logo
  • Develop
  • Deploy
  • Ecosystem
  • Learn
  • API Reference
Log in Start Free
logo
  • Develop
  • Deploy
  • Ecosystem
  • Learn
  • API Reference
Qdrant Beginner Course
  • Module 0: Setting Up Dependencies
    • Module Overview
    • Qdrant Setup
    • Implementing a Basic Vector Search
  • Module 1: Let's Understand Search
    • Module Overview
    • What Is Search?
    • Why Keyword Search Struggles
    • How Traditional Search Improved
    • How It Works: Embeddings
    • Comparing Meaning: Distance Metrics
    • Why Similarity Alone Is Not Enough
    • When a System Needs Both
    • Further Reading
  • Module 2: First Principles of Vector Search
    • Module Overview
    • From Idea to System
    • Core Data Model
    • Distance Metrics
    • Top-K Retrieval
    • Fast Approximate Search: HNSW
    • Payload Filtering
    • Chunking Strategies
    • Ingestion Pipeline: End-to-End
    • Further Reading
  • Module 3: Sparse vs Dense vs Hybrid Search
    • Module Overview
    • Where We Left Off
    • The Two Families of Search
    • Hybrid Search: Dense and Sparse
    • Setting Up Hybrid Search in Qdrant
    • Fusion Strategies
    • Filtering: Works with Any Retrieval Method
    • Knowledge Check
    • References and Further Reading
  • Module 4: Designing a Vector Search System
    • Module Overview
    • Where Design Decisions Live
    • Decide Before You Ingest
    • What Changes as the Collection Grows
    • Growing Past One Machine
    • From Results to an Answer
    • Where It Runs
    • Design Your Own System
    • References and Further Reading
  • Module 5: Multimodal Supplier Risk Intelligence
    • Module Overview
    • Project Overview
    • System Architecture
    • Signal Sources and Embedding Models
    • Ingestion Pipeline
    • Clustering Risk Signals
    • Analyst Queries
    • Knowledge Check
    • Course Summary
    • References and Further Reading
  • Module 6: Beyond Similarity (Bonus)
    • Module Overview
    • Find Your Problem
    • Measure First
    • Ranking: Score Boosting and Reranking
    • Diversity: Maximal Marginal Relevance
    • Grouping: One Slot per Document
    • Searching From Examples and Feedback
    • Inspecting a Collection
    • Knowledge Check
  • Qdrant Beginner Certification
Qdrant Essentials Course
  • Day 0: Setup and First Steps
    • Qdrant Setup
    • Implementing a Basic Vector Search
    • Project: Building Your First Vector Search System
  • Day 1: Vector Search Fundamentals
    • Points, Vectors and Payloads
    • Distance Metrics
    • Text Chunking Strategies
    • Demo: Semantic Movie Search
    • Project: Building a Semantic Search Engine
  • Day 2: Indexing and Performance
    • HNSW Indexing Fundamentals
    • Combining Vector Search and Filtering
    • Demo: HNSW Performance Tuning
    • Project: HNSW Performance Benchmarking
  • Day 3: Hybrid Search
    • Sparse Vectors and Inverted Indexes
    • Demo: Keyword Search with Sparse Vectors
    • Hybrid Search and the Universal Query API
    • Demo: Implementing a Hybrid Search System
    • Project: Building a Hybrid Search Engine
  • Day 4: Optimization and Scale
    • Vector Quantization Methods
    • Accuracy Recovery with Rescoring
    • Large-Scale Data Ingestion
    • Project: Quantization Performance Optimization
  • Day 5: Advanced APIs
    • Multivectors for Late Interaction Models
    • The Universal Query API
    • Demo: Universal Query for Hybrid Retrieval
    • Project: Building a Recommendation System
  • Day 6: Final Project - Building a Production-Grade Search Engine
    • Final Project: Production-Ready Documentation Search Engine
    • Course Completion and Next Steps
  • Day 7: Partner Ecosystem Integrations (Bonus)
    • Integrating with Haystack
    • Integrating with Unstructured.io
    • Integrating with Tensorlake
    • Integrating with Superlinked
    • Integrating with LlamaIndex
    • Integrating with Camel AI
    • Integrating with Jina AI
  • Qdrant Essentials Certification
    • Qdrant Essentials FAQs
      Multi-Vector Search Course
      • Module 0: Setting Up Dependencies
        • Qdrant Setup
        • Installing Dependencies
      • Module 1: Multi-Vector Representations for Textual Data
        • Late Interaction Basics
        • MaxSim Distance Metric
        • Use Cases for Multi-Vector Search
        • Problems of Multi-Vector Search
        • Multi-Vector Embeddings in Qdrant
      • Module 2: Multi-Vector Representations for Multi-Modal Data
        • How ColPali Models Work
        • ColPali Family Overview
        • Visual Interpretability of ColPali
      • Module 3: Scalability and Optimization
        • Multi-Stage Retrieval with Universal Query API
        • Vector Quantization Techniques
        • Pooling Techniques
        • MUVERA
        • Evaluating Search Pipelines
        • Final Project: Build Your Own Multi-Vector Search System
      • Qdrant Multi-Vector Certification

      Multi-Vector Search Course

      5%

      Course Overview
      Module 0: Setting Up Dependencies
        Qdrant Setup
        Installing Dependencies
      Module 1: Multi-Vector Representations for Textual Data
        Late Interaction Basics
        MaxSim Distance Metric
        Use Cases for Multi-Vector Search
        Problems of Multi-Vector Search
        Multi-Vector Embeddings in Qdrant
      Module 2: Multi-Vector Representations for Multi-Modal Data
        How ColPali Models Work
        ColPali Family Overview
        Visual Interpretability of ColPali
      Module 3: Scalability and Optimization
        Multi-Stage Retrieval with Universal Query API
        Vector Quantization Techniques
        Pooling Techniques
        MUVERA
        Evaluating Search Pipelines
        Final Project: Build Your Own Multi-Vector Search System
      Qdrant Multi-Vector Certification
      • Qdrant Academy
      • Multi-Vector Search Course
      • Module 0: Setting Up Dependencies
      Calendar Module 0

      Setting Up Dependencies

      Get your environment ready for exploring multi-vector search with Qdrant.


      Today’s path

      1. Qdrant Setup
      2. Installing Dependencies

      By the end, you’ll have a working development environment ready for multi-vector search experiments.

      Continue to Next Step

      On this page:

      • Setting Up Dependencies
        • Today’s path
      © 2026 Qdrant.
      Terms Privacy Policy Impressum Recruitment Privacy Policy Cookie Consent