preamble

routes/chat.py

@@ -148,48 +148,41 @@ async def generate_stream_response(agent, messages, pre_message_list, tool_respo
         yield f"data: {json.dumps(error_data, ensure_ascii=False)}\n\n"
 
 
-async def create_agent_and_generate_response(
+async def process_guidelines_and_terms(
     bot_id: str,
     api_key: str,
-    messages: list,
-    stream: bool,
-    tool_response: bool,
     model_name: str,
     model_server: str,
+    system_prompt: str,
+    messages: list,
+    agent_manager,
+    project_dir: Optional[str],
+    generate_cfg: Optional[dict],
     language: str,
-    system_prompt: Optional[str],
     mcp_settings: Optional[list],
     robot_type: str,
-    project_dir: Optional[str] = None,
+    user_identifier: Optional[str]
-    generate_cfg: Optional[dict] = None,
+) -> tuple:
-    user_identifier: Optional[str] = None
+    """
-) -> Union[ChatResponse, StreamingResponse]:
+    公共函数：处理guideline分析和terms处理，返回agent和analysis结果
-    """创建agent并生成响应的公共逻辑"""
-    if generate_cfg is None:
-        generate_cfg = {}
-    pre_message_list = []
-    query_text = get_user_last_message_content(messages)
-    chat_history = format_messages_to_chat_history(messages)
-    preamble_text = await call_preamble_llm(chat_history, query_text, get_preamble_text(language), language, model_name, api_key, model_server)
 
-    if preamble_text != '':
+    Returns:
-        pre_message_list.append({"role": "assistant","content": preamble_text})
+        tuple: (agent, processed_system_prompt, guideline_analysis, terms_analysis)
+    """
+    # 提取system_prompt中的guideline和terms
+    processed_system_prompt, guidelines_list, terms_list = extract_block_from_system_prompt(system_prompt)
 
-    # 1. 从system_prompt提取guideline和terms内容
+    # 处理terms
-    system_prompt, guidelines_list, terms_list = extract_block_from_system_prompt(system_prompt)
-
-    # 2. 如果有terms内容，先进行embedding（embedding需要缓存起来，这个可以tmp文件缓存，以{bot_id}_terms作为key）embedding实现情参考 @embedding/embedding.py 文件,可以在里面实现。拿到embedding后，可以进行相似性检索，检索方式先使用cos相似度，找到阈值相似性>0.7的匹配项，重新整理为terms_analysis，格式：1) Name: term_name1, Description: desc, Synonyms: syn1, syn2。
     terms_analysis = ""
     if terms_list:
-        logger.info(f"terms_list: {terms_list}")
+        logger.info(f"Processing terms: {len(terms_list)} terms")
-        # 使用embedding进行terms处理
         try:
             from embedding.embedding import process_terms_with_embedding
+            query_text = get_user_last_message_content(messages)
             terms_analysis = process_terms_with_embedding(terms_list, bot_id, query_text)
             if terms_analysis:
-                # 将terms分析结果也添加到消息中
+                processed_system_prompt = processed_system_prompt.replace("{terms}", terms_analysis)
-                system_prompt = system_prompt.replace("{terms}", terms_analysis)
+                logger.info(f"Terms analysis completed: {len(terms_analysis)} chars")
-                logger.info(f"Generated terms analysis: {terms_analysis[:200]}...")  # 只打印前200个字符
         except Exception as e:
             logger.error(f"Error processing terms with embedding: {e}")
             terms_analysis = ""
@@ -204,24 +197,22 @@ async def create_agent_and_generate_response(
         except Exception as e:
             logger.error(f"Error removing terms cache file: {e}")
 
-    # 3. 如果有guideline内容，进行并发处理
+    # 处理guidelines
     guideline_analysis = ""
+    agent = None
+
     if guidelines_list:
-        logger.info(f"guidelines_list: {guidelines_list}")
+        logger.info(f"Processing guidelines: {len(guidelines_list)} guidelines")
+        chat_history = format_messages_to_chat_history(messages)
+
         guidelines_count = len(guidelines_list)
-
-        if guidelines_count > 0:
-            # 获取最优批次数量（并发数）
         batch_count = _get_optimal_batch_size(guidelines_count)
-
-            # 计算每个批次应该包含多少条guideline
         guidelines_per_batch = max(1, guidelines_count // batch_count)
 
-            # 分批处理guidelines - 将字典列表转换为字符串列表以便处理
+        # 分批处理guidelines
         batches = []
         for i in range(0, guidelines_count, guidelines_per_batch):
             batch_guidelines = guidelines_list[i:i + guidelines_per_batch]
-                # 将格式化为字符串，保持原有的格式以便LLM处理
             batch_strings = []
             for guideline in batch_guidelines:
                 guideline_str = f"{guideline['id']}) Condition: {guideline['condition']} Action: {guideline['action']}"
@@ -230,16 +221,13 @@ async def create_agent_and_generate_response(
 
         # 确保批次数量不超过要求的并发数
         while len(batches) > batch_count:
-                # 将最后一个批次合并到倒数第二个批次
             batches[-2].extend(batches[-1])
             batches.pop()
 
-            logger.info(f"Processing {guidelines_count} guidelines in {len(batches)} batches with {batch_count} concurrent batches")
+        # 创建所有任务
-
-            # 并发执行所有任务：guideline批次处理 + agent创建
         tasks = []
 
-            # 添加所有guideline批次任务
+        # 添加guideline批次任务
         for batch in batches:
             task = process_guideline_batch(
                 guidelines_batch=batch,
@@ -260,47 +248,38 @@ async def create_agent_and_generate_response(
             model_server=model_server,
             generate_cfg=generate_cfg,
             language=language,
-                system_prompt=system_prompt,
+            system_prompt=processed_system_prompt,
             mcp_settings=mcp_settings,
             robot_type=robot_type,
             user_identifier=user_identifier
         )
         tasks.append(agent_task)
 
-            # 等待所有任务完成
+        # 并发执行所有任务
         all_results = await asyncio.gather(*tasks, return_exceptions=True)
 
-            # 处理结果：最后一个结果是agent，前面的是guideline批次结果
+        # 处理结果
         agent = all_results[-1]  # agent创建的结果
         batch_results = all_results[:-1]  # guideline批次的结果
-            logger.info(f"batch_results:{batch_results}")
 
-            # 合并guideline分析结果，使用JSON格式的checks数组
+        # 合并guideline分析结果
         all_checks = []
         for i, result in enumerate(batch_results):
             if isinstance(result, Exception):
                 logger.error(f"Guideline batch {i} failed: {result}")
                 continue
             if result and isinstance(result, dict) and 'checks' in result:
-                    # 如果是JSON对象且包含checks数组，只保留applies为true的checks
                 applicable_checks = [check for check in result['checks'] if check.get('applies') is True]
                 all_checks.extend(applicable_checks)
             elif result and isinstance(result, str) and result.strip():
-                    # 如果是普通文本，保留原有逻辑
                 logger.info(f"Non-JSON result from batch {i}: {result}")
 
         if all_checks:
-                # 将checks数组格式化为JSON字符串
             guideline_analysis = "\n".join([item["rationale"] for item in all_checks])
-                # guideline_analysis = json.dumps({"checks": all_checks}, ensure_ascii=False)
+            logger.info(f"Guideline analysis completed: {len(guideline_analysis)} chars")
-                logger.info(f"Merged guideline analysis result: {guideline_analysis}")
-
-                # 将分析结果添加到最后一个消息的内容中
-                if guideline_analysis:
-                    messages = append_user_last_message(messages, f"\n\nActive Guidelines:\n{guideline_analysis}\nPlease follow these guidelines in your response.")
 
     else:
-        # 3. 从全局管理器获取或创建助手实例
+        # 没有guidelines，直接创建agent
         agent = await agent_manager.get_or_create_agent(
             bot_id=bot_id,
             project_dir=project_dir,
@@ -309,28 +288,245 @@ async def create_agent_and_generate_response(
             model_server=model_server,
             generate_cfg=generate_cfg,
             language=language,
-            system_prompt=system_prompt,
+            system_prompt=processed_system_prompt,
             mcp_settings=mcp_settings,
             robot_type=robot_type,
             user_identifier=user_identifier
         )
 
-    messages = append_user_last_message(messages, f"\n\nlanguage:{get_language_text(language)}")
+    return agent, processed_system_prompt, guideline_analysis, terms_analysis
 
