修改完毕

metagpt/actions/ga_action_base.py

@@ -1,85 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-# @Desc   : 调用GPT
-# author: didi
-# Date:9.25
-
-import openai
-from metagpt.llm import DEFAULT_LLM
-# 直接调用Prompt生成
-# ga的prompt构建格式和metagpt完全不同。没有办法融合。
-
-
-# 特殊指令加入Prompt生成
-
-
-def final_response(prompt, special_instruction, example_output=None):
-    """
-    通过将特殊指令加入Prompt生成最终的响应。
-
-    参数：
-    - prompt：要生成响应的提示文本。
-    - special_instruction：要加入Prompt的特殊指令。
-    - example_output（可选）：示例输出的JSON字符串。
-
-    返回：
-    生成的最终响应。
-
-    """
-    prompt = '"""\n' + prompt + '\n"""\n'
-    prompt += f"Output the response to the prompt above in json. {special_instruction}\n"
-    if example_output:
-        prompt += "Example output json:\n"
-        prompt += '{"output": "' + str(example_output) + '"}'
-    return DEFAULT_LLM.ask(prompt)
-
-# prompt填充模板
-
-
-def prompt_generate(curr_input, prompt_lib_file):
-    """
-    Takes in the current input (e.g. comment that you want to classifiy) and 
-    the path to a prompt file. The prompt file contains the raw str prompt that
-    will be used, which contains the following substr: !<INPUT>! -- this 
-    function replaces this substr with the actual curr_input to produce the 
-    final promopt that will be sent to the GPT3 server. 
-    ARGS:
-    curr_input: the input we want to feed in (IF THERE ARE MORE THAN ONE
-                INPUT, THIS CAN BE A LIST.)
-    prompt_lib_file: the path to the promopt file. 
-    RETURNS: 
-    a str prompt that will be sent to OpenAI's GPT server.  
-    """
-    if type(curr_input) is type("string"):
-        curr_input = [curr_input]
-        curr_input = [str(i) for i in curr_input]
-
-    f = open(prompt_lib_file, "r")
-    prompt = f.read()
-    f.close()
-    for count, i in enumerate(curr_input):
-        prompt = prompt.replace(f"!<INPUT {count}>!", i)
-    if "<commentblockmarker>###</commentblockmarker>" in prompt:
-        prompt = prompt.split(
-            "<commentblockmarker>###</commentblockmarker>")[1]
-    return prompt.strip()
-
-# 使用OpenAI embedding库进行存储
-
-
-def embedding(query):
-    """
-    Generates an embedding for the given query.
-
-    Args:
-        query (str): The text query to be embedded.
-
-    Returns:
-        str: The embedding key generated for the query.
-    """
-    embedding_result = openai.Embedding.create(
-        model="text-embedding-ada-002",
-        input=query
-    )
-    embedding_key = embedding_result['data'][0]["embedding"]
-    return embedding_key

metagpt/reflect/reflect.py

@@ -9,7 +9,7 @@ from metagpt.actions.ga_action_base import final_response
 '''
 等待Agent和memory更新，保留相关引用但可以忽略。
 '''
-from metagpt.memory.GA_memory_storage import Agent_memory, Memory_basic
+from metagpt.memory.ga_memory_storage import AgentMemory, MemoryBasic
 
 
 def agent_reflect(agent):

metagpt/roles/role.py

@@ -18,6 +18,7 @@ from metagpt.llm import LLM
 from metagpt.logs import logger
 from metagpt.memory import Memory, LongTermMemory
 from metagpt.schema import Message
+from metagpt.reflect import agent_reflect
 
 PREFIX_TEMPLATE = """You are a {profile}, named {name}, your goal is {goal}, and the constraint is {constraints}. """
 
@@ -78,7 +79,8 @@ class RoleContext(BaseModel):
     def check(self, role_id: str):
         if hasattr(CONFIG, "long_term_memory") and CONFIG.long_term_memory:
             self.long_term_memory.recover_memory(role_id, self)
-            self.memory = self.long_term_memory  # use memory to act as long_term_memory for unify operation
+            # use memory to act as long_term_memory for unify operation
+            self.memory = self.long_term_memory
 
     @property
     def important_memory(self) -> list[Message]:
@@ -95,7 +97,8 @@ class Role:
 
     def __init__(self, name="", profile="", goal="", constraints="", desc=""):
         self._llm = LLM()
-        self._setting = RoleSetting(name=name, profile=profile, goal=goal, constraints=constraints, desc=desc)
+        self._setting = RoleSetting(
+            name=name, profile=profile, goal=goal, constraints=constraints, desc=desc)
         self._states = []
         self._actions = []
         self._role_id = str(self._setting)
@@ -169,9 +172,10 @@ class Role:
         # logger.info(response)
         if isinstance(response, ActionOutput):
             msg = Message(content=response.content, instruct_content=response.instruct_content,
-                        role=self.profile, cause_by=type(self._rc.todo))
+                          role=self.profile, cause_by=type(self._rc.todo))
         else:
-            msg = Message(content=response, role=self.profile, cause_by=type(self._rc.todo))
+            msg = Message(content=response, role=self.profile,
+                          cause_by=type(self._rc.todo))
         self._rc.memory.add(msg)
         # logger.debug(f"{response}")
 
@@ -184,8 +188,9 @@ class Role:
         env_msgs = self._rc.env.memory.get()
 
         observed = self._rc.env.memory.get_by_actions(self._rc.watch)
-        
-        self._rc.news = self._rc.memory.remember(observed)  # remember recent exact or similar memories
+
+        # remember recent exact or similar memories
+        self._rc.news = self._rc.memory.remember(observed)
 
         for i in env_msgs:
             self.recv(i)
@@ -205,7 +210,8 @@ class Role:
     async def _react(self) -> Message:
         """Think first, then act"""
         await self._think()
-        logger.debug(f"{self._setting}: {self._rc.state=}, will do {self._rc.todo}")
+        logger.debug(
+            f"{self._setting}: {self._rc.state=}, will do {self._rc.todo}")
         return await self._act()
 
     def recv(self, message: Message) -> None: