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// Import required modules from the LLM library for Google Gemini integration
use llm::{
builder::{LLMBackend, LLMBuilder},
chat::{ChatMessage, StructuredOutputFormat},
};
#[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());
// Define a simple JSON schema for structured output
let schema = r#"
{
"name": "student",
"schema": {
"type": "object",
"properties": {
"name": {
"type": "string"
},
"age": {
"type": "integer"
},
"is_student": {
"type": "boolean"
}
},
"required": ["name", "age", "is_student"]
}
}
"#;
let schema: StructuredOutputFormat = serde_json::from_str(schema)?;
// 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)
// Optional: Set system prompt
.system("You are a helpful AI assistant. Please generate a random student using the provided JSON schema.")
// Set JSON schema for structured output
.schema(schema)
.build()
.expect("Failed to build LLM (Google)");
// Prepare conversation history with example messages
let messages = vec![ChatMessage::user()
.content("Please generate a random student using the provided JSON schema.")
.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(())
}