some fixes + gliner first implementation

.gitignore

@@ -50,3 +50,6 @@ tree.txt
 
 # Ignore packaged crates
 *.crate
+
+# Ignore AI models
+/resources/models/*

Cargo.lock

@@ -3305,12 +3305,15 @@ dependencies = [
  "kira",
  "log",
  "mlua",
+ "ndarray",
  "nnnoiseless",
  "once_cell",
+ "ort",
  "parking_lot",
  "platform-dirs",
  "pv_recorder",
  "rand 0.8.5",
+ "regex",
  "reqwest 0.13.1",
  "rodio",
  "rustpotter",
@@ -3320,6 +3323,7 @@ dependencies = [
  "serde_yaml",
  "sha2",
  "tempfile",
+ "tokenizers",
  "tokio",
  "tokio-tungstenite",
  "toml 0.9.11+spec-1.1.0",

Cargo.toml

@@ -45,4 +45,10 @@ mlua = { version = "0.11.5", features = ["lua55", "vendored", "async", "serde"]
 reqwest = { version = "0.13.1", features = ["blocking", "json"] }
 tempfile = "^3.24"
 winrt-notification = "0.5"
-fastembed = { version = "^5.8.1", default-features = false, features = ["ort-download-binaries"] }
+
+fastembed = { version = "^5.8.1", default-features = false, features = ["ort-download-binaries"] }
+ort = { version = "=2.0.0-rc.11" }
+ndarray = "0.17"
+tokenizers = { version = "0.22", default-features = false }
+regex = "1"
+

crates/jarvis-app/src/app.rs

@@ -1,7 +1,7 @@
 use std::sync::mpsc::Receiver;
 use std::time::SystemTime;
 
-use jarvis_core::{audio_buffer::AudioRingBuffer, audio_processing, commands, config, listener, recorder, stt, COMMANDS_LIST, intent, voices, ipc::{self, IpcEvent}, i18n};
+use jarvis_core::{audio_buffer::AudioRingBuffer, audio_processing, commands, config, listener, recorder, stt, COMMANDS_LIST, intent, voices, ipc::{self, IpcEvent}, i18n, slots};
 use rand::seq::SliceRandom;
 
 use crate::should_stop;
@@ -23,8 +23,8 @@ fn main_loop(text_cmd_rx: Receiver<String>) -> Result<(), ()> {
     let sample_rate: usize = 16000;
     let mut frame_buffer: Vec<i16> = vec![0; frame_length];
     
-    // ring buffer: keeps last 2 seconds of audio (pre-roll)
+    // ring buffer: keeps last 5 seconds of audio (pre-roll)
-    let mut audio_buffer = AudioRingBuffer::new(2.0, frame_length, sample_rate);
+    let mut audio_buffer = AudioRingBuffer::new(5.0, frame_length, sample_rate);
 
     // VAD state
     let mut vad_state = VadState::WaitingForVoice;
@@ -162,8 +162,8 @@ fn recognize_command(
     let mut first_recognition = prefed_audio;
     
     // longer silence threshold for commands (user might pause to think)
-    // 2 seconds
+    // 5 seconds
-    let silence_threshold: u32 = ((2.0 * sample_rate as f32) / frame_length as f32) as u32;
+    let silence_threshold: u32 = ((5.0 * sample_rate as f32) / frame_length as f32) as u32;
     
     loop {
         if crate::should_stop() {
@@ -262,6 +262,11 @@ fn recognize_command(
 
                     recognized_voice = recognized_voice.trim().to_string();
                     
+                    if recognized_voice.len() < 5 {
+                        debug!("Ignoring too short recognition: '{}'", recognized_voice);
+                        continue;
+                    }
+
                     if recognized_voice.is_empty() {
                         continue;
                     }
@@ -360,7 +365,18 @@ fn execute_command(text: &str, rt: &tokio::runtime::Runtime) -> bool {
     if let Some((cmd_path, cmd_config)) = cmd_result {
         info!("Command found: {:?}", cmd_path);
         
-        match commands::execute_command(&cmd_path, &cmd_config, Some(&text)) {
+        // extract slots if needed
+        let extracted_slots = if !cmd_config.slots.is_empty() {
+            let s = slots::extract(text, &cmd_config.slots);
+            if !s.is_empty() {
+                info!("Extracted slots: {:?}", s);
+            }
+            Some(s)
+        } else {
+            None
+        };
+
+        match commands::execute_command(&cmd_path, &cmd_config, Some(&text), extracted_slots.as_ref()) {
             Ok(chain) => {
                 info!("Command executed successfully");
                 // voices::play_ok();

crates/jarvis-app/src/main.rs

@@ -1,3 +1,4 @@
+use jarvis_core::slots;
 use parking_lot::RwLock;
 use std::sync::Arc;
 use std::sync::atomic::{AtomicBool, Ordering};
@@ -105,6 +106,9 @@ fn main() -> Result<(), String> {
         }
     });
 
+    // init slots parsing engine
+    slots::init().map_err(|e| error!("Slot extraction init failed: {}", e)).ok();
+
     // init audio processing
     info!("Initializing audio processing...");
     if let Err(e) = audio_processing::init() {

crates/jarvis-core/Cargo.toml

@@ -43,13 +43,21 @@ reqwest = { workspace = true, optional = true }
 tempfile.workspace = true
 
 fastembed = { workspace = true, optional = true }
+ort = { workspace = true, optional = true }
+ndarray = { workspace = true, optional = true }
+tokenizers = { workspace = true, optional = true }
+regex = { workspace = true, optional = true }
 
 [target.'cfg(windows)'.dependencies]
 winrt-notification = { workspace = true, optional = true }
 
 [features]
 default = ["jarvis_app"]
-jarvis_app = ["vosk", "intent-classifier", "fastembed", "tokio", "nnnoiseless", "tokio-tungstenite", "futures-util", "lua"]
+jarvis_app = [
+    "vosk", "intent-classifier", "fastembed", "tokio", "nnnoiseless", "tokio-tungstenite", "futures-util",
+    "lua",
+    "ort", "ndarray", "tokenizers", "regex",]
+
 intent = ["intent-classifier", "tokio"]
 lua = ["mlua", "reqwest", "winrt-notification"]
 lua_only = ["lua", "tokio"]

crates/jarvis-core/src/audio_processing/noise_suppression/nnnoiseless.rs

@@ -45,7 +45,9 @@ impl NnnoiselessNS {
     }
 
     pub fn reset(&mut self) {
-        self.state = DenoiseState::new();
+        // self.state = DenoiseState::new();
+        // self.buffer.clear();
+
         self.buffer.clear();
     }
 }

crates/jarvis-core/src/commands.rs

@@ -1,3 +1,4 @@
+use std::collections::HashMap;
 use std::path::{Path, PathBuf};
 use std::fs;
 use std::time::Duration;
@@ -191,7 +192,8 @@ pub fn execute_cli(cmd: &str, args: &[String]) -> std::io::Result<Child> {
     }
 }
 
-pub fn execute_command(cmd_path: &PathBuf, cmd_config: &JCommand, phrase: Option<&str>) -> Result<bool, String> {
+pub fn execute_command(cmd_path: &PathBuf, cmd_config: &JCommand, phrase: Option<&str>, slots: Option<&HashMap<String, SlotValue>>) -> Result<bool, String> {
+    // execute command by the type
     match cmd_config.cmd_type.as_str() {
 
