complete_agent_flow_rule.py 33 KB

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  1. """
  2. 完整的智能体工作流 (Complete Agent Flow)
  3. =====================================
  4. 此工作流整合了规划、大纲生成和指标计算四个核心智能体,实现完整的报告生成流程。
  5. 包含的智能体:
  6. 1. PlanningAgent (规划智能体) - 分析状态并做出决策
  7. 2. OutlineAgent (大纲生成智能体) - 生成报告结构和指标需求
  8. 3. MetricCalculationAgent (指标计算智能体) - 执行标准指标计算
  9. 4. RulesEngineMetricCalculationAgent (规则引擎指标计算智能体) - 执行规则引擎指标计算
  10. 工作流程:
  11. 1. 规划节点 → 分析当前状态,决定下一步行动
  12. 2. 大纲生成节点 → 生成报告大纲和指标需求
  13. 3. 指标判断节点 → 根据大纲确定需要计算的指标
  14. 4. 指标计算节点 → 执行具体的指标计算任务
  15. 技术特点:
  16. - 基于LangGraph的状态机工作流
  17. - 支持条件路由和状态管理
  18. - 完善的错误处理机制
  19. - 详细的执行日志记录
  20. 作者: Big Agent Team
  21. 版本: 1.0.0
  22. 创建时间: 2024-12-20
  23. """
  24. import asyncio
  25. from typing import Dict, Any, List
  26. from datetime import datetime
  27. from langgraph.graph import StateGraph, START, END
  28. from llmops.workflow_state import (
  29. IntegratedWorkflowState,
  30. create_initial_integrated_state,
  31. get_calculation_progress,
  32. update_state_with_outline_generation,
  33. update_state_with_planning_decision,
  34. update_state_with_data_classified,
  35. convert_numpy_types,
  36. update_state_with_data_standardize
  37. )
  38. from llmops.agents.outline_agent import generate_report_outline
  39. from llmops.agents.planning_agent import plan_next_action
  40. from llmops.agents.rules_engine_metric_calculation_agent import RulesEngineMetricCalculationAgent
  41. from llmops.agents.data_manager import DataManager
  42. import os
  43. from llmops.agents.data_classify_agent import data_classify
  44. from llmops.config import DEEPSEEK_API_KEY, multimodal_api_url
  45. from llmops.agents.data_stardard import data_standize
  46. class CompleteAgentFlow:
  47. """完整的智能体工作流"""
  48. def __init__(self, api_key: str, base_url: str = "https://api.deepseek.com"):
  49. """
  50. 初始化完整的工作流
  51. Args:
  52. api_key: DeepSeek API密钥
  53. base_url: DeepSeek API基础URL
  54. """
  55. self.api_key = api_key
  56. self.base_url = base_url
  57. # 初始规则引擎智能体
  58. self.rules_engine_agent = RulesEngineMetricCalculationAgent(api_key, base_url)
  59. # 创建工作流图
  60. self.workflow = self._create_workflow()
  61. def _create_workflow(self) -> StateGraph:
  62. """创建LangGraph工作流"""
  63. workflow = StateGraph(IntegratedWorkflowState)
  64. # 添加节点
  65. workflow.add_node("planning_node", self._planning_node)
  66. workflow.add_node("outline_generator", self._outline_generator_node)
  67. workflow.add_node("metric_calculator", self._metric_calculator_node)
  68. workflow.add_node("data_classify", self._data_classify_node)
  69. workflow.add_node("data_standardize", self._data_standardize_node)
  70. # 设置入口点
  71. workflow.set_entry_point("planning_node")
  72. # 添加条件边 - 基于规划决策路由
  73. workflow.add_conditional_edges(
  74. "planning_node",
  75. self._route_from_planning,
  76. {
  77. "outline_generator": "outline_generator",
  78. "metric_calculator": "metric_calculator",
  79. "data_classify": "data_classify",
  80. "data_standardize": "data_standardize",
  81. END: END
  82. }
  83. )
  84. # 从各个节点返回规划节点重新决策
  85. workflow.add_edge("data_standardize", "planning_node")
  86. workflow.add_edge("data_classify", "planning_node")
  87. workflow.add_edge("outline_generator", "planning_node")
  88. workflow.add_edge("metric_calculator", "planning_node")
  89. return workflow
