remove global config

examples/agent_creator.py

@@ -6,12 +6,13 @@ Author: garylin2099
 import re
 
 from metagpt.actions import Action
-from metagpt.config2 import config
+from metagpt.config2 import Config
 from metagpt.const import METAGPT_ROOT
 from metagpt.logs import logger
 from metagpt.roles import Role
 from metagpt.schema import Message
 
+config = Config.default()
 EXAMPLE_CODE_FILE = METAGPT_ROOT / "examples/build_customized_agent.py"
 MULTI_ACTION_AGENT_CODE_EXAMPLE = EXAMPLE_CODE_FILE.read_text()
 

metagpt/actions/rebuild_class_view.py

@@ -14,7 +14,6 @@ from typing import Optional, Set, Tuple
 import aiofiles
 
 from metagpt.actions import Action
-from metagpt.config2 import config
 from metagpt.const import (
     AGGREGATION,
     COMPOSITION,
@@ -40,7 +39,7 @@ class RebuildClassView(Action):
 
     graph_db: Optional[GraphRepository] = None
 
-    async def run(self, with_messages=None, format=config.prompt_schema):
+    async def run(self, with_messages=None, format=None):
         """
         Implementation of `Action`'s `run` method.
 
@@ -48,6 +47,7 @@ class RebuildClassView(Action):
             with_messages (Optional[Type]): An optional argument specifying messages to react to.
             format (str): The format for the prompt schema.
         """
+        format = format if format else self.config.prompt_schema
         graph_repo_pathname = self.context.git_repo.workdir / GRAPH_REPO_FILE_REPO / self.context.git_repo.workdir.name
         self.graph_db = await DiGraphRepository.load_from(str(graph_repo_pathname.with_suffix(".json")))
         repo_parser = RepoParser(base_directory=Path(self.i_context))

metagpt/actions/rebuild_sequence_view.py

@@ -18,7 +18,6 @@ from pydantic import BaseModel
 from tenacity import retry, stop_after_attempt, wait_random_exponential
 
 from metagpt.actions import Action
-from metagpt.config2 import config
 from metagpt.const import GRAPH_REPO_FILE_REPO
 from metagpt.logs import logger
 from metagpt.repo_parser import CodeBlockInfo, DotClassInfo
@@ -84,7 +83,7 @@ class RebuildSequenceView(Action):
 
     graph_db: Optional[GraphRepository] = None
 
-    async def run(self, with_messages=None, format=config.prompt_schema):
+    async def run(self, with_messages=None, format=None):
         """
         Implementation of `Action`'s `run` method.
 
@@ -92,6 +91,7 @@ class RebuildSequenceView(Action):
             with_messages (Optional[Type]): An optional argument specifying messages to react to.
             format (str): The format for the prompt schema.
         """
+        format = format if format else self.config.prompt_schema
         graph_repo_pathname = self.context.git_repo.workdir / GRAPH_REPO_FILE_REPO / self.context.git_repo.workdir.name
         self.graph_db = await DiGraphRepository.load_from(str(graph_repo_pathname.with_suffix(".json")))
         if not self.i_context:

metagpt/actions/research.py

@@ -8,7 +8,6 @@ from typing import Any, Callable, Coroutine, Optional, Union
 from pydantic import TypeAdapter, model_validator
 
 from metagpt.actions import Action
-from metagpt.config2 import config
 from metagpt.logs import logger
 from metagpt.tools.search_engine import SearchEngine
 from metagpt.tools.web_browser_engine import WebBrowserEngine
@@ -134,8 +133,8 @@ class CollectLinks(Action):
                 if len(remove) == 0:
                     break
 
-        model_name = config.llm.model
-        prompt = reduce_message_length(gen_msg(), model_name, system_text, config.llm.max_token)
+        model_name = self.config.llm.model
+        prompt = reduce_message_length(gen_msg(), model_name, system_text, self.config.llm.max_token)
         logger.debug(prompt)
         queries = await self._aask(prompt, [system_text])
         try:

metagpt/actions/talk_action.py

@@ -9,7 +9,6 @@
 from typing import Optional
 
 from metagpt.actions import Action
-from metagpt.config2 import config
 from metagpt.logs import logger
 from metagpt.schema import Message
 
@@ -26,7 +25,7 @@ class TalkAction(Action):
 
     @property
     def language(self):
-        return self.context.kwargs.language or config.language
+        return self.context.kwargs.language or self.config.language
 
     @property
     def prompt(self):

metagpt/config2.py

@@ -97,20 +97,21 @@ class Config(CLIParams, YamlModel):
         return Config.from_yaml_file(pathname)
 
