从入门到精通：Agent任务分解终极指南，一篇彻底讲透技术栈与实战！-开发者社区

本文将详细介绍如何在金融、证券领域构建智能Agent系统，实现复杂问题的自动化任务分解、依赖管理和并行执行。通过大模型、意图识别、工具使用的协同配合，为用户提供高效、准确的金融数据分析和决策支持。

代码以逻辑为主，并非完整可运行，其中的 RAG检索和 NL2SQL 可以为独立的系统。因个人知识有限，难免会出现错误，欢迎批评指正哈，文章略长，建议先收藏，如果喜欢，请多多转发，谢谢😊

1. 系统架构概览

1.1 整体架构设计

1.2 核心组件说明

1.2.1 意图识别模块

功能：识别用户查询的业务意图和数据需求
输入：自然语言查询（如"分析平安银行2023年ROE变化趋势"）
输出：结构化意图信息（查询类型、目标实体、时间范围、指标类型等）

1.2.2 任务分解器

功能：将复杂金融问题分解为可执行的子任务
策略：基于金融业务场景的专业分解模式
输出：子任务列表及其执行要求

1.2.3 依赖关系分析器

功能：分析子任务间的数据依赖和逻辑依赖
输出：任务依赖图和执行约束

1.2.4 执行引擎

并行执行：独立子任务同时执行，提高效率
串行执行：有依赖关系的任务按序执行，保证正确性

2. 大模型与Agent协同架构

2.1 大模型在金融Agent中的核心作用

2.1.1 大模型的多层次应用

1. 理解层（Understanding Layer）

语义理解：解析复杂金融术语和业务逻辑
意图识别：识别用户的真实需求和查询目标
上下文感知：理解对话历史和业务背景

2. 推理层（Reasoning Layer）

逻辑推理：基于金融知识进行逻辑推断
因果分析：分析金融指标间的因果关系
趋势预测：基于历史数据预测未来趋势

3. 生成层（Generation Layer）

代码生成：自动生成SQL查询和数据处理代码
报告生成：生成专业的金融分析报告
解释生成：为分析结果提供可理解的解释

classFinancialLLMEngine: def__init__(self): self.llm_model = self._initialize_llm() self.financial_knowledge_base = FinancialKnowledgeBase() self.prompt_templates = FinancialPromptTemplates() defunderstand_query(self, user_input: str, context: dict = None) -> dict: """大模型理解用户查询""" # 构建理解提示词 understanding_prompt = self.prompt_templates.get_understanding_prompt( user_input=user_input, context=context, financial_context=self.financial_knowledge_base.get_relevant_context(user_input) ) # 大模型推理 understanding_result = self.llm_model.generate( prompt=understanding_prompt, max_tokens=1000, temperature=0.1 ) return self._parse_understanding_result(understanding_result) defgenerate_task_plan(self, intent_analysis: dict) -> List[dict]: """大模型生成任务执行计划""" planning_prompt = self.prompt_templates.get_planning_prompt( intent=intent_analysis["primary_intent"], entities=intent_analysis["entities"], complexity=intent_analysis["complexity"], available_tools=self._get_available_tools() ) plan_result = self.llm_model.generate( prompt=planning_prompt, max_tokens=2000, temperature=0.2 ) return self._parse_task_plan(plan_result) defgenerate_financial_analysis(self, data: dict, analysis_type: str) -> str: """大模型生成金融分析""" analysis_prompt = self.prompt_templates.get_analysis_prompt( data=data, analysis_type=analysis_type, financial_principles=self.financial_knowledge_base.get_analysis_principles(analysis_type) ) analysis_result = self.llm_model.generate( prompt=analysis_prompt, max_tokens=3000, temperature=0.3 ) return analysis_result

2.2 Agent架构设计模式

2.2.1 多Agent协作架构

2.2.2 Agent实现框架

from abc import ABC, abstractmethodfrom typing import Dict, List, Anyimport asyncioclassBaseFinancialAgent(ABC): """金融Agent基类""" def__init__(self, agent_id: str, llm_engine: FinancialLLMEngine): self.agent_id = agent_id self.llm_engine = llm_engine self.state = "idle" self.memory = AgentMemory() self.tools = {} @abstractmethod asyncdefprocess(self, input_data: Dict[str, Any]) -> Dict[str, Any]: """处理输入数据""" pass @abstractmethod defget_capabilities(self) -> List[str]: """获取Agent能力列表""" pass defupdate_memory(self, key: str, value: Any): """更新Agent记忆""" self.memory.update(key, value) defget_memory(self, key: str) -> Any: """获取Agent记忆""" return self.memory.get(key)classIntentRecognitionAgent(BaseFinancialAgent): """意图识别Agent""" def__init__(self, agent_id: str, llm_engine: FinancialLLMEngine): super().__init__(agent_id, llm_engine) self.intent_classifier = FinancialIntentClassifier() asyncdefprocess(self, input_data: Dict[str, Any]) -> Dict[str, Any]: """处理意图识别""" user_query = input_data.get("user_query", "") context = input_data.get("context", {}) # 使用大模型进行深度理解 llm_understanding = await self.llm_engine.understand_query(user_query, context) # 结合规则分类器 rule_classification = self.intent_classifier.classify_intent(user_query) # 融合结果 final_intent = self._merge_intent_results(llm_understanding, rule_classification) # 更新记忆 self.update_memory("last_intent", final_intent) return { "intent_result": final_intent, "confidence": final_intent.get("confidence", 0.0), "next_agent": "task_decomposition" } defget_capabilities(self) -> List[str]: return ["intent_recognition", "entity_extraction", "context_understanding"] def_merge_intent_results(self, llm_result: dict, rule_result: dict) -> dict: """融合大模型和规则的识别结果""" # 实现融合逻辑 merged_result = { "primary_intent": llm_result.get("intent", rule_result.get("primary_intent")), "entities": {**rule_result.get("entities", {}), **llm_result.get("entities", {})}, "confidence": max(llm_result.get("confidence", 0), rule_result.get("confidence", 0)), "complexity": llm_result.get("complexity", rule_result.get("complexity", "medium")), "reasoning": llm_result.get("reasoning", "") } return merged_resultclassTaskDecompositionAgent(BaseFinancialAgent): """任务分解Agent""" def__init__(self, agent_id: str, llm_engine: FinancialLLMEngine): super().__init__(agent_id, llm_engine) self.decomposer = FinancialTaskDecomposer() asyncdefprocess(self, input_data: Dict[str, Any]) -> Dict[str, Any]: """处理任务分解""" intent_result = input_data.get("intent_result", {}) # 使用大模型生成任务计划 llm_plan = await self.llm_engine.generate_task_plan(intent_result) # 结合规则分解器优化 rule_subtasks = self.decomposer.decompose_complex_query( intent_result.get("original_query", ""), intent_result ) # 融合和优化任务计划 optimized_plan = self._optimize_task_plan(llm_plan, rule_subtasks) # 依赖关系分析 dependency_analysis = self._analyze_dependencies(optimized_plan) return { "task_plan": optimized_plan, "dependency_analysis": dependency_analysis, "execution_strategy": self._determine_execution_strategy(dependency_analysis), "next_agent": "data_extraction" } defget_capabilities(self) -> List[str]: return ["task_decomposition", "dependency_analysis", "execution_planning"]classMasterCoordinatorAgent(BaseFinancialAgent): """主控协调Agent""" def__init__(self, agent_id: str, llm_engine: FinancialLLMEngine): super().__init__(agent_id, llm_engine) self.agents = {} self.execution_queue = asyncio.Queue() self.results_store = {} defregister_agent(self, agent: BaseFinancialAgent): """注册子Agent""" self.agents[agent.agent_id] = agent asyncdefprocess(self, input_data: Dict[str, Any]) -> Dict[str, Any]: """协调整个处理流程""" user_query = input_data.get("user_query", "") # 1. 意图识别 intent_result = await self.agents["intent_recognition"].process({ "user_query": user_query, "context": input_data.get("context", {}) }) # 2. 任务分解 decomposition_result = await self.agents["task_decomposition"].process({ "intent_result": intent_result["intent_result"], "original_query": user_query }) # 3. 执行任务计划 execution_results = await self._execute_task_plan( decomposition_result["task_plan"], decomposition_result["execution_strategy"] ) # 4. 结果整合 final_result = await self._integrate_results(execution_results, intent_result["intent_result"]) return final_result asyncdef_execute_task_plan(self, task_plan: List[dict], strategy: dict) -> dict: """执行任务计划""" results = {} if strategy["type"] == "parallel": # 并行执行 tasks = [] for task in task_plan: ifnot task.get("dependencies"): tasks.append(self._execute_single_task(task)) parallel_results = await asyncio.gather(*tasks) for i, result in enumerate(parallel_results): results[task_plan[i]["task_id"]] = result elif strategy["type"] == "sequential": # 串行执行 for task in task_plan: result = await self._execute_single_task(task) results[task["task_id"]] = result return results asyncdef_execute_single_task(self, task: dict) -> dict: """执行单个任务""" task_type = task.get("task_type") if task_type == "data_retrieval": returnawait self.agents["data_extraction"].process(task) elif task_type == "calculation": returnawait self.agents["calculation_analysis"].process(task) elif task_type == "report_generation": returnawait self.agents["report_generation"].process(task) return {"status": "unknown_task_type", "task_id": task.get("task_id")} defget_capabilities(self) -> List[str]: return ["coordination", "workflow_management", "result_integration"]

