Natuurlijk! Hieronder vind je een gedetailleerde uitleg en voorbeelden om een LangChain-agent te ontwikkelen die geavanceerde beslissingslogica kan uitvoeren voor automatische kredietbeoordeling en risicoanalyse. We richten ons op het gebruik van conditionele ketens, logische operatoren, aangepaste functies, foutafhandeling en optimalisaties. ### 1. Structuur en aanpak - **Conditionele ketens**: gebruik functies of methoden die verschillende regels en voorwaarden combineren. - **Logische operatoren**: implementeer AND, OR, NOT, en combinaties daarvan. - **Aangepaste functies**: maak functies die complexe berekeningen en beslissingen uitvoeren. - **Contextgevoeligheid**: gebruik variabelen en data om beslissingen dynamisch te sturen. - **Foutafhandeling**: gebruik try-except blokken en validatie. - **Optimalisaties**: cache resultaten, minimaliseer herhaalde berekeningen. --- ### 2. Voorbeeld: Basisstructuur met Python functies Hier is een voorbeeld van een gestructureerde aanpak: ```python def is_beschikbaar(gegevens): # Check of alle benodigde gegevens aanwezig zijn required_fields = ['inkomen', 'schulden', 'lening_bedrag', 'duur'] return all(gegevens.get(field) is not None for field in required_fields) def bereken_risico_score(gegevens): # Eenvoudige risico score berekening inkomen = gegevens['inkomen'] schulden = gegevens['schulden'] lening = gegevens['lening_bedrag'] # Voorbeeld van een eenvoudige formule schuld_ratio = schulden / inkomen lening_ratio = lening / inkomen score = 0 if schuld_ratio > 0.4: score += 2 elif schuld_ratio > 0.2: score += 1 if lening_ratio > 0.5: score += 2 elif lening_ratio > 0.3: score += 1 return score def neemt_beslissing(gegevens): # Combineser regels met conditionele logica try: if not is_beschikbaar(gegevens): return "Onvoldoende gegevens" risk_score = bereken_risico_score(gegevens) if risk_score >= 4: return "Afgewezen: hoog risico" elif risk_score >= 2: return "Voorwaardelijk goedkeuren" else: return "Goedgekeurd" except Exception as e: return f"Fout tijdens beoordeling: {str(e)}" ``` ### 3. Integratie met LangChain Gebruik een custom Tool of Agent met een PythonCodeExecutor: ```python from langchain.agents import initialize_agent, AgentType from langchain.tools import PythonREPLTool # Definieer de functies in een script of inline def krediet_beslissing(gegevens): # Include hier bovenstaande functies of importeer uit een module # Bijvoorbeeld: return neemt_beslissing(gegevens) # Maak een tool krediet_tool = PythonREPLTool() # Initialiseer de agent met de tool agent = initialize_agent( tools=[krediet_tool], agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True ) # Voorbeelddata gegevens = { 'inkomen': 3000, 'schulden': 800, 'lening_bedrag': 10000, 'duur': 60 } # Stel dat je de beslissing wilt opvragen resultaat = agent.run(f"Gegevens: {gegevens}\nWat is de kredietbeslissing?") print(resultaat) ``` ### 4. Complexere regels en conditionele ketens Voor meer complexiteit kun je de beslissingslogica uitbreiden met geneste voorwaarden, logische operatoren en dynamische regels: ```python def complexe_beslissing(gegevens): try: if not is_beschikbaar(gegevens): return "Onvoldoende gegevens" risk_score = bereken_risico_score(gegevens) inkomen = gegevens['inkomen'] lening = gegevens['lening_bedrag'] duur = gegevens['duur'] # Conditionele keten met meerdere voorwaarden if risk_score >= 5: return "Afgewezen: zeer hoog risico" elif risk_score >= 3: if inkomen < 2000 or lening > inkomen * 0.8: return "Afgewezen: risico te hoog" else: return "Voorwaardelijk goedkeuren" else: if duur > 60 and inkomen < 2500: return "Voorwaardelijk, korte termijn" else: return "Goedgekeurd" except Exception as e: return f"Fout: {str(e)}" ``` --- ### 5. Foutafhandeling en optimalisatie - **Foutafhandeling**: gebruik `try-except` blokken, log fouten, en geef duidelijke berichten. - **Caching**: indien mogelijk, cache uitkomsten van dure berekeningen. - **Validatie**: controleer inputdata voor consistentie en volledigheid. - **Configuratie**: maak regels parametriseerbaar via externe configuratiebestanden (bv. JSON of YAML). --- ### 6. Samenvatting - Gebruik functies voor regels en condities. - Combineer conditionele ketens met logische operatoren. - Maak de beslissingslogica dynamisch door data te gebruiken. - Implementeer foutafhandeling en validatie. - Integreer deze functies binnen een LangChain-agent via custom tools of Python code. Wil je dat ik een volledig werkend voorbeeld met data en integratie op maat voor jouw toepassing opstel?