     @classmethod
-    def default(cls):
+    def default(cls, reload: bool = False):
         """Load default config
         - Priority: env < default_config_paths
         - Inside default_config_paths, the latter one overwrites the former one
         """
-        default_config_paths: List[Path] = [
+        default_config_paths = (
             METAGPT_ROOT / "config/config2.yaml",
             CONFIG_ROOT / "config2.yaml",
-        ]
-
-        dicts = [dict(os.environ)]
-        dicts += [Config.read_yaml(path) for path in default_config_paths]
-        final = merge_dict(dicts)
-        return Config(**final)
+        )
+        if reload or default_config_paths not in _CONFIG_CACHE:
+            dicts = [dict(os.environ)]
+            dicts += [Config.read_yaml(path) for path in default_config_paths]
+            final = merge_dict(dicts)
+            _CONFIG_CACHE[default_config_paths] = Config(**final)
+        return _CONFIG_CACHE[default_config_paths]
 
     @classmethod
     def from_llm_config(cls, llm_config: dict):
@@ -160,4 +161,4 @@ def merge_dict(dicts: Iterable[Dict]) -> Dict:
     return result
 
 
-config = Config.default()
+_CONFIG_CACHE = {}

metagpt/configs/browser_config.py

@@ -5,12 +5,23 @@
 @Author  : alexanderwu
 @File    : browser_config.py
 """
+from enum import Enum
 from typing import Literal
 
-from metagpt.tools import WebBrowserEngineType
 from metagpt.utils.yaml_model import YamlModel
 
 
+class WebBrowserEngineType(Enum):
+    PLAYWRIGHT = "playwright"
+    SELENIUM = "selenium"
+    CUSTOM = "custom"
+
+    @classmethod
+    def __missing__(cls, key):
+        """Default type conversion"""
+        return cls.CUSTOM
+
+
 class BrowserConfig(YamlModel):
     """Config for Browser"""
 

metagpt/configs/search_config.py

@@ -5,14 +5,23 @@
 @Author  : alexanderwu
 @File    : search_config.py
 """
+from enum import Enum
 from typing import Callable, Optional
 
 from pydantic import ConfigDict, Field
 
-from metagpt.tools import SearchEngineType
 from metagpt.utils.yaml_model import YamlModel
 
 
+class SearchEngineType(Enum):
+    SERPAPI_GOOGLE = "serpapi"
+    SERPER_GOOGLE = "serper"
+    DIRECT_GOOGLE = "google"
+    DUCK_DUCK_GO = "ddg"
+    CUSTOM_ENGINE = "custom"
+    BING = "bing"
+
+
 class SearchConfig(YamlModel):
     """Config for Search"""
 

metagpt/context.py

@@ -10,7 +10,7 @@ from __future__ import annotations
 import os
 from typing import Any, Dict, Optional
 
-from pydantic import BaseModel, ConfigDict
+from pydantic import BaseModel, ConfigDict, Field
 
 from metagpt.config2 import Config
 from metagpt.configs.llm_config import LLMConfig, LLMType
@@ -61,7 +61,7 @@ class Context(BaseModel):
     model_config = ConfigDict(arbitrary_types_allowed=True)
 
     kwargs: AttrDict = AttrDict()
-    config: Config = Config.default()
+    config: Config = Field(default_factory=Config.default)
 
     cost_manager: CostManager = CostManager()
 

metagpt/environment/minecraft/minecraft_env.py

@@ -11,7 +11,7 @@ from typing import Any, Iterable
 from llama_index.vector_stores.chroma import ChromaVectorStore
 from pydantic import ConfigDict, Field
 
-from metagpt.config2 import config as CONFIG
+from metagpt.config2 import Config
 from metagpt.environment.base_env import Environment
 from metagpt.environment.minecraft.const import MC_CKPT_DIR
 from metagpt.environment.minecraft.minecraft_ext_env import MinecraftExtEnv
@@ -82,7 +82,7 @@ class MinecraftEnv(Environment, MinecraftExtEnv):
             persist_dir=f"{MC_CKPT_DIR}/skill/vectordb",
         )
 
-        if CONFIG.resume:
+        if Config.default().resume:
             logger.info(f"Loading Action Developer from {MC_CKPT_DIR}/action")
             self.chest_memory = read_json_file(f"{MC_CKPT_DIR}/action/chest_memory.json")
 

metagpt/exp_pool/decorator.py

@@ -6,7 +6,7 @@ from typing import Any, Callable, Optional, TypeVar
 
 from pydantic import BaseModel, ConfigDict, model_validator
 
-from metagpt.config2 import config
+from metagpt.config2 import Config
 from metagpt.exp_pool.context_builders import BaseContextBuilder, SimpleContextBuilder
 from metagpt.exp_pool.manager import ExperienceManager, exp_manager
 from metagpt.exp_pool.perfect_judges import BasePerfectJudge, SimplePerfectJudge
@@ -50,11 +50,14 @@ def exp_cache(
     """
 
     def decorator(func: Callable[..., ReturnType]) -> Callable[..., ReturnType]:
-        if not config.exp_pool.enabled:
-            return func
-
         @functools.wraps(func)
         async def get_or_create(args: Any, kwargs: Any) -> ReturnType:
+            config = Config.default()
+
+            if not config.exp_pool.enabled:
+                rsp = func(*args, **kwargs)
+                return await rsp if asyncio.iscoroutine(rsp) else rsp
+
             handler = ExpCacheHandler(
                 func=func,
                 args=args,

metagpt/exp_pool/manager.py

@@ -2,9 +2,9 @@
 
 from typing import TYPE_CHECKING, Any
 
-from pydantic import BaseModel, ConfigDict
+from pydantic import BaseModel, ConfigDict, Field
 
