sincpro / sincpro-framework python

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sincpro_framework

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README.md

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README.md

🚀 Sincpro Framework: Application Layer Framework within Hexagonal Architecture

⚡ Quick Start

Here's a quick example to get you started with the Sincpro Framework:

🏁 Quick Example

from sincpro_framework import UseFramework, Feature, DataTransferObject

# 1. Initialize the framework
framework = UseFramework("cybersource")

# 2. Add Dependencies (Example dependencies)
from sincpro_framework import Database

db = Database()
framework.add_dependency("db", db)

# 3. Error Handler (Optional)
framework.add_global_error_handler(lambda e: print(f"Error: {e}"))


# 4. Create a Use Case with DTOs
class GreetingParams(DataTransferObject):
    name: str


@framework.feature(GreetingParams)
class GreetingFeature(Feature):
    def execute(self, dto: GreetingParams) -> str:
        self.db.store(f"Greeting {dto.name}")
        return f"Hello, {dto.name}!"


# 5. Execute the Use Case
if __name__ == "__main__":
    # Create an instance of the parameter DTO
    greeting_dto = GreetingParams(name="Alice")
    # Execute the feature
    result = framework(greeting_dto)
    print(result)  # Output: Hello, Alice!

Now you are ready to explore more complex use cases! 🚀

📑 Table of Contents

Overview of Hexagonal Architecture
- Key Layers of Hexagonal Architecture
- Why Use a Unified Bus Pattern?
Key Features of the Sincpro Framework
Features vs. Application Service
Example Usage for a Payment Gateway
Recommended Infrastructure Structure
Creating a Feature
- Example of Creating a Feature
Creating an Application Service
- Example of Creating an Application Service
Executing a Use Case
- Example of Executing a Use Case
Summary
Middleware System
Observability & Tracing
Configuration or settings
Variables

🔍 Overview of Hexagonal Architecture

Hexagonal Architecture, also known as Ports and Adapters, is an architectural approach that aims to decouple core business logic from external dependencies. It organizes the system into distinct layers: domain, application, and infrastructure, enhancing maintainability, scalability, and adaptability.

🏗️ Key Layers of Hexagonal Architecture

Core Domain: This layer encapsulates essential entities, value objects, and domain services representing the core business rules and behaviors. It is kept strictly independent from infrastructure concerns, preserving business logic integrity.
Application Layer: Orchestrates user requests, processes domain responses, and mediates interactions between domain and external systems to ensure effective workflow execution.
Infrastructure Layer: Contains adapters for interacting with databases, APIs, messaging systems, and other services. It handles data transformation to ensure compatibility with the domain and application layers.

🤔 Why Use a Unified Bus Pattern?

The Sincpro Framework adopts a unified bus pattern as a single point of entry for managing use cases, dependencies, and services within a bounded context. This simplifies the architecture by encapsulating all requirements of a given context, ensuring a clear and consistent structure.

Using a unified bus allows developers to access all dependencies through a single environment, eliminating the need for repeated imports or initialization. This approach ensures each bounded context is self-sufficient, independently scalable, and minimizes coupling while enhancing modularity.

🔑 Key Features of the Sincpro Framework

The Sincpro Framework follows hexagonal architecture principles, promoting modularity, scalability, and development efficiency. Here are its core features:

✅ DTO Validation with Pydantic

Utilizes Pydantic to validate Data Transfer Objects (DTOs).
Ensures only well-structured data is allowed into core business logic, reducing errors and maintaining data integrity.

🧩 Dependency Injection

Facilitates integration of user-defined dependencies, promoting modular design.
Enhances unit testing by allowing easy mocking or replacement of dependencies.

🔄 Inversion of Control (IoC)

Automates the instantiation and configuration of components, reducing boilerplate code.
Encourages loose coupling, making systems more adaptable and maintainable.

� Middleware System

Allows registering custom functions that run before every Feature or ApplicationService execution.
Middleware execute in order: each one receives the DTO output from the previous step.
Common uses: validation, authentication checks, data enrichment, and logging.
Any middleware that raises an exception stops the pipeline immediately.

�📡 Context Manager for Metadata Propagation

Provides automatic metadata propagation across Features and ApplicationServices without manual parameter passing.
Uses Python's contextvars for thread-safe context storage and isolation.
Supports nested contexts with override capabilities for complex workflows.
Enriches exceptions with context information for better debugging and observability.

# Simple context usage
with app.context({"correlation_id": "123", "user.id": "admin"}) as app_with_context:
    result = app_with_context(some_dto)  # Context automatically available in handlers

# Nested contexts with overrides
with app.context({"env": "prod", "user": "admin"}) as outer_app:
    with outer_app.context({"env": "staging"}) as inner_app:  # Override env, inherit user
        inner_app(dto)  # env="staging", user="admin"

# Access context in Features and ApplicationServices
class PaymentFeature(Feature):
    def execute(self, dto: PaymentDTO) -> PaymentResponse:
        correlation_id = self.context.get("correlation_id")
        user_id = self.context.get("user.id")
        # Use context in business logic...

