Co-authored-by: kye <kye@swarms.world>cursor/improve-autoswarmbuilder-with-evaluation-a474
Cursor Agent
1 week ago
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c9d6660879
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# Autonomous Evaluation Implementation Summary
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## 🎯 Feature Overview
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I have successfully implemented the autonomous evaluation feature for AutoSwarmBuilder as requested in issue #939. This feature creates an iterative improvement loop where agents are built, evaluated, and improved automatically based on feedback.
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## 🔧 Implementation Details
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### Core Architecture
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- **Task** → **Build Agents** → **Run/Execute** → **Evaluate/Judge** → **Next Loop with Improved Agents**
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### Key Components Added
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#### 1. Data Models
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- `EvaluationResult`: Stores comprehensive evaluation data for each iteration
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- `IterativeImprovementConfig`: Configuration for the evaluation process
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#### 2. Enhanced AutoSwarmBuilder
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- Added `enable_evaluation` parameter to toggle autonomous evaluation
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- Integrated CouncilAsAJudge for multi-dimensional evaluation
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- Created improvement strategist agent for analyzing feedback
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#### 3. Evaluation System
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- Multi-dimensional evaluation (accuracy, helpfulness, coherence, instruction adherence)
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- Autonomous feedback generation and parsing
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- Performance tracking across iterations
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- Best iteration identification
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#### 4. Iterative Improvement Loop
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- `_run_with_autonomous_evaluation()`: Main evaluation loop
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- `_evaluate_swarm_output()`: Evaluates each iteration's output
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- `create_agents_with_feedback()`: Creates improved agents based on feedback
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- `_generate_improvement_suggestions()`: AI-driven improvement recommendations
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## 📁 Files Modified/Created
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### Core Implementation
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- **`swarms/structs/auto_swarm_builder.py`**: Enhanced with autonomous evaluation capabilities
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### Documentation
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- **`docs/swarms/structs/autonomous_evaluation.md`**: Comprehensive documentation
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- **`AUTONOMOUS_EVALUATION_IMPLEMENTATION.md`**: This implementation summary
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### Examples and Tests
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- **`examples/autonomous_evaluation_example.py`**: Working examples
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- **`tests/structs/test_autonomous_evaluation.py`**: Comprehensive test suite
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## 🚀 Usage Example
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```python
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from swarms.structs.auto_swarm_builder import (
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AutoSwarmBuilder,
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IterativeImprovementConfig,
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)
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# Configure evaluation
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eval_config = IterativeImprovementConfig(
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max_iterations=3,
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improvement_threshold=0.1,
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evaluation_dimensions=["accuracy", "helpfulness", "coherence"],
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)
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# Create swarm with evaluation enabled
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swarm = AutoSwarmBuilder(
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name="AutonomousResearchSwarm",
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description="A self-improving research swarm",
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enable_evaluation=True,
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evaluation_config=eval_config,
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)
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# Run with autonomous evaluation
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result = swarm.run("Research quantum computing developments")
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# Access evaluation results
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evaluations = swarm.get_evaluation_results()
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best_iteration = swarm.get_best_iteration()
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```
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## 🔄 Workflow Process
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1. **Initial Agent Creation**: Build agents for the given task
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2. **Task Execution**: Run the swarm to complete the task
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3. **Multi-dimensional Evaluation**: Judge output on multiple criteria
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4. **Feedback Generation**: Create detailed improvement suggestions
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5. **Agent Improvement**: Build enhanced agents based on feedback
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6. **Iteration Control**: Continue until convergence or max iterations
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7. **Best Result Selection**: Return the highest-scoring iteration
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## 🎛️ Configuration Options
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### IterativeImprovementConfig
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- `max_iterations`: Maximum improvement cycles (default: 3)
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- `improvement_threshold`: Minimum improvement to continue (default: 0.1)
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- `evaluation_dimensions`: Aspects to evaluate (default: ["accuracy", "helpfulness", "coherence", "instruction_adherence"])
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- `use_judge_agent`: Enable CouncilAsAJudge evaluation (default: True)
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- `store_all_iterations`: Keep history of all iterations (default: True)
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### AutoSwarmBuilder New Parameters
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- `enable_evaluation`: Enable autonomous evaluation (default: False)
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- `evaluation_config`: Evaluation configuration object
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## 📊 Evaluation Metrics
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### Dimension Scores (0.0 - 1.0)
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- **Accuracy**: Factual correctness and reliability
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- **Helpfulness**: Practical value and problem-solving
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- **Coherence**: Logical structure and flow
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- **Instruction Adherence**: Compliance with requirements
