提交版本

AlgoriAgent/projects/algoriAgent/agent/algori_agent.py

@@ -19,11 +19,17 @@ from agentscope.service import (
     ServiceExecStatus,
 )
 import json
+from AlgoriAgent.projects.algoriAgent.agent.score_agent import ScoreAgent
+from AlgoriAgent.projects.algoriAgent.agent.total_score_agent import TotalScoreAgent
+
+import re
+
+
 
 
 # 这个INSTRUCTION_PROMPT 交给免费Agent进行使用，不包含评测与得分标准，但是会在没有自己注释的大段代码或复制粘贴代码是，通知学生进入一次理解性测试。
-INSTRUCTION_PROMPT = """## Your Role:
-You are a powerful AI agent to teach student computer algorithm, you are to company a student to learn algorithm or solve problems.
+INSTRUCTION_PROMPT_EN = """## Your Role:
+You need to help the student learn algorithms, verify the learning process, ensure that the student understands the current chapter's content, and then guide the student to the next chapter.
 
 ## What You Should Do:
 0. You MUST follow the teaching instructions made by human teacher with several chapters, ONLY focus on one chapter at a time.
@@ -42,24 +48,78 @@ You are a powerful AI agent to teach student computer algorithm, you are to comp
 1. The tool functions you can use.
 2. The context, including the student's code, and teaching instructions.
 """  # noqa
+INSTRUCTION_PROMPT_CN = """## 你的角色：
+你需要帮助学生学习算法，检验学习过程，确保学生理解当前章节的内容后，带领学生进入下一章
+
+## 你需要做的：
+0. 你必须根据老师提供的教学大纲，按照章节顺序，依次学习。
+1. 学生可能会提出一些问题，你无需回答问题，而是根据上下文（比如学生自己的代码）来分析，并给出一步的解决方案。
+2. 教学大纲中会指明每个章节的学习目标，你按照目标学习。
+3. 系统会occasionally gather学生信息，你需要分析学生学习记录，调用必要的函数。
+
+## 注意事项：
+1. 确保使用工具函数时，提供的参数类型和值是正确的。
+2. 不要太相信自己，例如，当前位置，当前时间等，你可以尝试使用工具函数获取信息。
+3. 如果函数执行失败，你需要分析错误并尝试解决。
+4. 确保学生提出的问题或想法是否与当前章节相关，如果不相关，你应答并提醒学生关注当前章节。
+
+## 资源：
+1. 你可以使用的工具函数。
+2. 上下文，包括学生的代码，和教学大纲。
+"""
+
+
+LANGUAGE_HINT_EN = "Use English to reply"
+LANGUAGE_HINT_CN = "使用中文回复"
+
+
+
 CHAPTER_FINISH = 0
 CHAPTER_FOCUS = 1
 CHAPTER_LATTER = 2
 CHAPTER_FAILED = 3
 
 class  Chapter():
-    def __init__(self, No = 1, focus = 2,title="", chapter = "", chapter_prompt = "", score_prompt = "", append_tools : Callable[..., Any] = None) -> None:
+    
+    def __init__(self, course_id, lesson_id, No = 1, focus = 2,title="", chapter = "", chapter_prompt = "", 
+                 score_prompt = "", append_tools : list[Callable[..., Any], dict] = None) -> None:
+        '''
+        No : 序号
+        focus : 0: Finished, 1: Focus, 2: Latter, 3: Failed
+        title : 子章节标题
+        chapter : 教案子章节内容
+        chapter_prompt : 教案的提示
+        score_prompt : 评测标准
+        append_tools : 追加工具函数
+        '''
         self.No = No
         self.focus = focus
         self.chapter = chapter
+        self.chapter_prompt = chapter_prompt
+        self.score_prompt = score_prompt
         self.append_tools = append_tools
         self.memory = None
+        self.title =title
+        self.course_id = course_id
+        self.lesson_id = lesson_id 
+
+
     def GetChapter(self):
         if self.focus == CHAPTER_FINISH: f = "(Finished Chapter)"
         elif self.focus == CHAPTER_FOCUS: f = "(Your Now Chapter)"
         elif self.focus == CHAPTER_LATTER: f = "(Ignore for Latter Chapter)"
         elif self.focus == CHAPTER_FAILED: f = "(Failed Chapter)"
-        return f"{f} Chapter {self.No}: {self.chapter}"
+        # des = f"{f} Chapter {self.No}: {self.chapter if self.focus else 'Hidden for further study.'}"
+        des = f"{f} Chapter {self.No}: "
+        if self.focus == CHAPTER_FINISH or self.focus == CHAPTER_FAILED: des += 'Hidden due to finished.'
+        if self.focus == CHAPTER_LATTER: des += 'Hidden for further study.'
+        if self.focus == CHAPTER_FOCUS:
+            des+=self.chapter
+            des+='\n'
+            des+='Here are some concrete instructions:\n'
+            des+=self.chapter_prompt+'\n'
+        
+        return des
     
     def Finished(self, successful: bool = True):
         if successful: self.focus = 0
@@ -93,18 +153,21 @@ def next_chapter(name: str):
         name (`str`): The name of the agent to go to next chapter.
         