         // BRUH
@@ -200,7 +202,7 @@ pub fn execute_command(cmd_path: &PathBuf, cmd_config: &JCommand, phrase: Option
         // LUA command
         #[cfg(feature = "lua")]
         "lua" => {
-            execute_lua_command(cmd_path, cmd_config, phrase)
+            execute_lua_command(cmd_path, cmd_config, phrase, slots)
         }
 
         // AutoHotkey command
@@ -254,8 +256,10 @@ fn execute_lua_command(
     cmd_path: &PathBuf,
     cmd_config: &JCommand,
     phrase: Option<&str>,
+    slots: Option<&HashMap<String, SlotValue>>
 ) -> Result<bool, String> {
     // get script path
+
     let script_name = if cmd_config.script.is_empty() {
         "script.lua"
     } else {
@@ -270,13 +274,14 @@ fn execute_lua_command(
     
     // parse sandbox level
     let sandbox = SandboxLevel::from_str(&cmd_config.sandbox);
-    
+
     // create context
     let context = CommandContext {
         phrase: phrase.unwrap_or("").to_string(),
         command_id: cmd_config.id.clone(),
         command_path: cmd_path.clone(),
         language: i18n::get_language(),
+        slots: slots.map(|s| s.clone()),
     };
     
     // get timeout

crates/jarvis-core/src/commands/structs.rs

@@ -59,6 +59,9 @@ pub struct JCommand {
     #[serde(default)]
     pub phrases: HashMap<String, Vec<String>>,
 
+    // Slot definitions: slot_name -> how to extract it
+    #[serde(default)]
+    pub slots: HashMap<String, SlotDefinition>,
 
     // CACHE
     #[serde(skip, default)]
@@ -90,6 +93,8 @@ impl Clone for JCommand {
             sounds: self.sounds.clone(),
             phrases: self.phrases.clone(),
 
+            slots: self.slots.clone(),
+
             // empty caches for cloned instance
             sounds_cache: RwLock::new(HashMap::new()),
             phrases_cache: RwLock::new(HashMap::new()),
@@ -150,4 +155,25 @@ impl JCommand {
         // fallback to first available
         map.values().next().cloned().unwrap_or_default()
     }
+}
+
+#[derive(Serialize, Deserialize, Debug, Clone)]
+pub struct SlotDefinition {
+    // Entity label for GLiNER (e.g. "city name", "song title", "number")
+    // This is a free-form description - GLiNER matches it semantically
+    #[serde(default)]
+    pub entity: String,
+
+    // Optional: fallback context words for template-based extraction
+    // e.g. ["in", "for", "at"] for a city slot
+    #[serde(default)]
+    pub context: Vec<String>,
+}
+
+// Extracted slot value passed to commands
+#[derive(Debug, Clone, Serialize)]
+#[serde(untagged)]
+pub enum SlotValue {
+    Text(String),
+    Number(f64),
 }

crates/jarvis-core/src/config.rs

@@ -18,6 +18,7 @@ use rustpotter::{
 };
 
 use crate::IntentRecognitionEngine;
+use crate::SlotExtractionEngine;
 use crate::config::structs::NoiseSuppressionBackend;
 use crate::config::structs::VadBackend;
 use crate::{APP_CONFIG_DIR, APP_DIRS, APP_LOG_DIR};
@@ -156,6 +157,9 @@ pub const VOSK_SPEECH_PARTIAL_WORDS: bool = false;
 // IRE (intents recognition)
 pub const INTENT_CLASSIFIER_MIN_CONFIDENCE: f64 = 0.75;
 
+// SLOTS EXTRACTION
+pub const DEFAULT_SLOT_EXTRACTION_ENGINE: SlotExtractionEngine = SlotExtractionEngine::None;
+
 // embedding classifier
 pub const EMBEDDING_MIN_CONFIDENCE: f64 = 0.70;
 

crates/jarvis-core/src/config/structs.rs

@@ -45,6 +45,11 @@ pub enum AudioType {
     Kira,
 }
 
+#[derive(Clone, Copy, Serialize, Deserialize, Debug, PartialEq)]
+pub enum SlotExtractionEngine {
+    None,
+    GLiNER,
+}
 
 
 impl fmt::Display for WakeWordEngine {
@@ -77,6 +82,12 @@ impl fmt::Display for VadBackend {
     }
 }
 
+impl fmt::Display for SlotExtractionEngine {
+    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
+        write!(f, "{:?}", self)
+    }
+}
+
 // pub enum TextToSpeechEngine {}
 
 // pub enum IntentRecognitionEngine {}

crates/jarvis-core/src/db/structs.rs

@@ -6,6 +6,7 @@ use crate::config::structs::WakeWordEngine;
 use crate::config::structs::IntentRecognitionEngine;
 use crate::config::structs::NoiseSuppressionBackend;
 use crate::config::structs::VadBackend;
+use crate::config::structs::SlotExtractionEngine;
 
 #[derive(Serialize, Deserialize, Debug, Clone)]
 pub struct Settings {
@@ -14,6 +15,10 @@ pub struct Settings {
 
     pub wake_word_engine: WakeWordEngine,
     pub intent_recognition_engine: IntentRecognitionEngine,
+
+    pub slot_extraction_engine: SlotExtractionEngine,
+    pub gliner_model: String,
+
     pub speech_to_text_engine: SpeechToTextEngine,
     pub vosk_model: String,
 
@@ -35,6 +40,8 @@ impl Default for Settings {
 
             wake_word_engine: config::DEFAULT_WAKE_WORD_ENGINE,
             intent_recognition_engine: config::DEFAULT_INTENT_RECOGNITION_ENGINE,
+            slot_extraction_engine: SlotExtractionEngine::None,
+            gliner_model: String::new(),
             speech_to_text_engine: config::DEFAULT_SPEECH_TO_TEXT_ENGINE,
             vosk_model: String::from(""), // auto detect first available
 

