https://github.com/davidyz/vectorcode

A code repository indexing tool to supercharge your LLM completion experience.
https://github.com/davidyz/vectorcode

neovim-plugin retrieval-augmented

Last synced: 5 days ago
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A code repository indexing tool to supercharge your LLM completion experience.

• Host: GitHub
• URL: https://github.com/davidyz/vectorcode
• Owner: Davidyz
• License: mit
• Created: 2025-01-11T20:14:20.000Z (about 1 month ago)
• Default Branch: main
• Last Pushed: 2025-02-02T11:59:14.000Z (9 days ago)
• Last Synced: 2025-02-02T12:15:24.089Z (9 days ago)
• Topics: neovim-plugin, retrieval-augmented
• Language: Python
• Homepage:
• Size: 675 KB
• Stars: 63
• Watchers: 4
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-neovim - Davidyz/VectorCode - Supercharge your LLM experience with repository-level RAG. (AI / (requires Neovim 0.5))

README

        
# VectorCode

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](https://www.trychroma.com/), 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).

* [Why VectorCode?](#why-vectorcode)

* [Documentation](#documentation)

* [TODOs](#todos)

* [Credit](#credit)

## Why VectorCode?

LLMs usually have very limited understanding about close-source and/or infamous 

projects, as well as cutting edge developments that have not made it into the

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:

![](./images/sudoku_no_rag.png)

But with RAG context provided by VectorCode, my completion LLM was able to

provide completions that I actually implemented:

![](./images/sudoku_with_rag.png)

This makes the completion results far more usable. 

[A similar strategy](https://docs.continue.dev/customize/deep-dives/codebase) 

is implemented in [continue](https://www.continue.dev/), a popular AI completion

and chat plugin available on VSCode and JetBrain products.

## Documentation

- For the setup and usage of the command-line tool, see [the CLI documentation](./docs/cli.md);

- For neovim users, after you've gone through the CLI documentation, please refer to 

  [the neovim plugin documentation](./docs/neovim.md) for further instructions.

## TODOs

- [x] query by ~file path~ excluded paths;

- [ ] chunking support;

  - [x] add metadata for files;

  - [x] chunk-size configuration;

  - [ ] smarter chunking (semantics/syntax based);

  - [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](https://docs.trychroma.com/docs/run-chroma/persistent-client);

- [x] proper [remote Chromadb](https://docs.trychroma.com/production/administration/auth) 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**

## Credit

- Thank [@milanglacier](https://github.com/milanglacier) (and [minuet-ai.nvim](https://github.com/milanglacier/minuet-ai.nvim)) for the support when this project was still in early stage.