     Returns:
-        None
+        message (`str`): The result of the operation.
     """
+    res=""
     try:
-        ChapterChainAgent.Instance[name].next_chapter()
-    except:
+        print(ChapterChainAgent.Instance)
+        print(name in ChapterChainAgent.Instance)
+        res = ChapterChainAgent.Instance[name].next_chapter()
+    except Exception as e:
         status = ServiceExecStatus.ERROR
-        return ServiceResponse(status, "Failed to go to next chapter. name '"+name+"' not found in ChapterChainAgent.Instance")
+        return ServiceResponse(status, str(e))
 
     status = ServiceExecStatus.SUCCESS
-    return ServiceResponse(status, None)
-
+    return ServiceResponse(status, res)
 
+from AlgoriAgent.projects.algoriAgent.tools.file_tools import file_tools
 
 class ChapterChainAgent(AgentBase):
     """An agent class to preform chapter chain algorithm. 
@@ -122,6 +185,7 @@ class ChapterChainAgent(AgentBase):
         verbose: bool = True,
         chapter_chain: Sequence[Chapter] = None,
         stop_when_one_CHAPTER_failed: bool = True,
+        root_path: str = ".",
         **kwargs: Any,
     ) -> None:
         """
@@ -150,9 +214,10 @@ class ChapterChainAgent(AgentBase):
             sys_prompt=sys_prompt,
             model_config_name=model_config_name,
         )
-
+        self.model_config_name =  model_config_name
         # TODO: To compatible with the old version, which will be deprecated
         #  soon
+        self.name=name
         if "tools" in kwargs:
             logger.warning(
                 "The argument `tools` will be deprecated soon. "
@@ -186,6 +251,8 @@ class ChapterChainAgent(AgentBase):
         
         service_toolkit.add(record,name=name)
         service_toolkit.add(next_chapter,name=name)
+        for tool in file_tools:
+            service_toolkit.add(tool, root_path=root_path)
 
         self.service_toolkit = service_toolkit
         self.service_toolkit.agent = self
@@ -200,7 +267,7 @@ class ChapterChainAgent(AgentBase):
                 # The brief intro of the role and target
                 sys_prompt.format(name=self.name),
                 # The detailed instruction prompt for the agent
-                INSTRUCTION_PROMPT,
+                INSTRUCTION_PROMPT_CN,
             ],
         )
 
@@ -221,7 +288,7 @@ class ChapterChainAgent(AgentBase):
             # Only print the speak field when verbose is False
             keys_to_content=True if self.verbose else "speak",
         )
-
+        self.language_hint = LANGUAGE_HINT_CN
 
         self.chapter_chain = chapter_chain
 
@@ -232,13 +299,16 @@ class ChapterChainAgent(AgentBase):
 
 
         ChapterChainAgent.Instance[name] = self
+        self.agent_manager = None
+        self.root_path = root_path
 
     def chapter_chain_to_memory(self):
         mem = "##The Chapter Chain##\n"
         for chapter in self.chapter_chain:
+            if (chapter.focus == CHAPTER_FOCUS or chapter.focus == CHAPTER_FINISH):
                 mem+=chapter.GetChapter()+"\n"
         mem+="\n"
-        mem+= "Remember your now chapter is "+ self.chapter_chain[self.chapter_chain_now].GetChapter()+"\n"
+        # mem+= "Remember your now chapter is "+ self.chapter_chain[self.chapter_chain_now].GetChapter()+"\n"
         return Msg("system", mem, role="system")
 
     def _has_unfinished_chapter(self):
@@ -247,20 +317,45 @@ class ChapterChainAgent(AgentBase):
                 return True
         return False
     def next_chapter(self, successful: bool = True):
-        if self.chapter_chain[self.chapter_chain_now].append_tools is not None:
-            for callable_tools in self.chapter_chain[self.chapter_chain_now].append_tools:
+        if self.chapter_chain[self.chapter_chain_now].append_tools is not None or len(self.chapter_chain[self.chapter_chain_now].append_tools)==0:
+            for callable_tools, args in self.chapter_chain[self.chapter_chain_now].append_tools:
                 self.service_toolkit.Remove(callable_tools)
         
         self.chapter_chain[self.chapter_chain_now].Finished(successful)
+        try:
+            score_agent = ScoreAgent(name=self.name, model_config_name=self.model_config_name, memory=self.chapter_memory, 
+                                    chapter_score_prompt=self.chapter_chain[self.chapter_chain_now].score_prompt,
+                                    service_toolkit=self.service_toolkit, sys_prompt="", max_iters=self.max_iters, verbose=self.verbose)
+            res = score_agent.reply()
+
+            total_score_agent = TotalScoreAgent(name=self.name, model_config_name=self.model_config_name, input=res['content']['speak'], 
+                                                service_toolkit=self.service_toolkit, sys_prompt="", max_iters=self.max_iters, verbose=self.verbose)
+            score = total_score_agent.reply()
+
+        except Exception as e:
+            print("=*="*50)
+            print(e)
+        self.agent_manager.message_pass(self, "chapter_score", json.dumps({'chapter_id':self.chapter_chain_now+1, 'data':res}))
+        self.agent_manager.save_chapter_memory(self,self.chapter_chain[self.chapter_chain_now].course_id, self.chapter_chain[self.chapter_chain_now].lesson_id,  
+                                               self.chapter_chain[self.chapter_chain_now].title,  self.chapter_memory.export(to_mem = True),
+                                               score, is_rebuttal=False)
+        self.chapter_memory = TemporaryMemory()
+        self.agent_manager.message_pass(self, "next_chapter","")
+        self.agent_manager.system_message("进入下一节")
 
         for i in range(len(self.chapter_chain)):
-            if self.chapter_chain[i].focus == 2:
+            if self.chapter_chain[i].focus == CHAPTER_LATTER:
                 self.chapter_chain[i].Focus()
-                if self.chapter_chain[i].append_tools is not None:
-                    for callable_tools in self.chapter_chain[i].append_tools:
-                        self.service_toolkit.add(callable_tools)
+                if self.chapter_chain[i].append_tools is not None and len(self.chapter_chain[i].append_tools) > 0:
+                    for callable_tools, args in self.chapter_chain[i].append_tools:
+                        print("add tools:", callable_tools)
+                        print("args:", args)
+                        args['root_path'] = self.root_path
+                        self.service_toolkit.add(callable_tools, **args)
                 self.chapter_chain_now = i
-                return 
+                return "成功进入下一章"
+        # 找不到章节
+        return "学生完成了本课所有章节，已经没有下一章了！"
                 