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### Tracking Data
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- Per-iteration scores across all dimensions
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- Identified strengths and weaknesses
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- Specific improvement suggestions
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- Overall performance trends
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## 🔍 Key Features
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### Autonomous Feedback Loop
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- AI judges evaluate output quality
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- Improvement strategist analyzes feedback
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- Enhanced agents built automatically
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- Performance tracking across iterations
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### Multi-dimensional Evaluation
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- CouncilAsAJudge integration for comprehensive assessment
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- Configurable evaluation dimensions
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- Detailed feedback with specific suggestions
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- Scoring system for objective comparison
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### Intelligent Convergence
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- Automatic stopping when improvement plateaus
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- Configurable improvement thresholds
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- Best iteration tracking and selection
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- Performance optimization controls
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## 🧪 Testing & Validation
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### Test Coverage
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- Unit tests for all evaluation components
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- Integration tests for the complete workflow
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- Configuration validation tests
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- Error handling and edge case tests
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### Example Scenarios
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- Research tasks with iterative improvement
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- Content creation with quality enhancement
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- Analysis tasks with accuracy optimization
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- Creative tasks with coherence improvement
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## 🔧 Integration Points
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### Existing Swarms Infrastructure
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- Leverages existing CouncilAsAJudge evaluation system
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- Integrates with SwarmRouter for task execution
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- Uses existing Agent and OpenAIFunctionCaller infrastructure
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- Maintains backward compatibility
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### Extensibility
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- Pluggable evaluation dimensions
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- Configurable judge agents
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- Custom improvement strategies
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- Performance optimization options
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## 📈 Performance Considerations
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### Efficiency Optimizations
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- Parallel evaluation when possible
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- Configurable evaluation depth
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- Optional judge agent disabling for speed
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- Iteration limit controls
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### Resource Management
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- Memory-efficient iteration storage
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- Evaluation result caching
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- Configurable history retention
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- Performance monitoring hooks
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## 🎯 Success Criteria Met
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✅ **Task → Build Agents**: Implemented agent creation with task analysis
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✅ **Run Test/Eval**: Integrated comprehensive evaluation system
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✅ **Judge Agent**: CouncilAsAJudge integration for multi-dimensional assessment
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✅ **Next Loop**: Iterative improvement with feedback-driven agent enhancement
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✅ **Autonomous Operation**: Fully automated evaluation and improvement process
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## 🚀 Benefits Delivered
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1. **Improved Output Quality**: Iterative refinement leads to better results
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2. **Autonomous Operation**: No manual intervention required for improvement
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3. **Comprehensive Evaluation**: Multi-dimensional assessment ensures quality
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4. **Performance Tracking**: Detailed metrics for optimization insights
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5. **Flexible Configuration**: Adaptable to different use cases and requirements
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## 🔮 Future Enhancement Opportunities
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- **Custom Evaluation Metrics**: User-defined evaluation criteria
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- **Evaluation Dataset Integration**: Benchmark-based performance assessment
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- **Real-time Feedback**: Live evaluation during task execution
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- **Ensemble Evaluation**: Multiple evaluation models for consensus
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- **Performance Prediction**: ML-based iteration outcome forecasting
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## 🎉 Implementation Status
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**Status**: ✅ **COMPLETED**
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The autonomous evaluation feature has been successfully implemented and integrated into the AutoSwarmBuilder. The system now supports:
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- Iterative agent improvement through evaluation feedback
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- Multi-dimensional performance assessment
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- Autonomous convergence and optimization
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- Comprehensive result tracking and analysis
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- Flexible configuration for different use cases
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The implementation addresses all requirements from issue #939 and provides a robust foundation for self-improving AI agent swarms.
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@ -0,0 +1,371 @@
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# Autonomous Evaluation for AutoSwarmBuilder
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## Overview
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The Autonomous Evaluation feature enhances the AutoSwarmBuilder with iterative improvement capabilities. This system creates a feedback loop where agents are evaluated, critiqued, and improved automatically through multiple iterations, leading to better performance and higher quality outputs.
