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.

Table of Contents

• Minix
  • Table of Contents
  • Key Features
  • Architecture Overview
  • Installation
    • Requirements
    • Install via pip
    • Install from source (development)
  • Core Concepts
    • Bootstrap
    • Modules
    • Entities
      • SQL Entity
      • Qdrant Entity (Vector DB)
      • Redis Entity
    • Repositories
      • SQL Repository
      • Qdrant Repository
    • Services
    • Controllers
    • Connectors
      • SQL Connector (PostgreSQL/MySQL/ClickHouse)
      • Qdrant Connector
      • Object Storage Connector (S3-compatible)
    • Tasks & Scheduling
      • Async Task
      • Periodic Task
      • Running Tasks Manually
      • Workflows (DAG Scheduling)
        • Creating a Workflow
        • Dependency Result Passing
        • Running the Entire Workflow
        • Running a Specific Target Node
        • Workflow Execution Guarantees
    • Kafka Consumers
    • ML Models
      • Base Model
      • Embedding Model
      • MLflow Model (requires ai extra)
  • RAG Module
  • Configuration
    • Project config
    • Connectors from config
    • Environment overrides
  • Extras
    • SQL drivers
    • AI Capabilities
    • Vector DB (Qdrant)
    • ClickHouse Support
    • Install All Extras
  • Registry Usage
  • CLI Commands
    • Quick map
    • Global options
    • minix init
    • minix add module
      • SQL binding (all or none)
      • Flags
      • Naming rules
      • Module layout
    • Adding to an existing module
      • After scaffolding
  • License
  • Contributing
  • Author

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 PostgreSQL (default), 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
• RAG Module: Pluggable retrieval-augmented generation — collections, document ingestion, chunking, hybrid BM25 + vector retrieval, LLM reranking, and answers with citations
• Dependency Registry: Singleton-based service container for clean dependency injection
• Environment Management: config.py (pydantic-settings) with .env support

Architecture Overview

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

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 .

Core Concepts

Bootstrap

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

# Preferred: connectors and INSTALLED_MODULES come from config.py.
from minix.core.bootstrap import bootstrap_from_settings
bootstrap_from_settings()

# Deprecated (backward compatible): pass explicit lists.
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_binding(ProductEntity, ProductRepository, ProductService)
        self.add_controller(ProductController)
        self.add_periodic_task(SyncProductsTask)
        self.add_consumer(ProductEventConsumer)

Module Methods:

• add_binding(entity, repository, service, connection=None, *, provides_repository=None, provides_service=None) - Register entity + repository + service together (required unit; do not split them)
• add_entity / add_repository / add_service - Deprecated; use add_binding instead
• add_controller(controller, provides=None) - Register an API controller
• add_helper_service(helper) - Register a helper service
• 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

Swapping in a subclass (provides): provides=Base also registers the installed instance under Base, so every Registry().get(Base) lookup — including the framework's own — resolves to your subclass. This is how you extend a built-in module (e.g. the RAG module below) without editing it:

from minix.core.modules.rag.controllers import RagController
from minix.core.modules.rag.services import RagCollectionService

module = (
    BusinessModule("my_rag")
    .add_binding(MyCollectionEntity, MyCollectionRepository, MyCollectionService,
                 provides_service=RagCollectionService)
    .add_controller(MyRagController, provides=RagController)
)

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

Cross-module transactions (one session, one commit):

By default each repository call opens its own session and commits. Wrap related writes in sql_transaction() so every repository on that connector shares one session until the block exits:

from minix.core.connectors import sql_transaction
from minix.core.registry import Registry

with sql_transaction():
    Registry().get(OrderSqlRepository).save(order)
    Registry().get(PaymentSqlRepository).save(payment)
# committed once here; any exception rolls back all work in the block

with sql_transaction("mysql"):  # named DATABASES entry
    ...

Equivalent long form: Registry().get(SqlConnector).transaction(). Nested calls on the same connector use SAVEPOINTs. Custom repository methods that call session.commit() still join the outer unit (commit becomes flush while the transaction is active).