-from metagpt.config2 import Config, config
+from metagpt.config2 import Config
 from metagpt.exp_pool.schema import (
     DEFAULT_COLLECTION_NAME,
     DEFAULT_SIMILARITY_TOP_K,
@@ -29,7 +29,7 @@ class ExperienceManager(BaseModel):
 
     model_config = ConfigDict(arbitrary_types_allowed=True)
 
-    config: Config = config
+    config: Config = Field(default_factory=Config.default)
 
     _storage: Any = None
     _vector_store: Any = None

metagpt/ext/stanford_town/actions/st_action.py

@@ -8,7 +8,6 @@ from pathlib import Path
 from typing import Any, Optional, Union
 
 from metagpt.actions.action import Action
-from metagpt.config2 import config
 from metagpt.ext.stanford_town.utils.const import PROMPTS_DIR
 from metagpt.logs import logger
 
@@ -62,13 +61,13 @@ class STAction(Action):
     async def _run_gpt35_max_tokens(self, prompt: str, max_tokens: int = 50, retry: int = 3):
         for idx in range(retry):
             try:
-                tmp_max_tokens_rsp = getattr(config.llm, "max_token", 1500)
-                setattr(config.llm, "max_token", max_tokens)
+                tmp_max_tokens_rsp = getattr(self.config.llm, "max_token", 1500)
+                setattr(self.config.llm, "max_token", max_tokens)
                 self.llm.use_system_prompt = False  # to make it behave like a non-chat completions
 
                 llm_resp = await self._aask(prompt)
 
-                setattr(config.llm, "max_token", tmp_max_tokens_rsp)
+                setattr(self.config.llm, "max_token", tmp_max_tokens_rsp)
                 logger.info(f"Action: {self.cls_name} llm _run_gpt35_max_tokens raw resp: {llm_resp}")
                 if self._func_validate(llm_resp, prompt):
                     return self._func_cleanup(llm_resp, prompt)

metagpt/ext/stanford_town/utils/utils.py

@@ -13,7 +13,7 @@ from typing import Union
 
 from openai import OpenAI
 
-from metagpt.config2 import config
+from metagpt.config2 import Config
 from metagpt.logs import logger
 
 
@@ -48,6 +48,7 @@ def read_csv_to_list(curr_file: str, header=False, strip_trail=True):
 
 
 def get_embedding(text, model: str = "text-embedding-ada-002"):
+    config = Config.default()
     text = text.replace("\n", " ")
     if not text:
         text = "this is blank"

metagpt/learn/text_to_embedding.py

@@ -6,12 +6,13 @@
 @File    : text_to_embedding.py
 @Desc    : Text-to-Embedding skill, which provides text-to-embedding functionality.
 """
-import metagpt.config2
+from typing import Optional
+
 from metagpt.config2 import Config
 from metagpt.tools.openai_text_to_embedding import oas3_openai_text_to_embedding
 
 
-async def text_to_embedding(text, model="text-embedding-ada-002", config: Config = metagpt.config2.config):
+async def text_to_embedding(text, model="text-embedding-ada-002", config: Optional[Config] = None):
     """Text to embedding
 
     :param text: The text used for embedding.
@@ -19,6 +20,7 @@ async def text_to_embedding(text, model="text-embedding-ada-002", config: Config
     :param config: OpenAI config with API key, For more details, checkout: `https://platform.openai.com/account/api-keys`
     :return: A json object of :class:`ResultEmbedding` class if successful, otherwise `{}`.
     """
+    config = config if config else Config.default()
     openai_api_key = config.get_openai_llm().api_key
     proxy = config.get_openai_llm().proxy
     return await oas3_openai_text_to_embedding(text, model=model, openai_api_key=openai_api_key, proxy=proxy)

metagpt/learn/text_to_image.py

@@ -7,8 +7,8 @@
 @Desc    : Text-to-Image skill, which provides text-to-image functionality.
 """
 import base64
+from typing import Optional
 
-import metagpt.config2
 from metagpt.config2 import Config
 from metagpt.const import BASE64_FORMAT
 from metagpt.llm import LLM
@@ -17,7 +17,7 @@ from metagpt.tools.openai_text_to_image import oas3_openai_text_to_image
 from metagpt.utils.s3 import S3
 
 
-async def text_to_image(text, size_type: str = "512x512", config: Config = metagpt.config2.config):
+async def text_to_image(text, size_type: str = "512x512", config: Optional[Config] = None):
     """Text to image
 