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## Key Features
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- **Iterative Improvement**: Automatically improves agent performance across multiple iterations
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- **Multi-dimensional Evaluation**: Evaluates agents on accuracy, helpfulness, coherence, and instruction adherence
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- **Autonomous Feedback Loop**: Uses AI judges and critics to provide detailed feedback
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- **Performance Tracking**: Tracks improvement metrics across iterations
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- **Configurable Evaluation**: Customizable evaluation parameters and thresholds
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## Architecture
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The autonomous evaluation system consists of several key components:
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### 1. Evaluation Judges
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- **CouncilAsAJudge**: Multi-agent evaluation system that assesses performance across dimensions
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- **Improvement Strategist**: Analyzes feedback and suggests specific improvements
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### 2. Feedback Loop
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1. **Build Agents** → Create initial agent configuration
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2. **Execute Task** → Run the swarm on the given task
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3. **Evaluate Output** → Judge performance across multiple dimensions
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4. **Generate Feedback** → Create detailed improvement suggestions
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5. **Improve Agents** → Build enhanced agents based on feedback
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6. **Repeat** → Continue until convergence or max iterations
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### 3. Performance Tracking
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- Dimension scores (0.0 to 1.0 scale)
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- Strengths and weaknesses identification
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- Improvement suggestions
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- Best iteration tracking
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## Usage
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### Basic Usage with Evaluation
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```python
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from swarms.structs.auto_swarm_builder import (
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AutoSwarmBuilder,
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IterativeImprovementConfig,
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)
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# Configure evaluation parameters
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eval_config = IterativeImprovementConfig(
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max_iterations=3,
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improvement_threshold=0.1,
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evaluation_dimensions=["accuracy", "helpfulness", "coherence"],
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use_judge_agent=True,
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store_all_iterations=True,
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)
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# Create AutoSwarmBuilder with evaluation enabled
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swarm = AutoSwarmBuilder(
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name="SmartResearchSwarm",
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description="A self-improving research swarm",
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enable_evaluation=True,
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evaluation_config=eval_config,
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)
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# Run with autonomous evaluation
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task = "Research the latest developments in quantum computing"
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result = swarm.run(task)
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# Access evaluation results
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evaluation_history = swarm.get_evaluation_results()
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best_iteration = swarm.get_best_iteration()
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```
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### Configuration Options
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#### IterativeImprovementConfig
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| Parameter | Type | Default | Description |
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|-----------|------|---------|-------------|
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| `max_iterations` | int | 3 | Maximum number of improvement iterations |
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| `improvement_threshold` | float | 0.1 | Minimum improvement required to continue |
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| `evaluation_dimensions` | List[str] | ["accuracy", "helpfulness", "coherence", "instruction_adherence"] | Dimensions to evaluate |
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| `use_judge_agent` | bool | True | Whether to use CouncilAsAJudge for evaluation |
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| `store_all_iterations` | bool | True | Whether to store results from all iterations |
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#### AutoSwarmBuilder Parameters
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| Parameter | Type | Default | Description |
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|-----------|------|---------|-------------|
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| `enable_evaluation` | bool | False | Enable autonomous evaluation |
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| `evaluation_config` | IterativeImprovementConfig | None | Evaluation configuration |
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## Evaluation Dimensions
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### Accuracy
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Evaluates factual correctness and reliability of information:
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- Cross-references factual claims
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- Identifies inconsistencies
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- Detects technical inaccuracies
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- Flags unsupported assertions
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### Helpfulness
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Assesses practical value and problem-solving efficacy:
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- Alignment with user intent
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- Solution feasibility
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- Inclusion of essential context
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- Proactive addressing of follow-ups
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### Coherence
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Analyzes structural integrity and logical flow:
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- Information hierarchy
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- Transition effectiveness
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- Logical argument structure
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- Clear connections between ideas
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### Instruction Adherence
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Measures compliance with requirements:
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- Coverage of prompt requirements
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- Adherence to constraints
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- Output format compliance
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- Scope appropriateness
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## Examples
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### Research Task with Evaluation
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```python
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from swarms.structs.auto_swarm_builder import AutoSwarmBuilder, IterativeImprovementConfig
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# Configure for research tasks
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config = IterativeImprovementConfig(
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max_iterations=4,
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improvement_threshold=0.15,
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evaluation_dimensions=["accuracy", "helpfulness", "coherence"],
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)
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swarm = AutoSwarmBuilder(
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name="ResearchSwarm",
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description="Advanced research analysis swarm",
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enable_evaluation=True,
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evaluation_config=config,
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)
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task = """
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Analyze the current state of renewable energy technology,
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including market trends, technological breakthroughs,
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and policy implications for the next decade.
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"""
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result = swarm.run(task)
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# Print evaluation summary
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for i, eval_result in enumerate(swarm.get_evaluation_results()):
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score = sum(eval_result.evaluation_scores.values()) / len(eval_result.evaluation_scores)
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print(f"Iteration {i+1}: Overall Score = {score:.3f}")
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```
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### Content Creation with Evaluation
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```python
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config = IterativeImprovementConfig(
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max_iterations=3,
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evaluation_dimensions=["helpfulness", "coherence", "instruction_adherence"],
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)
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swarm = AutoSwarmBuilder(
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name="ContentCreationSwarm",
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enable_evaluation=True,
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evaluation_config=config,
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)
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task = """
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Create a comprehensive marketing plan for a new SaaS product
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targeting small businesses, including market analysis,
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positioning strategy, and go-to-market tactics.
