Use in-process Llama cleanup

AGENTS.md

@@ -5,6 +5,7 @@
 - `src/leld.py` is the primary entrypoint (X11 STT daemon).
 - `src/recorder.py` handles audio capture using PortAudio via `sounddevice`.
 - `src/leld.py` owns Whisper setup and transcription.
+- `src/aiprocess.py` runs the in-process Llama-3.2-3B cleanup.
 
 ## Build, Test, and Development Commands
 
@@ -33,3 +34,5 @@ System packages (example names): `portaudio`/`libportaudio2`, `libayatana-appind
 
 - Audio input is configured via the `recording.input` field in `config.json`.
 - STT model and device are configured via the `stt` section in `config.json`.
+- LLM model settings are locked; model downloads to `~/.cache/lel/models/`.
+- `-v/--verbose` enables verbose logs (including llama.cpp) with `llama::` prefix.

README.md

@@ -7,8 +7,9 @@ Python X11 STT daemon that records audio, runs Whisper, logs the transcript, and
 - X11 (not Wayland)
 - `sounddevice` (PortAudio)
 - `faster-whisper`
+- `llama-cpp-python`
 - Tray icon deps: `gtk3`, `libayatana-appindicator3`
-- Python deps: `pillow`, `python-xlib`, `faster-whisper`, `PyGObject`, `sounddevice`
+- Python deps: `pillow`, `python-xlib`, `faster-whisper`, `llama-cpp-python`, `PyGObject`, `sounddevice`
 
 System packages (example names): `portaudio`/`libportaudio2`.
 
@@ -35,19 +36,18 @@ Create `~/.config/lel/config.json`:
   "daemon": { "hotkey": "Cmd+m" },
   "recording": { "input": "0" },
   "stt": { "model": "base", "device": "cpu" },
-  "injection": { "backend": "clipboard" },
+  "injection": { "backend": "clipboard" }
-
-  "ai_cleanup": {
-    "model": "llama3.2:3b",
-    "base_url": "http://localhost:11434",
-    "api_key": ""
-  }
 }
 ```
 
 Recording input can be a device index (preferred) or a substring of the device
 name.
 
+The LLM model is downloaded on first startup to `~/.cache/lel/models/` and uses
+the locked Llama-3.2-3B GGUF model.
+Pass `-v/--verbose` to see verbose logs, including llama.cpp loader logs; these
+messages are prefixed with `llama::`.
+
 ## systemd user service
 
 ```bash

pyproject.toml

@@ -6,6 +6,7 @@ readme = "README.md"
 requires-python = ">=3.10"
 dependencies = [
   "faster-whisper",
+  "llama-cpp-python",
   "pillow",
   "python-xlib",
   "PyGObject",

src/aiprocess.py

@@ -1,12 +1,12 @@
 from __future__ import annotations
 
-import argparse
-import json
 import logging
-import sys
+import os
 import urllib.request
-from pathlib import Path
 from dataclasses import dataclass
+from pathlib import Path
+
+from llama_cpp import Llama  # type: ignore[import-not-found]
 
 
 SYSTEM_PROMPT = (
@@ -22,77 +22,95 @@ SYSTEM_PROMPT = (
     "   - \"let's ask Bob, I mean Janice, let's ask Janice\" -> \"let's ask Janice\"\n"
 )
 
+MODEL_NAME = "Llama-3.2-3B-Instruct-Q4_K_M.gguf"
+MODEL_URL = (
+    "https://huggingface.co/bartowski/Llama-3.2-3B-Instruct-GGUF/resolve/main/"
+    "Llama-3.2-3B-Instruct-Q4_K_M.gguf"
+)
+MODEL_DIR = Path.home() / ".cache" / "lel" / "models"
+MODEL_PATH = MODEL_DIR / MODEL_NAME
+LLM_LANGUAGE = "en"
+
 
 @dataclass
-class AIConfig:
+class LLMConfig:
-    model: str
+    model_path: Path
-    base_url: str
+    n_ctx: int = 4096
-    api_key: str
+    verbose: bool = False
-    timeout_sec: int
-    language_hint: str | None = None
-    wrap_transcript: bool = True
 
