🐋 DeepSeek-R1 - Document RAG (Retrieval-Augmented Generation)

📌 Overview

This project implements a Retrieval-Augmented Generation (RAG) system for document-based question answering using:

• DeepSeek-R1 (via Groq) for fast & efficient AI-powered responses.
• ChromaDB for storing and retrieving document embeddings.
• HuggingFace Embeddings for vectorization.
• Streamlit for an interactive UI.

🔹 Users can upload a PDF, process it into a vector database, and query it for answers!

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🚀 Features

• 📄 Upload and process PDF documents into a vector store.
• 🔍 Retrieve relevant document sections using ChromaDB.
• 💬 Generate AI-powered answers using DeepSeek-R1 via Groq.
• 🎛 Interactive UI built with Streamlit.

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📂 Project Structure

/project-folder
│-- main.py                # Streamlit UI for user interaction
│-- rag_utility.py         # Core logic for document processing & retrieval
│-- config.json            # Stores API keys
│-- requirements.txt       # Required dependencies

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🛠 Installation & Setup

1️⃣ Clone the Repository

git clone https://github.com/AymenGabsi/rag_deepseek_groq_doc_qa.git
cd your-repo

2️⃣ Set Up a Virtual Environment

python -m venv venv
source venv/bin/activate   # For macOS/Linux
venv\Scripts\activate     # For Windows

3️⃣ Install Dependencies

pip install --upgrade pip setuptools wheel
pip install -r requirements.txt

4️⃣ Configure API Keys

• Open config.json and replace your_groq_api_key with your actual Groq API Key:

{
  "GROQ_API_KEY": "your_groq_api_key"
}

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▶️ Running the Application

After setting up, launch the Streamlit app:

streamlit run main.py

This will open a web interface where you can:

1. Upload a PDF
2. Ask questions about its content
3. Receive AI-generated answers powered by DeepSeek-R1

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🏗 How It Works

1. Upload a PDF file.
2. Extract text and convert it into vector embeddings using HuggingFace.
3. Store embeddings in ChromaDB for efficient retrieval.
4. Retrieve relevant sections from ChromaDB when a user asks a question.
5. Send retrieved data to DeepSeek-R1 (Groq) LLM for final answer generation.

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📌 Technologies Used

• LangChain for orchestrating LLM-based workflows.
• DeepSeek-R1 via Groq for LLM-based response generation.
• ChromaDB for vector storage and retrieval.
• HuggingFace Embeddings for document vectorization.
• Streamlit for UI interaction.

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🤖 Future Enhancements

• 🔄 Support more document formats (TXT, DOCX, HTML, etc.)
• 📊 Improve retrieval accuracy using hybrid search (BM25 + embeddings).
• 🎨 Enhance UI with real-time answer explanations.
• 🚀 Optimize response speed by caching retrieval results.

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📜 License

This project is licensed under the MIT License.

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⭐ Contributing

Contributions are welcome! Feel free to fork this repository, submit issues, or create pull requests.

📧 For inquiries, reach out via GitHub Issues.

🚀 Happy Coding!