2.3 大模型Prompt工程

2.3.1 金融领域专用Prompt模板

classFinancialPromptTemplates: """金融领域Prompt模板库""" def__init__(self): self.templates = { "understanding": self._get_understanding_template(), "planning": self._get_planning_template(), "analysis": self._get_analysis_template(), "sql_generation": self._get_sql_template(), "report_generation": self._get_report_template() } def_get_understanding_template(self) -> str: return"""你是一个专业的金融分析师和AI助手，具备深厚的金融知识和数据分析能力。用户查询：{user_input}上下文信息：{context}金融背景：{financial_context}请分析用户的查询意图，包括：1. 主要意图类型（指标查询/对比分析/筛选过滤/计算分析/报告生成）2. 涉及的金融实体（公司、指标、时间等）3. 查询的复杂程度4. 需要的数据源类型5. 预期的输出格式请以JSON格式返回分析结果：{{ "intent": "主要意图类型", "entities": {{ "companies": ["公司列表"], "metrics": ["指标列表"], "time_period": "时间范围", "other": {{}} }}, "complexity": "simple/medium/complex", "data_sources": ["需要的数据源"], "output_format": "预期输出格式", "confidence": 0.95, "reasoning": "分析推理过程"}}""" def_get_planning_template(self) -> str: return"""作为金融数据分析专家，请为以下查询制定详细的执行计划。查询意图：{intent}实体信息：{entities}复杂程度：{complexity}可用工具：{available_tools}请将复杂查询分解为具体的子任务，每个子任务应该：1. 有明确的目标和输出2. 指定需要使用的工具3. 明确数据依赖关系4. 设置优先级请以JSON格式返回任务计划：{{ "tasks": [ {{ "task_id": "唯一标识", "description": "任务描述", "task_type": "任务类型", "tool_required": "需要的工具", "data_sources": ["数据源列表"], "expected_output": "预期输出", "dependencies": ["依赖的任务ID"], "priority": "high/medium/low", "estimated_time": "预估时间（秒）" }} ], "execution_strategy": "parallel/sequential/hybrid", "total_estimated_time": "总预估时间", "risk_factors": ["潜在风险"]}}""" def_get_analysis_template(self) -> str: return"""作为资深金融分析师，请对以下数据进行专业分析。分析类型：{analysis_type}数据内容：{data}分析原则：{financial_principles}请提供：1. 数据概况总结2. 关键指标分析3. 趋势变化解读4. 风险因素识别5. 投资建议（如适用）分析要求：- 使用专业的金融术语- 提供量化的分析结果- 给出明确的结论和建议- 注明分析的局限性请以结构化的方式组织分析报告。"""

2.3.2 Agent记忆与学习机制

classAgentMemory: """Agent记忆系统""" def__init__(self): self.short_term_memory = {} # 当前会话记忆 self.long_term_memory = {} # 持久化记忆 self.episodic_memory = [] # 历史交互记录 self.semantic_memory = {} # 知识库记忆 defupdate_short_term(self, key: str, value: Any): """更新短期记忆""" self.short_term_memory[key] = { "value": value, "timestamp": datetime.now(), "access_count": self.short_term_memory.get(key, {}).get("access_count", 0) + 1 } defconsolidate_to_long_term(self, threshold: int = 5): """将频繁访问的短期记忆转为长期记忆""" for key, memory_item in self.short_term_memory.items(): if memory_item["access_count"] >= threshold: self.long_term_memory[key] = memory_item defadd_episode(self, interaction: dict): """添加交互记录""" episode = { "timestamp": datetime.now(), "user_query": interaction.get("user_query"), "intent": interaction.get("intent"), "task_plan": interaction.get("task_plan"), "execution_result": interaction.get("execution_result"), "user_feedback": interaction.get("user_feedback"), "success_rate": interaction.get("success_rate") } self.episodic_memory.append(episode) # 保持记忆大小限制 if len(self.episodic_memory) > 1000: self.episodic_memory = self.episodic_memory[-1000:] deflearn_from_feedback(self, feedback: dict): """从用户反馈中学习""" if feedback.get("rating", 0) >= 4: # 高评分 # 强化成功模式 successful_pattern = { "query_pattern": feedback.get("query_pattern"), "task_decomposition": feedback.get("task_decomposition"), "tool_selection": feedback.get("tool_selection"), "success_score": feedback.get("rating") } self.semantic_memory["successful_patterns"] = \ self.semantic_memory.get("successful_patterns", []) + [successful_pattern] elif feedback.get("rating", 0) <= 2: # 低评分 # 记录失败模式 failure_pattern = { "query_pattern": feedback.get("query_pattern"), "error_type": feedback.get("error_type"), "improvement_suggestion": feedback.get("suggestion") } self.semantic_memory["failure_patterns"] = \ self.semantic_memory.get("failure_patterns", []) + [failure_pattern]classAdaptiveLearningAgent(BaseFinancialAgent): """自适应学习Agent""" def__init__(self, agent_id: str, llm_engine: FinancialLLMEngine): super().__init__(agent_id, llm_engine) self.learning_rate = 0.1 self.performance_history = [] defadapt_strategy(self, performance_metrics: dict): """根据性能指标调整策略""" self.performance_history.append(performance_metrics) # 分析性能趋势 if len(self.performance_history) >= 10: recent_performance = self.performance_history[-10:] avg_success_rate = sum(p["success_rate"] for p in recent_performance) / 10 if avg_success_rate < 0.8: # 性能下降 self._adjust_parameters("decrease_complexity") elif avg_success_rate > 0.95: # 性能优秀 self._adjust_parameters("increase_efficiency") def_adjust_parameters(self, adjustment_type: str): """调整Agent参数""" if adjustment_type == "decrease_complexity": # 降低任务分解复杂度 self.memory.update_short_term("max_subtasks", 5) self.memory.update_short_term("parallel_threshold", 2) elif adjustment_type == "increase_efficiency": # 提高执行效率 self.memory.update_short_term("max_subtasks", 10) self.memory.update_short_term("parallel_threshold", 4)

