DocAgent uses the following mechanisms to handle large code bases:
- hierarchical processing strategy: Prioritize the analysis of basic modules with fewer dependencies and gradually build the complete context to avoid loading all the code at once
- Intelligent Tasking: Multi-intelligence system automatically splits code base into logical units for parallel processing
- incremental generation: Supports modular operation, allowing pause and resume in the middle
Performance Optimization Recommendations::
- Modular processing: Generate documents in batches by functional modules for very large projects.
- Hardware configuration: Ensure when using local LLM:
- GPU memory ≥ 16GB (for 7B parameter model)
- SSD storage accelerates file reading
- parameter tuning: in
agent_config.yamlAdjustment in the middle:- diminish
max_tokensControlling Document Length - lower
temperatureValues reduce randomness
- diminish
- Cache Utilization: Automatically skips processed files when run repeatedly
For projects with more than 100,000 lines of code, it is recommended to run them during off-peak hours and monitor resource usage in real time through the web interface.
This answer comes from the articleDocAgent: A Smart Tool for Automating Python Code DocumentationThe
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