feat: Phase 1 - output quality verification + node-level auto-retry
- Add enterprise_review tool (35th builtin) for LLM-based quality assessment - Add evaluator workflow node type for quality gating in DAG - Add AgentRuntime built-in self-review with auto-correction loop - Rewrite error_handler node from stub to real retry mechanism - Add engine-level per-node retry with configurable max_retries/delay/on_exhausted - Add AgentExtension model for extension tracking - Enhance validation in agent_create_tool and tool_register_tool - Update 全能助手 system prompt with self-evolution workflow - Docs: 缺失能力.md and 解决缺失能力计划.md Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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renjianbo
@@ -4066,57 +4066,62 @@ class WorkflowEngine:
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}
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elif node_type == 'error_handler':
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# 错误处理节点：捕获错误、错误重试、错误通知
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# 注意：这个节点需要特殊处理，因为它应该包装其他节点的执行
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# 这里我们实现一个简化版本，主要用于错误重试和通知
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# 错误处理节点：捕获上游节点失败，执行重试/通知/停止策略
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node_data = node.get('data', {})
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retry_count = node_data.get('retry_count', 3)
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retry_delay = node_data.get('retry_delay', 1000) # 毫秒
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on_error = node_data.get('on_error', 'notify') # notify, retry, stop
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error_handler_workflow = node_data.get('error_handler_workflow', '')
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# 这个节点通常用于包装其他节点，但在这里我们只处理输入数据中的错误
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# 找到上游节点（从 edges 反查 source）
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predecessor_id = None
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for e in self.active_edges if hasattr(self, 'active_edges') else []:
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if e.get("target") == node_id:
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predecessor_id = e.get("source")
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break
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if not predecessor_id:
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for e in self.edges:
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if e.get("target") == node_id:
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predecessor_id = e.get("source")
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break
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try:
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# 检查输入数据中是否有错误
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if isinstance(input_data, dict) and input_data.get('status') == 'failed':
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error = input_data.get('error', '未知错误')
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if on_error == 'retry' and retry_count > 0:
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# 重试逻辑（这里简化处理，实际应该重新执行前一个节点）
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logger.warning(f"错误处理节点检测到错误，将重试: {error}")
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# 注意：实际重试需要重新执行前一个节点，这里只记录
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if on_error == 'retry':
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logger.warning(f"error_handler 检测到错误，请求重试前驱节点 {predecessor_id}: {error}")
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exec_result = {
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'output': input_data,
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'status': 'retry',
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'status': 'retry_predecessor',
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'predecessor_id': predecessor_id,
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'retry_count': retry_count,
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'error': error
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'retry_delay_ms': retry_delay,
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'error': error,
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}
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elif on_error == 'notify':
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# 通知错误（记录日志）
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logger.error(f"错误处理节点捕获错误: {error}")
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logger.error(f"error_handler 捕获错误并通知: {error}")
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exec_result = {
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'output': input_data,
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'status': 'error_handled',
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'error': error,
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'notified': True
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'notified': True,
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}
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else:
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# 停止执行
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else: # stop
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exec_result = {
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'output': input_data,
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'status': 'failed',
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'error': error,
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'stopped': True
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'stopped': True,
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}
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else:
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# 没有错误，正常通过
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# 上游正常，透传
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exec_result = {'output': input_data, 'status': 'success'}
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if self.logger:
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duration = int((time.time() - start_time) * 1000)
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self.logger.log_node_complete(node_id, node_type, exec_result.get('output'), duration)
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return exec_result
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except Exception as e:
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if self.logger:
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duration = int((time.time() - start_time) * 1000)
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@@ -4124,7 +4129,7 @@ class WorkflowEngine:
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return {
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'output': input_data,
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'status': 'failed',
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'error': f'错误处理失败: {str(e)}'
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'error': f'错误处理失败: {str(e)}',
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}
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elif node_type == 'csv':
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@@ -5427,6 +5432,89 @@ class WorkflowEngine:
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self.logger.log_node_complete(node_id, node_type, final_output, duration)
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return result
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elif node_type == 'evaluator':
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# 输出质量评估节点：用 LLM 评判上游节点的输出质量
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nd = node.get('data', {}) or {}
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criteria = nd.get('criteria', '回答必须准确、完整、切题')
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pass_threshold = float(nd.get('pass_threshold', 0.6))
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# 从输入数据提取待评估内容
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if isinstance(input_data, dict):
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content = input_data.get('output', input_data.get('content', str(input_data)))
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else:
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content = str(input_data) if input_data else ""
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# 调用 LLM 评判
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try:
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from app.services.llm_service import llm_service
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judge_prompt = (
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"你是严格的内容质量评审专家。请根据以下标准对内容进行评分。\n\n"
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f"【评判标准】\n{criteria}\n\n"
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f"【待评审内容】\n{str(content)[:8000]}\n\n"
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"请以 JSON 格式返回评审结果（严格只返回 JSON，不要任何其他文字）：\n"
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'{"score": 0.75, "passed": true, "issues": ["问题1"], '
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'"suggestions": ["建议1"], "summary": "一句话总结"}\n\n'
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"评分规则：1.0完美 0.8良好 0.6基本满足 0.4大部分未满足 0.2完全不满足\n"
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"score >= 0.6 时 passed=true，否则 passed=false\n"
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)
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messages = [{"role": "user", "content": judge_prompt}]
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resp = llm_service.call_openai_with_tools(
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provider="deepseek",
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model="deepseek-v4-flash",
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messages=messages,
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temperature=0.1,
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max_tokens=600,
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)
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judge_text = ""
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if isinstance(resp, dict):
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choices = resp.get("choices", [])
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if choices:
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judge_text = choices[0].get("message", {}).get("content", "")
