minix 0.1.57

A modular Python framework for backend, AI, and data projects

Minix

Minix is a modular Python framework for building backend, AI, and data-driven applications. It provides a clean, layered architecture with built-in support for REST APIs, task scheduling, message queues, and machine learning workflows.

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Table of Contents

• Key Features
• Architecture Overview
• Installation
• Core Concepts
  • Bootstrap
  • Modules
  • Entities
  • Repositories
  • Services
  • Controllers
  • Connectors
  • Tasks & Scheduling
    • Async Task
    • Periodic Task
    • Running Tasks Manually
    • Workflows (DAG Scheduling)
  • Kafka Consumers
  • ML Models
• Configuration
• Extras
• Registry Usage
• CLI Commands
• License
• Contributing
• Author

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Key Features

• FastAPI Integration: Build high-performance REST APIs with automatic OpenAPI documentation
• Modular Architecture: Organize code into self-contained modules with entities, repositories, services, and controllers
• Multi-Database Support: Built-in connectors for MySQL, ClickHouse, Redis, and Qdrant (vector DB)
• Task Scheduling: Celery-powered background tasks with RedBeat scheduler for periodic jobs
• Kafka Consumers: Async Kafka message processing with aiokafka
• Object Storage: S3-compatible storage support via boto3
• ML Workflows: Optional MLflow integration for model versioning and deployment
• Dependency Registry: Singleton-based service container for clean dependency injection
• Environment Management: Configuration via .env files using dotenv

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Architecture Overview

┌─────────────────────────────────────────────────────────────────┐
│                         Application                              │
├─────────────────────────────────────────────────────────────────┤
│  Bootstrap → Registers Connectors & Modules                      │
├─────────────────────────────────────────────────────────────────┤
│                          Modules                                 │
│  ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐            │
│  │Controller│ │ Service  │ │Repository│ │  Entity  │            │
│  │ (API)    │→│ (Logic)  │→│  (Data)  │→│ (Model)  │            │
│  └──────────┘ └──────────┘ └──────────┘ └──────────┘            │
├─────────────────────────────────────────────────────────────────┤
│  Tasks (Celery)  │  Consumers (Kafka)  │  Models (MLflow)       │
├─────────────────────────────────────────────────────────────────┤
│                        Connectors                                │
│  SQL (MySQL/ClickHouse) │ Redis │ Qdrant │ Object Storage (S3)  │
└─────────────────────────────────────────────────────────────────┘

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Installation

Requirements

• Python 3.10 or higher

Install via pip

pip install minix

Install from source (development)

git clone <repository-url>
cd minix
pip install -e .

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Core Concepts

Bootstrap

The bootstrap function initializes your application by registering connectors and modules:

from minix.core.bootstrap import bootstrap

bootstrap(
    modules=[Module1(), Module2()],
    connectors=[
        (sql_connector, None),           # Default connector
        (sql_connector_2, "analytics")   # Named connector with salt
    ]
)

Modules

Modules are self-contained units that group related functionality:

from minix.core.module.business_module import BusinessModule

class ProductModule(BusinessModule):
    def __init__(self):
        super().__init__("product")
        self.add_entity(ProductEntity)
        self.add_repository(ProductRepository)
        self.add_service(ProductService)
        self.add_controller(ProductController)
        self.add_periodic_task(SyncProductsTask)
        self.add_consumer(ProductEventConsumer)

Module Methods:

• add_entity(entity) - Register a data entity
• add_repository(repository, connector_salt) - Register a repository with optional connector
• add_service(service) - Register a service
• add_controller(controller) - Register an API controller
• add_task(task) - Register an async task
• add_periodic_task(periodic_task) - Register a scheduled task
• add_consumer(consumer) - Register a Kafka consumer
• add_model(model, config) - Register an ML model

Entities

Entities define your data models:

SQL Entity

from sqlalchemy import String
from sqlalchemy.orm import Mapped, mapped_column
from minix.core.entity import SqlEntity

class UserEntity(SqlEntity):
    __tablename__ = "users"

    name: Mapped[str] = mapped_column(String(100), nullable=False)
    email: Mapped[str] = mapped_column(String(255), unique=True, nullable=False)

SQL entities automatically include id, created_at, and updated_at columns.

