Self Hosting RAG Applications On Edge Devices

Deploying a Powerful AI Document Processor on a Raspberry Pi: A Step-by-Step Guide

This article details the process of transforming a Raspberry Pi into a robust tool for intelligent document processing, leveraging the power of advanced AI. We'll cover the setup, software installation, and development of a system capable of handling document ingestion and question-answering (QnA) tasks. Even a small device can achieve impressive results with the right approach.

Learning Objectives:

• Configure a Raspberry Pi for headless operation.
• Install and manage dependencies such as Ollama.
• Develop a system for loading and processing PDF documents.
• Build a Retrieval-Augmented Generation (RAG) pipeline for answering queries.
• Test application functionality.
• Deploy your application using FastAPI and create a user interface with Reflex.

(This article is part of the Data Science Blogathon.)

Table of Contents:

• Introduction
• Raspberry Pi Setup
• Application Development
  • Step 1: Environment Configuration
  • Step 2: Creating config.py
  • Step 3: The Ingestion Class
  • Step 4: The PDFLoader Class
  • Step 5: The QnA Pipeline
  • Step 6: Querying the RAG Chain
• Conclusion
• Frequently Asked Questions

Raspberry Pi Setup:

Begin by setting up your Raspberry Pi's operating system. We'll use Ubuntu Server 24.04 for this example, but other distributions are possible. You'll need a microSD card (at least 16GB).

Flashing the SD Card:

1. Download and install the Raspberry Pi Imager.
2. Select your SD card and choose Ubuntu Server 24.04 as the operating system.
3. Configure headless operation via SSH, providing a username and password. Add your Wi-Fi credentials for automatic network connection on boot.
4. Enable SSH in the Services tab.
5. Write the image to the SD card.

After booting, connect to your Raspberry Pi via SSH using its IP address (use a tool like Fing to find it):

ssh [username]@[IP address]

Updating Packages:

Update your system packages:

sudo apt update
sudo apt upgrade

Install Ollama:

curl -fsSL https://ollama.com/install.sh | sh

(If you encounter errors, install curl first: sudo apt install curl)

Download necessary models:

ollama pull phi3
ollama pull nomic-embed-text

Application Development:

Now, let's build the RAG application.

Step 1: Environment Setup:

Create a virtual environment and install the required Python packages (listed in the original article).

Step 2: Creating config.py:

Create a config.py file with the following:

LANGUAGE_MODEL_NAME = "phi3"
EMBEDDINGS_MODEL_NAME = "nomic-embed-text"
OLLAMA_URL = "http://localhost:11434"

(Steps 3-6 detailing the Ingestion Class, PDFLoader, QnA Pipeline, and RAG Chain querying are provided in the original article and are too extensive to reproduce here. Refer to the original for the code snippets.)

Conclusion:

This guide demonstrates setting up a Raspberry Pi for a functional RAG application. The next steps will involve wrapping the application with FastAPI and building a user interface using Reflex.

Key Takeaways:

• Raspberry Pi configuration for RAG applications.
• Dependency management (Ollama).
• PDF document ingestion and processing.
• RAG pipeline implementation.
• Application testing and deployment preparation.

Frequently Asked Questions:

(The FAQs from the original article are also too extensive to reproduce here. Refer to the original for the answers.)

(Note: Image URLs remain unchanged.)

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