llm_api_access

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

Current Status

This crate 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.

  async fn send_single_message(
        &self,
        message: &str,
        model: Option<&str>,
        config: Option<&LlmConfig>,
    ) -> Result<String, Box<dyn std::error::Error + Send + Sync>>;
  

• send_convo_message: Sends a list of messages as a conversation and returns the generated response.

  async fn send_convo_message(
        &self,
        messages: Vec<Message>,
        model: Option<&str>,
        config: Option<&LlmConfig>,
    ) -> Result<String, Box<dyn std::error::Error + Send + Sync>>;
  

• get_model_info: Gets information about a specific LLM model.

  async fn get_model_info(
        &self,
        model: &str,
    ) -> Result<ModelInfo, Box<dyn std::error::Error + Send + Sync>>;
  

• list_models: Lists all available LLM models.

  async fn list_models(&self)
        -> Result<Vec<ModelInfo>, Box<dyn std::error::Error + Send + Sync>>;
  

• count_tokens: Counts the number of tokens in a given text.

  async fn count_tokens(
        &self,
        text: &str,
        model: &str,
    ) -> Result<u32, Box<dyn std::error::Error + Send + Sync>>;
  

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.

LlmConfig

The LlmConfig struct allows you to configure provider-specific settings for the LLM calls. It uses a builder pattern for easy customization.

#[derive(Debug, Clone, Default)]
pub struct LlmConfig {
    pub temperature: Option<f64>,
    pub thinking_budget: Option<i32>,
    pub grounding_with_search: Option<bool>, // New: Enable grounding with Google Search for Gemini
    // Add other configuration options here
}

impl LlmConfig {
    pub fn new() -> Self {
        Self::default()
    }

    pub fn with_temperature(mut self, temperature: f64) -> Self {
        self.temperature = Some(temperature);
        self
    }

    pub fn with_thinking_budget(mut self, thinking_budget: i32) -> Self {
        self.thinking_budget = Some(thinking_budget);
        self
    }

    pub fn with_grounding_with_search(mut self, grounding_with_search: bool) -> Self {
        self.grounding_with_search = Some(grounding_with_search);
        self
    }
}

Grounding with Google Search (Gemini Only):

When grounding_with_search is set to true in the LlmConfig for Gemini models, the model can automatically use Google Search to access real-time web content. This helps increase factual accuracy, reduce hallucinations, and provide citations for its responses.

Example Usage:

use llm_api_access::config::LlmConfig;

// Default usage (no config)
let config = None;

// With thinking budget
let config = Some(LlmConfig::new().with_thinking_budget(1024));

// With multiple options
let config = Some(LlmConfig::new()
    .with_thinking_budget(2048)
    .with_temperature(0.7));

// With Google Search grounding enabled for Gemini
let config = Some(LlmConfig::new().with_grounding_with_search(true));

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};
use llm_api_access::config::LlmConfig; // Import LlmConfig

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

    // Send a single message to the LLM with no config
    let response = llm.send_single_message("Tell me a joke about programmers", None, None).await;

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

    //Send a single message to the LLM with a config
    let config = Some(LlmConfig::new().with_temperature(0.7));
    let response = llm.send_single_message("Tell me a joke about programmers", None, config.as_ref()).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;
use llm_api_access::config::LlmConfig; // Import LlmConfig

#[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 with no config
    let response = llm.send_convo_message(messages.clone(), None, None).await; // Clone messages for second use

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

    // Send the conversation messages to the LLM with a config (e.g., thinking budget)
    let config_budget = Some(LlmConfig::new().with_thinking_budget(2048));
    let response_budget = llm.send_convo_message(messages.clone(), None, config_budget.as_ref()).await; // Clone messages again

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

    // Send a conversation message to Gemini with Google Search grounding enabled
    let grounding_messages = vec![
        Message {
            role: "user".to_string(),
            content: "Who won the FIFA World Cup in 2022?".to_string(),
        },
    ];
    let config_grounding = Some(LlmConfig::new().with_grounding_with_search(true));
    let response_grounding = llm.send_convo_message(grounding_messages, None, config_grounding.as_ref()).await;

    match response_grounding {
        Ok(answer) => println!("\nAnswer with Grounding: {}", answer),
        Err(err) => eprintln!("\nError with Grounding: {}", 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. To run the tests, use:

cargo test