Cover dependencies installation

qdrant-landing/content/course/multi-vector-search/module-0/installing-dependencies.md

@@ -12,18 +12,122 @@ To work with multi-vector search in Qdrant, you'll need several Python libraries
 
 We'll set up a clean Python environment and install everything you need to start experimenting with multi-vector representations.
 
----
+## Python Environment Setup
 
-<div class="video">
-<iframe
-  src="https://www.youtube.com/embed/dQw4w9WgXcQ"
-  frameborder="0"
-  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
-  referrerpolicy="strict-origin-when-cross-origin"
-  allowfullscreen>
-</iframe>
-</div>
+### Using uv (Recommended)
 
----
+For this course, we recommend using [uv](https://docs.astral.sh/uv/), a modern Python package manager that's significantly faster and more reliable than traditional pip. It handles virtual environments and dependencies with better performance and dependency resolution.
 
-With your environment set up, you're ready to explore multi-vector representations for textual data in Module 1.
+**Install uv:**
+
+On macOS and Linux:
+```bash
+curl -LsSf https://astral.sh/uv/install.sh | sh
+```
+
+On Windows:
+```bash
+powershell -c "irm https://astral.sh/uv/install.ps1 | iex"
+```
+
+**Create a new virtual environment:**
+
+```bash
+uv venv
+source .venv/bin/activate  # On macOS/Linux
+# or
+.venv\Scripts\activate  # On Windows
+```
+
+### Alternative: Using Poetry
+
+If you prefer Poetry for dependency management, it offers robust project management with automatic virtual environment handling and dependency lock files.
+
+**Install Poetry:**
+
+On macOS and Linux:
+```bash
+curl -sSL https://install.python-poetry.org | python3 -
+```
+
+On Windows (PowerShell):
+```bash
+(Invoke-WebRequest -Uri https://install.python-poetry.org -UseBasicParsing).Content | py -
+```
+
+**Create a new project or add dependencies:**
+
+```bash
+# Initialize a new Poetry project
+poetry init
+
+# Activate the virtual environment
+poetry shell
+```
+
+**Python Version Requirements:**
+You'll need Python 3.10 or higher, as required by the qdrant-client library.
+
+## Installing Dependencies
+
+With your virtual environment activated, install the required libraries:
+
+**Using uv (recommended):**
+
+```bash
+uv pip install "qdrant-client==1.16.2" "fastembed==0.7.4"
+```
+
+**Using Poetry:**
+
+```bash
+poetry add "qdrant-client==1.16.2" "fastembed==0.7.4"
+```
+
+**Using pip:**
+
+```bash
+pip install "qdrant-client==1.16.2" "fastembed==0.7.4"
+```
+
+### What These Libraries Do
+
+- **qdrant-client (1.16.2)**: The official Python client for Qdrant, providing both synchronous and asynchronous APIs for vector search operations. This library contains full type definitions and supports all Qdrant features.
+
+- **fastembed (0.7.4)**: A fast, lightweight library for generating embeddings, maintained by the Qdrant team. It includes support for multi-vector embeddings which we'll use extensively in this course.
+
+## Verification Steps
+
+Let's verify that everything is installed correctly.
+
+**Test your imports:**
+
+```python
+from qdrant_client import QdrantClient
+from fastembed import TextEmbedding
+
+print("All dependencies installed successfully!")
+```
+
+**Quick connection test:**
+
+If you set up Qdrant in the previous lesson, verify you can connect:
+
+```python
+from qdrant_client import QdrantClient
+
+# For Qdrant Cloud
+client = QdrantClient(
+    url="https://your-cluster-url.cloud.qdrant.io",
+    api_key="your-api-key"
+)
+
+# For local Qdrant
+# client = QdrantClient(url="http://localhost:6333")
+
+print(f"Connected to Qdrant: {client.get_collections()}")
+```
+
+## Next Steps
+
+With your Python environment configured and dependencies installed, you're ready to dive into Module 1, where we'll explore the fundamentals of multi-vector search and understand how it differs from traditional single-vector approaches.

qdrant-landing/content/course/multi-vector-search/module-2/visual-interpretability.md

@@ -26,4 +26,4 @@ This transparency is invaluable for building trust in multi-modal search systems
 
 ---
 
-With a solid understanding of ColPali, let's start building a real application in Module 2.
+With a solid understanding of ColPali, let's start building a real application in Module 3.