use std::env;
use std::process::{Command, Stdio};
use std::io::{self, Write};

fn main() {
    let args: Vec<String> = env::args().collect();
    if args.len() < 2 {
        eprintln!("Usage: langchain_cli <prompt>");
        std::process::exit(1);
    }

    let prompt = &args[1];

    // Simulate a pipeline stage: e.g., tokenization, reasoning, response generation
    let stages = vec!["Tokenize", "Reason", "Generate"];

    for stage in &stages {
        println!("[Stage: {}] Processing...", stage);
    }

    // Example call to Python-based LangChain (assuming you have a script or API to call)
    // For placeholder purposes, we echo the prompt back.
    let output = Command::new("python3")
        .arg("-c")
        .arg(format!("print(\"LangChain Agent Response for: {}\")", prompt))
        .stdout(Stdio::piped())
        .spawn()
        .expect("failed to execute process")
        .wait_with_output()
        .expect("failed to wait on child");

    io::stdout().write_all(&output.stdout).unwrap();
}

/*
TODO (for future LangChain-style pipeline):
1. Implement trait-based agent components: Tokenizer, Retriever, Reasoner, Generator.
2. Allow config via YAML or TOML to define chain flow.
3. Async pipeline support with Tokio.
4. Optional integration with LLM APIs (OpenAI, Ollama, etc).
5. Rust-native vector search (e.g. using `tantivy`, `qdrant-client`).
*/
