ChatGPT-Powered PDF Assistant with Langchain & Streamlit

A conversational AI application that allows you to interact with PDF documents using OpenAI's LLM. Built as part of a tutorial project to demonstrate NLP skills, Langchain integration, and Streamlit UI development. Ideal for document analysis, Q&A, and chatbot functionalities.

🚀 Features

• Upload PDFs and extract text content.
• Split documents into manageable chunks for LLM processing.
• Generate embeddings and build a searchable index using Langchain.
• Ask questions in natural language and receive AI-powered answers.
• User-friendly interface built with Streamlit.

🔧 Technologies Used

• Langchain: Framework for LLM integration, text splitting, and embeddings.
• OpenAI: GPT model and API for generative responses.
• Streamlit: Frontend UI for PDF uploads and chat interface.
• PyPDF2: PDF text extraction.

📦 Installation

1. Clone the repository:

   git clone https://github.com/your-username/your-repo-name.git
   cd your-repo-name
   

2. Install dependencies:

   pip install langchain openai streamlit pypdf2 python-dotenv
   

3. Set up your OpenAI API key in a .env file:

   OPENAI_API_KEY="your-api-key-here
   

🖥️ Usage

1. Run the Streamlit app:

   streamlit run app.py
   

2. Upload a PDF file through the Streamlit interface.
3. Ask questions about the PDF content in the chatbox.
4. View the AI-generated responses in real-time.

🏗️ Architecture

Key Steps (Detailed View)

1. PDF Processing:

• Split PDF text into chunks.
• Generate embeddings for each chunk.
• Build a searchable index using Langchain.

2. Query Handling:

• Convert user questions into embeddings.
• Retrieve relevant text chunks from the index.
• Generate answers using OpenAI's LLM.

Tech Stack (Abstract View)

• Backend: Langchain for data processing, OpenAI API for LLM.
• Frontend: Streamlit for UI, chat interface, and PDF uploads.

📄 License MIT License. Feel free to adapt for your use case.

🔗 Connect with me on LinkedIn or GitHub.