43 lines
1.4 KiB
Rust
43 lines
1.4 KiB
Rust
use std::env;
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use std::process::{Command, Stdio};
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use std::io::{self, Write};
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fn main() {
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let args: Vec<String> = env::args().collect();
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if args.len() < 2 {
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eprintln!("Usage: langchain_cli <prompt>");
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std::process::exit(1);
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}
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let prompt = &args[1];
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// Simulate a pipeline stage: e.g., tokenization, reasoning, response generation
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let stages = vec!["Tokenize", "Reason", "Generate"];
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for stage in &stages {
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println!("[Stage: {}] Processing...", stage);
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}
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// Example call to Python-based LangChain (assuming you have a script or API to call)
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// For placeholder purposes, we echo the prompt back.
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let output = Command::new("python3")
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.arg("-c")
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.arg(format!("print(\"LangChain Agent Response for: {}\")", prompt))
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.stdout(Stdio::piped())
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.spawn()
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.expect("failed to execute process")
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.wait_with_output()
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.expect("failed to wait on child");
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io::stdout().write_all(&output.stdout).unwrap();
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}
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/*
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TODO (for future LangChain-style pipeline):
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1. Implement trait-based agent components: Tokenizer, Retriever, Reasoner, Generator.
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2. Allow config via YAML or TOML to define chain flow.
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3. Async pipeline support with Tokio.
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4. Optional integration with LLM APIs (OpenAI, Ollama, etc).
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5. Rust-native vector search (e.g. using `tantivy`, `qdrant-client`).
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*/
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