  90. def _route_from_planning(self, state: IntegratedWorkflowState) -> str:
  91. """
  92. 从规划节点路由到下一个节点
  93. Args:
  94. state: 当前状态
  95. Returns:
  96. 目标节点名称
  97. """
  98. print(f"\n🔍 [路由决策] 步骤={state['planning_step']}, "
  99. f"数据集分类打标数量={len(state.get('data_set_classified', []))}",
  100. f"大纲={state.get('outline_draft') is not None}, "
  101. f"指标需求={len(state.get('metrics_requirements', []))}")
  102. # 防止无限循环
  103. if state['planning_step'] > 30:
  104. print("⚠️ 规划步骤超过30次,强制结束流程")
  105. return END
  106. # 数据标准化状态 0 → 数据标准化
  107. if state.get("is_standardized", 0) == 0:
  108. print("→ 路由到 data_standardize(数据标准化)")
  109. return "data_standardize"
  110. # 数据分类打标数量为0 → 分类打标
  111. if len(state.get("data_set_classified", [])) == 0:
  112. print("→ 路由到 data_classify(分类打标)")
  113. return "data_classify"
  114. # 如果大纲为空 → 生成大纲
  115. if not state.get("outline_draft"):
  116. print("→ 路由到 outline_generator(生成大纲)")
  117. return "outline_generator"
  118. # 如果指标需求为空但大纲已生成 → 评估指标需求
  119. if not state.get("metrics_requirements") and state.get("outline_draft"):
  120. print("→ 路由到 metric_evaluator(评估指标需求)")
  121. return "metric_evaluator"
  122. # 计算覆盖率
  123. progress = get_calculation_progress(state)
  124. coverage = progress["coverage_rate"]
  125. print(f" 指标覆盖率 = {coverage:.2%}")
  126. # 如果有待计算指标且覆盖率 < 100% → 计算指标
  127. if state.get("pending_metric_ids") and coverage < 1.0:
  128. print(f"→ 路由到 metric_calculator(计算指标,覆盖率={coverage:.2%})")
  129. return "metric_calculator"
  130. # 检查是否应该结束流程
  131. pending_ids = state.get("pending_metric_ids", [])
  132. failed_attempts = state.get("failed_metric_attempts", {})
  133. max_retries = 3
  134. # 计算还有哪些指标可以重试(未达到最大重试次数)
  135. retryable_metrics = [
  136. mid for mid in pending_ids
  137. if failed_attempts.get(mid, 0) < max_retries
  138. ]
  139. # 如果覆盖率 >= 80%,或者没有可重试的指标 → 结束流程
  140. if coverage >= 0.8 or not retryable_metrics:
  141. reason = "覆盖率达到80%" if coverage >= 0.8 else "没有可重试指标"
  142. print(f"→ 结束流程(覆盖率={coverage:.2%},原因:{reason})")
  143. return END
  144. # 默认返回规划节点
  145. return "planning_node"
  146. async def _planning_node(self, state: IntegratedWorkflowState) -> IntegratedWorkflowState:
  147. """规划节点:分析状态并做出决策"""
  148. try:
  149. print("🧠 正在执行规划分析...")
  150. # 使用规划智能体做出决策
  151. decision = await plan_next_action(
  152. question=state["question"],
  153. industry=state["industry"],
  154. current_state=state,
  155. api_key=self.api_key
  156. )
  157. # 更新状态
  158. new_state = update_state_with_planning_decision(state, {
  159. "decision": decision.decision,
  160. "next_route": self._decision_to_route(decision.decision),
  161. "metrics_to_compute": decision.metrics_to_compute
  162. })
  163. # 添加决策消息
  164. decision_msg = self._format_decision_message(decision)
  165. new_state["messages"].append({
  166. "role": "assistant",
  167. "content": decision_msg,
  168. "timestamp": datetime.now().isoformat()
  169. })
  170. print(f"✅ 规划决策完成:{decision.decision}")
  171. return convert_numpy_types(new_state)
  172. except Exception as e:
  173. print(f"❌ 规划节点执行失败: {e}")
  174. new_state = state.copy()
  175. new_state["errors"].append(f"规划节点错误: {str(e)}")
  176. return convert_numpy_types(new_state)
  177. async def _outline_generator_node(self, state: IntegratedWorkflowState) -> IntegratedWorkflowState:
  178. """大纲生成节点"""
  179. try:
  180. print("📝 正在生成报告大纲...")
