MetaGPT源码 （ContextMixin 类）

在本文中，我们将探索 ContextMixin 类，它在多重继承场景中的集成及其在 Python 配置和上下文管理中的应用。此外，我们将通过测试验证其功能，以了解它如何简化模型配置的处理。让我们深入了解代码片段的详细解释。

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理解 ContextMixin

什么是 ContextMixin？

ContextMixin 是一个用于高效管理上下文和配置的 Python 类。继承该类的模型或对象能够：

1. 通过灵活的优先级规则处理上下文（private_context）和配置（private_config）。
2. 管理与 LLM（private_llm）实例的交互。
3. 支持动态设置属性的覆盖机制。

主要组件

• 私有上下文和配置：

  • private_context和 private_config 被设计为内部属性，为每个实例提供灵活的作用域。
  • 这些属性默认值为 None，但可以显式覆盖。

• LLM 管理：

  • 通过 private_llm 集成 LLM，支持从配置动态初始化。

实现细节

以下是核心 ContextMixin 类：

from typing import Optionalfrom pydantic import BaseModel, ConfigDict, Field, model_validatorfrom metagpt.config2 import Config
from metagpt.context import Context
from metagpt.provider.base_llm import BaseLLMclass ContextMixin(BaseModel):"""Mixin class for context and config"""model_config = ConfigDict(arbitrary_types_allowed=True, extra="allow")# Pydantic has bug on _private_attr when using inheritance, so we use private_* instead# - https://github.com/pydantic/pydantic/issues/7142# - https://github.com/pydantic/pydantic/issues/7083# - https://github.com/pydantic/pydantic/issues/7091# Env/Role/Action will use this context as private context, or use self.context as public contextprivate_context: Optional[Context] = Field(default=None, exclude=True)# Env/Role/Action will use this config as private config, or use self.context.config as public configprivate_config: Optional[Config] = Field(default=None, exclude=True)# Env/Role/Action will use this llm as private llm, or use self.context._llm instanceprivate_llm: Optional[BaseLLM] = Field(default=None, exclude=True)@model_validator(mode="after")def validate_context_mixin_extra(self):self._process_context_mixin_extra()return selfdef _process_context_mixin_extra(self):"""Process the extra field"""kwargs = self.model_extra or {}self.set_context(kwargs.pop("context", None))self.set_config(kwargs.pop("config", None))self.set_llm(kwargs.pop("llm", None))def set(self, k, v, override=False):"""Set attribute"""if override or not self.__dict__.get(k):self.__dict__[k] = vdef set_context(self, context: Context, override=True):"""Set context"""self.set("private_context", context, override)def set_config(self, config: Config, override=False):"""Set config"""self.set("private_config", config, override)if config is not None:_ = self.llm  # init llmdef set_llm(self, llm: BaseLLM, override=False):"""Set llm"""self.set("private_llm", llm, override)@propertydef config(self) -> Config:"""Role config: role config > context config"""if self.private_config:return self.private_configreturn [e-mail] config(self, config: Config) -> None:"""Set config"""self.set_config(config)@propertydef context(self) -> Context:"""Role context: role context > context"""if self.private_context:return self.private_contextreturn Context()@context.setterdef context(self, context: Context) -> None:"""Set context"""self.set_context(context)@propertydef llm(self) -> BaseLLM:"""Role llm: if not existed, init from role.config"""# print(f"class:{self.__class__.__name__}({self.name}), llm: {self._llm}, llm_config: {self._llm_config}")if not self.private_llm:self.private_llm = self.context.llm_with_cost_manager_from_llm_config(self.config.llm)return [e-mail] llm(self, llm: BaseLLM) -> None:"""Set llm"""self.private_llm = llm

ContextMixin 通过 Pydantic 进行模型验证和数据管理，在处理任意字段时提供了灵活性。

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测试 ContextMixin

示例：ModelX

为了演示 ContextMixin 的工作原理，我们创建了一个简单的模型 ModelX，继承自 ContextMixin, 验证 ModelX 能正确继承默认属性，同时保留 ContextMixin 的功能。

ContextMixin 可以无缝集成到多重继承的层次结构中,
ModelY 结合了 ContextMixin 和 WTFMixin，继承了两者的字段和功能。

class ModelX(ContextMixin, BaseModel):a: str = "a"b: str = "b"class WTFMixin(BaseModel):c: str = "c"d: str = "d"class ModelY(WTFMixin, ModelX):passdef test_config_mixin_1():new_model = ModelX()assert new_model.a == "a"assert new_model.b == "b"
test_config_mixin_1()

1. 配置优先级

from metagpt.configs.llm_config import LLMConfigmock_llm_config = LLMConfig(llm_type="mock",api_key="mock_api_key",base_url="mock_base_url",app_id="mock_app_id",api_secret="mock_api_secret",domain="mock_domain",
)
mock_llm_config_proxy = LLMConfig(llm_type="mock",api_key="mock_api_key",base_url="mock_base_url",proxy="http://localhost:8080",
)def test_config_mixin_2():i = Config(llm=mock_llm_config)j = Config(llm=mock_llm_config_proxy)obj = ModelX(config=i)assert obj.config == iassert obj.config.llm == mock_llm_configobj.set_config(j)# obj already has a config, so it will not be setassert obj.config == i
test_config_mixin_2()

