# -*- coding: utf-8 -*- # Copyright (c) Alibaba, Inc. and its affiliates. import copy from typing import Any, List from dashscope.api_entities.dashscope_response import ( DashScopeAPIResponse, Message, Role, ) from dashscope.client.base_api import BaseApi from dashscope.common.constants import ( CUSTOMIZED_MODEL_ID, DEPRECATED_MESSAGE, HISTORY, MESSAGES, PROMPT, ) from dashscope.common.error import InputRequired, ModelRequired from dashscope.common.logging import logger class Tokenization(BaseApi): FUNCTION = "tokenizer" """API for get tokenizer result.. """ class Models: """List of models currently supported""" qwen_turbo = "qwen-turbo" qwen_plus = "qwen-plus" qwen_7b_chat = "qwen-7b-chat" qwen_14b_chat = "qwen-14b-chat" llama2_7b_chat_v2 = "llama2-7b-chat-v2" llama2_13b_chat_v2 = "llama2-13b-chat-v2" text_embedding_v2 = "text-embedding-v2" qwen_72b_chat = "qwen-72b-chat" @classmethod # type: ignore[override] def call( # pylint: disable=arguments-renamed # noqa: E501 cls, model: str, input: Any = None, # pylint: disable=redefined-builtin prompt: Any = None, history: list = None, api_key: str = None, messages: List[Message] = None, workspace: str = None, **kwargs, ) -> DashScopeAPIResponse: """Call tokenization. Args: model (str): The requested model, such as qwen-v1 input: (Any): The model input body. prompt (Any): The input prompt, for qwen serial model. history (list):The user provided history, deprecated, use messages instead. examples: [{'user':'The weather is fine today.', 'bot': 'Suitable for outings'}]. Defaults to None. api_key (str, optional): The api api_key, can be None, if None, will get by default rule(TODO: api key doc). messages (list): The generation messages. examples: [{'role': 'user', 'content': 'The weather is fine today.'}, {'role': 'assistant', 'content': 'Suitable for outings'}] workspace (str): The dashscope workspace id. **kwargs: see model input. Raises: InputRequired: input is required. ModelRequired: model is required. Returns: DashScopeAPIResponse: The tokenizer output. """ if ( # pylint: disable=too-many-boolean-expressions (input is None or not input) and (prompt is None or not prompt) and (messages is None or not messages) ): raise InputRequired("prompt or messages is required!") if model is None or not model: raise ModelRequired("Model is required!") if input is None: input, parameters = cls._build_llm_parameters( model, prompt, history, messages, **kwargs, ) else: parameters = kwargs if kwargs.pop("stream", False): # not support stream logger.warning("streaming option not supported for tokenization.") return super().call( model=model, task_group=None, # type: ignore[arg-type] function=cls.FUNCTION, api_key=api_key, input=input, is_service=False, workspace=workspace, **parameters, ) @classmethod def _build_llm_parameters(cls, model, prompt, history, messages, **kwargs): parameters = {} input = {} # pylint: disable=redefined-builtin if history is not None: logger.warning(DEPRECATED_MESSAGE) input[HISTORY] = history if prompt is not None and prompt: input[PROMPT] = prompt elif messages is not None: msgs = copy.deepcopy(messages) if prompt is not None and prompt: msgs.append({"role": Role.USER, "content": prompt}) input = {MESSAGES: msgs} else: input[PROMPT] = prompt if model.startswith("qwen"): enable_search = kwargs.pop("enable_search", False) if enable_search: parameters["enable_search"] = enable_search elif model.startswith("bailian"): customized_model_id = kwargs.pop("customized_model_id", None) if customized_model_id is None: raise InputRequired( f"customized_model_id is required for {model}", ) input[CUSTOMIZED_MODEL_ID] = customized_model_id return input, {**parameters, **kwargs}