  181. # 生成大纲(支持重试机制)
  182. outline = await generate_report_outline(
  183. question=state["question"],
  184. industry=state["industry"],
  185. sample_data=state["data_set"][:3], # 使用前3个样本
  186. api_key=self.api_key,
  187. max_retries=3, # 最多重试5次
  188. retry_delay=3.0 # 每次重试间隔3秒
  189. )
  190. # 更新状态
  191. new_state = update_state_with_outline_generation(state, outline)
  192. print(f"✅ 大纲生成完成:{outline.report_title}")
  193. print(f" 包含 {len(outline.sections)} 个章节,{len(outline.global_metrics)} 个指标需求")
  194. # 分析并打印AI的指标选择推理过程
  195. self._print_ai_selection_analysis(outline)
  196. return convert_numpy_types(new_state)
  197. except Exception as e:
  198. print(f"❌ 大纲生成失败: {e}")
  199. new_state = state.copy()
  200. new_state["errors"].append(f"大纲生成错误: {str(e)}")
  201. return convert_numpy_types(new_state)
  202. async def _data_classify_node(self, state: IntegratedWorkflowState) -> IntegratedWorkflowState:
  203. """数据分类打标节点"""
  204. try:
  205. standardized_file_path = state["standardized_file_path"]
  206. file_name = os.path.basename(standardized_file_path)
  207. # 读取标准化后的数据文件
  208. data_set = DataManager.load_data_from_csv_file(standardized_file_path)
  209. # 加载测试数据集并展示两条样例
  210. print(f"📊 读取标准化数据文件: {file_name}, 加载 {len(data_set)} 条记录")
  211. print(f"测试数据样例: {data_set[0:1]}")
  212. print("📝 正在对数据进行分类打标...")
  213. # 对数据进行分类打标
  214. data_set_classified = await data_classify(
  215. industry=state["industry"],
  216. data_set=data_set,
  217. file_name=state["file_name"]
  218. )
  219. # 更新状态
  220. new_state = update_state_with_data_classified(state, data_set_classified)
  221. print(f"✅ 数据分类打标完成,打标记录数: {len(data_set_classified)}")
  222. return convert_numpy_types(new_state)
  223. except Exception as e:
  224. print(f"❌ 数据分类打标失败: {e}")
  225. new_state = state.copy()
  226. new_state["errors"].append(f"数据分类打标错误: {str(e)}")
  227. return convert_numpy_types(new_state)
  228. async def _data_standardize_node(self, state: IntegratedWorkflowState) -> IntegratedWorkflowState:
  229. """数据标准化节点"""
  230. try:
  231. print("📝 正在对数据进行标准化处理...")
  232. # 数据标准化处理
  233. result = await data_standize(
  234. api_key=self.api_key,
  235. base_url=self.base_url,
  236. multimodal_api_url=multimodal_api_url,
  237. input_file_path=state["original_file_path"]
  238. )
  239. is_succ = 0
  240. standardized_file_path = None
  241. if result["status"] == "success": # 数据标准化成功
  242. is_succ = 1
  243. standardized_file_path = result["file_path"]
  244. # 更新状态
  245. new_state = update_state_with_data_standardize(state, is_succ, standardized_file_path)
  246. print(f"✅ 数据标准化完成,处理状态: {is_succ},标准化文件路径:{standardized_file_path}")
  247. return convert_numpy_types(new_state)
  248. except Exception as e:
  249. print(f"❌ 数据标准化失败: {e}")
  250. new_state = state.copy()
  251. new_state["errors"].append(f"数据标准化错误: {str(e)}")
  252. return convert_numpy_types(new_state)
  253. def _print_ai_selection_analysis(self, outline):
  254. """打印AI指标选择的推理过程分析 - 完全通用版本"""
  255. print()
  256. print('╔══════════════════════════════════════════════════════════════════════════════╗')
  257. print('║ 🤖 AI指标选择分析 ║')
  258. print('╚══════════════════════════════════════════════════════════════════════════════╝')