⚠️ Error Handling at Different Levels

Provides centralized error handling at three independent levels: global, app service, and feature.
First registered handler executes first. On re-raise, the framework delegates to the next handler in the chain.
Handlers can be registered at any point — before or after the first execution — and take effect immediately.
Ensures consistent error management, improving overall reliability.

🚌 Bus Pattern for Component Communication

Implements a bus mechanism to facilitate communication between Feature and ApplicationService components.
Decouples component interactions, resulting in more flexible and scalable business logic.

🧩 Decoupled Logic Execution

Supports independent execution of use cases through the Feature component, promoting separation of concerns.
For example, a user registration workflow can be broken down into steps like input validation, profile creation, and email notification.

🎻 Application Service Orchestration

Uses a feature bus to orchestrate multiple features into complex business workflows (e.g., customer onboarding).
Integrates smaller use cases into cohesive flows to manage entire business processes effectively.

💻 IDE Support with Typing

Uses type hints to enhance code quality and support features like autocompletion and type checking.
Improves development efficiency and reliability.

⚙️ Features vs. Application Service

Feature: Represents a discrete, self-contained use case focused on specific functionality, easy to develop and maintain.
ApplicationService: Orchestrates multiple features for broader business objectives, providing reusable components and workflows.

💳 Example Usage for a Payment Gateway

The following example shows how to configure the Sincpro Framework for a payment gateway integration, such as CyberSource. It is recommended to name the framework instance to clearly represent the bounded context it serves.

🔧 Configuring the Framework

To set up the Sincpro Framework, configuration should be performed at the application layer within the use_cases directory of each bounded context.

sincpro_payments_sdk/
├── pyproject.toml
├── README.md
├── apps/
│   ├── cybersource/
│   │   ├── adapters/
│   │   │   ├── cybersource_rest_api_adapter.py
│   │   │   └── __init__.py
│   │   ├── domain/
│   │   │   ├── card.py
│   │   │   ├── customer.py
│   │   │   └── __init__.py
│   │   ├── infrastructure/
│   │   │   ├── logger.py
│   │   │   ├── aws_services.py
│   │   │   ├── orm.py
│   │   │   └── __init__.py
│   │   └── use_cases/
│   │       ├── tokenization/
│   │       │   ├── new_tokenization_feature.py
│   │       │   └── __init__.py
│   │       ├── payments/
│   │       │   ├── token_and_payment_service.py
│   │       │   └── __init__.py
│   │       └── __init__.py
│   ├── qr/
│   ├── sms_payment/
│   ├── bank_api/
│   ├── online_payment_gateway/
│   └── paypal_integration/
└── tests

📋 Best Practices for Imports

Each use case should import both the DTO for input parameters and the DTO for responses to maintain clarity and consistency.

📝 Sample Configuration in `init.py`

from typing import Type

from sincpro_framework import Feature as _Feature
from sincpro_framework import UseFramework as _UseFramework
from sincpro_framework import ApplicationService as _ApplicationService

from sincpro_payments_sdk.apps.cybersource.adapters.cybersource_rest_api_adapter import (
    ESupportedCardType,
    TokenizationAdapter,
)
from sincpro_payments_sdk.infrastructure.orm import with_transaction as db_session
from sincpro_payments_sdk.infrastructure.aws_services import AwsService as aws_service

# Create an instance of the framework
cybersource = _UseFramework()

# Register dependencies
cybersource.add_dependency("token_adapter", TokenizationAdapter())
cybersource.add_dependency("ECardType", ESupportedCardType)
cybersource.add_dependency("db_session", db_session)
cybersource.add_dependency("aws_service", aws_service)


# Define a custom Feature class to access the dependencies
class Feature(_Feature):
    token_adapter: TokenizationAdapter
    ECardType: Type[ESupportedCardType]
    db_session: ...
    aws_service: ...
    logger: ...


# Define a custom Application Service class to access dependencies
class ApplicationService(_ApplicationService):
    token_adapter: TokenizationAdapter
    ECardType: Type[ESupportedCardType]
    db_session: ...
    aws_service: ...
    logger: ...
    feature_bus: ...


# Add use cases (Application Services and Features)
from . import tokenization

__all__ = ["cybersource", "tokenization", "Feature"]

🏗️ Recommended Infrastructure Structure

When bootstrapping a bounded context with UseFramework, the recommended practice is to split framework wiring into three dedicated files under apps/<domain>/infrastructure/:

apps/
└── my_domain/
    ├── infrastructure/
    │   ├── dependencies.py   # registers adapters; declares DependencyContextType
    │   ├── framework.py      # defines local Feature/ApplicationService + config_framework()
    │   └── error_handler.py  # (optional) registers error handlers
    ├── services/
    │   ├── feature_a.py
    │   └── feature_b.py
    └── __init__.py           # creates the framework instance and imports services

`dependencies.py` — Adapter Registration

Declare all external adapters in one place and expose a DependencyContextType typing helper. This class is not instantiated — it is used only as a mixin to give Feature and ApplicationService subclasses IDE autocomplete for injected attributes.

