Generative AI Applications

This repository contains a collection of applications demonstrating different use cases of Generative AI. Each project is self-contained within its own directory.

Projects

1. Chat with Your Data

A customizable AI-powered chatbot that answers questions based on your own data. This application allows you to upload a PDF document and query its contents using a natural language web interface.

Location: build_chatbot_for_your_data/

Key Technologies:

• Backend: Python, Flask, LangChain
• LLM Integration: IBM Watsonx (default), Hugging Face (alternative)
• Embeddings: Hugging Face Instruct Embeddings, Sentence Transformers
• Vector Store: ChromaDB
• Frontend: HTML, CSS, JavaScript (jQuery)

Setup and Usage

1. Navigate to the project directory:

   cd build_chatbot_for_your_data

2. Install dependencies:

   pip install -r requirements.txt

3. Configure Credentials: Update worker.py with your credentials for IBM Watsonx or uncomment and configure the Hugging Face Hub section.

4. Run the application:

   python server.py

5. Open your web browser and navigate to http://0.0.0.0:8000.

6. Upload a PDF file and start asking questions about its content.

Docker Usage

You can also run the application using Docker:

cd build_chatbot_for_your_data
docker build -t chatbot-data .
docker run -p 8000:8000 chatbot-data

2. Software Development Chatbot

A lightweight client-server chatbot that demonstrates a direct integration with the OpenAI API. It's a simple Node.js application that serves a static frontend and proxies chat requests to the OpenAI API.

Location: software-dev-chatbot/

Key Technologies:

• Backend: Node.js, Express.js
• API: OpenAI gpt-3.5-turbo-1106
• Frontend: HTML, CSS, JavaScript

Setup and Usage

1. Navigate to the project directory:

   cd software-dev-chatbot

2. Install dependencies:

   npm install

3. Set your OpenAI API Key: You must set your OpenAI API key as an environment variable.

   export OPENAI_API_KEY='your-openai-api-key'

4. Start the server:

   node server.js

5. Open your web browser and go to http://localhost:3000.