收藏必备！金融智能Agent系统构建实战：从零开始掌握大模型驱动的金融分析平台

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。

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, abstractmethod
from typing import Dict, List, Any
import asyncio

classBaseFinancialAgent(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_result

classTaskDecompositionAgent(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

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

1. 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
2. 灵活的执行策略：支持并行和串行执行，优化资源利用
3. 丰富的工具集成：RAG检索、NL2SQL、数据计算等多种工具协同
4. 强大的容错能力：多层次错误处理和降级策略
5. 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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