Semi Technologies

Mentors: Student: Yash Gupta

This project aims to make more examples and tutorials for weaviate. As any beginner who wants to use weaviate, These examples and tutorials are the best way to start learning. So it's better that we have a good number of these examples. The project can use various datasets available at sites like Kaggle.com or https://paperswithcode.com/datasets. These examples will not only help the beginners but also help people who already are using weaviate as by watching these they can come up with a new similar idea of their own to use weaviate.

Mentors: Student: Aryan Narayan

Weaviate CLI offers very limited functionalities. Even though there are various other options to interact with a Weaviate instance, we cannot simple neglect the importance of a CLI. A CLI can be a lot faster and efficient than any other type of interface. It can also handle repetitive tasks easily. A CLI requires less memory to use in comparison to other interfaces. By adding more functionalities to the CLI we can increase it’s usability for the end user. This section describes some the existing functionalities of the CLI with their scope of improvement, and also proposes new functions to be added to the CLI. New functions and improvement is not limited to proposed functionalities below and more features can be added to the CLI in project duration.

Mentors: Student: Dasith Edirisinghe

Weaviate is a cloud-native vector search engine and a vector database. Weaviate is completely modularized and the functionalities of the weaviate can be enhanced by using modules. This project will be focusing on creating a custom text2text generator module that could potentially cover a variety of NLP tasks. Tasks range from text summarization, and translation to any other text2text transformations.