https://github.com/yyigitturan/medical-chatbot

RAG-powered medical chatbot providing evidence-based insights on disease causes and treatments.
https://github.com/yyigitturan/medical-chatbot

flask langchain pinecone python

Last synced: 2 months ago
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RAG-powered medical chatbot providing evidence-based insights on disease causes and treatments.

• Host: GitHub
• URL: https://github.com/yyigitturan/medical-chatbot
• Owner: yyigitturan
• License: mit
• Created: 2025-09-28T08:22:32.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2025-09-29T17:23:49.000Z (9 months ago)
• Last Synced: 2025-09-29T19:06:25.537Z (9 months ago)
• Topics: flask, langchain, pinecone, python
• Language: Jupyter Notebook
• Homepage: https://medical-ai-two.vercel.app
• Size: 12.8 MB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Medical-Chatbot 

[![Python 3.10+](https://img.shields.io/badge/Python-3.10%2B-3776AB?logo=python&logoColor=white)](https://python.org)

[![Flask](https://img.shields.io/badge/Flask-3.1.1-000000?logo=flask&logoColor=white)](https://flask.palletsprojects.com/)

[![Docker](https://img.shields.io/badge/Docker-Ready-2496ED?logo=docker&logoColor=white)](https://docker.com)

[![LangChain](https://img.shields.io/badge/LangChain-0.3.27-00FF00)](https://langchain.com)

[![Google Gemini](https://img.shields.io/badge/Gemini_AI-Powered-4285F4?logo=google&logoColor=white)](https://gemini.google.com)

[![Pinecone](https://img.shields.io/badge/Vector_DB-Pinecone-430098)](https://pinecone.io)

[![HuggingFace](https://img.shields.io/badge/HuggingFace-Transformers-FFD21E?logo=huggingface&logoColor=black)](https://huggingface.co)

[![SentenceTransformers](https://img.shields.io/badge/Sentence_Transformers-Embeddings-1D8F73)](https://www.sbert.net)

[![RAG Architecture](https://img.shields.io/badge/Architecture-RAG-8A2BE2)](https://github.com/yyigitturan/Medical-Chatbot)

# 🎯 Overview

This project implements Retrieval-Augmented Generation (RAG) system specifically designed for medical knowledge queries. By combining Pinecone's vector database with Google's Gemini LLM, it provides accurate, contextually relevant medical information grounded in verified literature.

# ✨ Project Features

- RAG Implementation

  - Semantic Chunking: Intelligent document segmentation preserving medical context

  - Multi-vector Retrieval: Top-K similarity search with relevance scoring

  - Context-Aware Generation: Prompt engineering optimized for medical accuracy

- Production-Ready Architecture

   - Containerized Deployment: Docker support with multi-stage builds

   - Environment Management: Secure API key handling and configuration

# 📊 Project Workflow

![alt text](flow.png)

# 🖥️ Application Interface



# 🚀 Tech Stack



| Category | Technologies |

|----------|--------------|

| **🤖 AI Framework & Models** | `LangChain` `Gemini LLM` `Sentence Transformers` |

| **🗄️ Vector Database** | `Pinecone` `HuggingFace` |

| **🌐 Backend** | `Flask` `Flask-CORS` |

| **🎨 Frontend** | `HTML5` `CSS3` `JavaScript` |

| **📄 Document Processing** | `PyPDF` |

| **🔧 Development & Deployment** | `Docker` `Python-Dotenv` `Python 3.10+` |



# 🛠️ Installation & Setup

### Prerequisites

- Pinecone API account

- Google Gemini API key

## Steps: 

Clone the repository 

```bash 

git clone https://github.com/yyigitturan/Medical-Chatbot.git

```

### Step 01- Create a environment after opening the repository 

```bash 

python -m venv medicalbot

source medicalbot/bin/activate  

``` 

### Step 02- Install Dependencies

```bash 

pip install -r requirements.txt

``` 

### Step 03- Environment Configuration

Create a .env file with your API keys: 

```bash 

PINECONE_API_KEY=your_pinecone_api_key

GEMINI_API_KEY=your_gemini_api_key

``` 

### Step 04- Initialize Vector Database

```bash 

python store_index.py

``` 

### Step 05- Run the Application

```bash 

python app.py

``` 

## Docker Deployment

Build the Image

```bash 

docker build -t medical-chatbot .

``` 

Run the Container

```bash 

docker run -p 5000:5000 --env-file .env medical-chatbot

``` 

# 💡 Usage

Start the application

Access the web interface at http://localhost:5000

Enter medical questions like:

"How can acne be prevented?"