     :param text: The text used for image conversion.
@@ -25,6 +25,7 @@ async def text_to_image(text, size_type: str = "512x512", config: Config = metag
     :param config: Config
     :return: The image data is returned in Base64 encoding.
     """
+    config = config if config else Config.default()
     image_declaration = "data:image/png;base64,"
 
     model_url = config.metagpt_tti_url

metagpt/learn/text_to_speech.py

@@ -6,7 +6,8 @@
 @File    : text_to_speech.py
 @Desc    : Text-to-Speech skill, which provides text-to-speech functionality
 """
-import metagpt.config2
+from typing import Optional
+
 from metagpt.config2 import Config
 from metagpt.const import BASE64_FORMAT
 from metagpt.tools.azure_tts import oas3_azsure_tts
@@ -20,7 +21,7 @@ async def text_to_speech(
     voice="zh-CN-XiaomoNeural",
     style="affectionate",
     role="Girl",
-    config: Config = metagpt.config2.config,
+    config: Optional[Config] = None,
 ):
     """Text to speech
     For more details, check out:`https://learn.microsoft.com/en-us/azure/ai-services/speech-service/language-support?tabs=tts`
@@ -38,7 +39,7 @@ async def text_to_speech(
     :return: Returns the Base64-encoded .wav/.mp3 file data if successful, otherwise an empty string.
 
     """
-
+    config = config if config else Config.default()
     subscription_key = config.azure_tts_subscription_key
     region = config.azure_tts_region
     if subscription_key and region:

metagpt/memory/brain_memory.py

@@ -12,9 +12,9 @@ import json
 import re
 from typing import Dict, List, Optional
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, field_validator
 
-from metagpt.config2 import config
+from metagpt.config2 import Config as _Config
 from metagpt.const import DEFAULT_MAX_TOKENS, DEFAULT_TOKEN_SIZE
 from metagpt.logs import logger
 from metagpt.provider import MetaGPTLLM
@@ -32,6 +32,12 @@ class BrainMemory(BaseModel):
     last_talk: Optional[str] = None
     cacheable: bool = True
     llm: Optional[BaseLLM] = Field(default=None, exclude=True)
+    config: Optional[_Config] = None
+
+    @field_validator("config")
+    @classmethod
+    def set_default_config(cls, config):
+        return config if config else _Config.default()
 
     class Config:
         arbitrary_types_allowed = True
@@ -54,9 +60,8 @@ class BrainMemory(BaseModel):
         texts = [m.content for m in self.knowledge]
         return "\n".join(texts)
 
-    @staticmethod
-    async def loads(redis_key: str) -> "BrainMemory":
-        redis = Redis(config.redis)
+    async def loads(self, redis_key: str) -> "BrainMemory":
+        redis = Redis(self.config.redis)
         if not redis_key:
             return BrainMemory()
         v = await redis.get(key=redis_key)
@@ -70,7 +75,7 @@ class BrainMemory(BaseModel):
     async def dumps(self, redis_key: str, timeout_sec: int = 30 * 60):
         if not self.is_dirty:
             return
-        redis = Redis(config.redis)
+        redis = Redis(self.config.redis)
         if not redis_key:
             return False
         v = self.model_dump_json()
@@ -140,7 +145,7 @@ class BrainMemory(BaseModel):
             return text
         summary = await self._summarize(text=text, max_words=max_words, keep_language=keep_language, limit=limit)
         if summary:
-            await self.set_history_summary(history_summary=summary, redis_key=config.redis_key)
+            await self.set_history_summary(history_summary=summary, redis_key=self.config.redis_key)
             return summary
         raise ValueError(f"text too long:{text_length}")
 
@@ -164,7 +169,7 @@ class BrainMemory(BaseModel):
         msgs.reverse()
         self.history = msgs
         self.is_dirty = True
-        await self.dumps(redis_key=config.redis.key)
+        await self.dumps(redis_key=self.config.redis.key)
         self.is_dirty = False
 
         return BrainMemory.to_metagpt_history_format(self.history)
@@ -181,7 +186,7 @@ class BrainMemory(BaseModel):
 
         summary = await self.summarize(llm=llm, max_words=500)
 
-        language = config.language
+        language = self.config.language
         command = f"Translate the above summary into a {language} title of less than {max_words} words."
         summaries = [summary, command]
         msg = "\n".join(summaries)

metagpt/rag/factories/embedding.py

@@ -1,7 +1,7 @@
 """RAG Embedding Factory."""
 from __future__ import annotations
 
-from typing import Any
+from typing import Any, Optional
 
 from llama_index.core.embeddings import BaseEmbedding
 from llama_index.embeddings.azure_openai import AzureOpenAIEmbedding
@@ -9,7 +9,7 @@ from llama_index.embeddings.gemini import GeminiEmbedding
 from llama_index.embeddings.ollama import OllamaEmbedding
 from llama_index.embeddings.openai import OpenAIEmbedding
 
-from metagpt.config2 import config
+from metagpt.config2 import Config
 from metagpt.configs.embedding_config import EmbeddingType
 from metagpt.configs.llm_config import LLMType
 from metagpt.rag.factories.base import GenericFactory
@@ -18,7 +18,7 @@ from metagpt.rag.factories.base import GenericFactory
 class RAGEmbeddingFactory(GenericFactory):
     """Create LlamaIndex Embedding with MetaGPT's embedding config."""
 
