fix(report_agent): handle API token overflow crash with context length error recovery

.env.example

@@ -4,6 +4,8 @@
 LLM_API_KEY=your_api_key_here
 LLM_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
 LLM_MODEL_NAME=qwen-plus
+# LLM最大输出token数（根据模型能力调整，默认4096）
+# LLM_MAX_TOKENS=4096
 
 # ===== ZEP记忆图谱配置 =====
 # 每月免费额度即可支撑简单使用：https://app.getzep.com/

backend/app/config.py

@@ -31,6 +31,7 @@ class Config:
     LLM_API_KEY = os.environ.get('LLM_API_KEY')
     LLM_BASE_URL = os.environ.get('LLM_BASE_URL', 'https://api.openai.com/v1')
     LLM_MODEL_NAME = os.environ.get('LLM_MODEL_NAME', 'gpt-4o-mini')
+    LLM_MAX_TOKENS = int(os.environ.get('LLM_MAX_TOKENS', '4096'))
     
     # Zep配置
     ZEP_API_KEY = os.environ.get('ZEP_API_KEY')

backend/app/services/report_agent.py

@@ -1294,11 +1294,17 @@ class ReportAgent:
         for iteration in range(max_iterations):
             if progress_callback:
                 progress_callback(
-                    "generating", 
+                    "generating",
                     int((iteration / max_iterations) * 100),
                     f"深度检索与撰写中 ({tool_calls_count}/{self.MAX_TOOL_CALLS_PER_SECTION})"
                 )
-            
+
+            # 防止消息历史无限增长导致上下文溢出
+            # 保留 system + user prompt（前2条）和最近的对话轮次
+            if len(messages) > 14:
+                messages = messages[:2] + messages[-12:]
+                logger.info(f"章节 {section.title}: 消息历史已裁剪至 {len(messages)} 条以防止上下文溢出")
+
             # 调用LLM
             response = self.llm.chat(
                 messages=messages,
@@ -1502,7 +1508,11 @@ class ReportAgent:
         # 达到最大迭代次数，强制生成内容
         logger.warning(f"章节 {section.title} 达到最大迭代次数，强制生成")
         messages.append({"role": "user", "content": REACT_FORCE_FINAL_MSG})
-        
+
+        # 裁剪消息以防止强制收尾时上下文溢出
+        if len(messages) > 14:
+            messages = messages[:2] + messages[-12:]
+
         response = self.llm.chat(
             messages=messages,
             temperature=0.5,

backend/app/utils/llm_client.py

@@ -4,16 +4,19 @@ LLM客户端封装
 """
 
 import json
+import logging
 import re
 from typing import Optional, Dict, Any, List
-from openai import OpenAI
+from openai import OpenAI, BadRequestError, APIError
 
 from ..config import Config
 
+logger = logging.getLogger(__name__)
+
 
 class LLMClient:
     """LLM客户端"""
-    
+
     def __init__(
         self,
         api_key: Optional[str] = None,
@@ -23,64 +26,105 @@ class LLMClient:
         self.api_key = api_key or Config.LLM_API_KEY
         self.base_url = base_url or Config.LLM_BASE_URL
         self.model = model or Config.LLM_MODEL_NAME
-        
+        self.default_max_tokens = Config.LLM_MAX_TOKENS
+
         if not self.api_key:
             raise ValueError("LLM_API_KEY 未配置")
-        
+
         self.client = OpenAI(
             api_key=self.api_key,
             base_url=self.base_url
         )
-    
+
+    @staticmethod
+    def _trim_messages(messages: List[Dict[str, str]]) -> List[Dict[str, str]]:
+        """
+        当消息列表过长导致上下文溢出时，裁剪中间的历史消息。
+        保留第一条（system prompt）和最后几条消息，移除中间部分。
+        """
+        if len(messages) <= 4:
+            return messages
+
+        # 保留 system prompt（第1条）+ 最近3轮对话（最后6条）
+        keep_tail = min(6, len(messages) - 1)
+        trimmed = [messages[0]] + messages[-keep_tail:]
+        logger.warning(
+            f"消息上下文过长，已裁剪: {len(messages)} -> {len(trimmed)} 条消息"
+        )
+        return trimmed
+
     def chat(
         self,
         messages: List[Dict[str, str]],
         temperature: float = 0.7,
-        max_tokens: int = 4096,
+        max_tokens: Optional[int] = None,
         response_format: Optional[Dict] = None
     ) -> str:
         """
         发送聊天请求
-        
+
         Args:
             messages: 消息列表
             temperature: 温度参数
-            max_tokens: 最大token数
+            max_tokens: 最大token数（默认使用配置值）
             response_format: 响应格式（如JSON模式）
-            
+
         Returns:
             模型响应文本
         """
+        if max_tokens is None:
+            max_tokens = self.default_max_tokens
+
         kwargs = {
             "model": self.model,
             "messages": messages,
             "temperature": temperature,
             "max_tokens": max_tokens,
         }
-        
+
         if response_format:
             kwargs["response_format"] = response_format
-        
+
-        response = self.client.chat.completions.create(**kwargs)
+        try:
+            response = self.client.chat.completions.create(**kwargs)
+        except BadRequestError as e:
+            error_msg = str(e).lower()
+            # 处理上下文长度超限错误：自动裁剪消息后重试一次
+            if "context_length" in error_msg or "maximum context" in error_msg or "token" in error_msg:
+                logger.warning(
+                    f"上下文长度超限，尝试裁剪消息后重试: {e}"
+                )
+                trimmed_messages = self._trim_messages(messages)
+                if len(trimmed_messages) == len(messages):
+                    # 无法进一步裁剪，向上抛出
+                    raise
+                kwargs["messages"] = trimmed_messages
+                response = self.client.chat.completions.create(**kwargs)
+            else:
+                raise
+        except APIError as e:
+            logger.error(f"LLM API 调用失败: {e}")
+            raise
+
         content = response.choices[0].message.content
         # 部分模型（如MiniMax M2.5）会在content中包含<think>思考内容，需要移除
         content = re.sub(r'<think>[\s\S]*?</think>', '', content).strip()
         return content
-    
+
     def chat_json(
         self,
         messages: List[Dict[str, str]],
         temperature: float = 0.3,
-        max_tokens: int = 4096
+        max_tokens: Optional[int] = None
     ) -> Dict[str, Any]:
         """
         发送聊天请求并返回JSON
-        
+
         Args:
             messages: 消息列表
             temperature: 温度参数
-            max_tokens: 最大token数
+            max_tokens: 最大token数（默认使用配置值）
-            
+
         Returns:
             解析后的JSON对象
         """
@@ -100,4 +144,3 @@ class LLMClient:
             return json.loads(cleaned_response)
         except json.JSONDecodeError:
             raise ValueError(f"LLM返回的JSON格式无效: {cleaned_response}")
-