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101T Work with: Text data in JavaScript

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 an 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

Units​

1. Set up Weaviate

Practical

Set up a Weaviate instance and connect to it.

Click here

2. Populate the database

Practical

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

Click here

3. Perform searches

Practical

Learn how to use search functions in Weaviate.

Click here

4. LLMs and Weaviate (RAG)

Practical

Use large language models to augment and transform retrieved data.

Click here