preamble
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朱潮
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@ -4,13 +4,7 @@ The actual message will be sent later by a smarter agent. Your job is only to ge
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## Scenario Detection Logic
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FIRST, determine if this is a SIMPLE CHAT scenario or COMPLEX scenario:
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**Simple Chat Scenarios (NO preamble needed - output "<empty>"):**
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- Basic greetings: "hi", "hello", "你好", "在吗", "早上好/晚上好"
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- Simple acknowledgments: "thanks", "ok", "好的", "谢谢"
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- Small talk: "how are you", "天气怎么样", "最近怎么样"
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- Simple farewells: "bye", "goodbye", "再见"
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FIRST, determine if this is a COMPLEX scenario that requires a preamble:
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**Complex Scenarios (preamble needed):**
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- Query scenarios: User is asking for information, searching, or looking up data
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@ -19,9 +13,16 @@ FIRST, determine if this is a SIMPLE CHAT scenario or COMPLEX scenario:
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- Problem-solving: User is reporting issues, asking for help with problems
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- Complex requests: Multi-step tasks, detailed instructions needed
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**ALL OTHER scenarios (output "<empty>"):**
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- Simple greetings: "hi", "hello", "你好", "在吗", "早上好/晚上好"
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- Simple acknowledgments: "thanks", "ok", "好的", "谢谢"
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- Small talk: "how are you", "天气怎么样", "最近怎么样"
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- Simple farewells: "bye", "goodbye", "再见"
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- Any other scenarios not explicitly listed as complex
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## Preamble Selection
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IF this is a COMPLEX scenario, choose from these preamble messages. You must ONLY choose one of these: ###
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ONLY IF this is a COMPLEX scenario, choose from these preamble messages. You must ONLY choose one of these: ###
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{preamble_choices_text}
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###
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@ -29,13 +30,13 @@ Basically, the preamble is something very short that continues the interaction n
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We leave that later response to another agent. Make sure you understand this.
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Instructions:
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- For SIMPLE CHAT scenarios: Always output preamble: "<empty>"
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- For COMPLEX scenarios:
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- Note that some of the choices are more generic, and some are more specific to a particular scenario.
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- If you're unsure what to choose --> prefer to go with a more generic, bland choice. This should be 80% of cases.
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Examples of generic choices: "Hey there!", "Just a moment.", "Hello.", "Got it."
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- If you see clear value in saying something more specific and nuanced --> then go with a more specific choice. This should be 20% or less of cases.
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Examples of specific choices: "Let me check that for you.", "Sorry to hear that.", "Thanks for your patience."
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- For ALL OTHER scenarios: Always output preamble: "<empty>"
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Chat History:
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@ -48,7 +49,7 @@ OUTPUT FORMAT:
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You must now choose the preamble message. You must produce a JSON object with a single key, "preamble", holding the preamble message as a string,
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EXACTLY as it is given (pay attention to subtleties like punctuation and copy your choice EXACTLY as it is given above).The content in JSON format needs to be wrapped in "```json" and "```".
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For SIMPLE CHAT scenarios:
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For ALL OTHER scenarios:
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```json
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{
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"preamble": "<empty>"
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109
routes/chat.py
109
routes/chat.py
@ -311,45 +311,66 @@ async def enhanced_generate_stream_response(
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robot_type: str,
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project_dir: Optional[str],
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generate_cfg: Optional[dict],
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user_identifier: Optional[str],
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pre_message_list: Optional[list]
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user_identifier: Optional[str]
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):
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"""增强的渐进式流式响应生成器"""
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try:
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# 第一阶段：立即传输preamble_text
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if pre_message_list:
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chunk_data = {
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"id": f"chatcmpl-preamble",
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"object": "chat.completion.chunk",
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"created": int(__import__('time').time()),
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"model": model_name,
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"choices": [{
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"index": 0,
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"delta": {
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"content": get_content_from_messages(pre_message_list, tool_response=tool_response)
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},
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"finish_reason": None
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}]
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}
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yield f"data: {json.dumps(chunk_data, ensure_ascii=False)}\n\n"
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# 第一阶段：并行启动preamble_text生成和第二阶段处理
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query_text = get_user_last_message_content(messages)
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chat_history = format_messages_to_chat_history(messages)
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# 第二阶段：使用公共函数处理guideline分析和agent创建
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agent, _, guideline_analysis, _ = await process_guidelines_and_terms(
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bot_id=bot_id,
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api_key=api_key,
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model_name=model_name,
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model_server=model_server,
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system_prompt=system_prompt,
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messages=messages,
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agent_manager=agent_manager,
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project_dir=project_dir,
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generate_cfg=generate_cfg,
