mapgraph 0.2.1

一个强大的对象控制工具, 覆盖了配置管理、状态管理、上下文管理和生命周期管理。

Mapgraph

mapgraph 提供了强大的对象控制工具, 覆盖了配置管理、状态管理、上下文管理和生命周期管理。

一阴一阳之谓道

功能

• 配置管理
  • 基于类型声明的配置
  • 支持实时变更
• 上下文管理
  • 基于描述器的依赖注入
  • 优雅的上下文切换
• 生命周期管理
  • 服务编排
• 状态管理

安装

pip install mapgraph

Or

pdm add mapgraph

入门指南

上下文管理

from mapgraph.context import InstanceContext

InstanceContext 实例上下文, 顾名思义是管理 对象 的抽象, 它通过两个维度对 对象 进行管理, 即 名义类型 与 值。

• 名义类型 可以理解为 变量名 或者是 变量类型, 总之它在当前实例上下文是唯一的 KEY
• 值 很好理解，即是某个被管理的 对象

from mapgraph.globals import GLOBAL_INSTANCE_CONTEXT

GLOBAL_INSTANCE_CONTEXT 全局实例上下文, 即最顶层的命名空间, 本质也是一个 InstanceContext。

基本使用

from typing import NewType

from mapgraph.globals import GLOBAL_INSTANCE_CONTEXT
from mapgraph.instance_of import get_instance

MYINT = NewType("MYINT", int)

GLOBAL_INSTANCE_CONTEXT.store(123, "test")
# GLOBAL_INSTANCE_CONTEXT.store({int: 123, str: "test"}) 
GLOBAL_INSTANCE_CONTEXT.store({list[int]: [1,2,3]})
GLOBAL_INSTANCE_CONTEXT.store({MYINT: -123})

print(get_instance(int)) # 123
print(get_instance(MYINT)) # 123
print(get_instance(MYINT, is_key=True)) # -123
print(get_instance(str)) # test
print(get_instance(list[int])) # [1,2,3]

• get_instance 可以通过类型获取 InstanceContext 中的值, 当 is_key=True 为 名义类型 匹配, 反之为 值 匹配
• .store 拥有可变位置参数 collection_or_targets 类型为 Mapping[type, Any] | Any
• .store(123, "test") 等价于 .store({int: 123, str: "test"})

因为运行时类型检查器拥有的局限性，所以分别引入了 名义类型 与 值，它们相互补充

多级实例上下文

from mapgraph.context import InstanceContext
from mapgraph.globals import GLOBAL_INSTANCE_CONTEXT
from mapgraph.instance_of import get_instance

loacl_context = InstanceContext()

GLOBAL_INSTANCE_CONTEXT.store(123, {list[int]: [1,2,3]})
loacl_context.store("test", -123)

print(get_instance(int)) # 123
# print(get_instance(str)) # ValueError: <class 'str'>

with loacl_context.scope():
    print(get_instance(str)) # test
    print(get_instance(int)) # -123
    print(get_instance(list[int])) # [1,2,3]

with loacl_context.scope(inherit=False):
    print(get_instance(str)) # test
    print(get_instance(int)) # -123
    print(get_instance(list[int])) # ValueError: <class 'list[int]'>

.scope 拥有关键字参数 inherit, 表示是否继承上一级 InstanceContext

描述器支持

from typing import NewType

from httpx import AsyncClient
from mapgraph.globals import GLOBAL_INSTANCE_CONTEXT
from mapgraph.instance_of import get_instance, InstanceOf, InstanceOfK, InstanceOfV

OPENAI_API_KEY = NewType("OPENAI_API_KEY", str)

class Test:
    client: InstanceOfV[AsyncClient] = InstanceOf(AsyncClient)
    openai_api_key: InstanceOfK[OPENAI_API_KEY] = InstanceOf(OPENAI_API_KEY, is_key=True)

GLOBAL_INSTANCE_CONTEXT.store({OPENAI_API_KEY: "test"}, AsyncClient())

test = Test()

print(test.client) # <httpx.AsyncClient object at 0x7f7f7f7f7f7f>
print(test.openai_api_key) # test

InstanceOf 是一个函数会返两种描述器 InstanceOfV[T] 与 InstanceOfK[T], 很好理解 前者被声明为通过 值 匹配, 后者被声明通过 名义类型 匹配。

描述器读取

from typing import NewType

from httpx import AsyncClient
from mapgraph.context import InstanceContext
from mapgraph.globals import GLOBAL_INSTANCE_CONTEXT
from mapgraph.instance_of import get_instance, InstanceOf, InstanceOfK, InstanceOfV

