate a response using sampling. Default is True.
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- `temperature` (number): A non-negative float controlling the randomness in generation. Lower temperatures result in less random generations. Default is 0.7.
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- `top_p` (number): Selection threshold for token inclusion based on cumulative probability. Default is 0.95.
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- `repetition_penalty` (number): Penalty for repetition in the generated conversation. Default is 1.
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- `stopping_tokens` (list of string): List of tokens where the conversation can be stopped/stopped.
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"""
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max_tokens: Optional[int] = None
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model: Optional[str] = None
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do_sample: Optional[bool] = None
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temperature: Optional[float] = None
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top_p: Optional[float] = None
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repetition_penalty: Optional[float] = None
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stopping_tokens: Optional[List[str]] = None
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class MaritalkConfig(OpenAIGPTConfig):
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def __init__(
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self,
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max_tokens: Optional[int] = None,
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model: Optional[str] = None,
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do_sample: Optional[bool] = None,
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temperature: Optional[float] = None,
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frequency_penalty: Optional[float] = None,
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presence_penalty: Optional[float] = None,
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top_p: Optional[float] = None,
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repetition_penalty: Optional[float] = None,
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stopping_tokens: Optional[List[str]] = None,
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top_k: Optional[int] = None,
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temperature: Optional[float] = None,
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max_tokens: Optional[int] = None,
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n: Optional[int] = None,
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stop: Optional[List[str]] = None,
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stream: Optional[bool] = None,
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stream_options: Optional[dict] = None,
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tools: Optional[List[dict]] = None,
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tool_choice: Optional[Union[str, dict]] = None,
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) -> None:
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locals_ = locals()
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for key, value in locals_.items():
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@ -65,129 +48,27 @@ class MaritTalkConfig:
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@classmethod
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def get_config(cls):
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return {
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k: v
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for k, v in cls.__dict__.items()
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if not k.startswith("__")
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and not isinstance(
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v,
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(
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types.FunctionType,
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types.BuiltinFunctionType,
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classmethod,
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staticmethod,
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),
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)
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and v is not None
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}
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return super().get_config()
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def get_supported_openai_params(self, model: str) -> List:
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return [
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"frequency_penalty",
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"presence_penalty",
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"top_p",
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"top_k",
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"temperature",
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"max_tokens",
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"n",
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"stop",
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"stream",
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"stream_options",
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"tools",
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"tool_choice",
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]
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def validate_environment(api_key):
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headers = {
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"accept": "application/json",
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"content-type": "application/json",
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}
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if api_key:
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headers["Authorization"] = f"Key {api_key}"
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return headers
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def completion(
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model: str,
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messages: list,
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api_base: str,
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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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litellm_params=None,
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logger_fn=None,
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):
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headers = validate_environment(api_key)
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completion_url = api_base
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model = model
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## Load Config
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config = litellm.MaritTalkConfig.get_config()
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for k, v in config.items():
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if (
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k not in optional_params
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): # completion(top_k=3) > maritalk_config(top_k=3) <- allows for dynamic variables to be passed in
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optional_params[k] = v
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data = {
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"messages": messages,
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**optional_params,
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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={"complete_input_dict": data},
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)
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## COMPLETION CALL
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response = requests.post(
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completion_url,
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headers=headers,
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data=json.dumps(data),
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stream=optional_params["stream"] if "stream" in optional_params else False,
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)
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if "stream" in optional_params and optional_params["stream"] is True:
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return response.iter_lines()
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else:
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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=api_key,
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original_response=response.text,
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additional_args={"complete_input_dict": data},
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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 MaritalkError(
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status_code=status_code, message=error_message, headers=headers
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)
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print_verbose(f"raw model_response: {response.text}")
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## RESPONSE OBJECT
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completion_response = response.json()
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if "error" in completion_response:
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raise MaritalkError(
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message=completion_response["error"],
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status_code=response.status_code,
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)
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else:
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try:
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if len(completion_response["answer"]) > 0:
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model_response.choices[0].message.content = completion_response[ # type: ignore
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"answer"
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]
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except Exception:
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raise MaritalkError(
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message=response.text, status_code=response.status_code
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)
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## CALCULATING USAGE
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prompt = "".join(m["content"] for m in messages)
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prompt_tokens = len(encoding.encode(prompt))
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completion_tokens = len(
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encoding.encode(model_response["choices"][0]["message"].get("content", ""))
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)
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model_response.created = int(time.time())
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model_response.model = model
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usage = Usage(
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prompt_tokens=prompt_tokens,
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completion_tokens=completion_tokens,
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total_tokens=prompt_tokens + completion_tokens,
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)
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setattr(model_response, "usage", usage)
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return model_response
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def embedding(
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model: str,
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input: list,
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api_key: Optional[str],
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logging_obj: Any,
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model_response=None,
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encoding=None,
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):
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pass
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