Ingesting Data for Semantic Searches in a Production-Ready Way

This tutorial demonstrates how to ingest large volumes of data, upload it to a vector database like Weaviate, run top K similarity searches against it, and monitor it in production using VectorFlow, Arize Phoenix, LlamaIndex, and other open-source tools. The process involves setting up a vector database, embedding the data with VectorFlow, querying the corpus with LlamaIndex, visualizing the data with Arize Phoenix, and adjusting configurations as needed for optimal results.