Qdrant Entity (Vector DB)

from minix.core.entity import QdrantEntity

class DocumentEntity(QdrantEntity):
    content: str

    @staticmethod
    def collection() -> str:
        return "documents"

Redis Entity

from minix.core.entity import RedisEntity

class CacheEntity(RedisEntity):
    pass

Repositories

Repositories handle data access:

SQL Repository

from minix.core.repository import SqlRepository

class UserRepository(SqlRepository[UserEntity]):
    pass

Built-in methods:

• save(entity) - Save a single entity
• save_all(entities) - Save multiple entities
• save_bulk(entities, chunk_size) - Bulk insert with chunking
• get_all() - Get all entities
• get_by_id(id) - Get by ID
• get_by(**kwargs) - Filter by attributes
• update(entity) - Update an entity
• delete(entity) - Delete an entity

Qdrant Repository

from minix.core.repository import QdrantRepository

class DocumentRepository(QdrantRepository[DocumentEntity]):
    pass

Methods:

• insert(entities) - Insert vectors
• query(entity, top_k) - Similarity search
• delete(entity_ids) - Delete vectors

Services

Services contain business logic:

from minix.core.service import Service

class UserService(Service[UserEntity]):
    def get_active_users(self):
        return self.repository.get_by(status="active")

    def create_user(self, name: str, email: str) -> UserEntity:
        user = UserEntity(name=name, email=email)
        return self.repository.save(user)

Controllers

Controllers define REST API endpoints:

from minix.core.controller import Controller
from minix.core.registry import Registry

class UserController(Controller):
    def get_prefix(self):
        return "/users"

    def define_routes(self):
        @self.router.get("/")
        def get_users():
            service = Registry().get(UserService)
            return service.get_repository().get_all()

        @self.router.post("/")
        def create_user(name: str, email: str):
            service = Registry().get(UserService)
            return service.create_user(name, email)

Connectors

SQL Connector (MySQL/ClickHouse)

from minix.core.connectors.sql_connector import SqlConnector, SqlConnectorConfig

config = SqlConnectorConfig(
    username="root",
    password="password",
    host="localhost",
    port=3306,
    database="mydb",
    driver="mysql",  # or "clickhouse"
    connect_timeout=30,
    pool_recycle=3600
)
connector = SqlConnector(config)

Qdrant Connector

from minix.core.connectors.qdrant_connector import QdrantConnector

connector = QdrantConnector(
    url="http://localhost:6333",
    api_key="your-api-key"
)
await connector.connect()

Object Storage Connector (S3-compatible)

from minix.core.connectors.object_storage_connector import ObjectStorageConnector
from minix.core.connectors.object_storage_connector.config import ObjectStorageConfig

config = ObjectStorageConfig(
    endpoint_url="https://s3.amazonaws.com",
    access_key="your-access-key",
    secret_key="your-secret-key",
    bucket_name="my-bucket"
)
connector = ObjectStorageConnector(config)

# Upload, download, delete files
await connector.upload_file(file_obj, "path/to/file.txt")
await connector.download_file("path/to/file.txt", local_file)
await connector.generate_presigned_url("path/to/file.txt", expiration=3600)

Tasks & Scheduling

Async Task

from minix.core.scheduler.task import Task

class ProcessOrderTask(Task):
    def get_name(self) -> str:
        return "process_order"

    def run(self, order_id: int):
        # Process the order
        pass

Periodic Task

from celery.schedules import crontab
from minix.core.scheduler.task import PeriodicTask

class DailyReportTask(PeriodicTask):
    def get_name(self) -> str:
        return "daily_report"

    def get_schedule(self) -> crontab:
        return crontab(hour=0, minute=0)  # Run at midnight

    def run(self):
        # Generate daily report
        pass

Running Tasks Manually

from minix.core.registry import Registry
from minix.core.scheduler import Scheduler

scheduler = Registry().get(Scheduler)
scheduler.run_task(ProcessOrderTask(order_id=123))

Workflows (DAG Scheduling)

Minix supports defining and executing workflows as DAGs (Directed Acyclic Graphs) where:

• Each node is a Task
• Nodes can depend on other nodes
• Dependency results can be passed to downstream tasks
• The workflow engine guarantees each node runs at most once during a workflow execution
• If the DAG has multiple sink nodes, all sinks are executed

Creating a Workflow

from minix.core.registry import Registry
from minix.core.scheduler import Scheduler
from minix.core.scheduler.workflow import Workflow

scheduler = Registry().get(Scheduler)

wf = Workflow("order_pipeline")

t1 = wf.add(DAGTaskTest1(in_a=aaaaa), node_id="task1")
t2 = wf.add(DAGTaskTest2(), node_id="task2", depends_on=[t1], consume_dependency_results=True)
t3 = wf.add(DAGTaskTest3(), node_id="task3", depends_on=[t2, t1], consume_dependency_results=True)
t4 = wf.add(DAGTaskTest4(), node_id="task4", depends_on=[t3], consume_dependency_results=True)

t5 = wf.add(DAGTaskTest5(), node_id="task5", depends_on=[t4], consume_dependency_results=True)

res = scheduler.run_workflow(wf)

Dependency Result Passing

For a node with consume_dependency_results=True:

• If it has 1 dependency, that dependency result is passed as the first positional argument (arg0) to the task.
• If it has multiple dependencies, a list of dependency results is passed as arg0 in the same order as depends_on.

