Is the PubMed ML Platform scalable for production use?

Yes, it supports scalable deployments using Docker Compose for local environments and Kubernetes for production. It also utilizes pgvector for efficient similarity search and Airflow for robust data orchestration.

What is the PubMed ML Platform designed for?

It's an end-to-end machine learning platform that builds a semantic search engine over PubMed biomedical abstracts, integrating data ingestion, advanced embeddings, model serving, and LLM tools for research.

How does it integrate with Large Language Models (LLMs)?

It includes an MCP (Model Context Protocol) server that exposes its core functionalities as tools, allowing compatible LLMs to leverage its semantic search and paper retrieval capabilities directly.

PubMed ML Platform

Name: PubMed ML Platform
Author: chibanaryan

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Establishes an end-to-end machine learning platform for semantic search across PubMed biomedical abstracts.

The PubMed ML Platform is a comprehensive machine learning infrastructure designed for semantic search over PubMed biomedical abstracts. It covers the entire ML lifecycle, from data ingestion using Airflow and PubMed's E-utilities API, to generating optimized vector embeddings with HuggingFace models and MLflow experiment tracking. The platform includes advanced techniques like contrastive fine-tuning, cross-encoder re-ranking, knowledge distillation, and ONNX export with INT8 quantization for superior performance. A robust FastAPI serving layer provides semantic search, similarity finding, and A/B testing capabilities, all wrapped as MCP tools for seamless integration with large language models, enabling powerful AI-driven research exploration.

Key Features

01End-to-end ML infrastructure for semantic search

02Advanced embedding pipeline with fine-tuning, re-ranking, and ONNX optimization

03FastAPI serving layer with A/B testing and Prometheus metrics

04Integrated with Model Context Protocol (MCP) for LLM tool utilization

05Scalable architecture with Docker Compose, Kubernetes, and pgvector for local and production deployments

061 GitHub stars

Use Cases

01Discover semantically similar research papers based on an existing article's content

02Perform semantic searches across a vast collection of biomedical abstracts from PubMed

03Integrate with large language models to enable advanced, context-aware queries and research workflows