/Chat-with-Github-Repo

This repository contains two Python scripts that demonstrate how to create a chatbot using Streamlit, OpenAI GPT-3.5-turbo, and Activeloop's Deep Lake.

Primary LanguagePythonMIT LicenseMIT

Chat-with-Github-Repo

This repository contains two Python scripts that demonstrate how to create a chatbot using Streamlit, OpenAI GPT-3.5-turbo, and Activeloop's Deep Lake.

The chatbot searches a dataset stored in Deep Lake to find relevant information and generates responses based on the user's input.

Files

github.py: This script clones a git repository, processes the text documents, computes embeddings using OpenAIEmbeddings, and stores the embeddings in a DeepLake instance.

chat.py: This script creates a Streamlit web application that interacts with the user and the DeepLake instance to generate chatbot responses using OpenAI GPT-3.5-turbo.

Setup

Before getting started, be sure to sign up for an Activeloop and OpenAI account and create API keys. You'll also want to create a Deep Lake dataset, which will generate a dataset path in the format hub://{username}/{repo_name} (where you define the repo_name).

To set up and run this project, follow these steps:

  1. Install the required packages with pip: 
    pip install -r requirements.txt
  2. Copy the .env.example file to .env and replace the variables, including API keys, GitHub URL, and site / Deep Lake information. Here's a brief explanation of the variables in the .env file:

OPENAI_API_KEY: Your OpenAI API key. You can obtain this from your OpenAI account dashboard. ACTIVELOOP_TOKEN: Your Activeloop API token. You can obtain this from your Activeloop account dashboard. DEEPLAKE_USERNAME: Your Activeloop username. DEEPLAKE_DATASET_PATH: The dataset path for your Deep Lake dataset. This is in the format hub://{username}/{repo_name}. Replace {username} with your Activeloop username, and {repo_name} with the desired name for your Deep Lake dataset (e.g., hub://johndoe/my-chatbot-dataset). REPO_URL: The URL of the GitHub repository you want to clone and process (e.g., https://github.com/username/repo_name). SITE_TITLE: The title for your Streamlit web application (e.g., "My Chatbot App").

  1. Run the github.py script to embed the GitHub repo, thus, storing the data in the specified Activeloop Deep Lake.
  2. Run the Streamlit chat app, which should default to http://localhost:8502 and allow you to ask questions about the repo: 
    streamlit run chat.py

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License

MIT License