use context instead of FileRepo... done main process.

metagpt/actions/talk_action.py

@@ -9,25 +9,31 @@
 from typing import Optional
 
 from metagpt.actions import Action
-from metagpt.config import CONFIG
-from metagpt.const import DEFAULT_LANGUAGE
+from metagpt.config2 import config
 from metagpt.logs import logger
 from metagpt.schema import Message
 
 
-# TOTEST
 class TalkAction(Action):
     context: str
     history_summary: str = ""
     knowledge: str = ""
     rsp: Optional[Message] = None
 
+    @property
+    def agent_description(self):
+        return self.g_context.kwargs["agent_description"]
+
+    @property
+    def language(self):
+        return self.g_context.kwargs["language"] or config.language
+
     @property
     def prompt(self):
         prompt = ""
-        if CONFIG.agent_description:
+        if self.agent_description:
             prompt = (
-                f"You are {CONFIG.agent_description}. Your responses should align with the role-play agreement, "
+                f"You are {self.agent_description}. Your responses should align with the role-play agreement, "
                 f"maintaining the character's persona and habits. When faced with unrelated questions, playfully "
                 f"decline to answer without revealing your AI nature to preserve the character's image.\n\n"
             )
@@ -36,7 +42,7 @@ class TalkAction(Action):
         prompt += (
             "If the information is insufficient, you can search in the historical conversation or knowledge above.\n"
         )
-        language = CONFIG.language or DEFAULT_LANGUAGE
+        language = self.language
         prompt += (
             f"Answer the following questions strictly in {language}, and the answers must follow the Markdown format.\n "
             f"{self.context}"
@@ -47,10 +53,10 @@ class TalkAction(Action):
     @property
     def prompt_gpt4(self):
         kvs = {
-            "{role}": CONFIG.agent_description or "",
+            "{role}": self.agent_description or "",
             "{history}": self.history_summary or "",
             "{knowledge}": self.knowledge or "",
-            "{language}": CONFIG.language or DEFAULT_LANGUAGE,
+            "{language}": self.language,
             "{ask}": self.context,
         }
         prompt = TalkActionPrompt.FORMATION_LOOSE
@@ -68,9 +74,9 @@ class TalkAction(Action):
 
     @property
     def aask_args(self):
-        language = CONFIG.language or DEFAULT_LANGUAGE
+        language = self.language
         system_msgs = [
-            f"You are {CONFIG.agent_description}.",
+            f"You are {self.agent_description}.",
             "Your responses should align with the role-play agreement, "
             "maintaining the character's persona and habits. When faced with unrelated questions, playfully "
             "decline to answer without revealing your AI nature to preserve the character's image.",

metagpt/actions/write_teaching_plan.py

@@ -75,7 +75,7 @@ class WriteTeachingPlanPart(Action):
         if "{" not in value:
             return value
 
-        # FIXME: 从Context中获取参数
+        # FIXME: 从Context中获取参数，而非从options
         merged_opts = CONFIG.options or {}
         try:
             return value.format(**merged_opts)

metagpt/config.py

@@ -80,7 +80,7 @@ class Config(metaclass=Singleton):
             LLMType.OPEN_LLM: self._is_valid_llm_key(self.OPEN_LLM_API_BASE),
             LLMType.GEMINI: self._is_valid_llm_key(self.GEMINI_API_KEY),
             LLMType.METAGPT: bool(self._is_valid_llm_key(self.OPENAI_API_KEY) and self.OPENAI_API_TYPE == "metagpt"),
-            LLMType.AZURE_OPENAI: bool(
+            LLMType.AZURE: bool(
                 self._is_valid_llm_key(self.OPENAI_API_KEY)
                 and self.OPENAI_API_TYPE == "azure"
                 and self.DEPLOYMENT_NAME
@@ -108,7 +108,7 @@ class Config(metaclass=Singleton):
         provider = provider or self.get_default_llm_provider_enum()
         model_mappings = {
             LLMType.OPENAI: self.OPENAI_API_MODEL,
-            LLMType.AZURE_OPENAI: self.DEPLOYMENT_NAME,
+            LLMType.AZURE: self.DEPLOYMENT_NAME,
         }
         return model_mappings.get(provider, "")
 

metagpt/config2.py

@@ -64,6 +64,7 @@ class Config(CLIParams, YamlModel):
     llm_for_researcher_summary: str = "gpt3"
     llm_for_researcher_report: str = "gpt3"
     METAGPT_TEXT_TO_IMAGE_MODEL_URL: str = ""
+    language: str = "English"
 
     @classmethod
     def default(cls):
@@ -103,14 +104,24 @@ class Config(CLIParams, YamlModel):
             raise ValueError(f"LLM {name} not found in config")
         return self.llm[name]
 
