PD_RAG_Conversational

PD_RAG_Conversational is a Retrieval-Augmented Generation (RAG) conversational AI system designed to provide accurate and context-aware responses by leveraging retrieval-based knowledge capabilities for simple and technical purposes .

message for twise peeps 'mr ahmed I believe '

After installation run streamlit run streamlit_app.py , all the scripts are available from data collection script , to creating vector db , to the rag pipeline to the integration into the streamlit app .

Features

• You can see both awnser and Retrieved context for the question !
• You can toggle between simple and technical response
• Implements RAG to enhance response accuracy
• Supports document retrieval for context-aware conversations
• Utilizes NLP techniques for better user interactions
• Scalable and modular architecture

Installation

Prerequisites

Ensure you have the following installed:

• Python 3.8+
• pip
• Virtual environment (optional but recommended)

Steps

1. Clone the repository:

   git clone https://github.com/RamiIbrahim2002/PD_RAG_Conversational.git
   cd PD_RAG_Conversational

2. Create and activate a virtual environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install dependencies:

   pip install -r requirements.txt

4. Run the application:

   python main.py

Usage

• Start the chatbot using main.py.
• Provide input queries and receive responses based on the retrieved knowledge.

Configuration

Add your api key to .env

Data

The data script + the script to scrape that data 9000 articales from PubMed is open-source :)

License

This project is licensed under the MIT License.