Hier is een overzicht en voorbeeldcode om een verklaarbare LangChain-agent te maken voor financieel advies, met focus op transparantie, traceerbaarheid en begrijpelijke uitleg. **Benadering:** - Gebruik van een LLM (bijvoorbeeld OpenAI) met expliciete prompts voor verklaringen. - Inbedden van verklaringsfuncties (bijvoorbeeld `explain_decision`) die de redeneerstappen verduidelijken. - Loggen van redeneerstappen binnen chains. - Gebruik van stapsgewijze, eenvoudige taal voor uitleg. --- ### 1. Basisopzet met LangChain Installeer benodigde pakketten: ```bash pip install langchain openai ``` ### 2. Voorbeeldcode ```python from langchain import LLMChain, PromptTemplate from langchain.chat_models import ChatOpenAI # Initializeer de LLM llm = ChatOpenAI(model="gpt-3.5-turbo") # of een ander model # Stap 1: Basis prompt voor financieel advies advies_prompt = PromptTemplate( input_variables=["klant_info"], template=""" Je bent een financieel adviseur. Geef een beknopt advies op basis van de volgende klantinformatie: Klantinformatie: {klant_info} Geef geen uitleg, alleen het advies. """ ) # Stap 2: Prompt voor verklaring verklaar_prompt = PromptTemplate( input_variables=["klant_info", "advies"], template=""" Je bent een financieel adviseur. Je hebt het volgende advies gegeven: "{advies}" op basis van de klantinformatie: "{klant_info}". Leg stap voor stap uit waarom je dit advies hebt gegeven. Gebruik eenvoudige taal, en beschrijf elke redeneerstap. """ ) # Chains maken advies_chain = LLMChain(llm=llm, prompt=advies_prompt) verklaar_chain = LLMChain(llm=llm, prompt=verklaar_prompt) # Functie voor transparant financieel advies def geef_advies_met_uitleg(klant_info): advies = advies_chain.run(klant_info=klant_info) uitleg = verklaren(klant_info, advies) return advies, uitleg # Verklaringsfunctie def verklaren(klant_info, advies): uitleg = verklaraar_chain.run(klant_info=klant_info, advies=advies) return uitleg # Voorbeeldgebruik klant_info = "De klant is 45 jaar, heeft een spaarrekening met 10.000 euro, geen schulden, en wil op lange termijn sparen voor pensioen." advies, uitleg = geef_advies_met_uitleg(klant_info) print("Advies:", advies) print("\nUitleg:\n", uitleg) ``` --- ### 3. Uitleg van de aanpak - **Transparantie & Traceerbaarheid:** De functie `verklaren` gebruikt een aparte prompt om de redeneerstappen uit te leggen, gebaseerd op de klantinformatie en het advies. Zo wordt het proces inzichtelijk. - **Menselijke Leesbaarheid:** Het prompt voor de uitleg vraagt expliciet om eenvoudige, stapsgewijze taal, waardoor de verklaring voor een niet-specialist begrijpelijk is. - **Loggen van redeneerstappen:** Door de uitleg expliciet te genereren met een aparte chain, wordt het logisch proces inzichtelijk gemaakt, en kunnen de stappen worden gevolgd. --- ### 4. Uitbreiding en verfijning - **Gedetailleerder verklaringen:** Pas de `verklaren` prompt aan voor meer of minder detail. - **Traceerbaarheid binnen chains:** Gebruik `langchain`'s callback- en logging-functionaliteit om stappen te loggen. - **Visualisatie:** Exporteer de redeneerstappen naar een gestructureerd formaat (bijvoorbeeld JSON) voor verdere analyse. --- ### 5. Samenvatting Deze aanpak maakt het mogelijk om een financieel adviesproces te voorzien van begrijpelijke, stapsgewijze uitleg, waardoor de transparantie en trust in de AI-adviezen wordt vergroot. --- Indien je verdere specificaties hebt of de uitleg in een bepaald formaat wil, laat het gerust weten!