     def add_chapters_after(self, CHAPTER_No, chapters: Sequence[Chapter]):
         for i in range(len(self.chapter_chain)):
@@ -273,8 +368,7 @@ class ChapterChainAgent(AgentBase):
                     self.chapter_chain[j].No += len(chapters)
                 break
     def reply(self, x: Optional[Union[Msg, Sequence[Msg]]] = None, user_backboard:str = "") -> Msg:
-            if self.chapter_memory.size() == 0:
-
+            if self.chapter_memory.size() <= 1:
                 self.chapter_chain[self.chapter_chain_now].memory = self.chapter_memory
                 for msg in self.memory.get_memory():
                     self.chapter_memory.add(msg)
@@ -283,7 +377,7 @@ class ChapterChainAgent(AgentBase):
             self.chapter_memory.add(x)
 
             
-            for _ in range(1):
+            for _ in range(self.max_iters):
                 # Step 1: Thought
                 if self.verbose:
                     self.speak(f" ITER {_+1}, STEP 1: REASONING ".center(70, "#"))
@@ -304,31 +398,29 @@ class ChapterChainAgent(AgentBase):
                 )
                 hint_msg = Msg(
                     "system",
-                    self.parser.format_instruction+"\n ####",
+                    self.parser.format_instruction+"\n ####" + "!!"+ self.language_hint +"!!",
                     role="system",
                     echo=self.verbose,
                 )
-
                 prompt = self.model.format(self.chapter_memory.get_memory(),
                     backboard_hint_msg, 
                     # The instruction prompt for tools
                     tools_hint_msg,
                     hint_msg)
-                print("*********************************************************************")
-                print(prompt)
                 if self.verbose:
                     self.speak(f"Prompt".center(70, "#"))
-                    self.speak(str(self.chapter_memory.get_memory()[-1].content))
-                    # print(prompt)
+                    self.speak(str(prompt))
                 
                 try:
+                    if self.verbose:
+                        self.speak(f"API Trigger".center(70, "#"))
                     res = self.model(
                         prompt,
                         parse_func=self.parser.parse,
-                        max_retries=1,
+                        max_retries=2,
+                        ######################
                     )
                     
-                    
                     if self.verbose:
                         self.speak(f"Result Parsed".center(70, "#"))
                         self.speak(str(res.parsed))
@@ -354,6 +446,7 @@ class ChapterChainAgent(AgentBase):
                     self.speak(msg_returned)
                     return msg_returned
                 
+                    
                     if self.verbose:
                         self.speak(f"Function".center(70, "#"))
                         self.speak(str(res.parsed["function"]))
@@ -382,27 +475,59 @@ class ChapterChainAgent(AgentBase):
 
                     self.chapter_memory.add([response_msg, error_msg])
 
-                    # Skip acting step to re-correct the response
-                    continue
                 
-                # 2. Acting
+
+    def function_call(self,  parsed_function) -> Msg:
                 if self.verbose:
-                    self.speak(f" ITER {_+1}, STEP 2: ACTING ".center(70, "#"))
+                    self.speak(f"ACTING ".center(70, "#"))
+                    print(str(parsed_function))
 
                 # Parse, check and execute the tool functions in service toolkit
                 try:
-                    
-                    
-                        
                     execute_results = self.service_toolkit.parse_and_call_func(
-                        res.parsed["function"],
+                        parsed_function# res.parsed["function"],
                     )
                     # 在这里补充任务结束行动
-                    if self.finish_now_chapter:
+                    if self.finish_now_chapter: self.finish()
+                        
+
+                    # Note: Observing the execution results and generate response
+                    # are finished in the next reasoning step. We just put the
+                    # execution results into memory, and wait for the next loop
+                    # to generate response.
+
+                    # Record execution results into memory as system message
+                    msg_res = Msg("system", execute_results, "system")
+                    self.speak(msg_res)
+                    self.chapter_memory.add(msg_res)
+                    pattern = r"\[RESULT\]: (.*)"
+                    match = re.search(pattern, msg_res['content'])
+                    result_content='正则匹配失败'
+                    if match:
+                        result_content = match.group(1)
+                    res = self.reply(Msg(name='system', content="命令执行结果如上，请总结并引导学生进行下一步操作", role="system"))
+
+                    self.agent_manager.system_message(parsed_function['name'] + "执行" + ("成功" if "[STATUS]: SUCCESS" in str(msg_res['content']) else "失败") + "，结果为：" + result_content)
+                    self.agent_manager.message_pass(self, "message", res.content['speak'])
+
+                except FunctionCallError as e:
+                    # Catch the function calling error that can be handled by
+                    # the model
+                    error_msg = Msg("system", str(e), "system")
+                    self.speak(error_msg)
+                    self.chapter_memory.add(error_msg)
+
+                except Exception as e:
+                    # Catch the function calling error that can not be handled
+                    error_msg = Msg("system", str(e), "system")
+                    self.speak(error_msg)
+                    self.chapter_memory.add(error_msg)
+
+    def finish(self):
         self.finish_now_chapter = False
         hint_msg = Msg(
             "system",
-                            "You have finished your own chapter."
+            "You have finished your now chapter."
             "Now generate a reply by summarizing the current "
             "situation. Remember this summary will be recorded in global memory. Make sure keep important information.",
             role="system",
@@ -417,35 +542,10 @@ class ChapterChainAgent(AgentBase):
         self.speak(res_msg)
         self.next_chapter(successful=True)
         self.success_own_chapter = True
-                        break
-
-                    # Note: Observing the execution results and generate response
-                    # are finished in the next reasoning step. We just put the
-                    # execution results into memory, and wait for the next loop
-                    # to generate response.
-
-                    # Record execution results into memory as system message
-                    msg_res = Msg("system", execute_results, "system")
-                    self.speak(msg_res)
-                    self.chapter_memory.add(msg_res)
-
-                except FunctionCallError as e:
-                    # Catch the function calling error that can be handled by
-                    # the model
-                    error_msg = Msg("system", str(e), "system")
-                    self.speak(error_msg)
-                    self.chapter_memory.add(error_msg)
-                    continue
-
-                except Exception as e:
-                    # Catch the function calling error that can not be handled
-                    error_msg = Msg("system", str(e), "system")
-                    self.speak(error_msg)
-                    self.chapter_memory.add(error_msg)
-                    continue
-
-
 
