Learn how to leverage Qdrant for text-based vector similarity search in this 43-minute tutorial that demonstrates creating vector embeddings from text data, implementing similarity search functionality, and building context-based recommendation systems. Explore practical examples of adding embeddings to Qdrant's vector database, performing efficient similarity searches, and generating recommendations based on document context within a corpus. Access hands-on implementation details through the provided GitHub repository and comprehensive documentation on the Qdrant website.