The llm_api_access crate provides a unified way to interact with different large language models (LLMs) like OpenAI, Gemini, and Anthropic.

LLM Enum

This enum represents the supported LLM providers:

• OpenAI: Represents the OpenAI language model.
• Gemini: Represents the Gemini language model.
• Anthropic: Represents the Anthropic language model.

Access Trait

The Access trait defines asynchronous methods for interacting with LLMs:

• send_single_message: Sends a single message and returns the generated response.
• send_convo_message: Sends a list of messages as a conversation and returns the generated response.
• get_model_info: Gets information about a specific LLM model.
• list_models: Lists all available LLM models.
• count_tokens: Counts the number of tokens in a given text.

The LLM enum implements Access, providing specific implementations for each method based on the chosen LLM provider.

Note: Currently, get_model_info, list_models, and count_tokens only work for the Gemini LLM. Other providers return an error indicating this functionality is not yet supported.

Loading API Credentials with dotenv

The llm_api_access crate uses the dotenv library to securely load API credentials from a .env file in your project's root directory. This file should contain key-value pairs for each LLM provider you want to use.

Example Structure:

OPEN_AI_ORG=your_openai_org
OPENAI_API_KEY=your_openai_api_key
GEMINI_API_KEY=your_gemini_api_key
ANTHROPIC_API_KEY=your_anthropic_api_key

Steps:

1. Create .env File: Create a file named .env at the root of your Rust project directory.
2. Add API Keys: Fill in the .env file with the following format, replacing placeholders with your actual API keys.

Important Note:

• Never commit your .env file to version control systems like Git. It contains sensitive information like API keys.

Example Usage

send_single_message Example

use llm::{LLM, Access};

#[tokio::main]
async fn main() {
    // Create an instance of the OpenAI LLM
    let llm = LLM::OpenAI;

    // Send a single message to the LLM
    let response = llm.send_single_message("Tell me a joke about programmers").await;

    match response {
        Ok(joke) => println!("Joke: {}", joke),
        Err(err) => eprintln!("Error: {}", err),
    }
}

This example creates an instance of the LLM::OpenAI provider and sends a single message using the send_single_message method. It then matches the result, printing the generated joke or an error message if an error occurred.

send_convo_message Example

use llm::{LLM, Access, Message};

#[tokio::main]
async fn main() {
    // Create an instance of the Gemini LLM
    let llm = LLM::Gemini;

    // Define the conversation messages
    let messages = vec![
        Message {
            role: "user".to_string(),
            content: "You are a helpful coding assistant.".to_string(),
        },
        Message {
            role: "model".to_string(),
            content: "You got it! I am ready to assist!".to_string(),
        },
        Message {
            role: "user".to_string(),
            content: "Generate a rust function that reverses a string.".to_string(),
        },
    ];

    // Send the conversation messages to the LLM
    let response = llm.send_convo_message(messages).await;

    match response {
        Ok(code) => println!("Code: {}", code),
        Err(err) => eprintln!("Error: {}", err),
    }
}

Note: This example requires API keys and configuration for the Gemini LLM provider.

Testing

The llm_api_access crate includes unit tests for various methods in the Access trait. These tests showcase usage and expected behavior with different LLM providers.