+    def change_language(self, language):
+        if(language == "zh"):self.language_hint = LANGUAGE_HINT_CN
+        if(language == "en"):self.language_hint = LANGUAGE_HINT_EN
 
     def GetProcess(self):
         process = []

AlgoriAgent/projects/algoriAgent/agent/score_agent.py

@@ -0,0 +1,180 @@
+
+from typing import Any, Callable, Optional, Union, Sequence
+
+from loguru import logger
+from agent.flex_service_toolkit import FlexServiceToolkit
+from agentscope.exception import ResponseParsingError, FunctionCallError
+from agentscope.agents import AgentBase
+from agentscope.memory.temporary_memory import TemporaryMemory
+from agentscope.message import Msg
+from agentscope.parsers import MarkdownJsonDictParser
+from agentscope.service.service_toolkit import ServiceFunction
+from agentscope.service import (
+    ServiceToolkit,
+    ServiceResponse,
+    ServiceExecStatus,
+)
+import json
+
+
+INSTRUCTION_SCORE_PROMPT_CN = """
+你是一名经验丰富的算法课程评分专家，你的任务是：基于学生对一个章节的学习日志、对话流，按照指定的评分标准进行评分。
+但是不同于普通老师，为了提高得分的区分度，你需要作为一名非常挑剔的专家，根据评分标准仔细挑剔学生的学习日志，并给出评分报告。
+
+重要的挑剔点：
+1. 存在错误的概念理解
+2. 存在矛盾的概念理解
+3. 存在不清晰、不连贯的概念表达
+4. 其他你认为可以挑剔的地方
+
+下面是学生的整个学习日志和对话流：
+"""
+
+CHAPTER_HINT_CN ="""
+上面就是学生的整个学习日志和对话流，请进行下面的各条章节评分标准进行挑剔与评分。
+"""
+
+RESPONSE_HINT_CN = """
+你的返回必须包含：
+对于每条章节评分标准，报告学生的得分以及挑剔的结果。
+例如：
+```
+{'评分标准1': {'得分': 8, '挑剔报告': '答案正确，但对于xxx概念表述有误'}
+}
+```
+"""
+
+INSTRUCTION_SCORE_PROMPT_EN = """
+You are a experienced algorithm course scoring expert, your task is to: Based on the students' learning logs and dialogues, score them according to the specified scoring standard.
+But unlike a normal teacher, to improve the distinction of the score, you need to be a very critical expert, carefully pick out the students' learning logs according to the scoring standard and give a score report.
+
+The important points to pick out:
+1. There are errors in the concept understanding
+2. There are contradictions in the concept understanding
+3. There are unclear, incoherent concept expressions
+4. Other places you think can be picked out
+Below is the students' entire learning log and dialogue:
+"""
+
+CHAPTER_HINT_EN = """
+Above is the students' entire learning log and dialogue, please pick out according to the following chapter scoring standard and score them.
+"""
+
+RESPONSE_HINT_EN="""
+Your response must contain:
+For each chapter scoring standard, report the students' score and pick out the results.
+For example:
+```
+{'Score standard 1': {'Score': 8, 'Pick out report': 'The answer is correct, but for the xxx concept expression is wrong'}
+}
+```
+"""
+
+
+class ScoreAgent(AgentBase):
+    """
+    ScoreAgent is a agent that can score students' learning logs.
+    """
+    def __init__(self, name: str, model_config_name: str, memory: TemporaryMemory,
+                 chapter_score_prompt: str,
+                 service_toolkit: ServiceToolkit, 
+                 sys_prompt: str = "You're a helpful assistant.",
+                 max_iters: int = 5,
+                 verbose: bool = True,
+                   **kwargs):
+        self.service_toolkit = service_toolkit
+        super().__init__(
+            name=name,
+            sys_prompt=sys_prompt,
+            model_config_name=model_config_name,
+        )
+        self.max_iters = max_iters
+        self.verbose = verbose
+        if not sys_prompt.endswith("\n"):
+            sys_prompt = sys_prompt + "\n"
+
+        self.sys_prompt = "\n".join(
+            [
+                # The brief intro of the role and target
+                sys_prompt.format(name=self.name),
+                # The detailed instruction prompt for the agent
+                INSTRUCTION_SCORE_PROMPT_CN,
+            ],
+        )
+        self.memory = TemporaryMemory()
+        self.memory.add(Msg("system", self.sys_prompt, role="system"))
+        
+        self.memory.add(memory.get_memory())
+
+        self.memory.add(Msg("system", CHAPTER_HINT_CN, role="system"))
+
+        self.memory.add(Msg("system", chapter_score_prompt, role="system"))
+
+        self.memory.add(Msg("system", RESPONSE_HINT_CN, role="system"))
+
+        self.parser = MarkdownJsonDictParser(
+            content_hint={
+                "thought": "what you thought",
+                "speak": "actual response in correct format mentioned above",
+            },
+            required_keys=["thought", "speak"],
+            # Only print the speak field when verbose is False
+            keys_to_content=True if self.verbose else "speak",
+        )
+
+    def reply(self, x = None, user_backboard = ""):
+        for _ in range(self.max_iters):
+            if self.verbose:
+                self.speak(f" ITER {_+1} ".center(70, "#"))
+            try:
+                
+                hint_msg = Msg(
+                    "system",
+                    self.parser.format_instruction+"\n",
+                    role="system",
+                    echo=self.verbose,
+                )
+                prompt = self.model.format(self.memory.get_memory(),hint_msg
+                    )
+                if self.verbose:
+                        self.speak(f"API Trigger".center(70, "#"))
+                        self.speak(str(prompt))
+                res = self.model(
+                    prompt,
+                    parse_func=self.parser.parse,
+                    max_retries=2,
+                    ######################
+                )
+
+                if self.verbose:
+                    self.speak(f"Result Parsed".center(70, "#"))
+                    self.speak(str(res.parsed))
+                    print(res.parsed)
+                self.memory.add(
+                        Msg(
+                            self.name,
+                            self.parser.to_memory(res.parsed),
+                            "assistant",
+                        ),
+                    )
+                msg_returned = Msg(
+                        self.name,
+                        self.parser.to_content(res.parsed),
+                        "assistant",
+                    )
+                return msg_returned
+            except ResponseParsingError as e:
+                    # Print out raw response from models for developers to debug
+                    response_msg = Msg(self.name, e.raw_response, "assistant")
+                    self.speak(response_msg)
+                    # Re-correct by model itself
+                    error_msg = Msg("system", str(e), "system")
+                    self.speak(error_msg)
+                    self.memory.add([response_msg, error_msg])
+            except Exception as e:
+                self.speak(f"Error: {e}".center(70, "#"))
+                self.speak(f"Retrying".center(70, "#"))
+                continue
+
+        return Msg(self.name, "Error: Max iterations reached", "assistant")
+

