CodeRAG is a novel retrieval-augmented framework that helps large language models generate code in real-world environments with complex dependencies and structures.

• Addresses the gap between simple code tasks and real-world programming needs
• Utilizes a bigraph-based retrieval system to understand code repositories' complex structure
• Provides supportive context from existing codebases to improve generation accuracy
• Designed specifically for software environments with interdependent components

This research significantly advances engineering capabilities by enabling LLMs to understand and navigate the complex dependencies in production codebases, making AI-assisted programming more practical for real-world applications.

CodeRAG: Supportive Code Retrieval on Bigraph for Real-World Code Generation