---
license: mit
title: 'Medical Document Assistant APP with LLM RAG framework'
sdk: docker
emoji: 📚
colorFrom: blue
colorTo: red
pinned: false
short_description: Search medical terms among uploaded document
app_port: 8080
---
## Necessary resources

### Model must be downloaded to local ai_workshop folder:
Llama 2 Model (Quantized one by the Bloke): https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/blob/main/llama-2-7b-chat.ggmlv3.q8_0.bin

### License and other reference
The code in all scripts subjects to a licence of 96harsh52/LLaMa_2_chatbot (https://github.com/96harsh52/LLaMa_2_chatbot)
Youtube instruction (https://www.youtube.com/watch?v=kXuHxI5ZcG0&list=PLrLEqwuz-mRIdQrfeCjeCyFZ-Pl6ffPIN&index=18)

Llama 2 HF Model (Original One): https://huggingface.co/meta-llama
Chainlit docs: https://github.com/Chainlit/chainlit

## Create virtual Environment

1. Create Virtual env:
	>`cd ai_workshop`
	>`python -m venv langchain`

2. Activate virtual evn:
	>`langchain\Scripts\activate`

*NOTE: if you see the read warning in cmd terminal said "running scripts is disabled on this system" , use Powershell to setup API server:
	1. open Powershell
	   > `Set-ExecutionPolicy Unrestricted -Scope Process`
	2. activate virtual env as previous steps

3. install requirements.txt
    > `python -m ensurepip --upgrade`
    > `python -m pip install --upgrade setuptools`
	> `python -m pip install -r requirements.txt`
   

## Create local vectors storage database

After activate virtual environment, run `python .\ingest.py`

## Setup Medical chatbot server with chainlit

After set up the database folder of "vectorstore/db_faiss", run `chainlit run .\model.py > logs.txt`