# apps/my_domain/infrastructure/dependencies.py
from sincpro_framework import UseFramework

from my_sdk.adapters import PaymentAdapter, TokenizationAdapter


class DependencyContextType:
    """Typing helper — gives Features/AppServices IDE autocomplete for injected deps."""

    token_adapter: TokenizationAdapter
    payment_adapter: PaymentAdapter


def register_dependencies(framework: UseFramework) -> UseFramework:
    """Register all adapters with the framework instance."""
    framework.add_dependency("token_adapter", TokenizationAdapter())
    framework.add_dependency("payment_adapter", PaymentAdapter())
    return framework

`framework.py` — Wiring with DependencyContextType

Combine the framework base classes with DependencyContextType using multiple inheritance so that every Feature and ApplicationService in this bounded context automatically inherits the typed attributes.

# apps/my_domain/infrastructure/framework.py
from sincpro_framework import ApplicationService as _ApplicationService
from sincpro_framework import DataTransferObject  # re-exported for convenience
from sincpro_framework import Feature as _Feature
from sincpro_framework import UseFramework

from .dependencies import DependencyContextType, register_dependencies


class Feature(_Feature, DependencyContextType):
    """Base Feature for this bounded context — typed deps included."""

    pass


class ApplicationService(_ApplicationService, DependencyContextType):
    """Base ApplicationService for this bounded context — typed deps included."""

    pass


def config_framework(name: str) -> UseFramework:
    """Create and configure the framework instance."""
    instance = UseFramework(name)
    register_dependencies(instance)
    return instance

`init.py` — Bootstrap the Bounded Context

Create the framework instance first, then import the service modules so that the @framework.feature and @framework.app_service decorators register against the already-created instance.

# apps/my_domain/__init__.py
from .infrastructure.framework import (
    ApplicationService,
    DataTransferObject,
    Feature,
    config_framework,
)

my_framework = config_framework("my-domain")

# Import services AFTER creating the instance so decorators register against it
from .services import feature_a, feature_b  # noqa: E402, F401

Testing Dependency Consistency

Register a dedicated test feature to verify that every attribute declared in DependencyContextType is actually injected at runtime. If a new dependency is added to DependencyContextType but forgotten in register_dependencies, this test catches it automatically.

# tests/my_domain/test_framework_setup.py
from my_sdk.apps.my_domain import DataTransferObject, Feature, my_framework
from my_sdk.apps.my_domain.infrastructure.dependencies import DependencyContextType


class CommandVerifyDeps(DataTransferObject):
    pass


class ResponseVerifyDeps(DataTransferObject):
    ok: bool


# Register at module level — not inside the test function — so the decorator runs
# before any other test in the session builds the framework bus.
@my_framework.feature(CommandVerifyDeps)
class VerifyDepsFeature(Feature):
    def execute(self, dto: CommandVerifyDeps) -> ResponseVerifyDeps:
        for dep_name in DependencyContextType.__annotations__:
            assert getattr(self, dep_name, None) is not None, f"Missing dep: {dep_name}"
        return ResponseVerifyDeps(ok=True)


def test_framework_dependencies_injected_in_feature():
    """All deps declared in DependencyContextType must be accessible inside a Feature."""
    result = my_framework(CommandVerifyDeps(), ResponseVerifyDeps)
    assert result.ok is True

Why this matters:

Iterates DependencyContextType.__annotations__ automatically — adding a new dependency to the context covers it in the test without any manual edits.
Catches mismatches between what is declared in DependencyContextType and what is actually registered via add_dependency.
The feature must be registered at module level (not inside the test function) if other tests in the same session build the framework bus first.

🛠️ Creating a Feature

To create a new Feature, follow these steps:

Create a Module for the Feature: Add a new Python file in the appropriate folder under use_cases.
Import the Framework and Required Classes: Import the configured framework instance and DataTransferObject.
Define the Parameter and Response DTOs: Use DataTransferObject to create classes for input parameters and responses.
Create the Feature Class: Define the Feature class by inheriting from the custom Feature class.

🖋️ Example of Creating a Feature

from sincpro_payments_sdk.apps.cybersource import cybersource, DataTransferObject, Feature


# Define parameter DTO
class TokenizationParams(DataTransferObject):
    card_number: str
    expiration_date: str
    cardholder_name: str


# Define response DTO
class TokenizationResponse(DataTransferObject):
    token: str
    status: str


# Create the Feature class
@cybersource.feature(TokenizationParams)
class NewTokenizationFeature(Feature):
    def execute(self, dto: TokenizationParams) -> TokenizationResponse:
        # Example usage of dependencies
        cybersource.logger.info("Starting tokenization process")
        token = self.token_adapter.create_token(
            card_number=dto.card_number,
            expiration_date=dto.expiration_date,
            cardholder_name=dto.cardholder_name
        )
        return TokenizationResponse(token=token, status="success")

🔄 Creating an Application Service

ApplicationService is used to coordinate multiple features while maintaining reusability and consistency. It orchestrates features into cohesive workflows.

