Day 5: Advanced APIs on Qdrant - Vector Search Engine

Multivectors for Late Interaction Models

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

Day 5

Multivectors for Late Interaction Models

Many embedding models represent data as a single vector. Transformer-based encoders achieve this by pooling the per-token vector matrix from the final layer into a single vector. That works great for most cases. But when your documents get more complex, cover multiple topics, or require context sensitivity, that one-size-fits-all compression starts to break down. You lose granularity and semantic alignment (though chunking and learned pooling mitigate this to an extent).

The Universal Query API

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

Day 5

The Universal Query API

Picture this: a customer types “leather jackets” into your store’s search bar. You want to show items that match the style semantically - so a bomber jacket surfaces even if it doesn’t mention “leather jackets” verbatim - but you also need to enforce your business rules. Only products under $200, only items in stock, only jackets released within the past year. Traditionally, you’d fire off a search, gather results, then apply filters and glue code. With Qdrant’s Universal Query API, all of that happens in one declarative request.

Demo: Universal Query for Hybrid Retrieval

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

Day 5

Demo: Universal Query for Hybrid Retrieval

In this hands-on demo, we’ll build a research paper discovery system using the arXiv dataset that showcases the full power of Qdrant’s Universal Query API. You’ll see how to combine dense semantics, sparse keywords, and ColBERT reranking to help researchers find exactly the papers they need - all in a single query.

Follow along in Colab:

The Challenge: Intelligent Research Discovery

Imagine you’re a machine learning researcher looking for “transformer architectures for multimodal learning with attention mechanisms.” You need to:

Project: Building a Recommendation System

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

Day 5

Project: Building a Recommendation System

Bring together dense, sparse, and multivectors in one atomic Universal Query. You’ll retrieve candidates, fuse signals, rerank with ColBERT, and apply business filters - in a single request.

Your Mission

Build a complete recommendation system using Qdrant’s Universal Query API with dense, sparse, and ColBERT multivectors in one request.

Estimated Time: 90 minutes

What You’ll Build

A hybrid recommendation system using:

Multi-vector architecture with dense, sparse, and ColBERT vectors
Universal Query API for atomic multi-stage search
RRF fusion for combining candidates
ColBERT reranking for fine-grained relevance scoring
Business rule filtering at multiple pipeline stages
Production-ready patterns for recommendation systems

Setup

Prerequisites

Qdrant Cloud cluster (URL + API key)
Python 3.9+ (or Google Colab)
Packages: qdrant-client, fastembed

Models

Dense: sentence-transformers/all-MiniLM-L6-v2 (384-dim)
Sparse: prithivida/Splade_PP_en_v1 (SPLADE)
Multivector: colbert-ir/colbertv2.0 (128-dim tokens)

Dataset

Scope: A small set of sample items (e.g., 10-20 movies).
Payload Fields: title, description, category, genre, year, rating, user_segment, popularity_score, release_date.
Filters Used: category, user_segment, release_date, popularity_score.

Build Steps

Step 1: Set Up the Hybrid Collection

Initialize Client and Collection

First, connect to Qdrant and create a clean collection for our recommendation system: