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RAG for a Codebase with 10k Repos
Discover how CodiumAI used RAG to bridge LLMs' context gaps, ensuring quality and integrity in generative AI coding.
However, naive chunking methods struggle with accurately delineating meaningful segments of code, leading to issues with boundary definition and the inclusion of irrelevant or incomplete information. For a query about a specific microservice architecture pattern, our system might first identify the top 5-10 repositories most likely to contain relevant information based on metadata and high-level content analysis. By focusing on intelligent chunking, enhanced embeddings, advanced retrieval techniques, and scalable architectures, we’ve developed a system that can effectively navigate and leverage the vast knowledge contained in enterprise-scale codebases.
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