 
-    if guideline_analysis != '':
+async def enhanced_generate_stream_response(
-        pre_message_list.append({"role": "assistant","reasoning_content": guideline_analysis})
+    agent_manager,
-    # 根据stream参数决定返回流式还是非流式响应
+    bot_id: str,
+    api_key: str,
+    messages: list,
+    tool_response: bool,
+    model_name: str,
+    model_server: str,
+    language: str,
+    system_prompt: str,
+    mcp_settings: Optional[list],
+    robot_type: str,
+    project_dir: Optional[str],
+    generate_cfg: Optional[dict],
+    user_identifier: Optional[str],
+    pre_message_list: Optional[list]
+):
+    """增强的渐进式流式响应生成器"""
+    try:
+        # 第一阶段：立即传输preamble_text
+        if pre_message_list:
+            chunk_data = {
+                "id": f"chatcmpl-preamble",
+                "object": "chat.completion.chunk",
+                "created": int(__import__('time').time()),
+                "model": model_name,
+                "choices": [{
+                    "index": 0,
+                    "delta": {
+                        "content": get_content_from_messages(pre_message_list, tool_response=tool_response)
+                    },
+                    "finish_reason": None
+                }]
+            }
+            yield f"data: {json.dumps(chunk_data, ensure_ascii=False)}\n\n"
+
+        # 第二阶段：使用公共函数处理guideline分析和agent创建
+        agent, _, guideline_analysis, _ = await process_guidelines_and_terms(
+            bot_id=bot_id,
+            api_key=api_key,
+            model_name=model_name,
+            model_server=model_server,
+            system_prompt=system_prompt,
+            messages=messages,
+            agent_manager=agent_manager,
+            project_dir=project_dir,
+            generate_cfg=generate_cfg,
+            language=language,
+            mcp_settings=mcp_settings,
+            robot_type=robot_type,
+            user_identifier=user_identifier
+        )
+
+        # 立即发送guideline_analysis
+        if guideline_analysis:
+            chunk_data = {
+                "id": f"chatcmpl-guideline",
+                "object": "chat.completion.chunk",
+                "created": int(__import__('time').time()),
+                "model": model_name,
+                "choices": [{
+                    "index": 0,
+                    "delta": {
+                        "content": get_content_from_messages([{"role": "assistant","reasoning_content": guideline_analysis}], tool_response=tool_response)
+                    },
+                    "finish_reason": None
+                }]
+            }
+            yield f"data: {json.dumps(chunk_data, ensure_ascii=False)}\n\n"
+
+        # 准备最终的消息
+        final_messages = messages.copy()
+        final_messages = append_user_last_message(final_messages, f"\n\nlanguage:{get_language_text(language)}")
+
+        if guideline_analysis:
+            final_messages = append_user_last_message(final_messages, f"\n\nActive Guidelines:\n{guideline_analysis}\nPlease follow these guidelines in your response.")
+
+        # 第三阶段：agent响应流式传输
+        logger.info(f"Starting agent stream response")
+        accumulated_content = ""
+        chunk_id = 0
+        for response in agent.run(messages=final_messages):
+            previous_content = accumulated_content
+            accumulated_content = get_content_from_messages(response, tool_response=tool_response)
+
+            # 计算新增的内容
+            if accumulated_content.startswith(previous_content):
+                new_content = accumulated_content[len(previous_content):]
+            else:
+                new_content = accumulated_content
+                previous_content = ""
+
+            # 只有当有新内容时才发送chunk
+            if new_content:
+                if chunk_id == 0:
+                    logger.info(f"Agent首个Token已生成, 开始流式输出")
+                chunk_id += 1
+                chunk_data = {
+                    "id": f"chatcmpl-{chunk_id}",
+                    "object": "chat.completion.chunk",
+                    "created": int(__import__('time').time()),
+                    "model": model_name,
+                    "choices": [{
+                        "index": 0,
+                        "delta": {
+                            "content": new_content
+                        },
+                        "finish_reason": None
+                    }]
+                }
+                yield f"data: {json.dumps(chunk_data, ensure_ascii=False)}\n\n"
+
+        # 发送最终完成标记
+        final_chunk = {
+            "id": f"chatcmpl-{chunk_id + 1}",
+            "object": "chat.completion.chunk",