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elif hasattr(resp, 'choices'):
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judge_text = resp.choices[0].message.content or ""
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# 解析 JSON
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import re as _eval_re
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judge_clean = judge_text.strip()
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if judge_clean.startswith("```"):
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lines = judge_clean.split("\n")
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judge_clean = "\n".join(lines[1:-1] if lines[-1].strip() == "```" else lines[1:])
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try:
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review = json.loads(judge_clean)
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except json.JSONDecodeError:
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m = _eval_re.search(r'\{[^{}]*"score"\s*:\s*[\d.]+[^{}]*\}', judge_text, _eval_re.DOTALL)
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if m:
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review = json.loads(m.group())
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else:
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review = {"score": 0.5, "passed": False, "issues": ["评审解析失败"], "suggestions": []}
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score = float(review.get("score", 0.5))
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passed = score >= pass_threshold
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exec_result = {
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'output': {
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'score': score,
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'passed': passed,
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'threshold': pass_threshold,
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'issues': review.get('issues', []),
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'suggestions': review.get('suggestions', []),
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'summary': review.get('summary', ''),
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'content': content if isinstance(content, str) else str(content),
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},
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'status': 'success' if passed else 'quality_failed',
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}
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except Exception as e:
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logger.error(f"evaluator 节点评审失败: {e}")
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exec_result = {
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'output': {'score': 0, 'passed': False, 'error': str(e), 'content': str(content)},
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'status': 'failed',
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}
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if self.logger:
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duration = int((time.time() - start_time) * 1000)
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self.logger.log_node_complete(node_id, node_type, exec_result.get('output'), duration)
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return exec_result
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else:
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# 未知节点类型
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logger.warning(f"[rjb] 未知节点类型: node_id={node_id}, node_type={node_type}, node keys={list(node.keys())}")
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@@ -5751,9 +5839,84 @@ class WorkflowEngine:
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# 继续查找循环体内的节点
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self._mark_loop_body_executed(target_id, executed_nodes, active_edges)
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else:
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# 执行失败，停止工作流
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# 执行失败或质量不达标 — 支持重试
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failed_status = result.get('status', 'failed')
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error_msg = result.get('error', '未知错误')
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node_type = node.get('type', 'unknown')
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# 处理 error_handler 返回的 retry_predecessor
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if failed_status == 'retry_predecessor':
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pred_id = result.get('predecessor_id')
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eh_retry_count = result.get('retry_count', 1)
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eh_retry_delay_ms = result.get('retry_delay_ms', 1000)
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# 检查是否已有重试计数（多次经过 error_handler）
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prev_counter = self.node_outputs.get(f"_eh_retry_{pred_id}", {})
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remaining = prev_counter.get("remaining", eh_retry_count)
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if pred_id and pred_id in self.nodes and remaining > 0:
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remaining -= 1
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logger.warning(
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f"error_handler 请求重试前驱节点 {pred_id}，剩余 {remaining} 次"
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)
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self.executed_nodes.discard(pred_id)
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self.node_outputs.pop(pred_id, None)
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self.node_outputs[f"_eh_retry_{pred_id}"] = {
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"remaining": remaining,
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"delay_ms": eh_retry_delay_ms,
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"error_handler_id": next_node_id,
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}
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executed_nodes.add(next_node_id)
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execution_sequence.append(next_node_id)
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results[next_node_id] = result
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await asyncio.sleep(eh_retry_delay_ms / 1000.0)
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continue
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elif remaining <= 0:
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logger.error(f"error_handler 重试次数耗尽，前驱节点 {pred_id} 停止")
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raise WorkflowExecutionError(
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detail=f"error_handler 重试次数耗尽: {error_msg}",
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node_id=pred_id,
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)
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# 检查节点级 retry_config
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node_data = node.get('data', {}) or {}
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retry_cfg = node_data.get('retry_config', {})
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max_retries = retry_cfg.get('max_retries', 0) if isinstance(retry_cfg, dict) else 0
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retry_delay_ms = retry_cfg.get('retry_delay_ms', 1000) if isinstance(retry_cfg, dict) else 1000
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on_exhausted = retry_cfg.get('on_exhausted', 'stop') if isinstance(retry_cfg, dict) else 'stop'
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retry_key = f"_retry_{next_node_id}"
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retries_done = self.node_outputs.get(retry_key, 0)
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if max_retries > 0 and retries_done < max_retries:
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self.node_outputs[retry_key] = retries_done + 1
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logger.warning(
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f"节点 {next_node_id} ({node_type}) 执行失败，重试 {retries_done + 1}/{max_retries}: {error_msg}"
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)
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await asyncio.sleep(retry_delay_ms / 1000.0)
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continue # 不标记已执行，下次循环重新执行
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# 重试耗尽或未配置重试
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if retries_done >= max_retries and max_retries > 0:
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if on_exhausted == 'skip':
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logger.warning(f"节点 {next_node_id} 重试耗尽，跳过: {error_msg}")
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self.node_outputs[next_node_id] = {
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'status': 'skipped', 'error': error_msg
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}
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executed_nodes.add(next_node_id)
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execution_sequence.append(next_node_id)
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results[next_node_id] = result
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continue
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elif on_exhausted == 'notify':
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logger.error(f"节点 {next_node_id} 重试耗尽，已通知: {error_msg}")
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self.node_outputs[next_node_id] = {
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'status': 'error_notified', 'error': error_msg
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}
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executed_nodes.add(next_node_id)
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execution_sequence.append(next_node_id)
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results[next_node_id] = result
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continue
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# 默认：停止工作流
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logger.error(f"工作流执行失败 - 节点: {next_node_id} ({node_type}), 错误: {error_msg}")
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raise WorkflowExecutionError(
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detail=error_msg,
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