mmar-ptag 1.3.0

pydantically-type-adapted-grpc framework for multimodal architectures team

mmar-ptag

pydantically-type-adapted-grpc framework for multimodal architectures team

The main goal: simplify defining type-safe Python interfaces to separate services.

Installation

pip install mmar-ptag

Quick Start

1. Define the Service

from types import SimpleNamespace
from mmar_ptag import deploy_server

class Greeter:
    def say_hello(self, *, name: str, count: int = 1) -> dict:
        return {"message": f"Hello, {name} x{count}"}

2. Run the Server

deploy_server(
    config_server=SimpleNamespace(port=50051, max_workers=10),
    service=Greeter()
)

3. Create a Client

from mmar_ptag import ptag_client

class Greeter:
    def say_hello(self, *, name: str, count: int = 1) -> dict:
        ...

client = ptag_client(Greeter, "localhost:50051")
result = client.say_hello(name="World", count=3)
print(result)  # {'message': 'Hello, World x3'}

Trace ID Support

Trace ID enables distributed tracing across your service mesh. Use installed_trace_id from mmar_mimpl to set the trace ID context.

from mmar_mimpl import installed_trace_id
from mmar_ptag import ptag_client

class UserService:
    def get_user(self, *, user_id: int) -> dict:
        ...

client = ptag_client(UserService, "localhost:50051")

# Set trace_id using installed_trace_id context manager
with installed_trace_id(trace_id="request-abc-123"):
    result = client.get_user(user_id=123)

The trace ID value is stored in mmar_mimpl.TRACE_ID_VAR, making it accessible throughout the request lifecycle and automatically propagated to downstream services.

How It Works

ptag uses Pydantic adapters to handle type conversion between Python and gRPC/protobuf.

Example: client.say_hello(name="World", count=3)

Client flow (sending request):

{name="World", count=3}
    -(args tuple)->
("World", 3)
    -(args_adapter.dump_json)->
["World", 3]
    -(wrap in PTAGRequest)->
b'\n\tsay_hello\x12\x0c["World", 3]'
    -(server receives)->

Server flow (processing & responding):

b'\n\tsay_hello\x12\x0c["World", 3]'
    -(args_adapter.validate_json)->
["World", 3]
    -(bind to kwargs)->
{name="World", count=3}
    -(say_hello method)->
{"message": "Hello, World x3"}
    -(result_adapter.dump_json)->
{"message": "Hello, World x3"}
    -(wrap in PTAGResponse)->
b'\n\tsay_hello\x12\x1e{"message": "Hello, World x3"}'
    -(client receives)->

Client flow (receiving response):

b'\n\tsay_hello\x12\x1e{"message": "Hello, World x3"}'
    -(return_adapter.validate_json)->
{"message": "Hello, World x3"}

This ensures:

• Arguments are validated and serialized before sending
• Return values are deserialized and validated after receiving
• Type safety across the wire without manual protobuf definitions

Features

• Type-safe RPC using Pydantic for validation
• Automatic reconnection with configurable retry attempts
• Built-in tracing with trace ID support
• Interface-based design – define services as Python classes
• Keyword-only arguments – explicit and readable API

API Reference

ptag_client(interface, address, reconnect_attempts=5)

Create a dynamic client for the given interface at the provided gRPC address.

• interface: Type (class) defining the service interface
• address: gRPC server address (e.g., "localhost:50051")
• reconnect_attempts: Number of retry attempts on connection failure (default: 5)

ptag_attach(server, service_object)

Attach a service object to an existing gRPC server.