2.4 大模型与Agent的实际应用场景

2.4.1 智能投资研究助手

2.4.2 实际应用代码示例

classInvestmentResearchAgent(MasterCoordinatorAgent): """投资研究Agent""" def__init__(self, llm_engine: FinancialLLMEngine): super().__init__("investment_research", llm_engine) self._initialize_specialized_agents() def_initialize_specialized_agents(self): """初始化专业Agent""" # 注册各种专业Agent self.register_agent(IntentRecognitionAgent("intent_recognition", self.llm_engine)) self.register_agent(TaskDecompositionAgent("task_decomposition", self.llm_engine)) self.register_agent(FinancialDataAgent("data_extraction", self.llm_engine)) self.register_agent(FinancialAnalysisAgent("financial_analysis", self.llm_engine)) self.register_agent(ReportGenerationAgent("report_generation", self.llm_engine)) asyncdefconduct_investment_research(self, research_query: str) -> dict: """执行投资研究""" # 记录开始时间 start_time = time.time() try: # 执行完整的研究流程 result = await self.process({ "user_query": research_query, "context": { "research_type": "investment_analysis", "output_format": "comprehensive_report", "urgency": "normal" } }) # 计算执行时间 execution_time = time.time() - start_time # 添加元数据 result["metadata"] = { "execution_time": execution_time, "agent_version": "v2.0", "llm_model": self.llm_engine.model_name, "timestamp": datetime.now().isoformat() } # 学习和优化 await self._learn_from_execution(research_query, result) return result except Exception as e: # 错误处理和恢复 returnawait self._handle_research_error(research_query, str(e)) asyncdef_learn_from_execution(self, query: str, result: dict): """从执行结果中学习""" # 分析执行效果 performance_metrics = { "success_rate": 1.0if result.get("status") == "success"else0.0, "execution_time": result.get("metadata", {}).get("execution_time", 0), "data_quality": self._assess_data_quality(result), "user_satisfaction": result.get("user_feedback", {}).get("rating", 3.0) } # 更新Agent记忆 for agent in self.agents.values(): if hasattr(agent, 'adapt_strategy'): agent.adapt_strategy(performance_metrics) # 记录成功模式 if performance_metrics["success_rate"] > 0.8: self.memory.add_episode({ "user_query": query, "task_plan": result.get("task_plan"), "execution_result": result, "success_rate": performance_metrics["success_rate"] })classFinancialAnalysisAgent(BaseFinancialAgent): """金融分析专业Agent""" def__init__(self, agent_id: str, llm_engine: FinancialLLMEngine): super().__init__(agent_id, llm_engine) self.analysis_tools = { "ratio_analysis": RatioAnalysisTool(), "trend_analysis": TrendAnalysisTool(), "peer_comparison": PeerComparisonTool(), "valuation_analysis": ValuationAnalysisTool() } asyncdefprocess(self, input_data: Dict[str, Any]) -> Dict[str, Any]: """执行金融分析""" analysis_type = input_data.get("analysis_type", "comprehensive") financial_data = input_data.get("financial_data", {}) # 选择合适的分析工具 selected_tools = self._select_analysis_tools(analysis_type, financial_data) # 执行分析 analysis_results = {} for tool_name in selected_tools: tool = self.analysis_tools[tool_name] result = await tool.analyze(financial_data) analysis_results[tool_name] = result # 使用大模型整合分析结果 integrated_analysis = await self.llm_engine.generate_financial_analysis( data=analysis_results, analysis_type=analysis_type ) return { "analysis_results": analysis_results, "integrated_analysis": integrated_analysis, "confidence_score": self._calculate_confidence(analysis_results), "recommendations": self._generate_recommendations(analysis_results) } def_select_analysis_tools(self, analysis_type: str, data: dict) -> List[str]: """智能选择分析工具""" tool_selection = { "comprehensive": ["ratio_analysis", "trend_analysis", "peer_comparison"], "valuation": ["valuation_analysis", "ratio_analysis"], "performance": ["ratio_analysis", "trend_analysis"], "comparison": ["peer_comparison", "ratio_analysis"] } return tool_selection.get(analysis_type, ["ratio_analysis"]) defget_capabilities(self) -> List[str]: return ["financial_analysis", "ratio_calculation", "trend_analysis", "peer_comparison"]

2.5 大模型在Agent系统中的核心价值

2.5.1 认知能力增强

大模型为Agent系统提供了强大的认知能力，主要体现在以下几个方面：

classCognitiveCapabilities: """大模型认知能力封装""" def__init__(self, llm_engine: FinancialLLMEngine): self.llm_engine = llm_engine self.knowledge_base = FinancialKnowledgeBase() asyncdefunderstand_context(self, query: str, context: dict) -> dict: """深度理解上下文""" # 构建理解提示 understanding_prompt = f""" 作为金融领域专家，请深度分析以下查询： 用户查询：{query} 上下文信息：{json.dumps(context, ensure_ascii=False, indent=2)} 请从以下维度进行分析： 1. 查询的核心意图和隐含需求 2. 涉及的金融概念和专业术语 3. 需要的数据类型和分析方法 4. 预期的输出格式和详细程度 5. 潜在的风险点和注意事项 请以JSON格式返回分析结果。 """ response = await self.llm_engine.generate( prompt=understanding_prompt, temperature=0.1, max_tokens=1000 ) return json.loads(response) asyncdefreason_about_task(self, task_description: str, available_tools: List[str]) -> dict: """任务推理和规划""" reasoning_prompt = f""" 任务描述：{task_description} 可用工具：{', '.join(available_tools)} 请进行以下推理： 6. 分析任务的复杂度和执行难度 7. 确定最优的执行策略（串行/并行） 8. 选择合适的工具组合 9. 预估执行时间和资源需求 10. 识别潜在的执行风险 返回详细的推理结果和执行建议。 """ response = await self.llm_engine.generate( prompt=reasoning_prompt, temperature=0.2, max_tokens=800 ) return {"reasoning_result": response, "confidence": 0.85} asyncdefsynthesize_information(self, data_sources: List[dict]) -> dict: """信息综合和洞察生成""" synthesis_prompt = f""" 请综合分析以下多个数据源的信息： {json.dumps(data_sources, ensure_ascii=False, indent=2)} 请进行以下分析： 11. 识别数据间的关联性和一致性 12. 发现潜在的矛盾或异常 13. 提取关键洞察和趋势 14. 生成综合性结论 15. 评估信息的可靠性 返回综合分析报告。 """ response = await self.llm_engine.generate( prompt=synthesis_prompt, temperature=0.3, max_tokens=1200 ) return {"synthesis_report": response, "data_quality_score": 0.9}

