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Python Conversation

1. Install Dora CLI and Dependencies​

First, ensure you have Python 3.11 installed.

Next, install uv if you haven't already:

pip install uv

Then, create and activate a virtual environment:

uv venv --seed -p 3.11
source .venv/bin/activate  # Linux/macOS
.venv\Scripts\activate     # Windows

Finally, install the Dora CLI:

pip install dora-rs-cli

2. Create a new dataflow​

Create a Python-based dataflow and navigate into the project directory:

dora new conversation_py --lang python
cd conversation_py

This creates the following conversation_py directory structure:

├── dataflow.yml
├── listener-1
│   ├── README.md
│   ├── listener_1
│   │   ├── __init__.py
│   │   ├── __main__.py
│   │   └── main.py
│   ├── pyproject.toml
│   └── tests
│       └── test_listener_1.py
└── talker-1
    ├── README.md
    ├── pyproject.toml
    ├── talker_1
    │   ├── __init__.py
    │   ├── __main__.py
    │   └── main.py
    └── tests
        └── test_talker_1.py

3. Add another node (optional)​

From inside the conversation_py project directory, you can add another node to the workspace:

dora new --kind node talker --lang python

Now open the talker-1/talker_1/main.py file in your text editor.

4. How the default node works​

Your node is very bare bones right now. Below is an explanation of how it works by default.

Importing and initializing the node​

from dora import Node
import pyarrow as pa

def main():
    node = Node()

Handling input events​

for event in node:
    if event["type"] == "INPUT":
        print(
            f"""Node received:
id: {event["id"]},
value: {event["value"]},
metadata: {event["metadata"]}"""
        )

Sending output to another node​

node.send_output("speech", pa.array(["Hello World"]))
  • send_output sends data to other nodes.
  • "speech" is the output ID referenced in the dataflow.
  • pa.array(["Hello World"]) creates an Apache Arrow array.
  • Metadata is omitted for simplicity in this tutorial.

5. Listener node breakdown​

File: listener-1/listener_1/main.py

from dora import Node

def main():
    node = Node()
    for event in node:
        if event["type"] == "INPUT":
            message = event["value"][0].as_py()
            print(f"I heard {message} from {event['id']}")

if __name__ == "__main__":
    main()
  • event["value"] is an Apache Arrow array.
  • [0] accesses the first element.
  • .as_py() converts it into a native Python value.

6. Running the dataflow​

Edit dataflow.yml to connect the nodes:

nodes:
  - id: talker
    path: talker-1/talker_1/main.py
    inputs:
      tick: dora/timer/secs/1
    outputs:
      - speech

  - id: listener
    path: listener-1/listener_1/main.py
    inputs:
      speech: talker/speech

Build and run the dataflow​

# Install dependencies
dora build dataflow.yml --uv

# Run the dataflow
dora run dataflow.yml --uv

You should see:

I heard Hello World from speech

To stop the dataflow, press Ctrl+C.

7. Conclusion​

Well done reaching the end of this tutorial.
You’ve created and run a Dora dataflow connecting a talker and listener node.

From here, try experimenting with different data types or exploring Dora’s advanced features.