Main2

backend/app/api/graph.py

@@ -213,12 +213,19 @@ def generate_ontology():
 
         # 生成本体
         logger.info("调用 LLM 生成本体定义...")
+        # till this point
         generator = OntologyGenerator()
-        ontology = generator.generate(
-            document_texts=document_texts,
-            simulation_requirement=simulation_requirement,
-            additional_context=additional_context if additional_context else None
-        )
+        logger.info("OntologyGenerator....................")
+        try:
+            ontology = generator.generate(
+                document_texts=document_texts,
+                simulation_requirement=simulation_requirement,
+                additional_context=additional_context if additional_context else None
+            )
+            logger.info("OntologyGenerator  try....................")
+        except Exception as e:
+            logger.error(f"本体生成失败: {traceback.format_exc()}")  # ADD
+            raise 
 
         # 保存本体到项目
         entity_count = len(ontology.get("entity_types", []))

backend/app/config.py

@@ -29,6 +29,8 @@ class Config:
 
     # LLM配置（统一使用OpenAI格式）
     LLM_API_KEY = os.environ.get('LLM_API_KEY')
+    LLM_API_KEY_2 = os.environ.get('LLM_API_KEY_2')
+    LLM_API_KEY_3 = os.environ.get('LLM_API_KEY_3')
     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')
 
@@ -45,7 +47,7 @@ class Config:
     DEFAULT_CHUNK_OVERLAP = 50  # 默认重叠大小
 
     # OASIS模拟配置
-    OASIS_DEFAULT_MAX_ROUNDS = int(os.environ.get('OASIS_DEFAULT_MAX_ROUNDS', '10'))
+    OASIS_DEFAULT_MAX_ROUNDS = int(os.environ.get('OASIS_DEFAULT_MAX_ROUNDS', '3'))
     OASIS_SIMULATION_DATA_DIR = os.path.join(os.path.dirname(__file__), '../uploads/simulations')
 
     # OASIS平台可用动作配置
@@ -59,9 +61,9 @@ class Config:
     ]
 
     # Report Agent配置
-    REPORT_AGENT_MAX_TOOL_CALLS = int(os.environ.get('REPORT_AGENT_MAX_TOOL_CALLS', '5'))
-    REPORT_AGENT_MAX_REFLECTION_ROUNDS = int(os.environ.get('REPORT_AGENT_MAX_REFLECTION_ROUNDS', '2'))
-    REPORT_AGENT_TEMPERATURE = float(os.environ.get('REPORT_AGENT_TEMPERATURE', '0.5'))
+    REPORT_AGENT_MAX_TOOL_CALLS = int(os.environ.get('REPORT_AGENT_MAX_TOOL_CALLS', '1'))
+    REPORT_AGENT_MAX_REFLECTION_ROUNDS = int(os.environ.get('REPORT_AGENT_MAX_REFLECTION_ROUNDS', '1'))
+    REPORT_AGENT_TEMPERATURE = float(os.environ.get('REPORT_AGENT_TEMPERATURE', '0.3'))
 
     @classmethod
     def validate(cls):
@@ -72,4 +74,3 @@ def validate(cls):
         if not cls.ZEP_API_KEY:
             errors.append("ZEP_API_KEY 未配置")
         return errors
-

backend/app/services/simulation_config_generator.py

@@ -227,6 +227,7 @@ def __init__(
         base_url: Optional[str] = None,
         model_name: Optional[str] = None
     ):
+
         self.api_key = api_key or Config.LLM_API_KEY
         self.base_url = base_url or Config.LLM_BASE_URL
         self.model_name = model_name or Config.LLM_MODEL_NAME

backend/app/services/simulation_ipc.py

@@ -118,7 +118,7 @@ def send_command(
         self,
         command_type: CommandType,
         args: Dict[str, Any],
-        timeout: float = 60.0,
+        timeout: float = 300.0,
         poll_interval: float = 0.5
     ) -> IPCResponse:
         """

