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An event-driven AI pipeline using FastAPI, Redpanda, and Docker

This minimal demo shows how to build an event-driven AI pipeline using FastAPI as the gateway, Redpanda (or Kafka) as the message broker, and Docker for deployment.

SourceHacker News AIAuthor: infodatamatrix

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A minimal demo for Video 3 showing how a FastAPI gateway hands work to Kafka and how separate workers process the event chain.

Project Structure

ai-kafka-pipeline-demo/ ├── api-gateway/ │ └── app/ │ ├── main.py │ ├── routes/submit.py │ ├── services/publisher.py │ └── config.py ├── workers/ │ ├── extractor/ │ ├── summarizer/ │ └── notifier/ ├── shared/ │ ├── kafka/ │ ├── schemas/ │ ├── config/ │ └── utils/ ├── Dockerfile ├── docker-compose.yml ├── requirements.txt └── .env

Demo Flow

POST /submit to the API gateway

Gateway publishes document.submitted

Extractor consumes and publishes text.extracted

Summarizer consumes and publishes summary.generated

Notifier consumes and logs final completion

Run

docker compose up --build

Test

Open another terminal:

curl -X POST http://localhost:8000/submit \ -H "Content-Type: application/json" \ -d '{ "user_id": "user-1", "content": "Kafka helps decouple AI pipeline stages for scalable processing in production systems." }'

What you should see

API returns Processing started

Extractor logs the incoming event

Summarizer logs the next event

Notifier logs the final pipeline completion

Suggested narration

FastAPI handles intake, not heavy processing.

Kafka turns the request into an event.

Each worker owns one stage.

Shared schemas keep the contracts explicit.

This is the simplest form of a production-style AI pipeline.

I also recorded a full visual code walkthrough breaking down the project structure and explaining the design trade-offs here: https://youtu.be/c2ijN2KAWXw

https://youtu.be/KjvbABpajUs

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This is a sample kafka driven demo

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