-    def get_openai_llm(self, name: Optional[str] = None) -> LLMConfig:
+    def get_llm_configs_by_type(self, llm_type: LLMType) -> List[LLMConfig]:
+        """Get LLM instance by type"""
+        return [v for k, v in self.llm.items() if v.api_type == llm_type]
+
+    def get_llm_config_by_type(self, llm_type: LLMType) -> Optional[LLMConfig]:
+        """Get LLM instance by type"""
+        llm = self.get_llm_configs_by_type(llm_type)
+        if llm:
+            return llm[0]
+        return None
+
+    def get_openai_llm(self) -> Optional[LLMConfig]:
         """Get OpenAI LLMConfig by name. If no OpenAI, raise Exception"""
-        if name is None:
-            # Use the first OpenAI LLM as default
-            name = [k for k, v in self.llm.items() if v.api_type == LLMType.OPENAI][0]
-        if name not in self.llm:
-            raise ValueError(f"OpenAI LLM {name} not found in config")
-        return self.llm[name]
+        return self.get_llm_config_by_type(LLMType.OPENAI)
+
+    def get_azure_llm(self) -> Optional[LLMConfig]:
+        """Get Azure LLMConfig by name. If no Azure, raise Exception"""
+        return self.get_llm_config_by_type(LLMType.AZURE)
 
 
 def merge_dict(dicts: Iterable[Dict]) -> Dict:

metagpt/configs/llm_config.py

@@ -22,7 +22,7 @@ class LLMType(Enum):
     OPEN_LLM = "open_llm"
     GEMINI = "gemini"
     METAGPT = "metagpt"
-    AZURE_OPENAI = "azure"
+    AZURE = "azure"
     OLLAMA = "ollama"
 
 

metagpt/memory/brain_memory.py

@@ -14,8 +14,8 @@ from typing import Dict, List, Optional
 
 from pydantic import BaseModel, Field
 
-from metagpt.config import CONFIG
-from metagpt.const import DEFAULT_LANGUAGE, DEFAULT_MAX_TOKENS, DEFAULT_TOKEN_SIZE
+from metagpt.config2 import config
+from metagpt.const import DEFAULT_MAX_TOKENS, DEFAULT_TOKEN_SIZE
 from metagpt.logs import logger
 from metagpt.provider import MetaGPTLLM
 from metagpt.provider.base_llm import BaseLLM
@@ -83,7 +83,7 @@ class BrainMemory(BaseModel):
     def to_redis_key(prefix: str, user_id: str, chat_id: str):
         return f"{prefix}:{user_id}:{chat_id}"
 
-    async def set_history_summary(self, history_summary, redis_key, redis_conf):
+    async def set_history_summary(self, history_summary, redis_key):
         if self.historical_summary == history_summary:
             if self.is_dirty:
                 await self.dumps(redis_key=redis_key)
@@ -140,7 +140,7 @@ 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, redis_conf=CONFIG.REDIS)
+            await self.set_history_summary(history_summary=summary, redis_key=config.redis.key)
             return summary
         raise ValueError(f"text too long:{text_length}")
 
@@ -164,7 +164,7 @@ 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=config.redis.key)
         self.is_dirty = False
 
         return BrainMemory.to_metagpt_history_format(self.history)
@@ -181,7 +181,7 @@ class BrainMemory(BaseModel):
 
         summary = await self.summarize(llm=llm, max_words=500)
 
-        language = CONFIG.language or DEFAULT_LANGUAGE
+        language = 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/provider/azure_openai_api.py

@@ -18,7 +18,7 @@ from metagpt.provider.llm_provider_registry import register_provider
 from metagpt.provider.openai_api import OpenAILLM
 
 
-@register_provider(LLMType.AZURE_OPENAI)
+@register_provider(LLMType.AZURE)
 class AzureOpenAILLM(OpenAILLM):
     """
     Check https://platform.openai.com/examples for examples

metagpt/utils/make_sk_kernel.py

@@ -13,20 +13,20 @@ from semantic_kernel.connectors.ai.open_ai.services.open_ai_chat_completion impo
     OpenAIChatCompletion,
 )
 
-from metagpt.config import CONFIG
+from metagpt.config2 import config
 
 
 def make_sk_kernel():
     kernel = sk.Kernel()
-    if CONFIG.OPENAI_API_TYPE == "azure":
+    if llm := config.get_openai_llm():
         kernel.add_chat_service(
             "chat_completion",
-            AzureChatCompletion(CONFIG.DEPLOYMENT_NAME, CONFIG.OPENAI_BASE_URL, CONFIG.OPENAI_API_KEY),
+            AzureChatCompletion(llm.model, llm.base_url, llm.api_key),
         )
     else:
         kernel.add_chat_service(
             "chat_completion",
-            OpenAIChatCompletion(CONFIG.OPENAI_API_MODEL, CONFIG.OPENAI_API_KEY),
+            OpenAIChatCompletion(llm.model, llm.api_key),
         )
 
     return kernel

tests/metagpt/actions/test_talk_action.py

@@ -9,7 +9,7 @@
 import pytest
 
 from metagpt.actions.talk_action import TalkAction
-from metagpt.config import CONFIG
+from metagpt.context import Context
 from metagpt.schema import Message
 
 
@@ -36,8 +36,9 @@ from metagpt.schema import Message
 @pytest.mark.usefixtures("llm_mock")
 async def test_prompt(agent_description, language, context, knowledge, history_summary):
     # Prerequisites
-    CONFIG.agent_description = agent_description
-    CONFIG.language = language
+    g_context = Context()
+    g_context.kwargs["agent_description"] = agent_description
+    g_context.kwargs["language"] = language
 
     action = TalkAction(context=context, knowledge=knowledge, history_summary=history_summary)
     assert "{" not in action.prompt