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LLM Evaluation Integration

This project now includes automatic LLM-based evaluation of agent responses, similar to the week2/user-memory project. When an agent generates a response, it is automatically evaluated for accuracy and completeness.

🎯 Overview

The LLM evaluation system automatically: 1. Evaluates agent responses after generation 2. Assigns a continuous reward score (0.0 to 1.0) 3. Determines pass/fail based on threshold (>= 0.6) 4. Provides detailed reasoning for the evaluation 5. Checks if required information was found

📋 Features

Automatic Evaluation

  • Triggered automatically after agent generates response
  • No manual intervention required
  • Integrated into the existing evaluation pipeline

Evaluation Metrics

  • Reward Score: Continuous score from 0.0 to 1.0
  • 0.0-0.2: Complete failure
  • 0.2-0.4: Poor performance
  • 0.4-0.6: Partial success
  • 0.6-0.8: Good performance
  • 0.8-1.0: Excellent performance
  • Pass/Fail: Determined by reward >= 0.6
  • Reasoning: Detailed explanation of the score
  • Required Information: Verification of key facts

Console Output

When evaluation runs, you'll see: 

============================================================
Running LLM Evaluation...
------------------------------------------------------------
LLM Evaluation Reward: 0.850/1.000
Passed: Yes
Reasoning: The agent correctly recalled the account number from the conversation history.
Required Information Found:
  ✓ account number: 123456789
  ✓ routing number: 071000013
  ✗ pin number: not found
============================================================

🔧 Implementation

Integration Points

  1. evaluator.py
  2. Imports LLMEvaluator from week2/user-memory-evaluation
  3. Initializes evaluator if available
  4. Runs evaluation after agent response

  5. Adds results to EvaluationResult

  6. main.py

  7. Displays LLM evaluation results in UI
  8. Shows reward score and pass/fail status

  9. Lists required information checks

  10. Report Generation

  11. Includes LLM evaluation metrics
  12. Shows average reward scores
  13. Tracks evaluation success rates

Code Changes

The key changes include:

# In evaluator.py - Automatic evaluation after agent response
if self.llm_evaluator and agent_answer:
    llm_result = self.llm_evaluator.evaluate(
        test_case=eval_test_case,
        agent_response=agent_answer,
        extracted_memory=None
    )

    # Process and log results
    logger.info(f"LLM Evaluation Reward: {llm_result.reward:.3f}/1.000")
    logger.info(f"Passed: {'Yes' if llm_result.passed else 'No'}")

📊 Evaluation Flow

User Question
    ↓
Agent Processing (RAG)
    ↓
Agent Response Generated
    ↓
[AUTOMATIC LLM EVALUATION]
    ├─ Send response to LLM
    ├─ Get reward score
    ├─ Check required info
    └─ Generate reasoning
    ↓
Display Results
    ├─ Agent answer
    ├─ LLM evaluation score
    ├─ Pass/fail status
    └─ Required info checks

🚀 Usage

Running with Evaluation

  1. Single Test Case: 

    python main.py
# Select option 4: Evaluate Single Test Case
# LLM evaluation runs automatically

  2. Batch Evaluation: 

    python main.py --mode batch --category layer1
# All test cases evaluated with LLM

  3. Check Integration: 

    python test_llm_evaluation.py

Viewing Results

Results include LLM evaluation details: - In console output during evaluation - In generated reports - In saved result files

📈 Benefits

  1. Objective Assessment: Consistent evaluation criteria
  2. Detailed Feedback: Reasoning for each score
  3. Automatic Verification: Checks required information
  4. Performance Tracking: Monitor improvement over time
  5. No Manual Review: Reduces human evaluation burden

⚙️ Configuration

Requirements

  • Access to week2/user-memory-evaluation module
  • Valid API keys for LLM evaluation
  • OpenAI-compatible API endpoint

Environment Variables

# For LLM evaluation (if using OpenAI)
OPENAI_API_KEY=your_key

# Or configure evaluator in week2/user-memory-evaluation/config.py

Disabling Evaluation

If LLM evaluation is not available: - System continues to work normally - Only RAG metrics are shown - Manual evaluation still possible

📝 Example Output

Successful Evaluation

Test: layer1_01_bank_account
Agent Answer: Your checking account number is 4429853327.

LLM Evaluation:
  Passed: Yes ✓
  Reward Score: 0.920/1.000
  Reasoning: The agent correctly extracted and provided the exact account number from the conversation. The response is accurate and directly addresses the user's question.

Required Information:
  ✓ checking account number
  ✓ account number format

Failed Evaluation

Test: layer2_01_multiple_vehicles
Agent Answer: You have a Honda Accord.

LLM Evaluation:
  Passed: No ✗
  Reward Score: 0.450/1.000
  Reasoning: The agent only mentioned one vehicle when the user has multiple vehicles. Missing information about the Tesla Model 3 and service scheduling details.

Required Information:
  ✓ Honda Accord mentioned
  ✗ Tesla Model 3 not mentioned
  ✗ Service scheduling information missing

🔍 Troubleshooting

LLM Evaluator Not Available

  • Check week2/user-memory-evaluation exists
  • Verify evaluator.py is present
  • Ensure API keys are configured

Evaluation Errors

  • Check API key validity
  • Verify network connectivity
  • Review error logs for details

Inconsistent Scores

  • LLM evaluation is probabilistic
  • Use temperature=0 for consistency
  • Review evaluation criteria