OPENAI_API_KEY = NewType("OPENAI_API_KEY", str)
BASE_URL = NewType("BASE_URL", str)

loacl_context = InstanceContext()

class Test:
    client: InstanceOfV[AsyncClient] = InstanceOf(AsyncClient)
    openai_api_key: InstanceOfK[OPENAI_API_KEY] = InstanceOf(OPENAI_API_KEY, is_key=True)
    base_url: InstanceOfK[BASE_URL] = InstanceOf(BASE_URL, is_key=True)

GLOBAL_INSTANCE_CONTEXT.store({OPENAI_API_KEY: "sk-proj-xxx", BASE_URL: "https://api.openai.com"}, AsyncClient())
loacl_context.store({BASE_URL: "https://api.xxxmy.com"})

test = Test()

print(test.client) # <httpx.AsyncClient object at 0x7f7f7f7f7f7f>
print(test.openai_api_key) # sk-proj-xxx
print(test.base_url) # https://api.openai.com

with loacl_context.scope():
    print(test.base_url) # https://api.xxxmy.com

描述器写入

from typing import NewType

from httpx import AsyncClient
from mapgraph.context import InstanceContext
from mapgraph.globals import GLOBAL_INSTANCE_CONTEXT
from mapgraph.instance_of import get_instance, InstanceOf, InstanceOfK, InstanceOfV

OPENAI_API_KEY = NewType("OPENAI_API_KEY", str)
BASE_URL = NewType("BASE_URL", str)

loacl_context = InstanceContext()

class Test:
    client: InstanceOfV[AsyncClient] = InstanceOf(AsyncClient)
    base_url: InstanceOfK[BASE_URL] = InstanceOf(BASE_URL, is_key=True)


GLOBAL_INSTANCE_CONTEXT.store(
    {BASE_URL: "https://api.openai.com"}, AsyncClient()
)
loacl_context.store({BASE_URL: "https://api.xxxmy.com"})

test = Test()

print(id(test.client))  # 139921911645712
print(test.base_url)  # https://api.openai.com

with loacl_context.scope():
    print(test.base_url)  # https://api.xxxmy.com
    print(id(test.client))  # 139921911645712
    test.base_url = {BASE_URL: BASE_URL("https://api.yyymy.com")}
    test.client = AsyncClient()
    print(test.base_url)  # https://api.yyymy.com
    print(id(test.client))  # 140043844997072

print(test.base_url)  # https://api.openai.com
print(id(test.client))  # 139921911645712

• 当描述器为 InstanceOfK[T] 时, 设置值需要一个 Mapping[Type[T], T]
• 当描述器为 InstanceOfV[T] 时, 设置值需要为 T

配置管理

.env

OPENAI_API_KEY = "sk-proj-xxx"
BASE_URL = "https://api.openai.com"
MYINT = 123
MYDICT = {"a": 1}
MYLIST = [1, 2, 3]

from typing import NewType

from mapgraph.config import load_env
from mapgraph.instance_of import InstanceOf

OPENAI_API_KEY = NewType("OPENAI_API_KEY", str)
BASE_URL = NewType("BASE_URL", str)
MYINT = NewType("MYINT", int)
MYLIST = NewType("MYLIST", list[int])
MYDICT = NewType("MYDICT", dict[str, int])
MYBOOL = NewType("MYBOOL", bool)

class ConfigData:
    a = InstanceOf(MYINT, is_key=True)
    b = InstanceOf(MYLIST, is_key=True)
    c = InstanceOf(MYDICT, is_key=True)
    d = InstanceOf(MYBOOL, is_key=True)

class Config:
    api_key = InstanceOf(OPENAI_API_KEY, is_key=True)
    base_url = InstanceOf(BASE_URL, is_key=True)
    data = ConfigData()


load_env(
    envs=(OPENAI_API_KEY, BASE_URL, MYINT, MYLIST, MYDICT), 
    envs_kv={MYBOOL: False}, # 设置默认值
    dotenv_path=".env"
)

config = Config()

print(config.api_key) # sk-proj-xxx
print(config.base_url) # https://api.openai.com
print(config.data.a) # 123
print(config.data.b) # [1, 2, 3]
print(config.data.c) # {'a': 1}
print(config.data.d) # False

最佳实践是控制所需的最细的粒度, 当配置较为复杂可以进行嵌套

状态管理

依赖于 statv 文档

生命周期管理

依赖于 launart 文档