提交版本

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

@@ -1,235 +1,329 @@
-import sys  
-import os  
-current_directory = os.path.dirname(os.path.abspath(__file__))    
-sys.path.append(current_directory)  
-
-import agentscope
-from agentscope.message import Msg
-import os
-import asyncio
-import threading
-import json
-
-from agent.flex_service_toolkit import *
-from AlgoriAgent.projects.algoriAgent.agent.algori_agent import *
-
-from agentscope.service import (
-    ServiceToolkit,
-    ServiceResponse,
-    ServiceExecStatus,
-)
-import configparser
-
-config = configparser.ConfigParser()
-config.read('config.ini')
-openai_api_key = config['Global']['api_key']
-
-
-
-OPENAI_CFG_DICT = {
-    "config_name": "openai_cfg",    # 此配置的名称，必须保证唯一
-    "model_type": "openai_chat",         # 模型类型
-    "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": {
-        "base_url": config['Global']['base_chat_url']
-    },
-
-}
-
-import uuid
-from AlgoriAgent.projects.algoriAgent.tools.judge_tools import judge
-EMPTY_CHAPTER_CHAIN = [ Chapter(1, CHAPTER_FOCUS, "本章是未打开某个具体章节时的默认章节。", "处于本章节时不会有任何章节跳转。请作为一名经验丰富的算法教师，回答用户的问题。") ]
-
-def tool_name_to_tool(tool_name_list):
-    tools = []
-    for tool_name in tool_name_list:
-        if tool_name == "":
-            continue
-        if tool_name == "judge":
-            tools.append(judge)
-            
-    return tools
-
-class AgentManager:
-    def __init__(self):
-
-        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):
-        '''
-        markdown: 教案的markdown文件内容
-        markdown_prompt: 教案的prompt的markdown文件内容
-        score_prompt: 评分的prompt的markdown文件内容
-        根据3个教案的prompt，生成一个agent，返回agent的id与agent
-        '''
-        markdown_list = markdown.split("\n")
-        markdown_prompt_list = markdown_prompt.split("\n")
-        score_prompt_list = score_prompt.split("\n")
-        # 获取 H1 标题
-        title = ""
-        for line in markdown_list:
-            if line.startswith("# "):
-                title = line[2:]
-                break
-        
-        # 对于 H3 标题 构造每一个 Chapter
-        # 首先找到所有的 H3 标题 
-        chapter_dict = {}
-        chapter_sequence = []
-        for line in markdown_list:
-            if line.startswith("### "):
-                chapter_name = line[4:]
-                chapter_dict[chapter_name] = {}
-                chapter_sequence.append(chapter_name)
-        # 将 H3 标题 和 其对应的内容 构造成一个 Chapter
-
-        h3_name = ""
-        content = ""
-        for i in range(len(markdown_list)):
-            if markdown_list[i].startswith("### "): 
-                if(h3_name != ""):
-                    chapter_dict[h3_name]["markdown"] = content
-                h3_name = markdown_list[i][4:]
-                content = ""
-                continue
-            if h3_name != "":
-                content += markdown_list[i]+"\n"
-        if(h3_name != ""):
-            chapter_dict[h3_name]["markdown"] = content
-        
-
-        h3_name = ""
-        content = ""
-        require_tools = []
-        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)
-                h3_name = markdown_prompt_list[i][4:]
-                content = ""
-                continue
-
-            if h3_name != "":
-                if markdown_prompt_list[i].startswith("_require_tools"):
-                    require_tools.append(markdown_prompt_list[i].split("=")[1].strip())
-                    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)
-            
-        
-        h3_name = ""
-        content = ""
-        for i in range(len(score_prompt_list)):
-            if score_prompt_list[i].startswith("### "): 
-                if(h3_name != ""):
-                    chapter_dict[h3_name]["score_prompt"] = content
-                h3_name = score_prompt_list[i][4:]
-                content = ""
-                continue
-
-            if h3_name != "":
-                content += score_prompt_list[i]+"\n"
-        if(h3_name != ""):
-            chapter_dict[h3_name]["score_prompt"] = content
-
-
-        chapter_chain = []
-        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"]))
-            No+=1
-        chapter_chain[0].Focus()
-
-
-        # Prepare the tools for the agent
-        service_toolkit = FlexServiceToolkit()
-        # for tool_function in unity_function_list:
-        #     service_toolkit.add(tool_function)
-        agent = ChapterChainAgent(
-                    name="assistant",
-                    model_config_name="openai_cfg",
-                    verbose=True,
-                    service_toolkit=service_toolkit,
-                    max_iters=5,
-                    chapter_chain=chapter_chain
-                )
-        self.agents[id] = agent
-        return id, agent
-        
-
-
-    def new_agent_with_chain(self, chapter_chain = None, id = None):
-        '''
-
-        '''
-        if chapter_chain is None:
-            chapter_chain = EMPTY_CHAPTER_CHAIN
-        
-        # Prepare the tools for the agent
-        service_toolkit = FlexServiceToolkit()
-        # for tool_function in unity_function_list:
-        #     service_toolkit.add(tool_function)
-        agent = ChapterChainAgent(
-                    name="assistant",
-                    model_config_name="openai_cfg",
-                    verbose=True,
-                    service_toolkit=service_toolkit,
-                    max_iters=5,
-                    chapter_chain=EMPTY_CHAPTER_CHAIN
-                )
-        if id is None: id = uuid.uuid4()
-        self.agents[id] = agent
-        return id, agent
-
-
-    def get_agent(self, id=None):
-        '''
-            如果在get agent之前没有new agent，并用id进行访问，就返回一个章节链为空的agent
-        '''
-        if id:
-            if  id in self.agents:
-                return self.agents[id]
-            self.agents[id] = self.new_agent(id)[1]
-        else:
-            id = str(uuid.uuid4())
-            self.agents[id] = self.new_agent()[1]
-        return id, self.agents[id]
-        
-
-    def invoke(self,id, query, user_backboard):
-        msg = Msg("user", query, role="user")
-        return self.agents[id](msg, user_backboard = user_backboard)
-    
-    
-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()
-
-    # Main thread logic
-    try:
-        # Your code that might be interrupted
-        while True:
-            input_data = input("用户：")
-            print(manager.invoke(id, input_data))
-            
-    except KeyboardInterrupt:
-        print("Process interrupted by user.")
-        # You can add any cleanup code here if needed
-    finally:
-        print("Exiting program.")
-        # Code to run before the program exits
+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
+import asyncio
+import threading
+import json
+
+from agent.flex_service_toolkit import *
+from AlgoriAgent.projects.algoriAgent.agent.algori_agent import *
+
+from agentscope.service import (
+    ServiceToolkit,
+    ServiceResponse,
+    ServiceExecStatus,
+)
+import configparser
+
+config = configparser.ConfigParser()
+config.read('config.ini')
+openai_api_key = config['Global']['api_key']
+
+
+
+OPENAI_CFG_DICT = {
+    "config_name": "openai_cfg",    # 此配置的名称，必须保证唯一
+    "model_type": "openai_chat",         # 模型类型
+    "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": {
+        "base_url": config['Global']['base_chat_url']
+    },
+
+}
+
+import uuid
+from AlgoriAgent.projects.algoriAgent.tools.judge_tools import judge
+EMPTY_CHAPTER_CHAIN = [ Chapter(1, CHAPTER_FOCUS, "本章是未打开某个具体章节时的默认章节。", "处于本章节时不会有任何章节跳转。请作为一名经验丰富的算法教师，回答用户的问题。") ]
+
+def tool_name_to_tool(tool_name_list):
+    tools = []
+    for tool_name in tool_name_list:
+        if tool_name == "":
+            continue
+        if tool_name == "judge":
+            tools.append(judge)
+            
+    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,max_iter = 5, app=None, socketio=None):
+
+        agentscope.init(model_configs=[OPENAI_CFG_DICT])#, studio_url="http://0.0.0.0:5000")
+        self.agents = {}
+        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
+        score_prompt: 评分的prompt的markdown文件内容
+        根据3个教案的prompt，生成一个agent，返回agent的id与agent
+        '''
+        markdown_list = markdown.split("\n")
+        markdown_prompt_list = markdown_prompt.split("\n")
+        score_prompt_list = score_prompt.split("\n")
+        # 获取 H1 标题
+        title = ""
+        for line in markdown_list:
+            if line.startswith("# "):
+                title = line[2:]
+                break
+        
+        # 对于 H3 标题 构造每一个 Chapter
+        # 首先找到所有的 H3 标题 
+        chapter_dict = {}
+        chapter_sequence = []
+        for line in markdown_list:
+            if line.startswith("### "):
+                chapter_name = line[4:]
+                chapter_dict[chapter_name] = {}
+                chapter_sequence.append(chapter_name)
+        # 将 H3 标题 和 其对应的内容 构造成一个 Chapter
+
+        h3_name = ""
+        content = ""
+        for i in range(len(markdown_list)):
+            if markdown_list[i].startswith("### "): 
+                if(h3_name != ""):
+                    chapter_dict[h3_name]["markdown"] = content
+                h3_name = markdown_list[i][4:]
+                content = ""
+                continue
+            if h3_name != "":
+                content += markdown_list[i]+"\n"
+        if(h3_name != ""):
+            chapter_dict[h3_name]["markdown"] = content
+        
+
+        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_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().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_with_args(require_tools, require_tools_args)
+            
+        
+        h3_name = ""
+        content = ""
+        for i in range(len(score_prompt_list)):
+            if score_prompt_list[i].startswith("### "): 
+                if(h3_name != ""):
+                    chapter_dict[h3_name]["score_prompt"] = content