AlgoriAgent/projects/algoriAgent/agent/total_score_agent.py

@@ -0,0 +1,152 @@
+
+from typing import Any, Callable, Optional, Union, Sequence
+
+from loguru import logger
+from agent.flex_service_toolkit import FlexServiceToolkit
+from agentscope.exception import ResponseParsingError, FunctionCallError
+from agentscope.agents import AgentBase
+from agentscope.memory.temporary_memory import TemporaryMemory
+from agentscope.message import Msg
+from agentscope.parsers import MarkdownJsonDictParser
+from agentscope.service.service_toolkit import ServiceFunction
+from agentscope.service import (
+    ServiceToolkit,
+    ServiceResponse,
+    ServiceExecStatus,
+)
+import json
+
+
+INSTRUCTION_TOTAL_SCORE_PROMPT_CN = """
+你是一名精通文件处理的专家，你的本次任务非常简单，给你一段由其他老师给出的评分评价，你需要从中提取每一条标准下的得分，并计算总分，返回总分即可。
+"""
+
+
+RESPONSE_TOTAL_SCORE_HINT_CN = """
+你的返回必须包含且仅包含一个整数，表示得分。
+例如：
+输入：
+```
+{'评分标准1': {'得分': 8, '挑剔报告': '答案正确，但对于xxx概念表述有误'},
+ '评分标准2': {'得分': 3, '挑剔报告': '答案错误，对于xxx概念理解完全错误，导致回答有误'}
+}
+```
+输出：
+11
+下面是真正的输入：
+"""
+
+INSTRUCTION_TOTAL_SCORE_PROMPT_EN = """
+You are an expert in file processing. Your task is very simple. You will be given a segment of evaluation comments provided by another teacher. You need to extract the scores for each criterion and calculate the total score, returning only the total score.
+"""
+
+
+RESPONSE_TOTAL_SCORE_HINT_EN="""
+Your return must contain and only contain an integer representing the score. For example: 
+Input:
+```
+{'Criterion 1': {'Score': 8, 'Critical Report': 'The answer is correct, but there is a mistake in the explanation of the xxx concept'},
+ 'Criterion 2': {'Score': 3, 'Critical Report': 'The answer is incorrect, with a complete misunderstanding of the xxx concept, leading to an incorrect response'}
+}
+```
+Output:
+```
+{'output': 11}
+```
+Below is the actual input:
+"""
+
+
+class TotalScoreAgent(AgentBase):
+    """
+    TotalScoreAgent is a agent that can gather students' all score.
+    """
+    def __init__(self, name: str, model_config_name: str, input: str,
+                 service_toolkit: ServiceToolkit, 
+                 sys_prompt: str = "You're a helpful assistant.",
+                 max_iters: int = 5,
+                 verbose: bool = True,
+                   **kwargs):
+        self.service_toolkit = service_toolkit
+        super().__init__(
+            name=name,
+            sys_prompt=sys_prompt,
+            model_config_name=model_config_name,
+        )
+        self.max_iters = max_iters
+        self.verbose = verbose
+        if not sys_prompt.endswith("\n"):
+            sys_prompt = sys_prompt + "\n"
+
+        self.sys_prompt = "\n".join(
+            [
+                # The brief intro of the role and target
+                sys_prompt.format(name=self.name),
+                # The detailed instruction prompt for the agent
+                INSTRUCTION_TOTAL_SCORE_PROMPT_CN,
+                RESPONSE_TOTAL_SCORE_HINT_CN
+            ],
+        )
+        self.memory = TemporaryMemory()
+        self.memory.add(Msg("system", self.sys_prompt, role="system"))
+        
+        self.memory.add(Msg("User", input, role="system"))
+        self.parser = MarkdownJsonDictParser(
+            content_hint={
+                "output": "total score",
+            },
+            required_keys=["output"],
+            # Only print the speak field when verbose is False
+            keys_to_content=True if self.verbose else "speak",
+        )
+
+    def reply(self, x = None, user_backboard = ""):
+        for _ in range(self.max_iters):
+            if self.verbose:
+                self.speak(f" ITER {_+1} ".center(70, "#"))
+            try:
+                
+                hint_msg = Msg(
+                    "system",
+                    self.parser.format_instruction+"\n",
+                    role="system",
+                    echo=self.verbose,
+                )
+                prompt = self.model.format(self.memory.get_memory(),hint_msg
+                    )
+                if self.verbose:
+                        self.speak(f"API Trigger".center(70, "#"))
+                        self.speak(str(prompt))
+                res = self.model(
+                    prompt,
+                    parse_func=self.parser.parse,
+                    max_retries=2,
+                    ######################
+                )
+
+                print('====-======-=====')
+
+                return res.parsed['output']
+
+
+                if self.verbose:
+                    self.speak(f"Result Parsed".center(70, "#"))
+                    self.speak(str(res))
+                    print(res)
+                    
+                return res
+            except ResponseParsingError as e:
+                    # Print out raw response from models for developers to debug
+                    response_msg = Msg(self.name, e.raw_response, "assistant")
+                    self.speak(response_msg)
+                    # Re-correct by model itself
+                    error_msg = Msg("system", str(e), "system")
+                    self.speak(error_msg)
+                    self.memory.add([response_msg, error_msg])
+            except Exception as e:
+                self.speak(f"Error: {e}".center(70, "#"))
+                self.speak(f"Retrying".center(70, "#"))
+                continue
+
+        return Msg(self.name, "Error: Max iterations reached", "assistant")
+

