Dev to Main: Refactor LLM Interface to Byte Stream for Improved Stability

Dev to Main: Refactor LLM Interface to Byte Stream for Improved Stability

@@ -5,7 +5,8 @@ Unified OpenAI-compatible LLM client for the Insight Engine, with retry support.
 import os
 import sys
 from datetime import datetime
-from typing import Any, Dict, Optional
+from typing import Any, Dict, Optional, Iterator, Generator
+from loguru import logger
 
 from openai import OpenAI
 
@@ -82,6 +83,76 @@ class LLMClient:
             return self.validate_response(response.choices[0].message.content)
         return ""
 
+    @with_retry(LLM_RETRY_CONFIG)
+    def stream_invoke(self, system_prompt: str, user_prompt: str, **kwargs) -> Generator[str, None, None]:
+        """
+        流式调用LLM，逐步返回响应内容
+        
+        Args:
+            system_prompt: 系统提示词
+            user_prompt: 用户提示词
+            **kwargs: 额外参数（temperature, top_p等）
+            
+        Yields:
+            响应文本块（str）
+        """
+        current_time = datetime.now().strftime("%Y年%m月%d日%H时%M分")
+        time_prefix = f"今天的实际时间是{current_time}"
+        if user_prompt:
+            user_prompt = f"{time_prefix}\n{user_prompt}"
+        else:
+            user_prompt = time_prefix
+        messages = [
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": user_prompt},
+        ]
+
+        allowed_keys = {"temperature", "top_p", "presence_penalty", "frequency_penalty"}
+        extra_params = {key: value for key, value in kwargs.items() if key in allowed_keys and value is not None}
+        # 强制使用流式
+        extra_params["stream"] = True
+
+        timeout = kwargs.pop("timeout", self.timeout)
+
+        try:
+            stream = self.client.chat.completions.create(
+                model=self.model_name,
+                messages=messages,
+                timeout=timeout,
+                **extra_params,
+            )
+            
+            for chunk in stream:
+                if chunk.choices and len(chunk.choices) > 0:
+                    delta = chunk.choices[0].delta
+                    if delta and delta.content:
+                        yield delta.content
+        except Exception as e:
+            logger.error(f"流式请求失败: {str(e)}")
+            raise e
+    
+    def stream_invoke_to_string(self, system_prompt: str, user_prompt: str, **kwargs) -> str:
+        """
+        流式调用LLM并安全地拼接为完整字符串（避免UTF-8多字节字符截断）
+        
+        Args:
+            system_prompt: 系统提示词
+            user_prompt: 用户提示词
+            **kwargs: 额外参数（temperature, top_p等）
+            
+        Returns:
+            完整的响应字符串
+        """
+        # 以字节形式收集所有块
+        byte_chunks = []
+        for chunk in self.stream_invoke(system_prompt, user_prompt, **kwargs):
+            byte_chunks.append(chunk.encode('utf-8'))
+        
+        # 拼接所有字节，然后一次性解码
+        if byte_chunks:
+            return b''.join(byte_chunks).decode('utf-8', errors='replace')
+        return ""
+
     @staticmethod
     def validate_response(response: Optional[str]) -> str:
         if response is None:

@@ -70,8 +70,8 @@ class ReportFormattingNode(BaseNode):
             
             logger.info("正在格式化最终报告")
             
-            # 调用LLM
-            response = self.llm_client.invoke(
+            # 调用LLM（流式，安全拼接UTF-8）
+            response = self.llm_client.stream_invoke_to_string(
                 SYSTEM_PROMPT_REPORT_FORMATTING,
                 message,
             )

@@ -51,8 +51,8 @@ class ReportStructureNode(StateMutationNode):
         try:
             logger.info(f"正在为查询生成报告结构: {self.query}")
             
-            # 调用LLM
-            response = self.llm_client.invoke(SYSTEM_PROMPT_REPORT_STRUCTURE, self.query)
+            # 调用LLM（流式，安全拼接UTF-8）
+            response = self.llm_client.stream_invoke_to_string(SYSTEM_PROMPT_REPORT_STRUCTURE, self.query)
             
             # 处理响应
             processed_response = self.process_output(response)

@@ -65,8 +65,8 @@ class FirstSearchNode(BaseNode):
             
             logger.info("正在生成首次搜索查询")
             
