Chatbot

Chatbots are one of the major LLM use-cases. The core features of chatbots are that they can have long-running conversations and have access to information that users want to know about.

Aside from basic prompting and LLMs, memory and retrieval are the core components of a chatbot. Memory allows a chatbot to remember past interactions, and retrieval provides a chatbot with up-to-date, domain-specific information.

Getting Started

To get started with this project, you'll need to clone the repository and set up a virtual environment. This will allow you to install the required dependencies without affecting your system-wide Python installation.

Cloning the Repository

git clone https://github.com/jkmathilda/gpt-Chatbot.git

Setting up a Virtual Environment

cd ./gpt-Chatbot

pyenv versions

pyenv local 3.11.6

echo '.env'  >> .gitignore
echo '.venv' >> .gitignore

ls -la

python -m venv .venv        # create a new virtual environment

source .venv/bin/activate   # Activate the virtual environment

python -V                   # Check a python version

Install the required dependencies

pip list

pip install -r requirements.txt

pip freeze | tee requirements.txt.detail

Configure the Application

To configure the application, there are a few properties that can be set the environment

echo 'OPENAI_API_KEY="sk-...."' >> .env

Running the Application

python3 -m streamlit run main.py

Deactivate the virtual environment

deactivate

Example

Reference

Chatbots (on LangChain)

Create a ChatGPT clone using Streamlit and LangChain