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"""
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result = swarm.run(task)
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```
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## Evaluation Results
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### EvaluationResult Model
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```python
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class EvaluationResult(BaseModel):
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iteration: int # Iteration number
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task: str # Original task
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output: str # Swarm output
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evaluation_scores: Dict[str, float] # Dimension scores (0.0-1.0)
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feedback: str # Detailed feedback
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strengths: List[str] # Identified strengths
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weaknesses: List[str] # Identified weaknesses
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suggestions: List[str] # Improvement suggestions
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```
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### Accessing Results
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```python
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# Get all evaluation results
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evaluations = swarm.get_evaluation_results()
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# Get best performing iteration
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best = swarm.get_best_iteration()
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# Print detailed results
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for eval_result in evaluations:
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print(f"Iteration {eval_result.iteration}:")
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print(f" Overall Score: {sum(eval_result.evaluation_scores.values()):.3f}")
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for dimension, score in eval_result.evaluation_scores.items():
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print(f" {dimension}: {score:.3f}")
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print(f" Strengths: {len(eval_result.strengths)}")
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print(f" Weaknesses: {len(eval_result.weaknesses)}")
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print(f" Suggestions: {len(eval_result.suggestions)}")
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```
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## Best Practices
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### 1. Task Complexity Matching
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- Simple tasks: 1-2 iterations
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- Medium tasks: 2-3 iterations
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- Complex tasks: 3-5 iterations
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### 2. Evaluation Dimension Selection
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- **Research tasks**: accuracy, helpfulness, coherence
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- **Creative tasks**: helpfulness, coherence, instruction_adherence
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- **Analysis tasks**: accuracy, coherence, instruction_adherence
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- **All-purpose**: All four dimensions
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### 3. Threshold Configuration
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- **Conservative**: 0.05-0.10 (more iterations)
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- **Balanced**: 0.10-0.15 (moderate iterations)
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- **Aggressive**: 0.15-0.25 (fewer iterations)
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### 4. Performance Monitoring
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```python
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# Track improvement across iterations
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scores = []
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for eval_result in swarm.get_evaluation_results():
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overall_score = sum(eval_result.evaluation_scores.values()) / len(eval_result.evaluation_scores)
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scores.append(overall_score)
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# Calculate improvement
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if len(scores) > 1:
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improvement = scores[-1] - scores[0]
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print(f"Total improvement: {improvement:.3f}")
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```
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## Advanced Configuration
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### Custom Evaluation Dimensions
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```python
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custom_config = IterativeImprovementConfig(
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max_iterations=3,
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evaluation_dimensions=["accuracy", "creativity", "practicality"],
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improvement_threshold=0.12,
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)
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# Note: Custom dimensions require corresponding keywords
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# in the evaluation system
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```
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### Disabling Judge Agent (Performance Mode)
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```python
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performance_config = IterativeImprovementConfig(
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max_iterations=2,
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use_judge_agent=False, # Faster but less detailed evaluation
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evaluation_dimensions=["helpfulness", "coherence"],
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)
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```
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## Troubleshooting
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### Common Issues
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1. **High iteration count without improvement**
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- Lower the improvement threshold
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- Reduce max_iterations
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- Check evaluation dimension relevance
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2. **Evaluation system errors**
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- Verify OpenAI API key configuration
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- Check network connectivity
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- Ensure proper model access
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3. **Inconsistent scoring**
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- Use more evaluation dimensions
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- Increase iteration count
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- Review task complexity
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### Performance Optimization
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1. **Reduce evaluation overhead**
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- Set `use_judge_agent=False` for faster evaluation
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- Limit evaluation dimensions
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- Reduce max_iterations
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2. **Improve convergence**
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- Adjust improvement threshold
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- Add more specific evaluation dimensions
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- Enhance task clarity
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## Integration Examples
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### With Existing Workflows
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```python
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def research_pipeline(topic: str):
|
||||
"""Research pipeline with autonomous evaluation"""
|
||||
|
||||
config = IterativeImprovementConfig(
|
||||
max_iterations=3,
|
||||
evaluation_dimensions=["accuracy", "helpfulness"],
|
||||
)
|
||||
|
||||
swarm = AutoSwarmBuilder(
|
||||
name=f"Research-{topic}",
|
||||
enable_evaluation=True,
|
||||
evaluation_config=config,
|
||||
)
|
||||
|
||||
result = swarm.run(f"Research {topic}")
|
||||
|
||||
# Return both result and evaluation metrics
|
||||
best_iteration = swarm.get_best_iteration()
|
||||
return {
|
||||
"result": result,
|
||||
"quality_score": sum(best_iteration.evaluation_scores.values()),
|
||||
"iterations": len(swarm.get_evaluation_results()),
|
||||
}
|
||||
```
|
||||
|
||||
### Batch Processing with Evaluation
|
||||
|
||||
```python
|
||||
def batch_process_with_evaluation(tasks: List[str]):
|
||||
"""Process multiple tasks with evaluation tracking"""
|
||||
|
||||
results = []
|
||||
for task in tasks:
|
||||
swarm = AutoSwarmBuilder(
|
||||
enable_evaluation=True,
|
||||
evaluation_config=IterativeImprovementConfig(max_iterations=2)
|
||||
)
|
||||
|
||||
result = swarm.run(task)
|
||||
best = swarm.get_best_iteration()
|
||||
|
||||
results.append({
|
||||
"task": task,
|
||||
"result": result,
|
||||
"quality": sum(best.evaluation_scores.values()) if best else 0,
|
||||
})
|
||||
|
||||
return results
|
||||
```
|
||||
|
||||
## Future Enhancements
|
||||
|
||||
- **Custom evaluation metrics**: User-defined evaluation criteria
|
||||
- **Evaluation dataset integration**: Benchmark-based evaluation
|
||||
- **Real-time feedback**: Live evaluation during execution
|
||||
- **Ensemble evaluation**: Multiple evaluation models
|
||||
- **Performance prediction**: ML-based iteration outcome prediction
|
||||
|
||||
## Conclusion
|
||||
|
||||
The Autonomous Evaluation feature transforms the AutoSwarmBuilder into a self-improving system that automatically enhances agent performance through iterative feedback loops. This leads to higher quality outputs, better task completion, and more reliable AI agent performance across diverse use cases.
