Lib.rs

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

Current Status

Currently I develop this because it is used to power an open-source coding assistant currently in active development. Gemini has been the main test target; OpenAI (including embeddings) and Anthropic are supported but have been exercised less. Development is self encouraged so updates can be far and few between, open an issue on github if you want something specific.

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_api_access::llm::{Access, LLM};

#[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_api_access::llm::{Access, LLM};
use llm_api_access::structs::general::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.

Embeddings

The crate provides support for generating text embeddings through the OpenAI API.

OpenAI Embeddings

The openai module includes functionality to generate vector embeddings:

pub async fn get_embedding(
    input: String,
    dimensions: Option<u32>,
) -> Result<Vec<f32>, Box<dyn std::error::Error + Send + Sync>>

This function takes:

• input: The text to generate embeddings for
• dimensions: Optional parameter to specify the number of dimensions (if omitted, uses the model default)

It returns a vector of floating point values representing the text embedding.

Example Usage:

use llm_api_access::openai::get_embedding;

#[tokio::main]
async fn main() {
    // Generate an embedding with default dimensions
    match get_embedding("This is a sample text for embedding".to_string(), None).await {
        Ok(embedding) => {
            println!("Generated embedding with {} dimensions", embedding.len());
            // Use embedding for semantic search, clustering, etc.
        },
        Err(err) => eprintln!("Error generating embedding: {}", err),
    }
    
    // Generate an embedding with custom dimensions
    match get_embedding("Custom dimension embedding".to_string(), Some(64)).await {
        Ok(embedding) => {
            println!("Generated custom embedding with {} dimensions", embedding.len());
            assert_eq!(embedding.len(), 64);
        },
        Err(err) => eprintln!("Error generating embedding: {}", err),
    }
}

The function uses the "text-embedding-3-small" model by default and requires the same environment variables as other OpenAI API calls (OPEN_AI_KEY and OPEN_AI_ORG).

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.