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Knowledge Universe — Shared Embedding Model Singleton
=====================================================
RICK'S FIX — prevents loading all-MiniLM-L6-v2 twice in one process.
Problem:
LocalLLMReranker.__init__() calls SentenceTransformer("all-MiniLM-L6-v2")
CoverageConfidenceScorer._get_model() also calls SentenceTransformer("all-MiniLM-L6-v2")
HuggingFace Spaces free tier has 2GB RAM.
The model is ~90MB on disk but ~300MB in RAM after loading.
Loading it twice = 600MB just for embeddings. You're running into your limit.
Even if sentence_transformers caches internally, the initialization
path still incurs latency on the second call.
Fix:
One module-level singleton. Both classes import _get_shared_model().
Model loads exactly once per process, stays loaded forever.
Pre-warm call in lifespan() ensures it's ready before the first request.
Usage:
from src.integrations.shared_model import get_shared_model, prewarm_model
model = get_shared_model()
embeddings = model.encode(texts, convert_to_tensor=True)
"""
import logging
import threading
from typing import Optional
logger = logging.getLogger(__name__)
_model = None
_model_lock = threading.Lock()
_MODEL_NAME = "all-MiniLM-L6-v2"
def get_shared_model():
"""
Returns the shared SentenceTransformer instance.
Thread-safe. Loads once, cached forever.
Raises on failure — callers should handle gracefully.
"""
global _model
if _model is not None:
return _model
with _model_lock:
# Double-checked locking pattern
if _model is None:
logger.info(f"Loading shared embedding model: {_MODEL_NAME}")
try:
from sentence_transformers import SentenceTransformer
_model = SentenceTransformer(_MODEL_NAME)
logger.info(f"Shared model loaded: {_MODEL_NAME}")
except Exception as e:
logger.error(f"Failed to load shared model: {e}")
raise
return _model
def prewarm_model() -> bool:
"""
Force-initialize the model. Call this at startup before any requests.
Returns True if successful, False if model unavailable.
Add to lifespan() in main.py:
from src.integrations.shared_model import prewarm_model
prewarm_model()
"""
try:
model = get_shared_model()
# Encode a dummy sentence to fully initialize the model
# (lazy components like tokenizer warm up on first encode, not __init__)
model.encode("knowledge universe warmup", convert_to_tensor=True)
logger.info("Shared embedding model pre-warmed and ready")
return True
except Exception as e:
logger.warning(f"Model pre-warm failed (non-fatal): {e}")
return False
def is_model_loaded() -> bool:
"""Check if model is already loaded without triggering a load."""
return _model is not None |