Code retrieval for technical documentation requires special handling:
- Document Preprocessing::
- Ensure that code blocks are clearly identified in the PDF/TXT (e.g., ``wrapped'')
- Maintaining a standardized code comment format in GitHub repositories
- Pipeline Configuration::
- utilization
AgenticRAGPipelineand setmax_steps=3Implementing multiple rounds of context matching - lower
k=3to improve code snippet retrieval accuracy
- utilization
- Query Optimization::
- The input question contains a specific function name/argument (e.g.
"pipeline.generate()的使用示例") - For high-frequency queries can be preset prompt templates to emphasize the output of the code
- The input question contains a specific function name/argument (e.g.
The empirical measurements show that the combination ofllama3model and a temperature parameter of 0.8 allows for more deterministic code generation results.
This answer comes from the articleRAGLight: Lightweight Retrieval Augmentation Generation Python LibraryThe
May not be reproduced without permission:AI productivity tools " How to achieve accurate code snippet retrieval of technical documents by RAGLight?
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