ai-box-lib 0.2.0

Python library for NXP Edge AI Industrial Platform

AI Box Library

Python library for the NXP Edge AI Industrial Platform.

Overview

ai-box-lib provides thin clients for component-to-component communication:

• DataCollectorClient for publishing data collector payloads
• PreProcessorClient for subscribing data collector payloads and publishing features
• ContextEngineClient for publishing context messages
• ChannelClient for generic pub/sub communication between any components

Installation

Install from PyPI:

pip install ai-box-lib

Quick Start

1) Publish data from a data collector

from ai_box_lib.data_collector_client import DataCollectorClient

client = DataCollectorClient[dict]()
client.connect()
client.publish_timestream({"random_number": "42"})

2) Subscribe and publish from a pre-processor

from ai_box_lib.pre_processor_client import PreProcessorClient

client = PreProcessorClient[dict, dict]()
client.connect()

def handle_raw(message: dict) -> None:
	processed = {"feature_a": [1.0, 2.0, 3.0]}
	client.publish_data(processed)

unsubscribe = client.subscribe_timestream(handle_raw)

3) Publish and subscribe to context data

from ai_box_lib.context_engine_client import ContextEngineClient

client = ContextEngineClient[dict]()
client.connect()
client.publish_data({"state": "ok"})

from ai_box_lib.context_engine_client import ContextEngineClient
client = ContextEngineClient[dict]()
client.connect()
client.subscribe()

client.context  # Access the latest context value at any time

def handle_context(message: dict) -> None:
	print(f"Context update: {message}")

client.subscribe(handle_context) # Subscribe with a handler to receive real-time updates

Optionally you can pass custom_asset_id to subscribe to a Context Engine on a different asset, as long as it's connected to the same Box.

4) Communicate over a generic channel

A channel lets any two components exchange messages without being tied to a specific pipeline stage. You define the channel by providing a channel_id string. Both publisher and subscriber must use the same channel_id.

Messages are delivered on the topic {asset_id}/channel/{channel_id}.

Publisher:

from ai_box_lib.channel_client import ChannelClient

client: ChannelClient[None, dict] = ChannelClient("my-alerts")
client.connect()
client.publish({"severity": "high", "value": 42.0})

Subscriber:

from ai_box_lib.channel_client import ChannelClient

client: ChannelClient[dict, None] = ChannelClient("my-alerts")
client.connect()

def handle_alert(message: dict) -> None:
    print(f"Alert received: {message}")

unsubscribe = client.subscribe(handle_alert)
# call unsubscribe() when done

A single client instance can both publish and subscribe on the same channel.

API Summary

All clients must call connect() before any publish or subscribe operation.

• DataCollectorClient.connect()
• DataCollectorClient.publish_timestream(data)
• DataCollectorClient.publish_audio(data)
• PreProcessorClient.connect()
• PreProcessorClient.subscribe_timestream(handler)
• PreProcessorClient.subscribe_audio(handler)
• PreProcessorClient.publish_data(data)
• ContextEngineClient.connect()
• ContextEngineClient.publish_data(data)
• ContextEngineClient.subscribe(handler?, custom_asset_id?)
• ContextEngineClient.context — latest received context value (read-only)
• ChannelClient(channel_id).connect()
• ChannelClient(channel_id).publish(data)
• ChannelClient(channel_id).subscribe(handler)

Validation and Limits

• DataCollectorClient validates message keys against CHANNELS.
• PreProcessorClient validates feature keys and feature shapes against FEATURES.
• Maximum publish payload size is 2 MB.

License

See LICENSE.