Welcome to NotebookRAG! This application leverages a local Language Learning Model (LLM), Llama3, through Ollama to provide Retrieval-Augmented Generation (RAG) and chat functionalities based on PDFs and website content, all without sending any data to an online server. NotebookRAG is designed to maintain user privacy and ensure data security while offering robust and interactive text analysis capabilities.
NotebookRAG integrates several key components to deliver its functionalities:
- Local LLM (Llama3): Utilizes Llama3, a powerful language model, for natural language understanding and generation.
- Ollama: Acts as the interface for interacting with the Llama3 model locally.
- Offline Encoder: Processes and encodes data from PDFs and websites, ensuring that all content handling is performed offline.
- RAG (Retrieval-Augmented Generation): Enhances the generative capabilities of the LLM by incorporating retrieved documents and snippets from the provided content sources.
- Local Processing: All operations are performed locally, ensuring data privacy and security.
- PDF and Web Content Integration: Easily incorporate and analyze text from PDFs and websites.
- Interactive Chat: Engage in meaningful conversations with the model based on your documents and data.
- Docker
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Install Docker:
- Follow the instructions on the Docker website to install Docker on your system.
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Download the NotebookRAG Docker image:
docker pull codemaivanngu/note_rag:latest
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Create and run a Docker container:
docker run -d -p 8501:8501 --name notebookrag_container codemaivanngu/note_rag:latest
This command will create and run a Docker container named
notebookrag_containerfrom the downloaded image and map port 8080 of the container to port 8080 of your host machine.
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Create and run a Docker container:
docker run -d -p 8501:8501 --name notebookrag_container codemaivanngu/note_rag:latest
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Open your web browser and navigate to:
http://localhost:8501 -
Upload PDF or provide website URLs:
- Use the provided interface to upload PDFs or input website URLs for analysis.
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Interact with the model:
- Use the chat functionality to ask questions and interact with the model based on the uploaded content.
This project is licensed under the MIT License - see the LICENSE file for details.
- Special thanks to the developers of Llama3 and Ollama for their incredible work.
- Thanks to the open-source community for the invaluable tools and resources.