AIPyChat

This project is a Streamlit application that allows users to upload PDF documents and ask questions about the content. The app leverages OpenAI’s GPT-3.5-turbo model to generate responses based on the content of the uploaded PDFs.

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Features

• PDF Upload: Users can upload PDF documents through the sidebar.
• Text Extraction: The text from the uploaded PDFs is extracted.
• Text Chunking: The extracted text is split into manageable chunks for processing.
• Embedding Generation: The text chunks are converted into embeddings using OpenAI's embeddings.
• Vector Store Creation: A FAISS vector store is created from the embeddings for efficient similarity search.
• Question Answering: Users can input questions, which are answered based on the content of the uploaded PDFs using OpenAI's GPT-3.5-turbo model.

Demo

chatbot_vid.mov

Installation

1. Clone this repository:

   git clone https://github.com/vineetsansare/AIPyChat.git   

2. Create a virtual environment and activate it:

   python3 -m venv env
   source env/bin/activate

3. Install the required packages:

   pip install -r requirements.txt

4. Set up your OpenAI API key:

   • Replace OPENAI_API_KEY in the code with your actual OpenAI API key.

Usage

1. Run the Streamlit app:

   streamlit run app.py

2. Upload a PDF file through the sidebar.

3. Ask questions related to the content of the uploaded PDF in the provided text input field.

Code Overview

The main components of the code are:

• PDF Upload: Uses Streamlit's file_uploader to upload PDF files.
• Text Extraction: Utilizes PyPDF2.PdfReader to extract text from the uploaded PDF.
• Text Chunking: Employs RecursiveCharacterTextSplitter to split the extracted text into chunks.
• Embedding Generation: Uses OpenAIEmbeddings to generate embeddings from the text chunks.
• Vector Store Creation: Creates a FAISS vector store from the embeddings for efficient similarity search.
• Question Answering: Uses ChatOpenAI and load_qa_chain to answer user questions based on the relevant text chunks.

Dependencies

• streamlit
• PyPDF2
• langchain
• langchain_community
• faiss-cpu (or faiss-gpu for GPU support)