 
-class GenericAPIProcessor:
+class LlamaProcessor:
-    def __init__(self, cfg: AIConfig):
+    def __init__(self, cfg: LLMConfig):
         self.cfg = cfg
-        self.system = SYSTEM_PROMPT
+        os.environ.setdefault("LLAMA_CPP_LOG_PREFIX", "llama")
+        os.environ.setdefault("LLAMA_CPP_LOG_PREFIX_SEPARATOR", "::")
+        self.client = Llama(
+            model_path=str(cfg.model_path),
+            n_ctx=cfg.n_ctx,
+            verbose=cfg.verbose,
+        )
 
     def process(self, text: str) -> str:
-        language = self.cfg.language_hint or ""
-        if self.cfg.wrap_transcript:
         user_content = f"<transcript>{text}</transcript>"
-        else:
+        if LLM_LANGUAGE:
-            user_content = text
+            user_content = f"<language>{LLM_LANGUAGE}</language>\n{user_content}"
-        if language:
+        response = self.client.create_chat_completion(
-            user_content = f"<language>{language}</language>\n{user_content}"
+            messages=[
-        payload = {
+                {"role": "system", "content": SYSTEM_PROMPT},
-            "model": self.cfg.model,
-            "messages": [
-                {"role": "system", "content": self.system},
                 {"role": "user", "content": user_content},
             ],
-            "temperature": 0.0,
+            temperature=0.0,
-        }
+        )
-        data = json.dumps(payload).encode("utf-8")
+        return _extract_chat_text(response)
-        url = _chat_completions_url(self.cfg.base_url)
-        req = urllib.request.Request(url, data=data, method="POST")
-        req.add_header("Content-Type", "application/json")
-        if self.cfg.api_key:
-            req.add_header("Authorization", f"Bearer {self.cfg.api_key}")
 
-        with urllib.request.urlopen(req, timeout=self.cfg.timeout_sec) as resp:
-            body = resp.read()
 
-        out = json.loads(body.decode("utf-8"))
+def build_processor(verbose: bool = False) -> LlamaProcessor:
+    model_path = ensure_model()
+    return LlamaProcessor(LLMConfig(model_path=model_path, verbose=verbose))
 
-        if isinstance(out, dict):
+
-            if "output" in out:
+def ensure_model() -> Path:
-                return str(out["output"]).strip()
+    if MODEL_PATH.exists():
-            if "response" in out:
+        return MODEL_PATH
-                return str(out["response"]).strip()
+    MODEL_DIR.mkdir(parents=True, exist_ok=True)
-            if "choices" in out and out["choices"]:
+    tmp_path = MODEL_PATH.with_suffix(MODEL_PATH.suffix + ".tmp")
-                choice = out["choices"][0]
+    logging.info("downloading model: %s", MODEL_NAME)
+    try:
+        with urllib.request.urlopen(MODEL_URL) as resp:
+            total = resp.getheader("Content-Length")
+            total_size = int(total) if total else None
+            downloaded = 0
+            next_log = 0
+            with open(tmp_path, "wb") as handle:
+                while True:
+                    chunk = resp.read(1024 * 1024)
+                    if not chunk:
+                        break
+                    handle.write(chunk)
+                    downloaded += len(chunk)
+                    if total_size:
+                        progress = downloaded / total_size
+                        if progress >= next_log:
+                            logging.info("model download %.0f%%", progress * 100)
+                            next_log += 0.1
+                    elif downloaded // (50 * 1024 * 1024) > (downloaded - len(chunk)) // (50 * 1024 * 1024):
+                        logging.info("model download %d MB", downloaded // (1024 * 1024))
+        os.replace(tmp_path, MODEL_PATH)
+    except Exception:
+        try:
+            if tmp_path.exists():
+                tmp_path.unlink()
+        except Exception:
+            pass
+        raise
+    return MODEL_PATH
+
+
+def _extract_chat_text(payload: dict) -> str:
+    if "choices" in payload and payload["choices"]:
+        choice = payload["choices"][0]
         msg = choice.get("message") or {}
         content = msg.get("content") or choice.get("text")
         if content is not None:
             return str(content).strip()
     raise RuntimeError("unexpected response format")
-
-
-def build_processor(cfg: AIConfig) -> GenericAPIProcessor:
-    if not cfg.base_url:
-        raise ValueError("ai_base_url is required for generic API")
-    return GenericAPIProcessor(cfg)
-
-
-def _chat_completions_url(base_url: str) -> str:
-    if not base_url:
-        return ""
-    trimmed = base_url.rstrip("/")
-    if "/v1/" in trimmed:
-        return trimmed
-    if trimmed.endswith("/v1"):
-        return trimmed + "/chat/completions"
-    return trimmed + "/v1/chat/completions"
-

