💡Creating recommendations for applications with minimal complexity using vector databases

This data not only trains AI systems, but is also the final output that you continue to work with. That's why it's so important to use "good" data. No matter how powerful the model is, if the input is bad data, the output will be the same.

This article is about an example of using the Weaviate database in Streamlit format to simplify working with vector databases. The authors believe that this will allow you to create a powerful search and recommendation system taking into account technical and cost factors.

📚For information, it is worth noting that:
✅Weaviate is an open-source vector database that allows users to store data objects and vector data from machine learning models and easily scales to billions of data objects. .

✅Streamlit is a Python framework. It contains a set of software tools that allow you to transfer a machine learning model to a website. The written "smart" program with this framework can be quickly turned into web applications.