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cd6f706 d0736f4 cd6f706 d0736f4 cd6f706 d0736f4 cd6f706 d0736f4 cd6f706 d0736f4 cd6f706 d0736f4 cd6f706 d0736f4 cd6f706 d0736f4 cd6f706 d0736f4 cd6f706 d0736f4 cd6f706 d0736f4 cd6f706 d0736f4 cd6f706 d0736f4 cd6f706 d0736f4 cd6f706 d0736f4 cd6f706 d0736f4 cd6f706 d0736f4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 | """Local GLiNER2 extraction service for the ``/json/no-ai`` route.
GLiNER2 performs zero-shot entity / structured-JSON extraction entirely
on-device -- no external AI/LLM API is called, hence the "no-ai" route name.
The model is loaded ONCE and cached for the life of the process (warmed at
application startup via the lifespan hook in ``app/api/server.py`` and lazily
loaded on first use if startup warm-up failed). Inference is CPU-bound and is
dispatched on the shared thread pool by the route layer; a bounded semaphore
serializes concurrent forwards on the shared model instance so a burst of
requests cannot oversubscribe the CPU or the model's memory buffers.
Inference backend: the ONNX export ``lion-ai/gliner2-base-v1-onnx`` running on
ONNX Runtime via ``GLiNER2ONNXEngine`` (see ``gliner_onnx.py``). This removes
the PyTorch dependency from the extraction hot path.
"""
from __future__ import annotations
import threading
import time
from typing import Any, Dict, List, Optional
from app.config import get_settings
from app.core.logger import get_logger
from app.services.gliner_onnx import GLiNER2ONNXEngine
logger = get_logger(__name__)
DEFAULT_MODEL_ID = "lion-ai/gliner2-base-v1-onnx"
DEFAULT_DEVICE = "cpu"
DEFAULT_MAX_CONCURRENT = 2
DEFAULT_MAX_CONTENT_LENGTH = 100_000
class GLiNERService:
"""Cached, concurrency-bounded wrapper around the GLiNER2 ONNX engine."""
def __init__(
self,
model_id: str = DEFAULT_MODEL_ID,
device: str = DEFAULT_DEVICE,
max_concurrent: int = DEFAULT_MAX_CONCURRENT,
max_content_length: int = DEFAULT_MAX_CONTENT_LENGTH,
) -> None:
self._model_id = model_id
self._device = device
self._max_content_length = max_content_length
self._max_concurrent = max(1, max_concurrent)
self._engine: Optional[GLiNER2ONNXEngine] = None
self._lock = threading.Lock()
self._semaphore = threading.BoundedSemaphore(self._max_concurrent)
self._load_error: Optional[str] = None
# ------------------------------------------------------------------ #
# Lifecycle
# ------------------------------------------------------------------ #
def load_model(self) -> None:
"""Load and cache the GLiNER2 ONNX model. Idempotent and thread-safe."""
if self.is_loaded():
return
with self._lock:
if self.is_loaded():
return
t0 = time.perf_counter()
try:
engine = GLiNER2ONNXEngine(model_id=self._model_id, device=self._device)
engine.load()
self._engine = engine
self._load_error = None
logger.info(
"GLiNER2 ONNX model loaded and cached (%s, device=%s) in %.2fs",
self._model_id,
self._device,
time.perf_counter() - t0,
)
except Exception as exc: # noqa: BLE001
self._load_error = str(exc)
logger.exception("GLiNER2 ONNX model load failed")
raise
def unload(self) -> None:
"""Release the cached model. Used at shutdown."""
with self._lock:
self._engine = None
self._load_error = None
def is_loaded(self) -> bool:
return self._engine is not None
def load_error(self) -> Optional[str]:
return self._load_error
@property
def model_id(self) -> str:
return self._model_id
@property
def device(self) -> str:
return self._device
@property
def max_content_length(self) -> int:
return self._max_content_length
# ------------------------------------------------------------------ #
# Inference (called on the shared thread pool)
# ------------------------------------------------------------------ #
def extract_json(self, text: str, structure: Dict[str, Any], threshold: float = 0.5) -> Dict[str, Any]:
"""Structured JSON extraction with the cached local ONNX model."""
self._ensure_loaded()
with self._semaphore:
return self._engine.extract_json(text, structure, threshold=threshold)
def extract_entities(self, text: str, labels: List[str], threshold: float = 0.5) -> Dict[str, Any]:
"""Zero-shot entity extraction with the cached local ONNX model."""
