landing_page/qdrant-landing/content/documentation/tutorials-basics/search-beginners.md

title, short_description, description, weight, aliases

title	short_description	description	weight	aliases
Semantic Search 101	Run your first semantic search on Qdrant Cloud: create a cluster, upload a small dataset, and query by meaning instead of keywords.	Step-by-step tutorial: spin up a Qdrant Cloud cluster, create a collection, upload sample data, and run semantic vector search queries in five minutes.	10	/documentation/tutorials/mighty.md/ /documentation/tutorials/search-beginners/ /documentation/beginner-tutorials/search-beginners/

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.

If you are using Python, you can use this Google Colab notebook.

1. Create a Qdrant Cluster

If you do not already have a Qdrant cluster, follow these steps to create one:

1. Register for a Qdrant Cloud account using your email, Google, or Github credentials.
2. Under Create a Free Cluster, enter a cluster name and select your preferred cloud provider and region.
3. Click Create Free Cluster.
4. Copy the API key when prompted and store it somewhere safe as it won’t be displayed again.
5. Copy the Cluster Endpoint. It should look something like https://xxx.cloud.qdrant.io.

2. Set up a Client Connection

First, install the Qdrant Client for your preferred programming language:

{{< code-snippet path="/documentation/headless/snippets/install-client/" >}}

This library allows you to interact with Qdrant from code.

Next, create a client connection to your Qdrant cluster using the endpoint and API key.

{{< code-snippet path="/documentation/headless/snippets/tutorial-semantic-search-101/" block="client-connection" >}}

Replace QDRANT_URL and QDRANT_API_KEY with the cluster endpoint and API key you obtained in the previous step. The cloud_inference=True parameter enables Qdrant Cloud's inference capabilities, allowing the cluster to generate vector embeddings without the need to manage your own embedding infrastructure.

3. Create a Collection

All data in Qdrant is organized within collections. Since you're storing books, let's create a collection named my_books.

{{< code-snippet path="/documentation/headless/snippets/tutorial-semantic-search-101/" block="create-collection" >}}

• The size parameter defines the dimensionality of the vectors for the collection. 384 corresponds to the output dimensionality of the embedding model used in this tutorial.
• The distance parameter specifies the function used to measure the distance between two points.

4. Upload Data to the Cluster

The dataset consists of a list of science fiction books. Each entry has a name, author, publication year, and short description.

{{< code-snippet path="/documentation/headless/snippets/tutorial-semantic-search-101/" block="upload-data" >}}

Store each book as a point in the my_books collection, with each point consisting of a unique ID, a vector generated from the description, and a payload containing the book's metadata:

{{< code-snippet path="/documentation/headless/snippets/tutorial-semantic-search-101/" block="upload-points" >}}

This code tells Qdrant Cloud to use the sentence-transformers/all-minilm-l6-v2 embedding model to generate vector embeddings from the book descriptions. This is one of the free models available on Qdrant Cloud. For a list of the available free and paid models, refer to the Inference tab of the Cluster Detail page in the Qdrant Cloud Console.

5. Query the Engine

Now that the data is stored in Qdrant, you can query it and receive semantically relevant results.

{{< code-snippet path="/documentation/headless/snippets/tutorial-semantic-search-101/" block="query-engine" >}}

This query uses the same embedding model to generate a vector for the query "alien invasion". The search engine then looks for the three most similar vectors in the collection and returns their payloads and similarity scores.

Response:

The search engine returns the three most relevant books related to an alien invasion. Each is assigned a score indicating its similarity to the query:

{'name': 'The War of the Worlds', 'description': 'A Martian invasion of Earth throws humanity into chaos.', 'author': 'H.G. Wells', 'year': 1898} score: 0.570093257022374
{'name': "The Hitchhiker's Guide to the Galaxy", 'description': 'A comedic science fiction series following the misadventures of an unwitting human and his alien friend.', 'author': 'Douglas Adams', 'year': 1979} score: 0.5040468703143637
{'name': 'The Three-Body Problem', 'description': 'Humans encounter an alien civilization that lives in a dying system.', 'author': 'Liu Cixin', 'year': 2008} score: 0.45902943411768216

Narrow down the Query

How about the most recent book from the early 2000s? Qdrant allows you to narrow down query results by applying a filter. To filter for books published after the year 2000, you can filter on the year field in the payload.

Before filtering on a payload field, create a payload index for that field:

{{< code-snippet path="/documentation/headless/snippets/tutorial-semantic-search-101/" block="create-payload-index" >}}

In a production environment, create payload indexes before uploading data to get the maximum benefit from indexing.

Now you can apply a filter to the query:

{{< code-snippet path="/documentation/headless/snippets/tutorial-semantic-search-101/" block="query-with-filter" >}}

Response:

The results have been narrowed down to one result from 2008:

{'name': 'The Three-Body Problem', 'description': 'Humans encounter an alien civilization that lives in a dying system.', 'author': 'Liu Cixin', 'year': 2008} score: 0.45902943411768216

Next Steps

Congratulations, you have just created your very first search engine! Trust us, the rest of Qdrant is not that complicated, either. For your next tutorial, try building your own hybrid search service or take the free Qdrant Essentials course.