backend/app/services/zep_tools.py

@@ -1380,7 +1380,7 @@ def interview_agents(
                 simulation_id=simulation_id,
                 interviews=interviews_request,
                 platform=None,  # 不指定platform，双平台采访
-                timeout=180.0   # 双平台需要更长超时
+                timeout=300.0   # 双平台需要更长超时
             )
 
             logger.info(f"采访API返回: {api_result.get('interviews_count', 0)} 个结果, success={api_result.get('success')}")

backend/app/utils/llm_client.py

@@ -12,7 +12,7 @@
 
 
 class LLMClient:
-    """LLM客户端"""
+    #LLM客户端
 
     def __init__(
         self,
@@ -39,8 +39,9 @@ def chat(
         max_tokens: int = 4096,
         response_format: Optional[Dict] = None
     ) -> str:
+
         """
-        发送聊天请求
+        .....发送聊天请求
 
         Args:
             messages: 消息列表
@@ -49,7 +50,7 @@ def chat(
             response_format: 响应格式（如JSON模式）
 
         Returns:
-            模型响应文本
+            模型响应文本......
         """
         kwargs = {
             "model": self.model,
@@ -66,14 +67,15 @@ def chat(
         # 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(
+    """
+    ....def chat_json(
         self,
         messages: List[Dict[str, str]],
         temperature: float = 0.3,
         max_tokens: int = 4096
     ) -> Dict[str, Any]:
         """
+    """
         发送聊天请求并返回JSON
 
         Args:
@@ -84,6 +86,7 @@ def chat_json(
         Returns:
             解析后的JSON对象
         """
+    """
         response = self.chat(
             messages=messages,
             temperature=temperature,
@@ -99,5 +102,38 @@ def chat_json(
         try:
             return json.loads(cleaned_response)
         except json.JSONDecodeError:
-            raise ValueError(f"LLM返回的JSON格式无效: {cleaned_response}")
+            raise ValueError(f"LLM返回的JSON格式无效: {cleaned_response}")....."""
+
+    def chat_json(
+        self,
+        messages: List[Dict[str, str]],
+        temperature: float = 0.3,
+        max_tokens: int = 4096
+    ) -> Dict[str, Any]:
+        # Try with json_object mode first, fall back if unsupported
+        try:
+            response = self.chat(
+                messages=messages,
+                temperature=temperature,
+                max_tokens=max_tokens,
+                response_format={"type": "json_object"}
+            )
+        except Exception:
+            # Model doesn't support response_format, call without it
+            response = self.chat(
+                messages=messages,
+                temperature=temperature,
+                max_tokens=max_tokens,
+            )
+
+        # Clean markdown fences
+        cleaned_response = response.strip()
+        cleaned_response = re.sub(r'^```(?:json)?\s*\n?', '', cleaned_response, flags=re.IGNORECASE)
+        cleaned_response = re.sub(r'\n?```\s*$', '', cleaned_response)
+        cleaned_response = cleaned_response.strip()
+
+        try:
+            return json.loads(cleaned_response)
+        except json.JSONDecodeError:
+            raise ValueError(f"LLM返回的JSON格式无效: {cleaned_response[:500]}")
 

backend/pyproject.toml

@@ -2,7 +2,7 @@
 name = "mirofish-backend"
 version = "0.1.0"
 description = "MiroFish - 简洁通用的群体智能引擎，预测万物"
-requires-python = ">=3.11"
+requires-python = ">=3.11, <3.12"
 license = { text = "AGPL-3.0" }
 authors = [
     { name = "MiroFish Team" }
@@ -22,6 +22,7 @@ dependencies = [
     # OASIS 社交媒体模拟
     "camel-oasis==0.2.5",
     "camel-ai==0.2.78",
+    "pillow==10.3.0",
 
     # 文件处理
     "PyMuPDF>=1.24.0",