QA of codebases:GPT 4o - chunhualiao/public-docs GitHub Wiki

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To effectively address questions about a large-scale C++ codebase using open-source solutions that leverage Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) techniques, consider the following tools and frameworks:

Open-Source Solutions:

  1. RAGFlow: An open-source RAG engine that combines LLMs with deep document understanding to provide accurate, citation-backed answers from complex data formats.

  2. LangChain: A framework designed for developing applications powered by LLMs. It offers modules for integrating with various data sources and supports RAG implementations, making it suitable for tasks like code analysis and question answering.

  3. Haystack by Deepset: An open-source Python framework that enables building NLP applications, including those utilizing RAG techniques. It supports document retrieval, question answering, and summarization, and integrates seamlessly with LLMs.

  4. QA-with-RAG: A containerized question-answering framework that allows users to query their documents and receive accurate answers by employing RAG methodologies.

Standardized Benchmarks and Leaderboards:

Evaluating the performance of codebase question-answering systems is crucial. While specific benchmarks for this task are still emerging, the following resources offer relevant evaluations:

  • Papers with Code – Question Answering: Provides a collection of benchmarks and leaderboards tracking progress in question answering tasks, which can be adapted for codebase-related evaluations.

  • EvoEval: Introduces a novel benchmark designed to evaluate the coding abilities of LLMs by addressing limitations of existing benchmarks, offering a more comprehensive assessment of code understanding and generation.

Additionally, platforms like LiveBench are emerging to offer benchmarks for LLMs, focusing on test set contamination and objective evaluation, which could be pertinent as the field progresses.

While the landscape of standardized benchmarks for codebase question answering is still developing, these resources provide a foundation for evaluating and improving the performance of systems designed to navigate and interpret large-scale C++ codebases using LLMs and RAG techniques.