Welcome to the Complete Noobs Guide to Vector Search! In this two-part series, we will cover everything you need to know about vector search and how to use it effectively.
Part 1 will focus on the process of converting text data into vectors. We will discuss topics such as tokenization, embedding, and vector representation. Additionally, we will provide step-by-step instructions on how to embed and upsert data into Qdrant, a powerful vector database.
In Part 2, we will delve into the concept of vector search itself. You will learn about similarity metrics and the HNSW algorithm, which is used for efficient searching through vector databases.
If you’re new to vector search or just want to brush up on your knowledge, this guide is perfect for you. Let’s get started!

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A two-part guide to vector search covering topics such as converting text data into vectors, embedding, vector representation, similarity metrics, and the HNSW algorithm.

A Complete Noobs Guide to Vector Search, Part 1