Interpreter Component

The Interpreter Component is responsible for transforming raw computational results into user-friendly explanations and insights.

Overview

The interpreter serves as the critical bridge between mathematical/computational results and human understanding, ensuring that complex findings are presented in a clear, accurate, and contextually appropriate manner.

Components

Interpreter Service

The main service (interpreter_service.py) orchestrates the interpretation pipeline:

• Initializes LLM clients for response translation and quality assessment
• Coordinates the interpretation flow through multiple processing stages
• Extracts key insights from computational results
• Generates technical and user-friendly explanations
• Assesses response quality
• Provides fallback mechanisms

Key methods:

• interpret_solution(): Transforms a solution package into user-friendly content
• _generate_technical_explanation(): Creates technical documentation of results
• _extract_key_insights(): Identifies the most important findings
• _generate_follow_up_suggestions(): Creates contextual follow-up questions

Response Translator

The Response Translator (response_translator.py) handles translation of technical content to appropriate user levels:

• Converts technical explanations to user-friendly language
• Adapts content based on user expertise level
• Enhances explanations with domain-specific context
• Optimizes readability and comprehension

Quality Assessor

The Quality Assessor (quality_assessor.py) evaluates the quality of generated interpretations:

• Assesses accuracy of technical content
• Evaluates clarity and readability
• Checks completeness of information
• Verifies domain-specific correctness
• Provides quality metrics for monitoring

Data Models

The component uses Pydantic models (models.py) for data validation:

• InterpretationRequest: Model for interpretation requests
• InterpretationResponse: Model for interpretation responses
• InterpretedSolution: Model for fully processed solutions
• ResponseQualityMetrics: Model for quality assessment metrics

API Router

The FastAPI router (router.py) implements the following endpoints:

• POST /api/interpreter/interpret: Process interpretation requests
• POST /api/interpreter/quality-check: Assess interpretation quality
• Additional utility endpoints for component testing

Process Flow

1. The interpreter receives a solution package from the solver
2. A technical explanation is generated from the solution
3. Key insights are extracted from conclusions and results
4. A user-friendly explanation is created based on user context
5. Follow-up suggestions are generated
6. Quality metrics are calculated for the interpretation
7. The complete interpreted solution is returned

Integration

The Interpreter Component integrates with:

• The Solver Component for receiving solution packages
• The Model Container for accessing LLM services
• The API Layer for handling requests
• The Quality Assessment system for validation
• The User Context system for personalization

Best Practices

1. Always validate input data using Pydantic models
2. Maintain clear separation between technical and user-friendly content
3. Include quality metrics with all interpretations
4. Handle edge cases and provide fallback mechanisms
5. Keep explanations concise but complete
6. Adapt content based on user expertise level