(v3) Zero to MVP
Course overview
This course was written for the Weaviate Python client API (v3), and is now deprecated.
If you are new to Weaviate, we recommend you start with one of the 100-level courses written with the v4 client API, such as those for working with text data, your own vectors, or multimodal data.
This course is designed to get you started with Weaviate, so that you can go from being new to Weaviate to building an MVP-level product with Weaviate in a short period of time.
Along the way, you'll develop intuitions about not only how Weaviate works, but also how vectors work, and how vector searches work. You'll also learn how to use Weaviate's client library so that you can get going in a language that you are familiar with.
By the time you're done with these short units, you'll be able to build your own instance of Weaviate with your own data, and have a suite of search tools at your disposal so that you can get the data you want in the format you want it.
Learning objectives
Here, we will cover:
Learning Goals- How to build a Weaviate instance and populate it with vectorized data, as well as how to construct queries to efficiently retrieve relevant data.
By the time you are finished, you will be able to:
Learning Outcomes- Use Weaviate Cloud to create an instance of Weaviate
- Use appropriate query types and syntax to retrieve desired objects
- Outline what vector search is and how it works
- Demonstrate how to efficiently populate an Weaviate instance with data
- Differentiate BM25 and hybrid search techniques from vector search techniques
Units
1. Hello, Weaviate
Start here: Learn what Weaviate is, and about its key capabilities and features, as well as about vectors that power Weaviate.
2. Queries 1
Learn how queries work in Weaviate, how to use similarity searches and use filters, as well as how search works under the hood.
3. Schema and imports
Learn what role the schema plays in Weaviate, and how to define it, before learning how to effectively populate Weaviate with data.
4. Queries 2
Learn about even more query types, from hybrid searches that combine keyword and vector searches to generative searches that transform your data at retrieval.