crates/jarvis-core/src/gliner_models.rs

@@ -0,0 +1,130 @@
+use std::collections::HashMap;
+use std::fs;
+use crate::APP_DIR;
+
+const GLINER_DIRS: &[&str] = &["gliner_small-v2.1", "gliner_multi-v2.1"];
+
+#[derive(Debug, Clone)]
+pub struct GlinerModelVariant {
+    // type id stored in settings, e.g. "int8", "fp16", "full"
+    pub value: String,
+    // shown in dropdown, e.g. "int8 (174MB / 332MB)"
+    pub display_name: String,
+}
+
+// scan both model dirs and return a deduplicated list of model types
+pub fn scan_gliner_variants() -> Vec<GlinerModelVariant> {
+    let base = APP_DIR.join("resources").join("models");
+
+    // collect: type -> { dir_name -> size_mb }
+    let mut types: HashMap<String, HashMap<String, u64>> = HashMap::new();
+
+    for dir_name in GLINER_DIRS {
+        let onnx_dir = base.join(dir_name).join("onnx");
+        if !onnx_dir.exists() { continue; }
+
+        let entries = match fs::read_dir(&onnx_dir) {
+            Ok(e) => e,
+            Err(_) => continue,
+        };
+
+        for entry in entries.flatten() {
+            let path = entry.path();
+            let file_name = match path.file_name().and_then(|n| n.to_str()) {
+                Some(n) if n.ends_with(".onnx") => n.to_string(),
+                _ => continue,
+            };
+
+            let variant_type = file_name_to_type(&file_name);
+            let size_mb = fs::metadata(&path).map(|m| m.len()).unwrap_or(0) / (1024 * 1024);
+
+            types.entry(variant_type)
+                .or_default()
+                .insert(dir_name.to_string(), size_mb);
+        }
+    }
+
+    let mut result: Vec<GlinerModelVariant> = types.into_iter().map(|(variant, sizes)| {
+        let size_str = build_size_string(&sizes);
+        let label = if variant == "full" { "Full".to_string() } else { variant.clone() };
+        GlinerModelVariant {
+            display_name: format!("{} ({})", label, size_str),
+            value: variant,
+        }
+    }).collect();
+
+    // sort: full first, then alphabetically
+    result.sort_by(|a, b| {
+        let a_full = a.value == "full";
+        let b_full = b.value == "full";
+        b_full.cmp(&a_full).then_with(|| a.value.cmp(&b.value))
+    });
+
+    result
+}
+
+// "model.onnx" -> "full", "model_int8.onnx" -> "int8"
+fn file_name_to_type(name: &str) -> String {
+    let stem = name.strip_suffix(".onnx").unwrap_or(name);
+    if stem == "model" {
+        "full".to_string()
+    } else if let Some(variant) = stem.strip_prefix("model_") {
+        variant.to_string()
+    } else {
+        stem.to_string()
+    }
+}
+
+// build size display: "174MB" if only one dir, "small: 174MB / multi: 332MB" if both
+fn build_size_string(sizes: &HashMap<String, u64>) -> String {
+    if sizes.len() == 1 {
+        let (dir, mb) = sizes.iter().next().unwrap();
+        let short = short_dir_name(dir);
+        return format!("{}: {}MB", short, mb);
+    }
+
+    let mut parts: Vec<String> = Vec::new();
+    for dir_name in GLINER_DIRS {
+        if let Some(mb) = sizes.get(*dir_name) {
+            parts.push(format!("{}: {}MB", short_dir_name(dir_name), mb));
+        }
+    }
+    parts.join(" / ")
+}
+
+fn short_dir_name(dir: &str) -> &str {
+    if dir.contains("small") { "small" }
+    else if dir.contains("multi") { "multi" }
+    else { dir }
+}
+
+// resolve variant type + language into actual file path
+// returns (model_dir_path, onnx_file_name) or None
+pub fn resolve_model(variant: &str, language: &str) -> Option<(std::path::PathBuf, String)> {
+    let base = APP_DIR.join("resources").join("models");
+    let file_name = type_to_file_name(variant);
+
+    // pick dir based on language
+    let preferred: &[&str] = match language {
+        "en" => &["gliner_small-v2.1", "gliner_multi-v2.1"],
+        _ => &["gliner_multi-v2.1", "gliner_small-v2.1"],
+    };
+
+    for dir_name in preferred {
+        let path = base.join(dir_name).join("onnx").join(&file_name);
+        if path.exists() {
+            return Some((base.join(dir_name), file_name));
+        }
+    }
+
+    None
+}
+
+// "full" -> "model.onnx", "int8" -> "model_int8.onnx"
+fn type_to_file_name(variant: &str) -> String {
+    if variant == "full" || variant.is_empty() {
+        "model.onnx".to_string()
+    } else {
+        format!("model_{}.onnx", variant)
+    }
+}

crates/jarvis-core/src/i18n/locales/en.ftl

@@ -122,6 +122,15 @@ notification-error = Error
 notification-assistant-started = Assistant started
 notification-assistant-stopped = Assistant stopped
 
+# SLOTS EXTRACTION
+settings-slot-engine = Slot extraction
+settings-slot-engine-desc = Extract parameters from voice commands (e.g. city name, number).
+settings-gliner-model = GLiNER ONNX model
+settings-gliner-model-desc =
+    Select model variant.
+    Smaller quantized models (int8, uint8) are faster but less accurate.
+settings-gliner-models-hint = No GLiNER models found.
+
 # ETC
 search-error-not-running = Assistant is not running
 search-error-failed = Failed to execute command

crates/jarvis-core/src/i18n/locales/ru.ftl

@@ -122,6 +122,15 @@ notification-error = Ошибка
 notification-assistant-started = Ассистент запущен
 notification-assistant-stopped = Ассистент остановлен
 
+# SLOTS EXTRACTION
+settings-slot-engine = Извлечение параметров
+settings-slot-engine-desc = Извлекает параметры из голосовых команд (напр. название города, число).
+settings-gliner-model = Модель GLiNER ONNX
+settings-gliner-model-desc =
+    Выберите вариант модели.
+    Квантизированные модели (int8, uint8) быстрее, но менее точны.
+settings-gliner-models-hint = Модели GLiNER не найдены.
+
 # ETC
 search-error-not-running = Ассистент не запущен
 search-error-failed = Не удалось выполнить команду

crates/jarvis-core/src/i18n/locales/ua.ftl

@@ -122,8 +122,16 @@ notification-error = Помилка
 notification-assistant-started = Асистент запущено
 notification-assistant-stopped = Асистент зупинено
 
-# ETC
+# SLOTS EXTRACTION
+settings-slot-engine = Витяг параметрів
+settings-slot-engine-desc = Витягує параметри з голосових команд (напр. назва міста, число).
+settings-gliner-model = Модель GLiNER ONNX
+settings-gliner-model-desc = 
+    Оберіть варіант моделі.
+    Квантизовані моделі (int8, uint8) швидші, але менш точні.
+settings-gliner-models-hint = Моделі GLiNER не знайдено.
 
+# ETC
 search-error-not-running = Асистент не запущено
 search-error-failed = Не вдалося виконати команду
 settings-no-voices = Голоси не знайдено

crates/jarvis-core/src/intent/embeddingclassifier.rs

@@ -40,10 +40,12 @@ pub fn init(commands: &[JCommandsList]) -> Result<(), String> {
     match i18n::get_language().as_str() {
         "en" => {
             // smaller model for English
+            info!("Loading all-MiniLM-L6-v2 ...");
             model_dir = APP_DIR.join("resources").join("models").join("all-MiniLM-L6-v2");
         },
         _ => {
             // bigger model for any other languages (multilingual)
+            info!("Loading paraphrase-multilingual-MiniLM-L12-v2-onnx-Q ...");
             model_dir = APP_DIR.join("resources").join("models").join("paraphrase-multilingual-MiniLM-L12-v2-onnx-Q");
         }
     }

crates/jarvis-core/src/lib.rs

@@ -26,7 +26,11 @@ pub mod stt;
 #[cfg(feature = "intent")]
 pub mod intent;
 
+#[cfg(feature = "jarvis_app")]
+pub mod slots;
+
 pub mod vosk_models;
+pub mod gliner_models;
 
 #[cfg(feature = "jarvis_app")]
 pub mod audio_processing;

crates/jarvis-core/src/lua/api/context.rs

@@ -3,6 +3,8 @@
 use mlua::{Lua, Table};
 use crate::lua::{CommandContext};
 
+use crate::commands::SlotValue;
+
 pub fn register(lua: &Lua, jarvis: &Table, ctx: &CommandContext) -> mlua::Result<()> {
     let context = lua.create_table()?;
     
@@ -25,6 +27,18 @@ pub fn register(lua: &Lua, jarvis: &Table, ctx: &CommandContext) -> mlua::Result
     time.set("timestamp", now.timestamp())?;
     context.set("time", time)?;
     
+    // slots
+    let slots_table = lua.create_table()?;
+    if let Some(ref slots) = ctx.slots {
+        for (name, value) in slots {
+            match value {
+                SlotValue::Text(t) => slots_table.set(name.as_str(), t.as_str())?,
+                SlotValue::Number(n) => slots_table.set(name.as_str(), *n)?,
+            }
+        }
+    }
+    context.set("slots", slots_table)?;
+
     jarvis.set("context", context)?;
     
