| | 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): |
| | |
| | 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 |
| |
|
