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import os, types |
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import json |
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from enum import Enum |
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import requests |
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import time, traceback |
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from typing import Callable, Optional, List |
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from litellm.utils import ModelResponse, Choices, Message, Usage |
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import litellm |
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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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) |
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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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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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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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) -> None: |
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locals_ = locals() |
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for key, value in locals_.items(): |
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if key != "self" and value is not None: |
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setattr(self.__class__, key, value) |
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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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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=None, |
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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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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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): |
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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_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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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"] == True: |
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return response.iter_lines() |
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else: |
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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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) |
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print_verbose(f"raw model_response: {response.text}") |
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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"][ |
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"content" |
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] = completion_response["answer"] |
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except Exception as e: |
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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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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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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] = None, |
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logging_obj=None, |
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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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