增加get_decrypted_password函数

apps/jingrow/frontend/src/core/features/flows/executors/flowExecutor.js

@@ -24,14 +24,7 @@ export class FlowExecutor {
    * @returns {Promise<Object>} 执行结果
    */
   async executeFlow(nodes, edges, initialData = {}) {
-    console.group('[FlowExecutor] executeFlow START');
-    console.log('Nodes count:', nodes?.length);
-    console.log('Edges count:', edges?.length);
-    console.log('Initial data:', initialData);
-    
     if (this.isExecuting) {
-      console.error('[FlowExecutor] Flow already executing');
-      console.groupEnd();
       throw new Error('流程正在执行中，请等待完成');
     }
 
@@ -44,20 +37,12 @@ export class FlowExecutor {
       const executionNodes = JSON.parse(JSON.stringify(nodes));
       const executionEdges = JSON.parse(JSON.stringify(edges));
       
-      console.log('Execution nodes:', executionNodes.map(n => ({ id: n.id, type: n.type })));
-      console.log('Execution edges:', executionEdges);
-      
       // 预构建执行图（一次性构建，避免重复计算）
       this.buildExecutionGraph(executionNodes, executionEdges);
-      console.log('Execution order:', this.executionOrder);
       
       // 执行流程
       const result = await this.executeGraph();
       
-      console.log('[FlowExecutor] executeFlow SUCCESS');
-      console.log('Final results:', result);
-      console.groupEnd();
-      
       return {
         success: true,
         result: result,
@@ -65,9 +50,6 @@ export class FlowExecutor {
         context: this.executionContext
       };
     } catch (error) {
-      console.error('[FlowExecutor] executeFlow FAILED:', error);
-      console.error('Error stack:', error.stack);
-      console.groupEnd();
       return {
         success: false,
         error: error.message,
@@ -190,9 +172,6 @@ export class FlowExecutor {
    * @returns {Promise<Object>} 执行结果
    */
   async executeGraph() {
-    console.log('[FlowExecutor] executeGraph START');
-    console.log('Execution order:', this.executionOrder);
-    
     const context = { 
       node_results: {},
       flow_id: this.executionContext.agent_name
@@ -204,19 +183,14 @@ export class FlowExecutor {
     // 按拓扑排序执行节点
     for (const nodeId of this.executionOrder) {
       const node = this.nodes.get(nodeId);
-      console.log(`[FlowExecutor] Processing node: ${nodeId} (${node?.type})`);
       
       // 检查节点是否应该被执行（O(1)时间复杂度）
       if (!this.shouldExecuteNode(node, context, executedNodes)) {
-        console.log(`[FlowExecutor] Skipping node ${nodeId} (shouldExecuteNode=false)`);
         continue;
       }
       
-      console.log(`[FlowExecutor] Executing node ${nodeId}...`);
       // 执行节点
       const result = await this.executeNode(node, context);
-      console.log(`[FlowExecutor] Node ${nodeId} result:`, result);
-      
       results[nodeId] = result;
       context.node_results[nodeId] = result;
       executedNodes.add(nodeId);
@@ -225,7 +199,6 @@ export class FlowExecutor {
       this.recordExecutionHistory(node, result);
     }
     
-    console.log('[FlowExecutor] executeGraph COMPLETE, results:', results);
     return results;
   }
 
@@ -294,44 +267,23 @@ export class FlowExecutor {
         edges: this.edges
       };
       
-      // Debug: log request details
-      console.group(`[FlowExecutor] Executing node: ${node.type} (${node.id})`);
-      console.log('Node data:', JSON.parse(JSON.stringify(node)));
-      console.log('Node inputs:', nodeInputs);
-      console.log('Node config:', config);
-      console.log('Context:', JSON.parse(JSON.stringify(context)));
-      console.log('Request URL:', `/jingrow/nodes/${node.type}/execute`);
-      console.log('CSRF Token:', window.jingrow?.csrf_token);
-      
-      const requestBody = JSON.stringify({ 
-        context, 
-        inputs: nodeInputs, 
-        config 
-      });
-      console.log('Request body size:', requestBody.length, 'bytes');
-      
       const response = await fetch(`/jingrow/nodes/${node.type}/execute`, {
         method: 'POST',
         headers: {
           'Content-Type': 'application/json',
           'X-Jingrow-CSRF-Token': window.jingrow?.csrf_token
         },
-        body: requestBody
+        body: JSON.stringify({ 
+          context, 
+          inputs: nodeInputs, 
+          config 
+        })
       });
       
-      // Debug: log response details
-      console.log('Response status:', response.status, response.statusText);
-      console.log('Response headers:', Object.fromEntries(response.headers.entries()));
-      
       const resJson = await response.json();
-      console.log('Response JSON:', resJson);
-      console.groupEnd();
       
       if (!resJson.message || resJson.message.success === false) {
-        const errorMsg = resJson.message?.error || resJson.error || '节点执行失败';
+        throw new Error(resJson.message?.error || '节点执行失败');
-        console.error('[FlowExecutor] Node execution failed:', errorMsg);
-        console.error('[FlowExecutor] Full response:', resJson);
-        throw new Error(errorMsg);
       }
       
       // 更新节点状态
@@ -342,9 +294,6 @@ export class FlowExecutor {
       return resJson.message;
       