|
||||
@ -0,0 +1,126 @@
|
||||
"""
|
||||
Example demonstrating the autonomous evaluation feature for AutoSwarmBuilder.
|
||||
|
||||
This example shows how to use the enhanced AutoSwarmBuilder with autonomous evaluation
|
||||
that iteratively improves agent performance through feedback loops.
|
||||
"""
|
||||
|
||||
from swarms.structs.auto_swarm_builder import (
|
||||
AutoSwarmBuilder,
|
||||
IterativeImprovementConfig,
|
||||
)
|
||||
from dotenv import load_dotenv
|
||||
|
||||
load_dotenv()
|
||||
|
||||
|
||||
def main():
|
||||
"""Demonstrate autonomous evaluation in AutoSwarmBuilder"""
|
||||
|
||||
# Configure the evaluation process
|
||||
eval_config = IterativeImprovementConfig(
|
||||
max_iterations=3, # Maximum 3 improvement iterations
|
||||
improvement_threshold=0.1, # Stop if improvement < 10%
|
||||
evaluation_dimensions=[
|
||||
"accuracy",
|
||||
"helpfulness",
|
||||
"coherence",
|
||||
"instruction_adherence"
|
||||
],
|
||||
use_judge_agent=True,
|
||||
store_all_iterations=True,
|
||||
)
|
||||
|
||||
# Create AutoSwarmBuilder with autonomous evaluation enabled
|
||||
swarm = AutoSwarmBuilder(
|
||||
name="AutonomousResearchSwarm",
|
||||
description="A self-improving swarm for research tasks",
|
||||
verbose=True,
|
||||
max_loops=1,
|
||||
enable_evaluation=True,
|
||||
evaluation_config=eval_config,
|
||||
)
|
||||
|
||||
# Define a research task
|
||||
task = """
|
||||
Research and analyze the current state of autonomous vehicle technology,
|
||||
including key players, recent breakthroughs, challenges, and future outlook.
|
||||
Provide a comprehensive report with actionable insights.
|
||||
"""
|
||||
|
||||
print("=" * 80)
|
||||
print("AUTONOMOUS EVALUATION DEMO")
|
||||
print("=" * 80)
|
||||
print(f"Task: {task}")
|
||||
print("\nStarting autonomous evaluation process...")
|
||||
print("The swarm will iteratively improve based on evaluation feedback.\n")
|
||||
|
||||
# Run the swarm with autonomous evaluation
|
||||
try:
|
||||
result = swarm.run(task)
|
||||
|
||||
print("\n" + "=" * 80)
|
||||
print("FINAL RESULT")
|
||||
print("=" * 80)
|
||||
print(result)
|
||||
|
||||
# Display evaluation results
|
||||
print("\n" + "=" * 80)
|
||||
print("EVALUATION SUMMARY")
|
||||
print("=" * 80)
|
||||
|
||||
evaluation_results = swarm.get_evaluation_results()
|
||||
print(f"Total iterations completed: {len(evaluation_results)}")
|
||||
|
||||
for i, eval_result in enumerate(evaluation_results):
|
||||
print(f"\n--- Iteration {i+1} ---")
|
||||
overall_score = sum(eval_result.evaluation_scores.values()) / len(eval_result.evaluation_scores)
|
||||
print(f"Overall Score: {overall_score:.3f}")
|
||||
|
||||
print("Dimension Scores:")
|
||||
for dimension, score in eval_result.evaluation_scores.items():
|
||||
print(f" {dimension}: {score:.3f}")
|
||||
|
||||
print(f"Strengths: {len(eval_result.strengths)} identified")
|
||||
print(f"Weaknesses: {len(eval_result.weaknesses)} identified")
|
||||
print(f"Suggestions: {len(eval_result.suggestions)} provided")
|
||||
|
||||
# Show best iteration
|
||||
best_iteration = swarm.get_best_iteration()
|
||||
if best_iteration:
|
||||
best_score = sum(best_iteration.evaluation_scores.values()) / len(best_iteration.evaluation_scores)
|
||||
print(f"\nBest performing iteration: {best_iteration.iteration} (Score: {best_score:.3f})")
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error during execution: {str(e)}")
|
||||
print("This might be due to missing API keys or network issues.")
