From 70aba1e7205691fc35310604013e62a1bb942b33 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kacper=20=C5=81ukawski?= Date: Thu, 22 Jan 2026 12:52:17 +0100 Subject: [PATCH] Add course overview --- .../course/multi-vector-search/_index.md | 152 +++++++++++++++++- 1 file changed, 149 insertions(+), 3 deletions(-) diff --git a/qdrant-landing/content/course/multi-vector-search/_index.md b/qdrant-landing/content/course/multi-vector-search/_index.md index e458f27f0..4231a9590 100644 --- a/qdrant-landing/content/course/multi-vector-search/_index.md +++ b/qdrant-landing/content/course/multi-vector-search/_index.md @@ -1,7 +1,7 @@ --- title: "Multi-Vector Search Course" page_title: "Qdrant Multi-Vector Search Course" -description: TBD +description: Master late interaction models, ColPali, and production optimization. Build scalable multi-vector search pipelines. content: sidebarTitle: "Multi-Vector Search Course" menuTitle: @@ -10,10 +10,156 @@ content: nextButton: Continue to Next Step nextDay: Complete title: "Multi-Vector Search" - description: tbd + description: Master late interaction models, ColPali, and production optimization. Build scalable multi-vector search pipelines. partition: course --- # Multi-Vector Search -TBD \ No newline at end of file +**Build production-ready multi-vector search pipelines** + +Go beyond single-vector embeddings with late interaction models like ColBERT and ColPali. Learn the MaxSim distance metric, optimize for billion-scale search, and evaluate your retrieval pipelines with industry-standard metrics. + +
+ +
+ +
+ +{{< cards-list >}} +- icon: /icons/outline/play-white.svg + title: 4 modules + content: Focused lessons building from fundamentals to production +- icon: /icons/outline/cloud-check-blue.svg + title: Shareable certificate + content: Earn a digital certificate upon completion +- icon: /icons/outline/time-blue.svg + title: Flexible schedule + content: Learn at your own pace (1–2 hours/module) +- icon: /icons/outline/plan.svg + title: Advanced level + content: Assumes familiarity with vector search basics + +{{< /cards-list >}} + +
+ +## What you'll learn +{{< course-card + title="Skills you'll gain:" + image="/icons/outline/training-white.svg" + type="wide-list">}} + +- Late interaction paradigm and MaxSim distance metric +- ColBERT for text and ColPali for visual documents +- Multi-stage retrieval with prefetch and reranking +- Quantization and pooling techniques for memory optimization +- MUVERA indexing for billion-scale search +- Evaluation metrics: Recall@k, NDCG, MRR + +{{< /course-card >}} + +### The Path + +**Module 0**: Setup. Configure Qdrant Cloud or local instance and install Python dependencies. + +**Module 1**: Text multi-vectors. Understand the late interaction paradigm, learn the MaxSim distance metric, explore use cases and challenges, and implement ColBERT with Qdrant. + +**Module 2**: Multi-modal search. Apply multi-vector representations to images and PDFs with ColPali. Explore model variants and leverage visual interpretability for debugging. + +**Module 3**: Optimization and evaluation. Master quantization, pooling, and MUVERA for memory-efficient search. Build multi-stage retrieval pipelines and evaluate with standard metrics. + +## How the course works + +{{< cards-list >}} + +- icon: /icons/outline/training-purple.svg + title: Video-first lessons + content: Clear, concise modules by the Qdrant team +- icon: /icons/outline/hacker-purple.svg + title: Final project + content: Build a production-ready multi-modal search system +- icon: /icons/outline/similarity-blue.svg + title: Hands-on notebooks + content: Practice each concept with Colab notebooks +- icon: /icons/outline/copy.svg + title: Progressive learning + content: Build from fundamentals to advanced optimization + {{< /cards-list >}} + +
+ +## Syllabus + +{{< accordion >}} +- title: "Module 0: Setting Up Dependencies" + content: | + - Qdrant Setup + - Installing Dependencies +
+
+

→ Start Module 0

+ +- title: "Module 1: Multi-Vector Representations for Textual Data" + content: | + - Late Interaction Basics + - MaxSim Distance Metric + - Use Cases for Multi-Vector Search + - Problems of Multi-Vector Search + - Multi-Vector Embeddings in Qdrant +
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+

→ Start Module 1

+ +- title: "Module 2: Multi-Vector Representations for Multi-Modal Data" + content: | + - How ColPali Models Work + - ColPali Family Overview + - Visual Interpretability of ColPali +
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→ Start Module 2

+ +- title: "Module 3: Scalability and Optimization" + content: | + - Multi-Stage Retrieval with Universal Query API + - Vector Quantization Techniques + - Pooling Techniques + - MUVERA Indexing + - Evaluating Search Pipelines + - Final Project +
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→ Start Module 3

+{{< /accordion >}} + + +## Who it's for + +ML, backend, and search engineers who want to go beyond single-vector embeddings. Requires intermediate Python, basic familiarity with vector search concepts (embeddings, similarity metrics), and comfort with APIs. + +## Time commitment + +- Duration: 4 modules at 2-3 hours/module +- Video learning: ~4 hours +- Hands-on notebooks: ~4 hours +- Final project: 2-4 hours +- Total: 8-12 hours + + +{{< course-card + title="Ready to master multi-vector search?" + image="/icons/outline/rocket-white-light.svg" + link="/course/multi-vector-search/module-0/">}} +**What you'll get** +- Build production-ready multi-vector pipelines +- Practice with real Colab notebooks +- Learn optimization techniques for scale +- Portfolio project and community support +{{< /course-card >}}