Rodrigo Rodriguez (Pragmatismo)
ff89298e61
- Execute GET requests in a dedicated thread with its own Tokio runtime, add timeout handling and clearer error messages. - Tighten `is_safe_path` checks and simplify HTTP/S3 logic. - Change `llm_keyword` to accept `Arc<AppState>`, add prompt builder, run LLM generation in an isolated thread with timeout. - Update keyword registration call in `basic/mod.rs`. - Convert template script to use `let` declarations and return a boolean. - Introduce connection‑status indicator in the web UI with styles, automatic reconnection attempts, and proper WS/WSS handling for voice.
97 lines
3.4 KiB
Rust
97 lines
3.4 KiB
Rust
use crate::shared::models::UserSession;
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use crate::shared::state::AppState;
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use log::{error, info};
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use rhai::{Dynamic, Engine};
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use std::sync::Arc;
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use std::time::Duration;
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/// Registers the `LLM` keyword for Rhai scripts.
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/// Example usage inside Rhai:
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/// ```rhai
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/// result = LLM "Summarize the following text about AI:";
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/// ```
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pub fn llm_keyword(state: Arc<AppState>, _user: UserSession, engine: &mut Engine) {
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let state_clone = Arc::clone(&state);
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engine
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.register_custom_syntax(&["LLM", "$expr$"], false, move |context, inputs| {
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let text = context.eval_expression_tree(&inputs[0])?.to_string();
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info!("LLM processing text: {}", text);
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let state_for_thread = Arc::clone(&state_clone);
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let prompt = build_llm_prompt(&text);
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// ---- safe runtime isolation: no deadlocks possible ----
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let (tx, rx) = std::sync::mpsc::channel();
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std::thread::spawn(move || {
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let rt = tokio::runtime::Builder::new_multi_thread()
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.worker_threads(2)
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.enable_all()
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.build();
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let send_err = if let Ok(rt) = rt {
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let result = rt.block_on(async move {
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execute_llm_generation(state_for_thread, prompt).await
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});
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tx.send(result).err()
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} else {
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tx.send(Err("failed to build tokio runtime".into())).err()
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};
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if send_err.is_some() {
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error!("Failed to send LLM thread result");
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}
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});
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match rx.recv_timeout(Duration::from_secs(60)) {
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Ok(Ok(result)) => Ok(Dynamic::from(result)),
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Ok(Err(e)) => Err(Box::new(rhai::EvalAltResult::ErrorRuntime(
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e.to_string().into(),
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rhai::Position::NONE,
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))),
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Err(std::sync::mpsc::RecvTimeoutError::Timeout) => {
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Err(Box::new(rhai::EvalAltResult::ErrorRuntime(
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"LLM generation timed out".into(),
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rhai::Position::NONE,
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)))
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}
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Err(e) => Err(Box::new(rhai::EvalAltResult::ErrorRuntime(
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format!("LLM thread failed: {e}").into(),
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rhai::Position::NONE,
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))),
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}
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})
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.unwrap();
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}
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/// Builds a consistent LLM prompt used by all Rhai scripts.
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/// You can change the style/structure here to guide the model's behavior.
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fn build_llm_prompt(user_text: &str) -> String {
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format!(
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"You are a AI assistant in form of KEYWORD called LLM
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running inside a General Bots BASIC environment.
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Task: Process and respond concisely to the following call to x = LLM 'prompt' syntax.
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---
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User Input:
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{}
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---
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Respond clearly and accurately in the same language as the input.",
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user_text.trim()
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)
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}
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/// Runs the async LLM provider call safely.
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pub async fn execute_llm_generation(
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state: Arc<AppState>,
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prompt: String,
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) -> Result<String, Box<dyn std::error::Error + Send + Sync>> {
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info!("Starting LLM generation for prompt: '{}'", prompt);
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state
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.llm_provider
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.generate(&prompt, &serde_json::Value::Null)
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.await
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.map_err(|e| format!("LLM call failed: {}", e).into())
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}
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