-    def __init__(self):
+    def __init__(self, config: Optional[Config] = None):
         creators = {
             EmbeddingType.OPENAI: self._create_openai,
             EmbeddingType.AZURE: self._create_azure,
@@ -29,6 +29,7 @@ class RAGEmbeddingFactory(GenericFactory):
             LLMType.AZURE: self._create_azure,
         }
         super().__init__(creators)
+        self.config = config if self.config else Config.default()
 
     def get_rag_embedding(self, key: EmbeddingType = None) -> BaseEmbedding:
         """Key is EmbeddingType."""
@@ -40,18 +41,18 @@ class RAGEmbeddingFactory(GenericFactory):
         If the embedding type is not specified, for backward compatibility, it checks if the LLM API type is either OPENAI or AZURE.
         Raise TypeError if embedding type not found.
         """
-        if config.embedding.api_type:
-            return config.embedding.api_type
+        if self.config.embedding.api_type:
+            return self.config.embedding.api_type
 
-        if config.llm.api_type in [LLMType.OPENAI, LLMType.AZURE]:
-            return config.llm.api_type
+        if self.config.llm.api_type in [LLMType.OPENAI, LLMType.AZURE]:
+            return self.config.llm.api_type
 
         raise TypeError("To use RAG, please set your embedding in config2.yaml.")
 
     def _create_openai(self) -> OpenAIEmbedding:
         params = dict(
-            api_key=config.embedding.api_key or config.llm.api_key,
-            api_base=config.embedding.base_url or config.llm.base_url,
+            api_key=self.config.embedding.api_key or self.config.llm.api_key,
+            api_base=self.config.embedding.base_url or self.config.llm.base_url,
         )
 
         self._try_set_model_and_batch_size(params)
@@ -60,9 +61,9 @@ class RAGEmbeddingFactory(GenericFactory):
 
     def _create_azure(self) -> AzureOpenAIEmbedding:
         params = dict(
-            api_key=config.embedding.api_key or config.llm.api_key,
-            azure_endpoint=config.embedding.base_url or config.llm.base_url,
-            api_version=config.embedding.api_version or config.llm.api_version,
+            api_key=self.config.embedding.api_key or self.config.llm.api_key,
+            azure_endpoint=self.config.embedding.base_url or self.config.llm.base_url,
+            api_version=self.config.embedding.api_version or self.config.llm.api_version,
         )
 
         self._try_set_model_and_batch_size(params)
@@ -71,8 +72,8 @@ class RAGEmbeddingFactory(GenericFactory):
 
     def _create_gemini(self) -> GeminiEmbedding:
         params = dict(
-            api_key=config.embedding.api_key,
-            api_base=config.embedding.base_url,
+            api_key=self.config.embedding.api_key,
+            api_base=self.config.embedding.base_url,
         )
 
         self._try_set_model_and_batch_size(params)
@@ -81,7 +82,7 @@ class RAGEmbeddingFactory(GenericFactory):
 
     def _create_ollama(self) -> OllamaEmbedding:
         params = dict(
-            base_url=config.embedding.base_url,
+            base_url=self.config.embedding.base_url,
         )
 
         self._try_set_model_and_batch_size(params)
@@ -90,14 +91,15 @@ class RAGEmbeddingFactory(GenericFactory):
 
     def _try_set_model_and_batch_size(self, params: dict):
         """Set the model_name and embed_batch_size only when they are specified."""
-        if config.embedding.model:
-            params["model_name"] = config.embedding.model
+        if self.config.embedding.model:
+            params["model_name"] = self.config.embedding.model
 
-        if config.embedding.embed_batch_size:
-            params["embed_batch_size"] = config.embedding.embed_batch_size
+        if self.config.embedding.embed_batch_size:
+            params["embed_batch_size"] = self.config.embedding.embed_batch_size
 
     def _raise_for_key(self, key: Any):
         raise ValueError(f"The embedding type is currently not supported: `{type(key)}`, {key}")
 
 
-get_rag_embedding = RAGEmbeddingFactory().get_rag_embedding
+def get_rag_embedding(key: EmbeddingType = None, config: Optional[Config] = None):
+    return RAGEmbeddingFactory(config=config).get_rag_embedding(key)

metagpt/rag/factories/llm.py

@@ -10,9 +10,9 @@ from llama_index.core.llms import (
     LLMMetadata,
 )
 from llama_index.core.llms.callbacks import llm_completion_callback
-from pydantic import Field
+from pydantic import Field, model_validator
 
