debug

app.py

@@ -2,7 +2,7 @@ from huggingface_hub import login
 from fastapi import FastAPI, Depends, HTTPException
 import logging
 from pydantic import BaseModel
-from sentence_transformers import SentenceTransformer
+from sentence_transformers import SentenceTransformer, logging as st_logging
 from services.qdrant_searcher import QdrantSearcher
 from services.openai_service import generate_rag_response
 from utils.auth import token_required
@@ -15,13 +15,13 @@ load_dotenv()
 # Initialize FastAPI application
 app = FastAPI()
 
+# Set the cache directory for Hugging Face
 os.environ["HF_HOME"] = "/tmp/huggingface_cache"
 
-
 # Ensure the cache directory exists
-cache_dir = os.environ["HF_HOME"]
-if not os.path.exists(cache_dir):
-    os.makedirs(cache_dir)
+hf_home_dir = os.environ["HF_HOME"]
+if not os.path.exists(hf_home_dir):
+    os.makedirs(hf_home_dir)
 
 # Setup logging
 logging.basicConfig(level=logging.INFO)
@@ -29,11 +29,15 @@ logging.basicConfig(level=logging.INFO)
 # Load Hugging Face token from environment variable
 huggingface_token = os.getenv('HUGGINGFACE_HUB_TOKEN')
 if huggingface_token:
-    login(token=huggingface_token, add_to_git_credential=True, write_permission=True)
+    try:
+        login(token=huggingface_token, add_to_git_credential=True)
+        logging.info("Successfully logged into Hugging Face Hub.")
+    except Exception as e:
+        logging.error(f"Failed to log into Hugging Face Hub: {e}")
+        raise HTTPException(status_code=500, detail="Failed to log into Hugging Face Hub.")
 else:
     raise ValueError("Hugging Face token is not set. Please set the HUGGINGFACE_HUB_TOKEN environment variable.")
 
-
 # Initialize the Qdrant searcher
 qdrant_url = os.getenv('QDRANT_URL')
 access_token = os.getenv('QDRANT_ACCESS_TOKEN')
@@ -41,9 +45,10 @@ access_token = os.getenv('QDRANT_ACCESS_TOKEN')
 if not qdrant_url or not access_token:
     raise ValueError("Qdrant URL or Access Token is not set. Please set the QDRANT_URL and QDRANT_ACCESS_TOKEN environment variables.")
 
-# Initialize the SentenceTransformer model
+# Initialize the SentenceTransformer model with the cache directory managed by HF_HOME
 try:
-    encoder = SentenceTransformer('nomic-ai/nomic-embed-text-v1.5', trust_remote_code=True)
+    st_logging.set_verbosity_info()
+    encoder = SentenceTransformer('nomic-ai/nomic-embed-text-v1.5')
     logging.info("Successfully loaded the SentenceTransformer model.")
 except Exception as e:
     logging.error(f"Failed to load the SentenceTransformer model: {e}")