AlgoriAgent/projects/algoriAgent/agent_manager.py

@@ -2,7 +2,7 @@ import sys
 import os  
 current_directory = os.path.dirname(os.path.abspath(__file__))    
 sys.path.append(current_directory)  
-
+from flask_socketio import SocketIO, join_room, emit, Namespace
 import agentscope
 from agentscope.message import Msg
 import os
@@ -32,7 +32,6 @@ OPENAI_CFG_DICT = {
     "model_name": "gpt-4o-mini",  # 模型名称
     #"model_name": "gpt-4",  # 模型名称
     #"model_name": "llama3",
-    
     "api_key": openai_api_key,      # OpenAI API key. 如果没有设置，将使用环境变量中的 OPENAI_API_KEY
 
     "client_args": {
@@ -55,13 +54,25 @@ def tool_name_to_tool(tool_name_list):
             
     return tools
 
+def tool_name_to_tool_with_args(tool_name_list, tool_args_list) -> list[tuple[Callable, dict]]:
+    tools = []
+    for tool_name, tool_args in zip(tool_name_list, tool_args_list):
+        if tool_name == "":
+            continue
+        if tool_name == "judge":
+            tools.append((judge, {"course_id": tool_args[0], "lesson_id": tool_args[1], "name": tool_args[2]}))
+            
+    return tools
 class AgentManager:
-    def __init__(self):
+    def __init__(self,max_iter = 5, app=None, socketio=None):
 
         agentscope.init(model_configs=[OPENAI_CFG_DICT])#, studio_url="http://0.0.0.0:5000")
         self.agents = {}
-
-    def new_agent(self, markdown:str, markdown_prompt:str, score_prompt:str, id = None):
+        self.max_iter = max_iter
+        self.agent_to_id = {}
+        self.app = app
+        self.socketio = socketio
+    def new_agent(self, course_id, lesson_id, markdown:str, markdown_prompt:str, score_prompt:str, id = "Defult Assistant", root_path="."):
         '''
         markdown: 教案的markdown文件内容
         markdown_prompt: 教案的prompt的markdown文件内容
@@ -107,23 +118,27 @@ class AgentManager:
         h3_name = ""
         content = ""
         require_tools = []
+        require_tools_args = []
         for i in range(len(markdown_prompt_list)):
             if markdown_prompt_list[i].startswith("### "): 
                 if(h3_name != ""):
                     chapter_dict[h3_name]["markdown_prompt"] = content
-                    chapter_dict[h3_name]["require_tools"] = tool_name_to_tool(require_tools)
+                    chapter_dict[h3_name]["require_tools"] = tool_name_to_tool_with_args(require_tools, require_tools_args)
                 h3_name = markdown_prompt_list[i][4:]
                 content = ""
+                require_tools=[]
+                require_tools_args = []
                 continue
 
             if h3_name != "":
                 if markdown_prompt_list[i].startswith("_require_tools"):
-                    require_tools.append(markdown_prompt_list[i].split("=")[1].strip())
+                    require_tools.append(markdown_prompt_list[i].split("=")[1].strip().split(",")[0])
+                    require_tools_args.append((markdown_prompt_list[i].split("=")[1].strip().split(",")[1:]))
                     continue
                 content += markdown_prompt_list[i]+"\n"
         if(h3_name != ""):
             chapter_dict[h3_name]["markdown_prompt"] = content
-            chapter_dict[h3_name]["require_tools"] = tool_name_to_tool(require_tools)
+            chapter_dict[h3_name]["require_tools"] = tool_name_to_tool_with_args(require_tools, require_tools_args)
             
         
         h3_name = ""
@@ -146,7 +161,7 @@ class AgentManager:
         No = 1
         print (chapter_dict)
         for chapter_name in chapter_sequence:
-            chapter_chain.append(Chapter(No, CHAPTER_LATTER, title, chapter_dict[chapter_name]["markdown"], chapter_dict[chapter_name]["markdown_prompt"], chapter_dict[chapter_name]["score_prompt"],chapter_dict[chapter_name]["require_tools"]))
+            chapter_chain.append(Chapter(course_id, lesson_id, No, CHAPTER_LATTER, chapter_name, chapter_dict[chapter_name]["markdown"], chapter_dict[chapter_name]["markdown_prompt"], chapter_dict[chapter_name]["score_prompt"],chapter_dict[chapter_name]["require_tools"]))
             No+=1
         chapter_chain[0].Focus()
 
