Fix init_collection_startups for latest qdrant

docker-compose-local.yaml

@@ -13,7 +13,7 @@ services:
 
   qdrant:
     container_name: qdrant_demo_qdrant
-    image: qdrant/qdrant:v1.7.3
+    image: qdrant/qdrant:latest
     ports:
       - "6333:6333"
       - "6334:6334"

qdrant_demo/init_collection_startups.py

@@ -1,5 +1,7 @@
 import json
 import os.path
+import logging
+from datetime import datetime
 
 from qdrant_client import QdrantClient, models
 from tqdm import tqdm
@@ -8,42 +10,49 @@
 
 
 def upload_embeddings():
+    print(f"{datetime.now().strftime('%Y-%m-%d %H:%M:%S,%f')[:-3]} - INFO - Initializing Qdrant client...")
     client = QdrantClient(
         url=QDRANT_URL,
         api_key=QDRANT_API_KEY,
         prefer_grpc=True,
     )
 
+    print(f"{datetime.now().strftime('%Y-%m-%d %H:%M:%S,%f')[:-3]} - INFO - Setting embedding model: {EMBEDDINGS_MODEL}")
     client.set_model(EMBEDDINGS_MODEL)
 
     payload_path = os.path.join(DATA_DIR, 'startups_demo.json')
+    print(f"{datetime.now().strftime('%Y-%m-%d %H:%M:%S,%f')[:-3]} - INFO - Loading data from file: {payload_path}")
     payload = []
     documents = []
 
     with open(payload_path) as fd:
+        print(f"{datetime.now().strftime('%Y-%m-%d %H:%M:%S,%f')[:-3]} - INFO - Processing data...")
         for line in fd:
             obj = json.loads(line)
-            # Rename fields to unified schema
             documents.append(obj.pop('description'))
             obj["logo_url"] = obj.pop("images")
             obj["homepage_url"] = obj.pop("link")
             payload.append(obj)
+
+    print(f"{datetime.now().strftime('%Y-%m-%d %H:%M:%S,%f')[:-3]} - INFO - Data processed: {len(documents)} documents")
 
+    print(f"{datetime.now().strftime('%Y-%m-%d %H:%M:%S,%f')[:-3]} - INFO - Recreating collection: {COLLECTION_NAME}")
     client.recreate_collection(
         collection_name=COLLECTION_NAME,
         vectors_config=client.get_fastembed_vector_params(on_disk=True),
-        # Quantization is optional, but it can significantly reduce the memory usage
         quantization_config=models.ScalarQuantization(
             scalar=models.ScalarQuantizationConfig(
                 type=models.ScalarType.INT8,
                 quantile=0.99,
                 always_ram=True
             )
+        ),
+        optimizers_config=models.OptimizersConfigDiff(
+            max_optimization_threads=2
         )
     )
 
-    # Create a payload index for text field.
-    # This index enables text search by the TEXT_FIELD_NAME field.
+    print(f"{datetime.now().strftime('%Y-%m-%d %H:%M:%S,%f')[:-3]} - INFO - Creating payload index for field: {TEXT_FIELD_NAME}")
     client.create_payload_index(
         collection_name=COLLECTION_NAME,
         field_name=TEXT_FIELD_NAME,
@@ -56,13 +65,16 @@ def upload_embeddings():
         )
     )
 
+    print(f"{datetime.now().strftime('%Y-%m-%d %H:%M:%S,%f')[:-3]} - INFO - Uploading documents to collection...")
     client.add(
         collection_name=COLLECTION_NAME,
         documents=documents,
         metadata=payload,
         ids=tqdm(range(len(payload))),
         parallel=0,
     )
+
+    print(f"{datetime.now().strftime('%Y-%m-%d %H:%M:%S,%f')[:-3]} - INFO - Upload completed successfully!")
 
 
 if __name__ == '__main__':