Set all leaf pages to isLesson: true for essentials course

2026-10-02 01:18:30 +02:00 · 2026-03-16 15:36:49 +01:00
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 title: "Qdrant Essentials Certification"
 description: Get officially certified by Qdrant today!
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 weight: 100
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 title: "Implementing a Basic Vector Search"
 description: Learn how to build a basic vector search in Qdrant. Create collections, insert vectors, and run your first similarity search step-by-step with Python. 
 weight: 3
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 title: "Project: Building Your First Vector Search System"
 description: Apply your Qdrant skills to build a complete vector search system. Create collections, insert data, run similarity and filtered searches, and share your results. 
 weight: 4
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 title: "Qdrant Setup"
 description: Set up your Qdrant Cloud cluster in minutes. Learn to create collections, manage data, access the Web UI, and connect securely from Python.
 weight: 2
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 title: "Text Chunking Strategies"
 description: Learn how to split text into meaningful chunks for vector search. Compare six chunking strategies and discover how metadata improves retrieval precision in Qdrant.
 weight: 4
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 title: "Distance Metrics"
 description: Learn how distance metrics like cosine, Euclidean, Manhattan, and dot product shape vector similarity in Qdrant. Discover which metric fits your data and use case. 
 weight: 3
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 title: "Points, Vectors and Payloads"
 description: Learn Qdrant’s core data model with points, vectors, payloads, and named vectors. Compare dense, sparse, and multivectors, understand dimensionality trade-offs, and master filtering with payload indexes for precise retrieval. 
 weight: 2
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 title: "Demo: Semantic Movie Search"
 description: Build a semantic movie search with Qdrant. Compare chunking strategies, embed descriptions, and combine cosine similarity with metadata filters and grouping for accurate, theme-aware recommendations. 
 weight: 5
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 title: "Project: Building a Semantic Search Engine"
 description: Build a semantic search engine with Qdrant. Compare chunking strategies, index embeddings, and query by meaning to discover what works best for your domain. 
 weight: 6
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 title: "Demo: HNSW Performance Tuning"
 description: Tune Qdrant’s HNSW index for speed and precision. Optimize bulk uploads, test filters, and benchmark performance on a real 100K OpenAI embedding dataset. 
 weight: 4
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 title: "Combining Vector Search and Filtering"
 description: Learn how Qdrant combines HNSW vector search with payload filtering. Understand Filterable HNSW, query planning, and payload indexing for accurate, high-performance retrieval.
 weight: 3
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 title: "Project: HNSW Performance Benchmarking"
 description: Optimize vector search with Qdrant. Test multiple HNSW configurations, time uploads and queries, and evaluate filtering with and without payload indexes to find the best settings for your domain.
 weight: 5
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 title: "HNSW Indexing Fundamentals"
 description: Learn how HNSW indexing powers fast, scalable vector search in Qdrant. Understand parameters like m, ef_construct, and hnsw_ef to balance recall, speed, and memory efficiency. 
 weight: 2
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 title: "Demo: Implementing a Hybrid Search System"
 description: Step-by-step demo on implementing hybrid search using Qdrant’s Universal Query API. Explore dense vs. sparse search, score fusion algorithms, and real-world evaluation techniques.
 weight: 5
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 title: "Hybrid Search and the Universal Query API"
 description: Master hybrid search in Qdrant using dense and sparse vectors. Explore retrieval, reranking, and Reciprocal Rank Fusion (RRF) to build efficient, adaptive search experiences.
 weight: 4
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 title: "Project: Building a Hybrid Search Engine"
 description: Build a hybrid search engine in Qdrant combining dense and sparse vectors with Reciprocal Rank Fusion. Compare performance, optimize retrieval, and understand when hybrid search outperforms single-vector methods.
 weight: 6
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 title: "Demo: Keyword Search with Sparse Vectors"
 description: Hands-on sparse retrieval in Qdrant—create BM25 collections, enable IDF, index with FastEmbed, try SPLADE++ expansion, and execute keyword queries via the Universal Query API.
 weight: 3
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 title: "Sparse Vectors and Inverted Indexes"
 description: Learn sparse vectors and inverted indexes in Qdrant, create named sparse vectors, store index–value pairs, run exact dot-product search, and prepare for hybrid search with dense vectors.
