add guideline
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朱潮
parent
363c80d9cb
commit
277fcca3d1
127
fastapi_app.py
127
fastapi_app.py
@ -21,6 +21,7 @@ from fastapi.staticfiles import StaticFiles
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from fastapi.middleware.cors import CORSMiddleware
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from file_manager_api import router as file_manager_router
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from qwen_agent.llm.schema import ASSISTANT, FUNCTION
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from qwen_agent.llm.oai import TextChatAtOAI
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from pydantic import BaseModel, Field
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# 导入语义检索服务
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@ -1044,6 +1045,115 @@ def process_messages(messages: List[Message], language: Optional[str] = None) ->
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return final_messages
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def extract_guidelines_from_system_prompt(system_prompt: Optional[str]) -> tuple[str, str]:
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"""从system_prompt中提取```guideline内容并清理原提示词
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Returns:
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tuple[str, str]: (清理后的system_prompt, 提取的guidelines内容)
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"""
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if not system_prompt:
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return "", ""
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# 使用正则表达式提取 ```guideline``` 包裹的内容
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pattern = r'```guideline\s*\n(.*?)\n```'
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matches = re.findall(pattern, system_prompt, re.DOTALL)
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guidelines_text = "\n".join(matches).strip()
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# # 从原始system_prompt中删除 ```guideline``` 内容块
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# cleaned_prompt = re.sub(pattern, '', system_prompt, flags=re.DOTALL)
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# # 清理多余的空行
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# cleaned_prompt = re.sub(r'\n\s*\n\s*\n', '\n\n', cleaned_prompt).strip()
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return guidelines_text
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def format_messages_to_chat_history(messages: List[Dict[str, str]]) -> str:
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"""将messages格式化为纯文本聊天记录
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Args:
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messages: 消息列表
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Returns:
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str: 格式化的聊天记录
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"""
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chat_history = []
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for message in messages:
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role = message.get('role', '')
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content = message.get('content', '')
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if role == 'user':
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chat_history.append(f"user: {content}")
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elif role == 'assistant':
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chat_history.append(f"assistant: {content}")
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# 忽略其他角色（如function等）
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return "\n".join(chat_history)
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async def call_guideline_llm(chat_history: str, guidelines_text: str, model_name: str, api_key: str, model_server: str) -> str:
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"""调用大语言模型处理guideline分析
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Args:
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chat_history: 聊天历史记录
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guidelines_text: 指导原则文本
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model_name: 模型名称
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api_key: API密钥
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model_server: 模型服务器地址
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Returns:
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str: 模型响应结果
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"""
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# 读取guideline提示词模板
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try:
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with open('./prompt/guideline_prompt.md', 'r', encoding='utf-8') as f:
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guideline_template = f.read()
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except Exception as e:
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print(f"Error reading guideline prompt template: {e}")
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return ""
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# 替换模板中的占位符
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system_prompt = guideline_template.replace('{chat_history}', chat_history).replace('{guidelines_text}', guidelines_text)
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# 配置LLM
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llm_config = {
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'model': model_name,
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'api_key': api_key,
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'model_server': model_server, # 使用传入的model_server参数
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}
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# 创建LLM实例
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llm_instance = TextChatAtOAI(llm_config)
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# 调用模型
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messages = [{'role': 'user', 'content': system_prompt}]
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try:
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# 设置stream=False来获取非流式响应
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response = llm_instance.chat(messages=messages, stream=False)
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# 处理响应
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if isinstance(response, list) and response:
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# 如果返回的是Message列表，提取内容
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if hasattr(response[0], 'content'):
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return response[0].content
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elif isinstance(response[0], dict) and 'content' in response[0]:
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return response[0]['content']
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# 如果是字符串，直接返回
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if isinstance(response, str):
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return response
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# 处理其他类型
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return str(response) if response else ""
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except Exception as e:
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print(f"Error calling guideline LLM: {e}")
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return ""
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async def create_agent_and_generate_response(
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bot_id: str,
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api_key: str,
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@ -1064,6 +1174,23 @@ async def create_agent_and_generate_response(
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if generate_cfg is None:
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generate_cfg = {}
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# 1. 从system_prompt提取guideline内容
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guidelines_text = extract_guidelines_from_system_prompt(system_prompt)
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# 2. 如果有guideline内容，调用LLM进行分析
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guideline_analysis = ""
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if guidelines_text:
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chat_history = format_messages_to_chat_history(messages)
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guideline_analysis = await call_guideline_llm(chat_history, guidelines_text, model_name, api_key, model_server)
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print(f"Guideline analysis result: {guideline_analysis}")
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# 将分析结果添加到最后一个消息的内容中
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if guideline_analysis and messages:
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last_message = messages[-1]
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if last_message.get('role') == 'user':
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messages[-1]['content'] += f"\n\nActive Guidelines:\n{guideline_analysis}\nPlease follow these guidelines in your response."
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print(messages[-1]['content'])
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# 从全局管理器获取或创建助手实例
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agent = await agent_manager.get_or_create_agent(
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bot_id=bot_id,
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167
prompt/guideline_prompt.md
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167
prompt/guideline_prompt.md
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@ -0,0 +1,167 @@
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GENERAL INSTRUCTIONS
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-----------------
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In our system, the behavior of a conversational AI agent is guided by "guidelines".
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Each guideline is composed of two parts:
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- "condition": This is a natural-language condition that specifies when a guideline should apply
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- "action": This is a natural-language instruction that should be followed by the agent
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Task Description
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----------------
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Your task is to evaluate the relevance and applicability of a set of provided 'when' conditions
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to the most recent state of an interaction between yourself (an AI agent) and a user.