  259. print()
  260. # 计算总指标数 - outline可能是字典格式,需要适配
  261. if hasattr(outline, 'sections'):
  262. # Pydantic模型格式
  263. total_metrics = sum(len(section.metrics_needed) for section in outline.sections)
  264. sections = outline.sections
  265. else:
  266. # 字典格式
  267. total_metrics = sum(len(section.get('metrics_needed', [])) for section in outline.get('sections', []))
  268. sections = outline.get('sections', [])
  269. # 获取可用指标总数(这里可以从状态或其他地方动态获取)
  270. available_count = 26 # 这个可以从API调用中动态获取
  271. print('📊 选择统计:')
  272. print(' ┌─────────────────────────────────────────────────────────────────────┐')
  273. print(' │ 系统可用指标: {}个 │ AI本次选择: {}个 │ 选择率: {:.1f}% │'.format(
  274. available_count, total_metrics, total_metrics/available_count*100 if available_count > 0 else 0))
  275. print(' └─────────────────────────────────────────────────────────────────────┘')
  276. print()
  277. print('📋 AI决策过程:')
  278. print(' 大模型已根据用户需求从{}个可用指标中选择了{}个最相关的指标。'.format(available_count, total_metrics))
  279. print(' 选择过程完全由大模型基于语义理解和业务逻辑进行,不涉及任何硬编码规则。')
  280. print()
  281. print('🔍 选择结果:')
  282. print(' • 总章节数: {}个'.format(len(sections)))
  283. print(' • 平均每章节指标数: {:.1f}个'.format(total_metrics/len(sections) if sections else 0))
  284. print(' • 选择策略: 基于用户需求的相关性分析')
  285. print()
  286. print('🎯 AI Agent核心能力:')
  287. print(' • 语义理解: 理解用户查询的业务意图和分析需求')
  288. print(' • 智能筛选: 从海量指标中挑选最相关的组合')
  289. print(' • 逻辑推理: 为每个分析维度提供充分的选择依据')
  290. print(' • 动态适配: 根据不同场景自动调整选择策略')
  291. print()
  292. print('💡 关键洞察:')
  293. print(' AI Agent通过大模型的推理能力,实现了超越传统规则引擎的智能化指标选择,')
  294. print(' 能够根据具体业务场景动态调整分析框架,确保分析的针对性和有效性。')
  295. print()
  296. async def _metric_calculator_node(self, state: IntegratedWorkflowState) -> IntegratedWorkflowState:
  297. """指标计算节点"""
  298. try:
  299. # 检查计算模式
  300. use_rules_engine_only = state.get("use_rules_engine_only", False)
  301. use_traditional_engine_only = state.get("use_traditional_engine_only", False)
  302. if use_rules_engine_only:
  303. print("🧮 正在执行规则引擎指标计算(专用模式)...")
  304. elif use_traditional_engine_only:
  305. print("🧮 正在执行传统引擎指标计算(专用模式)...")
  306. else:
  307. print("🧮 正在执行指标计算...")
  308. new_state = state.copy()
  309. # 使用规划决策指定的指标批次,如果没有指定则使用所有待计算指标
  310. current_batch = state.get("current_batch_metrics", [])
  311. if current_batch:
  312. pending_ids = current_batch
  313. print(f"🧮 本次计算批次包含 {len(pending_ids)} 个指标")
  314. else:
  315. pending_ids = state.get("pending_metric_ids", [])
  316. print(f"🧮 计算所有待计算指标,共 {len(pending_ids)} 个")
  317. if not pending_ids:
  318. print("⚠️ 没有待计算的指标")
  319. return convert_numpy_types(new_state)
  320. # 获取指标需求信息
  321. metrics_requirements = state.get("metrics_requirements", [])
  322. if not metrics_requirements:
  323. print("⚠️ 没有指标需求信息")
  324. return convert_numpy_types(new_state)
  325. # 计算成功和失败的指标
  326. successful_calculations = 0
  327. failed_calculations = 0
  328. # 遍历待计算的指标(创建副本避免修改时遍历的问题)
  329. for metric_id in pending_ids.copy():
  330. try:
  331. # 找到对应的指标需求
  332. metric_req = next((m for m in metrics_requirements if m.metric_id == metric_id), None)
  333. if not metric_req:
  334. # 修复:找不到指标需求时,创建临时的指标需求结构,避免跳过指标