src/config.py

@@ -9,13 +9,6 @@ class Config:
     recording: dict = field(default_factory=lambda: {"input": ""})
     stt: dict = field(default_factory=lambda: {"model": "base", "device": "cpu"})
     injection: dict = field(default_factory=lambda: {"backend": "clipboard"})
-    ai_cleanup: dict = field(
-        default_factory=lambda: {
-            "model": "llama3.2:3b",
-            "base_url": "http://localhost:11434",
-            "api_key": "",
-        }
-    )
 
 
 def default_path() -> Path:
@@ -27,7 +20,7 @@ def load(path: str | None) -> Config:
     p = Path(path) if path else default_path()
     if p.exists():
         data = json.loads(p.read_text(encoding="utf-8"))
-        if any(k in data for k in ("daemon", "recording", "stt", "injection", "ai_cleanup")):
+        if any(k in data for k in ("daemon", "recording", "stt", "injection")):
             for k, v in data.items():
                 if hasattr(cfg, k):
                     setattr(cfg, k, v)
@@ -37,9 +30,6 @@ def load(path: str | None) -> Config:
             cfg.stt["model"] = data.get("whisper_model", cfg.stt["model"])
             cfg.stt["device"] = data.get("whisper_device", cfg.stt["device"])
             cfg.injection["backend"] = data.get("injection_backend", cfg.injection["backend"])
-            cfg.ai_cleanup["model"] = data.get("ai_model", cfg.ai_cleanup["model"])
-            cfg.ai_cleanup["base_url"] = data.get("ai_base_url", cfg.ai_cleanup["base_url"])
-            cfg.ai_cleanup["api_key"] = data.get("ai_api_key", cfg.ai_cleanup["api_key"])
 
     if not isinstance(cfg.daemon, dict):
         cfg.daemon = {"hotkey": "Cmd+m"}
@@ -49,22 +39,12 @@ def load(path: str | None) -> Config:
         cfg.stt = {"model": "base", "device": "cpu"}
     if not isinstance(cfg.injection, dict):
         cfg.injection = {"backend": "clipboard"}
-    if not isinstance(cfg.ai_cleanup, dict):
-        cfg.ai_cleanup = {
-            "model": "llama3.2:3b",
-            "base_url": "http://localhost:11434",
-            "api_key": "",
-        }
     validate(cfg)
     return cfg
 
 
 def redacted_dict(cfg: Config) -> dict:
-    d = cfg.__dict__.copy()
+    return cfg.__dict__.copy()
-    if isinstance(d.get("ai_cleanup"), dict):
-        d["ai_cleanup"] = d["ai_cleanup"].copy()
-        d["ai_cleanup"]["api_key"] = ""
-    return d
 
 
 def validate(cfg: Config) -> None:

src/leld.py

@@ -15,7 +15,7 @@ from faster_whisper import WhisperModel
 
 from config import Config, load, redacted_dict
 from recorder import start_recording, stop_recording
-from aiprocess import AIConfig, build_processor
+from aiprocess import build_processor
 from inject import inject
 from x11_hotkey import listen
 
@@ -51,7 +51,7 @@ def _compute_type(device: str) -> str:
 
 
 class Daemon:
-    def __init__(self, cfg: Config):
+    def __init__(self, cfg: Config, *, llama_verbose: bool = False):
         self.cfg = cfg
         self.lock = threading.Lock()
         self.state = State.IDLE
@@ -63,6 +63,7 @@ class Daemon:
             device=cfg.stt.get("device", "cpu"),
             compute_type=_compute_type(cfg.stt.get("device", "cpu")),
         )
+        self.ai_processor = build_processor(verbose=llama_verbose)
         self.indicator = None
         self.status_icon = None
         if AppIndicator3 is not None:
@@ -183,23 +184,11 @@ class Daemon:
 
         logging.info("stt: %s", text)
 
-        ai_model = (self.cfg.ai_cleanup.get("model") or "").strip()
-        ai_base_url = (self.cfg.ai_cleanup.get("base_url") or "").strip()
-        if ai_model and ai_base_url:
         self.set_state(State.PROCESSING)
         logging.info("ai processing started")
         try:
-                processor = build_processor(
-                    AIConfig(
-                        model=ai_model,
-                        base_url=ai_base_url,
-                        api_key=self.cfg.ai_cleanup.get("api_key", ""),
-                        timeout_sec=25,
-                        language_hint="en",
-                    )
-                )
             ai_input = text
-                text = processor.process(ai_input) or text
+            text = self.ai_processor.process(ai_input) or text
         except Exception as exc:
             logging.error("ai process failed: %s", exc)
 