I'll help you create a LangChain agent with transparent decision-making capabilities for financial advising. Here's a comprehensive implementation with explainability features: ## Required Packages ```bash pip install langchain openai langchain-experimental python-dotenv ``` ## Environment Setup ```python import os from dotenv import load_dotenv from langchain.llms import OpenAI from langchain.agents import Tool, AgentExecutor, initialize_agent from langchain.agents import AgentType from langchain.memory import ConversationBufferMemory from langchain.chains import LLMChain from langchain.prompts import PromptTemplate from langchain.schema import AgentAction, AgentFinish from typing import List, Tuple, Any, Dict load_dotenv() ``` ## 1. Custom Agent with Explainability ```python class ExplainableFinancialAgent: def __init__(self): self.llm = OpenAI(temperature=0, model_name="gpt-3.5-turbo") self.memory = ConversationBufferMemory(memory_key="chat_history") self.reasoning_steps = [] def log_reasoning_step(self, step: str, details: Dict = None): """Log each reasoning step for transparency""" step_record = { "step": step, "details": details or {}, "timestamp": datetime.now().isoformat() } self.reasoning_steps.append(step_record) def get_explanation(self) -> str: """Generate human-readable explanation from reasoning steps""" explanation = "## Decision-Making Process Explanation:\n\n" for i, step in enumerate(self.reasoning_steps, 1): explanation += f"**Step {i}:** {step['step']}\n" if step['details']: explanation += f" - Details: {step['details']}\n" explanation += "\n" return explanation ``` ## 2. Financial Tools with Transparent Operations ```python def risk_assessment_tool(age: int, income: float, investment_goal: str) -> str: """Tool for risk assessment with transparent calculations""" reasoning = f"Assessing risk for age {age}, income ${income:,.2f}, goal: {investment_goal}" # Simple risk calculation logic risk_score = min(100, max(0, (100 - age) * 0.5 + (income / 10000) * 0.3)) explanation = { "calculation": f"Risk score = (100 - age) * 0.5 + (income / 10000) * 0.3", "result": f"Calculated risk score: {risk_score:.1f}", "factors_considered": ["age", "annual_income", "investment_goal"] } return f"Risk Score: {risk_score:.1f}", explanation def portfolio_recommendation_tool(risk_score: float, time_horizon: int) -> str: """Portfolio recommendation with transparent allocation logic""" reasoning = f"Creating portfolio for risk score {risk_score}, time horizon {risk_score} years" # Allocation logic equity_percent = min(100, max(0, risk_score * 0.8)) bond_percent = 100 - equity_percent explanation = { "allocation_logic": "Higher risk score → higher equity allocation", "equity_percent_calculation": f"min(100, max(0, risk_score * 0.8))", "bond_percent_calculation": "100 - equity_percent", "result": f"Recommended: {equity_percent:.1f}% equities, {bond_percent:.1f}% bonds" } return f"Allocation: {equity_percent:.1f}% equities, {bond_percent:.1f}% bonds", explanation ``` ## 3. Custom Agent with Step-by-Step Reasoning ```python class TransparentFinancialAgent(ExplainableFinancialAgent): def __init__(self): super().__init__() # Define tools with transparent operations self.tools = [ Tool( name="Risk Assessment", func=lambda params: risk_assessment_tool(**params), description="Assess investment risk based on age, income, and goals" ), Tool( name="Portfolio Recommendation", func=lambda params: portfolio_recommendation_tool(**params), description="Recommend portfolio allocation based on risk score and time horizon" ) ] # Custom prompt for explainability self.prompt = PromptTemplate( template=""" You are a transparent financial advisor. Always explain your reasoning step by step. Current conversation: {chat_history} Human: {input} Please think through each step and explain your reasoning clearly. Use tools when needed and explain why you're using each tool. {agent_scratchpad} """, input_variables=["input", "chat_history", "agent_scratchpad"] ) def process_query(self, query: str) -> Dict: """Process financial query with full transparency""" self.reasoning_steps = [] # Reset reasoning steps # Log initial query self.log_reasoning_step("Received user query", {"query": query}) try: # Initialize agent with custom prompt agent = initialize_agent( self.tools, self.llm, agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION, memory=self.memory, verbose=True, prompt=self.prompt ) # Process the query self.log_reasoning_step("Analyzing query to determine appropriate tools") response = agent.run(query) self.log_reasoning_step("Generated final response", {"response": response}) return { "final_response": response, "explanation": self.get_explanation(), "reasoning_steps": self.reasoning_steps } except Exception as e: self.log_reasoning_step("Error occurred during processing", {"error": str(e)}) return { "error": str(e), "explanation": self.get_explanation() } ``` ## 4. Usage Example ```python # Initialize the agent financial_agent = TransparentFinancialAgent() # Example usage query = """ I'm 35 years old, earn $85,000 annually, and want to save for retirement in 25 years. What's my risk profile and recommended portfolio allocation? """ result = financial_agent.process_query(query) print("=== FINAL RESPONSE ===") print(result['final_response']) print("\n=== EXPLANATION ===") print(result['explanation']) # Access detailed reasoning steps print("\n=== DETAILED REASONING STEPS ===") for step in result['reasoning_steps']: print(f"Step: {step['step']}") if step['details']: print(f" Details: {step['details']}") ``` ## 5. Enhanced Explanation Generator ```python class EnhancedExplanationGenerator: @staticmethod def generate_detailed_explanation(reasoning_steps: List[Dict]) -> str: """Generate comprehensive human-readable explanation""" explanation = "## Comprehensive Decision-Making Process\n\n" for i, step in enumerate(reasoning_steps, 1): explanation += f"### Step {i}: {step['step']}\n" if step['details']: explanation += "**Details:**\n" for key, value in step['details'].items(): explanation += f"- {key.replace('_', ' ').title()}: {value}\n" explanation += "\n" explanation += "## Key Considerations in This Analysis:\n" explanation += "- All calculations are based on standard financial principles\n" explanation += "- Risk assessment considers age, income, and investment goals\n" explanation += "- Portfolio allocation follows modern portfolio theory principles\n" explanation += "- Recommendations are general and should be reviewed with a certified financial advisor\n" return explanation ``` ## 6. Complete Implementation with Enhanced Features ```python class CompleteFinancialAdvisor: def __init__(self): self.agent = TransparentFinancialAgent() self.explanation_generator = EnhancedExplanationGenerator() def advise(self, query: str) -> Dict: """Provide financial advice with comprehensive explanations""" result = self.agent.process_query(query) if 'final_response' in result: result['enhanced_explanation'] = self.explanation_generator.generate_detailed_explanation( result['reasoning_steps'] ) return result # Example usage advisor = CompleteFinancialAdvisor() advice = advisor.advise(""" I'm 28, make $65,000, and want to buy a house in 5 years. What investment strategy should I consider? """) print(advice['final_response']) print(advice['enhanced_explanation']) ``` ## Key Explainability Features Implemented: 1. **Step-by-Step Reasoning Logging**: Every decision and tool usage is logged 2. **Transparent Calculations**: Mathematical formulas and logic are exposed 3. **Human-Readable Explanations**: Clear, simple language explanations 4. **Decision Traceability**: Full audit trail of reasoning steps 5. **Factor Disclosure**: Clear listing of considered factors and weights ## Format Requirements: - **Step-by-step numbering** for clear progression - **Simple language** avoiding financial jargon - **Mathematical transparency** showing calculations - **Factor disclosure** explaining what was considered - **Risk disclosure** stating limitations and assumptions This implementation provides a robust foundation for transparent financial advising with LangChain, ensuring users understand both the recommendations and the reasoning behind them.