Zek - Questions

1

Based on my review of the implementation, I have several technical questions about the code and design choices:

  1. Model Integration & Error Handling:

  • In the ModelManager class, you're using both OpenAI and Anthropic models. What was your reasoning behind choosing these specific model versions ("gpt-4o-mini" and "claude-3-5-sonnet-20240620")?

  • The retry mechanism uses exponential backoff. Could you explain why you chose these specific retry parameters (multiplier=1, min=4, max=10)?

  1. Document Processing:

  • The DocumentProcessor uses a chunk size of 2000 and overlap of 200 for text splitting. What factors influenced these specific values?

  • How would you handle documents with mixed content types (e.g., a PDF with both text and tables)?

  1. Summary Generation:

  • The SummaryGenerator processes chunks concurrently with max_concurrent_chunks=5. What considerations led to this limit?

  • How do you ensure consistency between summaries when processing chunks in parallel?

  1. Testing Strategy:

  • I notice the test_document_processor.py has good coverage, but how do you test the rate limiting functionality in ModelManager?

  • What additional test cases would you add to improve the test coverage?

  1. Architecture Decisions:

  • The code uses async/await patterns in certain places. What guided your decision on which operations should be async?

  • How would you modify the architecture to support streaming responses from the LLMs?

  1. Performance and Scalability:

  • Have you considered caching mechanisms for frequently requested summaries?

  • What improvements would you suggest for handling very large documents (>100MB)?

  1. Security:

  • How are you handling API key rotation and security?

  • What additional security measures would you implement in a production environment?

2

  1. Logging and Monitoring:

  • Your implementation uses a LoggerMixin. How would you extend this to support distributed tracing and monitoring in a microservices environment?

  • What metrics would you add to monitor the health and performance of the system?

  1. Input Validation:

  • The code uses Pydantic for validation. Can you explain how you handle validation for special characters or non-standard text encodings in documents?

  • What additional validation rules would you add for production use?

  1. Error Recovery:

  • In the SummaryGenerator, how do you handle partial failures when some chunks fail to process?

  • What strategies would you implement for resuming failed summary generations?

  1. Language Support:

  • The summary service supports multiple languages. How do you handle:

    • Right-to-left languages?

    • Languages with special characters?

    • Different text segmentation rules for different languages?

  1. API Design:

  • How would you modify the API to support:

    • Batch processing of multiple documents?

    • Progress tracking for long-running summaries?

    • Webhook notifications for completed summaries?

  1. Resource Management:

  • How do you handle memory usage when processing multiple large documents simultaneously?

  • What strategies would you implement for queue management and job prioritization?

  1. Code Organization:

  • The project uses a specific directory structure. What influenced this organization?

  • How would you modify the structure to support multiple model providers or document types?

  1. Configuration Management:

  • How do you handle different configurations for development, testing, and production environments?

  • What configuration parameters would you make dynamically adjustable?

  1. Dependency Management:

  • You're using Poetry for dependency management. What advantages did you find compared to requirements.txt?

  • How do you handle conflicting dependencies between different LLM providers?

  1. Quality Assurance:

  • Beyond unit tests, what additional quality assurance measures would you implement?

  • How would you set up continuous integration and deployment for this project?

These questions delve deeper into system design, scalability, and production readiness aspects of the implementation. Would you like me to elaborate on any of these areas?

3

  1. API Enhancement Task: "Add a new endpoint /analyze/batch/ that can process multiple documents simultaneously. The endpoint should:

  • Accept multiple file uploads

  • Return a unique batch ID

  • Implement a separate endpoint to check batch processing status

  • Handle partial failures (some documents succeed, others fail) How would you implement this while maintaining clean code principles?"

  1. Rate Limiting Implementation: "The current ModelManager has basic rate limiting. Implement a more sophisticated rate limiting system that:

  • Uses a token bucket algorithm

  • Considers both request count and token count

  • Supports different limits for different model providers

  • Persists rate limit state across application restarts Show your implementation."

  1. Caching Challenge: "Implement a caching system for document summaries that:

  • Uses document content hash as cache key

  • Supports TTL (Time To Live)

  • Handles cache invalidation when models are updated

  • Is thread-safe for concurrent access How would you modify the existing code to support this?"

  1. Error Handling Enhancement: "The current error handling in endpoints.py is basic. Implement a more comprehensive error handling system that:

  • Creates custom exception classes for different error types

  • Adds structured error responses with error codes

  • Includes request tracing IDs

  • Provides detailed error context for debugging Show the changes needed across the codebase."

  1. Streaming Implementation: "Modify the /analyze endpoint to support streaming responses. The implementation should:

  • Stream chunks of the summary as they're generated

  • Provide progress updates

  • Handle connection drops gracefully

  • Support backpressure How would you implement this?"

  1. Performance Optimization: "The document processing pipeline could be optimized. Implement changes to:

  • Add parallel processing for large documents

  • Implement smart chunking based on document structure

  • Add memory usage optimization for large files

  • Include performance metrics collection Show your implementation."

  1. Security Enhancement: "Implement additional security measures in the API:

  • Add rate limiting per API key

  • Implement request signing

  • Add input sanitization

  • Add audit logging How would you modify the existing code?"

  1. Testing Challenge: "Add comprehensive integration tests that:

  • Test the entire document processing pipeline

  • Include performance benchmarks

  • Test error conditions and edge cases

  • Mock external services effectively Write the test cases."

  1. Monitoring Implementation: "Add a monitoring system that:

  • Tracks API usage metrics

  • Monitors model performance

  • Alerts on error conditions

  • Provides usage analytics Show how you would implement this."

  1. Code Refactoring: "The handle_document_analysis function in endpoints.py could be improved. Refactor it to:

  • Follow SOLID principles better

  • Implement the Command pattern

  • Add better separation of concerns

  • Improve error handling Show your refactored implementation."

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