removed all python-dotenv and just let dev load from env, assuming they know variables have to be exported beforehand

qdrant-landing/content/course/essentials/day-4/large-scale-ingestion.md

@@ -57,9 +57,7 @@ The foundation of scalable ingestion is a well-designed collection configuration
 ```python
 from qdrant_client import QdrantClient, models
 import os
-from dotenv import load_dotenv
 
-load_dotenv()
 client = QdrantClient(url=os.getenv("QDRANT_URL"), api_key=os.getenv("QDRANT_API_KEY"))
 
 client.recreate_collection(

qdrant-landing/content/course/essentials/day-4/pitstop-project.md

@@ -29,7 +29,7 @@ A quantization-optimized search system that demonstrates:
 
 * Qdrant Cloud cluster (URL + API key)
 * Python 3.9+ (or Google Colab)
-* Packages: `qdrant-client`, `numpy`, `python-dotenv`
+* Packages: `qdrant-client`, `numpy`
 
 ### Models
 
@@ -55,9 +55,7 @@ import numpy as np
 
 from qdrant_client import QdrantClient, models
 import os
-from dotenv import load_dotenv
 
-load_dotenv()
 client = QdrantClient(url=os.getenv("QDRANT_URL"), api_key=os.getenv("QDRANT_API_KEY"))
 
 # For Colab:

qdrant-landing/content/course/essentials/day-4/rescoring-oversampling-indexing.md

@@ -46,9 +46,7 @@ For example, in our case with a limit of 4, a candidate that ranked 6th in the i
 ```python
 from qdrant_client import QdrantClient, models
 import os
-from dotenv import load_dotenv
 
-load_dotenv()
 client = QdrantClient(url=os.getenv("QDRANT_URL"), api_key=os.getenv("QDRANT_API_KEY"))
 
 # For Colab:

qdrant-landing/content/course/essentials/day-4/what-is-quantization.md

@@ -39,9 +39,7 @@ Scalar quantization excels as the production default because it [maintains 99%+
 ```python
 from qdrant_client import QdrantClient, models
 import os
-from dotenv import load_dotenv
 
-load_dotenv()
 client = QdrantClient(url=os.getenv("QDRANT_URL"), api_key=os.getenv("QDRANT_API_KEY"))
 
 # For Colab: