Day 3: Hybrid Search on Qdrant - Vector Search Engine

Sparse Vectors and Inverted Indexes

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

Day 3

Sparse Vectors and Inverted Indexes

Create and index sparse vector representations for keywords-based search and recommendations.

What You’ll Learn

Understanding sparse vector representations
Using sparse vectors in Qdrant

Sparse Vector Representations

Sparse vectors are high dimensional vectors, filled up with zeroes except for a few dimensions. Each dimension of a sparse vector refers to a certain object, and its value – a role of this object in this sparse representation.

Demo: Keyword Search with Sparse Vectors

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

Day 3

Demo: Keyword Search with Sparse Vectors

Use sparse vectors for keywords-based text retrieval.

What You’ll Learn

Connection between Sparse Vectors & keywords-based retrieval
Using BM25 in Qdrant
Sparse Neural Retrieval
Using SPLADE++ in Qdrant

Text Encoding

In sparse vectors, each non‑zero dimension represents an object that plays a specific role for the item being represented. When we work with text, the natural choice for these objects is words.

Hybrid Search and the Universal Query API

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

Day 3

Hybrid Search and the Universal Query API

Learn how to combine dense and sparse vector search methods to build powerful hybrid search pipelines that serve diverse user needs.

What You’ll Learn

Understand when to use dense vs. sparse vectors
Build hybrid search pipelines with Qdrant’s Universal Query API
Apply Reciprocal Rank Fusion (RRF) to combine results
Design multi-stage retrieval and reranking strategies

The Challenge: Different Users, Different Search Needs

The reality is that your users exist across a spectrum: from precise keyword searchers to vague natural language describers, and forcing a single search approach means disappointing part of your audience. Rather than compromising on search quality for different user types, hybrid search allows you to meet everyone where they are.

Demo: Implementing a Hybrid Search System

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

Day 3

Demo: Implementing a Hybrid Search System

Build a complete hybrid search system with hands-on examples.

What You’ll Learn

Step-by-step hybrid search implementation
RRF algorithm in practice
Performance optimization techniques
Testing and evaluation methods

Follow along in Colab:

What You’ll Discover

In the previous lesson, you learned the theory behind hybrid search and the Universal Query API. Today you’ll implement it hands-on with a real dataset, comparing dense and sparse vector search and combining them using fusion algorithms.

Project: Building a Hybrid Search Engine

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

Day 3

Project: Building a Hybrid Search Engine

Build a hybrid system that combines dense and sparse vectors with Reciprocal Rank Fusion, demonstrating how to get the best of both semantic understanding and keyword precision.

Your Mission

Create a production-ready hybrid search system that leverages both dense and sparse vectors to deliver superior search results. You’ll implement the complete hybrid pipeline and compare its performance against single-vector approaches.

Estimated Time: 75 minutes