RagBot

RagBot is an AI-powered chatbot that provides information and assistance to users based on a predefined context. It uses Streamlit for the user interface, Langchain for document processing and retrieval, and Hugging Face Transformers for sentence embeddings.

Features

• Answers user questions based on predefined question-answer pairs
• Handles out-of-scope questions by generating responses using LLMs and context from a JSON file
• Utilizes sentence embeddings to find the most relevant question-answer pair
• Provides a user-friendly interface using Streamlit

Prerequisites

Before running RagBot, ensure that you have the following:

• Python 3.7 or higher
• An OpenAI API key

Installation

1. Clone the repository:

   git clone https://github.com/codexnyctis/ragBot
   

2. Navigate to the project directory:

   cd ragBot
   

3. Install the required dependencies:

   pip install -r requirements.txt
   

4. Create a .env file in the project directory and add your OpenAI API key:

   OPENAI_API_KEY=your-api-key
   

Usage

1. Prepare your data/OR use the example data:

   • Create a data.json file in the project directory.
   • The file should contain an array of objects, where each object represents a question-answer pair in the following format:

     [
       {
         "question": "What is RagBot?",
         "answer": "RagBot is an AI-powered chatbot that provides information and assistance based on predefined context."
       },
       ...
     ]

2. Run the Streamlit app:

   streamlit run ragBot.py
   

3. Open the provided URL in your web browser.

4. Enter your question in the chat input field and press Enter.

5. RagBot will provide a response based on the provided context:

   • If the question is similar to one of the predefined questions in data.json, the corresponding answer will be displayed.
   • If the question is out-of-scope (i.e., not similar to any predefined question), RagBot will generate a response using the LLMs and the context from data.json.

Customization

• To modify RagBot's behavior or add more functionality, you can update the code in ragBot.py.
• To change the predefined questions and answers, modify the data.json file.
• You can customize the LLMs and models used by modifying the relevant code sections in ragBot.py.