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 (PostgreSQL/MySQL/ClickHouse)

PostgreSQL is the recommended default (DB_DRIVER=postgresql). Install the matching extra (minix[postgresql] and/or minix[mysql]). ClickHouse remains minix[clickhouse].

from minix.core.connectors import SqlConnector

# Reads DATABASES["default"] from settings / env
connector = SqlConnector()
# Secondary engine when both were selected at init:
# connector = SqlConnector(connection="mysql")

# Deprecated — still supported for backward compatibility:
# from minix.core.connectors import SqlConnector, SqlConnectorConfig
# connector = SqlConnector(SqlConnectorConfig(username="minix", driver="postgresql", ...))

Qdrant Connector

from minix.core.connectors import QdrantConnector

# Reads QDRANT_CONNECTIONS[connection] from settings / env
connector = QdrantConnector()
await connector.connect()

Object Storage Connector (S3-compatible)

from minix.core.object_storage import ObjectStorageConnector

# Reads OBJECT_STORAGES[connection] from settings / env
connector = ObjectStorageConnector()

# 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).

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)

RAG Module

minix.core.modules.rag is a ready-made retrieval-augmented generation module: a REST API over named collections of documents, with async ingestion, hybrid retrieval and LLM answers that cite their sources.

What it does

• Ingestion — upload a file (POST /rag/ingest/upload: PDF or images read by a vision model, or text formats) or post raw text (POST /rag/ingest/text). Ingestion runs in the background and returns a job_id to poll.
• Chunking — per-collection strategy: smart, fixed, by_heading, by_clause, qa, sentence or llm (instruction-driven). GET /rag/chunking/strategies returns UI labels for each.
• Metadata extraction — the LLM detects the document type and extracts typed metadata, stored in PostgreSQL and on every vector payload.
• Hybrid retrieval — PostgreSQL full-text search (tsvector) plus Qdrant cosine vectors, fused (weighted or RRF) and filtered by a relevance floor.
• LLM reranking — optional reranker that re-orders the retrieved chunks before the answer prompt (per request, or server default).
• Answers with citations — POST /rag/query (async, poll by query_id), single collection or an explicit multi-collection allowlist. Every response carries the retrieved and selected chunks and a refusal classification (out_of_corpus, out_of_scope, policy_violation, ambiguous). Retrieved text is fenced as untrusted data in the prompt.
• Search without an LLM — POST /rag/search (hybrid / vector / keyword).
• Multi-query match — POST /rag/match blends several weighted queries into one document ranking (synchronous).
• Management — collections, documents, chunks, jobs, GET /rag/health, GET /rag/me.

Install — the module is optional; a plain pip install minix does not pull in any of its dependencies:

pip install "minix[rag]"

At runtime it also needs a PostgreSQL server (keyword search + metadata) and a Qdrant server (vectors); PDF ingestion needs the poppler system package. Importing minix.core.modules.rag without the extra raises an ImportError that tells you the command above.

Enable it with the CLI:

# New project (PostgreSQL is the default)
minix init my_app --rag

# Existing project with PostgreSQL as its default database
minix add rag

--rag enables Qdrant, registers AuthModule and RagModule, adds model settings to the environment templates, and includes the rag extra and Poppler in Docker. --extras rag also enables RAG. RAG is enabled explicitly rather than inferred from installed packages. --rag --no-extras still includes the RAG requirements. MySQL alone is rejected; when both SQL drivers are selected, PostgreSQL is primary.

minix add rag preserves existing settings and secrets, registers missing modules, adds missing keys to .env and .env.example, adds the rag extra to the minix requirement in pyproject.toml (PEP 621 or Poetry) or requirements.txt, and updates the Dockerfile dependencies. It creates docker-compose.rag.yml for Qdrant and the app/worker connections, and sets COMPOSE_FILE in .env so a plain docker compose up --build loads it. Qdrant requires QDRANT_API_KEY (it rejects every request when the key is set but empty): .env gets a random key and .env.example a change-me placeholder.

