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added quote wrapping to meta description content
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kanungle
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title: "Qdrant Essentials Course"
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page_title: Qdrant Essentials Course
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short_description: "Build production vector search skills in seven days: hybrid retrieval, multivector reranking, quantization, sharding, and multitenancy."
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description: Learn hybrid search, multivectors, and production deployment in 7 days. Build and ship a docs search engine.
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description: "Learn hybrid search, multivectors, and production deployment in 7 days. Build and ship a docs search engine."
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content:
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sidebarTitle: Qdrant Essentials
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menuTitle:
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@@ -1,7 +1,7 @@
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---
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title: "Qdrant Essentials Certification"
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short_description: "Validate your Qdrant Essentials skills with an official certification exam covering HNSW indexing, hybrid search, and production optimization."
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description: Get officially certified by Qdrant today!
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description: "Get officially certified by Qdrant today!"
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isLesson: true
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weight: 100
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---
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@@ -1,7 +1,7 @@
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---
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title: "Day 0: Setup and First Steps"
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short_description: "Day 0 of Qdrant Essentials: set up Qdrant Cloud, build a first vector search, and ship a working starter project."
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description: Set up Qdrant and build your first vector search app. Learn how to configure Qdrant Cloud, run a basic search, and complete your first project.
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description: "Set up Qdrant and build your first vector search app. Learn how to configure Qdrant Cloud, run a basic search, and complete your first project."
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isLesson: true
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weight: 10
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---
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@@ -1,7 +1,7 @@
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---
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title: "Implementing a Basic Vector Search"
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short_description: "Walk through your first vector search: connect to Qdrant, create a collection, insert points, and run similarity queries with the Python client."
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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.
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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."
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weight: 3
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isLesson: true
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||||
---
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@@ -1,7 +1,7 @@
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---
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title: "Project: Building Your First Vector Search System"
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short_description: "Hands-on starter project: build a working vector search system with a collection, points, payloads, similarity search, and filter conditions."
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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.
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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."
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weight: 4
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isLesson: true
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---
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@@ -1,7 +1,7 @@
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---
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title: "Qdrant Setup"
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short_description: "Spin up a managed Qdrant Cloud cluster, generate API keys, and explore the Web UI for collections, points, and cluster monitoring."
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description: Set up your Qdrant Cloud cluster in minutes. Learn to create collections, manage data, access the Web UI, and connect securely from Python.
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description: "Set up your Qdrant Cloud cluster in minutes. Learn to create collections, manage data, access the Web UI, and connect securely from Python."
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weight: 2
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isLesson: true
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---
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---
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title: "Day 1: Vector Search Fundamentals"
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short_description: "Day 1 of Qdrant Essentials: points, vectors, payloads, distance metrics, chunking strategies, and a hands-on semantic search build."
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description: Learn vector search fundamentals in Qdrant. Explore points, payloads, and distance metrics, then apply them in a hands-on semantic movie search project.
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description: "Learn vector search fundamentals in Qdrant. Explore points, payloads, and distance metrics, then apply them in a hands-on semantic movie search project."
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isLesson: true
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weight: 20
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---
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@@ -1,7 +1,7 @@
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---
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title: "Text Chunking Strategies"
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short_description: "Compare text chunking strategies and learn how to split documents into chunks that align with embedding model context windows for precise retrieval."
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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.
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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."
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weight: 4
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isLesson: true
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---
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---
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title: "Distance Metrics"
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short_description: "Compare cosine, dot product, and Euclidean distance for vector search, and learn how to pick the right metric for your embedding model."
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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.
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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."
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weight: 3
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isLesson: true
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---
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---
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title: "Points, Vectors and Payloads"
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short_description: "Understand Qdrant's core data model: points, payloads, dense vectors, sparse vectors, and multivectors for flexible vector search."
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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.
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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."
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weight: 2
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isLesson: true
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---
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---
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title: "Demo: Semantic Movie Search"
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short_description: "Walk through a semantic movie search demo using sentence embeddings, payload metadata, and chunking to retrieve films by theme and concept."
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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.
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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."