     } catch (error) {
-      console.groupEnd();
-      console.error('[FlowExecutor] Node execution error:', error);
-      console.error('[FlowExecutor] Error stack:', error.stack);
       // 处理执行失败
       node.status = 'failed';
       node.error = error.message;

apps/jingrow/jingrow/utils/jingrow_cloud.py

@@ -4,20 +4,9 @@ Jingrow Cloud API 调用工具
 """
 
 import requests
-from functools import lru_cache
+import jingrow
 from jingrow.utils.auth import get_jingrow_cloud_api_headers, get_jingrow_cloud_api_url
-
+from jingrow.utils.password import get_decrypted_password
-
-@lru_cache(maxsize=1)
-def _get_cached_ai_settings():
-    """缓存 AI Settings 配置（含密码字段解密），避免重复请求"""
-    from jingrow.utils.jingrow_api import get_ai_settings_from_jingrow
-    return get_ai_settings_from_jingrow() or {}
-
-
-def clear_ai_settings_cache():
-    """清除 AI Settings 缓存，配置更新后调用"""
-    _get_cached_ai_settings.cache_clear()
 
 
 def call_jingrow_model(prompt: str,
@@ -98,10 +87,9 @@ def call_chatgpt_model(prompt: str,
                        model: str = None):
     """调用 ChatGPT 模型"""
     try:
-        ai_settings = _get_cached_ai_settings()
+        api_url = jingrow.get_single_value("Ai Settings", "chatgpt_api_url") or "https://api.openai.com/v1/chat/completions"
-        api_url = ai_settings.get('chatgpt_api_url') or "https://api.openai.com/v1/chat/completions"
+        api_key = get_decrypted_password("Ai Settings", "Ai Settings", "chatgpt_api_key", raise_exception=False)
-        api_key = ai_settings.get('chatgpt_api_key')
+        model_name = model or jingrow.get_single_value("Ai Settings", "chatgpt_api_model") or "gpt-4o"
-        model_name = model or ai_settings.get('chatgpt_api_model') or "gpt-4o"
         if not api_key:
             return {"success": False, "error": "ChatGPT API 未配置，请在 Ai Settings 中设置 chatgpt_api_key"}
         content = [{"type": "text", "text": prompt}]
@@ -145,10 +133,9 @@ def call_deepseek_model(prompt: str,
                         model: str = None):
     """调用 DeepSeek 模型"""
     try:
-        ai_settings = _get_cached_ai_settings()
+        api_url = jingrow.get_single_value("Ai Settings", "deepseek_api_url")
-        api_url = ai_settings.get('deepseek_api_url')
+        api_key = get_decrypted_password("Ai Settings", "Ai Settings", "deepseek_api_key", raise_exception=False)
-        api_key = ai_settings.get('deepseek_api_key')
+        model_name = model or jingrow.get_single_value("Ai Settings", "deepseek_api_model") or "deepseek-chat"
-        model_name = model or ai_settings.get('deepseek_api_model') or "deepseek-chat"
         if not api_url or not api_key:
             return {"success": False, "error": "DeepSeek API 未配置，请在 Ai Settings 中设置 deepseek_api_url 和 deepseek_api_key"}
         content = [{"type": "text", "text": prompt}]
@@ -192,10 +179,9 @@ def call_doubao_model(prompt: str,
                       model: str = None):
     """调用豆包模型"""
     try:
-        ai_settings = _get_cached_ai_settings()
+        api_url = jingrow.get_single_value("Ai Settings", "doubao_api_url")
-        api_url = ai_settings.get('doubao_api_url')
+        api_key = get_decrypted_password("Ai Settings", "Ai Settings", "doubao_api_key", raise_exception=False)
-        api_key = ai_settings.get('doubao_api_key')
+        model_name = model or jingrow.get_single_value("Ai Settings", "doubao_api_model") or "doubao-pro-32k-241215"
-        model_name = model or ai_settings.get('doubao_api_model') or "doubao-pro-32k-241215"
         if not api_url or not api_key:
             return {"success": False, "error": "豆包 API 未配置，请在 Ai Settings 中设置 doubao_api_url 和 doubao_api_key"}
         content = [{"type": "text", "text": prompt}]

apps/jingrow/jingrow/utils/password.py

@@ -0,0 +1,28 @@
+"""Password utilities for jlocal environment."""
+
+import requests
+from jingrow.config import Config
+from jingrow.utils.auth import get_jingrow_api_headers
+
+
+def get_decrypted_password(pagetype, name, fieldname="password", raise_exception=True):
+	"""Get decrypted password from remote Jingrow SaaS API."""
+	headers = get_jingrow_api_headers()
+	if not headers:
+		if raise_exception:
+			raise Exception("JINGROW_API_KEY or JINGROW_API_SECRET not configured")
+		return None
+	
+	resp = requests.post(
+		f"{Config.jingrow_server_url}/api/action/jingrow.client.get_password",
+		headers=headers,
+		json={"pagetype": pagetype, "name": name, "fieldname": fieldname},
+		timeout=10
+	)
+
+	if resp.status_code == 200:
+		result = resp.json()
+		return result.get("message") if isinstance(result, dict) else result
+
+	if raise_exception:
+		raise Exception(f"Password not found for {pagetype} {name} {fieldname}")