Repeated minix add rag calls leave the generated files unchanged. Afterwards, reinstall (for example poetry lock && poetry install), install Poppler for local PDF ingestion, set the model provider's API key in .env, and create the SQL tables as described below.

Manual registration — in a project from minix init with PostgreSQL as the default database, add the module to installed_modules in config.py:

installed_modules: list[str] = Field(
    default_factory=lambda: [
        "minix.core.modules.auth.AuthModule",
        "minix.core.modules.rag.RagModule",
    ]
)

bootstrap_from_settings() (already called in entries/api.py) then registers the connectors and installs the module. Create its tables once, or manage the schema with Alembic:

from minix.core.connectors import SqlConnector
from minix.core.entity.sql_entity import Base
from minix.core.registry import Registry

Base.metadata.create_all(Registry().get(SqlConnector).get_engine())

Qdrant: the module uses the default entry of qdrant_connections when there is one, and QDRANT_URL / QDRANT_API_KEY otherwise.

AuthModule provides the API keys the RAG routes accept (X-API-Key). With RAG_ACCESS_CONTROL_ENABLED=false (the default) requests without a key run as an anonymous admin; set it to true to require a key and enforce the key's role (readonly may read and query, user may also ingest and edit, admin may also delete collections).

Configuration (environment variables, read by minix.core.modules.rag.config.RagConfig)

How it fits the framework — it is a regular module: RagModule is a BusinessModule built from one add_binding(entity, repository, service) per table (collections, documents, chunks, jobs, search requests, query requests) plus a controller, exactly like AuthModule / OidcModule, and everything it needs (config, chunking, retrieval, document processors) lives inside minix/core/modules/rag/. It reads the SQL connector from the Registry like any repository, authenticates with the auth module's API keys, and imports nothing that the rest of minix imports back. Long-running work (ingestion, search, query) uses FastAPI background tasks plus persisted job rows, so the module needs no Celery broker or Kafka.

Providers — nothing is tied to one vendor: every model call (answers, reranking, embeddings, metadata extraction, document transcription, LLM chunking) goes through LiteLLM, so changing a model name is all it takes to move to another provider, a gateway or a local server:

LLM_MODEL=anthropic/claude-sonnet-5          # + ANTHROPIC_API_KEY
EMBEDDING_MODEL=ollama/nomic-embed-text         # local, no key
EMBEDDING_DIMENSION=768
RAG_EMBEDDING_SEND_DIMENSIONS=false
DOCUMENT_PROCESSOR_MODEL=anthropic/claude-sonnet-5

Any OpenAI-compatible endpoint (vLLM, LM Studio, a gateway) works with the openai/<model> prefix and OPENAI_API_BASE. Changing the embedding model or its size requires re-indexing: existing vectors were produced by the old one.

Extending it — RagController exposes protected hook methods (request scoping, gating and usage metering, per-collection access policy via _svc_access(), audit, input rewriting, answer generation, output shaping, provider-event and cost recording) and swappable response models (COLLECTION_INFO_MODEL, QUERY_RESPONSE_MODEL, …). All defaults are single-scope no-ops. Subclass the controller and register it with provides=RagController; add columns to the RAG tables with single-table-inheritance subclasses of the entities (no __tablename__), mounted through provides= on the paired repository and service.

Configuration

Minix uses a pydantic-settings config module (config.py). Values come from the environment / .env, with defaults on the Settings class.

Project config

minix init my_project

This creates config.py, .env, .env.example, Dockerfile, docker-compose.yml, and entries/. The name argument sets app_name; optional --*-port flags set Docker / env port defaults. .env includes MINIX_SETTINGS_MODULE=config.

from minix.core.conf import settings
from config import config

print(settings.APP_NAME)
print(settings.DATABASES["default"]["host"])
print(config.app_name)  # same pydantic instance

Override the module with MINIX_SETTINGS_MODULE. With no config module, framework defaults from global_settings are used.