 weight: 2
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 title: "Large-Scale Data Ingestion"
 description: Master large-scale vector ingestion in Qdrant. Explore batching, upload_points, and upload_collection methods, on-disk storage, and parallel streaming for billion-scale AI data pipelines.
 weight: 4
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 title: "Project: Quantization Performance Optimization"
 description: Apply vector quantization in Qdrant to boost search speed, reduce memory, and balance accuracy. Test scalar, binary, and 2-bit quantization with oversampling and rescoring optimization. 
 weight: 5
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 title: "Accuracy Recovery with Rescoring"
 description: Learn how oversampling and rescoring restore accuracy in quantized vector search. Improve Qdrant search precision while maintaining high performance using efficient reranking on original vectors. 
 weight: 3
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 title: "Vector Quantization Methods"
 description: Explore scalar, binary, and product quantization in Qdrant. Learn how compression boosts vector search speed, cuts memory costs, and balances accuracy for large-scale AI retrieval. 
 weight: 2
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 title: "Multivectors for Late Interaction Models"
 description: Learn how Qdrant supports late interaction models like ColBERT and ColPali using multivectors for token-level precision, enabling fine-grained, context-aware text and visual document retrieval.
 weight: 2
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 title: "Project: Building a Recommendation System"
 description: Build a hybrid AI recommendation system with Qdrant’s Universal Query API—combining dense, sparse, and multivector retrieval, ColBERT reranking, and RRF fusion in one atomic query. 
 weight: 5
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 title: "The Universal Query API"
 description: Learn how to run dense, sparse, and ColBERT multivector retrieval with Qdrant’s Universal Query API—fusing, filtering, and reranking results in a single atomic request.
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 title: "Demo: Universal Query for Hybrid Retrieval"
 description: Build a hybrid research discovery system using Qdrant’s Universal Query API—combine dense, sparse, and ColBERT vectors for semantic, keyword, and reranked retrieval in one query. 
 weight: 4
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 title: "Course Completion and Next Steps"
 description: Complete your Qdrant course by earning certification and mastering hybrid, multivector, and production-ready vector search techniques—skills to design, evaluate, and deploy real-world AI search systems. 
 weight: 3
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 title: "Final Project: Production-Ready Documentation Search Engine"
 description: Create a complete documentation search system with Qdrant, featuring hybrid retrieval, multivector reranking, and performance evaluation for a portfolio-ready, production-quality vector search application.
 weight: 2
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 title: "Integrating with Camel AI"
 description: Learn how Camel AI and Qdrant enable automated RAG pipelines with multi-agent communication, vector-based memory, and seamless integration into live environments like Discord bots. 
 weight: 8
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 title: "Integrating with Haystack"
 description: Learn how Qdrant and Haystack combine to deliver end-to-end search and recommendation systems with hybrid retrieval, semantic filtering, and agentic AI orchestration. 
 weight: 2
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 title: "Integrating with Jina AI"
 description: Learn how Jina AI’s Embeddings v4 and Qdrant enable advanced multimodal retrieval, supporting text-to-image, image-to-text, and hybrid search with high-performance vector storage.
 weight: 9
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 title: "Integrating with LlamaIndex"
 description: Learn how LlamaIndex and Qdrant power intelligent RAG pipelines, function-calling agents, and cloud-synced vector search systems with structured workflows and dynamic query handling.
 weight: 6
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 title: "Integrating with Quotient"
 description: Learn how Quotient and Qdrant combine to deliver end-to-end AI monitoring, hallucination detection, and performance analytics for reliable, high-quality retrieval-augmented generation systems.
 weight: 7
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 title: "Integrating with Superlinked"
 description: Learn how Superlinked’s Mixture of Encoders and Qdrant enable rich, multi-modal embeddings that fuse semantic, numerical, and temporal data for optimized vector retrieval.
 weight: 5
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 title: "Integrating with Tensorlake"
 description: Learn how TensorLake and Qdrant combine document parsing, knowledge graphs, and vector search to build scalable, structured data lakes for advanced RAG and research discovery applications.
 weight: 4
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 title: "Integrating with Unstructured.io"
 description: Learn how Unstructured.io and Qdrant transform unstructured enterprise data into structured embeddings through VLM document understanding, smart chunking, and secure, production-ready ETL pipelines. 
 weight: 3
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