This project demonstrates how to load a document, split it into chunks, embed it using OpenAI embeddings, store it in a Chroma vector database, and create a simple Streamlit app for question answering based on the document content. Prerequisites

Python 3.7+
pip
An OpenAI API key
A .env file with your OpenAI API key (OPENAI_API_KEY)

Installation

Clone the repository:

bash

git clone <repository-url>
cd <repository-directory>

Install the required packages:

bash

pip install -r requirements.txt

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

plaintext

OPENAI_API_KEY=your_openai_api_key

Usage

Place the PDF file you want to load in the data/ directory and update the script with the correct filename if necessary.

Run the script to process the document and start the Streamlit app:

bash

streamlit run app.py