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.
🔍 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.
Ensure you have the following installed:
- Python 3.9+
- pip (Python package manager)
- Git (for cloning the repository)
$ git clone https://github.com/mohd-faizy/RAG-DeepSeek.git
$ cd RAG-DeepSeek$ pip install -r requirements.txt-
Start Ollama service:
ollama serve
-
In a separate terminal, launch the chat interface:
streamlit run app/main.py
The application will launch in your browser at http://localhost:11434/.
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
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.
| 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 |
- Fork the repository
- Create a new feature branch (
git checkout -b feature-name) - Commit your changes (
git commit -m "Added new feature") - Push to your fork (
git push origin feature-name) - Open a pull request
This project is licensed under the MIT License. See the LICENSE file for details.
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➤ If you have questions or feedback, feel free to reach out!!!