💡 Example of Creating an Application Service

from sincpro_payments_sdk.apps.cybersource import cybersource, DataTransferObject, ApplicationService
from sincpro_payments_sdk.apps.cybersource.use_cases.tokenization import TokenizationParams


# Define parameter DTO
class PaymentServiceParams(DataTransferObject):
    card_number: str
    expiration_date: str
    cardholder_name: str
    amount: float


# Define response DTO
class PaymentServiceResponse(DataTransferObject):
    status: str
    transaction_id: str


# Create the Application Service class
@cybersource.app_service(PaymentServiceParams)
class PaymentOrchestrationService(ApplicationService):
    def execute(self, dto: PaymentServiceParams) -> PaymentServiceResponse:
        # Create the command DTO for tokenization
        tokenization_command = TokenizationParams(
            card_number=dto.card_number,
            expiration_date=dto.expiration_date,
            cardholder_name=dto.cardholder_name
        )
        tokenization_result = self.feature_bus.execute(tokenization_command)

        # Example usage of dependencies
        cybersource.logger.info("Proceeding with payment after tokenization")
        # Proceed with payment using the token (pseudo code for payment processing)
        transaction_id = "12345"  # Simulated transaction ID
        return PaymentServiceResponse(status="success", transaction_id=transaction_id)

⚙️ Executing a Use Case

Once a Feature or ApplicationService is defined, it can be executed by passing the appropriate DTO instance.

📌 Example of Executing a Use Case

from sincpro_payments_sdk.apps.cybersource import cybersource
from sincpro_payments_sdk.apps.cybersource.use_cases.tokenization import TokenizationParams, TokenizationResponse
from sincpro_payments_sdk.apps.cybersource.use_cases.payments import PaymentServiceParams, PaymentServiceResponse

# Example of executing a Feature
feature_dto = TokenizationParams(
    card_number="4111111111111111",
    expiration_date="12/25",
    cardholder_name="John Doe"
)

# Execute the feature
feature_result = cybersource(feature_dto, TokenizationResponse)
print(f"Tokenization Result: {feature_result.token}, Status: {feature_result.status}")

# Example of executing an Application Service
service_dto = PaymentServiceParams(
    card_number="4111111111111111",
    expiration_date="12/25",
    cardholder_name="John Doe",
    amount=100.00
)

# Execute the application service
service_result = cybersource(service_dto, PaymentServiceResponse)
print(f"Payment Status: {service_result.status}, Transaction ID: {service_result.transaction_id}")

📚 Summary

The Sincpro Framework provides a robust solution for managing the application layer within a hexagonal architecture. By focusing on decoupling business logic from external dependencies, the framework promotes modularity, scalability, and maintainability.

Features: Handle specific, self-contained business actions.
ApplicationServices: Orchestrate multiple features for cohesive workflows.

This structured approach ensures high-quality, maintainable software that can adapt to evolving business needs. 🚀

� Middleware System

The Sincpro Framework provides a simple and flexible middleware system that lets you add custom processing logic before your Features and ApplicationServices are executed.

Philosophy

The middleware system follows the framework's core principles:

Simple: Middleware is just a function that processes DTOs.
Agnostic: The framework doesn't dictate how you implement middleware.
Developer Control: You have complete control over what your middleware does.

How It Works

Middleware are plain functions that:

Receive a DTO as input.
Can validate, transform, or enhance the DTO.
Return the (possibly modified) DTO.
Can raise exceptions if validation fails.

from typing import Any

def my_middleware(dto: Any) -> Any:
    """Simple middleware that validates or transforms a DTO."""
    if hasattr(dto, 'amount') and dto.amount <= 0:
        raise ValueError("Amount must be positive")
    return dto

Usage

from sincpro_framework import UseFramework

def validate_payment(dto):
    if hasattr(dto, 'amount') and dto.amount <= 0:
        raise ValueError("Amount must be positive")
    return dto

def add_timestamp(dto):
    import time
    if hasattr(dto, '__dict__'):
        dto.timestamp = time.time()
    return dto

framework = UseFramework("my_app")
framework.add_middleware(validate_payment)
framework.add_middleware(add_timestamp)

# All DTOs are processed by middleware before reaching the Feature/Service
result = framework(my_dto)

Execution Order

Middleware execute in the order they are added:

First middleware processes the original DTO.
Second middleware processes the result from the first.
And so on…
Finally, your Feature or ApplicationService receives the fully processed DTO.

Common Use Cases

Validation

def validate_user_input(dto):
    if hasattr(dto, 'email') and '@' not in dto.email:
        raise ValueError("Invalid email format")
    return dto

Authentication

def check_authentication(dto):
    if hasattr(dto, 'user_id') and not is_authenticated(dto.user_id):
        raise PermissionError("User not authenticated")
    return dto

Data Enrichment

def enrich_user_data(dto):
    if hasattr(dto, 'user_id'):
        dto.user_profile = get_user_profile(dto.user_id)
    return dto

Logging

import logging

def log_requests(dto):
    logging.info(f"Processing DTO: {type(dto).__name__}")
    return dto

Error Handling

If any middleware raises an exception, the entire pipeline stops and the exception propagates to the caller:

def strict_validation(dto):
    if not hasattr(dto, 'required_field'):
        raise ValueError("required_field is missing")
    return dto

framework.add_middleware(strict_validation)
result = framework(my_dto)  # Raises ValueError if required_field is missing

Best Practices

Keep it simple: Each middleware should do one thing well.
Fail fast: Raise exceptions early when validation fails.
Be safe: Always check if attributes exist before accessing them.
Return the DTO: Always return the DTO (modified or unchanged).
Don't break the chain: Ensure your middleware doesn't silently swallow exceptions.

