llm-api-access 0.1.20

A package to query popular LLMs

LLM API Access

The llm_api_access library (available as a Rust crate and Python package) provides a unified way to interact with different large language models (LLMs) like OpenAI, Gemini, and Anthropic. It aims to simplify the process of sending messages, managing conversations, and generating embeddings across various LLM providers.

Key Features

• Unified LLM Access: Interact with OpenAI, Gemini, and Anthropic through a consistent interface.
• Conversation Management: Easily send single messages or manage multi-turn conversations.
• Embeddings Generation: Generate text embeddings using OpenAI.
• Secure Credential Loading: Utilizes dotenv to load API keys securely from a .env file.

Installation

Rust

Add llm_api_access to your Cargo.toml:

[dependencies]
llm_api_access = "0.1.0" # Use the latest version
tokio = { version = "1", features = ["full"] } # Required for async operations

Python

Install from PyPI:

pip install llm-api-access

Loading API Credentials with dotenv

The llm_api_access library 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 .env 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 project directory.
2. Add API Keys: Fill in the .env file with the format shown above, 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.

Rust Usage

The llm_api_access crate provides the LLM enum and the Access trait for interacting with LLMs.

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.

send_single_message Example (Rust)

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),
    }
}

send_convo_message Example (Rust)

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),
    }
}

Python Usage

The Python package exposes two main asynchronous functions: call_llm for interacting with LLMs and get_embedding for generating OpenAI embeddings.

call_llm Function (Python)

async def call_llm(llm_type: str, messages: list[dict]) -> str

This function takes:

• llm_type: A string representing the LLM provider ("openai", "gemini", or "anthropic").
• messages: A list of dictionaries, where each dictionary represents a message with "role" (e.g., "user", "model", "system") and "content".

It returns a string containing the LLM's response.

send_single_message Example (Python)

import asyncio
from llm_api_access import call_llm

async def main():
    # Send a single message to OpenAI
    messages = [{"role": "user", "content": "Hello, tell me a joke."}]
    response = await call_llm("openai", messages)
    print(f"OpenAI Joke: {response}")

if __name__ == "__main__":
    asyncio.run(main())

send_convo_message Example (Python)

import asyncio
from llm_api_access import call_llm

async def main():
    # Send a conversation to Gemini
    messages = [
        {"role": "user", "content": "Write the first line of a story."},
        {"role": "model", "content": "Once upon a time..."},
        {"role": "user", "content": "Continue the story in 1600s France."},
    ]
    response = await call_llm("gemini", messages)
    print(f"Gemini Story Continuation: {response}")

if __name__ == "__main__":
    asyncio.run(main())

Embeddings

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

OpenAI Embeddings (Rust)

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 (Rust):

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

get_embedding Function (Python)

async def get_embedding(input: str, dimensions: int | None) -> list[float]

This function takes:

• input: The text to generate embeddings for.
• dimensions: An optional integer parameter to specify the number of dimensions (if None, uses the model default).

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

Example Usage (Python):

import asyncio
from llm_api_access import get_embedding

async def main():
    # Generate an embedding with default dimensions
    try:
        embedding = await get_embedding("This is a test sentence.", None)
        print(f"Generated embedding with {len(embedding)} dimensions")
        print(f"Embedding snippet: {embedding[:5]}...") # Print first 5 elements
    except Exception as e:
        print(f"Error generating embedding: {e}")

    # Generate an embedding with custom dimensions
    try:
        embedding_with_dims = await get_embedding("Another test sentence.", 64)
        print(f"Generated custom embedding with {len(embedding_with_dims)} dimensions")
        assert len(embedding_with_dims) == 64
    except Exception as e:
        print(f"Error generating custom embedding: {e}")

if __name__ == "__main__":
    asyncio.run(main())

Testing

The llm_api_access library includes unit tests that showcase usage and expected behavior with different LLM providers and the embedding functionality.