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Examples of Guideline Match Evaluations:
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```
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示例 1: 旅游咨询
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Example #1: ###
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- **Chat History**:
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user: Hi, I'm planning a trip to Italy next month. What can I do there?
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ai_agent: That sounds exciting! I can help you with that. Do you prefer exploring cities or enjoying scenic landscapes?
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user: Can you help me figure out the best time to visit Rome and what to pack?
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user: Actually I'm also wondering — do I need any special visas or documents as an American citizen?
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- **Guidelines**:
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1) Condition: The customer is looking for flight or accommodation booking assistance. Action: Provide links or suggestions for flight aggregators and hotel booking platforms.
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2) Condition: The customer ask for activities recommendations. Action: Guide them in refining their preferences and suggest options that match what they're looking for
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3) Condition: The customer asks for logistical or legal requirements. Action: Provide a clear answer or direct them to a trusted official source if uncertain.
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- **Expected Result**:
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```json
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{
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"checks": [
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{
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"guideline_id": "<example-id-for-few-shots--do-not-use-this-in-output>",
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"condition": "The customer is looking for flight or accommodation booking assistance",
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"rationale": "There's no mention of booking logistics like flights or hotels",
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"applies": false
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},
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{
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"guideline_id": "<example-id-for-few-shots--do-not-use-this-in-output>",
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"condition": "The customer ask for activities recommendations",
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"rationale": "The customer has moved from seeking activity recommendations to asking about legal requirements. Since they are no longer pursuing their original inquiry about activities, this represents a new topic rather than a sub-issue",
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"applies": false
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},
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{
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"guideline_id": "<example-id-for-few-shots--do-not-use-this-in-output>",
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"condition": "The customer asks for logistical or legal requirements.",
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"rationale": "The customer now asked about visas and documents which are legal requirements",
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"applies": true
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}
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]
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}
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```
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示例 2: 课程咨询
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Example #2: ###
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- **Chat History**:
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user:Hi, I'm interested in your Python programming course, but I'm not sure if I'm ready for it.
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ai_agent:Happy to help! Could you share a bit about your background or experience with programming so far?
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user:I've done some HTML and CSS, but never written real code before.
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ai_agent:Thanks for sharing! That gives me a good idea. Our Python course is beginner-friendly, but it does assume you're comfortable with logic and problem solving. Would you like me to recommend a short prep course first?
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user:That sounds useful. But I'm also wondering — is the course self-paced? I work full time.
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- **Guidelines**:
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1) Condition: The customer mentions a constraint that related to commitment to the course. Action: Emphasize flexible learning options
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2) Condition: The user expresses hesitation or self-doubt. Action: Affirm that it's okay to be uncertain and provide confidence-building context
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3) Condition: The user asks about certification or course completion benefits. Action: Clearly explain what the user receives
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- **Expected Result**:
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```json
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{
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"checks": [
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{
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"guideline_id": "<example-id-for-few-shots--do-not-use-this-in-output>",
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"condition": "The customer mentions a constraint that related to commitment to the course",
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"rationale": "In the most recent message the customer mentions that they work full time which is a constraint",
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"applies": true
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},
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{
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"guideline_id": "<example-id-for-few-shots--do-not-use-this-in-output>",
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"condition": "The user expresses hesitation or self-doubt.",
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"rationale": "In the most recent message the user still sounds hesitating about their fit to the course",
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"applies": true
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},
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{
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"guideline_id": "<example-id-for-few-shots--do-not-use-this-in-output>",
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"condition": "The user asks about certification or course completion benefits.",
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"rationale": "The user didn't ask about certification or course completion benefits",
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"applies": false
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}
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]
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}
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```
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示例 3: 登录问题
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Example #3: ###
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- **Chat History**:
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user:I'm having trouble logging into my account.
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ai_agent:I'm sorry to hear that. Can you tell me what happens when you try to log in?
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user:It says my password is incorrect.
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ai_agent:Have you tried resetting your password?
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user:Yes, I did, but I can't access my mail to complete the reset.
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- **Guidelines**:
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1) Condition When the user is having a problem with login. Action: Help then identify the problem and solve it
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- **Expected Result**:
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```json
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{
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"checks": [
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{
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"guideline_id": "<example-id-for-few-shots--do-not-use-this-in-output>",
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"condition": "When the user is having a problem with login.",
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"rationale": "In the most recent message the customer is still pursuing their login problem, making the mail access problem a sub-issue rather than a new topic",
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"applies": true
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}
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]
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}
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示例 4: 退货政策
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Example #4: ###
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- **Chat History**:
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user: Hi, I'm thinking about ordering this coat, but I need to know — what's your return policy?
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ai_agent: You can return items within 30 days either in-store or using our prepaid return label.
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user: And what happens if I already wore it once?
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- **Guidelines**:
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1) Condition When the customer asks about how to return an item. Action: Mention both in-store and delivery service return options.
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- **Expected Result**:
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```json
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{
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"checks": [
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{
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"guideline_id": "<example-id-for-few-shots--do-not-use-this-in-output>",
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"condition": "When the customer asks about how to return an item.",
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"rationale": "In the most recent message the customer asks about what happens when they wore the item, which an inquiry regarding returning an item",
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"applies": true
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}
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]
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}
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```
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Chat History:
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{chat_history}
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Guidelines List:
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{guidelines_text}
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OUTPUT FORMAT:
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Please specify the applicability of each guideline:
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```json
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{{
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"checks": [
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{{
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"guideline_id": "1",
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"condition": "具体的条件描述",
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"rationale": "<解释为什么条件满足或不满足>",
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"applies": true/false
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}}
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]
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}}
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```
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