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//! Example demonstrating a multi-step prompt chain for exploring programming language features
//!
//! This example shows how to:
//! 1. Select a programming language topic
//! 2. Get advanced features for that language
//! 3. Generate a code example for one feature
//! 4. Get a detailed explanation of the example
use llm::{
builder::{LLMBackend, LLMBuilder},
chain::{ChainStepBuilder, ChainStepMode, PromptChain},
};
#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
// Initialize the LLM with OpenAI backend and configuration
let llm = LLMBuilder::new()
.backend(LLMBackend::OpenAI)
.api_key(std::env::var("OPENAI_API_KEY").unwrap_or("sk-TESTKEY".into()))
.model("gpt-4o")
.max_tokens(200)
.temperature(0.7)
.build()?;
// Create and execute a 4-step prompt chain
let chain_result = PromptChain::new(&*llm)
// Step 1: Choose a programming language topic
.step(
ChainStepBuilder::new("topic", "Suggest an interesting technical topic to explore among: Rust, Python, JavaScript, Go. Answer with a single word only.", ChainStepMode::Chat)
.temperature(0.8) // Higher temperature for more variety in topic selection
.build()
)
// Step 2: Get advanced features for the chosen language
.step(
ChainStepBuilder::new("features", "List 3 advanced features of {{topic}} that few developers know about. Format: one feature per line.", ChainStepMode::Chat)
.build()
)
// Step 3: Generate a code example for one feature
.step(
ChainStepBuilder::new("example", "Choose one of the features listed in {{features}} and show a commented code example that illustrates it.", ChainStepMode::Chat)
.build()
)
// Step 4: Get detailed explanation of the code example
.step(
ChainStepBuilder::new("explanation", "Explain in detail how the code example {{example}} works and why this feature is useful.", ChainStepMode::Chat)
.max_tokens(500) // Allow longer response for detailed explanation
.build()
)
.run().await?;
// Display the results from all chain steps
println!("Chain results: {chain_result:?}");
Ok(())
}