Merge branch 'main' into feature/talk_prompt

metagpt/actions/action.py

@@ -5,6 +5,7 @@
 @Author  : alexanderwu
 @File    : action.py
 @Modified By: mashenquan, 2023/8/20. Add function return annotations.
+@Modified By: mashenquan, 2023/9/8. Replace LLM with LLMFactory
 """
 from __future__ import annotations
 
@@ -14,16 +15,18 @@ from typing import Optional
 from tenacity import retry, stop_after_attempt, wait_fixed
 
 from metagpt.actions.action_output import ActionOutput
-from metagpt.llm import LLM
 from metagpt.logs import logger
+from metagpt.provider.base_gpt_api import BaseGPTAPI
 from metagpt.utils.common import OutputParser
 
 
 class Action(ABC):
-    def __init__(self, name: str = "", context=None, llm: LLM = None):
+    def __init__(self, name: str = "", context=None, llm: BaseGPTAPI = None):
         self.name: str = name
         if llm is None:
-            llm = LLM()
+            from metagpt.llm import LLMFactory
+
+            llm = LLMFactory.new_llm()
         self.llm = llm
         self.context = context
         self.prefix = ""

metagpt/actions/talk_action.py

@@ -6,10 +6,12 @@
 @File    : talk_action.py
 @Desc    : Act as it’s a talk
 """
+import json
 
 from metagpt.actions import Action, ActionOutput
 from metagpt.config import CONFIG
 from metagpt.const import DEFAULT_LANGUAGE
+from metagpt.llm import LLMType
 from metagpt.logs import logger
 
 
@@ -62,13 +64,40 @@ class TalkAction(Action):
         logger.info(f"PROMPT: {prompt}")
         return prompt
 
-    async def run(self, *args, **kwargs) -> ActionOutput:
+    async def run_old(self, *args, **kwargs) -> ActionOutput:
         prompt = self.prompt
         rsp = await self.llm.aask(msg=prompt, system_msgs=[])
         logger.debug(f"PROMPT:{prompt}\nRESULT:{rsp}\n")
         self._rsp = ActionOutput(content=rsp)
         return self._rsp
 
+    @property
+    def aask_args(self):
+        language = CONFIG.language or DEFAULT_LANGUAGE
+        system_msgs = [
+            f"You are {CONFIG.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.",
+            "If the information is insufficient, you can search in the context or knowledge.",
+            f"Answer the following questions strictly in {language}, and the answers must follow the Markdown format.",
+        ]
+        format_msgs = []
+        if self._knowledge:
+            format_msgs.append({"role": "assistant", "content": self._knowledge})
+        if self._history_summary:
+            if CONFIG.LLM_TYPE == LLMType.METAGPT.value:
+                format_msgs.extend(json.loads(self._history_summary))
+            else:
+                format_msgs.append({"role": "assistant", "content": self._history_summary})
+        return self._talk, format_msgs, system_msgs
+
+    async def run(self, *args, **kwargs) -> ActionOutput:
+        msg, format_msgs, system_msgs = self.aask_args
+        rsp = await self.llm.aask(msg=msg, format_msgs=format_msgs, system_msgs=system_msgs)
+        self._rsp = ActionOutput(content=rsp)
+        return self._rsp
+
     __FORMATION__ = """Formation: "Capacity and role" defines the role you are currently playing;
   "[HISTORY_BEGIN]" and "[HISTORY_END]" tags enclose the historical conversation;
   "[KNOWLEDGE_BEGIN]" and "[KNOWLEDGE_END]" tags enclose the knowledge may help for your responses;