2.5.2 动态决策支持

classDynamicDecisionSupport: """动态决策支持系统""" def__init__(self, llm_engine: FinancialLLMEngine): self.llm_engine = llm_engine self.decision_history = [] asyncdefmake_strategic_decision(self, situation: dict, options: List[dict]) -> dict: """战略决策制定""" decision_prompt = f""" 当前情况：{json.dumps(situation, ensure_ascii=False, indent=2)} 可选方案： {json.dumps(options, ensure_ascii=False, indent=2)} 作为金融专家，请进行决策分析： 1. 评估每个方案的优缺点 2. 分析风险和收益 3. 考虑市场环境和监管要求 4. 推荐最优方案并说明理由 5. 提供备选方案和应急预案 请提供详细的决策分析报告。 """ decision_analysis = await self.llm_engine.generate( prompt=decision_prompt, temperature=0.2, max_tokens=1500 ) # 记录决策历史 decision_record = { "timestamp": datetime.now(), "situation": situation, "options": options, "analysis": decision_analysis, "decision_id": str(uuid.uuid4()) } self.decision_history.append(decision_record) return decision_record asyncdefadapt_to_feedback(self, decision_id: str, feedback: dict) -> dict: """根据反馈调整决策""" # 找到原始决策 original_decision = next( (d for d in self.decision_history if d["decision_id"] == decision_id), None ) ifnot original_decision: return {"error": "Decision not found"} adaptation_prompt = f""" 原始决策：{json.dumps(original_decision, ensure_ascii=False, indent=2)} 反馈信息：{json.dumps(feedback, ensure_ascii=False, indent=2)} 请基于反馈调整决策： 6. 分析反馈的有效性和重要性 7. 识别原决策的不足之处 8. 提出改进建议 9. 更新决策方案 10. 制定实施计划 返回调整后的决策方案。 """ adapted_decision = await self.llm_engine.generate( prompt=adaptation_prompt, temperature=0.25, max_tokens=1200 ) return { "original_decision_id": decision_id, "adapted_decision": adapted_decision, "adaptation_timestamp": datetime.now(), "feedback_incorporated": feedback }

