Add pre-built ChromaDB with Git LFS

.gitattributes

@@ -0,0 +1 @@
+chroma_db/** filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -16,6 +16,6 @@ __pycache__/
 *.env
 .env.local
 
-chroma_db/
+#chroma_db/
 
 misc/chatgpt_chatbot_compare.md

chatbot_app.py

@@ -19,30 +19,30 @@ EMBEDDING_MODEL = 'BAAI/bge-large-en-v1.5'
 PRODUCTS_JSON_PATH = 'products.json'
 REVIEWS_JSON_PATH = 'product_reviews.json'
 
-# --- Check for and Build VectorDB if it doesn't exist ---
-# This is crucial for environments like HF Spaces where the file system is ephemeral.
-if not os.path.exists(DB_PATH):
-    logger.info(f"ChromaDB path '{DB_PATH}' not found. Running ETL pipeline to create and populate the database.")
-    logger.info("This may take a few moments...")
+# # --- Check for and Build VectorDB if it doesn't exist ---
+# # This is crucial for environments like HF Spaces where the file system is ephemeral.
+# if not os.path.exists(DB_PATH):
+#     logger.info(f"ChromaDB path '{DB_PATH}' not found. Running ETL pipeline to create and populate the database.")
+#     logger.info("This may take a few moments...")
     
-    # Check if data files exist before running ETL
-    if not os.path.exists(PRODUCTS_JSON_PATH) or not os.path.exists(REVIEWS_JSON_PATH):
-        logger.error(f"FATAL: Required data files ('{PRODUCTS_JSON_PATH}' or '{REVIEWS_JSON_PATH}') not found.")
-        # Exit if data is missing, as the app cannot function
-        exit()
+#     # Check if data files exist before running ETL
+#     if not os.path.exists(PRODUCTS_JSON_PATH) or not os.path.exists(REVIEWS_JSON_PATH):
+#         logger.error(f"FATAL: Required data files ('{PRODUCTS_JSON_PATH}' or '{REVIEWS_JSON_PATH}') not found.")
+#         # Exit if data is missing, as the app cannot function
+#         exit()
     
-    try:
-        run_etl_pipeline(
-            products_file=PRODUCTS_JSON_PATH,
-            reviews_file=REVIEWS_JSON_PATH,
-            db_path=DB_PATH,
-            model_name=EMBEDDING_MODEL
-        )
-        logger.info("ETL pipeline completed successfully.")
-    except Exception as e:
-        logger.error(f"FATAL: An error occurred during the ETL pipeline: {e}", exc_info=True)
-        # Exit if the ETL fails, as the app cannot function
-        exit()
+#     try:
+#         run_etl_pipeline(
+#             products_file=PRODUCTS_JSON_PATH,
+#             reviews_file=REVIEWS_JSON_PATH,
+#             db_path=DB_PATH,
+#             model_name=EMBEDDING_MODEL
+#         )
+#         logger.info("ETL pipeline completed successfully.")
+#     except Exception as e:
+#         logger.error(f"FATAL: An error occurred during the ETL pipeline: {e}", exc_info=True)
+#         # Exit if the ETL fails, as the app cannot function
+#         exit()
 
 # 1. Instantiate the retrieval manager
 # It will now connect to the newly created or existing database

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