  335. print(f"⚠️ 指标 {metric_id} 找不到需求信息,创建临时配置继续计算")
  336. metric_req = type('MetricRequirement', (), {
  337. 'metric_id': metric_id,
  338. 'metric_name': metric_id.replace('metric-', '') if metric_id.startswith('metric-') else metric_id,
  339. 'calculation_logic': f'计算 {metric_id}',
  340. 'required_fields': ['transactions'],
  341. 'dependencies': []
  342. })()
  343. print(f"🧮 计算指标: {metric_id} - {metric_req.metric_name}")
  344. # 根据模式决定使用哪种计算方式
  345. if use_rules_engine_only:
  346. # 只使用规则引擎计算
  347. use_rules_engine = True
  348. print(f" 使用规则引擎模式")
  349. elif use_traditional_engine_only:
  350. # 只使用传统引擎计算
  351. use_rules_engine = False
  352. print(f" 使用传统引擎模式")
  353. else:
  354. # 自动选择计算方式:优先使用规则引擎,只在规则引擎不可用时使用传统计算
  355. use_rules_engine = True # 默认使用规则引擎计算所有指标
  356. if use_rules_engine:
  357. # 使用规则引擎计算
  358. # 现在metric_id已经是知识ID,直接使用它作为配置名
  359. config_name = metric_id # metric_id 已经是知识ID,如 "metric-分析账户数量"
  360. intent_result = {
  361. "target_configs": [config_name],
  362. "intent_category": "指标计算"
  363. }
  364. print(f" 使用知识ID: {config_name}")
  365. # 将打好标的数据集传入指标计算函数中
  366. data_set_classified = state.get("data_set_classified", [])
  367. results = await self.rules_engine_agent.calculate_metrics(intent_result, data_set_classified)
  368. else:
  369. # 使用传统指标计算(模拟)
  370. # 这里简化处理,实际应该根据配置文件调用相应的API
  371. results = {
  372. "success": True,
  373. "results": [{
  374. "config_name": metric_req.metric_id,
  375. "result": {
  376. "success": True,
  377. "data": f"传统引擎计算结果:{metric_req.metric_name}",
  378. "value": 100.0 # 模拟数值
  379. }
  380. }]
  381. }
  382. # 处理计算结果
  383. calculation_success = False
  384. for result in results.get("results", []):
  385. if result.get("result", {}).get("success"):
  386. # 计算成功
  387. new_state["computed_metrics"][metric_id] = result["result"]
  388. successful_calculations += 1
  389. calculation_success = True
  390. print(f"✅ 指标 {metric_id} 计算成功")
  391. break # 找到一个成功的就算成功
  392. else:
  393. # 计算失败
  394. failed_calculations += 1
  395. print(f"❌ 指标 {metric_id} 计算失败")
  396. # 初始化失败尝试记录
  397. if "failed_metric_attempts" not in new_state:
  398. new_state["failed_metric_attempts"] = {}
  399. # 根据计算结果处理指标
  400. if calculation_success:
  401. # 计算成功:从待计算列表中移除
  402. if metric_id in new_state["pending_metric_ids"]:
  403. new_state["pending_metric_ids"].remove(metric_id)
  404. # 重置失败计数
  405. new_state["failed_metric_attempts"].pop(metric_id, None)
  406. else:
  407. # 计算失败:记录失败次数,不从待计算列表移除
  408. new_state["failed_metric_attempts"][metric_id] = new_state["failed_metric_attempts"].get(metric_id, 0) + 1
  409. max_retries = 3
  410. if new_state["failed_metric_attempts"][metric_id] >= max_retries:
  411. print(f"⚠️ 指标 {metric_id} 已达到最大重试次数 ({max_retries}),从待计算列表中移除")
  412. if metric_id in new_state["pending_metric_ids"]:
  413. new_state["pending_metric_ids"].remove(metric_id)
  414. except Exception as e:
  415. print(f"❌ 计算指标 {metric_id} 时发生异常: {e}")
  416. failed_calculations += 1
  417. # 初始化失败尝试记录
  418. if "failed_metric_attempts" not in new_state:
  419. new_state["failed_metric_attempts"] = {}
  420. # 记录失败次数
  421. new_state["failed_metric_attempts"][metric_id] = new_state["failed_metric_attempts"].get(metric_id, 0) + 1
