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-0/building-simple-vector-search.md

@@ -42,9 +42,7 @@ To connect to Qdrant Cloud, you need your cluster URL and API key from your Qdra
 
 ```python
 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-0/pitstop-project.md

@@ -29,7 +29,7 @@ A working search system with:
 ### Prerequisites
 - Qdrant Cloud cluster (URL + API key)
 - Python 3.9+ (or Colab)
-- Required packages: `qdrant-client`, `python-dotenv`.
+- Required packages: `qdrant-client`.
 
 ### Models
 - None. We will create vectors by hand.
@@ -52,9 +52,7 @@ For this tutorial, we'll use the **Product Categories** concept.
 ```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-0/qdrant-cloud.md

@@ -99,14 +99,12 @@ QDRANT_URL=https://YOUR-CLUSTER.cloud.qdrant.io:6333
 QDRANT_API_KEY=YOUR_API_KEY
 ```
 
-Load the credentials with `dotenv` and create a Qdrant client:
+Load the credentials from the environment and create a Qdrant client:
 
 ```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-1/embedding-models.md

@@ -122,9 +122,7 @@ To create a collection with Named Vectors, you need to specify a configuration f
 ```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-1/pitstop-project.md

@@ -31,7 +31,7 @@ A working semantic search engine that demonstrates:
 ### Prerequisites
 - Qdrant Cloud cluster (URL + API key)
 - Python 3.9+ (or Colab)
-- Packages: `qdrant-client`, `sentence-transformers`, `python-dotenv` (optional), `google.colab` (if using Colab)
+- Packages: `qdrant-client`, `sentence-transformers`, `google.colab` (if using Colab)
 
 ### Models
 - SentenceTransformer: `all-MiniLM-L6-v2` (384-dim)  
@@ -62,9 +62,7 @@ Pick something with rich, descriptive text where semantic search adds value:
 from sentence_transformers import SentenceTransformer
 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-2/collection-tuning-demo.md

@@ -43,7 +43,6 @@ Working with 100K high-dimensional vectors (1536 dimensions from OpenAI's text-e
 `qdrant-client`: Official Qdrant Python client for vector search operations   
 `tqdm`: Progress bars for bulk operations (essential for 100K upload tracking)   
 `openai`: Generate query embeddings compatible with the dataset   
-`python-dotenv`: Secure environment variable management
 
 ### Set Up API Keys
 
@@ -78,9 +77,7 @@ from tqdm import tqdm
 import openai
 import time
 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-2/filterable-hnsw.md

@@ -64,9 +64,7 @@ At query time, Qdrant uses a query planner to determine the appropriate strategy
 ```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-2/pitstop-project.md

@@ -31,7 +31,7 @@ A performance-optimized version of your Day 1 search engine that demonstrates:
 
 * Qdrant Cloud cluster (URL + API key)
 * Python 3.9+ (or Google Colab)
-* Packages: `qdrant-client`, `sentence-transformers`, `python-dotenv`, `numpy`
+* Packages: `qdrant-client`, `sentence-transformers`, `numpy`
 
 ### Models
 
@@ -54,9 +54,7 @@ from sentence_transformers import SentenceTransformer
 import time
 import numpy as np
 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-2/what-is-hnsw.md

@@ -140,9 +140,7 @@ Small collections or low-dimensional vectors may not trigger HNSW indexing at al
 ```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:
@@ -186,9 +184,7 @@ Let's test the performance. First we upload some toy data to a new collection:
 import time
 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/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:

qdrant-landing/content/course/essentials/day-5/colbert-multivectors.md

@@ -57,9 +57,7 @@ To use ColBERT for retrieval, create a collection with a multivector field confi
 ```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-5/pitstop-project.md

@@ -32,7 +32,7 @@ A hybrid recommendation system using:
 
 * Qdrant Cloud cluster (URL + API key)
 * Python 3.9+ (or Google Colab)
-* Packages: `qdrant-client`, `fastembed`, `python-dotenv`
+* Packages: `qdrant-client`, `fastembed`
 
 ### Models
 - **Dense**: `sentence-transformers/all-MiniLM-L6-v2` (384-dim)
@@ -56,9 +56,7 @@ First, connect to Qdrant and create a clean collection for our recommendation sy
 from datetime import datetime
 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-5/universal-query-api.md

@@ -17,9 +17,7 @@ First, you retrieve candidates from multiple sources in parallel and fuse their
 ```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-5/universal-query-demo.md

@@ -36,9 +36,7 @@ from datetime import datetime, timedelta
 
 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-6/final-project.md

@@ -43,7 +43,7 @@ This mirrors real-world retrieval challenges where users need precise answers fr
 ### Prerequisites
 * Qdrant Cloud cluster (URL + API key)
 * Python 3.9+ (or Google Colab)
-* Packages: `qdrant-client`, `numpy`, `python-dotenv`
+* Packages: `qdrant-client`, `numpy`
 
 ### Models
 - **Dense**: `BAAI/bge-small-en-v1.5` (384-dim) or `BAAI/bge-base-en-v1.5` (768-dim)
@@ -76,9 +76,7 @@ payload = {
 ```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: