openai_vision_example.rs

use std::fs;

// Import required modules from the LLM library for OpenAI integration
use llm::{
    builder::{LLMBackend, LLMBuilder}, // Builder pattern components
    chat::{ChatMessage, ImageMime},    // Chat-related structures
};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Get OpenAI API key from environment variable or use test key as fallback
    let api_key = std::env::var("OPENAI_API_KEY").unwrap_or("sk-TESTKEY".into());

    // Initialize and configure the LLM client
    let llm = LLMBuilder::new()
        .backend(LLMBackend::OpenAI) // Use OpenAI as the LLM provider
        .api_key(api_key) // Set the API key
        .model("gpt-4o") // Use GPT-3.5 Turbo model
        .max_tokens(1024) // Limit response length
        .temperature(0.7) // Control response randomness (0.0-1.0)
        .build()
        .expect("Failed to build LLM (OpenAI)");

    let content = fs::read("./examples/image001.jpg").expect("The image001.jpg file should exist");

    // Prepare conversation history with example messages
    let messages = vec![
        ChatMessage::user().image_url("https://media.istockphoto.com/id/1443562748/fr/photo/mignon-chat-gingembre.jpg?s=612x612&w=0&k=20&c=ygNVVnqLk9V8BWu4VQ0D21u7-daIyHUoyKlCcx3K1E8=").build(),
        ChatMessage::user().image(ImageMime::JPEG, content).build(),
        ChatMessage::user().content("What is in this image (image 1 and 2)?").build(),
    ];

    // Send chat request and handle the response
    match llm.chat(&messages).await {
        Ok(text) => println!("Chat response:\n{text}"),
        Err(e) => eprintln!("Chat error: {e}"),
    }

    Ok(())
}