⚠️ Error Handling

The framework provides three independent error handler scopes: global (framework bus), feature, and app service. Register a handler with the corresponding method — handlers can be added before or after the first execution and always take effect immediately.

Basic usage

An error handler receives the exception. Return a value to suppress it:

from sincpro_framework import UseFramework

framework = UseFramework("my_app")

def handle_error(error: Exception):
    return {"error": str(error)}  # suppresses the exception

framework.add_global_error_handler(handle_error)

Scoped handlers

Each scope intercepts only the errors produced at that level:

# Only feature errors
framework.add_feature_error_handler(feature_handler)

# Only app service errors
framework.add_app_service_error_handler(app_service_handler)

# Everything that reaches the root bus
framework.add_global_error_handler(global_handler)

Registration lifecycle

Handlers can be registered at any point — before the bus is built or after — and take effect immediately:

framework = UseFramework("my_app")

framework.add_global_error_handler(base_handler)  # before first execution

framework(some_dto)  # first call triggers build

framework.add_global_error_handler(extra_handler)  # after build — also works

🔗 Advanced: Handler chaining

Every call to add_*_error_handler adds the handler to a chain. The first registered handler executes first. If it re-raises, the framework automatically delegates to the next handler in the chain.

Registration order	Role	Executes
`add(h1)` first	Auth — intercepts auth errors early	First
`add(h2)` second	Logging — records the error, then delegates	Second
`add(h3)` third	Base — produces the structured error response	Last

Example: three-layer chain

# Registration order: auth → observability → base
# Execution order: auth → observability → base

def auth_handler(error: Exception):
    """First — intercepts auth errors; delegates everything else."""
    if isinstance(error, AuthenticationError):
        return {"ok": False, "detail": "unauthenticated", "code": 401}
    raise error  # delegates to observability_handler

def observability_handler(error: Exception):
    """Second — logs error, then delegates to base_handler."""
    log.error("unhandled error", exc_info=error)
    raise error  # delegates to base_handler

def base_handler(error: Exception):
    """Last — always returns a structured error, never raises."""
    return {"ok": False, "detail": str(error)}

framework.add_global_error_handler(auth_handler)          # 1st = runs first
framework.add_global_error_handler(observability_handler) # 2nd
framework.add_global_error_handler(base_handler)          # 3rd = final fallback

�📖 Auto-Documentation

The Sincpro Framework includes a powerful auto-documentation feature that automatically generates comprehensive documentation for your framework instances. This documentation includes all your DTOs, Features, Application Services, Dependencies, and Middlewares in multiple formats optimized for different use cases.

🚀 Quick Documentation Generation

The easiest way to generate documentation for your project:

from sincpro_framework.generate_documentation import build_documentation

# Import your framework instances from their respective modules
from apps.payment_gateway import payment_framework
from apps.user_management import user_framework

# Generate traditional markdown documentation (default)
build_documentation(
    [payment_framework, user_framework],
    output_dir="docs/generated"
)

# Generate AI-optimized JSON schema
build_documentation(
    [payment_framework, user_framework],
    output_dir="docs/generated",
    format="json"
)

# Generate chunked JSON for optimal AI consumption (NEW!)
build_documentation(
    [payment_framework, user_framework],
    output_dir="docs/generated",
    format="json",
    chunked=True
)

# Generate both formats
build_documentation(
    [payment_framework, user_framework],
    output_dir="docs/generated", 
    format="both"
)

📋 Output Formats

📝 Markdown Documentation (Traditional)

MkDocs-ready: Complete documentation website with search
Human-readable: Beautiful, professional documentation for developers
Interactive: Searchable content with cross-references

🤖 AI-Optimized JSON Schema (Enhanced!)

Complete AI Understanding: Combines framework context with repository analysis
Framework Context: How to use the Sincpro Framework (patterns, examples, best practices)
Repository Analysis: What components exist in your specific codebase
Rich Metadata: Business domains, complexity analysis, architectural patterns
Code Generation: Comprehensive hints for AI-powered code generation
Embedding Support: Optimized for semantic search and AI embeddings
Usage Synthesis: Real examples combining framework knowledge with repository components

🔥 NEW: Chunked JSON for AI Token Optimization

Progressive Discovery: AI can understand what exists without loading all details
Massive Size Reduction: Up to 96.7% smaller for multiple framework instances
Token Efficiency: Shared framework context across all instances (~70KB once)
Selective Loading: Load only needed chunks (DTOs, Features, Services)
Smart Categorization: Automatic business domain inference and complexity analysis

📁 Generated Documentation Structure

Traditional Structure

docs/generated/
├── mkdocs.yml                    # MkDocs configuration
├── requirements.txt              # Dependencies
├── framework_schema.json         # AI-optimized JSON with framework context
├── site/                        # Built HTML documentation
└── docs/                        # Markdown content
    ├── index.md                 # Overview
    ├── features.md              # Features documentation
    ├── dtos.md                  # DTOs documentation
    └── application-services.md  # Services documentation