Of course. I will guide you through developing a sophisticated LangChain agent for automated credit assessment. This involves creating conditional logic chains, integrating custom functions, and ensuring robust error handling. ### Core Conceptual Architecture A credit assessment agent doesn't just answer questions; it executes a **workflow**. The key is to break down the complex decision into sequential steps, each with its own conditions and data requirements. 1. **Data Ingestion & Parsing:** Extract and structure data from user input or documents (e.g., pay stubs, bank statements via OCR). 2. **Rule-Based Evaluation:** Apply a series of conditional rules (e.g., "If Debt-to-Income ratio > 40%, then flag as high risk"). 3. **LLM Reasoning & Justification:** Use the LLM to synthesize the rule-based outputs, provide a narrative explanation, and handle edge cases not covered by strict rules. 4. **Decision & Action:** Output a final decision (Approve/Deny/Refer) and recommended actions. We'll implement this using **LangChain's Expression Language (LCEL)**, which is the modern, recommended way to build complex, composable chains. --- ### Implementation Breakdown #### 1. Define Custom Tool Functions These functions encapsulate the deterministic parts of your risk analysis. The LLM agent will learn to call these tools. ```python from langchain.tools import tool from pydantic import BaseModel, Field from typing import Optional, Dict, Any import math # Define input models for type safety and better LLM tool calling class CreditScoreInput(BaseModel): applicant_id: str = Field(description="Unique identifier for the applicant") class DTIRatioInput(BaseModel): monthly_income: float = Field(description="The applicant's gross monthly income") monthly_debt_payments: float = Field(description="The applicant's total monthly debt obligations") class PaymentHistoryInput(BaseModel): applicant_id: str = Field(description="Unique identifier for the applicant") # Custom tool to get credit score (simulated) @tool(args_schema=CreditScoreInput) def get_credit_score(applicant_id: str) -> int: """Fetches the credit score for a given applicant ID from a database or API.""" # Simulate a database/API call. In reality, connect to Experian, Equifax, etc. # This is a simple mock; a real function would have error handling. credit_db = {"app_123": 720, "app_456": 650, "app_789": 580} return credit_db.get(applicant_id, 600) # Default to 600 if not found # Custom tool to calculate Debt-to-Income (DTI) Ratio @tool(args_schema=DTIRatioInput) def calculate_dti_ratio(monthly_income: float, monthly_debt_payments: float) -> Dict[str, Any]: """Calculates the Debt-to-Income (DTI) ratio and returns it with a risk category.""" if monthly_income <= 0: raise ValueError("Monthly income must be a positive number.") ratio = (monthly_debt_payments / monthly_income) * 100 ratio = round(ratio, 2) if ratio <= 35: category = "Low Risk" elif 36 <= ratio <= 43: category = "Moderate Risk" else: category = "High Risk" return {"dti_ratio": ratio, "risk_category": category} # Custom tool to check payment history (simulated) @tool(args_schema=PaymentHistoryInput) def check_payment_history(applicant_id: str) -> str: """Checks the payment history for a given applicant ID.""" # Simulate a check for late payments history_db = { "app_123": "No late payments in last 24 months", "app_456": "2 late payments in last 12 months", "app_789": "1 serious delinquency (90+ days) in last 24 months" } return history_db.get(applicant_id, "Limited history available") ``` #### 2. Define the Conditional Logic Chains We'll use LCEL's `RunnableLambda` and `RunnableBranch` to create conditional pathways. ```python from langchain_core.runnables import RunnableLambda, RunnableParallel from langchain_core.output_parsers import StrOutputParser # Define a function to route based on credit score def route_by_credit_score(info: Dict[str, Any]) -> str: """Determines the next step based on a pre-fetched credit score.""" credit_score = info.get("credit_score", 0) if credit_score >= 700: return "low_risk_path" elif 650 <= credit_score < 700: return "medium_risk_path" else: return "high_risk_path" # Define different chains for different risk profiles low_risk_chain = RunnableLambda(lambda x: f"Applicant has excellent credit ({x['credit_score']}). Proceeding with standard verification.") medium_risk_chain = RunnableLambda(lambda x: f"Applicant has moderate credit ({x['credit_score']}). Scrutinizing DTI and employment history closely.") high_risk_chain = RunnableLambda(lambda x: f"Applicant has poor credit ({x['credit_score']}). Application likely requires manual review or will be denied.") # Create a branch that chooses which chain to run credit_branch = RunnableBranch( (lambda x: route_by_credit_score(x) == "low_risk_path", low_risk_chain), (lambda x: route_by_credit_score(x) == "medium_risk_path", medium_risk_chain), high_risk_chain ) ``` #### 3. Build the Main Agent Chain with LCEL This is where we compose everything together. ```python from langchain.agents import AgentExecutor, create_tool_calling_agent from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder from langchain_openai import ChatOpenAI # Using OpenAI for this example # 1. Initialize the LLM llm = ChatOpenAI(model="gpt-4-turbo-preview", temperature=0) # 2. Define the tools list tools = [get_credit_score, calculate_dti_ratio, check_payment_history] # 3. Create the agent prompt prompt = ChatPromptTemplate.from_messages([ ("system", """You are an advanced AI credit risk analyst. Your goal is to assess loan applications based on hard rules and nuanced analysis. Follow these steps: 1. **Identify** the applicant and key financial data from the user's query. 