+                h3_name = score_prompt_list[i][4:]
+                content = ""
+                continue
+
+            if h3_name != "":
+                content += score_prompt_list[i]+"\n"
+        if(h3_name != ""):
+            chapter_dict[h3_name]["score_prompt"] = content
+
+
+        chapter_chain = []
+        No = 1
+        print (chapter_dict)
+        for chapter_name in chapter_sequence:
+            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()
+
+
+        # Prepare the tools for the agent
+        service_toolkit = FlexServiceToolkit()
+        # for tool_function in unity_function_list:
+        #     service_toolkit.add(tool_function)
+        agent = ChapterChainAgent(
+                    name=id,
+                    model_config_name="openai_cfg",
+                    verbose=True,
+                    service_toolkit=service_toolkit,
+                    max_iters=5,
+                    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
+        
+        # Prepare the tools for the agent
+        service_toolkit = FlexServiceToolkit()
+        # for tool_function in unity_function_list:
+        #     service_toolkit.add(tool_function)
+        agent = ChapterChainAgent(
+                    name="assistant",
+                    model_config_name="openai_cfg",
+                    verbose=True,
+                    service_toolkit=service_toolkit,
+                    max_iters=5,
+                    chapter_chain=EMPTY_CHAPTER_CHAIN
+                )
+        if id is None: id = uuid.uuid4()
+        self.agents[id] = agent
+        return id, agent
+
+
+    def get_agent(self, id=None):
+        '''
+            如果在get agent之前没有new agent，并用id进行访问，就返回一个章节链为空的agent
+        '''
+        if id:
+            if  id in self.agents:
+                return self.agents[id]
+            self.agents[id] = self.new_agent(id)[1]
+        else:
+            id = str(uuid.uuid4())
+            self.agents[id] = self.new_agent()[1]
+        return id, self.agents[id]
+        
+
+    def invoke(self,id, query, user_backboard, reset=False):
+        msg = Msg("user", query, role="user")
+        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__':
+    manager = AgentManager()
+    id, agent = manager.new_agent()
+
+    # Main thread logic
+    try:
+        # Your code that might be interrupted
+        while True:
+            input_data = input("用户：")
+            print(manager.invoke(id, input_data))
+            
+    except KeyboardInterrupt:
+        print("Process interrupted by user.")
+        # You can add any cleanup code here if needed
+    finally:
+        print("Exiting program.")
+        # Code to run before the program exits

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

@@ -1,64 +1,78 @@
-from agentscope.service.service_toolkit import ServiceFunction
-from agentscope.service import (
-    ServiceToolkit,
-    ServiceResponse,
-    ServiceExecStatus,
-)
-import os
-def judge(filepath:str, name: 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.
-
-    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
-    # 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)
-    Report = ""
-    passed_num=0
-    failed_num=0
-    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")
-        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}"
-            passed_num += 1
-        else:
-            re = "Failed {file}"
-            failed_num += 1
-        Report = Report + re + "\n"
-    os.remove(os.path.join(save_dir, f"_{name}.out"))
-    score = int (100 * passed_num / (passed_num + failed_num))
-    
-
-
-    status = ServiceExecStatus.SUCCESS
-    return ServiceResponse(status, (Report, score))
-
-
-def compare_file(path1:str, path2:str):
-    """
-        Compare two files and return true if they are the same.
-    """
-    with open(path1, 'r') as f1, open(path2, 'r') as f2:
-        # 逐行比较
-        for line1, line2 in zip(f1, f2):
-            if line1.strip() != line2.strip():
-                return False
-    return True
-        
+from agentscope.service.service_toolkit import ServiceFunction
+from agentscope.service import (
+    ServiceToolkit,
+    ServiceResponse,
+    ServiceExecStatus,
+)
+import os
+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/'+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 = root_path
+    Report = ""
+    passed_num=0
+    failed_num=0
+    erroroutput = ""
+    exceptoutput=""
+    for file in in_files:
+        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 = f"Passed {file}"
+            passed_num += 1
+        else:
+            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))
+    
+
+
+    status = ServiceExecStatus.SUCCESS
+    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):
+    """
+        Compare two files and return true if they are the same.
+    """
+    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
+        
 judge_tools = [judge]