How to prepare data for your RAG application with Qdrant and FastEmbed - create embeddings

Learn best practices to get your data into Qdrant to start building your AI application Are you ready to build a RAG application, but unsure of how to make your data ready? In AI and machine learning, preparing your data correctly is crucial for building effective applications. This webinar focuses on Retrieval-Augmented Generation (RAG) applications and how to leverage Qdrant, a powerful vector database, along with FastEmbed, an efficient embedding generation library. Whether you're new to RAG or looking to optimize your existing workflows, this session will provide valuable insights into data preparation techniques. We'll guide you through the process of transforming raw data into a format that's ready for AI consumption, ensuring your RAG applications are built on a solid foundation. What you will learn: What kind of data you can use with Qdrant What is chunking and a few common chunking methods How to use FastEmbed: Qdrant's efficient Python library for embedding generation What is indexing and a few common indexing methods How to load data into Qdrant Who this is for: This event is designed for machine learning practitioners, data scientists, and AI enthusiasts who are either building AI apps or curious to learn how.