> Explore Qdrant's agent skills catalog at https://skills.qdrant.tech/ > Search the documentation at https://skills.qdrant.tech/search?query=your+query+here > Use this file to discover all available pages: https://qdrant.tech/llms.txt # Qdrant Beginner 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.
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6 modules
From setting up dependencies to a hands-on capstone project
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Shareable certificate
Earn a digital certificate upon completion
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Flexible schedule
Learn at your own pace
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Beginner level
No prior experience required

## What You'll Learn
Icon Skills you'll gain:
### The Path **Module 0**: Setting Up Dependencies. 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. Anatomy of a vector - how data is stored, indexed, and retrieved in Qdrant. **Module 3**: Sparse vs Dense vs Hybrid Search. Understand dense vs sparse search, when each fails, and how hybrid systems combine them. **Module 4**: Designing a Vector Search System. How to design a vector search system - layers, filtering, RAG, and deployment. **Module 5**: Capstone - Multimodal Supplier Risk Intelligence. Ingest, cluster, and query multimodal supplier signals across languages. **Bonus Module**: Further Reading. A roundup of advanced techniques for further reading: score boosting, relevance feedback, MMR, and re-ranking. ## How the Course Works
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Bite-sized lessons
Short, friendly modules you can finish in one sitting
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Learn by doing
Follow along with real examples and hands-on exercises
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One step at a time
Each module builds on the last, so nothing feels out of reach
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Go at your own pace
Pause anytime and pick up right where you left off

## Syllabus
Module 0: Setting Up Dependencies
  • Qdrant Cloud Setup
  • Implementing a Basic Vector Search

→ Start Module 0

Module 1: Let's Understand Search
  • The Problem: Why Traditional Search Struggles
  • 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

→ Start Module 1

Module 2: First Principles of Vector Search
  • What is a Vector?
  • How Dimensions Represent Meaning
  • Similarity Under the Hood
  • Your First Qdrant Collection
  • Points, Payloads, and Queries

→ Start Module 2

Module 3: Sparse vs Dense vs Hybrid Search
  • The Two Families of Search
  • Hybrid Search: Dense + Sparse
  • Setting Up Hybrid Search in Qdrant
  • Fusion Strategies
  • Beyond Text: Multimodal Search

→ Start Module 3

Module 4: Designing a Vector Search System
  • Architecture Layers of a Search System
  • Filtering and Metadata Strategies
  • Retrieval-Augmented Generation (RAG) Patterns
  • Deployment Considerations

→ Start Module 4

Module 5: Capstone - Multimodal Supplier Risk Intelligence
  • Ingesting Multimodal Supplier Signals
  • Clustering Signals Across Languages
  • Querying the Capstone System
  • Putting It All Together

→ Start Module 5

Bonus Module: Further Reading
  • Score Boosting
  • Relevance Feedback
  • Maximal Marginal Relevance (MMR)
  • Re-ranking
  • Other Advanced Techniques

→ Start Module 6

## Who It's For Anyone new to vector search who wants to understand the fundamentals. No prior experience with Qdrant or vector search engines required. ## Time Commitment - Core course (Modules 0-4): under 2 hours - Capstone project (Module 5): ~3 hours - **Total: under 5 hours** - Bonus module: optional, not included in the total above - Self-paced, flexible schedule
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What you’ll get

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