📄 AI PDF Chat RAG Application

An AI-powered PDF Question Answering application built using Python, Streamlit, LangChain, Google Gemini, FAISS, and Retrieval-Augmented Generation (RAG).

The application allows users to upload any PDF document, ask questions in natural language, retrieve the most relevant information using a vector database, and generate accurate answers using Google's Gemini Large Language Model.

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🚀 Features

• 📂 Upload PDF documents
• 📖 Automatic PDF text extraction
• ✂️ Intelligent text chunking
• 🧠 Google Gemini Embeddings
• 🗄️ FAISS Vector Database
• 🔍 Similarity Search Retriever
• 🤖 Google Gemini 2.5 Flash LLM
• 💬 Context-aware Question Answering
• ⚡ Cached PDF Processing
• 🎨 Streamlit User Interface

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🏗️ System Architecture

                 User
                   │
                   ▼
            Upload PDF
                   │
                   ▼
          PyPDFLoader
                   │
                   ▼
   RecursiveCharacterTextSplitter
                   │
                   ▼
  Google Gemini Embeddings
                   │
                   ▼
        FAISS Vector Store
                   │
                   ▼
            Retriever
                   │
                   ▼
      Relevant PDF Chunks
                   │
                   ▼
     Google Gemini 2.5 Flash
                   │
                   ▼
           Final AI Answer

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🛠️ Tech Stack

Component	Technology
Programming Language	Python
Frontend	Streamlit
AI Framework	LangChain
Large Language Model	Google Gemini 2.5 Flash
Embeddings	Gemini Embedding Model
Vector Database	FAISS
PDF Loader	PyPDFLoader
Environment Variables	python-dotenv

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📂 Project Structure

rag-pdf-chat-gemini/

│── app.py
│── requirements.txt
│── README.md
│── SETUP.md
│── VERSION_FIX.md
│── .env.example
│── .gitignore

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⚙️ Installation

Clone the repository

git clone https://github.com/YOUR_USERNAME/rag-pdf-chat-gemini.git

Move into the project folder

cd rag-pdf-chat-gemini

Create Virtual Environment

python -m venv .venv

Activate Virtual Environment (Windows)

.venv\Scripts\activate

Install Required Packages

pip install -r requirements.txt

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🔑 Environment Variables

Create a file named

.env

Add your Google Gemini API Key

GOOGLE_API_KEY=YOUR_GOOGLE_API_KEY

Note: Never upload your .env file to GitHub.

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▶️ Run the Application

streamlit run app.py

The application will start on

http://localhost:8501

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📸 Application Workflow

Upload PDF
      │
      ▼
Read PDF
      │
      ▼
Split into Chunks
      │
      ▼
Generate Embeddings
      │
      ▼
Store in FAISS
      │
      ▼
Retrieve Relevant Chunks
      │
      ▼
Generate AI Answer
      │
      ▼
Display Answer in Streamlit

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📷 Screenshots

Home Screen

(Add Screenshot Here)

screenshots/home.png

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Upload PDF

(Add Screenshot Here)

screenshots/upload.png

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AI Generated Answer

(Add Screenshot Here)

screenshots/answer.png

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📖 How Retrieval-Augmented Generation (RAG) Works

1. User uploads a PDF.
2. The PDF is loaded using PyPDFLoader.
3. The extracted text is split into smaller chunks.
4. Each chunk is converted into vector embeddings using Google Gemini Embeddings.
5. The embeddings are stored inside a FAISS Vector Database.
6. When the user asks a question, the Retriever finds the most relevant chunks.
7. Those chunks are passed as context to Google Gemini 2.5 Flash.
8. Gemini generates an accurate answer based on the retrieved context.
9. The answer is displayed in the Streamlit application.

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🌟 Future Enhancements

• ✅ Multiple PDF Support
• ✅ Chat History
• ✅ Conversation Memory
• ✅ Source Page References
• ✅ Save FAISS Index Locally
• ✅ Authentication
• ✅ Dark Mode UI
• ✅ Streamlit Cloud Deployment

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📚 Learning Outcomes

This project demonstrates practical implementation of:

• Retrieval-Augmented Generation (RAG)
• LangChain Framework
• Google Gemini API
• Vector Embeddings
• FAISS Vector Database
• Streamlit Web Applications
• Prompt Engineering
• Semantic Search
• PDF Processing
• AI-powered Question Answering

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👨‍💻 Author

N. Gopalakrishnan

Generative AI Learner

GitHub: https://github.com/YOUR_USERNAME

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📄 License

This project is licensed under the MIT License.

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