-from metagpt.config2 import config
+from metagpt.config2 import Config
 from metagpt.llm import LLM
 from metagpt.provider.base_llm import BaseLLM
 from metagpt.utils.async_helper import NestAsyncio
@@ -26,9 +26,23 @@ class RAGLLM(CustomLLM):
     """
 
     model_infer: BaseLLM = Field(..., description="The MetaGPT's LLM.")
-    context_window: int = TOKEN_MAX.get(config.llm.model, DEFAULT_CONTEXT_WINDOW)
-    num_output: int = config.llm.max_token
-    model_name: str = config.llm.model
+    context_window: int = -1
+    num_output: int = -1
+    model_name: str = ""
+
+    @model_validator(mode="after")
+    def update_from_config(self):
+        config = Config.default()
+        if self.context_window < 0:
+            self.context_window = TOKEN_MAX.get(config.llm.model, DEFAULT_CONTEXT_WINDOW)
+
+        if self.num_output < 0:
+            self.num_output = config.llm.max_token
+
+        if not self.model_name:
+            self.model_name = config.llm.model
+
+        return self
 
     @property
     def metadata(self) -> LLMMetadata:

metagpt/rag/schema.py

@@ -10,7 +10,7 @@ from llama_index.core.schema import TextNode
 from llama_index.core.vector_stores.types import VectorStoreQueryMode
 from pydantic import BaseModel, ConfigDict, Field, PrivateAttr, model_validator
 
-from metagpt.config2 import config
+from metagpt.config2 import Config
 from metagpt.configs.embedding_config import EmbeddingType
 from metagpt.logs import logger
 from metagpt.rag.interface import RAGObject
@@ -45,6 +45,7 @@ class FAISSRetrieverConfig(IndexRetrieverConfig):
     @model_validator(mode="after")
     def check_dimensions(self):
         if self.dimensions == 0:
+            config = Config.default()
             self.dimensions = config.embedding.dimensions or self._embedding_type_to_dimensions.get(
                 config.embedding.api_type, 1536
             )

metagpt/software_company.py

@@ -27,7 +27,7 @@ def generate_repo(
     recover_path=None,
 ):
     """Run the startup logic. Can be called from CLI or other Python scripts."""
-    from metagpt.config2 import config
+    from metagpt.config2 import Config
     from metagpt.context import Context
     from metagpt.roles import (
         Architect,
@@ -38,6 +38,8 @@ def generate_repo(
     )
     from metagpt.team import Team
 
+    config = Config.default()
+
     config.update_via_cli(project_path, project_name, inc, reqa_file, max_auto_summarize_code)
     ctx = Context(config=config)
 

metagpt/tools/__init__.py

@@ -6,33 +6,18 @@
 @File    : __init__.py
 """
 
-from enum import Enum
 from metagpt.tools import libs  # this registers all tools
 from metagpt.tools.tool_registry import TOOL_REGISTRY
+from metagpt.configs.search_config import SearchEngineType
+from metagpt.configs.browser_config import WebBrowserEngineType
+
 
 _ = libs, TOOL_REGISTRY  # Avoid pre-commit error
 
 
-class SearchEngineType(Enum):
-    SERPAPI_GOOGLE = "serpapi"
-    SERPER_GOOGLE = "serper"
-    DIRECT_GOOGLE = "google"
-    DUCK_DUCK_GO = "ddg"
-    CUSTOM_ENGINE = "custom"
-    BING = "bing"
-
-
-class WebBrowserEngineType(Enum):
-    PLAYWRIGHT = "playwright"
-    SELENIUM = "selenium"
-    CUSTOM = "custom"
-
-    @classmethod
-    def __missing__(cls, key):
-        """Default type conversion"""
-        return cls.CUSTOM
-
-
 class SearchInterface:
     async def asearch(self, *args, **kwargs):
         ...
+
+
+__all__ = ["SearchEngineType", "WebBrowserEngineType", "TOOL_REGISTRY"]

metagpt/tools/libs/gpt_v_generator.py

@@ -7,7 +7,9 @@
 """
 import re
 from pathlib import Path
+from typing import Optional
 
+from metagpt.config2 import Config
 from metagpt.const import DEFAULT_WORKSPACE_ROOT
 from metagpt.logs import logger
 from metagpt.tools.tool_registry import register_tool
@@ -36,11 +38,11 @@ class GPTvGenerator:
     It utilizes a vision model to analyze the layout from an image and generate webpage codes accordingly.
     """
 
-    def __init__(self):
+    def __init__(self, config: Optional[Config]):
         """Initialize GPTvGenerator class with default values from the configuration."""
-        from metagpt.config2 import config
         from metagpt.llm import LLM
 
+        config = config if config else Config.default()
         self.llm = LLM(llm_config=config.get_openai_llm())
         self.llm.model = "gpt-4-vision-preview"
 

metagpt/tools/ut_writer.py

@@ -4,7 +4,7 @@
 import json
 from pathlib import Path
 
-from metagpt.config2 import config
+from metagpt.config2 import Config
 from metagpt.provider.openai_api import OpenAILLM as GPTAPI
 from metagpt.utils.common import awrite
 