@@ -286,6 +275,7 @@ def main():
     parser = argparse.ArgumentParser()
     parser.add_argument("--config", default="", help="path to config.json")
     parser.add_argument("--dry-run", action="store_true", help="log hotkey only")
+    parser.add_argument("-v", "--verbose", action="store_true", help="enable verbose logs")
     args = parser.parse_args()
 
     logging.basicConfig(
@@ -299,7 +289,13 @@ def main():
     logging.info("ready (hotkey: %s)", cfg.daemon.get("hotkey", ""))
     logging.info("config (%s):\n%s", args.config or str(Path.home() / ".config" / "lel" / "config.json"), json.dumps(redacted_dict(cfg), indent=2))
 
-    daemon = Daemon(cfg)
+    if args.verbose:
+        logging.getLogger().setLevel(logging.DEBUG)
+    try:
+        daemon = Daemon(cfg, llama_verbose=args.verbose)
+    except Exception as exc:
+        logging.error("startup failed: %s", exc)
+        raise SystemExit(1)
 
     def handle_signal(_sig, _frame):
         logging.info("signal received, shutting down")

uv.lock

@@ -232,6 +232,15 @@ wheels = [
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 ]
 
+[[package]]
+name = "diskcache"
+version = "5.6.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/3f/21/1c1ffc1a039ddcc459db43cc108658f32c57d271d7289a2794e401d0fdb6/diskcache-5.6.3.tar.gz", hash = "sha256:2c3a3fa2743d8535d832ec61c2054a1641f41775aa7c556758a109941e33e4fc", size = 67916, upload-time = "2023-08-31T06:12:00.316Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/3f/27/4570e78fc0bf5ea0ca45eb1de3818a23787af9b390c0b0a0033a1b8236f9/diskcache-5.6.3-py3-none-any.whl", hash = "sha256:5e31b2d5fbad117cc363ebaf6b689474db18a1f6438bc82358b024abd4c2ca19", size = 45550, upload-time = "2023-08-31T06:11:58.822Z" },
+]
+
 [[package]]
 name = "exceptiongroup"
 version = "1.3.1"
@@ -382,12 +391,25 @@ wheels = [
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 ]
 
+[[package]]
+name = "jinja2"
+version = "3.1.6"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "markupsafe" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/df/bf/f7da0350254c0ed7c72f3e33cef02e048281fec7ecec5f032d4aac52226b/jinja2-3.1.6.tar.gz", hash = "sha256:0137fb05990d35f1275a587e9aee6d56da821fc83491a0fb838183be43f66d6d", size = 245115, upload-time = "2025-03-05T20:05:02.478Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/62/a1/3d680cbfd5f4b8f15abc1d571870c5fc3e594bb582bc3b64ea099db13e56/jinja2-3.1.6-py3-none-any.whl", hash = "sha256:85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67", size = 134899, upload-time = "2025-03-05T20:05:00.369Z" },
+]
+
 [[package]]
 name = "lel"
 version = "0.0.0"
 source = { virtual = "." }
 dependencies = [
     { name = "faster-whisper" },
+    { name = "llama-cpp-python" },
     { name = "pillow" },
     { name = "pygobject" },
     { name = "python-xlib" },
@@ -397,12 +419,111 @@ dependencies = [
 [package.metadata]
 requires-dist = [
     { name = "faster-whisper" },
+    { name = "llama-cpp-python" },
     { name = "pillow" },
     { name = "pygobject" },
     { name = "python-xlib" },
     { name = "sounddevice" },
 ]
 
+[[package]]
+name = "llama-cpp-python"
+version = "0.3.16"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "diskcache" },
+    { name = "jinja2" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
+    { name = "numpy", version = "2.4.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/e4/b4/c8cd17629ced0b9644a71d399a91145aedef109c0333443bef015e45b704/llama_cpp_python-0.3.16.tar.gz", hash = "sha256:34ed0f9bd9431af045bb63d9324ae620ad0536653740e9bb163a2e1fcb973be6", size = 50688636, upload-time = "2025-08-15T04:58:29.212Z" }
+
+[[package]]
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+version = "3.0.3"
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