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Qdrant Essentials Course

    Qdrant Essentials

    100%

    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
        • Course
        • Essentials
        • 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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