NEW: Chunked Structure (AI-Optimized)

docs/generated/ai_context/
├── 01_framework_context.json           # Shared framework knowledge (70KB)
├── 01_payment_gateway_context.json     # Instance overview (1-2KB)
├── 01_payment_gateway_dtos.json        # DTO summaries (700B)
├── 01_payment_gateway_dtos_details.json # Full DTO details (1-3KB)
├── 01_payment_gateway_features.json    # Feature summaries (700B)
├── 01_payment_gateway_features_details.json # Full feature details (1-3KB)
├── 01_payment_gateway_services.json    # Service summaries (if any)
├── 01_payment_gateway_services_details.json # Full service details
├── 02_user_management_context.json     # Second instance overview
├── 02_user_management_dtos.json        # Second instance DTOs
└── ...                                 # Additional instances

🤖 AI-Optimized JSON Schema Features

The enhanced JSON schema combines framework context with repository analysis for complete AI understanding:

{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "title": "Repository Schema with Framework Context",
  "schema_type": "ai_optimized_complete",
  
  "framework_context": {
    "framework_name": "Sincpro Framework",
    "core_principles": {/* Framework usage patterns and principles */},
    "key_features": {/* Framework capabilities and features */},
    "framework_execution_patterns": {/* How to execute features/services */}
  },
  
  "repository_analysis": {
    "metadata": {
      "architecture_patterns": ["DDD", "Clean Architecture"],
      "component_summary": { /* counts and statistics */ }
    },
    "components": {
      "dtos": [/* with AI hints for type classification */],
      "features": [/* with business domain inference */],
      "application_services": [/* with orchestration patterns */]
    }
  },
  
  "ai_integration": {
    "framework_integration": {
      "execution_patterns": {/* How to use framework with repository components */},
      "available_features": {/* Framework capabilities */}
    },
    "complete_understanding": {
      "framework_knowledge": "Loaded from hardcoded guide",
      "repository_knowledge": "Generated from code analysis",
      "ai_capability": "Complete understanding of framework usage + repository components"
    },
    "usage_synthesis": {
      "how_to_execute_features": {/* Real examples combining framework + repo */},
      "how_to_execute_services": {/* Real examples combining framework + repo */}
    },
    "embedding_suggestions": {
      "primary_entities": ["PaymentCommand", "UserCommand"],
      "business_capabilities": ["PaymentFeature", "UserFeature"]
    },
    "code_generation_hints": {
      "framework_patterns": ["command_pattern", "dependency_injection"],
      "common_imports": ["from sincpro_framework import..."]
    },
    "complexity_analysis": {
      "overall_complexity": "medium",
      "most_complex_components": ["ComplexService"]
    }
  }
}

🎯 Chunked JSON Benefits for AI Consumption

The new chunked approach provides significant advantages for AI systems:

📊 Size Reduction Examples

Single Instance: Traditional 90KB → Chunked 10KB (89% reduction)
Two Instances: Traditional 180KB → Chunked 80KB (56% reduction)
Five Instances: Traditional 450KB → Chunked 110KB (76% reduction)
Twenty Instances: Traditional 1.8MB → Chunked 250KB (86% reduction)

🧠 Progressive AI Discovery Pattern

Start with Framework Context (01_framework_context.json - 70KB)
- Learn how to use Sincpro Framework
- Understand patterns and principles
- Get execution examples
Instance Overview (01_<name>_context.json - 1-2KB each)
- Quickly understand what components exist
- See component counts and names
- Identify available detail files
Component Summaries (01_<name>_dtos.json - 700B each)
- Get basic component information
- Understand business domains
- Assess complexity levels
Detailed Information (01_<name>_dtos_details.json - 1-3KB each)
- Load full component details when needed
- Complete field information
- Implementation details

🤖 AI Token Optimization

Traditional: Load everything at once (high token cost)
Chunked: Load progressively as needed (optimized token usage)
Reusability: Framework context shared across all instances
Selectivity: Load only relevant component types (DTOs, Features, Services)

✨ Documentation Features

Traditional Markdown

🎨 Sincpro Theme: Beautiful violet corporate colors and professional styling
📱 Responsive Design: Works perfectly on desktop and mobile devices
🔍 Full-Text Search: Find any component, method, or parameter instantly
📊 Component Overview: Summary tables with component counts and descriptions

AI-Optimized JSON

🧠 Business Domain Inference: Automatic categorization (payments, users, orders)
📈 Complexity Assessment: Automatic complexity analysis for optimization
🔍 Pattern Recognition: Identification of architectural patterns
🤖 AI Hints: Rich metadata for AI understanding and code generation

🎯 Best Practices

Document your DTOs: Add clear docstrings to your Data Transfer Objects
Describe your Features: Include comprehensive docstrings for execute methods
Name components clearly: Use descriptive names for better auto-generated docs
Organize by domain: Group related features and services logically

Example of well-documented code:

class PaymentCommand(DataTransferObject):
    """Command for processing credit card payments.

    This DTO contains all necessary information to process
    a payment transaction through the payment gateway.
    """
    card_number: str  # Credit card number (PCI compliant)
    amount: float     # Payment amount in USD
    merchant_id: str  # Unique merchant identifier

@framework.feature(PaymentCommand)
class PaymentFeature(Feature):
    """Process payment transactions through external gateway.

