heliotrapi 0.2.1

An API for small fast data analysis jobs at Diamond Light Source

heliotrapi

An API for small fast data analysis jobs at Diamond Light Source.

[HEE-LEE-OH-TRAY-PEE-EYE]

Helio - Like the sun, a very brilliant light source Heliotrope - An pink-purple indigo-like colour HeliotrAPI - The API that comes after blue

heliotrapi exposes an HTTP API to submit analysis jobs, return queued results, and optionally consume messages from RabbitMQ.

Source	https://github.com/DiamondLightSource/heliotrapi
PyPI	pip install heliotrapi
Docker	docker run ghcr.io/diamondlightsource/heliotrapi:latest
Releases	https://github.com/DiamondLightSource/heliotrapi/releases

Example Python usage:

from heliotrapi import __version__

print(f"Hello heliotrapi {__version__}")

To start the api server in dev mode on local host:

uvicorn heliotrapi.main:start_api --reload --factory --host 127.0.0.1 --port 8000

or

heliotrapi serve

Overview

The app accepts analysis jobs via HTTP or the client and stores results in memory for a configurable time-to-live. Jobs can also be ingested from RabbitMQ if rabbitmq.enabled is set.

Sending/Recieving results using the python client

    from heliotrapi.client import AnalysisClient

    client = AnalysisClient("https://ixx-analysis.diamond.ac.uk")

    print(client.available_analyses()) #see available analyses

    client.submit("name_of_analysis", examplekwarg1=1, examplekwarg2=2) # <- if kawrgs are invalid it will raise

    result = client.get_result() #returns an AnalysisResult basemodel

    print(result)

Sending/Recieving results as an http request

submit jobs to /analyse as a json blob via an HTTP POST request

if want to call the function "double" eg:

def double(number: float | int) -> float:
    """Example analysis that doubles a number."""
    return number * 2

then the json would be sent as a POST request like:

{"analysis_name": "double", "inputs": {"number": 5}}

the server will handle request_id time and created_at, but if you want you can also send it in it's full form and create a uuid and timestamp yourself:

{"analysis_name":"double","inputs":{"number":5},"request_id":"d68de927-79f5-4df3-83d9-d125445c758a","created_at":"2026-05-29T11:47:09.087317"}

you get then return the last results from /result/latest as a GET request

Using the WebUI

You can also navigate to the url or the ip address to be met with:

Request flow

• AnalysisClient submits jobs to /analyse

• Jobs are queued in QueueManager

• Workers process jobs in FIFO order

• Results are returned via /result/id/{request_id} or /result/latest

• Optional RabbitMQ listener can enqueue jobs automatically

                 AnalysisClient ─────--────────────────
                    │ ▲                │              │
                    ▼ │                ▼              ▼
    Analysis <-── heliotrapi ──---►  RabbitMQ ──---► Results
       Job   ─---►    ▲                │
                      │                │
                      │                │   
                RabbitListener <───────
  

Kubernetes deployment

This repository includes a Helm chart under ./helm/heliotrapi.

Config support

The service supports configuration from one of these sources:

• CONFIG_PATH environment variable
• mounted config file at /etc/config/config.yaml
• local config.yaml file in the current working directory

In Kubernetes, the Helm chart mounts config.yaml from a ConfigMap and sets:

env:
  - name: CONFIG_PATH
    value: "/etc/config/config.yaml"

RabbitMQ config

The Helm values now expose RabbitMQ settings in the same shape as the app expects:

config:
  rabbitmq:
    enabled: true
    host: ixx-analysis.diamond.ac.uk
    username: guest
    password: guest
    port: 61613
    destinations:
      - "/topic/public.worker.event"
      - "/topic/gda.messages.scan"
      - "/topic/gda.messages.processing"
      - "/topic/public.analysis.trigger"

Helm usage

1. Build and push your Docker image

podman build -t ghcr.io/diamondlightsource/heliotrapi:latest .
podman push ghcr.io/diamondlightsource/heliotrapi:latest

2. Render the chart

helm template heliotrapi ./helm/heliotrapi

3. Dry-run validation

helm template heliotrapi ./helm/heliotrapi | kubectl apply --dry-run=client -f -

4. Install the chart

helm install heliotrapi ./helm/heliotrapi

5. Verify the deployment

kubectl get pods
kubectl get svc

6. Test the API

kubectl port-forward svc/heliotrapi 8000:8000

Then open:

http://localhost:8000/