     Ok(())

crates/jarvis-core/src/lua/structs.rs

@@ -1,4 +1,6 @@
-use std::path::PathBuf;
+use std::{collections::HashMap, path::PathBuf};
+
+use crate::commands::SlotValue;
 
 // Context passed to Lua scripts
 #[derive(Debug, Clone)]
@@ -14,6 +16,9 @@ pub struct CommandContext {
 
     // Current language
     pub language: String,
+
+    // Slots
+    pub slots: Option<HashMap<String, SlotValue>>
 }
 
 // Result returned from Lua script execution

crates/jarvis-core/src/lua/tests.rs

@@ -13,6 +13,7 @@ mod tests {
             command_id: "test_cmd".to_string(),
             command_path: cmd_path,
             language: "en".to_string(),
+            slots: None,
         }
     }
     

crates/jarvis-core/src/slots.rs

@@ -0,0 +1,58 @@
+mod gliner;
+
+use std::collections::HashMap;
+use once_cell::sync::OnceCell;
+
+use crate::commands::{SlotDefinition, SlotValue};
+use crate::config::structs::SlotExtractionEngine;
+use crate::DB;
+
+static SLOT_ENGINE: OnceCell<SlotExtractionEngine> = OnceCell::new();
+
+pub fn init() -> Result<(), String> {
+    if SLOT_ENGINE.get().is_some() {
+        return Ok(());
+    }
+
+    let engine = DB.get()
+        .map(|db| db.read().slot_extraction_engine)
+        .unwrap_or(SlotExtractionEngine::None);
+
+    SLOT_ENGINE.set(engine).map_err(|_| "Slot engine already set")?;
+
+    match engine {
+        SlotExtractionEngine::None => {
+            info!("Slot extraction disabled");
+        }
+        SlotExtractionEngine::GLiNER => {
+            info!("Initializing GLiNER slot extraction backend.");
+            gliner::init()?;
+            info!("GLiNER slot extraction backend initialized.");
+        }
+    }
+
+    Ok(())
+}
+
+// Extract slot values from text using the configured engine
+pub fn extract(
+    text: &str,
+    slots: &HashMap<String, SlotDefinition>,
+) -> HashMap<String, SlotValue> {
+    if slots.is_empty() {
+        return HashMap::new();
+    }
+
+    match SLOT_ENGINE.get().unwrap_or(&SlotExtractionEngine::None) {
+        SlotExtractionEngine::None => HashMap::new(),
+        SlotExtractionEngine::GLiNER => {
+            match gliner::extract(text, slots) {
+                Ok(result) => result,
+                Err(e) => {
+                    error!("GLiNER slot extraction failed: {}", e);
+                    HashMap::new()
+                }
+            }
+        }
+    }
+}

