As AI continues to reshape how we build digital experiences, combining cloud-based AI services with modern CMS platforms like Drupal is becoming the new normal. Whether you’re looking to power up content generation, provide smart recommendations, or summarize long-form text — this blog walks you through using OpenAPI, AWS Bedrock, and Amazon SageMaker Studio in a Drupal 10 environment.
Introduction to AI Services
- What is AWS Bedrock?
AWS Bedrock is a fully managed service that allows you to build and scale generative AI applications using foundation models (FMs) from leading AI companies like Anthropic (Claude), Meta (LLaMA), Stability AI, and Amazon’s own Titan models — all without having to train or manage your own infrastructure.
Key Capabilities:
- Text summarization
- Q&A bots
- Content creation
- Code generation
It’s serverless, fast, and integrates easily with other AWS services.
- What is Amazon SageMaker Studio?
SageMaker Studio is a web-based, end-to-end ML development environment that enables data scientists and engineers to:
- Clean and prepare data
- Train, tune, and deploy machine learning models
- Monitor performance
- Run real-time inferences
It provides a visual interface for managing the ML lifecycle and integrates with AWS Bedrock to leverage pre-trained foundation models.
Use SageMaker when you want full control over your model pipeline, including custom training, while still tapping into AWS-hosted tools.
- What is OpenAPI Schema?
OpenAPI (formerly known as Swagger) is an industry-standard specification used to describe RESTful APIs in a machine-readable format (YAML or JSON). It helps developers:
- Define API endpoints
- Standardize request/response formats
- Document authentication and parameters
- Auto-generate SDKs and test tools
- Sample OpenAPI Schema (YAML)
paths:
/summary:
post:
summary: Get summary from Bedrock
requestBody:
content:
application/json:
schema:
type: object
properties:
prompt:
type: string
responses:
‘200’:
description: AI-generated summary
This schema becomes the contract between your AI service and the Drupal frontend.
How to Call AWS Bedrock or SageMaker and Get JSON Data
Here’s how you can trigger Bedrock from an external app and get a JSON response:
- Sample Python (Boto3) Code:
import boto3
import json
bedrock = boto3.client(‘bedrock-runtime’, region_name=’us-east-1′)
response = bedrock.invoke_model(
modelId=’anthropic.claude-v2′,
body=json.dumps({
“prompt”: “Summarize this article on climate change.”,
“max_tokens_to_sample”: 300
}),
contentType=’application/json’,
accept=’application/json’
)
print(response[‘body’].read().decode(‘utf-8’))
Sample Output:
json
{
“completion”: “The article discusses climate change trends, policy updates, and future predictions…”
}
You now have usable JSON data that can be consumed by any CMS including Drupal.
How to Integrate JSON Data in Drupal 10
- Create a REST Resource Plugin
Define a custom REST endpoint in Drupal that acts as a middleware:
- Accepts content or a request
- Sends it to Bedrock or SageMaker
- Returns the AI response in real time
This is perfect if you want Drupal to act as a bridge between the editor and the AI model.
Conclusion
The combination of OpenAPI, AWS Bedrock, and SageMaker Studio offers a scalable and intelligent backend for modern web applications. With Drupal 10 acting as the frontend layer, you can create experiences that are dynamic, personalized, and AI-powered — all while maintaining control and security.
Source: Read MoreÂ
