Initial Tasks for LLM-Based OCR Error Detection Implementation

Based on the Product Requirements Document (PRD), here are the first tasks needed to add the LLM-based scanno detection module to Codexis:

Phase 1: Core Infrastructure Implementation

1. Ollama Client Implementation

• Create basic Ollama client module with HTTP interface to local Ollama API
• Implement error handling and retry mechanisms
• Add connection pooling for efficient handling of multiple requests
• Develop structured output handling with JSON schema-based responses
• Create mock client for testing purposes

2. LaTeX Processing Foundation

• Implement text extraction from LaTeX documents
• Add position tracking for accurate correction placement
• Develop basic content segmentation (text blocks, commands, math blocks)
• Create handlers for special LaTeX environments
• Implement context extraction for improved detection

3. Basic Scanno Detection

• Design initial prompts for LLM-based error detection
• Implement sequential processing of text blocks
• Add confidence scoring for suggested corrections
• Create basic validation for suggested corrections
• Implement simple correction application mechanism

4. Project Setup and Dependencies

• Add required dependencies to mix.exs:

  defp deps do
    [
      {:req, "~> 0.5.0"},     # HTTP client for Ollama API
      {:jason, "~> 1.4"},     # JSON parsing
      {:nimble_pool, "~> 1.0"}, # Connection pooling
      {:flow, "~> 1.2"}       # Parallel processing
    ]
  end
  

• Create module structure and namespaces

• Set up basic configuration system

• Implement logging and telemetry foundation

• Create initial test framework

5. API and CLI Interface

• Design and implement the main API module (Codexis.BuildManager.ScannoIntegration)
• Create the check_manuscript_for_scannos function with options for model selection and interactive mode
• Implement the CLI interface (mix codexis.check_scannos)
• Add command-line options (model selection, interactive mode toggle)
• Create basic result reporting structure

6. Build Pipeline Integration

• Create build pipeline configuration module (Codexis.BuildPipeline.Config)
• Implement build pipeline stage for scanno detection (Codexis.BuildPipeline.ScannoStage)
• Add build hooks for pre-build and post-build processing
• Implement caching mechanism for improved performance
• Create CI/CD integration for automated scanno detection

7. Monitoring and Metrics

• Implement telemetry for key metrics collection
• Create handlers for logging and reporting
• Add performance monitoring
• Implement health checks and diagnostics

Next Steps After Phase 1

After completing the core infrastructure, the next phases would involve:

1. Enhanced detection capabilities (specialized detectors, pattern filtering)
2. User experience improvements and advanced build pipeline integration
3. Advanced features and optimization

Getting Started

To begin implementation:

1. Set up the development environment with Ollama installed

2. Pull required models:

   ollama pull qwen3.0:8b
   ollama pull deepseek-r1:8b
   

3. Create the initial module structure

4. Implement the Ollama client as the first component

Implementation Strategy

Recommended Order of Implementation

1. Start with the Ollama client to establish the foundation for LLM interaction
2. Implement the LaTeX processor to handle manuscript content
3. Develop the basic scanno detection functionality
4. Create the API and CLI interface for user interaction
5. Implement the build pipeline integration
6. Add monitoring and metrics

Development Approach

• Use test-driven development (TDD) to ensure reliability
• Implement features incrementally, starting with minimal viable functionality
• Focus on core functionality first, then add advanced features
• Regularly test with real manuscripts to validate effectiveness

Conclusion

The implementation of the LLM-based OCR error detection module represents a significant enhancement to the Codexis platform. By following the tasks outlined in this document, the development team can systematically add this functionality to Codexis in a structured and efficient manner.

The initial focus should be on establishing the core infrastructure (Phase 1), which will provide the foundation for the more advanced features in later phases. Regular testing with real manuscripts will be crucial to ensure the system effectively detects and corrects OCR errors.

Once implemented, this module will significantly improve the quality of digitized manuscripts in the Codexis build pipeline by leveraging the power of locally-hosted LLMs to detect context-dependent OCR errors that traditional pattern-matching approaches miss.