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  • Deploy
  • Ecosystem
  • Learn
  • API Reference
Beginners Course
  • Module 0: Setting Up Dependencies
    • Qdrant Setup
  • Module 5: Capstone - Multimodal Supplier Risk Intelligence
    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

        Qdrant Essentials

        80%

        Course Overview
        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
            • Qdrant Academy
            • Qdrant Essentials Course
            • Day 7: Partner Ecosystem Integrations (Bonus)
            Calendar Day 7

            Partner Ecosystem Integrations (Bonus)

            Explore the Qdrant ecosystem and learn how to integrate with leading AI and data platforms.


            Partner Integrations Overview

            Learn about the Qdrant ecosystem and integration strategies.

            ➡️ Partner Integrations


            Choose Your Integration

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            Haystack
            Build end-to-end agentic pipelines with Qdrant
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            Tensorlake
            Build scalable data lakes with vector capabilities
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            LlamaIndex
            Build agentic workflows for complex enterprise documents
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            Unstructured.io
            Process and vectorize documents from any format
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            Quotient
            Advanced analytics with vector data
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            Superlinked
            Advanced feature engineering for vectors
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            Camel AI
            Agentic RAG with multi-agent systems
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            Jina AI
            Advanced multimodal embeddings with Qdrant
            Continue to Next Step

            On this page:

            • Partner Ecosystem Integrations (Bonus)
              • Partner Integrations Overview
              • Choose Your Integration
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