2.5.3 知识图谱增强

```plaintext
classKnowledgeGraphEnhancement: “”“知识图谱增强系统”“” def__init__(self, llm_engine: FinancialLLMEngine): self.llm_engine = llm_engine self.knowledge_graph = FinancialKnowledgeGraph() asyncdefextract_financial_entities(self, text: str) -> List[dict]: “”“提取金融实体”“” extraction_prompt = f"“” 请从以下文本中提取金融相关实体：文本：{text} 请识别以下类型的实体： 1. 公司名称（包括简称和全称） 2. 金融指标（如ROE、PE、营收等） 3. 时间期间（如2023年、Q1等） 4. 金融产品（如股票、债券、基金等） 5. 市场名称（如A股、港股等） 6. 监管机构（如证监会、银保监会等）返回JSON格式的实体列表，包含实体名称、类型、置信度。 “”" response = await self.llm_engine.generate( prompt=extraction_prompt, temperature=0.1, max_tokens=800 ) return json.loads(response) asyncdefinfer_relationships(self, entities: List[dict], context: str) -> List[dict]: “”“推理实体关系”“” relationship_prompt = f"“” 实体列表：{json.dumps(entities, ensure_ascii=False, indent=2)} 上下文：{context} 请推理实体之间的关系： 7. 所属关系（如公司-行业） 8. 比较关系（如公司A vs 公司B） 9. 时间关系（如2022年 vs 2023年） 10. 因果关系（如政策-市场影响） 11. 层级关系（如集团-子公司）返回关系三元组列表：[主体, 关系, 客体, 置信度] “”" response = await self.llm_engine.generate( prompt=relationship_prompt, temperature=0.15, max_tokens=1000 ) return json.loads(response) asyncdefenhance_agent_knowledge(self, agent: BaseFinancialAgent, domain: str) -> dict: “”“增强Agent知识”“” # 获取领域相关知识 domain_knowledge = await self.knowledge_graph.get_domain_knowledge(domain) # 生成知识增强提示 enhancement_prompt = f"“” 领域：{domain} 现有知识：{json.dumps(domain_knowledge, ensure_ascii=False, indent=2)} 请为Agent生成增强知识： 12. 关键概念和定义 13. 常见分析方法 14. 重要的计算公式 15. 行业最佳实践 16. 风险控制要点返回结构化的知识增强包。 “”" enhanced_knowledge = await self.llm_engine.generate( prompt=enhancement_prompt, temperature=0.2, max_tokens=1500 ) # 更新Agent知识库 agent.update_knowledge_base(json.loads(enhanced_knowledge)) return {“status”: “success”, “enhanced_knowledge”: enhanced_knowledge}

3. 金融领域意图识别

3.1 金融查询意图分类

3.2 意图识别实现

classFinancialIntentClassifier: def__init__(self): self.intent_patterns = { "indicator_query": { "keywords": ["ROE", "净利润", "营业收入", "资产", "负债", "现金流", "毛利率"], "patterns": [r".*查询.*指标.*", r".*的.*是多少", r".*指标.*情况"], "entities": ["financial_metrics", "companies", "time_period"] }, "comparison_analysis": { "keywords": ["对比", "比较", "排名", "同比", "环比", "增长"], "patterns": [r".*对比.*", r".*比较.*", r".*排名.*", r".*同比.*"], "entities": ["comparison_targets", "comparison_dimensions"] }, "filtering_screening": { "keywords": ["筛选", "过滤", "条件", "满足", "符合", "大于", "小于"], "patterns": [r".*筛选.*", r".*条件.*", r".*满足.*"], "entities": ["filter_conditions", "target_universe"] }, "calculation_analysis": { "keywords": ["计算", "分析", "评估", "测算", "预测"], "patterns": [r".*计算.*", r".*分析.*", r".*评估.*"], "entities": ["calculation_type", "input_data"] }, "report_generation": { "keywords": ["报告", "总结", "分析", "研究", "建议"], "patterns": [r".*报告.*", r".*总结.*", r".*研究.*"], "entities": ["report_type", "analysis_scope"] } } self.financial_entities = { "companies": ["平安银行", "招商银行", "工商银行", "建设银行", "中国银行"], "financial_metrics": ["ROE", "ROA", "净利润", "营业收入", "总资产", "净资产"], "time_periods": ["2023年", "2022年", "Q1", "Q2", "Q3", "Q4", "上半年", "全年"], "industries": ["银行业", "证券业", "保险业", "房地产", "制造业"], "regions": ["北京", "上海", "深圳", "广州", "杭州"] } defclassify_intent(self, user_query: str) -> dict: """分类用户查询意图""" intent_scores = {} # 计算各意图类型的匹配分数 for intent_type, patterns in self.intent_patterns.items(): score = self._calculate_intent_score(user_query, patterns) intent_scores[intent_type] = score # 确定主要意图 primary_intent = max(intent_scores, key=intent_scores.get) confidence = intent_scores[primary_intent] # 提取实体信息 entities = self._extract_financial_entities(user_query) # 分析查询复杂度 complexity = self._analyze_query_complexity(user_query, entities) return { "primary_intent": primary_intent, "confidence": confidence, "entities": entities, "complexity": complexity, "query_type": self._determine_query_type(primary_intent, entities) } def_extract_financial_entities(self, query: str) -> dict: """提取金融相关实体""" entities = {} for entity_type, entity_list in self.financial_entities.items(): found_entities = [entity for entity in entity_list if entity in query] if found_entities: entities[entity_type] = found_entities # 提取数值和时间 import re entities["numbers"] = re.findall(r'\d+(?:\.\d+)?', query) entities["years"] = re.findall(r'\d{4}年', query) entities["quarters"] = re.findall(r'Q[1-4]|[一二三四]季度', query) return entities def_analyze_query_complexity(self, query: str, entities: dict) -> str: """分析查询复杂度""" complexity_indicators = { "simple": len(entities) <= 2and len(query) < 50, "medium": 2 < len(entities) <= 4and50 <= len(query) < 100, "complex": len(entities) > 4or len(query) >= 100 } for level, condition in complexity_indicators.items(): if condition: return level return"medium"

4. 任务分解策略

4.1 金融业务任务分解模式

4.2 具体分解示例

4.2.1 复杂查询示例：“分析平安银行2023年ROE变化趋势，与同业对比，并给出投资建议”

任务分解结果：

classFinancialTaskDecomposer: defdecompose_complex_query(self, query: str, intent_result: dict) -> List[SubTask]: """分解复杂金融查询""" # 示例："分析平安银行2023年ROE变化趋势，与同业对比，并给出投资建议" subtasks = [ SubTask( task_id="data_collection_1", description="收集平安银行2023年各季度ROE数据", task_type="data_retrieval", tool_required="rag_search", data_sources=["财务报告库", "Wind数据库"], expected_output="平安银行2023年Q1-Q4 ROE数据", dependencies=[], priority="high" ), SubTask( task_id="data_collection_2", description="收集同业银行2023年ROE数据", task_type="data_retrieval", tool_required="nl2sql", data_sources=["Wind数据库"], expected_output="招商银行、工商银行、建设银行等ROE数据", dependencies=[], priority="high" ), SubTask( task_id="trend_analysis", description="分析平安银行ROE变化趋势", task_type="calculation", tool_required="data_analysis", expected_output="ROE趋势分析结果（增长率、波动性等）", dependencies=["data_collection_1"], priority="medium" ), SubTask( task_id="peer_comparison", description="进行同业ROE对比分析", task_type="comparison", tool_required="data_analysis", expected_output="平安银行与同业ROE对比结果", dependencies=["data_collection_1", "data_collection_2"], priority="medium" ), SubTask( task_id="investment_recommendation", description="基于分析结果生成投资建议", task_type="report_generation", tool_required="llm_analysis", expected_output="投资建议报告", dependencies=["trend_analysis", "peer_comparison"], priority="low" ) ] return subtasks

4.3 依赖关系管理

依赖关系分析器实现：

classDependencyAnalyzer: def__init__(self): self.dependency_graph = {} self.execution_groups = [] defanalyze_dependencies(self, subtasks: List[SubTask]) -> dict: """分析任务依赖关系""" # 构建依赖图 self.dependency_graph = self._build_dependency_graph(subtasks) # 检测循环依赖 if self._has_circular_dependency(): raise ValueError("检测到循环依赖，请检查任务设计") # 生成执行组（拓扑排序） self.execution_groups = self._generate_execution_groups() # 识别并行执行机会 parallel_groups = self._identify_parallel_groups() return { "dependency_graph": self.dependency_graph, "execution_groups": self.execution_groups, "parallel_groups": parallel_groups, "total_stages": len(self.execution_groups) } def_build_dependency_graph(self, subtasks: List[SubTask]) -> dict: """构建依赖关系图""" graph = {} for task in subtasks: graph[task.task_id] = { "task": task, "dependencies": task.dependencies, "dependents": [] } # 建立反向依赖关系 for task_id, task_info in graph.items(): for dep in task_info["dependencies"]: if dep in graph: graph[dep]["dependents"].append(task_id) return graph def_generate_execution_groups(self) -> List[List[str]]: """生成执行组（拓扑排序）""" groups = [] remaining_tasks = set(self.dependency_graph.keys()) while remaining_tasks: # 找到当前可执行的任务（无未完成依赖） current_group = [] for task_id in remaining_tasks: dependencies = self.dependency_graph[task_id]["dependencies"] if all(dep notin remaining_tasks for dep in dependencies): current_group.append(task_id) ifnot current_group: raise ValueError("无法解析依赖关系，可能存在循环依赖") groups.append(current_group) remaining_tasks -= set(current_group) return groups def_identify_parallel_groups(self) -> List[dict]: """识别可并行执行的任务组""" parallel_groups = [] for i, group in enumerate(self.execution_groups): if len(group) > 1: # 分析并行任务的资源需求 resource_analysis = self._analyze_resource_requirements(group) parallel_groups.append({ "stage": i + 1, "tasks": group, "parallelizable": True, "resource_requirements": resource_analysis, "estimated_time_saving": self._estimate_time_saving(group) }) return parallel_groups def_analyze_resource_requirements(self, task_group: List[str]) -> dict: """分析资源需求""" resource_usage = { "database_connections": 0, "api_calls": 0, "memory_intensive": False, "cpu_intensive": False } for task_id in task_group: task = self.dependency_graph[task_id]["task"] if task.tool_required in ["nl2sql", "database_query"]: resource_usage["database_connections"] += 1 if task.tool_required in ["rag_search", "api_call"]: resource_usage["api_calls"] += 1 if task.task_type in ["data_analysis", "calculation"]: resource_usage["cpu_intensive"] = True if"large_dataset"in task.expected_output.lower(): resource_usage["memory_intensive"] = True return resource_usage

