https://github.com/gyanbardhan/medichat

MediChat: An AI-powered medical chatbot using the Llama-2-7B-Chat model for precise clinical responses. Integrates Chroma DB and all-MiniLM-L6-v2 embeddings trained on medical literature, including texts like Clinical Emergency Medicine and Gale Encyclopedia. Accurate, fast, and reliable for healthcare queries.
https://github.com/gyanbardhan/medichat

all-minilm-l6-v2 chromadb flask generative-ai langchain llama2 llm llms meta sentence-transformers vector-database

Last synced: 21 days ago
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MediChat: An AI-powered medical chatbot using the Llama-2-7B-Chat model for precise clinical responses. Integrates Chroma DB and all-MiniLM-L6-v2 embeddings trained on medical literature, including texts like Clinical Emergency Medicine and Gale Encyclopedia. Accurate, fast, and reliable for healthcare queries.

• Host: GitHub
• URL: https://github.com/gyanbardhan/medichat
• Owner: Gyanbardhan
• Created: 2024-07-05T17:41:19.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2024-07-05T18:30:46.000Z (7 months ago)
• Last Synced: 2024-12-17T23:03:46.139Z (about 2 months ago)
• Topics: all-minilm-l6-v2, chromadb, flask, generative-ai, langchain, llama2, llm, llms, meta, sentence-transformers, vector-database
• Language: Jupyter Notebook
• Homepage: https://drive.google.com/file/d/1Z2c27T0WiH8-AycAJbTcVged6y0ng6rG/view?usp=drive_link
• Size: 22.1 MB
• Stars: 4
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# MediChat

MediChat is an advanced medical chatbot designed to assist with clinical queries and provide information based on medical literature. It leverages state-of-the-art models and embeddings to deliver accurate and reliable responses.

### [Demo for MediChat](https://drive.google.com/file/d/1Z2c27T0WiH8-AycAJbTcVged6y0ng6rG/view?usp=drive_link)

## Features

### Medical Expertise: 

Provides information based on extensive training on medical literature.

### Advanced Model: 

Utilizes the [Llama-2-7B-Chat](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML) model from Hugging Face.

### Vector Database: 

Incorporates Chroma DB for efficient data retrieval.

### High-Quality Embeddings: 

Uses [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) embeddings from Hugging Face.

## Model Details

### Llama-2-7B-Chat Model:

- Source: [Link](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/blob/main/llama-2-7b-chat.ggmlv3.q4_0.bin)

- Description: An open-source large language model optimized for chat-based interactions, capable of understanding and generating human-like text.

## Database and Embeddings

### Chroma DB: 

A vector database that allows for efficient storage and retrieval of embeddings.

### Embeddings Model:

- Source: [Link](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)

- Description: A small, fast, and high-quality embedding model that provides dense vector representations of text.

## Training Data

MediChat is trained on embeddings derived from the following medical books:

- Clinical Emergency Medicine (PDFDrive.com)

- Current Essentials of Medicine

- Gale Encyclopedia of Medicine Vol. 4 (N-S)

## Installation

To set up MediChat, follow these steps:

Clone the Repository:

- git clone https://github.com/Gyanbardhan/MediChat.git

- cd MediChat

## Install Dependencies:

- pip install -r requirements.txt

## Download the Model and Embeddings:

- Llama-2-7B-Chat model: Download from Hugging Face and place it in the models directory.

- Sentence-Transformers embeddings: Download from Hugging Face and place them in the embeddings directory.

- Set Up Chroma DB

#### Run the MediChat application with the following command:

- python app.py

#### Interact with MediChat via the provided interface, asking medical questions and receiving expert responses based on the embedded medical literature.