Performance and Best Practices Improvements

[klauseduard]

Code Efficiency and Best Practices Improvements

Current State

The codebase has several areas where efficiency and best practices could be improved, particularly in the search functionality implementation.

Issues

1. Code Duplication and Organization

• build_corpus_and_hashes exists in both search_semantic.py and search_utils.py
• Deprecated code versions are still present (e.g., load_embeddings_and_hashes_old)
• build_and_save_embeddings has multiple variants with slight differences

2. Memory Management

• Model and embeddings are loaded into memory at startup and kept there
• No lazy loading of resources
• Potential memory leaks during index rebuilding

3. Search Performance

• Sequential processing in get_semantic_scores instead of vectorized operations
• Redundant cosine similarity computations
• Could benefit from batch processing

4. File Operations

• Redundant file reads (e.g., indices.json)
• No file locking mechanism for concurrent access
• No cleanup of old index files
• No atomic operations for index updates

5. Error Handling

• Inconsistent error handling across modules
• Some errors printed to stdout instead of using logger
• No retry mechanism for temporary failures

Proposed Solutions

Short term

1. Consolidate duplicate code into single implementations
2. Remove deprecated code
3. Implement proper logging throughout
4. Add basic file locking

Medium term

1. Implement lazy loading for model and embeddings
2. Vectorize search operations
3. Add proper error handling hierarchy
4. Implement atomic file operations

Long term

1. Consider implementing a proper search index class
2. Add caching layer
3. Implement background index rebuilding
4. Add monitoring and performance metrics

Technical Details

• Affects files: search_semantic.py, search_utils.py, web_interface.py
• Impact: Performance, memory usage, code maintainability
• Priority: Medium
• Complexity: Medium to High

Labels

• enhancement
• performance
• technical-debt