RAG Chatbot

---

🤖 A chatbot that uses Retrieval-Augmented Generation (RAG) to provide accurate and context-aware responses.

📝 Table of Contents

• About
• Demo / Working
• How it works
• Usage
• Getting Started
• Deploying your own bot
• Built Using
• TODO
• Contributing
• Authors
• Acknowledgments

🧐 About

The RAG Chatbot is designed to leverage the power of Retrieval-Augmented Generation to provide users with accurate and contextually relevant responses. It combines the strengths of retrieval-based and generative models to enhance the user experience.

💭 How it works

The chatbot first retrieves relevant documents from a pre-defined knowledge base using a retrieval model. It then uses a generative model to generate a response based on the retrieved documents and the user's query.

The bot uses the OpenAI API for the generative model and a Chroma vectorstore for the retrieval model. The entire bot is written in Python 3.12.

🎈 Usage

The bot has a simple UI where you can input your question and set the number of references to use. After submitting your question, the bot will provide a response based on the retrieved documents and the generative model. You will also see the PDF pages that were used to generate the response.

🏁 Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

You need to have Python and pip installed on your machine.

sudo apt-get install python3
sudo apt-get install python3-pip

Installing

A step by step series of examples that tell you how to get a development env running.

Clone the repository:

git clone https://github.com/TatjanaUtz/rag_chatbot.git
cd rag_chatbot

Create a virtual environment and activate it:

python3 -m venv env
source env/bin/activate

Install the dependencies:

pip install -r requirements.txt

Add your PDF file(s) to the data/raw directory.

Run the script to create the vectorstore:

python src/create_vectorstore.py

Run the bot:

python src/main.py

The chatbot will be accessible on http://localhost:7860.

🚀 Deploying your own bot

To deploy this project on a live system using Docker and Docker Compose, follow these steps:

Prerequisites

Ensure you have Docker and Docker Compose installed on your machine.

sudo apt-get install docker
sudo apt-get install docker-compose

Steps

1. Clone the repository

git clone https://github.com/tutz/translation-service.git
cd translation-service

2. Build the Docker images

docker-compose build

3. Start the services

docker-compose up -d

This will start the application and its dependencies in the background.

4. Verify the services are running

docker-compose ps

You should see the translation service and its dependencies listed and running.

Accessing the Service

The translation service will be available at http://localhost:7860.

To stop the services, run:

docker-compose down

This will stop and remove the containers, networks, and volumes created by Docker Compose.

⛏️ Built Using

• Docker - Containerization
• pytest - Testing Framework
• pre-commit - Git Hook Scripts
• mypy - Static Type Checker
• Ruff - Linter
• OpenAI - Generative Model
• Gradio - User Interface

✍️ Authors

• @TatjanaUtz - Idea & Initial work