About VectorCode
VectorCode is a code repository indexing tool that helps build better prompts for coding LLMs. It includes a command-line tool and a neovim plugin with APIs for AI plugin development, and is currently in beta.
Key features
- 01Indexes repositories and retrieves relevant code
- 02Respects .gitignore when selecting files
- 03Syntax-aware chunking built on tree-sitter
- 04Configurable document selection from query results
- 05Neovim plugin with a Lua API
- 06Joint search through MCP with codecompanion.nvim
Use cases
- 01Injecting project-specific context into coding LLM prompts
- 02Adding repository retrieval to AI plugins in neovim
- 03Letting MCP clients query an indexed local repository
MCP support
VectorCode connects to MCP clients such as codecompanion.nvim, which lets them use its repository retrieval through MCP.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEVectorCode
VectorCode is a code repository indexing tool. It helps you build 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 that provides a set of APIs for you to build or enhance AI plugins, and integrations for some of the popular plugins.
Note
This project is in beta quality and is undergoing rapid iterations. I know there are plenty of rooms for improvements, and any help is welcomed.
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. With VectorCode, you can easily (and programmatically) inject task-relevant context from the project into the prompt. This significantly improves the quality of the model output and reduce hallucination.
Documentation
Note
The documentation on the
mainbranch reflects the code on the latest commit. To check for the documentation for the version you're using, you can check out the corresponding tags.
- For the setup and usage of the command-line tool, see the CLI documentation;
- For neovim users, after you've gone through the CLI documentation, please refer to the neovim plugin documentation (and optionally the lua API reference) for further instructions.
- Additional resources:
- the wiki for extra tricks and tips that will help you get the most out of VectorCode;
- the discussions where you can ask general questions and share your cool usages about VectorCode.
- If you're feeling adanvturous, feel free to check out the pull requests for WIP features.
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.
About Versioning
This project follows an adapted semantic versioning:
- Until 1.0.0 is released, the major version number stays 0 which indicates that this project is still in early stage, and features/interfaces may change from time to time;
- The minor version number indicates breaking changes. When I decide to remove a
feature/config option, the actual removal will happen when I bump the minor
version number. Therefore, if you want to avoid breaking a working setup, you
may choose to use a version constraint like
"vectorcode<0.7.0"; - The patch version number indicates non-breaking changes. This can include new features and bug fixes. When I decide to deprecate things, I will make a new release with bumped patch version. Until the minor version number is bumped, the deprecated feature will still work but you'll see a warning. It's recommended to update your setup to adapt the new features.
TODOs
- query by
file pathexcluded paths; - chunking support;
- add metadata for files;
- chunk-size configuration;
- smarter chunking (semantics/syntax based), implemented with py-tree-sitter and tree-sitter-language-pack;
- configurable document selection from query results.
-
NeoVim Lua API with cache to skip the retrieval when a project has not been indexedReturns empty array instead; - job pool for async caching;
- persistent-client;
- proper remote Chromadb support (with authentication, etc.);
- respect
.gitignore; - implement some sort of project-root anchors (such as
.gitor a custom.vectorcode.json) that enhances automatic project-root detection. Implemented project-level.vectorcode/and.gitas root anchor - ability to view and delete files in a collection;
- joint search (kinda, using codecompanion.nvim/MCP);
- Nix support (unofficial packages here);
- Query rewriting (#124).
Credit
- @milanglacier (and minuet-ai.nvim) for the support when this project was still in early stage;
- @olimorris for the help (personally and from codecompanion.nvim) when this project made initial attempts at tool-calling;
- @ravitemer for the help to interface VectorCode with MCP;
- The nix community (especially @sarahec and @GaetanLepage) for maintaining the nix packages.
Special Thanks
Star History
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.