self._ensure_loaded()
with self._semaphore:
return self._engine.extract_entities(text, labels, threshold=threshold)
def _ensure_loaded(self) -> None:
if not self.is_loaded():
self.load_model()
_settings = get_settings()
gliner_service = GLiNERService(
model_id=_settings.gliner_model,
device=_settings.gliner_device,
max_concurrent=_settings.gliner_max_concurrent,
max_content_length=_settings.gliner_max_content_length,
)
# =============================================================================
# DEPRECATED -- PyTorch (``gliner2`` / torch) backend.
# -----------------------------------------------------------------------------
# Kept for reference and easy rollback / later fallback implementation. The old
# implementation loaded ``fastino/gliner2-base-v1`` via the ``gliner2`` PyTorch
# library, which pulled torch into the extraction hot path and required
# redirecting stdout to swallow the emoji config banner.
#
# To restore the PyTorch backend:
# 1. Set GLINER_MODEL back to a PyTorch repo (e.g. fastino/gliner2-base-v1)
# in config/.env/.env.example and the Dockerfile.
# 2. Un-comment the whole block below and delete/rename the ONNX
# ``GLiNERService`` above (or point the module-level ``gliner_service``
# instantiation at the deprecated class).
# =============================================================================
# from __future__ import annotations
#
# import contextlib
# import io
# import threading
# import time
# from typing import Any, Dict, List, Optional
#
# from app.config import get_settings
# from app.core.logger import get_logger
#
# logger = get_logger(__name__)
#
# DEFAULT_MODEL_ID = "fastino/gliner2-base-v1"
# DEFAULT_DEVICE = "cpu"
# DEFAULT_MAX_CONCURRENT = 2
# DEFAULT_MAX_CONTENT_LENGTH = 100_000
#
#
# class GLiNERService:
# """Cached, concurrency-bounded wrapper around a GLiNER2 model instance."""
#
# def __init__(
# self,
# model_id: str = DEFAULT_MODEL_ID,
# device: str = DEFAULT_DEVICE,
# max_concurrent: int = DEFAULT_MAX_CONCURRENT,
# max_content_length: int = DEFAULT_MAX_CONTENT_LENGTH,
# ) -> None:
# self._model_id = model_id
# self._device = device
# self._max_content_length = max_content_length
# self._max_concurrent = max(1, max_concurrent)
# self._model: Any = None
# self._lock = threading.Lock()
# self._semaphore = threading.BoundedSemaphore(self._max_concurrent)
# self._load_error: Optional[str] = None
#
# # ------------------------------------------------------------------ #
# # Lifecycle
# # ------------------------------------------------------------------ #
#
# def load_model(self) -> None:
# """Load and cache the GLiNER2 model. Idempotent and thread-safe."""
# if self.is_loaded():
# return
# with self._lock:
# if self.is_loaded():
# return
# t0 = time.perf_counter()
# try:
# from gliner2 import GLiNER2
#
# # GLiNER2 prints an emoji config banner to stdout on load, which
# # crashes consoles with a non-UTF-8 encoding (e.g. Windows cp1252).
# # Swallow it so loading works everywhere.
# with contextlib.redirect_stdout(io.StringIO()):
# self._model = GLiNER2.from_pretrained(self._model_id, map_location=self._device)
# self._load_error = None
# logger.info(
# "GLiNER2 model loaded and cached (%s, device=%s) in %.2fs",
# self._model_id,
# self._device,
# time.perf_counter() - t0,
# )
# except Exception as exc: # noqa: BLE001
# self._load_error = str(exc)
# logger.exception("GLiNER2 model load failed")
# raise
#
# def unload(self) -> None:
# """Release the cached model (frees ~1.3 GB). Used at shutdown."""
# with self._lock:
# self._model = None
# self._load_error = None
#
# def is_loaded(self) -> bool:
# return self._model is not None
#
# def load_error(self) -> Optional[str]:
# return self._load_error
#
# @property
# def model_id(self) -> str:
# return self._model_id
#
# @property
# def device(self) -> str:
# return self._device
#
# @property
# def max_content_length(self) -> int:
# return self._max_content_length
#
# # ------------------------------------------------------------------ #
# # Inference (called on the shared thread pool)
# # ------------------------------------------------------------------ #
#
# def extract_json(self, text: str, structure: Dict[str, Any], threshold: float = 0.5) -> Dict[str, Any]:
# """Structured JSON extraction with the cached local model."""
# self._ensure_loaded()
# with self._semaphore:
# return self._model.extract_json(text, structure, threshold=threshold)
#
# def extract_entities(self, text: str, labels: List[str], threshold: float = 0.5) -> Dict[str, Any]:
# """Zero-shot entity extraction with the cached local model."""
# self._ensure_loaded()
# with self._semaphore:
# return self._model.extract_entities(text, labels, threshold=threshold)
#
# def _ensure_loaded(self) -> None:
# if not self.is_loaded():
# self.load_model()
#
#
# _settings = get_settings()
#
# gliner_service = GLiNERService(
# model_id=_settings.gliner_model,
# device=_settings.gliner_device,
# max_concurrent=_settings.gliner_max_concurrent,
# max_content_length=_settings.gliner_max_content_length,
# )
# ============================================================================= |