@@ -282,6 +282,7 @@ class UTGenerator:
         """Choose based on different calling methods"""
         result = ""
         if self.chatgpt_method == "API":
+            config = Config.default()
             result = await GPTAPI(config.get_openai_llm()).aask_code(messages=messages)
 
         return result

metagpt/utils/embedding.py

@@ -7,10 +7,11 @@
 """
 from llama_index.embeddings.openai import OpenAIEmbedding
 
-from metagpt.config2 import config
+from metagpt.config2 import Config
 
 
 def get_embedding() -> OpenAIEmbedding:
+    config = Config.default()
     llm = config.get_openai_llm()
     if llm is None:
         raise ValueError("To use OpenAIEmbedding, please ensure that config.llm.api_type is correctly set to 'openai'.")

metagpt/utils/make_sk_kernel.py

@@ -13,10 +13,11 @@ from semantic_kernel.connectors.ai.open_ai.services.open_ai_chat_completion impo
     OpenAIChatCompletion,
 )
 
-from metagpt.config2 import config
+from metagpt.config2 import Config
 
 
 def make_sk_kernel():
+    config = Config.default()
     kernel = sk.Kernel()
     if llm := config.get_azure_llm():
         kernel.add_chat_service(

metagpt/utils/mermaid.py

@@ -9,12 +9,14 @@ import asyncio
 import os
 from pathlib import Path
 
-from metagpt.config2 import config
+from metagpt.config2 import Config
 from metagpt.logs import logger
 from metagpt.utils.common import awrite, check_cmd_exists
 
 
-async def mermaid_to_file(engine, mermaid_code, output_file_without_suffix, width=2048, height=2048) -> int:
+async def mermaid_to_file(
+    engine, mermaid_code, output_file_without_suffix, width=2048, height=2048, config=None
+) -> int:
     """suffix: png/svg/pdf
 
     :param mermaid_code: mermaid code
@@ -24,6 +26,7 @@ async def mermaid_to_file(engine, mermaid_code, output_file_without_suffix, widt
     :return: 0 if succeed, -1 if failed
     """
     # Write the Mermaid code to a temporary file
+    config = config if config else Config.default()
     dir_name = os.path.dirname(output_file_without_suffix)
     if dir_name and not os.path.exists(dir_name):
         os.makedirs(dir_name)

metagpt/utils/mmdc_pyppeteer.py

@@ -10,11 +10,11 @@ from urllib.parse import urljoin
 
 from pyppeteer import launch
 
-from metagpt.config2 import config
+from metagpt.config2 import Config
 from metagpt.logs import logger
 
 
-async def mermaid_to_file(mermaid_code, output_file_without_suffix, width=2048, height=2048) -> int:
+async def mermaid_to_file(mermaid_code, output_file_without_suffix, width=2048, height=2048, config=None) -> int:
     """
     Converts the given Mermaid code to various output formats and saves them to files.
 
@@ -27,6 +27,7 @@ async def mermaid_to_file(mermaid_code, output_file_without_suffix, width=2048,
     Returns:
         int: Returns 1 if the conversion and saving were successful, -1 otherwise.
     """
+    config = config if config else Config.default()
     suffixes = ["png", "svg", "pdf"]
     __dirname = os.path.dirname(os.path.abspath(__file__))
 

metagpt/utils/repair_llm_raw_output.py

@@ -4,12 +4,12 @@
 
 import copy
 from enum import Enum
-from typing import Callable, Union
+from typing import Callable, Optional, Union
 
 import regex as re
 from tenacity import RetryCallState, retry, stop_after_attempt, wait_fixed
 
-from metagpt.config2 import config
+from metagpt.config2 import Config
 from metagpt.logs import logger
 from metagpt.utils.custom_decoder import CustomDecoder
 
@@ -154,7 +154,9 @@ def _repair_llm_raw_output(output: str, req_key: str, repair_type: RepairType =
     return output
 
 
-def repair_llm_raw_output(output: str, req_keys: list[str], repair_type: RepairType = None) -> str:
+def repair_llm_raw_output(
+    output: str, req_keys: list[str], repair_type: RepairType = None, config: Optional[Config] = None
+) -> str:
     """
     in open-source llm model, it usually can't follow the instruction well, the output may be incomplete,
     so here we try to repair it and use all repair methods by default.
@@ -169,6 +171,7 @@ def repair_llm_raw_output(output: str, req_keys: list[str], repair_type: RepairT
             target: { xxx }
             output: { xxx }]
     """
+    config = config if config else Config.default()
     if not config.repair_llm_output:
         return output
 
@@ -256,6 +259,7 @@ def run_after_exp_and_passon_next_retry(logger: "loguru.Logger") -> Callable[["R
                 "next_action":"None"
             }
         """
+        config = Config.default()
         if retry_state.outcome.failed:
             if retry_state.args:
                 # # can't be used as args=retry_state.args
@@ -276,8 +280,12 @@ def run_after_exp_and_passon_next_retry(logger: "loguru.Logger") -> Callable[["R
     return run_and_passon
 
