70 lines
2.8 KiB
Python
70 lines
2.8 KiB
Python
from toolbox import get_conf
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import threading
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import logging
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timeout_bot_msg = '[Local Message] Request timeout. Network error.'
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class ZhipuRequestInstance():
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def __init__(self):
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self.time_to_yield_event = threading.Event()
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self.time_to_exit_event = threading.Event()
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self.result_buf = ""
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def generate(self, inputs, llm_kwargs, history, system_prompt):
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# import _thread as thread
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import zhipuai
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ZHIPUAI_API_KEY, ZHIPUAI_MODEL = get_conf("ZHIPUAI_API_KEY", "ZHIPUAI_MODEL")
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zhipuai.api_key = ZHIPUAI_API_KEY
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self.result_buf = ""
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response = zhipuai.model_api.sse_invoke(
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model=ZHIPUAI_MODEL,
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prompt=generate_message_payload(inputs, llm_kwargs, history, system_prompt),
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top_p=llm_kwargs['top_p'],
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temperature=llm_kwargs['temperature'],
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)
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for event in response.events():
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if event.event == "add":
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# if self.result_buf == "" and event.data.startswith(" "):
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# event.data = event.data.lstrip(" ") # 每次智谱为啥都要带个空格开头呢？
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self.result_buf += event.data
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yield self.result_buf
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elif event.event == "error" or event.event == "interrupted":
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raise RuntimeError("Unknown error:" + event.data)
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elif event.event == "finish":
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yield self.result_buf
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break
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else:
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raise RuntimeError("Unknown error:" + str(event))
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logging.info(f'[raw_input] {inputs}')
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logging.info(f'[response] {self.result_buf}')
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return self.result_buf
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def generate_message_payload(inputs, llm_kwargs, history, system_prompt):
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conversation_cnt = len(history) // 2
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messages = [{"role": "user", "content": system_prompt}, {"role": "assistant", "content": "Certainly!"}]
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if conversation_cnt:
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for index in range(0, 2*conversation_cnt, 2):
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what_i_have_asked = {}
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what_i_have_asked["role"] = "user"
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what_i_have_asked["content"] = history[index]
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what_gpt_answer = {}
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what_gpt_answer["role"] = "assistant"
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what_gpt_answer["content"] = history[index+1]
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if what_i_have_asked["content"] != "":
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if what_gpt_answer["content"] == "":
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continue
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if what_gpt_answer["content"] == timeout_bot_msg:
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continue
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messages.append(what_i_have_asked)
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messages.append(what_gpt_answer)
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else:
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messages[-1]['content'] = what_gpt_answer['content']
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what_i_ask_now = {}
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what_i_ask_now["role"] = "user"
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what_i_ask_now["content"] = inputs
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messages.append(what_i_ask_now)
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return messages
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