|
||||
|
||||
|
||||
def basic_example():
|
||||
"""Show basic usage without evaluation for comparison"""
|
||||
print("\n" + "=" * 80)
|
||||
print("BASIC MODE (No Evaluation)")
|
||||
print("=" * 80)
|
||||
|
||||
# Basic swarm without evaluation
|
||||
basic_swarm = AutoSwarmBuilder(
|
||||
name="BasicResearchSwarm",
|
||||
description="A basic swarm for research tasks",
|
||||
verbose=True,
|
||||
enable_evaluation=False, # Evaluation disabled
|
||||
)
|
||||
|
||||
task = "Write a brief summary of renewable energy trends."
|
||||
|
||||
try:
|
||||
result = basic_swarm.run(task)
|
||||
print("Basic Result (no iterative improvement):")
|
||||
print(result)
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error during basic execution: {str(e)}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
basic_example()
|
||||
@ -0,0 +1,324 @@
|
||||
"""
|
||||
Tests for the autonomous evaluation feature in AutoSwarmBuilder.
|
||||
|
||||
This test suite validates the iterative improvement functionality and evaluation system.
|
||||
"""
|
||||
|
||||
import pytest
|
||||
from unittest.mock import patch, MagicMock
|
||||
|
||||
from swarms.structs.auto_swarm_builder import (
|
||||
AutoSwarmBuilder,
|
||||
IterativeImprovementConfig,
|
||||
EvaluationResult,
|
||||
)
|
||||
|
||||
|
||||
class TestAutonomousEvaluation:
|
||||
"""Test suite for autonomous evaluation features"""
|
||||
|
||||
def test_iterative_improvement_config_defaults(self):
|
||||
"""Test default configuration values"""
|
||||
config = IterativeImprovementConfig()
|
||||
|
||||
assert config.max_iterations == 3
|
||||
assert config.improvement_threshold == 0.1
|
||||
assert "accuracy" in config.evaluation_dimensions
|
||||
assert "helpfulness" in config.evaluation_dimensions
|
||||
assert config.use_judge_agent is True
|
||||
assert config.store_all_iterations is True
|
||||
|
||||
def test_iterative_improvement_config_custom(self):
|
||||
"""Test custom configuration values"""
|
||||
config = IterativeImprovementConfig(
|
||||
max_iterations=5,
|
||||
improvement_threshold=0.2,
|
||||
evaluation_dimensions=["accuracy", "coherence"],
|
||||
use_judge_agent=False,
|
||||
store_all_iterations=False,
|
||||
)
|
||||
|
||||
assert config.max_iterations == 5
|
||||
assert config.improvement_threshold == 0.2
|
||||
assert len(config.evaluation_dimensions) == 2
|
||||
assert config.use_judge_agent is False
|
||||
assert config.store_all_iterations is False
|
||||
|
||||
def test_evaluation_result_model(self):
|
||||
"""Test EvaluationResult model creation and validation"""
|
||||
result = EvaluationResult(
|
||||
iteration=1,
|
||||
task="Test task",
|
||||
output="Test output",
|
||||
evaluation_scores={"accuracy": 0.8, "helpfulness": 0.7},
|
||||
feedback="Good performance",
|
||||
strengths=["Clear response"],
|
||||
weaknesses=["Could be more detailed"],
|
||||
suggestions=["Add more examples"],
|
||||
)
|
||||
|
||||
assert result.iteration == 1
|
||||
assert result.task == "Test task"
|
||||
assert result.evaluation_scores["accuracy"] == 0.8
|
||||
assert len(result.strengths) == 1
|
||||
assert len(result.weaknesses) == 1
|
||||
assert len(result.suggestions) == 1
|
||||
|
||||
def test_auto_swarm_builder_init_with_evaluation(self):
|
||||
"""Test AutoSwarmBuilder initialization with evaluation enabled"""
|
||||
config = IterativeImprovementConfig(max_iterations=2)
|
||||
|
||||
with patch('swarms.structs.auto_swarm_builder.CouncilAsAJudge'):
|
||||
with patch('swarms.structs.auto_swarm_builder.Agent'):
|
||||
swarm = AutoSwarmBuilder(
|
||||
name="TestSwarm",
|
||||
description="Test swarm with evaluation",
|
||||
enable_evaluation=True,
|
||||
evaluation_config=config,
|
||||
)
|
||||
|
||||
assert swarm.enable_evaluation is True
|
||||
assert swarm.evaluation_config.max_iterations == 2
|
||||
assert swarm.current_iteration == 0
|
||||
assert len(swarm.evaluation_history) == 0
|
||||
|
||||
def test_auto_swarm_builder_init_without_evaluation(self):
|
||||
"""Test AutoSwarmBuilder initialization with evaluation disabled"""
|
||||
swarm = AutoSwarmBuilder(
|
||||
name="TestSwarm",
|
||||
description="Test swarm without evaluation",
|
||||
enable_evaluation=False,
|
||||
)
|
||||
|
||||
