litellm-mirror/litellm/llms/maritalk.py

import os, types
import json
from enum import Enum
import requests
import time, traceback
from typing import Callable, Optional, List
from litellm.utils import ModelResponse, Choices, Message, Usage
import litellm

class MaritalkError(Exception):
    def __init__(self, status_code, message):
        self.status_code = status_code
        self.message = message
        super().__init__(
            self.message
        )  # Call the base class constructor with the parameters it needs

class MaritTalkConfig():
    """
    The class `MaritTalkConfig` provides configuration for the MaritTalk's API interface. Here are the parameters:
        
    - `max_tokens` (integer): Maximum number of tokens the model will generate as part of the response. Default is 1.
        
    - `model` (string): The model used for conversation. Default is 'maritalk'.
        
    - `do_sample` (boolean): If set to True, the API will generate a response using sampling. Default is True.
        
    - `temperature` (number): A non-negative float controlling the randomness in generation. Lower temperatures result in less random generations. Default is 0.7.
        
    - `top_p` (number): Selection threshold for token inclusion based on cumulative probability. Default is 0.95.
        
    - `repetition_penalty` (number): Penalty for repetition in the generated conversation. Default is 1.
        
    - `stopping_tokens` (list of string): List of tokens where the conversation can be stopped/stopped.
    """
    max_tokens: Optional[int] = None
    model: Optional[str] = None
    do_sample: Optional[bool] = None
    temperature: Optional[float] = None
    top_p: Optional[float] = None
    repetition_penalty: Optional[float] = None
    stopping_tokens: Optional[List[str]] = None

    def __init__(self,
                 max_tokens: Optional[int]=None,
                 model: Optional[str] = None,
                 do_sample: Optional[bool] = None,
                 temperature: Optional[float] = None,
                 top_p: Optional[float] = None,
                 repetition_penalty: Optional[float] = None,
                 stopping_tokens: Optional[List[str]] = None) -> None:
        
        locals_ = locals()
        for key, value in locals_.items():
            if key != 'self' and value is not None:
                setattr(self.__class__, key, value)
   
    @classmethod
    def get_config(cls):
        return {k: v for k, v in cls.__dict__.items() 
                if not k.startswith('__') 
                and not isinstance(v, (types.FunctionType, types.BuiltinFunctionType, classmethod, staticmethod)) 
                and v is not None}
        
def validate_environment(api_key):
    headers = {
        "accept": "application/json",
        "content-type": "application/json",
    }
    if api_key:
        headers["Authorization"] = f"Key {api_key}"
    return headers

def completion(
    model: str,
    messages: list,
    api_base: str,
    model_response: ModelResponse,
    print_verbose: Callable,
    encoding,
    api_key,
    logging_obj,
    optional_params=None,
    litellm_params=None,
    logger_fn=None,
):
    headers = validate_environment(api_key)
    completion_url = api_base
    model = model

    ## Load Config
    config=litellm.MaritTalkConfig.get_config()
    for k, v in config.items():
        if k not in optional_params: # completion(top_k=3) > maritalk_config(top_k=3) <- allows for dynamic variables to be passed in
            optional_params[k] = v

    data = {
        "messages": messages,
        **optional_params,
    }

    ## LOGGING
    logging_obj.pre_call(
            input=messages,
            api_key=api_key,
            additional_args={"complete_input_dict": data},
        )
    ## COMPLETION CALL
    response = requests.post(
        completion_url, headers=headers, data=json.dumps(data), stream=optional_params["stream"] if "stream" in optional_params else False
    )
    if "stream" in optional_params and optional_params["stream"] == True:
        return response.iter_lines()
    else:
        ## LOGGING
        logging_obj.post_call(
                input=messages,
                api_key=api_key,
                original_response=response.text,
                additional_args={"complete_input_dict": data},
            )
        print_verbose(f"raw model_response: {response.text}")
        ## RESPONSE OBJECT
        completion_response = response.json()
        if "error" in completion_response:
            raise MaritalkError(
                message=completion_response["error"],
                status_code=response.status_code,
            )
        else:
            try:
                if len(completion_response["answer"]) > 0:
                    model_response["choices"][0]["message"]["content"] = completion_response["answer"]
            except Exception as e:
                raise MaritalkError(message=response.text, status_code=response.status_code)

        ## CALCULATING USAGE
        prompt = "".join(m["content"] for m in messages)
        prompt_tokens = len(
            encoding.encode(prompt)
        ) 
        completion_tokens = len(
            encoding.encode(model_response["choices"][0]["message"].get("content", ""))
        )

        model_response["created"] = int(time.time())
        model_response["model"] = model
        usage = Usage(
            prompt_tokens=prompt_tokens,
            completion_tokens=completion_tokens,
            total_tokens=prompt_tokens + completion_tokens
        )
        model_response.usage = usage
        return model_response

def embedding(
    model: str,
    input: list,
    api_key: Optional[str] = None,
    logging_obj=None,
    model_response=None,
    encoding=None,
):
    pass