Connectors from config

Edit the Settings class (or the exported DATABASES map) in config.py. When bootstrap_from_settings() is called without connectors=, Minix registers one connector per map key:

# config.py — add another database entry under DATABASES
DATABASES = {
    "default": {...},
    "analytics": {
        "user": "...",
        "password": "...",
        "host": "...",
        "port": 5432,
        "name": "analytics",
        "driver": "postgresql",
    },
}

# entries/api.py
bootstrap_from_settings()

Maps wired automatically: DATABASES, OBJECT_STORAGES, QDRANT_CONNECTIONS, REDIS_CONNECTIONS. Each key becomes Registry salt ("default" → no salt).

Modules are listed in INSTALLED_MODULES as dotted paths::

INSTALLED_MODULES = [
    "minix.core.modules.auth.AuthModule",
    "src.modules.example.ExampleModule",
]

Pass an explicit connectors=[...] / modules=[...] list to take full control (existing apps keep working). Pass connectors=[] / modules=[] to register/install none.

Multiple Celery apps use CELERY_CONNECTIONS; scheduler_config(connection="priority") picks a named entry. Bootstrap still registers the "default" scheduler when modules need tasks.

Missing keys raise ImproperlyConfigured.

Passing an explicit SqlConnectorConfig / URL still works but is deprecated; prefer connection= with settings maps.

Environment overrides

Any key in .env overrides the default in config.py:

DB_HOST=localhost
DB_DATABASE=myapp
CELERY_BROKER_URL=redis://localhost:6379/0
KAFKA_BOOTSTRAP_SERVERS=localhost:9092
MLFLOW_TRACKING_URL=http://localhost:5000

Extras

SQL drivers

pip install "minix[postgresql]"   # recommended default
pip install "minix[mysql]"
pip install "minix[postgresql,mysql]"

AI Capabilities

Install with AI tools (PyTorch, MLflow):

pip install "minix[ai]"

Vector DB (Qdrant)

pip install "minix[vdb]"

ClickHouse Support

pip install "minix[clickhouse]"

RAG Module

Install the RAG module's dependencies, PostgreSQL driver included (see RAG Module):

pip install "minix[rag]"

Install All Extras

pip install "minix[postgresql,mysql,ai,vdb,clickhouse,rag]"

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")

CLI Commands

Install Minix, then use the minix entry point. Prefer these commands over hand-writing project or module boilerplate.

minix --help
minix init --help
minix add module --help

Quick map

Global options

minix -v
minix --version

minix init

Scaffold a project in the current directory.

minix init APP_NAME [OPTIONS]

Interactive prompt (first question, multi-select):

Which SQL database(s) do you want? (comma-separated for multiple)
  1) postgresql  (recommended) [default]
  2) mysql
Examples: 1   |   2   |   1,2

Press Enter for PostgreSQL only. Select 1,2 when you need both. Pass --db-driver postgresql, --db-driver mysql, or --db-driver postgresql,mysql to skip the prompt (also used when stdin is not a TTY).

SQL drivers are optional extras (minix[postgresql], minix[mysql]). Docker only installs the engines you selected.

Creates:

App wiring after init (settings, not legacy app_connectors.py / app_modules.py):

• Connectors: DATABASES, OBJECT_STORAGES, QDRANT_CONNECTIONS, … in config.py
• Modules: INSTALLED_MODULES list of dotted paths
• Boot: bootstrap_from_settings() in entries/api.py

Behavior:

• Fails with exit code 1 if config.py or settings.py already exists.
• Existing optional files (.env, .gitignore, Docker files, entries/) are kept (.env only gains MINIX_SETTINGS_MODULE when missing).
• Port flags bake defaults into Dockerfile, docker-compose.yml, .env, and config.py.
• SQL choice (one or more) selects compose services, pip extras (minix[postgresql] / minix[mysql]), Dockerfile client libs, and DATABASES entries. If both are selected, PostgreSQL is the primary default connection and MySQL is registered as DATABASES["mysql"].
• Optional extras: Non-SQL extras can still be inferred from the current environment (e.g. minix[vdb]). Override with --extras … or --no-extras.
• Version pin: The Dockerfile uses a compatible release on the installed minor (e.g. minix~=0.2.3 → latest 0.2.x ≥ 0.2.3, not 0.3), capped to the newest version published on PyPI so a local unreleased build cannot break docker compose build. Prefer letting minix init manage the pin.