@@ -156,22 +171,42 @@ class AgentManager:
         # for tool_function in unity_function_list:
         #     service_toolkit.add(tool_function)
         agent = ChapterChainAgent(
-                    name="assistant",
+                    name=id,
                     model_config_name="openai_cfg",
                     verbose=True,
                     service_toolkit=service_toolkit,
                     max_iters=5,
-                    chapter_chain=chapter_chain
+                    chapter_chain=chapter_chain,
+                    root_path = root_path,
                 )
         self.agents[id] = agent
+        self.agent_to_id[agent] = id
+        agent.agent_manager = self
         return id, agent
         
+    def message_pass(self, agent, messagetype, message):
+        id = self.agent_to_id[agent]
+        print("message_pass", id, messagetype, message)
+        with self.app.app_context(): 
+            try:
+                self.socketio.emit(messagetype, message, room=id, namespace='/agent')
+            except Exception as e:
+                print("message_pass",e)
+
+    def system_message(self, message):
+        with self.app.app_context(): 
+            try:
+                self.socketio.emit('system_message', message, room=id, namespace='/agent')
+            except Exception as e:
+                print("ERROR: system_message",e)
+
+
+    def save_chapter_memory(self, agent,course_id, lesson_id, subchapter_title, mem_list, score, is_rebuttal):
+        id = self.agent_to_id[agent]
+        self.app.my_function.save_chapter_memory(id, course_id, lesson_id, subchapter_title, mem_list, score, is_rebuttal)
 
 
     def new_agent_with_chain(self, chapter_chain = None, id = None):
-        '''
-
-        '''
         if chapter_chain is None:
             chapter_chain = EMPTY_CHAPTER_CHAIN
         
@@ -206,17 +241,76 @@ class AgentManager:
         return id, self.agents[id]
         
 
-    def invoke(self,id, query, user_backboard):
+    def invoke(self,id, query, user_backboard, reset=False):
         msg = Msg("user", query, role="user")
-        return self.agents[id](msg, user_backboard = user_backboard)
+        agent = self.agents[id]
+        res  = agent(msg, user_backboard=user_backboard)
+        return res
+        # 暂时取消生成器的写法
+        # 假设 agent 是一个生成器
+        # if reset or not hasattr(agent, '_generator'):
+        #     agent._generator = agent(msg, user_backboard=user_backboard)
+        
+        # try:
+        #     response = next(agent._generator)
+        #     yield response
+        # except StopIteration:
+        #     return
+    
+    def function_call(self, id, arg_function):
+        agent = self.agents[id]
+        return agent.function_call(arg_function)
+
+    def change_language(self, id, language):
+        agent = self.agents[id]
+        agent.change_language(language)
+
+    def sample_judge(self, id, bb):
+        agent = self.agents[id]
+        
+        with self.app.app_context():
+            try:
+                    print(bb.get_active_file_reletive_path())
+                    assert bb is not None and bb.active_file_path != "" , "no path specified"
+                    if bb.get_active_file_reletive_path().endswith(".py"):
+                        self.socketio.emit('terminal', {'data': f"python {bb.get_active_file_reletive_path()}"}, room=id, namespace='/vscode')
+                        self.socketio.emit('system_message', "代码试运行尚在建设，推荐直接通过命令行执行！", room=id, namespace='/agent')
+                    else:
+                        self.socketio.emit('system_message', "代码试运行尚仅支持python文件", room=id, namespace='/agent')
+            except Exception as e:
+                print(e)
+                if (str(e) == "no path specified"):
+                    self.socketio.emit("system_message", "未加载IDE文件同步，请尝试重新打开IDE文件或刷新页面，并确保插件已连接", room=id, namespace='/agent')
+
+    def judge(self, id, bb):
+        agent = self.agents[id]
+        with self.app.app_context():
+            try:
+                if (agent.chapter_chain[agent.chapter_chain_now].append_tools is not None and len(agent.chapter_chain[agent.chapter_chain_now].append_tools)):
+                    for tool, tool_kwargs in agent.chapter_chain[agent.chapter_chain_now].append_tools:
+                        if tool.__name__ == "judge":
+                                print(bb.get_active_file_reletive_path())
+                                assert bb is not None and bb.active_file_path != "" , "no path specified"
+                                if bb.get_active_file_reletive_path().endswith(".py"):
+                                    # self.socketio.emit('terminal', {'data': f"python {bb.get_active_file_reletive_path()}"}, room=id, namespace='/vscode')
+                                    self.socketio.emit('system_message', "代码评测中，请稍后", room=id, namespace='/agent')
+                                    agent.function_call({'name':'judge','arguments':{'filepath': bb.get_active_file_reletive_path()}})
+                                else:
+                                    self.socketio.emit('system_message', "代码评测尚仅支持python文件", room=id, namespace='/agent')
+
+                        else:
+                            with self.app.app_context():
+                                    self.socketio.emit("system_message", "本章学习无需评测", room=id, namespace='/agent')
+                else:
+                    with self.app.app_context():
+                            self.socketio.emit("system_message", "本章学习无需评测", room=id, namespace='/agent')
+            except Exception as e:
+                if (str(e) == "no path specified"):
+                    self.socketio.emit("system_message", "未加载IDE文件同步，请尝试重新打开IDE文件或刷新页面，并确保插件已连接", room=id, namespace='/agent')
 
 
 import time
 if __name__ == '__main__':
-    # tool_demo = ToolDemo("Lava")
-    # response = tool_demo.invoke("I want such a Gurouce, when use it, it will be thrown and fly towards the mouse position for a short time, and stop. And will damage Enemy who step on it. I call it Lava Gurouce.")
-    # print(response)
-    # Start the WebSocket server in a separate thread
     manager = AgentManager()
     id, agent = manager.new_agent()
 

