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Introduction to Text Retrieval with Embeddings
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Text Retrieval with Embeddings

Introduction

Welcome to this course on text retrieval with embeddings! Here, we'll tackle the broad and challenging domain of text retrieval, leveraging text embeddings as our primary tool. This approach, often referred to as semantic search, capitalizes on the latest in the field of NLP, utilizing the latest advancements in large language models (LLMs) and vector databases.

This course is your gateway to mastering text retrieval in the modern AI landscape. You'll learn all about the theory behind text retrieval and embeddings, as well as how to build modern text retrieval solutions using state-of-the-art libraries and APIs. Additionally, we'll guide you through scaling these solutions using high-performance vector databases, ensuring you're equipped with the skills and confidence to build robust, scalable text retrieval systems.

🔎 The task of text retrieval using embeddings has many names, such as semantic search, similarity search, and vector search. In this course, we will primarily refer to it as semantic search, insofar as it applies to text (and not other mediums like images or audio).

Before diving in, let's unpack the problem space of text retrieval, explain why it's such an essential tool for your toolbelt, and explore a brief history of the space.