vention-communication 0.2.0

A framework for storing and managing component and application data for machine apps.

Vention Communication

A thin, FastAPI-powered RPC layer for machine-apps that exposes Connect-compatible request-response and server-streaming endpoints — plus .proto generation from Python decorators, allowing typed SDKs to be generated separately via Buf.

Table of Contents

• ✨ Features
• 🧠 Concepts & Overview
• ⚙️ Installation & Setup
• 🚀 Quickstart Tutorial
• 🛠 How-to Guides
• 📖 API Reference
• 🔍 Troubleshooting & FAQ

✨ Features

• Zero boilerplate RPCs: Expose any async Python function as a network API with a single decorator.
• Strong typing: Request and response models derived directly from Python annotations.
• Built-in schema emission: Generates a .proto file at runtime, ready for SDK code generation.
• Single service surface: All methods exposed under /rpc/<package.Service>/<Method>.
• Connect-compatible transport: Works seamlessly with @connectrpc/connect-web and connectrpc-python.

🧠 Concepts & Overview

The Problem

Machine-app developers write Python daily but shouldn’t need to know REST, gRPC, or frontend networking. They just need a way to say “this function should be callable from anywhere.”

The Solution

vention-communication bridges that gap by turning annotated Python functions into typed RPC endpoints automatically.

@action() → defines a one-request / one-response method.

@stream() → defines a live telemetry or event stream that other services can subscribe to.

VentionApp.finalize() → scans for decorators, builds a Connect router, and emits a .proto schema.

Once the .proto exists, SDKs for TypeScript, Python, or Go can be generated using Buf. The result: your frontend gets auto-completed methods for every RPC, no HTTP or JSON code required.

Core Concepts

• Actions (Request-Response) — send a request, get a response back. Input and output types are inferred from function annotations. If either is missing, google.protobuf.Empty is used.
• Streams (Server streaming) — continuous updates broadcast to all subscribers. Each stream can optionally replay the last value when someone subscribes. Queues default to size-1 to always show the latest value.
• Service Surface — all actions and streams belong to one service, e.g. vention.app.v1.<YourAppName>Service, with routes mounted under /rpc.
• Proto Generation — VentionApp.finalize() writes a .proto to disk, capturing all decorated RPCs, inferred models, and scalar wrappers. SDK generation (via Buf) is handled externally.

⚙️ Installation & Setup

Requirements:

• Python 3.10+
• FastAPI
• Uvicorn (for serving)

Install:

pip install vention-communication

Optional client libraries:

• TypeScript: @connectrpc/connect-web
• Python: connectrpc with httpx.AsyncClient

🚀 Quickstart Tutorial

A complete "hello world" in three steps.

1. Define your RPCs

from pydantic import BaseModel
from vention_communication import VentionApp, action, stream
import time, random

class PingRequest(BaseModel):
    message: str

class PingResponse(BaseModel):
    message: str

class Heartbeat(BaseModel):
    value: str
    timestamp: int

app = VentionApp(name="DemoApp", emit_proto=True)

@action()
async def ping(req: PingRequest) -> PingResponse:
    return PingResponse(message=f"Pong: {req.message}")

@stream(name="heartbeat", payload=Heartbeat, replay=True)
async def heartbeat():
    """Broadcast a live heartbeat value to all subscribers."""
    return Heartbeat(value=f"{random.uniform(0,100):.2f}", timestamp=int(time.time()))

app.finalize()

Run:

uvicorn demo.main:app --reload

Endpoints are automatically registered under /rpc/vention.app.v1.DemoAppService.

2. Generated .proto

After startup, proto/app.proto is emitted automatically.

You can now use Buf or protoc to generate client SDKs:

buf generate --template buf.gen.ts.yaml
buf generate --template buf.gen.python.yaml

SDK generation is external to vention-communication — allowing you to control versions and plugins.

3. Example TypeScript Client

import { createClient } from "@connectrpc/connect";
import { createConnectTransport } from "@connectrpc/connect-web";
import { DemoAppService } from "./gen/connect/proto/app_connect";

const transport = createConnectTransport({
  baseUrl: "http://localhost:8000/rpc",
  useBinaryFormat: false,
});

const client = createClient(DemoAppService, transport);

const res = await client.ping({ message: "Hello" });
console.log(res.message);

for await (const hb of client.heartbeat({})) {
  console.log("Heartbeat", hb.value, hb.timestamp);
}

🛠 How-to Guides

Add a new request-response endpoint

@action()
async def get_status() -> dict:
    return {"ok": True}

Add a new stream

@stream(name="Status", payload=dict)
async def publish_status() -> dict:
    return {"ok": True}

Emit proto to a custom path

app = VentionApp(name="MyService", emit_proto=True, proto_path="out/myservice.proto")
app.finalize()

📖 API Reference

VentionApp

VentionApp(
  name: str = "VentionApp",
  *,
  emit_proto: bool = False,
  proto_path: str = "proto/app.proto",
  **fastapi_kwargs
)

Methods:

• .register_rpc_plugin(bundle: RpcBundle) — merges external action/stream definitions (e.g., from state-machine or storage).
• .finalize() — registers routes, emits .proto, and makes publishers available.

Attributes:

• connect_router: internal FastAPI router for Connect RPCs.
• proto_path: location of the emitted .proto.

Decorators

@action(name: Optional[str] = None)
# → Registers a request-response handler

@stream(
    name: str,
    payload: type,
    replay: bool = True,
    queue_maxsize: int = 1,
    policy: Literal["latest", "fifo"] = "latest"
)
# → Registers a server-streaming RPC and publisher

Stream Parameters:

• name: Unique name for the stream
• payload: Type of data to stream (Pydantic model or JSON-serializable type)
• replay: Whether new subscribers receive the last value (default: True)
• queue_maxsize: Maximum items per subscriber queue (default: 1)
• policy: Delivery policy when queue is full - "latest" drops old items, "fifo" waits for space (default: "latest")

Stream Configuration Options

When creating a stream with @stream(), you can configure how updates are delivered to subscribers:

replay (default: True)

Controls whether new subscribers receive the last published value immediately when they subscribe.

• replay=True: New subscribers instantly receive the most recent value (if one exists). Useful for state streams where clients need the current state immediately upon connection.
• replay=False: New subscribers only receive values published after they subscribe. Useful for event streams where you only want to see new events.

queue_maxsize (default: 1)

The maximum number of items that can be queued for each subscriber before the delivery policy kicks in.

• queue_maxsize=1: Only the latest value is kept. Perfect for state streams where you only care about the current state.
• queue_maxsize=N (N > 1): Allows buffering up to N items. Useful when subscribers might process items slower than they're published, but you still want to limit memory usage.

# Only keep latest temperature reading
@stream(name="Temperature", payload=Temperature, queue_maxsize=1)

# Buffer up to 10 sensor readings
@stream(name="SensorData", payload=SensorReading, queue_maxsize=10)

policy (default: "latest")

Defines what happens when a subscriber’s queue is full and a new value is published.

Each subscriber maintains its own in-memory queue of pending messages. When you publish faster than a client can consume, the queue eventually fills — the policy determines what happens next.

policy="latest" — “drop oldest, never block”

• The publisher never waits.
• If a subscriber’s queue is full, the oldest item is dropped and the new one is inserted immediately.
• Fast subscribers receive every message; slow subscribers skip intermediate values but always see the most recent state.

✅ Pros

• Zero backpressure — publisher performance unaffected by slow clients.
• Keeps UI dashboards and telemetry feeds current (“latest value always wins”).
• Ideal for high-frequency data (positions, sensor readings, machine state).

⚠️ Cons

• Drops messages for slow clients (they may miss intermediate updates).
• Subscribers can diverge — one may receive more updates than another.

Example:

@stream(name="Temperature", payload=TempReading,
        policy="latest", queue_maxsize=1)
# → publisher never blocks; subscribers always see the most recent temperature

policy="fifo" — “deliver all, may block”

• The publisher awaits until there’s space in every subscriber’s queue.
• Guarantees that all messages are delivered in order to every subscriber.
• A slow subscriber can stall the entire stream, because the distributor waits for that subscriber’s queue to make room before continuing.

✅ Pros

• Preserves every event and strict ordering.
• Reliable for command sequences, audit logs, and event-driven logic.

⚠️ Cons

• One slow or paused subscriber can block all others.
• Publishing rate is limited by the slowest client.
• In extreme cases, a throttled browser or dropped connection can cause the distributor to stall until the queue frees or the subscriber is removed.

Example:

@stream(name="Events", payload=MachineEvent,
        policy="fifo", queue_maxsize=100)
# → guarantees ordered delivery but can back-pressure the publisher

Common Combinations:

• State monitoring (default): replay=True, queue_maxsize=1, policy="latest" — subscribers get current state immediately and always see the latest value.
• Event streaming: replay=False, queue_maxsize=100, policy="fifo" — subscribers only see new events and process them in order.

🔍 Troubleshooting & FAQ

Q: Can I disable proto generation at runtime?

Yes — set emit_proto=False in VentionApp(...).

Q: Publishing raises KeyError: Unknown stream.

Ensure app.finalize() has been called before publishing or subscribing.

Q: How do I integrate this with other libraries (state machine, storage, etc.)?

Use app.register_rpc_plugin() to merge additional RPC definitions before calling .finalize().