2. **Use your tools** to retrieve the applicant's credit score, calculate their DTI ratio, and check their payment history. 3. **Apply the core rules**: - Rule 1: Credit Score < 580 → Automatic Denial. - Rule 2: DTI Ratio > 45% → Automatic Denial. - Rule 3: Serious delinquency (90+ days late) in last 12 months → Refer to senior analyst. 4. **Synthesize** the tool outputs and rules into a final decision: APPROVE, DENY, or REFER. 5. **Provide a clear, professional justification** for your decision, citing the specific rules and data points. If any tool fails or data is missing, state this clearly and explain how it impacts the assessment. """), MessagesPlaceholder(variable_name="chat_history", optional=True), ("human", "{input}"), MessagesPlaceholder(variable_name="agent_scratchpad"), ]) # 4. Create the agent and executor agent = create_tool_calling_agent(llm, tools, prompt) agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True, handle_parsing_errors=True) # 5. (Optional) Compose a more complex sequential chain using LCEL # This chain first gets the credit score, then branches. sequential_workflow = RunnableParallel( # Fetch data in parallel credit_score=RunnableLambda(lambda x: get_credit_score.invoke({"applicant_id": x["applicant_id"]})), original_input=lambda x: x ) | RunnableLambda(lambda x: { # Add credit score to the dict for the branch "credit_score": x["credit_score"], "applicant_id": x["original_input"]["applicant_id"] }) | credit_branch # Example usage for the sequential chain: # result = sequential_workflow.invoke({"applicant_id": "app_123"}) # print(result) ``` #### 4. Advanced: Error Handling and Optimizations **a) Robust Error Handling in Tools:** Modify the tools to be more resilient. ```python @tool(args_schema=CreditScoreInput) def get_credit_score(applicant_id: str) -> str: """Fetches the credit score for a given applicant ID.""" try: credit_db = {"app_123": 720, "app_456": 650, "app_789": 580} score = credit_db[applicant_id] # Use [] to force a KeyError for missing IDs return f"Credit score retrieved successfully: {score}" except KeyError: return f"Error: No credit score found for applicant ID '{applicant_id}'. Please verify the ID." except Exception as e: return f"An unexpected error occurred while fetching the credit score: {str(e)}" ``` **b) Agent-Level Error Handling:** The `AgentExecutor` has a `handle_parsing_errors` parameter. For more control, you can use a custom lambda. ```python # Custom handler for when the LLM outputs bad JSON/tool calls def handle_parsing_error(error) -> str: return f"Sorry, I encountered an internal error. Please rephrase your request. Technical details: {str(error)}" agent_executor = AgentExecutor( agent=agent, tools=tools, verbose=True, handle_parsing_errors=True, # Basic handling # max_iterations=5, # Prevent infinite loops # early_stopping_method='generate', # Stop if no tool is called ) ``` **c) Optimizations:** * **Caching:** Use `LangChain's SQLiteCache` (`from langchain.cache import SQLiteCache`) to cache expensive LLM responses and tool calls for identical inputs, reducing cost and latency. * **Parallel Tool Calls:** If using a model that supports it (like `gpt-4-turbo`), the agent can call multiple non-dependent tools in parallel, significantly speeding up the workflow. * **Validation:** Add Pydantic validation to the inputs of your custom functions to catch errors early. ### Example Execution ```python # Example 1: Simple query for the agent result = agent_executor.invoke({ "input": "Please assess applicant app_789. Their monthly income is $3000 and their total monthly debt payments are $1500." }) print(result["output"]) # Example 2: Using the sequential chain branch_result = sequential_workflow.invoke({"applicant_id": "app_456"}) print(branch_result) ``` ### Expected Output (Example 1): > **> Entering new AgentExecutor chain...** > > **Action:** `get_credit_score` with args `{'applicant_id': 'app_789'}` > **Observation:** 580 > > **Action:** `calculate_dti_ratio` with args `{'monthly_income': 3000.0, 'monthly_debt_payments': 1500.0}` > **Observation:** {'dti_ratio': 50.0, 'risk_category': 'High Risk'} > > **Action:** `check_payment_history` with args `{'applicant_id': 'app_789'}` > **Observation:** 1 serious delinquency (90+ days) in last 24 months > > **Final Output:** > **DECISION: DENY** > > **Justification:** > The application for applicant `app_789` has been denied based on the following rule violations: > 1. **Rule 2 Violation:** The calculated Debt-to-Income (DTI) ratio is 50.0%, which exceeds the maximum allowable threshold of 45%. This indicates insufficient income to cover debt obligations. > 2. **Rule 1 Consideration:** The applicant's credit score of 580 is at the threshold for automatic denial, further supporting the high-risk profile. > 3. **Rule 3 Consideration:** The payment history shows a serious delinquency, which, while outside the 12-month automatic referral window, adds to the overall risk assessment. > > This decision is consistent with our credit policy aimed at mitigating default risk. This architecture provides a powerful, transparent, and maintainable framework for building complex, rule-driven LLM agents for financial analysis.