Cross-sectional analysis of multiple Repo


If I want to analyze across multiple Repo’s, can I create a workspace and display multiple folders so that Codebase can analyze multiple files?

If anyone has any good ideas, please let me know.

*However, since Codebase’s reference area is limited, we think we need to use it in combination with Langchain or something similar.
*By the way, I am a non-programmer.

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If you use the “Add Folder to Workspace…” option to add a second folder to your workspace, the Codebase context features still only use the codebase of the first folder in your workspace. But with the “@” symbol, you can still reference files from both folders. This seems to be a bug because the advanced codebase context settings provide the option to use “all” or a specific folder. We’ll investigate.

However, if you put both folders into one folder and open that one parent folder in Cursor, the codebase context features can use both folders simultaneously. So I would recommend that you do that.

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・Thanks for your suggestion well said as you suggested.
・On the other hand, what I’m wondering is whether GPT holds that much memory space. While it can take in large amounts of information, can it actually process all of that information?
・Does Cursor introduce its own storage space, etc. in Langchain compared to, for example, the web version of GPT?

The AI itself can only remember a limited amount of text at once. However, what codebase indexing does is make your codebase highly searchable in a smart manner. When you ask a question, it retrieves the most relevant snippets from your codebase related to your question and presents them within the limited context of the AI. This way, the AI only sees what it needs to answer your question, and its memory space doesn’t need to be excessively large.

・Okay, so if I still want to implement Langchain, can I combine Cursor and Langchain? Probably not…
・On a related note, you can’t read a large number of files like this like all Codebase can with the web version of GPT, can Codebase increase the accuracy of GPT compared to regular GPT? Or is it the same?

  1. I’m not sure if I understand your question correctly. LangChain is a framework for developing applications powered by language models. It has helpful methods that you can use when developing your own AI application to make the AI context-aware, for example. Cursor is its own application that has its own logic to make the AI context-aware.

  2. The ChatGPT website can’t read a large number of files because it’s limited by its own context size. It doesn’t have indexing logic built into it like Cursor, which makes it able to retrieve the most relevant snippets, as I described in my previous answer. Yes, it will greatly improve the accuracy of the AI answers when you ask something about your codebase. With ChatGPT, the relevant parts of your codebase would likely be excluded from the limited context, and it wouldn’t even know what code you wrote.

・Thanks for the great answer, github copilot doesn’t have an indexing feature either, right?

Correct. As far as I know, Copilot only sees the code that’s actually visible in your Visual Studio Code window, and that can’t be a lot of code, so they don’t need indexing.


@boqihua123 Please read my reply here. Cursor can handle multiple folders.