5. 工具使用与数据源集成

5.1 工具架构设计

5.2 RAG检索工具实现

classFinancialRAGTool: def__init__(self): self.vector_db = VectorDatabase() self.document_parser = DocumentParser() self.embedding_model = EmbeddingModel() # 文档类型映射 self.document_types = { "audit_report": "审计报告", "bond_prospectus": "债券募集说明书", "financial_report": "财务报告", "ipo_document": "IPO文档" } defsearch_financial_documents(self, query: str, doc_types: List[str] = None, companies: List[str] = None, time_range: dict = None) -> List[dict]: """搜索金融文档""" # 构建搜索向量 query_embedding = self.embedding_model.encode(query) # 构建过滤条件 filters = self._build_search_filters(doc_types, companies, time_range) # 执行向量搜索 search_results = self.vector_db.search( query_vector=query_embedding, filters=filters, top_k=20, similarity_threshold=0.7 ) # 重排序和后处理 ranked_results = self._rerank_results(search_results, query) return ranked_results def_build_search_filters(self, doc_types: List[str], companies: List[str], time_range: dict) -> dict: """构建搜索过滤条件""" filters = {} if doc_types: filters["document_type"] = {"$in": doc_types} if companies: filters["company_name"] = {"$in": companies} if time_range: filters["report_date"] = { "$gte": time_range.get("start_date"), "$lte": time_range.get("end_date") } return filters defextract_financial_metrics(self, documents: List[dict], metrics: List[str]) -> dict: """从文档中提取财务指标""" extracted_data = {} for doc in documents: doc_content = doc["content"] company = doc["company_name"] report_date = doc["report_date"] # 使用NER和规则提取指标 metrics_data = self._extract_metrics_from_text(doc_content, metrics) if company notin extracted_data: extracted_data[company] = {} extracted_data[company][report_date] = metrics_data return extracted_data def_extract_metrics_from_text(self, text: str, metrics: List[str]) -> dict: """从文本中提取具体指标""" import re extracted = {} # 定义指标提取模式 metric_patterns = { "ROE": r"净资产收益率[：:]*\s*([\d.]+)%?", "ROA": r"总资产收益率[：:]*\s*([\d.]+)%?", "净利润": r"净利润[：:]*\s*([\d,.]+)\s*[万亿]?元", "营业收入": r"营业收入[：:]*\s*([\d,.]+)\s*[万亿]?元", "总资产": r"总资产[：:]*\s*([\d,.]+)\s*[万亿]?元" } for metric in metrics: if metric in metric_patterns: pattern = metric_patterns[metric] matches = re.findall(pattern, text) if matches: extracted[metric] = self._normalize_number(matches[0]) return extracted def_normalize_number(self, number_str: str) -> float: """标准化数字格式""" # 移除逗号和其他格式字符 cleaned = re.sub(r'[,，]', '', number_str) try: return float(cleaned) except ValueError: returnNone

5.3 NL2SQL工具实现

classFinancialNL2SQLTool: def__init__(self): self.sql_generator = SQLGenerator() self.query_optimizer = QueryOptimizer() self.database_connector = DatabaseConnector() # 数据库schema信息 self.schema_info = { "wind_database": { "tables": { "stock_basic_info": ["stock_code", "stock_name", "industry", "list_date"], "financial_indicators": ["stock_code", "report_date", "roe", "roa", "net_profit"], "market_data": ["stock_code", "trade_date", "close_price", "volume", "market_cap"] }, "relationships": [ ("stock_basic_info.stock_code", "financial_indicators.stock_code"), ("stock_basic_info.stock_code", "market_data.stock_code") ] } } defnatural_language_to_sql(self, nl_query: str, database: str = "wind_database") -> dict: """将自然语言转换为SQL查询""" # 解析自然语言查询 parsed_query = self._parse_nl_query(nl_query) # 生成SQL语句 sql_query = self._generate_sql(parsed_query, database) # 优化查询 optimized_sql = self.query_optimizer.optimize(sql_query) # 执行查询 results = self._execute_query(optimized_sql, database) return { "original_query": nl_query, "parsed_query": parsed_query, "sql_query": optimized_sql, "results": results, "execution_time": self._get_execution_time() } def_parse_nl_query(self, nl_query: str) -> dict: """解析自然语言查询""" parsed = { "select_fields": [], "tables": [], "conditions": [], "aggregations": [], "order_by": [], "limit": None } # 识别查询字段 field_patterns = { "ROE": "roe", "净资产收益率": "roe", "ROA": "roa", "总资产收益率": "roa", "净利润": "net_profit", "股票代码": "stock_code", "股票名称": "stock_name", "行业": "industry" } for pattern, field in field_patterns.items(): if pattern in nl_query: parsed["select_fields"].append(field) # 识别条件 condition_patterns = { r"(\d{4})年": lambda m: f"report_date LIKE '{m.group(1)}%'", r"ROE\s*[>大于]\s*([\d.]+)": lambda m: f"roe > {m.group(1)}", r"行业\s*[=是]\s*([\u4e00-\u9fa5]+)": lambda m: f"industry = '{m.group(1)}'" } import re for pattern, condition_func in condition_patterns.items(): matches = re.finditer(pattern, nl_query) for match in matches: parsed["conditions"].append(condition_func(match)) # 识别排序和限制 if"排名"in nl_query or"前"in nl_query: parsed["order_by"].append("roe DESC") # 提取数量限制 limit_match = re.search(r"前(\d+)", nl_query) if limit_match: parsed["limit"] = int(limit_match.group(1)) return parsed def_generate_sql(self, parsed_query: dict, database: str) -> str: """生成SQL查询语句""" # 确定需要的表 required_tables = self._determine_required_tables(parsed_query["select_fields"]) # 构建SELECT子句 select_clause = "SELECT " + ", ".join(parsed_query["select_fields"]) # 构建FROM子句 from_clause = "FROM " + " JOIN ".join(required_tables) # 构建WHERE子句 where_clause = "" if parsed_query["conditions"]: where_clause = "WHERE " + " AND ".join(parsed_query["conditions"]) # 构建ORDER BY子句 order_clause = "" if parsed_query["order_by"]: order_clause = "ORDER BY " + ", ".join(parsed_query["order_by"]) # 构建LIMIT子句 limit_clause = "" if parsed_query["limit"]: limit_clause = f"LIMIT {parsed_query['limit']}" # 组合完整SQL sql_parts = [select_clause, from_clause, where_clause, order_clause, limit_clause] sql_query = " ".join([part for part in sql_parts if part]) return sql_query def_determine_required_tables(self, fields: List[str]) -> List[str]: """确定查询所需的表""" required_tables = set() field_table_mapping = { "stock_code": "stock_basic_info", "stock_name": "stock_basic_info", "industry": "stock_basic_info", "roe": "financial_indicators", "roa": "financial_indicators", "net_profit": "financial_indicators", "close_price": "market_data", "market_cap": "market_data" } for field in fields: if field in field_table_mapping: required_tables.add(field_table_mapping[field]) return list(required_tables)

6. 执行引擎与调度

6.1 任务调度器设计

6.2 调度器实现