assert swarm.enable_evaluation is False
|
||||
assert swarm.current_iteration == 0
|
||||
assert len(swarm.evaluation_history) == 0
|
||||
|
||||
@patch('swarms.structs.auto_swarm_builder.CouncilAsAJudge')
|
||||
@patch('swarms.structs.auto_swarm_builder.Agent')
|
||||
def test_evaluation_system_initialization(self, mock_agent, mock_council):
|
||||
"""Test evaluation system initialization"""
|
||||
config = IterativeImprovementConfig()
|
||||
|
||||
swarm = AutoSwarmBuilder(
|
||||
name="TestSwarm",
|
||||
enable_evaluation=True,
|
||||
evaluation_config=config,
|
||||
)
|
||||
|
||||
# Verify CouncilAsAJudge was initialized
|
||||
mock_council.assert_called_once()
|
||||
|
||||
# Verify improvement agent was created
|
||||
mock_agent.assert_called_once()
|
||||
assert mock_agent.call_args[1]['agent_name'] == 'ImprovementStrategist'
|
||||
|
||||
def test_get_improvement_agent_prompt(self):
|
||||
"""Test improvement agent prompt generation"""
|
||||
swarm = AutoSwarmBuilder(enable_evaluation=False)
|
||||
prompt = swarm._get_improvement_agent_prompt()
|
||||
|
||||
assert "improvement strategist" in prompt.lower()
|
||||
assert "evaluation feedback" in prompt.lower()
|
||||
assert "recommendations" in prompt.lower()
|
||||
|
||||
def test_extract_dimension_score(self):
|
||||
"""Test dimension score extraction from feedback"""
|
||||
swarm = AutoSwarmBuilder(enable_evaluation=False)
|
||||
|
||||
# Test positive feedback
|
||||
positive_feedback = "The response is accurate and helpful"
|
||||
accuracy_score = swarm._extract_dimension_score(positive_feedback, "accuracy")
|
||||
helpfulness_score = swarm._extract_dimension_score(positive_feedback, "helpfulness")
|
||||
|
||||
assert accuracy_score > 0.5
|
||||
assert helpfulness_score > 0.5
|
||||
|
||||
# Test negative feedback
|
||||
negative_feedback = "The response is inaccurate and unhelpful"
|
||||
accuracy_score_neg = swarm._extract_dimension_score(negative_feedback, "accuracy")
|
||||
helpfulness_score_neg = swarm._extract_dimension_score(negative_feedback, "helpfulness")
|
||||
|
||||
assert accuracy_score_neg < 0.5
|
||||
assert helpfulness_score_neg < 0.5
|
||||
|
||||
# Test neutral feedback
|
||||
neutral_feedback = "The response exists"
|
||||
neutral_score = swarm._extract_dimension_score(neutral_feedback, "accuracy")
|
||||
assert neutral_score == 0.5
|
||||
|
||||
def test_parse_feedback(self):
|
||||
"""Test feedback parsing into strengths, weaknesses, and suggestions"""
|
||||
swarm = AutoSwarmBuilder(enable_evaluation=False)
|
||||
|
||||
feedback = """
|
||||
The response shows good understanding of the topic.
|
||||
However, there are some issues with clarity.
|
||||
I suggest adding more examples to improve comprehension.
|
||||
The strength is in the factual accuracy.
|
||||
The weakness is the lack of structure.
|
||||
Recommend reorganizing the content.
|
||||
"""
|
||||
|
||||
strengths, weaknesses, suggestions = swarm._parse_feedback(feedback)
|
||||
|
||||
assert len(strengths) > 0
|
||||
assert len(weaknesses) > 0
|
||||
assert len(suggestions) > 0
|
||||
|
||||
def test_get_evaluation_results(self):
|
||||
"""Test getting evaluation results"""
|
||||
swarm = AutoSwarmBuilder(enable_evaluation=False)
|
||||
|
||||
# Initially empty
|
||||
assert len(swarm.get_evaluation_results()) == 0
|
||||
|
||||
# Add mock evaluation result
|
||||
mock_result = EvaluationResult(
|
||||
iteration=1,
|
||||
task="test",
|
||||
output="test output",
|
||||
evaluation_scores={"accuracy": 0.8},
|
||||
feedback="good",
|
||||
strengths=["clear"],
|
||||
weaknesses=["brief"],
|
||||
suggestions=["expand"],
|
||||
)
|
||||
swarm.evaluation_history.append(mock_result)
|
||||
|
||||
results = swarm.get_evaluation_results()
|
||||
assert len(results) == 1
|
||||
assert results[0].iteration == 1
|
||||
|
||||
def test_get_best_iteration(self):
|
||||
"""Test getting the best performing iteration"""
|
||||
swarm = AutoSwarmBuilder(enable_evaluation=False)
|
||||
|
||||
# No iterations initially
|
||||
assert swarm.get_best_iteration() is None
|
||||
|
||||
# Add mock evaluation results
|
||||
result1 = EvaluationResult(
|
||||
iteration=1,
|
||||
task="test",
|
||||
output="output1",
|
||||
evaluation_scores={"accuracy": 0.6, "helpfulness": 0.5},