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language=language,
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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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# 创建preamble_text生成任务
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preamble_task = asyncio.create_task(
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call_preamble_llm(chat_history, query_text, get_preamble_text(language), language, model_name, api_key, model_server)
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)
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# 创建guideline分析和agent创建任务
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guidelines_task = asyncio.create_task(
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process_guidelines_and_terms(
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bot_id=bot_id,
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api_key=api_key,
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model_name=model_name,
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model_server=model_server,
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system_prompt=system_prompt,
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messages=messages,
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agent_manager=agent_manager,
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project_dir=project_dir,
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generate_cfg=generate_cfg,
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language=language,
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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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)
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# 等待preamble_text任务完成
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try:
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preamble_text = await preamble_task
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# 只有当preamble_text不为空且不为"<empty>"时才输出
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if preamble_text and preamble_text.strip() and preamble_text != "<empty>":
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chunk_data = {
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"id": f"chatcmpl-preamble",
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"object": "chat.completion.chunk",
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"created": int(__import__('time').time()),
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"model": model_name,
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"choices": [{
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"index": 0,
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"delta": {
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"content": get_content_from_messages([{"role": "assistant","content": preamble_text + "\n"}], tool_response=tool_response)
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},
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"finish_reason": None
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}]
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}
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yield f"data: {json.dumps(chunk_data, ensure_ascii=False)}\n\n"
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logger.info(f"Stream mode: Generated preamble text ({len(preamble_text)} chars)")
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else:
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logger.info("Stream mode: Skipped empty preamble text")
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except Exception as e:
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logger.error(f"Error generating preamble text: {e}")
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# 等待guideline分析任务完成
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agent, _, guideline_analysis, _ = await guidelines_task
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# 立即发送guideline_analysis
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if guideline_analysis:
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chunk_data = {
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@ -360,7 +381,7 @@ async def enhanced_generate_stream_response(
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"choices": [{
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"index": 0,
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"delta": {
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"content": get_content_from_messages([{"role": "assistant","reasoning_content": guideline_analysis}], tool_response=tool_response)
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"content": get_content_from_messages([{"role": "assistant","reasoning_content": guideline_analysis+ "\n"}], tool_response=tool_response)
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},
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"finish_reason": None
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}]
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@ -462,21 +483,6 @@ 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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pre_message_list = []
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# 只在stream=True时生成preamble_text
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preamble_text = ""
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if stream:
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query_text = get_user_last_message_content(messages)
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chat_history = format_messages_to_chat_history(messages)
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preamble_text = await call_preamble_llm(chat_history, query_text, get_preamble_text(language), language, model_name, api_key, model_server)
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# 只有当preamble_text不为空且不为"<empty>"时才添加到消息列表
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if preamble_text and preamble_text.strip() and preamble_text != "<empty>":
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pre_message_list.append({"role": "assistant","content": preamble_text+"\n"})
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logger.info(f"Stream mode: Generated preamble text ({len(preamble_text)} chars)")
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else:
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logger.info("Stream mode: Skipped empty preamble text")
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# 如果是流式模式，使用增强的流式响应生成器
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if stream:
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return StreamingResponse(
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@ -494,8 +500,7 @@ async def create_agent_and_generate_response(
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robot_type=robot_type,
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project_dir=project_dir,
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generate_cfg=generate_cfg,
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user_identifier=user_identifier,
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pre_message_list=pre_message_list
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user_identifier=user_identifier
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),
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media_type="text/event-stream",
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headers={"Cache-Control": "no-cache", "Connection": "keep-alive"}
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@ -526,7 +531,7 @@ async def create_agent_and_generate_response(
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if guideline_analysis:
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final_messages = append_user_last_message(final_messages, f"\n\nActive Guidelines:\n{guideline_analysis}\nPlease follow these guidelines in your response.")
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pre_message_list.append({"role": "assistant","reasoning_content": guideline_analysis})
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pre_message_list.append({"role": "assistant","reasoning_content": guideline_analysis+ "\n"})
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# 非流式响应
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agent_responses = agent.run_nonstream(final_messages)
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