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101T Work with: Text data

Course overview

In this project-based course, you will learn how to work with any text data using Weaviate and a movie dataset.

You will get hands-on experience on how to store and index text data by meaning, using Weaviate's vectorization capabilities. You will learn how to search through that data using semantic, keyword and hybrid searches, as well as filters. You will also learn how to use Weaviate's retrieval augmented generation (RAG) capabilities to generate outputs based on the retrieved objects.

Learning objectives

  Here, we will cover:

Learning Goals
  • How to create a Weaviate instance, add data to it to enable semantic searching, and use AI through retrieval augmented generation.

  By the time you are finished, you will be able to:

Learning Outcomes
  • Create a instance of Weaviate for you to use
  • Produce, store and index semantic (vector) data from source text
  • Perform semantic, keyword and hybrid searches
  • Use AI (large language models) to augment and transform retrieved data


1. Set up Weaviate


Set up a Weaviate instance and connect to it.

2. Populate the database


Create a collection and import data, and have Weaviate create vectors for you.

3. Perform searches


Learn how to use search functions in Weaviate.

4. LLMs and Weaviate (RAG)


Use large language models to augment and transform retrieved data.