ai_rs

ai_rs is a Rust library that provides a unified interface to interact with multiple AI models. This library is designed to be modular, allowing you to easily add and use different AI models.

Features

• Modular design: Each AI model is implemented in its own module.
• Easy setup: Initialize and configure AI models with simple method calls.
• Logging: Configurable logging levels using an .env file.

Installation

Add ai_rs to your Cargo.toml:

[dependencies]
ai_rs = "0.0.1"

Usage

Example

Here's an example of how to use the ai_rs library with the GeminiClient and xyzClient:

use ai_rs::{init_logging, GeminiClient};

fn main() {
    init_logging();

    let gemini_ai = GeminiClient::setup("your_api_key").model("gemini-1.5-pro");
    let gemini_response = gemini_ai.generate_content("Hello, Gemini!");
    println!("{}", gemini_response);
}

Logging

The library uses the log crate for logging and the env_logger crate to configure logging levels via an .env file. Create a .env file in the root of your project to specify the logging level:

RUST_LOG=info

Adding New Models

To add a new AI model, create a new module in the src directory and implement the necessary methods. Update src/lib.rs to export the new module.

For example, to add a new model called xyz:

1. Create a folder for the xyz model:

mkdir src/xyz

2. Create a xyz.rs file inside the xyz folder with proper api calls and test cases. Sample code:

use log::{info, debug, error};

pub struct xyzClient {
  api_key: String,
  model: String,
}

impl xyzClient {
  pub fn setup(api_key: &str) -> Self {
      info!("Setting up xyzClient with API key");
      xyzClient {
          api_key: api_key.to_string(),
          model: String::new(),
      }
  }

  pub fn model(mut self, model: &str) -> Self {
      info!("Setting model to {}", model);
      self.model = model.to_string();
      self
  }

  pub fn generate_content(&self, prompt: &str) -> String {
      info!("Generating content for prompt: '{}'", prompt);
      // Mock implementation of content generation
      let response = format!(
          "Generated content for prompt: '{}', using model: '{}'",
          prompt, self.model
      );
      debug!("Generated response: {}", response);
      response
  }
}

3. Update src/lib.rs to export the xyz module:

   pub mod gemini;
   pub mod xyz;
   
   pub use gemini::GeminiClient;
   pub use xyz::xyzClient;
   
   use dotenv::dotenv;
   use std::env;
   
   pub fn init_logging() {
       dotenv().ok();
       env_logger::init();
   }
   

testing

• Run example to ensure proper working:

cargo run --example file_name # for example main, ollama, gemini

• run build

cargo build --release

• run tests

cargo test

• get documentation

cargo doc --open --release

Contributing

Contributions are welcome! Please open an issue or submit a pull request.

License