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weight: 5
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isLesson: true
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---
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---
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title: "Project: Building a Semantic Search Engine"
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short_description: "Build a domain-specific semantic search engine and compare chunking strategies to see how preprocessing shapes retrieval quality."
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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.
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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."
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weight: 6
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isLesson: true
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---
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---
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title: "Day 2: Indexing and Performance"
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short_description: "Day 2 of Qdrant Essentials: HNSW indexing, filterable HNSW, payload indexes, and tuning recall and latency for fast vector search."
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description: Learn how Qdrant’s HNSW indexing accelerates vector search. Explore index parameters, filtering integration, and performance tuning to balance speed, recall, and precision.
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description: "Learn how Qdrant’s HNSW indexing accelerates vector search. Explore index parameters, filtering integration, and performance tuning to balance speed, recall, and precision."
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isLesson: true
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weight: 30
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---
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---
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title: "Demo: HNSW Performance Tuning"
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short_description: "Tune HNSW parameters and payload indexes on a large embedding dataset to optimize bulk upload speed, search latency, and filter performance."
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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.
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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."
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weight: 4
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isLesson: true
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---
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---
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title: "Combining Vector Search and Filtering"
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short_description: "Combine vector search with payload filters using Qdrant's filterable HNSW index to keep recall high without sacrificing query speed."
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description: Learn how Qdrant combines HNSW vector search with payload filtering. Understand Filterable HNSW, query planning, and payload indexing for accurate, high-performance retrieval.
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description: "Learn how Qdrant combines HNSW vector search with payload filtering. Understand Filterable HNSW, query planning, and payload indexing for accurate, high-performance retrieval."
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weight: 3
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isLesson: true
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---
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---
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title: "Project: HNSW Performance Benchmarking"
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short_description: "Benchmark HNSW configurations and payload indexes on your own dataset to find the parameter mix that fits your workload."
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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.
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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."
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weight: 5
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isLesson: true
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---
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---
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title: "HNSW Indexing Fundamentals"
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short_description: "Learn how HNSW powers approximate nearest neighbor search in Qdrant and how its layered graph structure scales to billions of vectors."
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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.
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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."
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weight: 2
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isLesson: true
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---
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---
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title: "Day 3: Hybrid Search"
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short_description: "Day 3 of Qdrant Essentials: sparse vectors, inverted indexes, BM25, hybrid search, and score fusion with the Universal Query API."
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description: Learn how to combine dense and sparse vector search in Qdrant. Master hybrid search, score fusion, and keyword indexing to boost retrieval precision and recall.
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description: "Learn how to combine dense and sparse vector search in Qdrant. Master hybrid search, score fusion, and keyword indexing to boost retrieval precision and recall."
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isLesson: true
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weight: 40
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---
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---
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title: "Demo: Implementing a Hybrid Search System"
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short_description: "Implement hybrid search end to end: dense plus sparse named vectors, RRF fusion, and side-by-side comparisons of retrieval strategies."
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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.
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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."
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weight: 5
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isLesson: true
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---
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---
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title: "Hybrid Search and the Universal Query API"
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short_description: "Combine dense and sparse vectors with Qdrant's Universal Query API and Reciprocal Rank Fusion to serve both semantic and keyword queries."
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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.
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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."
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weight: 4
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isLesson: true
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---
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---
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title: "Project: Building a Hybrid Search Engine"
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short_description: "Build a hybrid search engine with dense and sparse vectors, fuse results with RRF, and benchmark against single-vector baselines."
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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.
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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."
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weight: 6
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isLesson: true
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---
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---
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title: "Demo: Keyword Search with Sparse Vectors"
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short_description: "Run keyword retrieval with sparse vectors in Qdrant using BM25 and SPLADE-style neural sparse models for precise lexical matching."
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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.
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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."
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weight: 3
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isLesson: true
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---
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---
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title: "Sparse Vectors and Inverted Indexes"
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short_description: "Use sparse vectors and inverted indexes for keyword search and recommendations, with index-value pairs and dot-product scoring."
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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.
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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."
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weight: 2
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isLesson: true
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---
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---
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title: "Day 4: Optimization and Scale"
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short_description: "Day 4 of Qdrant Essentials: scalar, binary, and product quantization, oversampling and rescoring, plus high-throughput ingestion patterns."
