https://github.com/airtai/faststream-gen
The faststream-gen library uses advanced AI to generate FastStream code from user descriptions, speeding up FastStream app development.
https://github.com/airtai/faststream-gen
fastkafka faststream faststream-gen
Last synced: 6 months ago
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The faststream-gen library uses advanced AI to generate FastStream code from user descriptions, speeding up FastStream app development.
- Host: GitHub
- URL: https://github.com/airtai/faststream-gen
- Owner: airtai
- License: apache-2.0
- Created: 2023-08-08T07:13:15.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-03-12T06:34:56.000Z (over 1 year ago)
- Last Synced: 2025-03-27T09:11:28.279Z (6 months ago)
- Topics: fastkafka, faststream, faststream-gen
- Language: Jupyter Notebook
- Homepage: https://faststream-gen.airt.ai
- Size: 13.9 MB
- Stars: 46
- Watchers: 4
- Forks: 6
- Open Issues: 22
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
Awesome Lists containing this project
README
# Code generator for FastStream
`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
------------------------------------------------------------------------
## Getting Started
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:
- **Automatic FastStream project generation**: `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.
- **Tested code**: `faststream-gen` provides dependable code through
rigorous testing, including pre-implemented integration tests,
ensuring stability and functionality, saving development time, and
preventing common bugs.
- **Script Templates**: Streamline the deployment of your FastStream
application using faststream-gen’s built-in scripts, tailored for
initiating, subscribing to Kafka topic and shutting down the local
Kafka broker.
- **GitHub workflow files**: `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.
[](https://github.com/airtai/faststream-gen/assets/32619626/2731caa1-1108-4f4e-8507-9bf1168419e4 "faststream-gen")
### Quick start
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.
#### Install
`faststream-gen` is published as a Python package and can be installed
with pip:
``` shell
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:
``` shell
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. │
╰──────────────────────────────────────────────────────────────────────────────╯
#### Generate new project
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:
``` shell
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})
#### Start localhost Kafka broker
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:
``` sh
cd my-awesome-project
# make all shell scripts executable
chmod +x scripts/*.sh
# start local kafka broker
./scripts/start_kafka_broker_locally.sh
```
#### Start application
To start the FastKafka application, run the following command:
``` sh
faststream run app.application:app
```
#### Stop application
To stop the FastKafka application, run the following command:
``` sh
./scripts/stop_kafka_broker_locally.sh
```
## Copyright
Copyright © 2023 onwards airt technologies ltd, Inc.
## License
This project is licensed under the terms of the
Apache
License 2.0