A Rust-based tool that automates the generation, compilation, and testing of code using Large Language Models (LLMs). It interacts with LLMs to generate code based on user-provided explanations, compiles the code, resolves dependencies, and runs tests to ensure functionality.
Project name "rustsn" is a combination of "Rust" and "Snippet" words. Code snippets are generated by the tool.
- Code Generation: Generates Rust functions based on user explanations using LLMs.
- Automated Compilation: Compiles the generated code and handles compilation errors.
- Dependency Resolution: Determines and adds necessary dependencies.
- Test Generation: Creates tests for the generated code using LLMs.
- Error Correction: Iteratively rewrites code or tests to fix errors until successful compilation and testing.
- Caching Mechanism: Implements caching to store and retrieve previous computations for efficiency.
- JavaScript
- Java
- Rust
- Scala
stateDiagram
[*] --> GenerateCode
GenerateCode --> CompileCode
CompileCode --> GenerateTests : Compilation successful
CompileCode --> CheckDependencies : Compilation failed
CheckDependencies --> GenerateDependencies : Dependencies required
GenerateDependencies --> CompileCode
CheckDependencies --> RewriteCode : No dependencies required
RewriteCode --> CompileCode
GenerateTests --> RunTests
RunTests --> TestsPass : Tests passed
RunTests --> DecideFix : Tests failed
DecideFix --> RewriteCode : Error in code
DecideFix --> RewriteTests : Error in tests
RewriteCode --> CompileCode
RewriteTests --> RunTests
TestsPass --> [*]
- Rust: Ensure you have Rust installed. You can install it from here.
- Make a decision: Use Ollama (free and launched on your machine) or the OpenAI API (paid and launched on OpenAI servers).
- If you choose Ollama: Required for LLM interactions. Install from Ollama's official site.
- Download Ollam model
After downloading the model, you can say "hello" to the model to check if it is working correctly. After that, you can use type "Ctrl+D" to exit the model.ollama run gemma2:27b
- If you choose OpenAI API Key: Create file "token.txt" in the root folder and put your OpenAI API key there.
git clone https://github.com/evgenyigumnov/rustsn.git
cd rustsn-
Start the Program
cargo run -- --lang=RUST
-
Provide an Explanation
The program will prompt:
Explain what the function should do:Enter a detailed explanation of the function you want to generate.
parse json string and return struct User (age, name) -
Automatic Processing
The tool will:
- Generate the function code using an LLM.
- Attempt to compile the code.
- If compilation fails, it will check for missing dependencies and attempt to resolve them.
- Generate tests for the function.
- Run the tests and iteratively fix any errors.
-
Completion
Once the code compiles and all tests pass, the final code and tests will be displayed and result of work will be saved in
sandboxfolder.
For example:
[dependencies]
serde = { version = "1.0", features = ["derive"] }
serde_json = "1.0"
use serde::{Deserialize, Serialize};
#[derive(Deserialize, Serialize, Debug)]
struct User {
name: String,
age: u32,
}
fn solution(json_string: &str) -> Result<User, serde_json::Error> {
let user: User = serde_json::from_str(json_string)?;
Ok(user)
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_solution() {
let json_string = r#"{"name": "John Doe", "age": 30}"#;
let user = solution(json_string).unwrap();
assert_eq!(user.name, "John Doe");
assert_eq!(user.age, 30);
}
#[test]
fn test_solution_invalid_json() {
let json_string = r#"{"name": "John Doe", "age": }"#;
assert!(solution(json_string).is_err());
}
}
Finished
-
take 2 params and multiply and return result -
take 1 parameter multiply by random number and return tuple with result and random number -
parse json string and return struct User (age, name)
I would love to see contributions from the community. If you experience bugs, feel free to open an issue. If you would like to implement a new feature or bug fix, please follow the steps:
- Read "Contributor License Agreement (CLA)"
- Do fork
- Add comment to the issue that you are going to work on it
- Create pull request
- Add JavaScript language support
- Add Scala language support
- Add Java language support
- Simplify state machine logic and remove logic.md file
- Simplify prompt.txt file
- Add --lang parameter to specify the language of the generated code (Rust, Python, C, JavaScript, Java, TypeScript, CPP, Scala, Kotlin, Swift)
- Make decision to support multi-language code generation: Python, C, JavaScript, Java, TypeScript, CPP, Scala, Kotlin, Swift
- Extract_code function replaced by extract_code, extract_dep, extract_test functions
- Support OpenAI API
- Moved prompts from code to "rust.prompt" file
- Moved logic from code to "logic.md" file
- Code Generation
- Automated Compilation
- Dependency Resolution
- Test Generation
- Error Correction
- Caching Mechanism