Hierarchical Analysis of DocAgent and Multi-Intelligent Body Collaboration Scheme
To address the problem of insufficient contextual understanding of traditional document generation tools, DocAgent usesFour-step quality improvement strategy::
- Dependency analysis: Build code call relationship graphs via AST parser, prioritize base function documentation
- multiple intelligences division of labor (MID): Decompose tasks to specialized intelligences (grammar analysis/document generation/quality validation), components collaborate via message queues
- Closed Loop Document Evaluation: Built-in AST checker to verify the integrity of parameter/return value descriptions, supports manual secondary validation
- Parameter tuning recommendations: Modify generation_settings in agent_config.yaml to lower the temperature value (0.3-0.7) to improve the accuracy
Hands-on program:
- Check the "Enable Context Awareness" option in the web interface.
- Configure the stringency level of the quality check (Basic/Strict mode)
- Use a split-generation strategy for key modules, with base modules followed by composite modules
This answer comes from the articleDocAgent: A Smart Tool for Automating Python Code DocumentationThe
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