Beginners course module 1

qdrant-landing/content/course/beginners/_index.md

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+---
+title: "Beginners Course"
+page_title: "Qdrant Beginners Course"
+short_description: "Learn the fundamentals of vector search: why keyword search fails, how semantic search works, embeddings, distance metrics, and hybrid systems."
+description: "Understand the fundamentals of vector search. Learn why keyword search breaks, how semantic search with vectors solves it, and build your first search system."
+content:
+  sidebarTitle: "Beginners Course"
+  menuTitle:
+    text: Course Overview
+    url: /course/beginners/
+  nextButton: Continue to Next Step
+  nextDay: Complete
+  title: "Beginners Course"
+  description: "Understand the fundamentals of vector search. Learn why keyword search breaks, how semantic search with vectors solves it, and build your first search system."
+partition: course
+isLesson: true
+---
+
+# Beginners Course
+
+**Learn the fundamentals of vector search**
+
+Understand why traditional search struggles and how modern semantic search improves it. Learn about embeddings, distance metrics, and hybrid search systems.
+
+<br/>
+
+{{< cards-list >}}
+- icon: /icons/outline/play-white.svg
+  title: Multiple modules
+  content: Focused lessons building from fundamentals to practical applications
+- icon: /icons/outline/cloud-check-blue.svg
+  title: Shareable certificate
+  content: Earn a digital certificate upon completion
+- icon: /icons/outline/time-blue.svg
+  title: Flexible schedule
+  content: Learn at your own pace
+- icon: /icons/outline/plan.svg
+  title: Beginner level
+  content: No prior experience required
+
+{{< /cards-list >}}
+
+<br/>
+
+## What you'll learn
+{{< course-card
+ title="Skills you'll gain:"
+ image="/icons/outline/training-white.svg"
+ type="wide-list">}}
+
+- Why keyword search breaks and how semantic search solves it
+- How embeddings convert text to vectors that capture meaning
+- Distance metrics: cosine similarity, dot product, and Euclidean
+- Hybrid search: combining dense and sparse retrieval
+- Building your first Qdrant collection and queries
+
+{{< /course-card >}}
+
+### The Path
+
+**Module 0**: Setup. Configure your environment and get started with the basics.
+
+**Module 1**: Let's Understand Search. Understand why traditional search struggles and how modern semantic search improves it.
+
+**Module 2**: First Principles of Vector Search. Learn what vectors are, how dimensions represent meaning, similarity metrics, and build your first Qdrant collection.
+
+## How the course works
+
+{{< cards-list >}}
+
+- icon: /icons/outline/training-purple.svg
+  title: Clear lessons
+  content: Focused modules by the Qdrant team
+- icon: /icons/outline/hacker-purple.svg
+  title: Hands-on learning
+  content: Practical examples and exercises
+- icon: /icons/outline/similarity-blue.svg
+  title: Progressive learning
+  content: Build from fundamentals to advanced concepts
+- icon: /icons/outline/copy.svg
+  title: Self-paced
+  content: Learn at your own speed
+  {{< /cards-list >}}
+
+<br/>
+
+## Syllabus
+
+{{< accordion >}}
+- title: "Module 0: Setting Up Dependencies"
+  content: |
+    - Qdrant Cloud Setup
+    - Implementing a Basic Vector Search
+    - Project: Building Your First Vector Search System
+    <br>
+    <br>
+    <p style="margin-left: 0px;"><a href="/course/beginners/module-0/">→ Start Module 0</a></p>
+
+- title: "Module 1: Let's Understand Search"
+  content: |
+    - The Problem: Why Keyword Search Breaks
+    - How Traditional Search Improved
+    - Enter Semantic Search
+    - How It Works: Embeddings
+    - Comparing Meaning: Distance Metrics
+    - Why Similarity Alone Is Not Enough
+    - Modern Search = Hybrid Systems
+    - References & Further Reading
+    <br>
+    <br>
+    <p style="margin-left: 0px;">→ Coming soon</p>
+
+- title: "Module 2: First Principles of Vector Search"
+  content: |
+    - What is a Vector?
+    - How Dimensions Represent Meaning
+    - Similarity Under the Hood
+    - Your First Qdrant Collection
+    - Points, Payloads, and Queries
+    <br>
+    <br>
+    <p style="margin-left: 0px;">→ Coming soon</p>
+
+- title: "Module 3: Sparse vs Dense vs Hybrid Search"
+  content: |
+    - The Two Families of Search
+    - Hybrid Search: Dense + Sparse + Filters
+    - Setting Up Hybrid Search in Qdrant
+    - Fusion Strategies
+    - Beyond Text: Multimodal Search
+    - Real-World Use Cases
+    <br>
+    <br>
+    <p style="margin-left: 0px;">→ Coming soon</p>
+{{< /accordion >}}
+
+## Who it's for
+
+Anyone new to vector search who wants to understand the fundamentals. No prior experience with Qdrant or vector databases required.
+
+## Time commitment
+
+- Duration: Multiple modules
+- Self-paced learning
+- Flexible schedule
+
+
+{{< course-card
+ title="Ready to start your vector search journey?"
+ image="/icons/outline/rocket-white-light.svg"
+ link="/course/beginners/module-0/">}}
+**What you'll get**
+- Understand the fundamentals of vector search
+- Learn why semantic search outperforms keyword search
+- Build your first Qdrant collection
+- Foundation for advanced courses
+{{< /course-card >}}