crates/jarvis-core/src/slots/gliner.rs

@@ -0,0 +1,487 @@
+// BASED ON: gline-rs crate source code
+// https://github.com/fbilhaut/gline-rs
+
+use std::collections::HashMap;
+use std::path::PathBuf;
+use once_cell::sync::OnceCell;
+use parking_lot::Mutex;
+use ndarray::Array;
+use regex::Regex;
+use tokenizers::Tokenizer;
+use ort::value::Tensor;
+
+pub mod structs;
+use structs::GlinerModelInfo;
+
+use std::fs;
+
+use crate::commands::{SlotDefinition, SlotValue};
+use crate::{APP_DIR, i18n};
+
+// MODEL STATE
+
+struct GlinerModel {
+    session: ort::session::Session,
+    tokenizer: Tokenizer,
+    splitter: Regex,
+}
+
+unsafe impl Send for GlinerModel {}
+unsafe impl Sync for GlinerModel {}
+
+static MODEL: OnceCell<Mutex<GlinerModel>> = OnceCell::new();
+
+// GLiNER defaults (same as gline-rs Parameters::default())
+const THRESHOLD: f32 = 0.3;
+const MAX_WIDTH: usize = 12;
+const MAX_LENGTH: usize = 512;
+
+// applied after decoding
+const MIN_CONFIDENCE: f32 = 0.4;
+
+// word splitting regex (gline-rs RegexSplitter default)
+const WORD_REGEX: &str = r"\w+(?:[-_]\w+)*|\S";
+
+// INIT
+
+pub fn init() -> Result<(), String> {
+    if MODEL.get().is_some() {
+        return Ok(());
+    }
+
+    let variant = crate::DB.get()
+        .map(|db| db.read().gliner_model.clone())
+        .unwrap_or_default();
+
+    let language = i18n::get_language();
+
+    let (model_dir, onnx_file) = if variant.is_empty() {
+        (select_model_dir(), "model.onnx".to_string())
+    } else {
+        crate::gliner_models::resolve_model(&variant, &language)
+            .unwrap_or_else(|| (select_model_dir(), "model.onnx".to_string()))
+    };
+
+    let model_path = model_dir.join("onnx").join(&onnx_file);
+    let tokenizer_path = model_dir.join("tokenizer.json");
+
+    info!("Loading GLiNER model from: {}, variant {}", model_dir.display(), variant);
+
+    let session = ort::session::Session::builder()
+        .map_err(|e| format!("Failed to create ort session builder: {}", e))?
+        .commit_from_file(&model_path)
+        .map_err(|e| format!("Failed to load ONNX model: {}", e))?;
+
+    let tokenizer = Tokenizer::from_file(&tokenizer_path)
+        .map_err(|e| format!("Failed to load tokenizer: {}", e))?;
+
+    let splitter = Regex::new(WORD_REGEX)
+        .map_err(|e| format!("Failed to compile word regex: {}", e))?;
+
+    MODEL.set(Mutex::new(GlinerModel { session, tokenizer, splitter }))
+        .map_err(|_| "GLiNER model already initialized".to_string())?;
+
+    info!("GLiNER model loaded");
+    Ok(())
+}
+
+fn select_model_dir() -> PathBuf {
+    let base = APP_DIR.join("resources").join("models");
+
+    match i18n::get_language().as_str() {
+        "en" => {
+            let path = base.join("gliner_small-v2.1");
+            if path.exists() { return path; }
+        }
+        _ => {}
+    }
+
+    // multilingual (covers RU, UA, EN)
+    let multi = base.join("gliner_multi-v2.1");
+    if multi.exists() { return multi; }
+
+    // fallback
+    base.join("gliner_small-v2.1")
+}
+
+// WORD SPLITTING
+
+struct WordToken {
+    start: usize,
+    end: usize,
+    text: String,
+}
+
+fn split_words(splitter: &Regex, text: &str, limit: Option<usize>) -> Vec<WordToken> {
+    let mut tokens = Vec::new();
+    for m in splitter.find_iter(text) {
+        tokens.push(WordToken {
+            start: m.start(),
+            end: m.end(),
+            text: m.as_str().to_string(),
+        });
+        if let Some(lim) = limit {
+            if tokens.len() >= lim { break; }
+        }
+    }
+    tokens
+}
+
+// PROMPT CONSTRUCTION
+//
+// GLiNER prompt format:
+//   [<<ENT>>, label1_w1, label1_w2, <<ENT>>, label2_w1, ..., <<SEP>>, word1, word2, ..., wordN]
+
+fn build_prompt(entities: &[&str], words: &[WordToken]) -> (Vec<String>, usize) {
+    let mut prompt = Vec::with_capacity(entities.len() * 2 + 1 + words.len());
+
+    for entity in entities {
+        prompt.push("<<ENT>>".to_string());
+        prompt.push(entity.to_string()); // whole string, no split
+    }
+    prompt.push("<<SEP>>".to_string());
+
+    let entities_len = prompt.len();
+
+    for w in words {
+        prompt.push(w.text.clone());
+    }
+
+    (prompt, entities_len)
+}
+
+// ENCODING
+
+struct EncodedBatch {
+    input_ids: ndarray::Array2<i64>,
+    attention_masks: ndarray::Array2<i64>,
+    word_masks: ndarray::Array2<i64>,
+    text_lengths: ndarray::Array2<i64>,
+    num_words: usize,
+}
+
+fn encode_single(
+    tokenizer: &Tokenizer,
+    _text: &str,
+    entities: &[&str],
+    words: &[WordToken],
+) -> Result<EncodedBatch, String> {
+    let (prompt, ent_len) = build_prompt(entities, words);
+    let text_word_count = words.len();
+
+    let mut word_encodings: Vec<Vec<u32>> = Vec::with_capacity(prompt.len());
+    let mut total_tokens: usize = 2; // BOS + EOS
+    let mut entity_tokens: usize = 0;
+
+    for (pos, word) in prompt.iter().enumerate() {
+        let encoding = tokenizer.encode(word.as_str(), false)
+            .map_err(|e| format!("Tokenizer encode error: {}", e))?;
+        let ids = encoding.get_ids().to_vec();
+        total_tokens += ids.len();
+        if pos < ent_len {
+            entity_tokens += ids.len();
+        }
+        word_encodings.push(ids);
+    }
+
+    // text_offset: index where text tokens start (after BOS + entity tokens)
+    let text_offset = entity_tokens + 1;
+
+    // DEBUG
+    debug!("GLiNER prompt ({} total, ent_len={}, text_offset={}):", prompt.len(), ent_len, text_offset);
+    for (i, (word, enc)) in prompt.iter().zip(word_encodings.iter()).enumerate() {
+        debug!("  [{}]{} '{}' -> {:?}", i, if i < ent_len { " ENT" } else { " TXT" }, word, enc);
+    }
+
+    let mut input_ids = Array::zeros((1, total_tokens));
+    let mut attention_masks = Array::zeros((1, total_tokens));
+    let mut word_masks = Array::zeros((1, total_tokens));
+
+    let mut idx: usize = 0;
+    let mut word_id: i64 = 0;
+
+    // BOS
+    input_ids[[0, idx]] = 1;
+    attention_masks[[0, idx]] = 1;
+    idx += 1;
+
+    // encode each word - matching gline-rs idx-based logic exactly
+    for word_enc in word_encodings.iter() {
+        for (token_idx, &token_id) in word_enc.iter().enumerate() {
+            input_ids[[0, idx]] = token_id as i64;
+            attention_masks[[0, idx]] = 1;
+            // word mask: only for text tokens (past text_offset), first sub-token only
+            if idx >= text_offset && token_idx == 0 {
+                word_masks[[0, idx]] = word_id;
+            }
+            idx += 1;
+        }
+        // increment word_id for any word whose tokens end past text_offset
+        if idx >= text_offset {
+            word_id += 1;
+        }
+    }
+
+    // EOS
+    input_ids[[0, idx]] = 2;
+    attention_masks[[0, idx]] = 1;
+
+    let mut text_lengths = Array::zeros((1, 1));
+    text_lengths[[0, 0]] = (text_word_count + 1) as i64;
+
+    debug!("GLiNER input_ids: {:?}", input_ids.as_slice().unwrap());
+    debug!("GLiNER word_masks: {:?}", word_masks.as_slice().unwrap());
+    debug!("GLiNER text_lengths: {}", text_word_count);
+
+    Ok(EncodedBatch {
+        input_ids,
+        attention_masks,
+        word_masks,
+        text_lengths,
+        num_words: text_word_count + 1,
+    })
+}
+
+// SPAN TENSORS
+
+fn make_span_tensors(num_words: usize, max_width: usize) -> (ndarray::Array3<i64>, ndarray::Array2<bool>) {
+    let num_spans = num_words * max_width;
+
+    let mut span_idx = Array::zeros((1, num_spans, 2));
+    let mut span_mask = Array::from_elem((1, num_spans), false);
+
+    for start in 0..num_words {
+        let remaining = num_words - start;
+        let actual_max = max_width.min(remaining);
+        for width in 0..actual_max {
+            let dim = start * max_width + width;
+            span_idx[[0, dim, 0]] = start as i64;
+            span_idx[[0, dim, 1]] = (start + width) as i64;
+            span_mask[[0, dim]] = true;
+        }
+    }
+
+    (span_idx, span_mask)
+}
+
+// DECODE + GREEDY SEARCH
+
+fn sigmoid(x: f32) -> f32 {
+    1.0 / (1.0 + (-x).exp())
+}
+
+struct Entity {
+    text: String,
+    label: String,
+    prob: f32,
+    start: usize,
+    end: usize,
+}
+
+fn decode_and_search(
+    logits_data: &[f32],
+    logits_shape: &[usize],
+    words: &[WordToken],
+    text: &str,
+    entities: &[&str],
+    max_width: usize,
+    threshold: f32,
+) -> Vec<Entity> {
+    let num_tokens = words.len();
+
+    let dim_mw = logits_shape.get(2).copied().unwrap_or(0);
+    let dim_e = logits_shape.get(3).copied().unwrap_or(0);
+
+    let mut spans: Vec<Entity> = Vec::new();
+
+    for start in 1..=num_tokens {
+        let max_end = (start + max_width).min(num_tokens + 1);
+        for end in start..max_end {
+            let width = end - start;
+            for (class_idx, &entity_label) in entities.iter().enumerate() {
+                let flat_idx = start * dim_mw * dim_e + width * dim_e + class_idx;
+                if flat_idx >= logits_data.len() { continue; }
+
+                let raw_score = logits_data[flat_idx];
+                let prob = sigmoid(raw_score);
+                if prob >= threshold {
+                    let w_start = start - 1;
+                    let w_end = end - 1;
+                    let start_offset = words[w_start].start;
+                    let end_offset = words[w_end].end;
+                    let span_text = text[start_offset..end_offset].to_string();
+                    spans.push(Entity {
+                        text: span_text,
+                        label: entity_label.to_string(),
+                        prob,
+                        start: start_offset,
+                        end: end_offset,
+                    });
+                }
+            }
+        }
+    }
+
+    spans.sort_unstable_by(|a, b| (a.start, a.end).cmp(&(b.start, b.end)));
+    greedy_flat(&spans)
+}
+
+fn greedy_flat(spans: &[Entity]) -> Vec<Entity> {
+    if spans.is_empty() {
+        return Vec::new();
+    }
+
+    let mut result: Vec<Entity> = Vec::new();
+    let mut prev = 0usize;
+    let mut next = 1usize;
+
+    while next < spans.len() {
+        let p = &spans[prev];
+        let n = &spans[next];
+
+        if n.start >= p.end || p.start >= n.end {
+            result.push(Entity {
+                text: p.text.clone(),
+                label: p.label.clone(),
+                prob: p.prob,
+                start: p.start,
+                end: p.end,
+            });
+            prev = next;
+        } else if p.prob < n.prob {
+            prev = next;
+        }
+        next += 1;
+    }
+
+    let last = &spans[prev];
+    result.push(Entity {
+        text: last.text.clone(),
+        label: last.label.clone(),
+        prob: last.prob,
+        start: last.start,
+        end: last.end,
+    });
+
+    result
+}
+
+// PUBLIC API
+
+pub fn extract(
+    text: &str,
+    slots: &HashMap<String, SlotDefinition>,
+) -> Result<HashMap<String, SlotValue>, String> {
+    let mut model = MODEL.get().ok_or("GLiNER not initialized")?.lock();
+
+    let mut label_to_slots: HashMap<&str, Vec<&str>> = HashMap::new();
+    for (slot_name, def) in slots {
+        if !def.entity.is_empty() {
+            label_to_slots
+                .entry(def.entity.as_str())
+                .or_default()
+                .push(slot_name.as_str());
+        }
+    }
+
+    if label_to_slots.is_empty() {
+        return Ok(HashMap::new());
+    }
+
+    let labels: Vec<&str> = label_to_slots.keys().copied().collect();
+
+    debug!("GLiNER extract: text='{}', labels={:?}", text, labels);
+
+    let words = split_words(&model.splitter, text, Some(MAX_LENGTH));
+    if words.is_empty() {
+        return Ok(HashMap::new());
+    }
+
+    let encoded = encode_single(&model.tokenizer, text, &labels, &words)?;
+
+    let (span_idx, span_mask) = make_span_tensors(encoded.num_words, MAX_WIDTH);
+
+    let t_input_ids = Tensor::from_array(encoded.input_ids).map_err(|e| format!("tensor: {}", e))?;
+    let t_attn = Tensor::from_array(encoded.attention_masks).map_err(|e| format!("tensor: {}", e))?;
+    let t_words = Tensor::from_array(encoded.word_masks).map_err(|e| format!("tensor: {}", e))?;
+    let t_lengths = Tensor::from_array(encoded.text_lengths).map_err(|e| format!("tensor: {}", e))?;
+    let t_span_idx = Tensor::from_array(span_idx).map_err(|e| format!("tensor: {}", e))?;
+    let t_span_mask = Tensor::from_array(span_mask).map_err(|e| format!("tensor: {}", e))?;
+
+    let outputs = model.session.run(
+        ort::inputs! {
+            "input_ids" => t_input_ids,
+            "attention_mask" => t_attn,
+            "words_mask" => t_words,
+            "text_lengths" => t_lengths,
+            "span_idx" => t_span_idx,
+            "span_mask" => t_span_mask,
+        }
+    ).map_err(|e| format!("ort inference error: {}", e))?;
+
+    let (shape, logits_data) = outputs["logits"]
+        .try_extract_tensor::<f32>()
+        .map_err(|e| format!("Failed to extract logits: {}", e))?;
+
+    let logits_shape: Vec<usize> = shape.iter().map(|&d| d as usize).collect();
+
+    debug!("GLiNER logits shape: {:?}, data len: {}", logits_shape, logits_data.len());
+    let max_logit = logits_data.iter().cloned().fold(f32::NEG_INFINITY, f32::max);
+    debug!("GLiNER max logit: {:.4}, sigmoid: {:.4}", max_logit, sigmoid(max_logit));
+
+    // dump all scores above 5%
+    let num_words = logits_shape.get(1).copied().unwrap_or(0);
+    let dim_mw = logits_shape.get(2).copied().unwrap_or(0);
+    let dim_e = logits_shape.get(3).copied().unwrap_or(0);
+    for start in 0..num_words {
+        for width in 0..dim_mw.min(num_words - start) {
+            for class_idx in 0..dim_e {
+                let flat_idx = start * dim_mw * dim_e + width * dim_e + class_idx;
+                if flat_idx < logits_data.len() {
+                    let score = logits_data[flat_idx];
+                    let prob = sigmoid(score);
+                    if prob > 0.05 {
+                        let end = start + width;
+                        let w_start = if start < words.len() { &words[start].text } else { "?" };
+                        let w_end = if end < words.len() { &words[end].text } else { "?" };
+                        debug!("  span[{}..{}] '{}'->'{}' label={} score={:.3} prob={:.3}",
+                            start, end, w_start, w_end, labels.get(class_idx).unwrap_or(&"?"), score, prob);
+                    }
+                }
+            }
+        }
+    }
+
+    let entities = decode_and_search(
+        logits_data, &logits_shape, &words, text, &labels, MAX_WIDTH, THRESHOLD,
+    );
+
+    let mut result = HashMap::new();
+
+    for entity in &entities {
+        if entity.prob < MIN_CONFIDENCE {
+            continue;
+        }
+
+        debug!("GLiNER entity: '{}' -> '{}' ({:.1}%)",
+            entity.text, entity.label, entity.prob * 100.0);
+
+        if let Some(slot_names) = label_to_slots.get(entity.label.as_str()) {
+            for &slot_name in slot_names {
+                if !result.contains_key(slot_name) {
+                    let value = parse_slot_value(&entity.text);
+                    result.insert(slot_name.to_string(), value);
+                }
+            }
+        }
+    }
+
+    Ok(result)
+}
+
+fn parse_slot_value(text: &str) -> SlotValue {
+    if let Ok(n) = text.parse::<f64>() {
+        return SlotValue::Number(n);
+    }
+    SlotValue::Text(text.to_string())
+}

