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Rust executable for Refact Agent, it lives inside your IDE and keeps AST and VecDB indexes up to date, offers agentic tools for an AI model to call. Yes, it works as a LSP server from IDE point of view.

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smallcloudai/refact-lsp

👋 Welcome Hacktoberfest Contributors!

We're excited to have you join us for Hacktoberfest 2024! Check out issues labeled for Hacktoberfest!

At Refact.ai, we're currently building an Autonomous AI Agent that will handle engineering tasks end-to-end. Exciting, isn't it?

We already have an AI Coding Assistant, designed to empower developers. It helps you get work done faster with context-aware code completion and integrated in-IDE chat.

We're building the future of programming, and you can join us today!

To show our appreciation, if you submit just one Pull Request (PR) to any of our open issues, we'll reward you with 3 months of free access to the Refact.ai tool! 🚀

Get started by checking out our open issues and the contributing.md for all the details you need to start making an impact.

To maintain a positive and respectful community, we encourage all users to review our Code of Conduct.

Join our community in our Discord server where you can get help, discuss ideas and be aware of our new releases and news.

Happy coding! 💻✨

Refact Agent (Rust)

This is a small executable written in Rust, a part of the Refact Agent project. Its main job is to live inside your IDE quietly and keep AST and VecDB indexes up to date. It is well-written: it will not break if you edit your files quickly or switch branches, it caches vectorization model responses so you don't have to wait for VecDB to complete indexing, AST supports connection graph between definitions and usages in many popular programming languages.

Yes, it looks like an LSP server to IDE, hence the name. It can also work within a python program, check out the Text UI below, you can talk about your project in the command line!

Progress

  • Code completion with RAG
  • Chat with tool usage
  • definition() references() tools
  • vecdb search() with scope
  • @file @tree @web @definition @references @search mentions in chat
  • locate() uses test-time compute to find good project cross-section
  • Latest gpt-4o gpt-4o-mini
  • Claude-3-5-sonnet
  • Llama-3.1 (passthrough)
  • Llama-3.2 (passthrough)
  • Llama-3.2 (scratchpad)
  • Bring-your-own-key
  • Memory (--experimental)
  • Docker integration (--experimental)
  • git integration (--experimental)
  • pdb python debugger integration (--experimental)
  • More debuggers
  • Github integration (--experimental)
  • Gitlab integration
  • Jira integration

Refact Agent

Installable by the end user:

Refact Self-Hosting Server:

Other important repos:

Compiling and Running

It will automatically pick up OPENAI_API_KEY, or maybe you have Refact cloud key or Refact Self-Hosting Server:

cargo build
target/debug/refact-lsp --http-port 8001 --logs-stderr
target/debug/refact-lsp --address-url Refact --api-key $REFACT_API_KEY --http-port 8001 --logs-stderr
target/debug/refact-lsp --address-url http://my-refact-self-hosting/ --api-key $REFACT_API_KEY --http-port 8001 --logs-stderr

Try --help for more options.

Things to Try

Code completion:

curl http://127.0.0.1:8001/v1/code-completion -k \
  -H 'Content-Type: application/json' \
  -d '{
  "inputs": {
    "sources": {"hello.py": "def hello_world():"},
    "cursor": {
      "file": "hello.py",
      "line": 0,
      "character": 18
    },
    "multiline": true
  },
  "stream": false,
  "parameters": {
    "temperature": 0.1,
    "max_new_tokens": 20
  }
}'

RAG status:

curl http://127.0.0.1:8001/v1/rag-status

Chat, the not-very-standard version, it has deterministic_messages in response for all your @-mentions. The more standard version is at /v1/chat/completions.

curl http://127.0.0.1:8001/v1/chat -k \
  -H 'Content-Type: application/json' \
  -d '{
  "messages": [
    {"role": "user", "content": "Who is Bill Clinton? What is his favorite programming language?"}
  ],
  "stream": false,
  "temperature": 0.1,
  "max_tokens": 20
}'

Telemetry

The flag --basic-telemetry means send counters and error messages. It is "compressed" into .cache/refact/telemetry/compressed folder, then from time to time it's sent and moved to .cache/refact/telemetry/sent folder.

To be clear: without these flags, no telemetry is sent. At no point it sends your code.

"Compressed" means similar records are joined together, increasing the counter. "Sent" means the rust binary communicates with a HTTP endpoint specified in caps (see Caps section below) and sends .json file exactly how you see it in .cache/refact/telemetry. The files are human-readable.

When using Refact self-hosted server, telemetry goes to the self-hosted server, not to the cloud.

Caps File

The capabilities file stores the same things as bring-your-own-key.yaml, the file describes how to access AI models. The --address-url parameter controls where to get this file, it defaults to ~/.cache/refact/bring-your-own-key.yaml. If it's a URL, the executable fetches $URL/refact-caps to know what to do. This is especially useful to connect to Refact Self-Hosting Server, because the configuration does not need to be copy-pasted among engineers who use the server.

AST

Supported languages:

  • Java
  • JavaScript
  • TypeScript
  • Python
  • Rust
  • C#

You can still use Refact for other languages, just the AST capabilities will be missing.

CLI

You can compile and use Refact Agent from command line with this repo alone, and it's a not an afterthought, it works great!

cargo build --release
cp target/release/refact-lsp python_binding_and_cmdline/refact/bin/
pip install -e python_binding_and_cmdline/

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Rust executable for Refact Agent, it lives inside your IDE and keeps AST and VecDB indexes up to date, offers agentic tools for an AI model to call. Yes, it works as a LSP server from IDE point of view.

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