For a node with consume_dependency_results=False, no dependency result is injected.

This design allows tasks to explicitly opt in/out of consuming upstream values, while still receiving their own constructor-provided args/kwargs.

Running the Entire Workflow

res = scheduler.run_workflow(wf)
print(res.id)  # Celery task id
# output = res.get(timeout=...)  # if you want to block and fetch the final result

Final result shape when running the whole workflow:

• If the workflow has one sink, the final result is that sink’s output.
• If the workflow has multiple sinks, the final result is a dictionary mapping sink node ids to outputs:

  {
    "task5": <result_of_task6>,
    "task4": <result_of_task9>
  }
  

Running a Specific Target Node

You can execute only the subgraph required to compute a particular node (useful for partial runs or debugging):

res = scheduler.run_workflow(wf, target_node_id="task9")
# output = res.get(timeout=...)

In this mode, only task9 and its dependency closure are executed, and the returned value corresponds to the output of the target node (task9).

Workflow Execution Guarantees

When executing a workflow:

• No duplicate execution: if a node is required by multiple downstream nodes, it runs once.
• Multiple sinks are supported: all sink nodes run when executing the whole workflow.
• Full-graph execution: when no target_node_id is provided, the workflow runs the entire DAG (not only the ancestry of a single sink).

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Kafka Consumers

from minix.core.consumer import AsyncConsumer, AsyncConsumerConfig

class OrderEventConsumer(AsyncConsumer):
    def get_config(self) -> AsyncConsumerConfig:
        return AsyncConsumerConfig(
            name="order_consumer",
            topics=["orders"],
            group_id="order_service",
            bootstrap_servers=["localhost:9092"]
        )

    async def run(self, message: dict):
        # Process the message
        order_id = message.get("order_id")
        print(f"Processing order: {order_id}")

ML Models

Base Model

from minix.core.model import Model

class MyModel(Model):
    def get_model_name(self):
        return "my_model"

    def predict(self, model_input: dict):
        # Prediction logic
        pass

    def set_device(self, device: str):
        self.device = device

Embedding Model

from minix.core.model import EmbeddingModel
import numpy as np

class TextEmbedder(EmbeddingModel):
    def get_model_name(self):
        return "text_embedder"

    def embed(self, text: str) -> np.ndarray:
        # Return embedding vector
        pass

    def embed_batch(self, texts: list[str]) -> np.ndarray:
        # Return batch of embeddings
        pass

    def set_device(self, device: str):
        self.device = device

MLflow Model (requires ai extra)

from minix.core.model import MlflowModel

class MyMlflowModel(MlflowModel):
    def __init__(self):
        super().__init__(
            name="my_model",
            version=1,
            packages=["torch", "transformers"]
        )

    def get_model(self):
        # Return your model instance
        pass

    def predict(self, model_input):
        model = self.load_model()
        return model.predict(model_input)

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Configuration

Minix uses environment variables for configuration. Create a .env file in your project root:

# Celery Configuration
CELERY_BROKER_URL=redis://localhost:6379/0
CELERY_RESULT_BACKEND=db+mysql://root:password@localhost:3306/celery_results

# Kafka Configuration
KAFKA_BOOTSTRAP_SERVERS=localhost:9092

# MLflow Configuration (for AI extras)
MLFLOW_TRACKING_URL=http://localhost:5000
PYTHON_VERSION=3.10

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Extras

AI Capabilities

Install with AI tools (PyTorch, MLflow, Qdrant):

pip install "minix[ai]"

ClickHouse Support

Install with ClickHouse support:

pip install "minix[clickhouse]"

Development Tools

Install development dependencies:

pip install "minix[dev]"

Install All Extras

pip install "minix[ai,clickhouse,dev]"

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Registry Usage

The Registry is a singleton-based dependency container:

from minix.core.registry import Registry

# Register a service
Registry().register(MyService, MyService())

# Register with a salt (for multiple instances)
Registry().register(SqlConnector, connector, salt="analytics")

# Retrieve a service
service = Registry().get(MyService)

# Retrieve with salt
connector = Registry().get(SqlConnector, salt="analytics")

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CLI Commands

# Initialize a new project
minix init <project_name>

# Show framework version
minix version

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License

Minix is licensed under the MIT License. See the LICENSE file for more details.

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Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

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Author

AmirHossein Advari - [e-mail]
Shirin Dehghani - [e-mail]
Parsa Mohammadpour - [e-mail]