Building Your Own "Google Maps for Codebases": A Practical Guide to Codebase Q&A with AI

library, such as all-MiniLM-L6-v2 or multi-qa-mpnet-base-cos-v1, for better performance on code-specific tasks.

By following these steps and making incremental improvements, you'll be well on your way to building a powerful code navigation tool that can significantly enhance developer productivity. Here are some additional tips and considerations as you proceed:

Tips for Improvement

1. Advanced Chunking Techniques

• AST-Based Splitting: Use libraries like asttokens (for Python) or equivalent tools in other languages to split the code into meaningful units based on AST nodes.
• Contextual Overlap: Ensure that chunks have some overlap, especially for functions and methods, so that context is preserved.

2. Enhanced Metadata

• Symbol Information: Store information about symbols (functions, classes) in each chunk to help the LLM understand relationships between code snippets.
• Dependency Graphs: Build a dependency graph of modules and files to provide more context when answering questions.

3. Hybrid Search Strategies

• BM25 + Embeddings: Combine keyword search (e.g., BM25) with semantic

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