The convergence of IoT and advanced data processing opens a realm of possibilities for innovation. In this practical exercise, we explore how to deploy a RAG model on an IoT device, specifically a Raspberry Pi, using Flask as our web server framework. By setting up a Flask server on a Raspberry Pi, we demonstrate a lightweight yet powerful approach to integrating AI capabilities into IoT devices. This exercise provides a hands-on experience in deploying AI models to edge devices, highlighting the potential of IoT systems to not only collect data but to host services that can interpret and provide intelligence.

RAG combines the retrieval of informational documents with the generation of responses, enabling the system to provide informative, contextually relevant answers to user queries. We will be accomplishing this by using one of the newer emerging technologies for generative AI: Amazon Bedrock. Amazon Bedrock is a service that offers advanced AI tools to businesses, enabling them to create interactive, intelligent applications. One of these tools is called Claude, a smart program that can understand and generate human-like text. Claude can read through large documents and help with tasks such as answering customer questions, managing emails, or even coding. It’s designed to be very safe and reliable, aiming to communicate effectively and without causing any misunderstandings or issues. This makes Claude an efficient assistant in various fields, including customer service, office administration, and legal work.

The goal is straightforward: to empower your IoT device with the ability to process queries and return informed responses, showcasing the integration of machine learning (ML) models within an IoT architecture. This serves as a practical step into the future of IoT, where devices are not mere data collectors but knowledge providers.

Setup for the working directory

We will start out by making sure that we have the right dependencies for the project:

If you have not yet done so, boot up your Raspberry Pi.

Create a new directory on your Raspberry Pi for where you want your project files to be located.

Create a requirements.txt file. Inside it, put the following code:
Flask
numpy
boto3
langchain

Run the following command:
$ pip install -r requirements.txt

After executing this command, you should see the dependencies installed accordingly.

With that, we are set up for development! Now, we will begin creating the code for the HTML landing page.

Coding up the HTML landing page

The first component of the coding starts with setting up the HTML landing page for us to be able to submit queries:

We first need to create the HTML code that will be used for the landing page. Create a Templates folder in the working directory.

Change directory into Templates and create a file named query_form.html.

Insert the following code into the file:
<!DOCTYPE html>
<html lang=”en”>
<head>
    <meta charset=”UTF-8″>
    <title>Query Form</title>
</head>
<body>
    <form action=”/query” method=”post”>
        <input type=”text” name=”query” placeholder=”Enter your query here” required>
        <input type=”submit” value=”Submit”>
    </form>
</body>
</html>

The code defines a simple web page with a form intended for user interaction. The language is set to English. The form comprises a single text input field where users can type a query. The text field is labeled Enter your query here, which serves as a placeholder inside the box, and it is a required field, meaning the form cannot be submitted without filling it out. There is also a submit button labeled Submit. When the form is submitted, it sends a POST request to the /query URL, which is typically handled by a server-side script to process the query. The form’s functionality is often used for search operations or data retrieval requests on a website.

With that, we have been able to create the landing page for the site. We can now move on to creating the RAG component of the site.

Leave a Reply

Your email address will not be published. Required fields are marked *