12 results for “topic:medical-chatbot-with-llm-and-rag”
AI-powered Medical Chatbot 🤖 that provides health advice, disease-symptom analysis, and multilingual support using NLP and machine learning. It helps users receive personalized recommendations and health tips in multiple languages.
This project builds a medical Q&A chatbot using a Large Language Model (LLM) with Retrieval-Augmented Generation (RAG). Developed with LangChain and Docker, it employs Prompt Engineering and a vector database to ensure accurate, relevant answers.
No description provided.
RAG-based clinical chatbot for conversational and semantic access to EsSalud public clinical practice guidelines, delivering context-aware responses with traceable references to official public sources.
An AI-powered medical assistant built using Flask, LangChain, HuggingFace, and Pinecone that answers health-related queries by retrieving information from authenticated medical PDFs.
includes both voice and vision
Medical Chat Bot
HealthBuddy AI is a medical chatbot that uses Retrieval-Augmented Generation (RAG) to answer health-related queries from a medical knowledge base built from PDF books. It uses LangChain, GPT-4o, and Pinecone for semantic search and accurate responses, with a web interface built using Flask.
A modern AI-powered medical chatbot using Retrieval-Augmented Generation (RAG) with Pinecone, LangChain, and Gemini 2.5 Flash.
A medical chatbot using LLMs, LangChain, Pinecone, Flask, and AWS for intelligent, scalable, and secure healthcare queries.
This application is a Medical Domain Chatbot built using Retrieval-Augmented Generation (RAG). It allows users to upload their own medical documents (e.g., textbooks, reports), and the system intelligently answers queries by retrieving the most relevant content before generating a final response.
🩺 Build a medical Q&A chatbot using LangChain and Pinecone to extract answers from PDF documents, ensuring accurate and reliable information.