crates/jarvis-core/src/slots/gliner/structs.rs

@@ -0,0 +1,7 @@
+#[derive(Debug, Clone)]
+pub struct GlinerModelInfo {
+    pub model_dir: String,
+    pub file_name: String,
+    pub display_name: String,
+    pub value: String,
+}

crates/jarvis-core/src/stt/vosk.rs

@@ -163,10 +163,26 @@ fn get_configured_model_path() -> Result<std::path::PathBuf, String> {
         }
     }
     
-    // auto-detect: use first available model
+    // auto-detect: prefer model matching current language
     let available = vosk_models::scan_vosk_models();
+    let language = i18n::get_language();
+
+    // try language match first
+    let lang_code = match language.as_str() {
+        "ru" => "ru",
+        "en" => "us",  // vosk uses "us" not "en"
+        "ua" => "uk",  // vosk uses "uk" not "ua"
+        other => other,
+    };
+
+    if let Some(matched) = available.iter().find(|m| m.language == lang_code) {
+        info!("Auto-detected Vosk model for '{}': {}", language, matched.name);
+        return Ok(matched.path.clone());
+    }
+
+    // fallback to first available
     if let Some(first) = available.first() {
-        info!("Auto-detected Vosk model: {}", first.name);
+        info!("Auto-detected Vosk model (no language match): {}", first.name);
         return Ok(first.path.clone());
     }
     

crates/jarvis-core/src/vosk_models.rs

@@ -18,7 +18,7 @@ pub fn scan_vosk_models() -> Vec<VoskModelInfo> {
     };
     let mut models = Vec::new();
     
-    info!("TESTTTTTTTTTTTTT: {}", models_dir.display());
+    info!("VOSK MODELS DIR: {}", models_dir.display());
 
     if !models_dir.exists() {
         warn!("Vosk models directory not found: {}", models_dir.display());

crates/jarvis-gui/src/main.rs

@@ -85,6 +85,9 @@ fn main() {
             // vosk
             tauri_commands::list_vosk_models,
 
