Basics on Qdrant - Vector Search Engine

Hugging Face Dataset Ingestion

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

Load Hugging Face Datasets into Qdrant

Hugging Face provides a platform for sharing and using ML models and datasets. Qdrant also publishes datasets along with the embeddings that you can use to practice with Qdrant and build your applications based on semantic search. Please let us know if you’d like to see a specific dataset!

arxiv-titles-instructorxl-embeddings

This dataset contains embeddings generated from the paper titles only. Each vector has a payload with the title used to create it, along with the DOI (Digital Object Identifier).

Semantic Search 101

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

Build a Semantic Search Engine in 5 Minutes

Time: 5 - 15 min	Level: Beginner

There are two versions of this tutorial:

The version on this page uses Qdrant Cloud. You’ll deploy a cluster and generate vector embedding in the cloud using Qdrant Cloud’s forever free tier (no credit card required).

Alternatively, you can run Qdrant on your own machine. This requires you to manage your own cluster and vector embedding infrastructure. If you prefer this option, check out the local deployment version of this tutorial.

Overview

If you are new to vector search engines, this tutorial is for you. In 5 minutes you will build a semantic search engine for science fiction books. After you set it up, you will ask the engine about an impending alien threat. Your creation will recommend books as preparation for a potential space attack.

Hybrid Search

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

Hybrid Search Using Qdrant Cloud Inference

Time: 30 min	Level: Intermediate

In this tutorial, we’ll walkthrough building a hybrid semantic search engine using Qdrant Cloud’s built-in inference capabilities. You’ll learn how to:

Automatically embed your data using cloud Inference without needing to run local models,
Combine dense semantic embeddings with sparse BM25 keywords, and
Perform hybrid search using Reciprocal Rank Fusion (RRF) to retrieve the most relevant results.

Initialize the Client

Initialize the Qdrant client after creating a Qdrant Cloud account and a dedicated paid cluster. Set cloud_inference to True to enable cloud inference.

Hybrid Search with Reranking

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

Qdrant Hybrid Search with Reranking

Time: 40 min	Level: Intermediate

Reranking is a powerful technique for improving search precision: rather than running an expensive model over your entire corpus, you apply it to a smaller set of candidates already retrieved by a faster method. This keeps latency low while surfacing the most relevant results.

Reranking pairs especially well with hybrid search, which casts a wide retrieval net, maximizing recall across several retrieval paths. Reranking can sort the hybrid search results with a deeper relevance signal. A late interaction model, for instance, represents both query and document as multiple vectors, enabling more nuanced term-level comparisons than a single embedding can capture.

Semantic Search 101

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

Build a Semantic Search Engine in 5 Minutes

Time: 5 - 15 min	Level: Beginner

There are two versions of this tutorial:

With the version on this page, you’ll run Qdrant on your own machine. This requires you to manage your own cluster and vector embedding infrastructure.

Alternatively, you can use Qdrant Cloud to deploy a cluster and generate vector embeddings using Qdrant Cloud’s forever free tier (no credit card required). If you prefer this option, check out the Qdrant Cloud version of this tutorial.