classFinancialTaskScheduler: def__init__(self): self.task_queue = TaskQueue() self.dependency_manager = DependencyManager() self.execution_engine = ExecutionEngine() self.result_manager = ResultManager() self.error_handler = ErrorHandler() # 执行状态跟踪 self.task_status = {} self.execution_history = [] defschedule_and_execute(self, subtasks: List[SubTask]) -> dict: """调度和执行任务""" # 初始化任务状态 for task in subtasks: self.task_status[task.task_id] = "pending" self.task_queue.add_task(task) # 分析依赖关系 dependency_analysis = self.dependency_manager.analyze_dependencies(subtasks) execution_groups = dependency_analysis["execution_groups"] # 按组执行任务 overall_results = {} for group_index, task_group in enumerate(execution_groups): group_results = self._execute_task_group(task_group, group_index) overall_results.update(group_results) # 检查是否有失败的关键任务 if self._has_critical_failures(group_results): return self._handle_critical_failure(overall_results) # 整合最终结果 final_result = self.result_manager.integrate_results(overall_results) return { "status": "completed", "results": final_result, "execution_summary": self._generate_execution_summary(), "performance_metrics": self._calculate_performance_metrics() } def_execute_task_group(self, task_group: List[str], group_index: int) -> dict: """执行任务组""" group_results = {} if len(task_group) == 1: # 串行执行单个任务 task_id = task_group[0] result = self._execute_single_task(task_id) group_results[task_id] = result else: # 并行执行多个任务 group_results = self._execute_parallel_tasks(task_group) return group_results def_execute_parallel_tasks(self, task_group: List[str]) -> dict: """并行执行任务组""" import concurrent.futures import threading results = {} # 创建线程池 with concurrent.futures.ThreadPoolExecutor(max_workers=len(task_group)) as executor: # 提交所有任务 future_to_task = { executor.submit(self._execute_single_task, task_id): task_id for task_id in task_group } # 收集结果 for future in concurrent.futures.as_completed(future_to_task): task_id = future_to_task[future] try: result = future.result(timeout=300) # 5分钟超时 results[task_id] = result self.task_status[task_id] = "completed" except Exception as e: results[task_id] = {"status": "failed", "error": str(e)} self.task_status[task_id] = "failed" return results def_execute_single_task(self, task_id: str) -> dict: """执行单个任务""" task = self.task_queue.get_task(task_id) try: self.task_status[task_id] = "executing" # 根据任务类型选择执行器 if task.task_type == "data_retrieval": result = self._execute_data_retrieval_task(task) elif task.task_type == "calculation": result = self._execute_calculation_task(task) elif task.task_type == "comparison": result = self._execute_comparison_task(task) elif task.task_type == "report_generation": result = self._execute_report_generation_task(task) else: raise ValueError(f"未知任务类型: {task.task_type}") self.task_status[task_id] = "completed" # 记录执行历史 self.execution_history.append({ "task_id": task_id, "status": "completed", "execution_time": result.get("execution_time", 0), "timestamp": datetime.now() }) return result except Exception as e: self.task_status[task_id] = "failed" error_result = self.error_handler.handle_task_error(task, e) # 记录错误 self.execution_history.append({ "task_id": task_id, "status": "failed", "error": str(e), "timestamp": datetime.now() }) return error_result def_execute_data_retrieval_task(self, task: SubTask) -> dict: """执行数据检索任务""" start_time = time.time() if task.tool_required == "rag_search": # 使用RAG工具 rag_tool = FinancialRAGTool() results = rag_tool.search_financial_documents( query=task.description, doc_types=task.data_sources ) elif task.tool_required == "nl2sql": # 使用NL2SQL工具 nl2sql_tool = FinancialNL2SQLTool() results = nl2sql_tool.natural_language_to_sql(task.description) else: raise ValueError(f"不支持的工具: {task.tool_required}") execution_time = time.time() - start_time return { "status": "completed", "data": results, "execution_time": execution_time, "task_type": task.task_type } def_execute_calculation_task(self, task: SubTask) -> dict: """执行计算任务""" start_time = time.time() # 获取依赖任务的结果 input_data = self._get_dependency_results(task.dependencies) # 执行计算 calculator = FinancialCalculator() if"趋势分析"in task.description: results = calculator.trend_analysis(input_data) elif"增长率"in task.description: results = calculator.growth_rate_calculation(input_data) elif"波动性"in task.description: results = calculator.volatility_analysis(input_data) else: results = calculator.general_calculation(task.description, input_data) execution_time = time.time() - start_time return { "status": "completed", "data": results, "execution_time": execution_time, "task_type": task.task_type } def_get_dependency_results(self, dependencies: List[str]) -> dict: """获取依赖任务的结果""" dependency_data = {} for dep_task_id in dependencies: if dep_task_id in self.result_manager.results: dependency_data[dep_task_id] = self.result_manager.results[dep_task_id] else: raise ValueError(f"依赖任务 {dep_task_id} 的结果不可用") return dependency_data

7. 常见业务场景实现

7.1 场景分类与处理策略

7.2 具体场景实现

7.2.1 场景一：“查询平安银行2023年ROE，并计算同比增长率”

classROEQueryScenario: def__init__(self): self.scenario_name = "ROE查询与同比计算" self.complexity = "medium" defdecompose_task(self, query: str) -> List[SubTask]: """任务分解""" return [ SubTask( task_id="roe_current_year", description="查询平安银行2023年ROE数据", task_type="data_retrieval", tool_required="nl2sql", data_sources=["Wind数据库"], sql_template="SELECT roe FROM financial_indicators WHERE stock_name='平安银行' AND report_date LIKE '2023%'", expected_output="2023年各季度ROE数据", dependencies=[], priority="high" ), SubTask( task_id="roe_previous_year", description="查询平安银行2022年ROE数据", task_type="data_retrieval", tool_required="nl2sql", data_sources=["Wind数据库"], sql_template="SELECT roe FROM financial_indicators WHERE stock_name='平安银行' AND report_date LIKE '2022%'", expected_output="2022年各季度ROE数据", dependencies=[], priority="high" ), SubTask( task_id="yoy_calculation", description="计算ROE同比增长率", task_type="calculation", tool_required="data_analysis", calculation_formula="(ROE_2023 - ROE_2022) / ROE_2022 * 100", expected_output="ROE同比增长率", dependencies=["roe_current_year", "roe_previous_year"], priority="medium" ) ] defexecute_scenario(self, query: str) -> dict: """执行场景""" # 任务分解 subtasks = self.decompose_task(query) # 调度执行 scheduler = FinancialTaskScheduler() results = scheduler.schedule_and_execute(subtasks) # 结果格式化 formatted_result = self._format_roe_result(results) return formatted_result def_format_roe_result(self, results: dict) -> dict: """格式化ROE查询结果""" roe_2023 = results["results"]["roe_current_year"]["data"] roe_2022 = results["results"]["roe_previous_year"]["data"] yoy_growth = results["results"]["yoy_calculation"]["data"] return { "company": "平安银行", "metric": "ROE", "current_year": { "year": 2023, "value": roe_2023, "unit": "%" }, "previous_year": { "year": 2022, "value": roe_2022, "unit": "%" }, "yoy_growth": { "value": yoy_growth, "unit": "%", "interpretation": self._interpret_growth(yoy_growth) }, "analysis_summary": self._generate_summary(roe_2023, roe_2022, yoy_growth) } def_interpret_growth(self, growth_rate: float) -> str: """解释增长率""" if growth_rate > 10: return"显著增长" elif growth_rate > 0: return"正增长" elif growth_rate > -10: return"轻微下降" else: return"显著下降"

7.2.2 场景二：“筛选ROE大于15%的银行股，按市值排序”

classBankStockScreeningScenario: def__init__(self): self.scenario_name = "银行股筛选排序" self.complexity = "medium" defdecompose_task(self, query: str) -> List[SubTask]: """任务分解""" return [ SubTask( task_id="get_bank_list", description="获取所有银行股列表", task_type="data_retrieval", tool_required="nl2sql", sql_template="SELECT stock_code, stock_name FROM stock_basic_info WHERE industry='银行业'", expected_output="银行股票列表", dependencies=[], priority="high" ), SubTask( task_id="get_roe_data", description="获取银行股ROE数据", task_type="data_retrieval", tool_required="nl2sql", sql_template="SELECT stock_code, roe FROM financial_indicators WHERE stock_code IN (银行股代码) AND report_date='2023-12-31'", expected_output="银行股ROE数据", dependencies=["get_bank_list"], priority="high" ), SubTask( task_id="filter_by_roe", description="筛选ROE大于15%的银行股", task_type="filtering", tool_required="data_analysis", filter_condition="roe > 15", expected_output="符合ROE条件的银行股", dependencies=["get_roe_data"], priority="medium" ), SubTask( task_id="get_market_cap", description="获取筛选后银行股的市值数据", task_type="data_retrieval", tool_required="nl2sql", sql_template="SELECT stock_code, market_cap FROM market_data WHERE stock_code IN (筛选后股票) AND trade_date='2023-12-31'", expected_output="银行股市值数据", dependencies=["filter_by_roe"], priority="medium" ), SubTask( task_id="sort_by_market_cap", description="按市值排序", task_type="sorting", tool_required="data_analysis", sort_criteria="market_cap DESC", expected_output="按市值排序的银行股列表", dependencies=["get_market_cap"], priority="low" ) ]

7.2.3 场景三：“生成银行业2023年度分析报告”

classBankingIndustryReportScenario: def__init__(self): self.scenario_name = "银行业年度分析报告" self.complexity = "high" defdecompose_task(self, query: str) -> List[SubTask]: """任务分解""" return [ # 数据收集阶段（可并行） SubTask( task_id="collect_industry_overview", description="收集银行业整体概况数据", task_type="data_retrieval", tool_required="rag_search", data_sources=["行业报告库", "监管公告库"], search_keywords=["银行业", "2023年", "行业概况", "发展趋势"], expected_output="银行业整体发展情况", dependencies=[], priority="high" ), SubTask( task_id="collect_financial_data", description="收集主要银行财务数据", task_type="data_retrieval", tool_required="nl2sql", sql_template="SELECT * FROM financial_indicators WHERE industry='银行业' AND report_date LIKE '2023%'", expected_output="银行业财务指标数据", dependencies=[], priority="high" ), SubTask( task_id="collect_market_data", description="收集银行股市场表现数据", task_type="data_retrieval", tool_required="nl2sql", sql_template="SELECT * FROM market_data WHERE industry='银行业' AND trade_date BETWEEN '2023-01-01' AND '2023-12-31'", expected_output="银行股市场表现数据", dependencies=[], priority="high" ), SubTask( task_id="collect_regulatory_data", description="收集监管政策和合规数据", task_type="data_retrieval", tool_required="rag_search", data_sources=["监管文件库", "政策公告库"], search_keywords=["银行监管", "2023年", "政策变化", "合规要求"], expected_output="监管政策变化情况", dependencies=[], priority="medium" ), # 分析计算阶段（依赖数据收集） SubTask( task_id="financial_analysis", description="进行财务指标分析", task_type="calculation", tool_required="data_analysis", analysis_types=["盈利能力分析", "资产质量分析", "资本充足率分析"], expected_output="财务分析结果", dependencies=["collect_financial_data"], priority="medium" ), SubTask( task_id="market_performance_analysis", description="进行市场表现分析", task_type="calculation", tool_required="data_analysis", analysis_types=["股价表现", "估值水平", "投资者情绪"], expected_output="市场分析结果", dependencies=["collect_market_data"], priority="medium" ), SubTask( task_id="competitive_analysis", description="进行竞争格局分析", task_type="comparison", tool_required="data_analysis", comparison_dimensions=["市场份额", "业务结构", "创新能力"], expected_output="竞争分析结果", dependencies=["collect_financial_data", "collect_market_data"], priority="medium" ), # 报告生成阶段（依赖所有分析） SubTask( task_id="generate_executive_summary", description="生成执行摘要", task_type="report_generation", tool_required="llm_analysis", report_section="executive_summary", expected_output="执行摘要内容", dependencies=["financial_analysis", "market_performance_analysis"], priority="low" ), SubTask( task_id="generate_detailed_analysis", description="生成详细分析章节", task_type="report_generation", tool_required="llm_analysis", report_section="detailed_analysis", expected_output="详细分析内容", dependencies=["financial_analysis", "market_performance_analysis", "competitive_analysis"], priority="low" ), SubTask( task_id="generate_conclusions", description="生成结论和建议", task_type="report_generation", tool_required="llm_analysis", report_section="conclusions_recommendations", expected_output="结论和投资建议", dependencies=["generate_detailed_analysis"], priority="low" ), SubTask( task_id="format_final_report", description="格式化最终报告", task_type="report_generation", tool_required="report_formatter", output_format="pdf", expected_output="完整的银行业分析报告", dependencies=["generate_executive_summary", "generate_detailed_analysis", "generate_conclusions"], priority="low" ) ]

8. 性能优化与最佳实践

8.1 性能优化策略

8.2 缓存策略实现

classFinancialDataCache: def__init__(self): self.redis_client = redis.Redis(host='localhost', port=6379, db=0) self.cache_ttl = { "financial_data": 3600, # 1小时 "market_data": 300, # 5分钟 "calculation_results": 1800, # 30分钟 "report_content": 7200 # 2小时 } defget_cached_result(self, cache_key: str, data_type: str) -> dict: """获取缓存结果""" try: cached_data = self.redis_client.get(cache_key) if cached_data: return json.loads(cached_data) except Exception as e: logger.warning(f"缓存读取失败: {e}") returnNone defcache_result(self, cache_key: str, data: dict, data_type: str): """缓存结果""" try: ttl = self.cache_ttl.get(data_type, 3600) self.redis_client.setex( cache_key, ttl, json.dumps(data, ensure_ascii=False) ) except Exception as e: logger.warning(f"缓存写入失败: {e}") defgenerate_cache_key(self, task: SubTask) -> str: """生成缓存键""" key_components = [ task.task_type, task.description, str(hash(str(task.dependencies))) ] return"financial_agent:" + ":".join(key_components)

9. 错误处理与容错机制

9.1 错误分类与处理策略

9.2 容错机制实现

classFinancialErrorHandler: def__init__(self): self.retry_config = { "max_retries": 3, "base_delay": 1.0, "max_delay": 60.0, "backoff_factor": 2.0 } self.fallback_strategies = { "data_source_unavailable": self._fallback_to_alternative_source, "calculation_error": self._fallback_to_simplified_calculation, "api_rate_limit": self._fallback_to_cached_data, "network_timeout": self._fallback_to_local_data } defhandle_task_error(self, task: SubTask, error: Exception) -> dict: """处理任务错误""" error_type = self._classify_error(error) # 尝试重试 if self._should_retry(error_type): return self._retry_task(task, error) # 尝试降级处理 if error_type in self.fallback_strategies: return self.fallback_strategies[error_type](task, error) # 记录错误并返回失败结果 self._log_error(task, error) return { "status": "failed", "error_type": error_type, "error_message": str(error), "fallback_available": False } def_retry_task(self, task: SubTask, error: Exception) -> dict: """重试任务""" for attempt in range(self.retry_config["max_retries"]): try: # 计算延迟时间 delay = min( self.retry_config["base_delay"] * (self.retry_config["backoff_factor"] ** attempt), self.retry_config["max_delay"] ) time.sleep(delay) # 重新执行任务 result = self._execute_task_with_retry(task) if result["status"] == "completed": return result except Exception as retry_error: if attempt == self.retry_config["max_retries"] - 1: return { "status": "failed_after_retry", "original_error": str(error), "final_error": str(retry_error), "retry_attempts": attempt + 1 } return {"status": "retry_exhausted"}

10. 监控与日志

10.1 监控指标体系

classFinancialAgentMonitor: def__init__(self): self.metrics_collector = MetricsCollector() self.alert_manager = AlertManager() # 关键性能指标 self.kpi_metrics = { "task_success_rate": 0.0, "average_execution_time": 0.0, "data_quality_score": 0.0, "user_satisfaction_score": 0.0, "system_availability": 0.0 } deftrack_task_execution(self, task: SubTask, result: dict): """跟踪任务执行""" execution_metrics = { "task_id": task.task_id, "task_type": task.task_type, "execution_time": result.get("execution_time", 0), "status": result.get("status"), "data_quality": self._assess_data_quality(result), "timestamp": datetime.now() } self.metrics_collector.record_metrics(execution_metrics) # 检查是否需要告警 self._check_alerts(execution_metrics) defgenerate_performance_report(self, time_range: dict) -> dict: """生成性能报告""" metrics_data = self.metrics_collector.get_metrics(time_range) return { "summary": { "total_tasks": len(metrics_data), "success_rate": self._calculate_success_rate(metrics_data), "average_execution_time": self._calculate_avg_time(metrics_data), "error_rate": self._calculate_error_rate(metrics_data) }, "task_type_breakdown": self._analyze_by_task_type(metrics_data), "performance_trends": self._analyze_trends(metrics_data), "recommendations": self._generate_recommendations(metrics_data) }

11. 总结与展望

11.1 系统优势

智能化任务分解：基于意图识别的自动任务分解，提高处理效率
灵活的执行策略：支持并行和串行执行，优化资源利用
丰富的工具集成：RAG检索、NL2SQL、数据计算等多种工具协同
强大的容错能力：多层次错误处理和降级策略
全面的监控体系：实时性能监控和质量评估

11.2 应用价值

提升分析效率：自动化复杂金融分析流程，减少人工干预
保证数据质量：多源数据整合和质量检查机制
降低技术门槛：自然语言交互，降低专业技能要求
增强决策支持：提供全面、准确的分析结果和建议

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如果你能在15天内完成所有的任务，那你堪称天才。然而，如果你能完成 60-70% 的内容，你就已经开始具备成为一名大模型 AI 的正确特征了。