https://github.com/davidyz/vectorcode
A code repository indexing tool to supercharge your LLM experience.
https://github.com/davidyz/vectorcode
embeddings mcp mcp-server neovim-plugin rag retrieval-augmented
Last synced: 20 days ago
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A code repository indexing tool to supercharge your LLM experience.
- Host: GitHub
- URL: https://github.com/davidyz/vectorcode
- Owner: Davidyz
- License: mit
- Created: 2025-01-11T20:14:20.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-04-02T05:53:19.000Z (27 days ago)
- Last Synced: 2025-04-02T06:31:53.430Z (27 days ago)
- Topics: embeddings, mcp, mcp-server, neovim-plugin, rag, retrieval-augmented
- Language: Python
- Homepage:
- Size: 932 KB
- Stars: 208
- Watchers: 5
- Forks: 9
- Open Issues: 5
-
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
[](https://codecov.io/github/Davidyz/VectorCode)
[](https://github.com/Davidyz/VectorCode/actions/workflows/test_and_cov.yml)
[](https://pypi.org/project/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 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](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
> [!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](https://github.com/Davidyz/VectorCode/tags).
- 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.
If you're trying to contribute to this project, take a look at [the contribution
guide](./docs/CONTRIBUTING.md), 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](https://github.com/tree-sitter/py-tree-sitter) and
[tree-sitter-language-pack](https://github.com/Goldziher/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](https://docs.trychroma.com/docs/run-chroma/persistent-client);
- [-] 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**
- [ ] 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
- 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;
- Thank [@olimorris](https://github.com/olimorris) for the help (personally and
from [codecompanion.nvim](https://github.com/olimorris/codecompanion.nvim))
when this project made initial attempts at tool-calling;
- Thank [@ravitemer](https://github.com/ravitemer) for the help to interface
VectorCode with [MCP](https://modelcontextprotocol.io/introduction).