-            # 调用LLM
-            response = self.llm_client.invoke(SYSTEM_PROMPT_FIRST_SEARCH, message)
+            # 调用LLM（流式，安全拼接UTF-8）
+            response = self.llm_client.stream_invoke_to_string(SYSTEM_PROMPT_FIRST_SEARCH, message)
             
             # 处理响应
             processed_response = self.process_output(response)
@@ -200,8 +200,8 @@ class ReflectionNode(BaseNode):
             
             logger.info("正在进行反思并生成新搜索查询")
             
-            # 调用LLM
-            response = self.llm_client.invoke(SYSTEM_PROMPT_REFLECTION, message)
+            # 调用LLM（流式，安全拼接UTF-8）
+            response = self.llm_client.stream_invoke_to_string(SYSTEM_PROMPT_REFLECTION, message)
             
             # 处理响应
             processed_response = self.process_output(response)

@@ -99,8 +99,8 @@ class FirstSummaryNode(StateMutationNode):
             
             logger.info("正在生成首次段落总结")
             
-            # 调用LLM
-            response = self.llm_client.invoke(SYSTEM_PROMPT_FIRST_SUMMARY, message)
+            # 调用LLM（流式，安全拼接UTF-8）
+            response = self.llm_client.stream_invoke_to_string(SYSTEM_PROMPT_FIRST_SUMMARY, message)
             
             # 处理响应
             processed_response = self.process_output(response)
@@ -264,8 +264,8 @@ class ReflectionSummaryNode(StateMutationNode):
             
             logger.info("正在生成反思总结")
             
-            # 调用LLM
-            response = self.llm_client.invoke(SYSTEM_PROMPT_REFLECTION_SUMMARY, message)
+            # 调用LLM（流式，安全拼接UTF-8）
+            response = self.llm_client.stream_invoke_to_string(SYSTEM_PROMPT_REFLECTION_SUMMARY, message)
             
             # 处理响应
             processed_response = self.process_output(response)

@@ -5,7 +5,8 @@ Unified OpenAI-compatible LLM client for the Media Engine, with retry support.
 import os
 import sys
 from datetime import datetime
-from typing import Any, Dict, Optional
+from typing import Any, Dict, Optional, Generator
+from loguru import logger
 
 from openai import OpenAI
 
@@ -85,6 +86,76 @@ class LLMClient:
             return self.validate_response(response.choices[0].message.content)
         return ""
 
+    @with_retry(LLM_RETRY_CONFIG)
+    def stream_invoke(self, system_prompt: str, user_prompt: str, **kwargs) -> Generator[str, None, None]:
+        """
+        流式调用LLM，逐步返回响应内容
+        
+        Args:
+            system_prompt: 系统提示词
+            user_prompt: 用户提示词
+            **kwargs: 额外参数（temperature, top_p等）
+            
+        Yields:
+            响应文本块（str）
+        """
+        current_time = datetime.now().strftime("%Y年%m月%d日%H时%M分")
+        time_prefix = f"今天的实际时间是{current_time}"
+        if user_prompt:
+            user_prompt = f"{time_prefix}\n{user_prompt}"
+        else:
+            user_prompt = time_prefix
+        messages = [
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": user_prompt},
+        ]
+
+        allowed_keys = {"temperature", "top_p", "presence_penalty", "frequency_penalty"}
+        extra_params = {key: value for key, value in kwargs.items() if key in allowed_keys and value is not None}
+        # 强制使用流式
+        extra_params["stream"] = True
+
+        timeout = kwargs.pop("timeout", self.timeout)
+
+        try:
+            stream = self.client.chat.completions.create(
+                model=self.model_name,
+                messages=messages,
+                timeout=timeout,
+                **extra_params,
+            )
+            
+            for chunk in stream:
+                if chunk.choices and len(chunk.choices) > 0:
+                    delta = chunk.choices[0].delta
+                    if delta and delta.content:
+                        yield delta.content
+        except Exception as e:
+            logger.error(f"流式请求失败: {str(e)}")
+            raise e
+    
+    def stream_invoke_to_string(self, system_prompt: str, user_prompt: str, **kwargs) -> str:
+        """
+        流式调用LLM并安全地拼接为完整字符串（避免UTF-8多字节字符截断）
+        
+        Args:
+            system_prompt: 系统提示词
+            user_prompt: 用户提示词
+            **kwargs: 额外参数（temperature, top_p等）
+            
+        Returns:
+            完整的响应字符串
+        """
+        # 以字节形式收集所有块
+        byte_chunks = []
+        for chunk in self.stream_invoke(system_prompt, user_prompt, **kwargs):
+            byte_chunks.append(chunk.encode('utf-8'))
+        
+        # 拼接所有字节，然后一次性解码
+        if byte_chunks:
+            return b''.join(byte_chunks).decode('utf-8', errors='replace')
+        return ""
+
     @staticmethod
     def validate_response(response: Optional[str]) -> str:
         if response is None:

