add guideline
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朱潮
parent
277fcca3d1
commit
7cc05c2c24
155
fastapi_app.py
155
fastapi_app.py
@ -1154,6 +1154,38 @@ async def call_guideline_llm(chat_history: str, guidelines_text: str, model_name
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return ""
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def _get_optimal_batch_size(guidelines_count: int) -> int:
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"""根据guidelines数量决定最优批次数量（并发数）"""
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if guidelines_count <= 10:
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return 1
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elif guidelines_count <= 20:
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return 2
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elif guidelines_count <= 30:
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return 3
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else:
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return 5
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async def process_guideline_batch(
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guidelines_batch: List[str],
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chat_history: str,
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model_name: str,
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api_key: str,
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model_server: str
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) -> str:
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"""处理单个guideline批次"""
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try:
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# 调用LLM分析这批guidelines
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batch_guidelines_text = "\n".join(guidelines_batch)
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batch_analysis = await call_guideline_llm(chat_history, batch_guidelines_text, model_name, api_key, model_server)
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return batch_analysis
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except Exception as e:
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print(f"Error processing guideline batch: {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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@ -1176,35 +1208,110 @@ async def create_agent_and_generate_response(
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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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print(f"guidelines_text: {guidelines_text}")
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# 2. 如果有guideline内容，调用LLM进行分析
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# 2. 如果有guideline内容，进行并发处理
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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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# 按换行符分割guidelines
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guidelines_list = [g.strip() for g in guidelines_text.split('\n') if g.strip()]
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guidelines_count = len(guidelines_list)
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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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if guidelines_count > 0:
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# 获取最优批次数量（并发数）
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batch_count = _get_optimal_batch_size(guidelines_count)
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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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project_dir=project_dir,
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model_name=model_name,
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api_key=api_key,
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model_server=model_server,
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generate_cfg=generate_cfg,
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language=language,
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system_prompt=system_prompt,
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mcp_settings=mcp_settings,
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robot_type=robot_type,
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user_identifier=user_identifier
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)
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# 计算每个批次应该包含多少条guideline
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guidelines_per_batch = max(1, guidelines_count // batch_count)
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# 分批处理guidelines
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batches = []
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for i in range(0, guidelines_count, guidelines_per_batch):
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batch = guidelines_list[i:i + guidelines_per_batch]
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batches.append(batch)
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# 确保批次数量不超过要求的并发数
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while len(batches) > batch_count:
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# 将最后一个批次合并到倒数第二个批次
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batches[-2].extend(batches[-1])
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batches.pop()
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print(f"Processing {guidelines_count} guidelines in {len(batches)} batches with {batch_count} concurrent batches")
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# 准备chat_history
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chat_history = format_messages_to_chat_history(messages)
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# 并发执行所有任务：guideline批次处理 + agent创建
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import asyncio
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tasks = []
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# 添加所有guideline批次任务
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for batch in batches:
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task = process_guideline_batch(
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guidelines_batch=batch,
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chat_history=chat_history,
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model_name=model_name,
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api_key=api_key,
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model_server=model_server
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)
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tasks.append(task)
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# 添加agent创建任务
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agent_task = agent_manager.get_or_create_agent(
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bot_id=bot_id,
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project_dir=project_dir,
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model_name=model_name,
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api_key=api_key,
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model_server=model_server,
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generate_cfg=generate_cfg,
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language=language,
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system_prompt=system_prompt,
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mcp_settings=mcp_settings,
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robot_type=robot_type,
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user_identifier=user_identifier
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)
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tasks.append(agent_task)
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# 等待所有任务完成
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all_results = await asyncio.gather(*tasks, return_exceptions=True)
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# 处理结果：最后一个结果是agent，前面的是guideline批次结果
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agent = all_results[-1] # agent创建的结果
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batch_results = all_results[:-1] # guideline批次的结果
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# 合并guideline分析结果
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valid_results = []
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for i, result in enumerate(batch_results):
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if isinstance(result, Exception):
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print(f"Guideline batch {i} failed: {result}")
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continue
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if result and result.strip():
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valid_results.append(result.strip())
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if valid_results:
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guideline_analysis = "\n\n".join(valid_results)
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print(f"Merged 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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else:
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# 3. 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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project_dir=project_dir,
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model_name=model_name,
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api_key=api_key,
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model_server=model_server,
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generate_cfg=generate_cfg,
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language=language,
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system_prompt=system_prompt,
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mcp_settings=mcp_settings,
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robot_type=robot_type,
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user_identifier=user_identifier
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)
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# 根据stream参数决定返回流式还是非流式响应
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if stream:
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@ -13,9 +13,7 @@ to the most recent state of an interaction between yourself (an AI agent) and a
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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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Example #1: travel consultation
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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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@ -52,9 +50,8 @@ Examples of Guideline Match Evaluations:
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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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Example #2: Course Consultation
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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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@ -93,9 +90,7 @@ Examples of Guideline Match Evaluations:
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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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Example #3: Login issue
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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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@ -119,9 +114,7 @@ Examples of Guideline Match Evaluations:
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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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Example #4: Return Policy
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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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@ -152,6 +145,7 @@ Guidelines List:
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{guidelines_text}
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OUTPUT FORMAT:
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The content in JSON format needs to be wrapped in "```json" and "```".
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Please specify the applicability of each guideline:
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```json
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{{
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