Learn how to build a medical search engine using hybrid search with NLP information retrieval models in Python. Explore the implementation of hybrid search combining sentence transformers and SPLADE for medical question-answering. Discover how to leverage both dense and sparse vectors to cover semantics and enable exact matching and keyword search. Dive into SPLADE, a powerful sparse embedding method outperforming BM25, and learn how it minimizes vocabulary mismatch problems. Follow along with a practical demo using SPLADE and a sentence transformer model trained on MS-MARCO, implemented via Hugging Face transformers. Gain hands-on experience with the Pinecone vector database for the search component, supporting SPLADE vectors natively. Cover topics including data preprocessing, creating dense and sparse vector embeddings, preparing data for Pinecone, creating a sparse-dense index, and making hybrid search queries.