This project implements a Document-based Question Answering (QA) system by implement Retrieval Augmented Generation (RAG) using OpenAI's API. The system is designed to answer questions based on the content of documents provided to it.
- Utilizes OpenAI's powerful language model for accurate and context-aware answers.
- Supports various document formats such as plain text, PDF, and HTML.
- Can handle a wide range of questions on diverse topics.
- Provides easy-to-use API endpoints for integration into other applications.
- Customizable parameters for fine-tuning performance.
I have removed the API key but if you want to run this project then you have to enter the API key
To use this system, follow these steps:
- Clone the repository to your local machine:
git clone https://github.com/SannketNikam/Document-QnA.git
- Navigate to the project directory:
cd Document-QnA
- Create a Conda environment named "documentqna" and activate it
conda create --name documentqna
conda activate documentqna
- Install the required dependencies
pip install -r requirements.txt