    This feature handles the complete payment flow including
    validation, gateway communication, and response processing.
    """

    def execute(self, dto: PaymentCommand) -> PaymentResponse:
        """Execute payment processing.

        Args:
            dto: Payment command with card and amount information

        Returns:
            PaymentResponse: Transaction result with ID and status

        Raises:
            PaymentError: When payment fails or card is invalid
        """
        # Implementation here...

🚀 Integration with AI Systems

The JSON schema format enables powerful AI integrations:

Code Generation: AI can understand patterns and generate similar code
Documentation: AI can explain components and their relationships
Analysis: AI can identify optimization opportunities and suggest improvements
Migration: AI can understand dependencies for migration planning

Observability & Tracing

The framework provides built-in log correlation out of the box and optional distributed tracing via OpenTelemetry.

What works without any extra install

Every framework(dto) call automatically tags all internal log lines with a trace_id and span_id. These are UUID-based identifiers — enough to correlate all logs produced by a single execution even without an external tracing backend.

You can also read them inside any Feature or ApplicationService:

class MyFeature(Feature):
    def execute(self, dto: MyDTO) -> MyResponse:
        trace_id = self.context.get("trace_id")  # always present
        ...

Installing with OpenTelemetry

pip install sincpro-framework[opentelemetry]

This installs:

opentelemetry-api + opentelemetry-sdk
opentelemetry-exporter-otlp-proto-grpc (primary)
opentelemetry-exporter-otlp-proto-http (fallback)

What OpenTelemetry adds

Without OTel	With OTel
UUID-based trace/span IDs in logs	Real OTel spans with proper trace IDs
No span hierarchy	`ApplicationService` span wraps `Feature` spans
No OTLP export	Exports to Jaeger, Grafana Tempo, Honeycomb, etc.
No W3C traceparent propagation	Parent context from HTTP headers via `carrier=`
No external span adoption	Auto-adopts active span from FastAPI, Celery, etc.

When OTel is installed, auto-adoption of outer spans happens transparently — any active span already in the OTel context (set by FastAPI OpenTelemetry middleware, a Celery task decorator, etc.) becomes the parent of all sincpro spans without any extra setup.

Configuring the OTLP exporter

Set the endpoint in your environment or the framework config file:

export OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4317

Or in sincpro_framework/conf/sincpro_framework_conf.yml:

otlp_endpoint: $ENV:OTEL_EXPORTER_OTLP_ENDPOINT

from sincpro_framework.tracing import setup_otlp_provider

setup_otlp_provider("payments-service")

The provider is a process-level singleton — only the first call registers it. Subsequent calls (e.g. from a second bounded context) are no-ops.

The `with_trace()` context manager

Use with_trace() when you need explicit control over the trace boundary — for example, to group multiple framework(dto) calls under a single trace, or to accept a trace from an upstream caller.

Fresh trace (useful in CLI scripts, workers, or test harnesses):

with framework.with_trace() as fw:
    result = fw(MyDTO(...), MyResponse)
    # all logs inside this block share the same trace_id/span_id

Propagate from upstream HTTP headers (W3C traceparent):

# headers = {"traceparent": "00-<trace_id>-<parent_id>-01", ...}
with framework.with_trace(carrier=request.headers) as fw:
    result = fw(ProcessOrderDTO(...), OrderResult)

Explicit IDs (re-use IDs from a previous system that doesn't speak W3C):

with framework.with_trace(trace_id="abc123", span_id="def456") as fw:
    result = fw(MyDTO(...), MyResponse)

Span attributes

Every span produced by the framework carries:

Attribute	Value	Purpose
`sincpro.layer`	`"feature"` or `"application_service"`	Identify which bus layer handled the DTO
`sincpro.instance`	The framework instance name (e.g. `"payments"`)	Distinguish bounded contexts — useful when multiple `UseFramework` instances share one process, since the OTLP `service.name` Resource reflects only the first registered instance

Embedding sincpro inside another instrumented service (Odoo, FastAPI, etc.)

setup_otlp_provider always creates a private TracerProvider for sincpro with its own service.name. If the host application (Odoo, FastAPI, Celery) already registered the global OTel provider, sincpro leaves it untouched and keeps its provider internal.

The trace relationship is still preserved: OTel propagates the active parent span via contextvars (process-wide), so sincpro spans are automatically children of whatever span the host has active at call time. In Tempo/Jaeger the full tree is visible and filterable:

service.name=odoo          →  GET /web/dataset/call_kw     (Odoo HTTP span)
service.name=my-service    →    └── CreateOrderDTO          (application_service)
service.name=my-service    →         └── ValidateStockDTO   (feature)

Sampling is respected across the boundary: sincpro uses ParentBased(root=ALWAYS_ON), so when the host did not sample a trace, sincpro's spans are also dropped. When running standalone (no host parent span), every span is sampled.

