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| from http import HTTPStatus | |
| from toolbox import get_conf | |
| import threading | |
| import logging | |
| timeout_bot_msg = '[Local Message] Request timeout. Network error.' | |
| class QwenRequestInstance(): | |
| def __init__(self): | |
| import dashscope | |
| self.time_to_yield_event = threading.Event() | |
| self.time_to_exit_event = threading.Event() | |
| self.result_buf = "" | |
| def validate_key(): | |
| DASHSCOPE_API_KEY = get_conf("DASHSCOPE_API_KEY") | |
| if DASHSCOPE_API_KEY == '': return False | |
| return True | |
| if not validate_key(): | |
| raise RuntimeError('请配置 DASHSCOPE_API_KEY') | |
| dashscope.api_key = get_conf("DASHSCOPE_API_KEY") | |
| def generate(self, inputs, llm_kwargs, history, system_prompt): | |
| # import _thread as thread | |
| from dashscope import Generation | |
| QWEN_MODEL = { | |
| 'qwen-turbo': Generation.Models.qwen_turbo, | |
| 'qwen-plus': Generation.Models.qwen_plus, | |
| 'qwen-max': Generation.Models.qwen_max, | |
| }[llm_kwargs['llm_model']] | |
| top_p = llm_kwargs.get('top_p', 0.8) | |
| if top_p == 0: top_p += 1e-5 | |
| if top_p == 1: top_p -= 1e-5 | |
| self.result_buf = "" | |
| responses = Generation.call( | |
| model=QWEN_MODEL, | |
| messages=generate_message_payload(inputs, llm_kwargs, history, system_prompt), | |
| top_p=top_p, | |
| temperature=llm_kwargs.get('temperature', 1.0), | |
| result_format='message', | |
| stream=True, | |
| incremental_output=True | |
| ) | |
| for response in responses: | |
| if response.status_code == HTTPStatus.OK: | |
| if response.output.choices[0].finish_reason == 'stop': | |
| yield self.result_buf | |
| break | |
| elif response.output.choices[0].finish_reason == 'length': | |
| self.result_buf += "[Local Message] 生成长度过长，后续输出被截断" | |
| yield self.result_buf | |
| break | |
| else: | |
| self.result_buf += response.output.choices[0].message.content | |
| yield self.result_buf | |
| else: | |
| self.result_buf += f"[Local Message] 请求错误：状态码：{response.status_code}，错误码:{response.code}，消息：{response.message}" | |
| yield self.result_buf | |
| break | |
| logging.info(f'[raw_input] {inputs}') | |
| logging.info(f'[response] {self.result_buf}') | |
| return self.result_buf | |
| def generate_message_payload(inputs, llm_kwargs, history, system_prompt): | |
| conversation_cnt = len(history) // 2 | |
| if system_prompt == '': system_prompt = 'Hello!' | |
| messages = [{"role": "user", "content": system_prompt}, {"role": "assistant", "content": "Certainly!"}] | |
| if conversation_cnt: | |
| for index in range(0, 2*conversation_cnt, 2): | |
| what_i_have_asked = {} | |
| what_i_have_asked["role"] = "user" | |
| what_i_have_asked["content"] = history[index] | |
| what_gpt_answer = {} | |
| what_gpt_answer["role"] = "assistant" | |
| what_gpt_answer["content"] = history[index+1] | |
| if what_i_have_asked["content"] != "": | |
| if what_gpt_answer["content"] == "": | |
| continue | |
| if what_gpt_answer["content"] == timeout_bot_msg: | |
| continue | |
| messages.append(what_i_have_asked) | |
| messages.append(what_gpt_answer) | |
| else: | |
| messages[-1]['content'] = what_gpt_answer['content'] | |
| what_i_ask_now = {} | |
| what_i_ask_now["role"] = "user" | |
| what_i_ask_now["content"] = inputs | |
| messages.append(what_i_ask_now) | |
| return messages | |