Cancer RAG Web Application

Overview

The Cancer RAG Web Application is a Flask-based web tool that leverages Retrieval-Augmented Generation (RAG) using state-of-the-art LLMs and vector search to help users query and retrieve insights about cancer-related information.
The system combines modern machine learning techniques with a clean and user-friendly interface for a seamless querying experience.

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Features

• RAG Architecture: Implements Retrieval-Augmented Generation using ChromaDB and LlamaIndex.
• Powered by LLMs: Uses Ollama's qwen2:0.5b model and HuggingFace embeddings.
• Persistent Vector Store: Efficient document retrieval with ChromaDB integration.
• Customizable Indexing: Dynamically load cancer-related data to enhance query capabilities.
• Interactive UI: A clean, responsive front-end for entering queries and viewing results.

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Installation

Follow these steps to set up and run the application locally:

Prerequisites

1. Python 3.9+ installed.
2. pip (Python package manager).

Steps

1. Clone the repository:

   git clone https://github.com/your_username/cancer-rag-webapp.git  
   cd cancer-rag-webapp  

2. Install dependencies:

   pip install -r requirements.txt  

3. Set up your data directory:

   • Place your cancer-related documents in a folder named data in the project root directory.

4. Start the application:

   python main1.py  

5. Open your browser and navigate to:

   http://127.0.0.1:5000  
   

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How It Works

1. Data Ingestion:

   • Documents placed in the data/ directory are indexed into a vector store using HuggingFace embeddings and ChromaDB.

2. Query Processing:

   • When a user inputs a query, the system retrieves relevant information from the vector store and processes it using the qwen2:0.5b LLM.

3. Interactive Response:

   • The response is displayed on the web interface for the user.

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Project Structure

cancer-rag-webapp/  
├── data/                   # Directory for documents to be indexed.  
├── templates/              # HTML templates for the Flask UI.  
├── static/                 # CSS/JS assets for the UI.  
├── main1.py                # Entry point for the Flask application.  
├── llm.py                  # Logic for indexing and query handling.  
├── requirements.txt        # List of dependencies.  
├── README.md               # Documentation for the project.  

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Sample Output

1. Navigate to the application in your browser.

2. Enter a query like:

   What are the common symptoms of lung cancer?  
   

3. The application will display an AI-generated, factually accurate response based on the indexed data.

   

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Contributions

Contributions are welcome! Feel free to submit issues or pull requests.

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License

This project is licensed under the MIT License.