qdrant-landing/content/course/beginners/module-0/_index.md

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+---
+title: "Module 0: Setting Up Dependencies"
+short_description: "Module 0 of the Beginners course: set up Qdrant Cloud, build a first vector search, and get started with the basics."
+description: "Set up Qdrant and build your first vector search app. Learn how to configure Qdrant Cloud, run a basic search, and get started with the fundamentals."
+isLesson: true
+weight: 10
+---
+
+{{< date >}} Module 0 {{< /date >}}
+
+# Setting Up Dependencies
+
+Get started with Qdrant by setting up your environment and building your first vector search application.
+
+## Today's path
+
+1. Qdrant Cloud Setup
+2. Implementing a Basic Vector Search
+3. Project: Building Your First Vector Search System
+
+By the end, you'll have a working Qdrant setup and a complete first search running.

qdrant-landing/content/course/beginners/module-0/qdrant-cloud.md

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+---
+title: "Qdrant Setup"
+short_description: "Spin up a managed Qdrant Cloud cluster, generate API keys, and explore the Web UI for collections, points, and cluster monitoring."
+description: Set up your Qdrant Cloud cluster in minutes. Learn to create collections, manage data, access the Web UI, and connect securely from Python.
+weight: 2
+isLesson: true
+---
+
+{{< date >}} Module 0 {{< /date >}}
+
+# Qdrant Setup
+
+<div class="video">
+<iframe 
+  src="https://www.youtube.com/embed/9JBlgNBQoOY?si=7t3LAvMsUUtlUMN7&rel=0"
+  frameborder="0"
+  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
+  referrerpolicy="strict-origin-when-cross-origin"
+  allowfullscreen>
+</iframe>
+</div>
+
+<br/>
+
+Spin up production-grade vector search in minutes. Qdrant Cloud gives you a managed endpoint with TLS, automatic backups, high-availability options, and a clean API.
+
+## Create your cluster
+
+1. Sign up at [cloud.qdrant.io](https://cloud.qdrant.io/signup) with email, Google, or GitHub.
+2. Open **Clusters** → **Create a Free Cluster**. The Free Tier is enough for this course.
+
+![Create cluster](/docs/gettingstarted/gui-quickstart/create-cluster.png)
+
+3. Pick a region close to your users or app.
+4. When the cluster is ready, copy the API key and store it securely. You can make new keys later from **API Keys** on the cluster page.
+
+![Get API key](/docs/gettingstarted/gui-quickstart/api-key.png)
+
+
+## Access the Web UI
+
+1. Click **Cluster UI** in the top-right of the cluster page to open the dashboard.
+
+![Access dashboard](/docs/gettingstarted/gui-quickstart/access-dashboard.png)
+
+### What you can do in the Web UI
+
+Use the Web UI to manage collections, inspect data, and debug search performance.
+
+#### Main Navigation
+
+**Console**: Run REST calls in the browser. Test endpoints, inspect responses, and debug queries without writing code. Handy for exploring the full API.
+
+**Collections**: See and manage all collections. Create collections, upload snapshots, and track status, size, and configuration at a glance.
+
+**Tutorial**: Follow an interactive walkthrough with sample data. Create a collection, add vectors, and run semantic search with live results.
+
+![Interactive tutorial](/docs/gettingstarted/gui-quickstart/interactive-tutorial.png)
+
+**Datasets**: Bulk-load preconfigured public datasets into your cluster.
+
+#### Inside a Collection
+
+When you open a collection by clicking it's name,
+
+![Select collection](/courses/day0/select-collection.png)
+
+you'll get a detailed view with these tabs:
+
+![Collection points](/courses/day0/collection-points.png)
+
+* **Points Tab**: Inspect, search, and manage individual points. Use the search bar to find by ID or filter by payload fields (e.g., `colony: "Mars"`). For each point, you can:
+
+  * See its payload and vector(s).
+  * Click **Find Similar** to run an ad-hoc similarity search.
