#Pubmed
Showing 14 of 14 repositories tagged #pubmed, ranked by stars
~95% on SimpleQA (e.g. Qwen3.6-27B on a 3090). Supports all local and cloud LLMs (llama.cpp, Ollama, Google, ...). 10+ search engines - arXiv, PubMed, your private documents. Everything Local & Encrypted.
🔬🦞 A self-evolving AI research colleague for scientists. 285 skills, zero hallucination, persistent memory.
ScienceClaw is a personal research assistant built with LangChain DeepAgents and AIO Sandbox infrastructure, adopting a completely new architecture beyond OpenClaw. It offers stronger security, better transparency, and a more user-friendly experience.
BioMCP: Biomedical Model Context Protocol
An agentic AI application that allows you to chat with your papers and gather also information from papers on ArXiv and on PubMed
VerifAI initiative to build open-source easy-to-deploy generative question-answering engine that can reference and verify answers for correctness (using posteriori model)
Pull high-quality, efficient embeddings for PubMed, arXiv and Wikipedia from Huggingface and use for local LLM inference/Retrieval Augmented Generation (RAG)
Scientific paper search API for AI agents: REST, Python, OpenAPI, and MCP with structured full-text evidence.
Enhance your knowledge in medical research with the help of LLM and RAG.
Enhancing Medical Question-Answering System through Advanced Information Retrieval Strategies
AMG-RAG (Agentic Medical Graph-RAG) is a comprehensive framework that automates the construction and continuous updating of Medical Knowledge Graphs (MKGs), integrates reasoning, and retrieves current external evidence for medical Question Answering (QA).
A PMC ID in. Clean, loss-aware article JSON out. Parse PubMed Central and JATS XML for biomedical AI, RAG, search, and literature pipelines.
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.
Co-occurrence analysis in pubmed and faers of two lists of terms.