Update app.py

app.py

@@ -1296,102 +1296,131 @@ def parse_query(query: str) -> dict:
 
 def autogen_multi_document_analysis(query: str, docs: list, file_names: list) -> str:
     try:
+        # 建立絕對路徑的暫存目錄，並確保它存在
+        import tempfile
+        import os
+        
+        # 建立一個臨時工作目錄
+        temp_dir = tempfile.mkdtemp(dir="/tmp")
+        os.environ["OPENAI_CACHE_DIR"] = temp_dir
+        
+        # 設置 AutoGen 的工作目錄
+        os.environ["AUTOGEN_CACHE_PATH"] = temp_dir
+        os.environ["AUTOGEN_CACHEDIR"] = temp_dir
+        os.environ["OPENAI_CACHE_PATH"] = temp_dir
+        
+        # 強制 AutoGen 使用我們的臨時目錄而不是 ./.cache
+        import autogen
+        if hasattr(autogen, "set_cache_dir"):
+            autogen.set_cache_dir(temp_dir)
+        
         # 準備文件上下文
         context = "\n\n".join(
             f"Document {name}:\n{doc[:2000]}..." 
             for name, doc in zip(file_names, docs)
         )
 
-        # 配置 LLM，使用最新的配置方法
+        # 配置 LLM
         config_list = [{
             "model": "gpt-4",
             "api_key": openai_api_key
         }]
 
-        # 簡化的基礎配置
+        # 基礎配置 - 不包含任何緩存相關參數
         llm_config = {
             "config_list": config_list,
             "temperature": 0
-            # 移除所有緩存相關參數
         }
+        
+        # 在進行 AutoGen 處理前，切換到臨時目錄
+        original_dir = os.getcwd()
+        os.chdir(temp_dir)
+        
+        try:
+            # 以下是您的 AutoGen 處理代碼
+            user_proxy = UserProxyAgent(
+                name="User",
+                system_message="A user seeking information from multiple documents.",
+                human_input_mode="NEVER",
+                code_execution_config={"use_docker": False},
+                llm_config=llm_config
+            )
 
-        # 定義用戶代理
-        user_proxy = UserProxyAgent(
-            name="User",
-            system_message="A user seeking information from multiple documents.",
-            human_input_mode="NEVER",
-            code_execution_config={"use_docker": False},
-            llm_config=llm_config
-        )
-
-        # 定義文檔分析專家
-        doc_analyzer = AssistantAgent(
-            name="DocumentAnalyzer",
-            system_message="""You are an expert at analyzing and comparing documents. Focus on:
-            1. Key similarities and differences
-            2. Main themes and topics
-            3. Relationships between documents
-            4. Evidence-based analysis""",
-            llm_config=llm_config
-        )
-
-        # 定義問答專家
-        qa_expert = AssistantAgent(
-            name="QAExpert",
-            system_message="""You are an expert at extracting specific information. Focus on:
-            1. Finding relevant details
-            2. Answering specific questions
-            3. Cross-referencing information
-            4. Providing evidence""",
-            llm_config=llm_config
-        )
-
-        # 定義總結專家
-        summarizer = AssistantAgent(
-            name="Summarizer",
-            system_message="""You are an expert at summarizing content. Focus on:
-            1. Key points and findings
-            2. Important relationships
-            3. Critical conclusions
-            4. Comprehensive overview""",
-            llm_config=llm_config
-        )
 
-        # 創建群組聊天
-        groupchat = GroupChat(
-            agents=[user_proxy, doc_analyzer, qa_expert, summarizer],
-            messages=[],
-            max_round=5
-        )
 
-        # 創建管理器
-        manager = GroupChatManager(
-            groupchat=groupchat,
-            llm_config=llm_config
-        )
+            # 定義文檔分析專家
+            doc_analyzer = AssistantAgent(
+                name="DocumentAnalyzer",
+                system_message="""You are an expert at analyzing and comparing documents. Focus on:
+                1. Key similarities and differences
+                2. Main themes and topics
+                3. Relationships between documents
+                4. Evidence-based analysis""",
+                llm_config=llm_config
+            )
 
-        # 準備任務提示
-        task_prompt = f"""Analyze these documents and answer the query:
-        
-        Query: {query}
+            # 定義問答專家
+            qa_expert = AssistantAgent(
+                name="QAExpert",
+                system_message="""You are an expert at extracting specific information. Focus on:
+                1. Finding relevant details
+                2. Answering specific questions
+                3. Cross-referencing information
+                4. Providing evidence""",
+                llm_config=llm_config
+            )
 
-        Documents Context:
-        {context}
+            # 定義總結專家
+            summarizer = AssistantAgent(
+                name="Summarizer",
+                system_message="""You are an expert at summarizing content. Focus on:
+                1. Key points and findings
+                2. Important relationships
+                3. Critical conclusions
+                4. Comprehensive overview""",
+                llm_config=llm_config
+            )
 
-        Requirements:
-        1. Provide a direct and clear answer
-        2. Support all claims with evidence from the documents
-        3. Consider relationships between all documents
-        4. If comparing, analyze all relevant aspects
-        5. If summarizing, cover all important points
-        6. If looking for specific content, search thoroughly
-        7. If analyzing relationships, consider all connections
+            # 創建群組聊天
+            groupchat = GroupChat(
+                agents=[user_proxy, doc_analyzer, qa_expert, summarizer],
+                messages=[],
+                max_round=5
+            )
 
-        Please provide a comprehensive and well-structured answer."""
+            # 創建管理器
+            manager = GroupChatManager(
+                groupchat=groupchat,
+                llm_config=llm_config
+            )
 
-        # 執行群組討論
-        user_proxy.initiate_chat(manager, message=task_prompt)
-        return user_proxy.last_message()["content"]
+            # 準備任務提示
+            task_prompt = f"""Analyze these documents and answer the query:
+        
+            Query: {query}
+
+            Documents Context:
+            {context}
+
+            Requirements:
+            1. Provide a direct and clear answer
+            2. Support all claims with evidence from the documents
+            3. Consider relationships between all documents
+            4. If comparing, analyze all relevant aspects
+            5. If summarizing, cover all important points
+            6. If looking for specific content, search thoroughly
+            7. If analyzing relationships, consider all connections
+
+            Please provide a comprehensive and well-structured answer."""
+
+            # 執行群組討論
+            user_proxy.initiate_chat(manager, message=task_prompt)
+            return user_proxy.last_message()["content"]
+        finally:
+            # 完成後，切回原始目錄
+            os.chdir(original_dir)
+            
+        return result
 
     except Exception as e:
         print(f"ERROR in AutoGen processing: {str(e)}")