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description: Learn Qdrant performance optimization with vector quantization, accuracy recovery with rescoring, and high-throughput data ingestion. Compare memory usage, recall, and speed to tune large-scale vector search.
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description: "Learn Qdrant performance optimization with vector quantization, accuracy recovery with rescoring, and high-throughput data ingestion. Compare memory usage, recall, and speed to tune large-scale vector search."
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isLesson: true
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weight: 50
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---
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---
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title: "Large-Scale Data Ingestion"
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short_description: "Choose the right ingestion strategy for Qdrant: batched upserts, upload_points, and streaming uploads for million- and billion-scale workloads."
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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.
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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."
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weight: 4
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isLesson: true
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---
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---
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title: "Project: Quantization Performance Optimization"
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short_description: "Apply scalar and binary quantization to your search engine and tune oversampling plus rescoring for better recall and lower memory."
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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.
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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."
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weight: 5
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isLesson: true
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---
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---
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title: "Accuracy Recovery with Rescoring"
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short_description: "Recover accuracy after quantization with oversampling and rescoring, using original vectors to rerank candidates from the quantized index."
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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.
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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."
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weight: 3
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isLesson: true
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---
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---
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title: "Vector Quantization Methods"
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short_description: "Compare scalar, binary, and product quantization to compress vectors, cut memory cost, and keep retrieval quality high in production."
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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.
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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."
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weight: 2
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isLesson: true
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---
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---
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title: "Day 5: Advanced APIs"
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short_description: "Day 5 of Qdrant Essentials: multivectors, ColBERT late interaction, and the Universal Query API for advanced retrieval pipelines."
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description: Learn advanced Qdrant APIs, including multivectors and the Universal Query API, to power hybrid retrieval, late interaction models, and recommendation systems with high accuracy and flexibility.
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||||
description: "Learn advanced Qdrant APIs, including multivectors and the Universal Query API, to power hybrid retrieval, late interaction models, and recommendation systems with high accuracy and flexibility."
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isLesson: true
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weight: 60
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---
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---
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title: "Multivectors for Late Interaction Models"
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short_description: "Use ColBERT multivectors and MaxSim scoring in Qdrant for token-level late-interaction retrieval that captures fine-grained relevance."
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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.
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||||
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."
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weight: 2
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isLesson: true
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||||
---
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---
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title: "Project: Building a Recommendation System"
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short_description: "Build a recommendation system that fuses dense, sparse, and ColBERT multivectors in a single Universal Query with reranking and filters."
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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.
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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."
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weight: 5
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isLesson: true
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||||
---
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---
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title: "The Universal Query API"
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short_description: "Compose dense retrieval, sparse retrieval, fusion, and ColBERT reranking in one declarative request with Qdrant's Universal Query API."
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||||
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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||||
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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weight: 3
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isLesson: true
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||||
---
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---
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title: "Demo: Universal Query for Hybrid Retrieval"
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short_description: "Build a research paper discovery pipeline that blends dense semantics, sparse keywords, ColBERT reranking, and metadata filters in one query."
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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.
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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."
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weight: 4
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isLesson: true
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||||
---
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||||
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@@ -1,7 +1,7 @@
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---
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title: "Day 6: Final Project - Building a Production-Grade Search Engine"
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short_description: "Day 6 of Qdrant Essentials: ship a portfolio-ready documentation search engine with hybrid retrieval, reranking, and rigorous evaluation."
|
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description: Build a production-grade documentation search engine with Qdrant—combining hybrid retrieval, multivector reranking, and evaluation to showcase real-world, portfolio-ready vector search expertise.
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||||
description: "Build a production-grade documentation search engine with Qdrant—combining hybrid retrieval, multivector reranking, and evaluation to showcase real-world, portfolio-ready vector search expertise."
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isLesson: true
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||||
weight: 70
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---
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@@ -1,7 +1,7 @@
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---
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title: "Course Completion and Next Steps"
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short_description: "Wrap up Qdrant Essentials and review the production skills you gained: hybrid retrieval, quantization, scaling, and search evaluation."
