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use std::fs;
// Import required modules from the LLM library for Google Gemini integration
use mirror::{
builder::{LLMBackend, LLMBuilder}, // Builder pattern components
chat::ChatMessage, // Chat-related structures
};
#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
// Get Google API key from environment variable or use test key as fallback
let api_key = std::env::var("GOOGLE_API_KEY").unwrap_or("google-key".into());
// Initialize and configure the LLM client
let llm = LLMBuilder::new()
.backend(LLMBackend::Google) // Use Google as the LLM provider
.api_key(api_key) // Set the API key
.model("gemini-2.0-flash-exp") // Use Gemini Pro model
.max_tokens(8512) // Limit response length
.temperature(0.7) // Control response randomness (0.0-1.0)
.stream(false) // Disable streaming responses
// Optional: Set system prompt
.system("You are a helpful AI assistant specialized in programming.")
.build()
.expect("Failed to build LLM (Google)");
let content = fs::read("./examples/dummy.pdf").expect("The dummy.pdf file should exist");
// Prepare conversation history with example messages
let messages = vec![
ChatMessage::user()
.content("Explain what is in the PDF")
.build(),
ChatMessage::user().pdf(content).build(),
];
// Send chat request and handle the response
match llm.chat(&messages).await {
Ok(text) => println!("Google Gemini response:\n{}", text),
Err(e) => eprintln!("Chat error: {}", e),
}
Ok(())
}