Prepare course structrure

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

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+---
+title: "Installing Dependencies"
+description: Install Python dependencies including FastEmbed and Qdrant client.
+weight: 2
+---
+
+{{< date >}} Module 0 {{< /date >}}
+
+# Installing Dependencies
+
+To work with multi-vector search in Qdrant, you'll need several Python libraries: Qdrant client for search and FastEmbed for multi-vector embeddings.
+
+We'll set up a clean Python environment and install everything you need to start experimenting with multi-vector representations.
+
+---
+
+TBD
+
+---
+
+With your environment set up, you're ready to explore multi-vector representations for textual data in Module 1.

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

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+---
+title: "Qdrant Setup"
+description: Set up Qdrant for multi-vector search. Learn how to create a collection and configure it for multi-vector embeddings.
+weight: 1
+---
+
+{{< date >}} Module 0 {{< /date >}}
+
+# Qdrant Setup
+
+Before diving into multi-vector search, you need a running Qdrant instance. Whether you choose Qdrant Cloud for a managed solution or a local deployment, this lesson will get you up and running.
+
+Multi-vector search requires specific collection configurations that differ from traditional single-vector setups. We'll cover the essentials to prepare your environment.
+
+---
+
+## Qdrant Cloud Setup
+
+<!-- TODO: Add instructions for setting up Qdrant Cloud account -->
+<!-- TODO: Include screenshot of Cloud dashboard -->
+<!-- TODO: Add cluster creation steps -->
+
+## Local Qdrant Installation
+
+<!-- TODO: Add Docker installation instructions -->
+
+```bash
+# TODO: Add Docker command to run Qdrant locally
+```
+
+<!-- TODO: Add alternative installation methods (pip, binary) -->
+
+## Creating Your First Multi-Vector Collection
+
+<!-- TODO: Explain multi-vector collection requirements -->
+<!-- TODO: Add Python code example for creating a collection -->
+
+```python
+# TODO: Add example code for creating a multi-vector collection
+```
+
+## Verifying Your Setup
+
+<!-- TODO: Add steps to verify Qdrant is running correctly -->
+<!-- TODO: Add simple query example to test connection -->
+
+```python
+# TODO: Add verification code
+```
+
+---
+
+Next, you'll install the Python dependencies needed to work with multi-vector embeddings.