use std::env; use std::process::{Command, Stdio}; use std::io::{self, Write}; fn main() { let args: Vec = env::args().collect(); if args.len() < 2 { eprintln!("Usage: langchain_cli "); 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`). */