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This is a RAG implementation using Open Source stack. BioMistral 7B has been used to build this app along with PubMedBert as an embedding model, Qdrant as a self hosted Vector DB, and Langchain & Llama CPP as an orchestration frameworks.
In this project, my colleague Catherine Lee (Rutgers) and I employ computational text analysis to examine quantitative trends in the use of diversity terms, OMB/Census terms, and other population labels in a sample of 2.6+ million biomedical abstracts spanning the last 30 years.
In BIOASQ TASK 9A total Number of articles present are 15,559,157 which is around 25.6 GB in size and Total Number of MeSH Covered in These articles are 29,369
Application that helps a searcher to evaluate iterations of a PubMed search strategy. Streamlines the process of creating a validation set of items and automatically tests successive iterations of a search strategy against that set.