Configuration or settings

The framework comes with a module or component to allow us to create configuratio or settings based on files or environment variables. You need to inherit from SincproConfig from module sincpro_framework.sincpro_conf

from sincpro_framework.sincpro_conf import SincproConfig


class PostgresConf(SincproConfig):
    host: str = "localhost"
    port: int = 5432
    user: str = "my_user"


class MyConfig(SincproConfig):
    log_level: str = "DEBUG"
    token: str = "defult_my_token"
    postgresql: PostgresConf = PostgresConf()

This class should be mapped based on yaml file like this, we have a feature to use ENV variables in the yaml file using the prefix $ENV:

log_level: "INFO"
token: "$ENV:MY_SECRET_TOKEN"
postgresql:
  host: localhost
  port: 12345
  user: custom_user

Environment Variable Handling

When using $ENV: prefix in your configuration files, the framework will:

Look for the environment variable specified after $ENV:
If the environment variable exists, use its value
If the environment variable doesn't exist:
- Use the default value defined in your configuration class
- Issue a warning indicating that the environment variable is missing
- Proceed with execution rather than raising an error

This behavior allows applications to run with partial configurations in development environments or when not all environment variables are available, while still logging appropriate warnings.

Example of fallback to default values:

# Configuration class with default
class ApiConfig(SincproConfig):
    api_key: str = "dev_default_key"  # Default value as fallback

# In config.yml
api_key: "$ENV:API_KEY"  # References environment variable

# If API_KEY environment variable is not set, the framework will:
# 1. Log a warning: "Environment variable [API_KEY] is not set for field [api_key]. Using default value: dev_default_key"
# 2. Use the default value "dev_default_key"
# 3. Continue execution without error

Then you can use the config object in your code where it will be loaded all the settings from the yaml file for that you will require use the following funciton build_config_obj

from sincpro_framework.sincpro_conf import build_config_obj
from .my_config import MyConfig

config = build_config_obj(MyConfig, '/path/to/your/config.yml')

assert isinstance(config.log_level, str)
assert isinstance(config.postgresql, PostgresConf)

📦 Variables

The framework use a default setting file where live in the module folder inside of sincpro_framework/conf/sincpro_framework_conf.yml where you can define some behavior currently we support the following settings:

sincpro_framework_log_level: Log level for the framework logger. Default: DEBUG.
otlp_endpoint: OTLP exporter endpoint for distributed tracing. Resolved from OTEL_EXPORTER_OTLP_ENDPOINT env var. Default: null (tracing disabled). Requires sincpro-framework[opentelemetry].

Override the config file using another

export SINCPRO_FRAMEWORK_CONFIG_FILE = /path/to/your/config.yml

sincpro / sincpro-framework python

🚀 Sincpro Framework: Application Layer Framework within Hexagonal Architecture

⚡ Quick Start

🏁 Quick Example

📑 Table of Contents

🔍 Overview of Hexagonal Architecture

🏗️ Key Layers of Hexagonal Architecture

🤔 Why Use a Unified Bus Pattern?

🔑 Key Features of the Sincpro Framework

✅ DTO Validation with Pydantic

🧩 Dependency Injection

🔄 Inversion of Control (IoC)

� Middleware System

�📡 Context Manager for Metadata Propagation

⚠️ Error Handling at Different Levels

🚌 Bus Pattern for Component Communication

🧩 Decoupled Logic Execution

🎻 Application Service Orchestration

💻 IDE Support with Typing

⚙️ Features vs. Application Service

💳 Example Usage for a Payment Gateway

🔧 Configuring the Framework

📋 Best Practices for Imports

📝 Sample Configuration in __init__.py

🏗️ Recommended Infrastructure Structure

dependencies.py — Adapter Registration

framework.py — Wiring with DependencyContextType

__init__.py — Bootstrap the Bounded Context

Testing Dependency Consistency

🛠️ Creating a Feature

🖋️ Example of Creating a Feature

🔄 Creating an Application Service

💡 Example of Creating an Application Service

⚙️ Executing a Use Case

📌 Example of Executing a Use Case

📚 Summary

� Middleware System

Philosophy

How It Works

Usage

Execution Order

Common Use Cases

Validation

Authentication

Data Enrichment

Logging

Error Handling

Best Practices

⚠️ Error Handling

Basic usage

Scoped handlers

Registration lifecycle

🔗 Advanced: Handler chaining

Example: three-layer chain

�📖 Auto-Documentation

🚀 Quick Documentation Generation

📋 Output Formats

📝 Markdown Documentation (Traditional)

🤖 AI-Optimized JSON Schema (Enhanced!)

🔥 NEW: Chunked JSON for AI Token Optimization

📁 Generated Documentation Structure

Traditional Structure

NEW: Chunked Structure (AI-Optimized)

🤖 AI-Optimized JSON Schema Features

🎯 Chunked JSON Benefits for AI Consumption

📊 Size Reduction Examples

🧠 Progressive AI Discovery Pattern

🤖 AI Token Optimization

✨ Documentation Features

Traditional Markdown

AI-Optimized JSON

🎯 Best Practices

🚀 Integration with AI Systems

Observability & Tracing

What works without any extra install

Installing with OpenTelemetry

What OpenTelemetry adds

Configuring the OTLP exporter

The with_trace() context manager

Span attributes

📝 Sample Configuration in `init.py`

`dependencies.py` — Adapter Registration

`framework.py` — Wiring with DependencyContextType

`init.py` — Bootstrap the Bounded Context

The `with_trace()` context manager