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tmp/agent/src/main.rs

@@ -0,0 +1,42 @@
+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`).
+*/