Medical Search Engine with SPLADE + Sentence Transformers in Python

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.

Hybrid search for medical field
Hybrid search process
Prerequisites and Installs
Pubmed QA data preprocessing step
Creating dense vectors with sentence-transformers
Creating sparse vector embeddings with SPLADE
Preparing sparse-dense format for Pinecone
Creating the Pinecone sparse-dense index
Making hybrid search queries
Final thoughts on sparse-dense with SPLADE