📄 RAG Chat Application – Chat with Your PDF using GPT-4

This is a simple RAG (Retrieval-Augmented Generation) application built using Streamlit, LangChain, OpenAI, and Qdrant as the vector database. The app lets you upload a PDF file and chat with it, powered by GPT-4. It stores vectorized content from the PDF and retrieves relevant chunks to answer your questions.

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🚀 Features

• Upload any PDF
• PDF is split into chunks and stored as vector embeddings in Qdrant
• Ask questions about the PDF in natural language
• Get answers from GPT-4 based on the content of your uploaded PDF

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🛠️ Tech Stack

• Frontend: Streamlit
• LLM: OpenAI GPT-4.1
• Embeddings: text-embedding-3-large from OpenAI
• Vector DB: Qdrant
• Chunking: LangChain's RecursiveCharacterTextSplitter

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🧑‍💻 Getting Started

1️⃣ Clone the Repository

git clone https://github.com/your-username/rag-chat-pdf.git
cd rag-chat-pdf

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2️⃣ Set Up Your Environment

Install dependencies:

pip install -r requirements.txt

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3️⃣ Add Your API Key

Create a .env file in the root directory:

OPENAI_API_KEY=sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx

⚠️ Never hard-code your API key into the Python files.

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4️⃣ Start Qdrant Locally

You can run Qdrant using Docker:

docker run -p 6333:6333 qdrant/qdrant

Make sure Qdrant is running before starting the app.

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5️⃣ Launch the Streamlit App

streamlit run app.py

This will open the Streamlit app in your browser.

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📁 Project Structure

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├── app.py               # Streamlit app with Upload & Chat UI
├── .env                 # Your API key is stored here (DO NOT COMMIT)
├── requirements.txt     # Python dependencies
└── README.md            # Project documentation

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⚠️ Notes

• Only one PDF is processed at a time. If a new PDF is uploaded, the vector store is overwritten.
• GPT will answer only based on content from the uploaded PDF.
• You can expand this into a multi-document or session-based app using LangChain memory and persistent vector stores.

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✅ To-Do (Optional Extensions)

• Support multiple documents
• Session-based chat memory
• File-type support beyond PDFs (Word, Text, etc.)
• Cloud-based deployment (Streamlit Cloud, HuggingFace Spaces)

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🙏 Acknowledgements

• LangChain
• Qdrant Vector DB
• Streamlit
• OpenAI GPT-4