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kanungle
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title: "Multi-Vector Search Course"
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page_title: "Qdrant Multi-Vector Search Course"
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short_description: "Master multi-vector search with ColBERT and ColPali: late interaction, MaxSim scoring, multi-stage retrieval, MUVERA indexing, and evaluation."
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description: Master late interaction models, ColPali, and production optimization. Build scalable multi-vector search pipelines.
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description: "Master late interaction models, ColPali, and production optimization. Build scalable multi-vector search pipelines."
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content:
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sidebarTitle: "Multi-Vector Search Course"
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menuTitle:
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---
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title: "Installing Dependencies"
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short_description: "Set up a Python environment for multi-vector search with the Qdrant client and FastEmbed for late-interaction embeddings."
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description: Install Python dependencies including FastEmbed and Qdrant client.
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description: "Install Python dependencies including FastEmbed and Qdrant client."
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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: "Qdrant Setup"
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short_description: "Stand up Qdrant Cloud or a local Qdrant instance and configure credentials before running multi-vector search experiments."
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description: Set up Qdrant for multi-vector search. Learn how to create a collection and configure it for multi-vector embeddings.
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description: "Set up Qdrant for multi-vector search. Learn how to create a collection and configure it for multi-vector embeddings."
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weight: 1
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isLesson: true
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---
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---
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title: "Late Interaction Basics"
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short_description: "Compare no-interaction, early-interaction, and late-interaction retrieval to see why ColBERT-style multivectors outperform single-vector search."
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description: Understand the late interaction paradigm and how it differs from traditional dense embeddings for text search.
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description: "Understand the late interaction paradigm and how it differs from traditional dense embeddings for text search."
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weight: 1
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isLesson: true
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---
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---
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title: "MaxSim Distance Metric"
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short_description: "Understand the MaxSim distance metric for late interaction: how token-level matches aggregate into a single score for multi-vector retrieval."
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description: Learn about the MaxSim distance metric used in multi-vector search and how it computes similarity between multi-vector representations.
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description: "Learn about the MaxSim distance metric used in multi-vector search and how it computes similarity between multi-vector representations."
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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: "Multi-Vector Embeddings in Qdrant"
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short_description: "Configure Qdrant collections for multi-vector search: enable MaxSim, index ColBERT-style embeddings, and run late-interaction queries."
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description: Configure Qdrant collections for multi-vector embeddings and learn how to index and query multi-vector data.
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description: "Configure Qdrant collections for multi-vector embeddings and learn how to index and query multi-vector data."
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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: "Problems of Multi-Vector Search"
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short_description: "Understand the trade-offs of multi-vector search: HNSW incompatibility, asymmetric MaxSim, and the storage and compute overhead per document."
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description: Understand the challenges and limitations of multi-vector search at scale, including memory and performance considerations.
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description: "Understand the challenges and limitations of multi-vector search at scale, including memory and performance considerations."
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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: "Use Cases for Multi-Vector Search"
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short_description: "See when multi-vector search outperforms single-vector embeddings: token-level matching for precise, fine-grained retrieval on complex queries."
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description: Discover scenarios where multi-vector search outperforms single-vector embeddings and provides better retrieval quality.
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description: "Discover scenarios where multi-vector search outperforms single-vector embeddings and provides better retrieval quality."
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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: "ColPali Family Overview"
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short_description: "Compare ColPali variants for visual document retrieval, from compact ColSmol and ColFlor to multilingual models, and pick the right fit."
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description: Explore the ColPali model family and their capabilities for multi-modal document understanding and retrieval.
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description: "Explore the ColPali model family and their capabilities for multi-modal document understanding and 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: "How ColPali Models Work"
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short_description: "Learn how ColPali turns document images into multi-vector representations, retrieving from PDFs, charts, and tables without an OCR step."
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description: Understand the inner workings of ColPali models and how they generate multi-vector representations for images and documents.
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description: "Understand the inner workings of ColPali models and how they generate multi-vector representations for images and documents."
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weight: 1
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isLesson: true
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---
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---
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title: "Visual Interpretability of ColPali"
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short_description: "Visualize ColPali matches by mapping query tokens to image patches, making multi-modal retrieval results inspectable and debuggable."
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description: Learn how to visualize and interpret ColPali embeddings to understand what the model focuses on in images.
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description: "Learn how to visualize and interpret ColPali embeddings to understand what the model focuses on in images."
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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: "Evaluating Search Pipelines"
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short_description: "Evaluate retrieval pipelines with Recall@k, NDCG, and MRR using qrels to balance cost, latency, and search quality across configurations."
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description: Learn how to evaluate different search configurations in terms of cost, latency, and retrieval quality using ground truth datasets and standardized metrics.
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description: "Learn how to evaluate different search configurations in terms of cost, latency, and retrieval quality using ground truth datasets and standardized metrics."
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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: "Final Project: Build Your Own Multi-Vector Search System"
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short_description: "Capstone project: build an end-to-end multi-vector search system with ColPali, optimization techniques, and a measurable quality benchmark."
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description: Apply everything you've learned to build a multi-vector search system that solves a real problem of your choosing.
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description: "Apply everything you've learned to build a multi-vector search system that solves a real problem of your choosing."
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weight: 7
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isLesson: true
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---
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---
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title: "Multi-Stage Retrieval with Universal Query API"
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short_description: "Build multi-stage retrieval with the Universal Query API: prefetch with fast vectors, then rerank with ColBERT for high-quality results."
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description: Combine multiple optimization techniques in multi-stage retrieval pipelines using Qdrant's Universal Query API.
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description: "Combine multiple optimization techniques in multi-stage retrieval pipelines using Qdrant's Universal Query API."
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weight: 1
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isLesson: true
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---
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---
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title: "MUVERA"
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short_description: "Use MUVERA to approximate multi-vector documents as single vectors so HNSW can index them, enabling fast late-interaction retrieval at scale."
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description: Understand MUVERA and how it enables HNSW indexing for multi-vector search despite MaxSim asymmetry.
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description: "Understand MUVERA and how it enables HNSW indexing for multi-vector search despite MaxSim asymmetry."
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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: "Pooling Techniques"
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short_description: "Apply pooling to multi-vector documents to cut the number of vectors per document while preserving late-interaction retrieval quality."
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description: Reduce the number of vectors per document using row/column pooling and hierarchical token pooling strategies.
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description: "Reduce the number of vectors per document using row/column pooling and hierarchical token pooling strategies."
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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 Techniques"
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short_description: "Compress multi-vector embeddings with scalar, binary, and product quantization in Qdrant to slash memory cost without losing retrieval quality."
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description: Learn how to reduce memory usage with scalar quantization, binary quantization, and other compression methods.
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description: "Learn how to reduce memory usage with scalar quantization, binary quantization, and other compression methods."
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weight: 2
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isLesson: true
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---
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