PDF Chatbot with RAG (Retrieval-Augmented Generation)

A powerful chatbot that allows you to upload PDF documents and ask questions about their content using AI. Built with Streamlit, LangChain, and Groq LLM.

Features

• 📄 Upload and process multiple PDF files
• 🤖 AI-powered question answering using Groq's LLM
• 🔍 Semantic search with FAISS vector database
• 💬 Interactive chat interface
• 📚 View source documents and page references
• ⚡ Fast response times with LLaMA 3.1 model

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Table of Contents

1. Installation
2. Getting API Keys
3. Configuration
4. Running Locally
5. Deploying to Streamlit Cloud
6. Usage
7. Common Errors & FAQs
8. Project Structure
9. Technologies Used

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Installation

Step 1: Clone the Repository

git clone https://github.com/yourusername/pdf-chatbot-rag.git
cd pdf-chatbot-rag

Step 2: Create Virtual Environment (Recommended)

On Windows:

python -m venv venv
venv\Scripts\activate

On macOS/Linux:

python3 -m venv venv
source venv/bin/activate

Step 3: Install Dependencies

pip install -r requirements.txt

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Getting API Keys

🔑 Groq API Key (Required)

Groq provides fast LLM inference with their optimized infrastructure.

Steps to Get Groq API Key:

1. Visit Groq Console

   • Go to: https://console.groq.com

2. Sign Up / Login

   • Click "Sign Up" if you're new
   • Use Google/GitHub for quick signup
   • Or create account with email

3. Navigate to API Keys

   • After login, go to the left sidebar
   • Click on "API Keys"

4. Create New API Key

   • Click "Create API Key" button
   • Give it a name (e.g., "PDF Chatbot")
   • Click "Submit"

5. Copy Your API Key

   • IMPORTANT: Copy the key immediately
   • You won't be able to see it again!
   • It looks like: gsk_xxxxxxxxxxxxxxxxxxxxxxxxxxxx

6. Save It Securely

   • Store it in a password manager
   • Never share it publicly
   • Don't commit it to GitHub

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Configuration

Step 1: Create Environment File

We've provided a sample.env file. Follow these steps:

1. Locate the sample.env file in the project root

2. Copy and rename it to .env:

   On Windows (Command Prompt):

   copy sample.env .env

   On macOS/Linux (Terminal):

   cp sample.env .env

   Or manually:

   • Right-click sample.env
   • Select "Copy"
   • Paste and rename to .env

Step 2: Add Your API Key

Open the .env file in any text editor and replace the placeholder:

Before:

GROQ_API_KEY=your_groq_api_key_here

After:

GROQ_API_KEY=gsk_1a2b3c4d5e6f7g8h9i0j1k2l3m4n5o6p7q8r9s0t

⚠️ Important:

• No spaces around the = sign
• No quotes needed
• Keep the .env file in the project root directory

Step 3: Verify .gitignore

Make sure your .env file won't be committed to Git:

Check if .gitignore contains:

.env
venv/
__pycache__/
*.pyc

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Running Locally

Step 1: Activate Virtual Environment

Make sure your virtual environment is activated (see Installation Step 2)

Step 2: Run the Streamlit App

streamlit run main2.py

Or if you named your file differently:

streamlit run your_filename.py

Step 3: Access the Application

The app will automatically open in your default browser at:

http://localhost:8501

If it doesn't open automatically, manually go to the URL shown in the terminal.

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Deploying to Streamlit Cloud

Prerequisites

• GitHub account
• Your code pushed to a GitHub repository

Step 1: Push Code to GitHub

git add .
git commit -m "Initial commit"
git push origin main

⚠️ Make sure .env is in .gitignore - Never push API keys to GitHub!

Step 2: Go to Streamlit Cloud

1. Visit: https://streamlit.io/cloud
2. Click "Sign up" or "Sign in with GitHub"
3. Authorize Streamlit to access your GitHub

Step 3: Deploy New App

1. Click "New app" button
2. Fill in the deployment settings:
   • Repository: Select your GitHub repo
   • Branch: main (or your default branch)
   • Main file path: main2.py

Step 4: Add Secrets (API Keys)

This is crucial - you need to add your API key securely:

1. Click on "Advanced settings"
2. In the "Secrets" section, add:

GROQ_API_KEY = "gsk_your_actual_groq_api_key_here"

⚠️ Important:

• Use TOML format (with quotes)
• Must match the variable name in your .env file

Step 5: Deploy

1. Click "Deploy!"
2. Wait for the app to build (usually 2-5 minutes)
3. Your app will be live at: https://your-app-name.streamlit.app

Step 6: Share Your App

Copy the URL and share it with anyone! They can use the app without needing API keys.

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Usage

1. Upload PDFs

• Click "Browse files" in the sidebar
• Select one or multiple PDF files
• Click "Process Documents"
• Wait for processing to complete

2. Ask Questions

• Type your question in the chat input at the bottom
• Press Enter or click Send
• The AI will answer based on your documents

3. View Sources

• Click "View document sources" below any answer
• See which pages and documents were used
• Verify the accuracy of responses

Example Questions:

• "What is this document about?"
• "Summarize the main points"
• "What are the key findings in section 3?"
• "Who are the authors mentioned?"
• "What is the conclusion?"

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Common Errors & FAQs

❌ Error: "ModuleNotFoundError"

Problem: Missing dependencies

Solution:

pip install -r requirements.txt --upgrade

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❌ Error: "GROQ_API_KEY not found"

Problem: API key not configured properly

Solution:

1. Check if .env file exists in the project root
2. Verify the key is spelled correctly: GROQ_API_KEY
3. No spaces around = sign
4. Restart the Streamlit app after adding the key

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❌ Error: "Rate limit exceeded"

Problem: Too many requests to Groq API

Solution:

• Wait a few minutes before trying again
• Groq has generous free tier limits
• Check your usage at: https://console.groq.com

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❌ Error: "Failed to process documents"

Problem: PDF processing issue

Solution:

1. Ensure PDFs are not password-protected
2. Check if PDFs contain actual text (not just images)
3. Try with a smaller PDF first
4. Check file size - very large PDFs may timeout

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❓ FAQ: "Can I use other LLM models?"

Answer: Yes! Edit line 91 in main2.py:

model_name="llama-3.1-8b-instant"  # Current model

Available Groq models:

• llama-3.1-8b-instant (fastest)
• llama-3.1-70b-versatile (more capable)
• mixtral-8x7b-32768 (good balance)
• gemma-7b-it (Google's model)

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❓ FAQ: "How much does this cost?"

Answer:

• Groq offers a generous free tier
• No credit card required to start
• Check current limits at: https://console.groq.com/docs/rate-limits

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❓ FAQ: "Can I process multiple PDFs at once?"

Answer: Yes!

• Select multiple files when uploading
• They'll all be processed into one searchable database
• Ask questions across all documents

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❓ FAQ: "My chat history disappeared!"

Answer:

• Chat history is stored in session state
• It clears when you refresh the page
• Use the "Clear Chat History" button to reset intentionally

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❓ FAQ: "Can I deploy this for free?"

Answer: Yes!

• Streamlit Cloud has a free tier
• Groq API has a free tier
• Perfect for personal projects and demos

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❓ FAQ: "How do I update the app after changes?"

Local:

# Save your changes
# Restart Streamlit
streamlit run main2.py

Streamlit Cloud:

• Just push to GitHub
• Streamlit automatically redeploys
• No manual action needed!

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❓ FAQ: "PDF contains images/tables - will it work?"

Answer:

• Text is extracted automatically
• Tables might not format perfectly
• Image-only PDFs won't work (text needed)
• For scanned PDFs, use OCR preprocessing

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Project Structure

pdf-chatbot-rag/
│
├── main2.py                 # Main application file
├── requirements.txt         # Python dependencies
├── sample.env              # Sample environment file
├── .env                    # Your actual API keys (not in Git)
├── .gitignore             # Files to ignore in Git
├── README.md              # This file
│
└── .streamlit/            # Streamlit configuration (optional)
    └── config.toml        # Theme and settings

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Technologies Used

Technology	Purpose
Streamlit	Web interface and deployment
LangChain	LLM orchestration framework
Groq	Fast LLM inference (LLaMA 3.1)
FAISS	Vector database for semantic search
HuggingFace	Sentence embeddings
PyPDF	PDF text extraction

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Requirements.txt

streamlit
langchain>=0.1.0
langchain-groq
langchain-community
langchain-text-splitters
langchain-core
langchain-huggingface
faiss-cpu
pypdf
sentence-transformers
python-dotenv

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Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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License

This project is licensed under the MIT License - see the LICENSE file for details.

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Support

If you encounter any issues:

1. Check the Common Errors & FAQs section
2. Open an issue on GitHub
3. Contact: your.email@example.com

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Acknowledgments

• Groq for providing fast LLM inference
• LangChain for the excellent framework
• Streamlit for making deployment easy
• HuggingFace for embeddings models

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Star the Project ⭐

If you find this useful, please give it a star on GitHub!

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Made with ❤️ by RAJNI GANGWAR

Last Updated: November 2025