Getting Started with Qdrant: A Beginner's Guide to Vector Search
If you're new to vector databases and looking to incorporate vector search into your applications, Qdrant is a powerful and user-friendly option to consider. Here's a step-by-step guide to help you get started with Qdrant, even if you're a beginner in the field of vector search.
Understanding the Basics
Qdrant is a vector database and a similarity search engine that allows you to store and query high-dimensional vectors efficiently. It is particularly well-suited for tasks such as semantic search, recommendation systems, and similarity matching.
Vector search is a machine learning technique that leverages mathematical representations of data, known as vectors, to find and retrieve similar items efficiently.
It works by associating similar mathematical representations of data and converting queries into the same vector representation.
This allows for the comparison of items based on the distance between their vectors, with closer vectors indicating greater similarity.
Unlike traditional keyword-based search, vector search focuses on the similarity between items, enabling tasks such as semantic search, recommendation systems, image and text retrieval, natural language processing, and anomaly detection.
Vector search is like finding things that are similar to each other. It's a bit like when you look for a toy in a big box of toys, and you want to find one that's similar to the toy you have in your hand.
So, you compare the toys based on how they look or what they can do. In the same way, vector search compares things based on their special numbers (vectors) to find the ones that are most alike.
To store and find toys by color using Qdrant, you can represent the color of each toy in the RGB space as a vector and then perform similarity searches based on these vectors.
For example, a red color can be represented as (255, 0, 0) in the RGB space, 3 dimensions for this case. You can then insert toys with their corresponding color vectors into the Qdrant vector store and perform searches to find toys by color.
See how the vectors for similar colors, violet and magenta are closer together, whereas the vectors for dissimilar colors are farther apart. This is the main idea behind how vectors can capture similarities versus differences in the original data.
Colors in a vector space
In terms of text or natural language, most LLMs are trained with what they call an embedding layer. This means that the semantics of the text are organized into many dimensions to capture the meaning of the text. When you convert text to embeddings, the result is this semantic dimension. That is the reason why you can search for similar terms by using unstructured data like text.
Given images are nothing but a pixel matrix, you can search on images because similar images tend to have similar vectors. You can go beyond and extract what is in the image with computer vision and look for similar text by using that information. This means you can search within images.
Given a video is a set of images, it means you can also search within videos.
You can also use speech-to-text to convert audio and search within audio as well.
You can go beyond and extract the audio patterns as vectors and search for patterns in audio files!
Useful Links:
[1] tutorials
[2] quick-start