+            // gliner
+            tauri_commands::list_gliner_models,
+
             // i18n
             tauri_commands::get_translations,
             tauri_commands::translate,

crates/jarvis-gui/src/tauri_commands/db.rs

@@ -10,6 +10,8 @@ pub fn db_read(state: tauri::State<'_, AppState>, key: &str) -> String {
         "assistant_voice" => settings.voice.clone(),
         "selected_wake_word_engine" => format!("{:?}", settings.wake_word_engine),
         "selected_intent_recognition_engine" => format!("{:?}", settings.intent_recognition_engine),
+        "selected_slot_extraction_engine" => format!("{:?}", settings.slot_extraction_engine),
+        "selected_gliner_model" => settings.gliner_model.clone(),
         "selected_vosk_model" => settings.vosk_model.clone(),
         "speech_to_text_engine" => format!("{:?}", settings.speech_to_text_engine),
         "noise_suppression" => format!("{:?}", settings.noise_suppression),
@@ -54,6 +56,16 @@ pub fn db_write(state: tauri::State<'_, AppState>, key: &str, val: &str) -> bool
                     _ => return false,
                 }
             }
+            "selected_slot_extraction_engine" => {
+                match val.to_lowercase().as_str() {
+                    "none" => settings.slot_extraction_engine = jarvis_core::config::structs::SlotExtractionEngine::None,
+                    "gliner" => settings.slot_extraction_engine = jarvis_core::config::structs::SlotExtractionEngine::GLiNER,
+                    _ => return false,
+                }
+            }
+            "selected_gliner_model" => {
+                settings.gliner_model = val.to_string();
+            }
             "selected_vosk_model" => {
                 settings.vosk_model = val.to_string();
             }

crates/jarvis-gui/src/tauri_commands/stt.rs

@@ -1,4 +1,4 @@
-use jarvis_core::{vosk_models, DB};
+use jarvis_core::{vosk_models, gliner_models};
 use serde::Serialize;
 
 #[derive(Serialize)]
@@ -8,6 +8,12 @@ pub struct VoskModel {
     pub size: String,
 }
 
+#[derive(Serialize)]
+pub struct GlinerVariant {
+    pub display_name: String,
+    pub value: String,
+}
+
 #[tauri::command]
 pub fn list_vosk_models() -> Vec<VoskModel> {
     vosk_models::scan_vosk_models()
@@ -18,4 +24,15 @@ pub fn list_vosk_models() -> Vec<VoskModel> {
             size: m.size,
         })
         .collect()
-}
+}
+
+#[tauri::command]
+pub fn list_gliner_models() -> Vec<GlinerVariant> {
+    gliner_models::scan_gliner_variants()
+        .into_iter()
+        .map(|m| GlinerVariant {
+            display_name: m.display_name,
+            value: m.value,
+        })
+        .collect()
+}

frontend/src/routes/settings/index.svelte

@@ -67,6 +67,7 @@
 
     let availableMicrophones: MicrophoneOption[] = []
     let availableVoskModels: { label: string; value: string }[] = []
+    let availableGlinerModels: { label: string; value: string }[] = []
     let settingsSaved = false
     let saveButtonDisabled = false
 
@@ -75,6 +76,8 @@
     let selectedMicrophone = ""
     let selectedWakeWordEngine = ""
     let selectedIntentRecognitionEngine = ""
+    let selectedSlotExtractionEngine = ""
+    let selectedGlinerModel = ""
     let selectedVoskModel = ""
     let selectedNoiseSuppression = ""
     let selectedVad = ""
@@ -105,6 +108,8 @@
                 invoke("db_write", { key: "selected_microphone", val: selectedMicrophone }),
                 invoke("db_write", { key: "selected_wake_word_engine", val: selectedWakeWordEngine }),
                 invoke("db_write", { key: "selected_intent_recognition_engine", val: selectedIntentRecognitionEngine }),
+                invoke("db_write", { key: "selected_slot_extraction_engine", val: selectedSlotExtractionEngine }),
+                invoke("db_write", { key: "selected_gliner_model", val: selectedGlinerModel }),
                 invoke("db_write", { key: "selected_vosk_model", val: selectedVoskModel }),
 
                 invoke("db_write", { key: "noise_suppression", val: selectedNoiseSuppression }),
@@ -173,13 +178,22 @@
                 value: m.name
             }))
 
+            // load gliner models
+            const glinerModels = await invoke<{ display_name: string; value: string }[]>("list_gliner_models")
+            availableGlinerModels = glinerModels.map(m => ({
+                label: m.display_name,
+                value: m.value,
+            }))
+
             // load settings from db
-            const [mic, wakeWord, intentReco, voskModel,
+            const [mic, wakeWord, intentReco, slotEngine, glinerModel, voskModel,
                    noiseSuppression, vad, gainNormalizer,
                    pico, openai] = await Promise.all([
                 invoke<string>("db_read", { key: "selected_microphone" }),
                 invoke<string>("db_read", { key: "selected_wake_word_engine" }),
                 invoke<string>("db_read", { key: "selected_intent_recognition_engine" }),
+                invoke<string>("db_read", { key: "selected_slot_extraction_engine" }),
+                invoke<string>("db_read", { key: "selected_gliner_model" }),
                 invoke<string>("db_read", { key: "selected_vosk_model" }),
 
                 invoke<string>("db_read", { key: "noise_suppression" }),
@@ -193,7 +207,9 @@
             selectedMicrophone = mic
             selectedWakeWordEngine = wakeWord
             selectedIntentRecognitionEngine = intentReco
+            selectedSlotExtractionEngine = slotEngine
             selectedVoskModel = voskModel
+            selectedGlinerModel = glinerModel
             selectedNoiseSuppression = noiseSuppression
             selectedVad = vad
             gainNormalizerEnabled = gainNormalizer === "true"
@@ -368,7 +384,43 @@
         />
 
         <Space h="xl" />
+        <NativeSelect
+            data={[
+                { label: t('settings-disabled'), value: "None" },
+                { label: "GLiNER (NER)", value: "GLiNER" }
+            ]}
+            label={t('settings-slot-engine')}
+            description={t('settings-slot-engine-desc')}
+            variant="filled"
+            bind:value={selectedSlotExtractionEngine}
+        />
 
+        {#if selectedSlotExtractionEngine === "GLiNER"}
+            <Space h="sm" />
+            {#key availableGlinerModels}
+            <NativeSelect
+                data={[
+                    { label: t('settings-auto-detect'), value: "" },
+                    ...availableGlinerModels
+                ]}
+                label={t('settings-gliner-model')}
+                description={t('settings-gliner-model-desc')}
+                variant="filled"
+                bind:value={selectedGlinerModel}
+            />
+            {/key}
+
+            {#if availableGlinerModels.length === 0}
+                <Space h="sm" />
+                <Alert title={t('settings-models-not-found')} color="orange" variant="outline">
+                    <Text size="sm" color="gray">
+                        {t('settings-gliner-models-hint')}
+                    </Text>
+                </Alert>
+            {/if}
+        {/if}
+
+        <Space h="xl" />
         <NativeSelect
             data={[
                 { label: t('settings-disabled'), value: "None" },

post_build.py

@@ -7,6 +7,7 @@ import os
 from pathlib import Path
 import shutil
 import sys
+import filecmp
 
 # some config vars
 # format: (source, destination_name)
@@ -37,8 +38,87 @@ TARGET_DIRS = (
 
 ABS_PATH = os.getcwd() + "/"
 
-# check for force flag
+# flags
-force_overwrite = "-force" in sys.argv
+force_overwrite = "--force" in sys.argv
+sync_mode = "--sync" in sys.argv
+
+if sync_mode:
+    print("[*] Sync mode: will update changed files and remove orphans")
+
+
+def files_differ(src, dst):
+    """check if two files differ by size or content"""
+    if not os.path.isfile(dst):
+        return True
+    # quick check: size
+    if os.path.getsize(src) != os.path.getsize(dst):
+        return True
+    # compare modification time (src newer = needs update)
+    if os.path.getmtime(src) > os.path.getmtime(dst):
+        return True
+    return False
+
+
+def sync_directory(src_dir, dst_dir):
+    """sync dst_dir to match src_dir: copy new/changed, remove orphans"""
+    copied = 0
+    updated = 0
+    removed = 0
+
+    # walk source, copy new/changed files
+    for root, dirs, files in os.walk(src_dir):
+        rel_root = os.path.relpath(root, src_dir)
+        dst_root = os.path.join(dst_dir, rel_root) if rel_root != "." else dst_dir
+
+        # ensure dir exists
+        os.makedirs(dst_root, exist_ok=True)
+
+        for f in files:
+            src_file = os.path.join(root, f)
+            dst_file = os.path.join(dst_root, f)
+
+            if not os.path.exists(dst_file):
+                shutil.copy2(src_file, dst_file)
+                copied += 1
+            elif files_differ(src_file, dst_file):
+                shutil.copy2(src_file, dst_file)
+                updated += 1
+
+    # walk destination, remove files/dirs not in source
+    for root, dirs, files in os.walk(dst_dir, topdown=False):
+        rel_root = os.path.relpath(root, dst_dir)
+        src_root = os.path.join(src_dir, rel_root) if rel_root != "." else src_dir
+
+        for f in files:
+            dst_file = os.path.join(root, f)
+            src_file = os.path.join(src_root, f)
+            if not os.path.exists(src_file):
+                os.remove(dst_file)
+                print(f"  [-] Removed orphan file: {os.path.relpath(dst_file, dst_dir)}")
+                removed += 1
+
+        for d in dirs:
+            dst_sub = os.path.join(root, d)
+            src_sub = os.path.join(src_root, d)
+            if not os.path.exists(src_sub):
+                shutil.rmtree(dst_sub)
+                print(f"  [-] Removed orphan dir: {os.path.relpath(dst_sub, dst_dir)}")
+                removed += 1
+
+    return copied, updated, removed
+
+
+def sync_file(src_path, dst_path):
+    """sync a single file, returns (copied, updated)"""
+    if not os.path.exists(dst_path):
+        os.makedirs(os.path.dirname(dst_path), exist_ok=True)
+        shutil.copy2(src_path, dst_path)
+        return 1, 0
+    elif files_differ(src_path, dst_path):
+        shutil.copy2(src_path, dst_path)
+        return 0, 1
+    return 0, 0
+
 
 for tdir in TARGET_DIRS:
     tdir = ABS_PATH + tdir
@@ -47,7 +127,6 @@ for tdir in TARGET_DIRS:
         print("Skipping target, not a directory: ", tdir)
         continue
 
-    # copy lib files
     for entry in SOURCE:
         if isinstance(entry, tuple):
             src, dest_name = entry
@@ -57,11 +136,22 @@ for tdir in TARGET_DIRS:
         src_path = ABS_PATH + src
 
         if os.path.isdir(src_path):
-            # copy the whole directory
             target_name = dest_name if dest_name else os.path.basename(src.rstrip('/'))
             full_target_dir_path = os.path.join(tdir, target_name)
-            
+
-            if os.path.isdir(full_target_dir_path):
+            if sync_mode:
+                # sync: update changed, add new, remove orphans
+                if os.path.isdir(full_target_dir_path):
+                    c, u, r = sync_directory(src_path, full_target_dir_path)
+                    if c or u or r:
+                        print(f"[~] Synced: {src} -> {target_name} (+{c} new, ~{u} updated, -{r} removed)")
+                    else:
+                        print(f"[=] Up to date: {src} -> {target_name}")
+                else:
+                    shutil.copytree(src_path, full_target_dir_path)
+                    print("[+] Directory copied: ", src, "->", target_name)
+
+            elif os.path.isdir(full_target_dir_path):
                 if force_overwrite:
                     shutil.rmtree(full_target_dir_path)
                     shutil.copytree(src_path, full_target_dir_path)
@@ -73,11 +163,19 @@ for tdir in TARGET_DIRS:
                 print("[+] Directory copied: ", src, "->", target_name)
 
         elif os.path.isfile(src_path):
-            # copy file
             target_name = dest_name if dest_name else os.path.basename(src)
             full_target_file_path = os.path.join(tdir, target_name)
-            
+
-            if os.path.isfile(full_target_file_path):
+            if sync_mode:
+                c, u = sync_file(src_path, full_target_file_path)
+                if c:
+                    print("[+] File copied: ", src, "->", target_name)
+                elif u:
+                    print("[~] File updated: ", src, "->", target_name)
+                else:
+                    print("[=] Up to date: ", src, "->", target_name)
+
+            elif os.path.isfile(full_target_file_path):
                 if force_overwrite:
                     os.remove(full_target_file_path)
                     shutil.copy(src_path, full_target_file_path)
@@ -90,4 +188,4 @@ for tdir in TARGET_DIRS:
         else:
             print("[?] Unknown entity to copy: ", src)
 
-    print("Post compile build done.")
+    print("Post compile build done.")

resources/commands/hello/command.toml

@@ -1,14 +0,0 @@
-[[commands]]
-id = "hello"
-type = "lua"
-script = "script.lua"
-sandbox = "minimal"
-timeout = 5000
-phrases.ru = [
-    "привет",
-    "здравствуй",
-]
-phrases.en = [
-    "hello",
-    "hi",
-]

resources/commands/hello/script.lua

@@ -1,21 +0,0 @@
--- simple test hello command
-
-local lang = jarvis.context.language
-local hour = tonumber(jarvis.context.time.hour)
-
--- determine greeting based on time
-local greeting
-if hour >= 5 and hour < 12 then
-    greeting = lang == "ru" and "Доброе утро" or "Good morning"
-elseif hour >= 12 and hour < 17 then
-    greeting = lang == "ru" and "Добрый день" or "Good afternoon"
-elseif hour >= 17 and hour < 22 then
-    greeting = lang == "ru" and "Добрый вечер" or "Good evening"
-else
-    greeting = lang == "ru" and "Доброй ночи" or "Good night"
-end
-
-jarvis.log("info", "Greeting user: " .. greeting)
-jarvis.audio.play_reply()
-
-return { chain = true }

resources/commands/test_slots/command.toml

@@ -0,0 +1,14 @@
+[[commands]]
+id = "test_greet_name"
+type = "lua"
+script = "greet.lua"
+
+[commands.phrases]
+en = ["say hello to {name}", "greet {name}"]
+ru = ["поздоровайся с {name}", "привет {name}"]
+
+[commands.slots.name]
+entity = "person name"
+
+[commands.slots.city]
+entity = "city name"

resources/commands/test_slots/greet.lua

@@ -0,0 +1,2 @@
+local name = jarvis.context.slots.name or "[UNKNOWN]"
+print("Hello, " .. name .. "!")

resources/commands/weather/command.toml

@@ -6,19 +6,16 @@ timeout = 5000
 sandbox = "standard"
 
 [commands.phrases]
-ru = [
+ru = ["какая погода в {city}", "погода {city}"]
-    "какая погода",
+en = ["what's the weather in {city}", "weather in {city}"]
-    "погода"
-]
-en = [
-    "what's the weather",
-    "weather"
-]
 
 [commands.sounds]
 ru = ["weather_ru_1", "weather_ru_2"]
 en = ["weather_en_1", "weather_en_2"]
 
+[commands.slots.city]
+entity = "city name"
+context = ["in", "for", "at", "в", "для"]
 
 
 [[commands]]