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  • 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

      Qdrant Essentials

      81%

      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 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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          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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