87 lines
4.0 KiB
Python
87 lines
4.0 KiB
Python
# encoding: utf-8
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# @Time : 2024/1/22
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# @Author : Kilig947 & binary husky
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# @Descr : 兼容最新的智谱Ai
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from toolbox import get_conf
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from zhipuai import ZhipuAI
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from toolbox import get_conf, encode_image, get_pictures_list
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import logging, os
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def input_encode_handler(inputs:str, llm_kwargs:dict):
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if llm_kwargs["most_recent_uploaded"].get("path"):
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image_paths = get_pictures_list(llm_kwargs["most_recent_uploaded"]["path"])
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md_encode = []
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for md_path in image_paths:
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type_ = os.path.splitext(md_path)[1].replace(".", "")
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type_ = "jpeg" if type_ == "jpg" else type_
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md_encode.append({"data": encode_image(md_path), "type": type_})
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return inputs, md_encode
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class ZhipuChatInit:
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def __init__(self):
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ZHIPUAI_API_KEY, ZHIPUAI_MODEL = get_conf("ZHIPUAI_API_KEY", "ZHIPUAI_MODEL")
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if len(ZHIPUAI_MODEL) > 0:
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logging.error('ZHIPUAI_MODEL 配置项选项已经弃用，请在LLM_MODEL中配置')
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self.zhipu_bro = ZhipuAI(api_key=ZHIPUAI_API_KEY)
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self.model = ''
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def __conversation_user(self, user_input: str, llm_kwargs:dict):
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if self.model not in ["glm-4v"]:
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return {"role": "user", "content": user_input}
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else:
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input_, encode_img = input_encode_handler(user_input, llm_kwargs=llm_kwargs)
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what_i_have_asked = {"role": "user", "content": []}
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what_i_have_asked['content'].append({"type": 'text', "text": user_input})
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if encode_img:
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img_d = {"type": "image_url",
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"image_url": {'url': encode_img}}
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what_i_have_asked['content'].append(img_d)
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return what_i_have_asked
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def __conversation_history(self, history:list, llm_kwargs:dict):
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messages = []
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conversation_cnt = len(history) // 2
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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 = self.__conversation_user(history[index], llm_kwargs)
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what_gpt_answer = {
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"role": "assistant",
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"content": history[index + 1]
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}
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messages.append(what_i_have_asked)
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messages.append(what_gpt_answer)
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return messages
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def __conversation_message_payload(self, inputs:str, llm_kwargs:dict, history:list, system_prompt:str):
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messages = []
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if system_prompt:
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messages.append({"role": "system", "content": system_prompt})
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self.model = llm_kwargs['llm_model']
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messages.extend(self.__conversation_history(history, llm_kwargs)) # 处理 history
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if inputs.strip() == "": # 处理空输入导致报错的问题 https://github.com/binary-husky/gpt_academic/issues/1640 提示 {"error":{"code":"1214","message":"messages[1]:content和tool_calls 字段不能同时为空"}
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inputs = "." # 空格、换行、空字符串都会报错，所以用最没有意义的一个点代替
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messages.append(self.__conversation_user(inputs, llm_kwargs)) # 处理用户对话
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response = self.zhipu_bro.chat.completions.create(
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model=self.model, messages=messages, stream=True,
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temperature=llm_kwargs.get('temperature', 0.95) * 0.95, # 只能传默认的 temperature 和 top_p
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top_p=llm_kwargs.get('top_p', 0.7) * 0.7,
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max_tokens=llm_kwargs.get('max_tokens', 1024 * 4), # 最大输出模型的一半
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)
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return response
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def generate_chat(self, inputs:str, llm_kwargs:dict, history:list, system_prompt:str):
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self.model = llm_kwargs['llm_model']
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response = self.__conversation_message_payload(inputs, llm_kwargs, history, system_prompt)
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bro_results = ''
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for chunk in response:
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bro_results += chunk.choices[0].delta.content
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yield chunk.choices[0].delta.content, bro_results
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if __name__ == '__main__':
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zhipu = ZhipuChatInit()
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zhipu.generate_chat('你好', {'llm_model': 'glm-4'}, [], '你是WPSAi')
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