 
+def repair_stop_after_attempt(retry_state):
+    return stop_after_attempt(3 if Config.default().repair_llm_output else 0)(retry_state)
+
+
 @retry(
-    stop=stop_after_attempt(3 if config.repair_llm_output else 0),
+    stop=repair_stop_after_attempt,
     wait=wait_fixed(1),
     after=run_after_exp_and_passon_next_retry(logger),
 )

tests/metagpt/roles/di/run_swe_agent_for_benchmark.py

@@ -2,13 +2,14 @@ import asyncio
 import json
 from datetime import datetime
 
-from metagpt.config2 import config
+from metagpt.config2 import Config
 from metagpt.const import DEFAULT_WORKSPACE_ROOT, METAGPT_ROOT
 from metagpt.logs import logger
 from metagpt.roles.di.swe_agent import SWEAgent
 from metagpt.tools.libs.terminal import Terminal
 from metagpt.tools.swe_agent_commands.swe_agent_utils import load_hf_dataset
 
+config = Config.default()
 # Specify by yourself
 TEST_REPO_DIR = METAGPT_ROOT / "data" / "test_repo"
 DATA_DIR = METAGPT_ROOT / "data/hugging_face"

tests/metagpt/test_document.py

@@ -5,10 +5,12 @@
 @Author  : alexanderwu
 @File    : test_document.py
 """
-from metagpt.config2 import config
+from metagpt.config2 import Config
 from metagpt.document import Repo
 from metagpt.logs import logger
 
+config = Config.default()
+
 
 def set_existing_repo(path):
     repo1 = Repo.from_path(path)

tests/metagpt/tools/test_azure_tts.py

@@ -12,9 +12,11 @@ from pathlib import Path
 import pytest
 from azure.cognitiveservices.speech import ResultReason, SpeechSynthesizer
 
-from metagpt.config2 import config
+from metagpt.config2 import Config
 from metagpt.tools.azure_tts import AzureTTS
 
+config = Config.default()
+
 
 @pytest.mark.asyncio
 async def test_azure_tts(mocker):

tests/metagpt/tools/test_metagpt_text_to_image.py

@@ -10,9 +10,11 @@ from unittest.mock import AsyncMock
 
 import pytest
 
-from metagpt.config2 import config
+from metagpt.config2 import Config
 from metagpt.tools.metagpt_text_to_image import oas3_metagpt_text_to_image
 
+config = Config.default()
+
 
 @pytest.mark.asyncio
 async def test_draw(mocker):

tests/metagpt/tools/test_moderation.py

@@ -8,10 +8,12 @@
 
 import pytest
 
-from metagpt.config2 import config
+from metagpt.config2 import Config
 from metagpt.llm import LLM
 from metagpt.tools.moderation import Moderation
 
+config = Config.default()
+
 
 @pytest.mark.asyncio
 @pytest.mark.parametrize(

tests/metagpt/tools/test_openai_text_to_image.py

@@ -11,7 +11,7 @@ import openai
 import pytest
 from pydantic import BaseModel
 
-from metagpt.config2 import config
+from metagpt.config2 import Config
 from metagpt.llm import LLM
 from metagpt.tools.openai_text_to_image import (
     OpenAIText2Image,
@@ -19,6 +19,8 @@ from metagpt.tools.openai_text_to_image import (
 )
 from metagpt.utils.s3 import S3
 
+config = Config.default()
+
 
 @pytest.mark.asyncio
 async def test_draw(mocker):

tests/metagpt/tools/test_ut_writer.py

@@ -20,10 +20,12 @@ from openai.types.chat.chat_completion_message_tool_call import (
     Function,
 )
 
-from metagpt.config2 import config
+from metagpt.config2 import Config
 from metagpt.const import API_QUESTIONS_PATH, UT_PY_PATH
 from metagpt.tools.ut_writer import YFT_PROMPT_PREFIX, UTGenerator
 
+config = Config.default()
+
 
 class TestUTWriter:
     @pytest.mark.asyncio

tests/metagpt/utils/test_repair_llm_raw_output.py

@@ -2,7 +2,9 @@
 # -*- coding: utf-8 -*-
 # @Desc   : unittest of repair_llm_raw_output
 
-from metagpt.config2 import config
+from metagpt.config2 import Config
+
+config = Config.default()
 
 """
 CONFIG.repair_llm_output should be True before retry_parse_json_text imported.

tests/mock/mock_llm.py

@@ -1,7 +1,7 @@
 import json
 from typing import Optional, Union
 
-from metagpt.config2 import config
+from metagpt.config2 import Config
 from metagpt.configs.llm_config import LLMType
 from metagpt.const import LLM_API_TIMEOUT
 from metagpt.logs import logger
@@ -10,6 +10,8 @@ from metagpt.provider.constant import GENERAL_FUNCTION_SCHEMA
 from metagpt.provider.openai_api import OpenAILLM
 from metagpt.schema import Message
 
+config = Config.default()
+
 OriginalLLM = OpenAILLM if config.llm.api_type == LLMType.OPENAI else AzureOpenAILLM