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🤖 RAG PDF Assistant: Powered by 🏆 DeepSeek-R1 ~ 1.5B , Ollama, Streamlit & FAISS

author Python 3.9+ Streamlit Ollama PDFPlumber LangChain HuggingFace FAISS

🌟 Overview

RAG PDF Assistant is an AI-powered Retrieval-Augmented Generation (RAG) chatbot that enables intelligent PDF document search and retrieval. It combines:

✅ DeepSeek-R1 (1.5B) – Advanced AI-powered language model for accurate responses.
✅ FAISS – Fast vector search for efficient document retrieval.
✅ Ollama – Lightweight model serving and seamless inference.
✅ LangChain – Modular AI framework for query execution and reasoning.
✅ Streamlit – Intuitive and interactive web-based UI.

🎯 Use Cases

🔍 Quickly search through large PDF documents.
📄 Summarize reports, research papers, contracts, and more.
📘 Extract relevant information with AI-driven accuracy.
🤖 Ask natural language questions and get concise answers.

🚀 Demo Screenshots

📌 UI Preview

Demo

📌 Application Workflow

Workflow

🛠️ Installation & Setup

🔧 Prerequisites

Ensure you have the following installed:

  • Python 3.9+
  • pip (Python package manager)
  • Git (for cloning the repository)

📥 Step 1: Clone the Repository

$ git clone https://github.com/mohd-faizy/RAG-DeepSeek.git
$ cd RAG-DeepSeek

📦 Step 2: Install Dependencies

$ pip install -r requirements.txt

⚙️ Step 3: Run the Application

  1. Start Ollama service:

    ollama serve

  2. In a separate terminal, launch the chat interface:

    streamlit run app/main.py

The application will launch in your browser at http://localhost:11434/.

📁 Directory Structure

RAG-DeepSeek/
├── app/
│   ├── __init__.py          # Python package initialization
│   ├── main.py              # Main Streamlit application file
│   ├── utils.py             # Utility functions for PDF processing, embeddings, retrieval
├── assets/                  # Static files like images, CSS, etc.
├── requirements.txt         # Python dependencies
├── .gitignore               # Files ignored by Git
├── README.md                # Project documentation

🧠 How It Works

1️⃣ Upload a PDF → The AI extracts and indexes the content.
2️⃣ Ask a question → The system searches for the most relevant passages.
3️⃣ AI answers your query → Based on retrieved document content.

🔹 Uses FAISS for fast, efficient document retrieval.
🔹 DeepSeek-R1 ensures high-quality, context-aware answers.

🔗 Technologies Used

Technology Purpose
DeepSeek-R1 (1.5B) Language model for intelligent responses
Ollama Model serving and inference
FAISS Vector search for document retrieval
LangChain AI-driven reasoning and query handling
Streamlit User-friendly web interface
PDFPlumber Extracting text from PDFs

🤝 Steps to Contribute

  1. Fork the repository
  2. Create a new feature branch (git checkout -b feature-name)
  3. Commit your changes (git commit -m "Added new feature")
  4. Push to your fork (git push origin feature-name)
  5. Open a pull request

⚖ ➤ License

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

❤️ Support

If you find this repository helpful, show your support by starring it! For questions or feedback, reach out on Twitter(X).

🔗Connect with me

➤ If you have questions or feedback, feel free to reach out!!!