Intelligent Q&A system based on RAG technology
DeepWiki-Open integrates Retrieval Augmented Generation (RAG) technology to provide users with intelligent question and answer functionality related to the content of the code repository. This innovative feature allows developers to ask questions directly to the code repository, and the system generates accurate context-sensitive answers based on its understanding of the code.
The working mechanism of the Q&A function is that first the system indexes the key information and context of the entire code base to build a knowledge graph. When a user asks a question, the RAG engine retrieves the relevant knowledge fragment and generates a professional answer through the Google Gemini model. The results are presented in real-time in a streaming manner, which greatly improves the interaction experience.
Typical application scenarios include: querying the implementation method of a particular function, understanding the call relationship between modules, analyzing the data flow and so on. For example, a user can ask "How does this repository handle user authentication?" The system will extract relevant implementation details from the code and generate clear explanations.
This answer comes from the articleDeepWiki-Open: Automatically Generating AI Documentation for GitHub, GitLab RepositoriesThe
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