The faststream-gen library uses advanced AI to generate FastStream code from user descriptions, speeding up FastStream app development.
faststream-gen is a Python library that uses generative AI to
automatically generate
FastStream
applications. Simply describe your application requirements, and
faststream-gen will generate a production-grade FastStream project
that is ready to deploy in no time.
Documentation: https://faststream-gen.airt.ai
Source Code: https://github.com/airtai/faststream-gen
The code generator for FastStream is a Python library that automates the process of creating FastStream applications. It works by taking your application requirements and swiftly turning them into a ready-to-deploy FastStream application.
The key features are:
faststream-gen enables
you to easily generate complete FastStream application with minimal
effort. This library allows you to outline your application
requirements, and it will quickly transform them into a fully-fledged
FastStream project.faststream-gen provides dependable code through
rigorous testing, including pre-implemented integration tests,
ensuring stability and functionality, saving development time, and
preventing common bugs.faststream-gen integrates seamlessly with
your version control and continuous integration pipeline through its
GitHub workflow files. These predefined configuration files are
optimized for FastStream projects, enabling smooth integration with
GitHub Actions. You can automate tasks such as code validation,
testing, and deployment, ensuring that your FastStream application
remains in top shape throughout its development lifecycle.
The following quick start guide will walk you through installing and
configuring the faststream-gen library, demonstrating the creation of
a new FastStream project in seconds.
faststream-gen is published as a Python package and can be installed
with pip:
pip install faststream-gen
If the installation was successful, you should now have the faststream-gen installed on your system. Run the below command from the terminal to see the full list of available commands:
faststream_gen --help
Usage: faststream_gen [OPTIONS] [DESCRIPTION]
Effortlessly create a new FastStream project based on the app description.
╭─ Arguments ──────────────────────────────────────────────────────────────────╮
│ description [DESCRIPTION] Summarize your FastStream application in a │
│ few sentences! │
│ │
│ Include details about messages, topics, │
│ servers, and a brief overview of the │
│ intended business logic. │
│ │
│ The simpler and more specific the app │
│ description is, the better the generated │
│ app will be. Please refer to the below │
│ example for inspiration: │
│ │
│ Create a FastStream application using │
│ localhost broker for testing and use the │
│ default port number. It should consume │
│ messages from the "input_data" topic, │
│ where each message is a JSON encoded │
│ object containing a single attribute: │
│ 'data'. For each consumed message, create │
│ a new message object and increment the │
│ value of the data attribute by 1. Finally, │
│ send the modified message to the │
│ 'output_data' topic. │
│ [default: None] │
╰──────────────────────────────────────────────────────────────────────────────╯
╭─ Options ────────────────────────────────────────────────────────────────────╮
│ --input_file -i TEXT The path to the file │
│ with the app │
│ desription. This path │
│ should be relative to │
│ the current working │
│ directory. │
│ If the app description │
│ is passed via both a │
│ --input_file and a │
│ command line argument, │
│ the description from │
│ the command line will │
│ be used to create the │
│ application. │
│ [default: None] │
│ --output_path -o TEXT The path to the output │
│ directory where the │
│ generated project files │
│ will be saved. This │
│ path should be relative │
│ to the current working │
│ directory. │
│ [default: .] │
│ --model -m [gpt-3.5-turbo-16k|gp The OpenAI model that │
│ t-4] will be used to create │
│ the FastStream project. │
│ For better results, we │
│ recommend using │
│ 'gpt-4'. │
│ [default: │
│ gpt-3.5-turbo-16k] │
│ --verbose -v Enable verbose logging │
│ by setting the logger │
│ level to INFO. │
│ --dev -d Save the complete logs │
│ generated by │
│ faststream-gen inside │
│ the output_path │
│ directory. │
│ --install-completion Install completion for │
│ the current shell. │
│ --show-completion Show completion for the │
│ current shell, to copy │
│ it or customize the │
│ installation. │
│ --help Show this message and │
│ exit. │
╰──────────────────────────────────────────────────────────────────────────────╯
The faststream-gen library uses OpenAI’s model to generate FastStream projects. In order to use the library, you’ll need to create an API key for OpenAI.
Once you have your API key, store it in the OPENAI_API_KEY environment variable. This is a necessary step for the library to work.
We’re now ready to create a new FastStream application with the
faststream-gen library.
Simply run the following command to create a new FastStream application
in the my-awesome-project directory:
faststream_gen "Create a FastStream application using localhost broker for testing and use the default port number. It should consume messages from the 'input_data' topic, where each message is a JSON encoded object containing a single attribute: 'data'. While consuming from the topic, increment the value of the data attribute by 1. Finally, send message to the 'output_data' topic." -o "./my-awesome-project"
✨ Generating a new FastStream application!
✔ Application description validated.
✔ FastStream app skeleton code generated. akes around 15 to 45 seconds)...
✔ The app and the tests are generated. around 30 to 90 seconds)...
✔ New FastStream project created.
✔ Integration tests were successfully completed.
Tokens used: 9398
Total Cost (USD): $0.02865
✨ All files were successfully generated!
Here’s a look at the directory hierarchy:
my-awesome-project
├── .github
│ └── workflows
│ ├── deploy_docs.yml
│ └── test.yml
├── .gitignore
├── LICENSE
├── README.md
├── app
│ ├── __init__.py
│ └── application.py
├── dev_requirements.txt
├── requirements.txt
├── scripts
│ ├── services.yml
│ ├── start_kafka_broker_locally.sh
│ ├── stop_kafka_broker_locally.sh
│ └── subscribe_to_kafka_broker_locally.sh
└── tests
└── test_application.py
5 directories, 14 files
Let’s take a quick look at the generated application and test code.
application.py:
from faststream import FastStream, Logger
from faststream.kafka import KafkaBroker
broker = KafkaBroker("localhost:9092")
app = FastStream(broker)
to_output_data = broker.publisher("output_data")
@broker.subscriber("input_data")
async def on_input_data(msg: dict, logger: Logger) -> None:
logger.info(f"{msg=}")
incremented_data = msg["data"] + 1
await to_output_data.publish({"data": incremented_data})
test_application.py:
import pytest
from faststream import Context
from faststream.kafka import TestKafkaBroker
from app.application import broker, on_input_data
@broker.subscriber("output_data")
async def on_output_data(msg: dict, key: bytes = Context("message.raw_message.key")):
pass
@pytest.mark.asyncio
async def test_data_was_incremented():
async with TestKafkaBroker(broker):
await broker.publish({"data": 1}, "input_data")
on_input_data.mock.assert_called_with({"data": 1})
on_output_data.mock.assert_called_with({"data": 2})
In order for FastStream applications to publish and consume messages
from the Kafka broker, it is necessary to have a running Kafka broker.
Along with application and test, faststream-gen also generated
scripts directory. You can start local Kafka broker (inside docker
container) by executing following commands:
cd my-awesome-project
# make all shell scripts executable
chmod +x scripts/*.sh
# start local kafka broker
./scripts/start_kafka_broker_locally.sh
To start the FastKafka application, run the following command:
faststream run app.application:app
To stop the FastKafka application, run the following command:
./scripts/stop_kafka_broker_locally.sh
Copyright © 2023 onwards airt technologies ltd, Inc.
This project is licensed under the terms of the Apache License 2.0
FAQs
The faststream-gen library uses advanced AI to generate FastStream code from user descriptions, speeding up FastStream app development.
We found that faststream-gen demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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