AlgoriAgent/projects/algoriAgent/tools/file_tools.py

@@ -0,0 +1,54 @@
+from agentscope.service.service_toolkit import ServiceFunction
+from agentscope.service import (
+    ServiceToolkit,
+    ServiceResponse,
+    ServiceExecStatus,
+)
+import os
+def read_file(filepath:str, root_path:str):
+    """Read the file and return the content. Remember use 'judge' function if you want to judge file, instead of this function.
+
+    Args:
+        filepath (`str`): The path to the file.
+        root_path (`str`): The root path of now student's workspace.
+
+    Returns:
+        content (`str`): The content of the file.
+    """
+    # 从books/code_tests/name/中获取所有.in文件
+
+    filepath = os.path.join('..', root_path, filepath)
+    try:
+        with open(filepath, 'r') as f:
+            content = f.read()
+    except Exception as e:
+        status = ServiceExecStatus.ERROR
+        content = str(e)
+        return ServiceResponse(status, (content))
+    
+    status = ServiceExecStatus.SUCCESS
+    return ServiceResponse(status, ('```\n'+content+'```\n'))
+
+
+def write_file(filepath:str, content:str, root_path:str):
+    """Write content to the file.
+
+    Args:
+        filepath (`str`): The path to the file.
+        content (`str`): The content to be written to the file.
+        root_path (`str`): The root path of now student's workspace.
+
+    Returns:
+        None.
+    """
+    filepath = os.path.join('..', root_path, filepath)
+    try:
+        with open(filepath, 'w') as f:
+            f.write(content)
+    except Exception as e:
+        status = ServiceExecStatus.ERROR
+        return ServiceResponse(status, None)
+    status = ServiceExecStatus.SUCCESS
+    return ServiceResponse(status, None)
+        
+file_tools = [read_file, write_file]

AlgoriAgent/projects/algoriAgent/tools/judge_tools.py

@@ -5,41 +5,53 @@ from agentscope.service import (
     ServiceExecStatus,
 )
 import os
-def judge(filepath:str, name: str):
+def judge(filepath:str, name: str, course_id: str, lesson_id: str, root_path:str):
     """Call this method to judge the student's solution after they have completed the problem.
 
     Args:
         filepath (`str`): The path to the file containing the student's solution.
         name (`str`): The name of the problem.
+        course_id (`str`): The id of the course.
+        lesson_id (`str`): The id of the lesson.
+        root_path (`str`): The root path of the user's workspace project.
 
     Returns:
         Report `tuple(int,str)`: The report of the student's solution.
     """
     # 从books/code_tests/name/中获取所有.in文件
 
-    directory = 'books/code_tests/'+name
+    directory = 'books/code_tests/'+course_id+'/'+name
     # 如果目录不存在，报错
     if not os.path.exists(directory):
         status = ServiceExecStatus.FAILED
         return ServiceResponse(status, f"The name {name} is not found.")
     files = os.listdir(directory)
     in_files = [file for file in files if file.endswith('.in')]
-    save_dir = os.path.dirname(filepath)
+    save_dir = root_path
     Report = ""
     passed_num=0
     failed_num=0
+    erroroutput = ""
+    exceptoutput=""
     for file in in_files:
-        os.remove(os.path.join(save_dir, f"_{name}.out"))
-        cmd = "cat " + os.path.join(directory, file) + " | " + "python " + filepath +" > " + os.path.join(save_dir, f"_{name}.out")
+        file_path = os.path.join(save_dir, f"_{name}.out")
+        if os.path.exists(file_path):
+            os.remove(file_path)
+        with open(file_path, 'w') as ff:
+            ff.write("") 
+        
+        cmd = "cat " + os.path.join(directory, file) + " | " + "python " + os.path.join(root_path, filepath)  +" > " + os.path.join(save_dir, f"_{name}.out")
         os.system(cmd)
         # 比较结果
         re = ""
         if compare_file(os.path.join(save_dir, f"_{name}.out"), os.path.join(directory, file.replace(".in", ".out"))):
-            re = "Passed {file}"
+            re = f"Passed {file}"
             passed_num += 1
         else:
-            re = "Failed {file}"
+            re = f"Failed {file}"
             failed_num += 1
+            with open(os.path.join(directory, file.replace(".in", ".out")),'r') as f: exceptoutput = f.read()
+            with open(os.path.join(save_dir, f"_{name}.out"),'r') as f: erroroutput = f.read()
         Report = Report + re + "\n"
     os.remove(os.path.join(save_dir, f"_{name}.out"))
     score = int (100 * passed_num / (passed_num + failed_num))
@@ -47,7 +59,7 @@ def judge(filepath:str, name: str):
 
 
     status = ServiceExecStatus.SUCCESS
-    return ServiceResponse(status, (Report, score))
+    return ServiceResponse(status, (score, f"评测得分为 {score}, 在 {passed_num + failed_num} 个测试用例中通过了 {passed_num}个。最后一个错误样例期望输出：'{exceptoutput}'，但你的输出：'{erroroutput}"))
 
 
 def compare_file(path1:str, path2:str):
@@ -56,7 +68,9 @@ def compare_file(path1:str, path2:str):
     """
     with open(path1, 'r') as f1, open(path2, 'r') as f2:
         # 逐行比较
+        print('-------------------COMPARE-------------------------')
         for line1, line2 in zip(f1, f2):
+            print(line1.strip(), "||",line2.strip())
             if line1.strip() != line2.strip():
                 return False
     return True