CodeRAG FAQs

Question 1

What is CodeRAG?

Accepted Answer

CodeRAG is a high-performance tool designed for lightning-fast hybrid code search. It combines TF-IDF and vector embeddings to provide semantic code search, specifically optimized to prepare code context for RAG-ready AI assistants.

Question 2

How does CodeRAG ensure accurate code search results?

Accepted Answer

CodeRAG employs a sophisticated hybrid search mechanism, leveraging both semantic vector embeddings for conceptual understanding and TF-IDF for keyword matching. This ensures highly relevant results, even for nuanced queries, combined with intelligent AST-based chunking for precise code splitting.

Question 3

What does 'RAG-ready for AI assistants' mean for CodeRAG?

Accepted Answer

Being 'RAG-ready' means CodeRAG efficiently extracts and prepares relevant code snippets (chunks) from your codebase, providing high-quality context to Retrieval Augmented Generation (RAG) AI models. This significantly enhances the AI's ability to understand, answer questions, and generate accurate responses about your code, with out-of-the-box MCP integration for Claude Desktop.

Question 4

Is CodeRAG fast and efficient for large codebases?

Accepted Answer

Yes, CodeRAG boasts impressive performance, with initial indexing speeds of 1000-2000 files/second. It utilizes SQLite for persistent storage, enabling instant startup (<100ms) and real-time file watching for incremental, smart delta updates, ensuring your index is always current without full rebuilds.

Question 5

What is AST-based chunking and why is it important?

Accepted Answer

AST-based (Abstract Syntax Tree) chunking is an intelligent method CodeRAG uses to split code into meaningful, context-rich segments. Unlike simple line-based splitting, AST-based chunking understands the code's structure, ensuring that each retrieved chunk provides complete and coherent information, which is crucial for AI assistants to interpret correctly.

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主要功能

使用案例