+  * Click **Open Graph** to jump to a graph view of its HNSW connections.
+
+* **Info Tab**: Get a full overview of collection health, config, and stats. Key fields:
+
+  * `status`: `green` means healthy.
+  * `points_count`: Number of active points.
+  * `indexed_vectors_count`: Points currently in the HNSW index. If this lags behind `points_count`, background indexing is still running.
+  * `config`: JSON view of all parameters, from vector settings to optimizer options.
+
+* **Cluster Tab**: See how shards are placed across nodes. Use it to monitor health, find hot spots, and verify shard placement in distributed setups.
+
+* **Search Quality Tab**: Evaluate and benchmark retrieval precision against ground truth. Tune parameters and measure the impact on accuracy.
+
+* **Snapshots Tab**: Manage backups for this collection. Create a [snapshot](/documentation/snapshots/), restore it later, or migrate it to another cluster.
+
+* **Visualize Tab**: Explore your vector space with an interactive 2D projection. See clusters, spot outliers, and build intuition about your embeddings.
+
+* **Graph Tab**: Explore the HNSW graph interactively. Start from any point, follow nearest neighbors, and see how the graph structure powers fast search.
+
+## Connect from Python
+
+Store credentials in an `.env` file at the root of your working directory or in colab:
+
+```env
+QDRANT_URL=https://YOUR-CLUSTER.cloud.qdrant.io:6333
+QDRANT_API_KEY=YOUR_API_KEY
+```
+
+Load the credentials from the environment and create a Qdrant client:
+
+```python
+from qdrant_client import QdrantClient, models
+import os
+
+client = QdrantClient(url=os.getenv("QDRANT_URL"), api_key=os.getenv("QDRANT_API_KEY"))
+
+# For Colab:
+# from google.colab import userdata
+# client = QdrantClient(url=userdata.get("QDRANT_URL"), api_key=userdata.get("QDRANT_API_KEY"))
+
+# Quick health check
+collections = client.get_collections()
+print(f"Connected to Qdrant Cloud: {len(collections.collections)} collections")
+```
+
+## Other ways to connect
+
+You can also send your key in the `Authorization` header:
+
+```bash
+# Using api-key header
+curl -X GET https://xyz-example.eu-central.aws.cloud.qdrant.io:6333/collections \
+  --header 'api-key: <your-api-key>'
+
+# Using Authorization header  
+curl -X GET https://xyz-example.eu-central.aws.cloud.qdrant.io:6333/collections \
+  --header 'Authorization: Bearer <your-api-key>'
+```
+
+## Quick validation
+
+Check basic connectivity:
+
+```bash
+# Service health
+curl -s "$QDRANT_URL/healthz" -H "api-key: $QDRANT_API_KEY"
+
+# List collections
+curl -s "$QDRANT_URL/collections" -H "api-key: $QDRANT_API_KEY"
+```
+
+## Good practices
+
+* Keep secrets out of code; use environment variables or a secret manager.
+* Restrict access with IP allow-lists or private networking.
+* Rotate API keys regularly from the cluster **Access** tab.
+* Use HTTPS only; turn on RBAC and strict limits when exposing endpoints to untrusted clients.
+
+## Common issues
+
+* **Authentication error**: Recheck the API key and the `api-key` header.
+* **Connection error**: Confirm cluster status and region URL; some corporate proxies block outbound TLS.
+
+## Qdrant Cloud Inference
+
+Qdrant Cloud also offers **[Cloud Inference](/cloud-inference/)**—managed embedding generation for text and images. Skip running your own embedding models; create vectors in Qdrant Cloud and write them straight into your collections.
+
+<div class="video">
+<iframe
+  src="https://www.youtube.com/embed/nJIX0zhrBL4?rel=0"
+  frameborder="0"
+  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
+  referrerpolicy="strict-origin-when-cross-origin"
+  allowfullscreen>
+</iframe>
+</div>
+
+Cut steps from your pipeline: send raw text or images to Qdrant, get vectors and search results in one API call. This helps prototypes and production systems alike by ending the separate embedding-infrastructure layer.
+
+Learn more: [Qdrant Cloud Inference](/documentation/cloud/inference/)
+