@@ -68,8 +68,8 @@ class ReportFormattingNode(BaseNode):
             
             logger.info("正在格式化最终报告")
             
-            # 调用LLM生成Markdown格式
-            response = self.llm_client.invoke(
+            # 调用LLM生成Markdown格式（流式，安全拼接UTF-8）
+            response = self.llm_client.stream_invoke_to_string(
                 SYSTEM_PROMPT_REPORT_FORMATTING,
                 message,
             )

@@ -52,7 +52,7 @@ class ReportStructureNode(StateMutationNode):
             logger.info(f"正在为查询生成报告结构: {self.query}")
             
             # 调用LLM
-            response = self.llm_client.invoke(SYSTEM_PROMPT_REPORT_STRUCTURE, self.query)
+            response = self.llm_client.stream_invoke_to_string(SYSTEM_PROMPT_REPORT_STRUCTURE, self.query)
             
             # 处理响应
             processed_response = self.process_output(response)

@@ -66,7 +66,7 @@ class FirstSearchNode(BaseNode):
             logger.info("正在生成首次搜索查询")
             
             # 调用LLM
-            response = self.llm_client.invoke(SYSTEM_PROMPT_FIRST_SEARCH, message)
+            response = self.llm_client.stream_invoke_to_string(SYSTEM_PROMPT_FIRST_SEARCH, message)
             
             # 处理响应
             processed_response = self.process_output(response)
@@ -201,7 +201,7 @@ class ReflectionNode(BaseNode):
             logger.info("正在进行反思并生成新搜索查询")
             
             # 调用LLM
-            response = self.llm_client.invoke(SYSTEM_PROMPT_REFLECTION, message)
+            response = self.llm_client.stream_invoke_to_string(SYSTEM_PROMPT_REFLECTION, message)
             
             # 处理响应
             processed_response = self.process_output(response)

@@ -99,8 +99,8 @@ class FirstSummaryNode(StateMutationNode):
             
             logger.info("正在生成首次段落总结")
             
-            # 调用LLM生成总结
-            response = self.llm_client.invoke(
+            # 调用LLM生成总结（流式，安全拼接UTF-8）
+            response = self.llm_client.stream_invoke_to_string(
                 SYSTEM_PROMPT_FIRST_SUMMARY,
                 message,
             )
@@ -267,8 +267,8 @@ class ReflectionSummaryNode(StateMutationNode):
             
             logger.info("正在生成反思总结")
             
-            # 调用LLM生成总结
-            response = self.llm_client.invoke(
+            # 调用LLM生成总结（流式，安全拼接UTF-8）
+            response = self.llm_client.stream_invoke_to_string(
                 SYSTEM_PROMPT_REFLECTION_SUMMARY,
                 message,
             )

@@ -5,7 +5,8 @@ Unified OpenAI-compatible LLM client for the Query Engine, with retry support.
 import os
 import sys
 from datetime import datetime
-from typing import Any, Dict, Optional
+from typing import Any, Dict, Optional, Generator
+from loguru import logger
 
 from openai import OpenAI
 
@@ -82,6 +83,76 @@ class LLMClient:
             return self.validate_response(response.choices[0].message.content)
         return ""
 
+    @with_retry(LLM_RETRY_CONFIG)
+    def stream_invoke(self, system_prompt: str, user_prompt: str, **kwargs) -> Generator[str, None, None]:
+        """
+        流式调用LLM，逐步返回响应内容
+        
+        Args:
+            system_prompt: 系统提示词
+            user_prompt: 用户提示词
+            **kwargs: 额外参数（temperature, top_p等）
+            
+        Yields:
+            响应文本块（str）
+        """
+        current_time = datetime.now().strftime("%Y年%m月%d日%H时%M分")
+        time_prefix = f"今天的实际时间是{current_time}"
+        if user_prompt:
+            user_prompt = f"{time_prefix}\n{user_prompt}"
+        else:
+            user_prompt = time_prefix
+        messages = [
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": user_prompt},
+        ]
+
+        allowed_keys = {"temperature", "top_p", "presence_penalty", "frequency_penalty"}
+        extra_params = {key: value for key, value in kwargs.items() if key in allowed_keys and value is not None}
+        # 强制使用流式
+        extra_params["stream"] = True
+
+        timeout = kwargs.pop("timeout", self.timeout)
+
+        try:
+            stream = self.client.chat.completions.create(
+                model=self.model_name,
+                messages=messages,
+                timeout=timeout,
+                **extra_params,
+            )
+            
+            for chunk in stream:
+                if chunk.choices and len(chunk.choices) > 0:
+                    delta = chunk.choices[0].delta
+                    if delta and delta.content:
+                        yield delta.content
+        except Exception as e:
+            logger.error(f"流式请求失败: {str(e)}")
+            raise e
+    
+    def stream_invoke_to_string(self, system_prompt: str, user_prompt: str, **kwargs) -> str:
+        """
+        流式调用LLM并安全地拼接为完整字符串（避免UTF-8多字节字符截断）
+        
+        Args:
+            system_prompt: 系统提示词
+            user_prompt: 用户提示词
+            **kwargs: 额外参数（temperature, top_p等）
+            
+        Returns:
+            完整的响应字符串
+        """
+        # 以字节形式收集所有块
+        byte_chunks = []
+        for chunk in self.stream_invoke(system_prompt, user_prompt, **kwargs):
+            byte_chunks.append(chunk.encode('utf-8'))
+        
+        # 拼接所有字节，然后一次性解码
+        if byte_chunks:
+            return b''.join(byte_chunks).decode('utf-8', errors='replace')
+        return ""
+
     @staticmethod
     def validate_response(response: Optional[str]) -> str:
         if response is None:

@@ -68,8 +68,8 @@ class ReportFormattingNode(BaseNode):
             
             logger.info("正在格式化最终报告")
             
-            # 调用LLM生成Markdown格式
-            response = self.llm_client.invoke(
+            # 调用LLM生成Markdown格式（流式，安全拼接UTF-8）
+            response = self.llm_client.stream_invoke_to_string(
                 SYSTEM_PROMPT_REPORT_FORMATTING,
                 message,
             )

@@ -52,7 +52,7 @@ class ReportStructureNode(StateMutationNode):
             logger.info(f"正在为查询生成报告结构: {self.query}")
             
             # 调用LLM
-            response = self.llm_client.invoke(SYSTEM_PROMPT_REPORT_STRUCTURE, self.query)
+            response = self.llm_client.stream_invoke_to_string(SYSTEM_PROMPT_REPORT_STRUCTURE, self.query)
             
             # 处理响应
             processed_response = self.process_output(response)

@@ -66,7 +66,7 @@ class FirstSearchNode(BaseNode):
             logger.info("正在生成首次搜索查询")
             
             # 调用LLM
-            response = self.llm_client.invoke(SYSTEM_PROMPT_FIRST_SEARCH, message)
+            response = self.llm_client.stream_invoke_to_string(SYSTEM_PROMPT_FIRST_SEARCH, message)
             
             # 处理响应
             processed_response = self.process_output(response)
@@ -201,7 +201,7 @@ class ReflectionNode(BaseNode):
             logger.info("正在进行反思并生成新搜索查询")
             
             # 调用LLM
-            response = self.llm_client.invoke(SYSTEM_PROMPT_REFLECTION, message)
+            response = self.llm_client.stream_invoke_to_string(SYSTEM_PROMPT_REFLECTION, message)
             
             # 处理响应
             processed_response = self.process_output(response)

@@ -99,8 +99,8 @@ class FirstSummaryNode(StateMutationNode):
             
             logger.info("正在生成首次段落总结")
             
-            # 调用LLM生成总结
-            response = self.llm_client.invoke(
+            # 调用LLM生成总结（流式，安全拼接UTF-8）
+            response = self.llm_client.stream_invoke_to_string(
                 SYSTEM_PROMPT_FIRST_SUMMARY,
                 message,
             )
@@ -267,8 +267,8 @@ class ReflectionSummaryNode(StateMutationNode):
             
             logger.info("正在生成反思总结")
             
-            # 调用LLM生成总结
-            response = self.llm_client.invoke(
+            # 调用LLM生成总结（流式，安全拼接UTF-8）
+            response = self.llm_client.stream_invoke_to_string(
                 SYSTEM_PROMPT_REFLECTION_SUMMARY,
                 message,
             )

@@ -4,7 +4,8 @@ Unified OpenAI-compatible LLM client for the Report Engine, with retry support.
 
 import os
 import sys
-from typing import Any, Dict, Optional
+from typing import Any, Dict, Optional, Generator
+from loguru import logger
 
 from openai import OpenAI
 
@@ -75,6 +76,70 @@ class LLMClient:
             return self.validate_response(response.choices[0].message.content)
         return ""
 
+    @with_retry(LLM_RETRY_CONFIG)
+    def stream_invoke(self, system_prompt: str, user_prompt: str, **kwargs) -> Generator[str, None, None]:
+        """
+        流式调用LLM，逐步返回响应内容
+        
+        Args:
+            system_prompt: 系统提示词
+            user_prompt: 用户提示词
+            **kwargs: 额外参数（temperature, top_p等）
+            
+        Yields:
+            响应文本块（str）
+        """
+        messages = [
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": user_prompt},
+        ]
+
+        allowed_keys = {"temperature", "top_p", "presence_penalty", "frequency_penalty"}
+        extra_params = {key: value for key, value in kwargs.items() if key in allowed_keys and value is not None}
+        # 强制使用流式
+        extra_params["stream"] = True
+
+        timeout = kwargs.pop("timeout", self.timeout)
+
+        try:
+            stream = self.client.chat.completions.create(
+                model=self.model_name,
+                messages=messages,
+                timeout=timeout,
+                **extra_params,
+            )
+            
+            for chunk in stream:
+                if chunk.choices and len(chunk.choices) > 0:
+                    delta = chunk.choices[0].delta
+                    if delta and delta.content:
+                        yield delta.content
+        except Exception as e:
+            logger.error(f"流式请求失败: {str(e)}")
+            raise e
+    
+    def stream_invoke_to_string(self, system_prompt: str, user_prompt: str, **kwargs) -> str:
+        """
+        流式调用LLM并安全地拼接为完整字符串（避免UTF-8多字节字符截断）
+        
+        Args:
+            system_prompt: 系统提示词
+            user_prompt: 用户提示词
+            **kwargs: 额外参数（temperature, top_p等）
+            
+        Returns:
+            完整的响应字符串
+        """
+        # 以字节形式收集所有块
+        byte_chunks = []
+        for chunk in self.stream_invoke(system_prompt, user_prompt, **kwargs):
+            byte_chunks.append(chunk.encode('utf-8'))
+        
+        # 拼接所有字节，然后一次性解码
+        if byte_chunks:
+            return b''.join(byte_chunks).decode('utf-8', errors='replace')
+        return ""
+
     @staticmethod
     def validate_response(response: Optional[str]) -> str:
         if response is None:

@@ -60,7 +60,7 @@ class HTMLGenerationNode(StateMutationNode):
             message = json.dumps(llm_input, ensure_ascii=False, indent=2)
             
             # 调用LLM生成HTML
-            response = self.llm_client.invoke(SYSTEM_PROMPT_HTML_GENERATION, message)
+            response = self.llm_client.stream_invoke_to_string(SYSTEM_PROMPT_HTML_GENERATION, message)
             
             # 处理响应（简化版）
             processed_response = self.process_output(response)

@@ -115,7 +115,7 @@ class TemplateSelectionNode(BaseNode):
 请根据查询内容、报告内容和论坛日志的具体情况，选择最合适的模板。"""
         
         # 调用LLM
-        response = self.llm_client.invoke(SYSTEM_PROMPT_TEMPLATE_SELECTION, user_message)
+        response = self.llm_client.stream_invoke_to_string(SYSTEM_PROMPT_TEMPLATE_SELECTION, user_message)
         
         # 检查响应是否为空
         if not response or not response.strip():

@@ -6,10 +6,7 @@ Forum日志读取工具
 import re
 from pathlib import Path
 from typing import Optional, List, Dict
-import logging
-
-logger = logging.getLogger(__name__)
-
+from loguru import logger
 
 def get_latest_host_speech(log_dir: str = "logs") -> Optional[str]:
     """