minix init my_app
minix init my_app --db-driver postgresql
minix init my_app --db-driver mysql --db-port 3307
minix init my_app --db-driver postgresql,mysql
minix init my_app --app-port 8001 --redis-port 6380
pip install "minix[vdb]"
minix init my_app --extras vdb
minix init my_app --no-extras

Install SQL drivers in an existing environment with:

pip install "minix[postgresql]"
pip install "minix[mysql]"
pip install "minix[postgresql,mysql]"

minix add module

Scaffold a feature module under src/modules/<name>/.

minix add module MODULE_NAME [OPTIONS]

Default stack: entity + repository + service + controller. The SQL trio is wired with a single add_binding(...) in module.py. Tasks / helpers / consumers / periodic tasks are opt-in.

SQL binding (all or none)

Entity, repository, and service are one unit. The CLI never generates a partial binding:

• Default: all three are created.
• --no-binding: skip all three.
• --entity= / --repository= / --service= (or -e / -r / -s) only rename; missing pieces are still filled so the binding stays complete.
• Combining --no-binding with any of those flags is an error.
• There are no --no-entity / --no-repository / --no-service flags.

Flags

Naming rules

• Long flags accept an optional value with =: --controller=ShopController, --task=ShipOrder.
• Short flags (-c, -t, …) are presence-only — do not pass a separate value after them.
• Safe: minix add module orders -t --no-register
• Avoid: minix add module orders -t ShipOrder (use --task=ShipOrder)

# Default CRUD/API stack + auto-register in config.py
minix add module orders

# Custom class names (binding still complete)
minix add module orders --entity=ShopOrder --controller=ShopController

# Background task add-on
minix add module orders -t
minix add module orders --task=FulfillOrder -H

# Everything
minix add module orders -a

# Worker-oriented: no HTTP controller
minix add module orders --no-controller -t

# No SQL binding (e.g. helper/consumer-only style module)
minix add module orders --no-binding -H --consumer=OrderEventsConsumer

Module layout

src/modules/<name>/
  __init__.py          # exports <Name>Module
  module.py            # BusinessModule + add_binding / add_*
  entities/            # present when binding is on
  repositories/
  services/
  controllers/         # optional
  tasks/               # optional (Task + PeriodicTask)
  consumers/           # optional

After scaffolding

1. Confirm INSTALLED_MODULES contains e.g. "src.modules.orders.OrdersModule".
2. Implement fields on the entity; add an Alembic migration if the app uses one.
3. Expand service / controller as needed.
4. Restart API / worker.

from src.modules.orders import OrdersModule

Cross-module links: put the FK on the owning entity and resolve the other side with Registry().get(OtherService) in services (do not merge unrelated entities into one module only because they relate).

Adding to an existing module

To add a piece you skipped at creation time (for example, after --no-controller), use minix add <component> [NAME] --module=MODULE. The CLI writes the file, exports it from the subpackage __init__.py, and appends the matching .add_*(...) call to the BusinessModule chain in module.py. Existing code is not rewritten. Running the command again does nothing, because the file is kept and the call is already wired.

--module/-m (required) names the target module. The optional NAME argument sets a custom class name (binding takes --entity, --repository, --service instead). --path DIR points to a non-default package directory, and --force/-f overwrites the component file. A controller added to a module that already has a binding uses that binding's service.

minix add module orders --no-controller
minix add controller --module=orders             # OrderController
minix add controller OrderAdmin --module=orders  # OrderAdminController
minix add task ShipOrder -m orders               # ShipOrderTask
minix add binding -m payments --entity=Payment

License

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

Contributing

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

Author

AmirHossein Advari - amiradvari@gmail.com
Shirin Dehghani - shirin.dehghani1996@gmail.com
Parsa Mohammadpour - parsa.mohammadpour01@gmail.com Emad Sudani - sudani.emad@gmail.com