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# encoding: utf-8
# @Time : 2024/1/22
# @Author : Kilig947 & binary husky
# @Descr : 兼容最新的智谱Ai
from toolbox import get_conf
from zhipuai import ZhipuAI
from toolbox import get_conf, encode_image, get_pictures_list
import logging, os
def input_encode_handler(inputs, llm_kwargs):
if llm_kwargs["most_recent_uploaded"].get("path"):
image_paths = get_pictures_list(llm_kwargs["most_recent_uploaded"]["path"])
md_encode = []
for md_path in image_paths:
type_ = os.path.splitext(md_path)[1].replace(".", "")
type_ = "jpeg" if type_ == "jpg" else type_
md_encode.append({"data": encode_image(md_path), "type": type_})
return inputs, md_encode
class ZhipuChatInit:
def __init__(self):
ZHIPUAI_API_KEY, ZHIPUAI_MODEL = get_conf("ZHIPUAI_API_KEY", "ZHIPUAI_MODEL")
if len(ZHIPUAI_MODEL) > 0:
logging.error('ZHIPUAI_MODEL 配置项选项已经弃用，请在LLM_MODEL中配置')
self.zhipu_bro = ZhipuAI(api_key=ZHIPUAI_API_KEY)
self.model = ''
def __conversation_user(self, user_input: str, llm_kwargs):
if self.model not in ["glm-4v"]:
return {"role": "user", "content": user_input}
else:
input_, encode_img = input_encode_handler(user_input, llm_kwargs=llm_kwargs)
what_i_have_asked = {"role": "user", "content": []}
what_i_have_asked['content'].append({"type": 'text', "text": user_input})
if encode_img:
img_d = {"type": "image_url",
"image_url": {'url': encode_img}}
what_i_have_asked['content'].append(img_d)
return what_i_have_asked
def __conversation_history(self, history, llm_kwargs):
messages = []
conversation_cnt = len(history) // 2
if conversation_cnt:
for index in range(0, 2 * conversation_cnt, 2):
what_i_have_asked = self.__conversation_user(history[index], llm_kwargs)
what_gpt_answer = {
"role": "assistant",
"content": history[index + 1]
}
messages.append(what_i_have_asked)
messages.append(what_gpt_answer)
return messages
def __conversation_message_payload(self, inputs, llm_kwargs, history, system_prompt):
messages = []
if system_prompt:
messages.append({"role": "system", "content": system_prompt})
self.model = llm_kwargs['llm_model']
messages.extend(self.__conversation_history(history, llm_kwargs)) # 处理 history
messages.append(self.__conversation_user(inputs, llm_kwargs)) # 处理用户对话
response = self.zhipu_bro.chat.completions.create(
model=self.model, messages=messages, stream=True,
temperature=llm_kwargs.get('temperature', 0.95) * 0.95, # 只能传默认的 temperature 和 top_p
top_p=llm_kwargs.get('top_p', 0.7) * 0.7,
max_tokens=llm_kwargs.get('max_tokens', 1024 * 4), # 最大输出模型的一半
)
return response
def generate_chat(self, inputs, llm_kwargs, history, system_prompt):
self.model = llm_kwargs['llm_model']
response = self.__conversation_message_payload(inputs, llm_kwargs, history, system_prompt)
bro_results = ''
for chunk in response:
bro_results += chunk.choices[0].delta.content
yield chunk.choices[0].delta.content, bro_results
if __name__ == '__main__':
zhipu = ZhipuChatInit()
zhipu.generate_chat('你好', {'llm_model': 'glm-4'}, [], '你是WPSAi')
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