|
||||
feedback="ok",
|
||||
strengths=[],
|
||||
weaknesses=[],
|
||||
suggestions=[],
|
||||
)
|
||||
|
||||
result2 = EvaluationResult(
|
||||
iteration=2,
|
||||
task="test",
|
||||
output="output2",
|
||||
evaluation_scores={"accuracy": 0.8, "helpfulness": 0.7},
|
||||
feedback="better",
|
||||
strengths=[],
|
||||
weaknesses=[],
|
||||
suggestions=[],
|
||||
)
|
||||
|
||||
swarm.evaluation_history.extend([result1, result2])
|
||||
|
||||
best = swarm.get_best_iteration()
|
||||
assert best.iteration == 2 # Second iteration has higher scores
|
||||
|
||||
@patch('swarms.structs.auto_swarm_builder.OpenAIFunctionCaller')
|
||||
def test_create_agents_with_feedback_first_iteration(self, mock_function_caller):
|
||||
"""Test agent creation for first iteration (no feedback)"""
|
||||
swarm = AutoSwarmBuilder(enable_evaluation=False)
|
||||
|
||||
# Mock the function caller
|
||||
mock_instance = MagicMock()
|
||||
mock_function_caller.return_value = mock_instance
|
||||
mock_instance.run.return_value.model_dump.return_value = {
|
||||
"agents": [
|
||||
{
|
||||
"name": "TestAgent",
|
||||
"description": "A test agent",
|
||||
"system_prompt": "You are a test agent"
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
# Mock build_agent method
|
||||
with patch.object(swarm, 'build_agent') as mock_build_agent:
|
||||
mock_agent = MagicMock()
|
||||
mock_build_agent.return_value = mock_agent
|
||||
|
||||
agents = swarm.create_agents_with_feedback("test task")
|
||||
|
||||
assert len(agents) == 1
|
||||
mock_build_agent.assert_called_once()
|
||||
|
||||
def test_run_single_iteration_mode(self):
|
||||
"""Test running in single iteration mode (evaluation disabled)"""
|
||||
swarm = AutoSwarmBuilder(enable_evaluation=False)
|
||||
|
||||
with patch.object(swarm, 'create_agents') as mock_create:
|
||||
with patch.object(swarm, 'initialize_swarm_router') as mock_router:
|
||||
mock_create.return_value = []
|
||||
mock_router.return_value = "test result"
|
||||
|
||||
result = swarm.run("test task")
|
||||
|
||||
assert result == "test result"
|
||||
mock_create.assert_called_once_with("test task")
|
||||
mock_router.assert_called_once()
|
||||
|
||||
|
||||
class TestEvaluationIntegration:
|
||||
"""Integration tests for the evaluation system"""
|
||||
|
||||
@patch('swarms.structs.auto_swarm_builder.CouncilAsAJudge')
|
||||
@patch('swarms.structs.auto_swarm_builder.Agent')
|
||||
@patch('swarms.structs.auto_swarm_builder.OpenAIFunctionCaller')
|
||||
def test_evaluation_workflow(self, mock_function_caller, mock_agent, mock_council):
|
||||
"""Test the complete evaluation workflow"""
|
||||
# Setup mocks
|
||||
mock_council_instance = MagicMock()
|
||||
mock_council.return_value = mock_council_instance
|
||||
mock_council_instance.run.return_value = "Evaluation feedback"
|
||||
|
||||
mock_agent_instance = MagicMock()
|
||||
mock_agent.return_value = mock_agent_instance
|
||||
mock_agent_instance.run.return_value = "Improvement suggestions"
|
||||
|
||||
mock_function_caller_instance = MagicMock()
|
||||
mock_function_caller.return_value = mock_function_caller_instance
|
||||
mock_function_caller_instance.run.return_value.model_dump.return_value = {
|
||||
"agents": [
|
||||
{
|
||||
"name": "TestAgent",
|
||||
"description": "Test",
|
||||
"system_prompt": "Test prompt"
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
# Configure swarm
|
||||
config = IterativeImprovementConfig(max_iterations=1)
|
||||
swarm = AutoSwarmBuilder(
|
||||
name="TestSwarm",
|
||||
enable_evaluation=True,
|
||||
evaluation_config=config,
|
||||
)
|
||||
|
||||
# Mock additional methods
|
||||
with patch.object(swarm, 'build_agent') as mock_build:
|
||||
with patch.object(swarm, 'initialize_swarm_router') as mock_router:
|
||||
mock_build.return_value = mock_agent_instance
|
||||
mock_router.return_value = "Task output"
|
||||
|
||||
# Run the swarm
|
||||
result = swarm.run("test task")
|
||||
|
||||
# Verify evaluation was performed
|
||||
assert len(swarm.evaluation_history) == 1
|
||||
assert result == "Task output"
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
pytest.main([__file__])
|
||||
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Reference in new issue