+            "created": int(__import__('time').time()),
+            "model": model_name,
+            "choices": [{
+                "index": 0,
+                "delta": {},
+                "finish_reason": "stop"
+            }]
+        }
+        yield f"data: {json.dumps(final_chunk, ensure_ascii=False)}\n\n"
+
+        # 发送结束标记
+        yield "data: [DONE]\n\n"
+        logger.info(f"Enhanced stream response completed, total chunks: {chunk_id}")
+
+    except Exception as e:
+        import traceback
+        error_details = traceback.format_exc()
+        logger.error(f"Error in enhanced_generate_stream_response: {str(e)}")
+        logger.error(f"Full traceback: {error_details}")
+
+        error_data = {
+            "error": {
+                "message": f"Stream error: {str(e)}",
+                "type": "internal_error"
+            }
+        }
+        yield f"data: {json.dumps(error_data, ensure_ascii=False)}\n\n"
+
+
+async def create_agent_and_generate_response(
+    bot_id: str,
+    api_key: str,
+    messages: list,
+    stream: bool,
+    tool_response: bool,
+    model_name: str,
+    model_server: str,
+    language: str,
+    system_prompt: Optional[str],
+    mcp_settings: Optional[list],
+    robot_type: str,
+    project_dir: Optional[str] = None,
+    generate_cfg: Optional[dict] = None,
+    user_identifier: Optional[str] = None
+) -> Union[ChatResponse, StreamingResponse]:
+    """创建agent并生成响应的公共逻辑"""
+    if generate_cfg is None:
+        generate_cfg = {}
+
+    pre_message_list = []
+    # 只在stream=True时生成preamble_text
+    preamble_text = ""
+    if stream:
+        query_text = get_user_last_message_content(messages)
+        chat_history = format_messages_to_chat_history(messages)
+        preamble_text = await call_preamble_llm(chat_history, query_text, get_preamble_text(language), language, model_name, api_key, model_server)
+        pre_message_list.append({"role": "assistant","content": preamble_text})
+        logger.info(f"Stream mode: Generated preamble text ({len(preamble_text)} chars)")
+
+
+    # 如果是流式模式，使用增强的流式响应生成器
     if stream:
         return StreamingResponse(
-            generate_stream_response(agent, messages, pre_message_list, tool_response, model_name),
+            enhanced_generate_stream_response(
+                agent_manager=agent_manager,
+                bot_id=bot_id,
+                api_key=api_key,
+                messages=messages,
+                tool_response=tool_response,
+                model_name=model_name,
+                model_server=model_server,
+                language=language,
+                system_prompt=system_prompt or "",
+                mcp_settings=mcp_settings,
+                robot_type=robot_type,
+                project_dir=project_dir,
+                generate_cfg=generate_cfg,
+                user_identifier=user_identifier,
+                pre_message_list=pre_message_list
+            ),
             media_type="text/event-stream",
             headers={"Cache-Control": "no-cache", "Connection": "keep-alive"}
         )
-    else:
+
+
+
+    # 使用公共函数处理所有逻辑
+    agent, _, guideline_analysis, _ = await process_guidelines_and_terms(
+        bot_id=bot_id,
+        api_key=api_key,
+        model_name=model_name,
+        model_server=model_server,
+        system_prompt=system_prompt or "",
+        messages=messages,
+        agent_manager=agent_manager,
+        project_dir=project_dir,
+        generate_cfg=generate_cfg,
+        language=language,
+        mcp_settings=mcp_settings,
+        robot_type=robot_type,
+        user_identifier=user_identifier
+    )
+
+    # 准备最终的消息
+    final_messages = messages.copy()
+    final_messages = append_user_last_message(final_messages, f"\n\nlanguage:{get_language_text(language)}")
+
+    if guideline_analysis:
+        final_messages = append_user_last_message(final_messages, f"\n\nActive Guidelines:\n{guideline_analysis}\nPlease follow these guidelines in your response.")
+        pre_message_list.append({"role": "assistant","reasoning_content": guideline_analysis})
+
     # 非流式响应
-        agent_responses = agent.run_nonstream(messages)
+    agent_responses = agent.run_nonstream(final_messages)
-        final_responses = pre_message_list+agent_responses
+    final_responses = pre_message_list + agent_responses
     if final_responses and len(final_responses) > 0:
         # 使用 get_content_from_messages 处理响应，支持 tool_response 参数
         content = get_content_from_messages(final_responses, tool_response=tool_response)