This simple project leverages the power of Retrieval Augmented Generation (RAG) to provide conversational interfaces that answer questions directly from multiple PDFs using Vector Embedding. Dive deep into your document's content with the chatbot interface and fetch relevant insights. This project is a modification of Alejandro AO's Implementation
PDF Content Extraction: Seamlessly extract and process text content from multiple PDFs. Conversational Interface: Engage in a chat-like experience to ask questions about your documents. Retrieval Augmented Generation: Uses advanced OpenAI or HuggingFace Models to retrieve relevant information from the processed PDFs to generate contextual answers.
Python 3.x
OpenAI or HuggingFace API key.
Clone the repository:
git clone https://github.com/MC-Meesh/PDFChatbot.git
Navigate to the project directory:
cd PDFChatbot
Install the required packages:
pip install -r requirements.txt
To use this bot, you'll need to have an API key for either OpenAI or HuggingFace.
Store your API key in a .env file in the root directory of the project. Here's the format:
OPENAI_API_KEY=your_openai_key_here
or
HUGGINGFACE_API_KEY=your_huggingface_key_here
Load the environment variables:
from dotenv import load_dotenv
load_dotenv()
Run the main script:
streamlit run app.py
Follow the on-screen instructions to upload your PDFs and start asking questions about your documents!
The PDFChatbot App is released under the MIT License.