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VectorCode

by Davidyz

Development/Toolscode indexingRAGneovimvector database

VectorCode is a code repository indexing tool that helps you write better prompts for coding LLMs. It indexes and provides information about the code repository you're working on.

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VectorCode

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codecov

Test and Coverage

Test and Coverage
pypi

pypi

VectorCode is a code repository indexing tool. It helps you write better prompt for your coding LLMs by indexing and providing information about the code repository you're working on. This repository also contains the corresponding neovim plugin because that's what I used to write this tool.

[!NOTE] This project is in beta quality and only implements very basic retrieval and embedding functionalities. There are plenty of rooms for improvements and any help is welcomed.

[!NOTE] Chromadb, the vector database backend behind this project, supports multiple embedding engines. I developed this tool using SentenceTransformer, but if you encounter any issues with a different embedding function, please open an issue (or even better, a pull request :D).

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Why VectorCode?

LLMs usually have very limited understanding about close-source projects, projects that are not well-known, and cutting edge developments that have not made it into releases. Their capabilities on these projects are quite limited. Take my little toy sudoku-solving project as an example: When I wrote the first few lines and want the LLM to fill in the list of solvers that I implemented in solver_candidates, without project context, the completions are simply random guesses that might be part of another sudoku project:

But with RAG context provided by VectorCode, my completion LLM was able to provide completions that I actually implemented:
This makes the completion results far more usable. A similar strategy is implemented in continue, a popular AI completion and chat plugin available on VSCode and JetBrain products.

Documentation

[!NOTE] The documentation on the main branch reflects the code on the latest commit (apologies if I forget to update the docs, but this will be what I aim for). To check for the documentation for the version you're using, you can check out the corresponding tags.

If you're trying to contribute to this project, take a look at the contribution guide, which contains information about some basic guidelines that you should follow and tips that you may find helpful.

TODOs

  • [x] query by ~file path~ excluded paths;
  • [x] chunking support;
    • [x] add metadata for files;
    • [x] chunk-size configuration;
    • [x] smarter chunking (semantics/syntax based), implemented with py-tree-sitter and tree-sitter-language-pack;
    • [x] configurable document selection from query results.
  • [x] ~NeoVim Lua API with cache to skip the retrieval when a project has not been indexed~ Returns empty array instead;
  • [x] job pool for async caching;
  • [x] persistent-client;
  • [-] proper remote Chromadb support (with authentication, etc.);
  • [x] respect .gitignore;
  • [x] implement some sort of project-root anchors (such as .git or a custom .vectorcode.json) that enhances automatic project-root detection. Implemented project-level .vectorcode/ and .git as root anchor
  • [ ] ability to view and delete files in a collection (atm you can only drop and vectorise again);
  • [x] joint search (kinda, using codecompanion.nvim/MCP).

Credit