node-api-python

name	node-api-python
description	Use for Python dora node development questions. Triggers on: dora.Node, python node, dora-rs python, event type, INPUT, STOP, send_output, for event in node, python dataflow, Python节点, Python API, dora python
globs	["**/*.py"]
source	https://dora-rs.ai/docs/guides/getting-started/conversation_py/

Python Node API

Complete guide to building dora nodes in Python

Installation

pip install dora-rs

Basic Usage

from dora import Node

node = Node()

for event in node:
    if event["type"] == "INPUT":
        input_id = event["id"]
        data = event["value"]  # numpy array or pyarrow array
        metadata = event["metadata"]

        # Process data...

        # Send output
        node.send_output("output_id", data)

    elif event["type"] == "STOP":
        break

Event Types

event = {
    "type": "INPUT" | "STOP" | "INPUT_CLOSED" | "ERROR",
    "id": "input_name",           # For INPUT events
    "value": data,                # numpy/pyarrow array
    "metadata": {...},            # Timestamp, type info
    "error": "message",           # For ERROR events
}

Handling Events

from dora import Node

node = Node()

for event in node:
    match event["type"]:
        case "INPUT":
            handle_input(event["id"], event["value"])
        case "INPUT_CLOSED":
            print(f"Input {event['id']} closed")
        case "STOP":
            print("Stopping...")
            break
        case "ERROR":
            print(f"Error: {event['error']}")

Sending Outputs

NumPy Arrays

import numpy as np

# Integer array
data = np.array([1, 2, 3], dtype=np.int32)
node.send_output("numbers", data)

# Float array
data = np.array([1.0, 2.5, 3.14], dtype=np.float64)
node.send_output("floats", data)

# Image (HWC format)
image = np.zeros((480, 640, 3), dtype=np.uint8)
node.send_output("image", image)

PyArrow Arrays

import pyarrow as pa

# String array
data = pa.array(["hello", "world"])
node.send_output("text", data)

# Struct array (for complex data)
data = pa.array([{
    "x": 1.0,
    "y": 2.0,
    "label": "point"
}])
node.send_output("point", data)

With Metadata

metadata = {
    "watermark": 1000,
    "deadline": 5000,
}
node.send_output("data", array, metadata)

Complete Examples

Camera Node

# camera_node.py
import cv2
import numpy as np
from dora import Node

node = Node()
cap = cv2.VideoCapture(0)

for event in node:
    if event["type"] == "INPUT" and event["id"] == "tick":
        ret, frame = cap.read()
        if ret:
            # Convert BGR to RGB
            frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
            node.send_output("image", frame_rgb)

    elif event["type"] == "STOP":
        break

cap.release()

Processing Node

# processor_node.py
import numpy as np
from dora import Node

node = Node()

for event in node:
    if event["type"] == "INPUT":
        if event["id"] == "image":
            image = event["value"]

            # Process image
            gray = np.mean(image, axis=2).astype(np.uint8)

            node.send_output("processed", gray)

    elif event["type"] == "STOP":
        break

Detection Node

# detector_node.py
import numpy as np
import pyarrow as pa
from dora import Node
from ultralytics import YOLO

node = Node()
model = YOLO("yolov8n.pt")

for event in node:
    if event["type"] == "INPUT" and event["id"] == "image":
        image = event["value"]

        # Run detection
        results = model(image)

        # Convert to bounding boxes
        boxes = []
        for r in results:
            for box in r.boxes:
                boxes.append({
                    "x1": float(box.xyxy[0][0]),
                    "y1": float(box.xyxy[0][1]),
                    "x2": float(box.xyxy[0][2]),
                    "y2": float(box.xyxy[0][3]),
                    "confidence": float(box.conf[0]),
                    "class": int(box.cls[0]),
                })

        node.send_output("bbox", pa.array(boxes))

    elif event["type"] == "STOP":
        break

Dataflow Configuration

nodes:
  - id: camera
    path: camera_node.py
    inputs:
      tick: dora/timer/millis/33
    outputs:
      - image

  - id: processor
    path: processor_node.py
    inputs:
      image: camera/image
    outputs:
      - processed

  - id: detector
    path: detector_node.py
    inputs:
      image: camera/image
    outputs:
      - bbox

Async Python (Experimental)

import asyncio
from dora import Node

async def main():
    node = Node()

    async for event in node:
        if event["type"] == "INPUT":
            # Async processing
            result = await process_async(event["value"])
            node.send_output("result", result)

        elif event["type"] == "STOP":
            break

asyncio.run(main())

Environment Variables

Access environment variables from dataflow config:

import os

model_path = os.environ.get("MODEL_PATH", "default.pt")
device = os.environ.get("DEVICE", "cpu")

Best Practices

1. Use NumPy for numerical data - Native support, zero-copy when possible
2. Use PyArrow for structured data - Complex types, nested structures
3. Handle STOP events - Clean up resources properly
4. Check input IDs - Multiple inputs may arrive
5. Use environment variables - Keep config in YAML

Debugging

Interactive Mode

Run node directly for testing:

python my_node.py

Without DORA_NODE_CONFIG set, the node enters interactive mode where you can manually provide inputs.

Logging

import logging

logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger(__name__)

for event in node:
    logger.debug(f"Received event: {event['type']}")

Related Skills

• dataflow-config - YAML configuration
• node-api-rust - Rust alternative
• hub-nodes - Pre-built Python nodes