apa.py

apa.py

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+import gradio as gr
+from zhipuai import ZhipuAI
+import json
+import os
+
+api_key = os.environ['ZHIPUAI_API_KEY']
+
+def convert_to_openai_format(nested_chat):
+    openai_format = []
+    for dialogue in nested_chat:
+        user_dialogue = {"role": "user", "content": dialogue[0]}
+        assistant_dialogue = {"role": "assistant", "content": dialogue[1]}
+        openai_format.extend([user_dialogue, assistant_dialogue])
+    return openai_format
+
+def master_llm(user_prompt, history):
+    # 生成针对专家LLM的系统提示
+    # 示例: 根据用户提问生成一个简单的系统提示
+    if history != []:
+        last_round = history[-1]
+        last_record_text = f"'''\n# 用户:\n{last_round[0]}\n\n\n# AI:\n{last_round[1]}\n\n\n# 用户:\n{user_prompt}\n'''"
+    else:
+        last_record_text = f"'''\n# 用户:\n{user_prompt}\n'''"
+    syst_prompt = """根据用户与AI的对话或提问，判断未来对话需要什么领域专家，并写出对应领域的AI专家的system prompt。
+    
+以以下JSON型式返回，请严格遵守`{}`与`""`的闭合（注意，所有参数都是string）：
+
+```
+{
+    "expert_system_prompt":"你是一个...AI，你有着...的经验...，你的思维...。现在，你的任务是...",
+    "temperature":"0.01",
+    "top_p":"1"
+}
+```
+
+参数解释：
+temperature为AI回复时的随机程度，值越小意味着回答逻辑越发散。取值为(0,1)，但不能等于0或1。
+top_p为AI会考虑的候选采样范围，比如0.1指只会选择前10%推荐的候选token。取值为(0,1)，但不能等于0或1。
+行业内，一般来说如果需要创意类型的AI，就会让这两个参数的值高一些，如果需要严格服从型的AI，则需要temperature尽量低一点。
+
+注意，请不要刻意生成专家，如果无法判断需要什么领域的专家（比如无上下文），则直接回复此默认设定：
+```
+{{
+    "expert_system_prompt":"根据用户的提问与需求（如有上下文，请根据上下文），返回合适的回应。",
+    "temperature":"0.5",
+    "top_p":"0.5"
+}}
+```
+    """
+    messages = [
+        {"role":"system","content":syst_prompt},
+        {"role":"user","content":last_record_text}
+    ]
+
+    client = ZhipuAI(api_key=api_key)
+    response = client.chat.completions.create(
+        model = "glm-4",
+        messages = messages,
+        temperature = 0.01,
+        top_p = 0.01,
+        do_sample = True
+    )
+
+    response_text = response.choices[0].message.content
+    response_json = json.loads(response_text[response_text.find('{'):response_text.rfind('}')+1])
+    expert_system_prompt = response_json['expert_system_prompt']
+    temperature = response_json['temperature']
+    top_p = response_json['top_p']
+    print(response_text)
+    
+    return expert_system_prompt, temperature, top_p
+
+def expert_llm(user_prompt, history, expert_system_prompt, temperature, top_p):
+    client = ZhipuAI(api_key=api_key)
+    if history != []:
+        prompt_records = convert_to_openai_format(history)
+        messages = [{"role":"system","content":expert_system_prompt}] + prompt_records + [{"role":"user","content":user_prompt}]
+    else:
+        messages = [{"role":"system","content":expert_system_prompt},{"role":"user","content":user_prompt}]
+    response = client.chat.completions.create(
+        model = "glm-4",
+        messages = messages,
+        temperature = float(temperature),
+        top_p = float(top_p),
+        do_sample = True
+    )
+    return response.choices[0].message.content
+
+def gradio_fn(message, history):
+    expert_system_prompt, temperature, top_p = master_llm(message, history)
+    expert_response = expert_llm(message, history, expert_system_prompt, temperature, top_p)
+    return expert_response
+
+demo = gr.ChatInterface(fn=gradio_fn)
+
+if __name__ == "__main__":
+    demo.launch()