The text discusses the development and functionality of CodeQA, a tool designed to enhance codebase understanding through codebase indexing and retrieval. Built using LanceDB, CodeQA can handle Java, Python, Rust, and JavaScript, and aims to answer natural language questions about a codebase by providing relevant snippets and context. The process involves indexing codebases using tree-sitter, a parser-generator tool that efficiently constructs abstract syntax trees (ASTs) for various programming languages, enabling syntax-level chunking to maintain semantic integrity. This approach is contrasted with in-context learning (ICL) using large language models (LLMs), which can suffer performance degradation as context windows fill up. Instead of relying solely on LLMs, CodeQA employs semantic search with vector embeddings to improve the retrieval of relevant code snippets, thereby reducing the risk of hallucination and enhancing the quality of responses. The text emphasizes the need for effective chunking and embedding strategies, highlighting the importance of maintaining code structure to ensure high-quality embeddings for semantic search.