Executive Summary
A developer built a retrieval benchmark on the Apache Kafka 4.0.0 broker core to evaluate how accurately Code-RAG systems find the correct files in a large polyglot codebase. The benchmark uses a five-layer schema to define correct answers, query variations, plausible incorrect files, rationale, and evaluation metrics. It isolates retrieval quality from generative model quality by focusing on embedding-based retrieval and ranking of relevant code files. The study revealed retrieval instability depending on query phrasing and chunking strategies, emphasizing the need for rigorous benchmarking to avoid overestimating model performance.
ResultDeveloped a rigorous benchmark methodology with a five-layer schema and multiple query phrasings to measure retrieval accuracy and error types. Found that retrieval qual...
Implementation ComplexityMedium effort
Best forSoftware Development / Developer Tools / Developer / Researcher / Code-RAG retrieval pipeline with embedding models (e.g., nomic-embed-text, mistral/codestral-embed, OpenAI text-embedding-3-small)