faststream-gen 0.0.1.dev2023091404

The faststream-gen library uses advanced AI to generate FastStream code from user descriptions, speeding up FastStream app development.

Code generator for FastStream

The faststream-gen library uses advanced AI to generate FastStream code from the application description, speeding up FastStream app development.

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If you’ve heard of the FastStream library and wanted to try it but didn’t have time to read the documentation, no worries! faststream-gen will generate FastStream applications for you automatically.

It not only creates your FastStream application, but it also generates the necessary tests to ensure its functionality. Simply describe your application in english, and faststream-gen will take care of the rest.

Install

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 options:

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-4]        │
│ --verbose             -v                             Enable verbose logging  │
│                                                      by setting the logger   │
│                                                      level to INFO.          │
│ --dev                 -d                             Save the intermediate   │
│                                                      faststream-gen files    │
│                                                      within the output_path. │
│ --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.                   │
╰──────────────────────────────────────────────────────────────────────────────╯

How to use

Setup

For generating FastStream applications, faststream-gen is using OPENAI models. So the first step is exporting your OPENAI_API_KEY.

export OPENAI_API_KEY="<your_openai_api_key>"

If you don’t already have OPENAI_API_KEY, you can create one at OPENAI API keys

Code generation

This library is very easy to use. The only thing you need to do is prepare a description of your FastStream application, i.e. write what your application should do and pass it to the faststream_gen command as a string:

faststream_gen "<your_app_description>"

Example

faststream_gen  "Write a faststream application with with one consumer function and two producer functions. The consumer function should receive the a message posted on 'new_joinee' topic. The message should contain 'employee_name', 'age', 'location' and 'experience' attributes. After consuming the consumer function should send the details to the 'project_team' and 'admin_team' topics. Use only localhost broker" 
✨  Generating a new FastStream application!
 ✔ Application description validated 
 ✔ FastStream app skeleton generated and saved at: ./faststream-gen/application_skeleton.py 
 ✔ FastStream app generated and saved at: ./faststream-gen/application.py 
 ✔ Tests are generated and saved at: ./faststream-gen/test.py 
 Tokens used: 18572
 Total Cost (USD): $0.05635
✨  All files were successfully generated!

Note: If you have longer application description (and we encourage writing a detailed description), it is easier to save description into the file and use:

faststream_gen -i <path_to_app_description.txt>

The faststream_gen CLI command will create faststream-gen folder (you can set different output folder by providing --output_path option in the CLI) and save generated FastStream application.py inside the folder. We know that no one likes code full of errors, that’s why faststream_gen generates and run tests along with the generated application!

Next to application.py you will find the test.py script for testing your FastStream application!

In the faststream-gen folder you will find following files:

application.py

from pydantic import BaseModel, Field

from faststream import FastStream, Logger
from faststream.kafka import KafkaBroker


class Employee(BaseModel):
    employee_name: str = Field(
        ..., examples=["John Doe"], description="Employee name"
    )
    age: int = Field(
        ..., examples=[30], description="Age"
    )
    location: str = Field(
        ..., examples=["New York"], description="Location"
    )
    experience: int = Field(
        ..., examples=[5], description="Experience in years"
    )


broker = KafkaBroker("localhost:9092")
app = FastStream(broker)


@broker.subscriber("new_joinee")
@broker.publisher("project_team")
@broker.publisher("admin_team")
async def on_new_joinee(msg: Employee, logger: Logger):
    logger.info(msg)
    return msg

test.py

import pytest

from faststream.kafka import TestKafkaBroker

from application import *


@broker.subscriber("project_team")
async def on_project_team(msg: Employee):
    pass

@broker.subscriber("admin_team")
async def on_admin_team(msg: Employee):
    pass

@pytest.mark.asyncio
async def test_app():
    async with TestKafkaBroker(broker):
        await broker.publish(Employee(employee_name="John Doe", age=30, location="New York", experience=5), "new_joinee")

        on_new_joinee.mock.assert_called_with(dict(Employee(employee_name="John Doe", age=30, location="New York", experience=5)))
        on_project_team.mock.assert_called_with(dict(Employee(employee_name="John Doe", age=30, location="New York", experience=5)))
        on_admin_team.mock.assert_called_with(dict(Employee(employee_name="John Doe", age=30, location="New York", experience=5)))

Finally, you can personally check whether all the generated tests pass:

pytest faststream-gen/test.py

Copyright

Copyright © 2023 onwards airt technologies ltd, Inc.

License

This project is licensed under the terms of the Apache License 2.0