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Beginners Course
  • Module 0: Setting Up Dependencies
    • Qdrant Setup
  • Module 1: Let's Understand Search
    Multi-Vector Search Course
    • Qdrant Multi-Vector Certification
    • 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 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 Quotient
      • Integrating with Camel AI
      • Integrating with Jina AI
    • Qdrant Essentials Certification
      • Qdrant Essentials FAQs

        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
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