faststream-gen
spec
| faststream-gen | spec | |
|---|---|---|
| 3 | 43 | |
| 34 | 3,913 | |
| - | 2.8% | |
| 9.3 | 7.9 | |
| 3 months ago | 10 days ago | |
| Jupyter Notebook | JavaScript | |
| Apache License 2.0 | Apache License 2.0 |
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
faststream-gen
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FastStream: Python's framework for Efficient Message Queue Handling
Built for Automatic Code Generation: FastStream is optimized for automatic code generation using advanced models like GPT. This means you can leverage the power of code generation to boost your productivity. Checkout the amazing tool we built for the microservice code generation: faststream-gen.
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Generating production-level streaming microservices using GPT
faststream-gen(https://github.com/airtai/faststream-gen/) uses GPT models to automatically generate microservices using the FastStream(https://github.com/airtai/faststream) framework for Apache Kafka, RabbitMQ and NATS. Simply describe your microservice in plain English, and it will generate a production-level FastStream application ready to deploy in a few minutes and under $1 cost, together with unit and integration tests, documentation and Docker images.
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Generating production-level streaming microservices using AI
faststream-gen is a Python library that uses generative AI to automatically generate FastStream applications. Simply describe your microservice in plain English, and faststream-gen will generate a production-level FastStream application ready to deploy in a few minutes and under $1 cost.
spec
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10 realtime data sources you won't believe are free!
AsyncAPI: Interested in how to define your WebSocket APIs? One of the most advanced realtime specifications is the AsyncAPI specification, which comes with various generators for code and documentation, as well as renderers for the specifications.
- Comunicar microservicios con: ¿Kafka, RabbitMQ u otro? ¿Por qué?
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FastStream: Python's framework for Efficient Message Queue Handling
Our journey with FastStream started when we needed to integrate our machine learning models into a customer's Apache Kafka environment. To streamline this process, we created FastKafka using AIOKafka, AsyncAPI, and asyncio. It was our first step in making message queue management easier.
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Introducing FastStream: the easiest way to write microservices for Apache Kafka and RabbitMQ in Python
Automatic Docs: Stay ahead with automatic AsyncAPI documentation
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FastStream: the easiest way to add Kafka and RabbitMQ support to FastAPI services
FastStream supports in-memory testing, AsyncAPI schema generation and more... If you are interested, please support our project by giving a GH start and joining our discord server.
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An AsyncAPI Example: Building Your First Event-driven API
However, in order for the system to work effectively, there must be a common understanding between the components regarding events and their data structures. This is where AsyncAPI comes in; it helps define a contract that describes how the components communicate and behave effectively.
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Is this a viable approach to a chat microservice?
You can also take a look at https://www.asyncapi.com/ (a spec for asynchronous APIs). It's useful for this use case, that is, building a well structured websocket interface with pub/sub.
- OpenAPI v4 Proposal
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Propan 0.1.2 - new way to interact Kafka from Python
Sure! Next step I am working on AsyncAPI scheme generation by your application code. It's also includes a project generation from scheme, scheme web view (lika the Swagger for OpanAPI), etc. It will a much difficult than just another broker implementation...
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Make API product lifecycle management easy
Onboarding - Enable developers to quickly learn how to consume the exposed APIs. For example, offer OpenAPI or AsyncAPI documentation and provide a portal and sandbox.
What are some alternatives?
cookiecutter-faststream - Cookiecutter template for FastStream apps
springdoc-openapi - Library for OpenAPI 3 with spring-boot
faststream - FastStream is a powerful and easy-to-use Python framework for building asynchronous services interacting with event streams such as Apache Kafka, RabbitMQ, NATS and Redis.
WatermelonDB - 🍉 Reactive & asynchronous database for powerful React and React Native apps ⚡️
aiokafka - asyncio client for kafka
asyncapi-react - React component for rendering documentation from your specification in real-time in the browser. It also provides a WebComponent and bundle for Angular and Vue
RabbitMQ - Open source RabbitMQ: core server and tier 1 (built-in) plugins
mqtt-venstar-bridge - Simple MQTT bridge to the venstar HTTP API
fastkafka - FastKafka is a powerful and easy-to-use Python library for building asynchronous web services that interact with Kafka topics. Built on top of Pydantic, AIOKafka and AsyncAPI, FastKafka simplifies the process of writing producers and consumers for Kafka topics.
eventbridge-atlas - Open-source tool to document, discover, and share your Amazon EventBridge schemas.
cookiecutter - A cross-platform command-line utility that creates projects from cookiecutters (project templates), e.g. Python package projects, C projects.
Flask-SocketIO - Socket.IO integration for Flask applications.