{"repo":"AIAnytime/Medical-RAG-using-Bio-Mistral-7B","free":true,"listed":false,"github":"https://github.com/AIAnytime/Medical-RAG-using-Bio-Mistral-7B","clone":"git clone https://github.com/AIAnytime/Medical-RAG-using-Bio-Mistral-7B.git","description":"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.","language":"HTML","stars":90,"topics":[],"license":"MIT","category":"ai-agents","readme_excerpt":"Medical-RAG-using-Bio-Mistral-7B 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. Reference Implementation on Intel AI PC Disclaimer This demo is intended only for the purpose of exploring new LLM use cases at the edge and not recommended for production-grade medical chatbot Device Under Test Processor: Intel® Core™ Ultra 7 165H OS: Windows 11 Pro 23H2 RAM: 64GB Python 3.11.9 Prerequisite Install Microsoft Visual C++ compiler toolset required for installing llama-cpp-python Steps 1. Clone this repository 2. Create a Python virtual environment and install the dependencies 3. Download the INT4 version of BioMistral-7B model in GGUF format 4. Download the embedding model 5. Install Docker Desktop for Windows with optional proxy settings 6. Create the Qdrant container Access the Dashboard using http://localhost:6333/dashboard 7. Create embeddings for the new documents in data Check the new Collection on the Qdrant Dashboard 8. Run the application Sample Outputs","default_branch":null,"files":null,"tree":[],"storefront":"/r/AIAnytime","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/AIAnytime/Medical-RAG-using-Bio-Mistral-7B/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}