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||||
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.
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||||
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."
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weight: 3
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||||
isLesson: true
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||||
---
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@@ -1,7 +1,7 @@
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---
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title: "Final Project: Production-Ready Documentation Search Engine"
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short_description: "Final project: build a documentation search engine with hybrid retrieval, multivector reranking, and Recall@10, MRR, and latency metrics."
|
||||
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.
|
||||
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
|
||||
isLesson: true
|
||||
---
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
---
|
||||
title: "Day 7: Partner Ecosystem Integrations (Bonus)"
|
||||
short_description: "Bonus day on the Qdrant ecosystem: integrations with LlamaIndex, Haystack, Jina, Unstructured, and other AI and data platforms."
|
||||
description: Learn how to extend Qdrant with integrations across top AI platforms like Haystack, LlamaIndex, and Unstructured.io for scalable, intelligent, and agentic search pipelines.
|
||||
description: "Learn how to extend Qdrant with integrations across top AI platforms like Haystack, LlamaIndex, and Unstructured.io for scalable, intelligent, and agentic search pipelines."
|
||||
isLesson: true
|
||||
weight: 80
|
||||
---
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
---
|
||||
title: "Integrating with Camel AI"
|
||||
short_description: "Build agentic RAG with Camel AI's multi-agent framework and Qdrant for automated context retrieval and collaborative agent workflows."
|
||||
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.
|
||||
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
|
||||
isLesson: true
|
||||
---
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
---
|
||||
title: "Integrating with Haystack"
|
||||
short_description: "Build agentic RAG pipelines with Haystack and Qdrant: sparse vector search, metadata filtering, and LLM-driven query routing."
|
||||
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.
|
||||
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
|
||||
isLesson: true
|
||||
---
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
---
|
||||
title: "Integrating with Jina AI"
|
||||
short_description: "Power multimodal text and image retrieval by pairing Jina embedding models with Qdrant for cross-modal search across mixed content."
|
||||
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.
|
||||
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
|
||||
isLesson: true
|
||||
---
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
---
|
||||
title: "Integrating with LlamaIndex"
|
||||
short_description: "Build LLM applications and agentic workflows with LlamaIndex and Qdrant: data loaders, RAG pipelines, function agents, and custom workflows."
|
||||
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.
|
||||
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
|
||||
isLesson: true
|
||||
---
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
---
|
||||
title: "Integrating with Quotient"
|
||||
short_description: "Monitor RAG quality with Quotient AI and Qdrant: detect hallucinations, score document relevance, and debug retrieval-augmented agents."
|
||||
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.
|
||||
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
|
||||
isLesson: true
|
||||
---
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
---
|
||||
title: "Integrating with Superlinked"
|
||||
short_description: "Engineer richer vector representations with Superlinked's mixture-of-encoders approach over text, numeric, categorical, and temporal data in Qdrant."
|
||||
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.
|
||||
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
|
||||
isLesson: true
|
||||
---
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
---
|
||||
title: "Integrating with Tensorlake"
|
||||
short_description: "Use Tensorlake to parse documents into knowledge graphs and feed structured embeddings into Qdrant for richer semantic retrieval."
|
||||
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.
|
||||
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
|
||||
isLesson: true
|
||||
---
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
---
|
||||
title: "Integrating with Unstructured.io"
|
||||
short_description: "Process PDFs, Word docs, and emails with Unstructured.io and load chunked, vectorized content into Qdrant for enterprise RAG pipelines."
|
||||
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.
|
||||
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
|
||||
isLesson: true
|
||||
---
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
---
|
||||
title: "Qdrant Essentials FAQs"
|
||||
short_description: "Frequently asked questions about the Qdrant Essentials course: who it's for, time commitment, tooling, certification, and support."
|
||||
description: Learn everything about the Qdrant Essentials course — who it’s for, tools required, certification details, setup options, and how to get help while building your vector search expertise.
|
||||
description: "Learn everything about the Qdrant Essentials course — who it’s for, tools required, certification details, setup options, and how to get help while building your vector search expertise."
|
||||
weight: 200
|
||||
---
|
||||
|
||||
|
||||
Reference in New Issue
Block a user