101M Work with: Multimodal data
Course overview
In this project-based course, you will learn how to work with multimodal data using Weaviate and a movie dataset.
You will get hands-on experience on how to store and index text and image data to be searchable together by meaning, using Weaviate's vectorization capabilities. You will learn how to search through that data using multimodal search methods, 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 local Weaviate instance, add data to it to enable multi-modal searching, and use AI through retrieval augmented generation.
By the time you are finished, you will be able to:
Learning Outcomes- Create a local instance of Weaviate with a multimodal vectorizer module
- Produce, store and index multimodal data
- Perform multimodal searches
- Use AI (large language models) to augment and transform retrieved data
Units
1. Weaviate for multimodal data
Create a local Weaviate instance for multimodal data.
2. Populate the database
Create a collection and import multimodal 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.