  422. max_retries = 3
  423. if new_state["failed_metric_attempts"][metric_id] >= max_retries:
  424. print(f"⚠️ 指标 {metric_id} 异常已达到最大重试次数 ({max_retries}),从待计算列表中移除")
  425. if metric_id in new_state["pending_metric_ids"]:
  426. new_state["pending_metric_ids"].remove(metric_id)
  427. # 更新计算结果统计
  428. new_state["calculation_results"] = {
  429. "total_configs": len(pending_ids),
  430. "successful_calculations": successful_calculations,
  431. "failed_calculations": failed_calculations
  432. }
  433. # 添加消息
  434. if use_rules_engine_only:
  435. message_content = f"🧮 规则引擎指标计算完成:{successful_calculations} 成功,{failed_calculations} 失败"
  436. elif use_traditional_engine_only:
  437. message_content = f"🧮 传统引擎指标计算完成:{successful_calculations} 成功,{failed_calculations} 失败"
  438. else:
  439. message_content = f"🧮 指标计算完成:{successful_calculations} 成功,{failed_calculations} 失败"
  440. new_state["messages"].append({
  441. "role": "assistant",
  442. "content": message_content,
  443. "timestamp": datetime.now().isoformat()
  444. })
  445. if use_rules_engine_only:
  446. print(f"✅ 规则引擎指标计算完成:{successful_calculations} 成功,{failed_calculations} 失败")
  447. elif use_traditional_engine_only:
  448. print(f"✅ 传统引擎指标计算完成:{successful_calculations} 成功,{failed_calculations} 失败")
  449. else:
  450. print(f"✅ 指标计算完成:{successful_calculations} 成功,{failed_calculations} 失败")
  451. return convert_numpy_types(new_state)
  452. except Exception as e:
  453. print(f"❌ 指标计算节点失败: {e}")
  454. new_state = state.copy()
  455. new_state["errors"].append(f"指标计算错误: {str(e)}")
  456. return convert_numpy_types(new_state)
  457. def _decision_to_route(self, decision: str) -> str:
  458. """将规划决策转换为路由"""
  459. decision_routes = {
  460. "data_classify": "data_classify",
  461. "generate_outline": "outline_generator",
  462. "compute_metrics": "metric_calculator",
  463. "finalize_report": END # 直接结束流程
  464. }
  465. return decision_routes.get(decision, "planning_node")
  466. def _format_decision_message(self, decision: Any) -> str:
  467. """格式化决策消息"""
  468. try:
  469. decision_type = getattr(decision, 'decision', 'unknown')
  470. reasoning = getattr(decision, 'reasoning', '')
  471. if decision_type == "compute_metrics" and hasattr(decision, 'metrics_to_compute'):
  472. metrics = decision.metrics_to_compute
  473. return f"🧮 规划决策:计算 {len(metrics)} 个指标"
  474. elif decision_type == "finalize_report":
  475. return f"✅ 规划决策:生成最终报告"
  476. elif decision_type == "generate_outline":
  477. return f"📋 规划决策:生成大纲"
  478. else:
  479. return f"🤔 规划决策:{decision_type}"
  480. except:
  481. return "🤔 规划决策已完成"
  482. async def run_workflow(self, question: str, industry: str, original_file_path: str, session_id: str = None, use_rules_engine_only: bool = False, use_traditional_engine_only: bool = False) -> Dict[str, Any]:
  483. """
  484. 运行完整的工作流
  485. Args:
  486. question: 用户查询
  487. industry: 行业
  488. data: 数据集
  489. original_file_path: 原始文件路径
  490. session_id: 会话ID
  491. use_rules_engine_only: 是否只使用规则引擎指标计算
  492. use_traditional_engine_only: 是否只使用传统引擎指标计算
  493. Returns:
  494. 工作流结果
  495. """
  496. try:
  497. print("🚀 启动完整智能体工作流...")
