Phenomenal020/dia_coursework

DIA Coursework Application

How well does a decoder-only agent like GPT-3.5 handle user queries with different levels of proficiency in a conversational chatbot?

Does adding an encoder like BERT to the pipeline improve the agent’s performance?

This README provides instructions on how to set up and run the application.

Prerequisites

• Python
• Streamlit
• OpenAI API key

Setup Instructions

1. Create a Virtual Environment

You can create a virtual environment to manage dependencies:

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

Alternatively, if you need to install pip, run:

• curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
• python get-pip.py

2. Install Required Packages

Install the necessary packages by running:

• pip install -r requirements.txt

3. Setup API Keys - MongoDB, Trip advisor, and OpenAI

• Add to a .env file to be used for connecting to these platforms.
• Copy the OpenAI api key and paste in the sidebar

4. Train the Encoder

Note: Due to size constraints, the BERT model is not included in this repo. To use this, run the following command to train the encoder for inference:

• python train_encoder.py

5. Run the Application

To chat with the decoder-only agent:

• streamlit run decoder_agent.py

To chat with the seq2seq (encoder-decoder) agent:

• streamlit run encoder_decoder_agent.py