2. 多继承

def test_config_mixin_3_multi_inheritance_not_override_config():"""Test config mixin with multiple inheritance"""i = Config(llm=mock_llm_config)j = Config(llm=mock_llm_config_proxy)obj = ModelY(config=i)assert obj.config == iassert obj.config.llm == mock_llm_configobj.set_config(j)# obj already has a config, so it will not be setassert obj.config == iassert obj.config.llm == mock_llm_configassert obj.a == "a"assert obj.b == "b"assert obj.c == "c"assert obj.d == "d"print(obj.__dict__.keys())print(obj.__dict__)assert "private_config" in obj.__dict__.keys()test_config_mixin_3_multi_inheritance_not_override_config()

dict_keys(['private_context', 'private_config', 'private_llm', 'a', 'b', 'c', 'd'])
{'private_context': None, 'private_config': Config(extra_fields=None, project_path='', project_name='', inc=False, reqa_file='', max_auto_summarize_code=0, git_reinit=False, llm=LLMConfig(extra_fields=None, api_key='mock_api_key', api_type=<LLMType.OPENAI: 'openai'>, base_url='mock_base_url', api_version=None, model=None, pricing_plan=None, access_key=None, secret_key=None, session_token=None, endpoint=None, app_id='mock_app_id', api_secret='mock_api_secret', domain='mock_domain', max_token=4096, temperature=0.0, top_p=1.0, top_k=0, repetition_penalty=1.0, stop=None, presence_penalty=0.0, frequency_penalty=0.0, best_of=None, n=None, stream=True, seed=None, logprobs=None, top_logprobs=None, timeout=600, context_length=None, region_name=None, proxy=None, calc_usage=True, use_system_prompt=True), embedding=EmbeddingConfig(extra_fields=None, api_type=None, api_key=None, base_url=None, api_version=None, model=None, embed_batch_size=None, dimensions=None), omniparse=OmniParseConfig(extra_fields=None, api_key='', base_url=''), proxy='', search=SearchConfig(extra_fields=None, api_type=<SearchEngineType.DUCK_DUCK_GO: 'ddg'>, api_key='', cse_id='', search_func=None, params={'engine': 'google', 'google_domain': 'google.com', 'gl': 'us', 'hl': 'en'}), browser=BrowserConfig(extra_fields=None, engine=<WebBrowserEngineType.PLAYWRIGHT: 'playwright'>, browser_type='chromium'), mermaid=MermaidConfig(extra_fields=None, engine='nodejs', path='mmdc', puppeteer_config='', pyppeteer_path='/usr/bin/google-chrome-stable'), s3=None, redis=None, repair_llm_output=False, prompt_schema='json', workspace=WorkspaceConfig(extra_fields=None, path=WindowsPath('d:/llm/metagpt/workspace'), use_uid=False, uid=''), enable_longterm_memory=False, code_review_k_times=2, agentops_api_key='', metagpt_tti_url='', language='English', redis_key='placeholder', iflytek_app_id='', iflytek_api_secret='', iflytek_api_key='', azure_tts_subscription_key='', azure_tts_region=''), 'private_llm': <metagpt.provider.openai_api.OpenAILLM object at 0x00000128F0753910>, 'a': 'a', 'b': 'b', 'c': 'c', 'd': 'd'}

3. 多继承重写

mock_llm_config_zhipu = LLMConfig(llm_type="zhipu",api_key="mock_api_key.zhipu",base_url="mock_base_url",model="mock_zhipu_model",proxy="http://localhost:8080",
)def test_config_mixin_4_multi_inheritance_override_config():"""Test config mixin with multiple inheritance"""i = Config(llm=mock_llm_config)j = Config(llm=mock_llm_config_zhipu)obj = ModelY(config=i)assert obj.config == iassert obj.config.llm == mock_llm_configobj.set_config(j, override=True)# override obj.configassert obj.config == jassert obj.config.llm == mock_llm_config_zhipuassert obj.a == "a"assert obj.b == "b"assert obj.c == "c"assert obj.d == "d"print(obj.__dict__.keys())assert "private_config" in obj.__dict__.keys()assert obj.config.llm.model == "mock_zhipu_model"
test_config_mixin_4_multi_inheritance_override_config()

dict_keys(['private_context', 'private_config', 'private_llm', 'a', 'b', 'c', 'd'])

4. 配置优先级

from pathlib import Path
import pytest
from metagpt.actions import Action
from metagpt.config2 import Config
from metagpt.const import CONFIG_ROOT
from metagpt.environment import Environment
from metagpt.roles import Role
from metagpt.team import [e-mail]
async def test_config_priority():"""If action's config is set, then its llm will be set, otherwise, it will use the role's llm"""home_dir = Path.home() / CONFIG_ROOTgpt4t = Config.from_home("gpt-4-turbo.yaml")if not home_dir.exists():assert gpt4t is Nonegpt35 = Config.default()gpt35.llm.model = "gpt35"gpt4 = Config.default()gpt4.llm.model = "gpt-4-0613"a1 = Action(name="Say", instruction="Say your opinion with emotion and don't repeat it", config=gpt4t)a2 = Action(name="Say", instruction="Say your opinion with emotion and don't repeat it")a3 = Action(name="Vote", instruction="Vote for the candidate, and say why you vote for him/her")# it will not work for a1 because the config is already setA = Role(name="A", profile="Democratic candidate", goal="Win the election", actions=[a1], watch=[a2], config=gpt4)# it will work for a2 because the config is not setB = Role(name="B", profile="Republican candidate", goal="Win the election", actions=[a2], watch=[a1], config=gpt4)# dittoC = Role(name="C", profile="Voter", goal="Vote for the candidate", actions=[a3], watch=[a1, a2], config=gpt35)env = Environment(desc="US election live broadcast")Team(investment=10.0, env=env, roles=[A, B, C])assert a1.llm.model == "gpt-4-turbo" if Path(home_dir / "gpt-4-turbo.yaml").exists() else "gpt-4-0613"assert a2.llm.model == "gpt-4-0613"assert a3.llm.model == "gpt35"await test_config_priority()

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