  498. print(f"问题:{question}")
  499. print(f"行业:{industry}")
  500. print(f"数据文件:{original_file_path}")
  501. # print(f"数据条数:{len(data)}")
  502. if use_rules_engine_only:
  503. print("计算模式:只使用规则引擎")
  504. elif use_traditional_engine_only:
  505. print("计算模式:只使用传统引擎")
  506. else:
  507. print("计算模式:标准模式")
  508. # 创建初始状态
  509. initial_state = create_initial_integrated_state(question, industry, original_file_path, session_id)
  510. # 设置计算模式标记
  511. if use_rules_engine_only:
  512. initial_state["use_rules_engine_only"] = True
  513. initial_state["use_traditional_engine_only"] = False
  514. elif use_traditional_engine_only:
  515. initial_state["use_rules_engine_only"] = False
  516. initial_state["use_traditional_engine_only"] = True
  517. else:
  518. initial_state["use_rules_engine_only"] = False
  519. initial_state["use_traditional_engine_only"] = False
  520. # 编译工作流
  521. app = self.workflow.compile()
  522. # 执行工作流
  523. result = await app.ainvoke(initial_state)
  524. print("✅ 工作流执行完成")
  525. return {
  526. "success": True,
  527. "result": result,
  528. "answer": result.get("answer"),
  529. "report": result.get("report_draft"),
  530. "session_id": result.get("session_id"),
  531. "execution_summary": {
  532. "planning_steps": result.get("planning_step", 0),
  533. "outline_generated": result.get("outline_draft") is not None,
  534. "metrics_computed": len(result.get("computed_metrics", {})),
  535. "completion_rate": result.get("completeness_score", 0)
  536. }
  537. }
  538. except Exception as e:
  539. print(f"❌ 工作流执行失败: {e}")
  540. return {
  541. "success": False,
  542. "error": str(e),
  543. "result": None
  544. }
  545. # 便捷函数
  546. async def run_complete_agent_flow(question: str, industry: str, data: List[Dict[str, Any]], file_name: str, api_key: str, session_id: str = None, use_rules_engine_only: bool = False, use_traditional_engine_only: bool = False) -> Dict[str, Any]:
  547. """
  548. 运行完整智能体工作流的便捷函数
  549. Args:
  550. question: 用户查询
  551. data: 数据集
  552. file_name: 数据文件名称
  553. api_key: API密钥
  554. session_id: 会话ID
  555. use_rules_engine_only: 是否只使用规则引擎指标计算
  556. use_traditional_engine_only: 是否只使用传统引擎指标计算
  557. Returns:
  558. 工作流结果
  559. """
  560. workflow = CompleteAgentFlow(api_key)
  561. return await workflow.run_workflow(question, industry, data, file_name, session_id, use_rules_engine_only, use_traditional_engine_only)
  562. # 便捷函数
  563. async def run_flow(question: str, industry: str, original_file_path: str, api_key: str, session_id: str = None, use_rules_engine_only: bool = False, use_traditional_engine_only: bool = False) -> Dict[str, Any]:
  564. """
  565. 运行完整智能体工作流的便捷函数
  566. Args:
  567. question: 用户查询
  568. data: 数据集
  569. original_file_path: 原始文件路径(pdf/img/csv)
  570. api_key: API密钥
  571. session_id: 会话ID
  572. use_rules_engine_only: 是否只使用规则引擎指标计算
  573. use_traditional_engine_only: 是否只使用传统引擎指标计算
  574. Returns:
  575. 工作流结果
  576. """
  577. workflow = CompleteAgentFlow(api_key)
  578. return await workflow.run_workflow(question, industry, original_file_path, session_id, use_rules_engine_only, use_traditional_engine_only)
  579. # 主函数用于测试
  580. async def main():
  581. """主函数:执行系统测试"""
  582. import os
  583. os.environ["LANGCHAIN_TRACING_V2"] = "false"
  584. os.environ["LANGCHAIN_API_KEY"] = ""
  585. # 禁用 LangGraph 的追踪
  586. os.environ["LANGSMITH_TRACING"] = "false"
  587. print("🚀 执行CompleteAgentFlow系统测试")
  588. print("=" * 50)
  589. if not DEEPSEEK_API_KEY:
  590. print("❌ 未找到API密钥")
  591. return
  592. # 行业
  593. industry = "农业"
  594. # 测试文件(pdf/img/csv)
  595. file_name = "11111.png"
  596. curr_dir = os.path.dirname(os.path.abspath(__file__))
  597. file_path = os.path.join(curr_dir, "..", "data_files", file_name)
  598. # 执行测试
  599. result = await run_flow(
  600. question="请生成一份详细的农业经营贷流水分析报告,需要包含:1.总收入和总支出统计 2.收入笔数和支出笔数 3.各类型收入支出占比分析 4.交易对手收入支出TOP3排名 5.按月份的收入支出趋势分析 6.账户数量和交易时间范围统计 7.资金流入流出月度统计等全面指标",
  601. industry = industry,
  602. original_file_path=file_path,
  603. api_key=DEEPSEEK_API_KEY,
  604. session_id="direct-test"
  605. )
  606. print(f"📋 结果: {'✅ 成功' if result.get('success') else '❌ 失败'}")
  607. if result.get('success'):
  608. summary = result.get('execution_summary', {})
  609. print(f" 规划步骤: {summary.get('planning_steps', 0)}")
  610. print(f" 指标计算: {summary.get('metrics_computed', 0)}")
  611. print("🎉 测试成功!")
  612. return result
  613. if __name__ == "__main__":
  614. import asyncio
  615. asyncio.run(main())