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2024-12-03 16:21:19 +08:00
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Html/runs/run_20241123-164807_sy4ep8/.config Normal file
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{
"project": "TrDbTY",
"name": "sy4ep8",
"run_id": "run_20241123-164807_sy4ep8",
"timestamp": "2024-11-23 16:48:07",
"pid": 8872
}

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from flask import Flask, session, request, jsonify, render_template, send_from_directory
from flask_cors import CORS
from flask_socketio import SocketIO, join_room, emit
import markdown
import os
import uuid
import shutil
import sys
import json
parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
sys.path.insert(0, parent_dir)
current_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '.'))
sys.path.insert(0, current_dir)
from AlgoriAgent.projects.algoriAgent.agent_manager import AgentManager
app = Flask(__name__)
app.secret_key = 'cakebaker'
# 配置 CORS
CORS(app, resources={r"/*": {"origins": "http://localhost:9888"}}, supports_credentials=True)
userid_recorder = {} # user_id&path -> session['user_id']
'''
Backboard
'''
from backboardManager import BackBoardManager
backboard_manager = BackBoardManager()
@app.route('/backboard')
def backboard():
return jsonify({'status': 'success'})
'''
Agent and Chat
'''
socketio = SocketIO(app, cors_allowed_origins="*")
agent_manager = AgentManager()
def realtime_response(config, realtime_action):
print("config"+str(config))
print("realtime_action"+str(realtime_action))
pass
@socketio.on('connect')
def handle_connect():
user = request.args.get('username')
folder_name = request.args.get('folder')
user_id = userid_recorder[user+'&'+folder_name]
session['user_id'] = user_id
print(f'User connected with session user_id: {user_id}')
# 从markdown、markdown_prompts、score_prompts中各读取数据folder.md并进行new_agent
with open(f'books/markdown/{folder_name}.md','r') as fmd,\
open(f'books/markdown_prompts/{folder_name}.md','r')as fmdp,\
open(f'books/score_prompts/{folder_name}.md','r') as fsp:
markdown = fmd.read()
markdown_prompts = fmdp.read()
score_prompts = fsp.read()
print('-----------------------')
print(user_id)
user_id, agent = agent_manager.new_agent(markdown, markdown_prompts, score_prompts, id=user_id)
join_room(user_id) # 将该用户加入以user_id为名的room
# 当接收到前端的消息时，触发此事件
@socketio.on('send_message')
def handle_message(data):
print(f"Message from client: {data}")
id = session.get('user_id')
# 构造回复消息
res = agent_manager.invoke(id, data)
reply = f"AI: {res.content['speak']}"
# 将回复发送回前端
emit('receive_message', reply)
'''
Markdown to HTML
'''
# 配置文件路径
MARKDOWN_DIR = 'books/markdown' # 存放 markdown 文件的文件夹
STATIC_DIR = 'static' # 存放生成的 HTML 和图片资源的文件夹
IMAGE_DIR = 'image' # 图片资源相对于 markdown 的位置
@app.route('/<filename>-markdown', methods=['GET'])
def convert_md(filename):
md_file_path = os.path.join(MARKDOWN_DIR, f'{filename}.md')
if not os.path.exists(md_file_path):
return jsonify({'error': 'Markdown file not found'}), 404
# 读取 markdown 文件内容
with open(md_file_path, 'r', encoding='utf-8') as file:
md_content = file.read()
# 将 markdown 转换为 HTML
html_content = markdown.markdown(md_content)
# 插入 CSS 样式，限制图片最大宽度为 100%，高度自动调整
html_with_styles = f"""
<html>
<head>
<style>
img {{
max-width: 100%;
height: auto;
}}
</style>
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/github-markdown-css/5.2.0/github-markdown.min.css">
</head>
<body>
{html_content}
</body>
</html>
"""
# 保存 HTML 文件到 static 文件夹
html_output_path = os.path.join(STATIC_DIR, f'{filename}.html')
with open(html_output_path, 'w', encoding='utf-8') as html_file:
html_file.write(html_with_styles)
# 处理图片资源，将 image/xxx 文件夹拷贝到 static/image/xxx
image_source_dir = os.path.join(MARKDOWN_DIR, IMAGE_DIR, filename)
image_target_dir = os.path.join(STATIC_DIR, IMAGE_DIR, filename)
print(f"Copying image resources from {image_source_dir} to {image_target_dir}")
if os.path.exists(image_source_dir):
# 确保 static/image/xxx 目录存在
if not os.path.exists(image_target_dir):
os.makedirs(image_target_dir)
# 拷贝图片资源
for image_file in os.listdir(image_source_dir):
full_image_file_path = os.path.join(image_source_dir, image_file)
if os.path.isfile(full_image_file_path):
shutil.copy(full_image_file_path, image_target_dir)
return jsonify({'html_url': f'/static/{filename}.html'})
# 静态文件（HTML 和图片资源）的访问
@app.route('/static/<path:filename>')
def serve_static(filename):
return send_from_directory(STATIC_DIR, filename)
'''
Vscode
'''
@app.route('/desktop/<user_id>/<path>')
def hello_world(user_id, path):
if 'user_id' not in session:
session['user_id'] = 'user_' + str(uuid.uuid4())
userid_recorder[user_id+'&'+path] = session['user_id']
# 在上层 目录下创建一个名为 user_id_path 的文件夹
path_dir = os.path.join(parent_dir, user_id, path)
os.makedirs(path_dir, exist_ok=True)
# 在此文件夹内部创建一个.config文件，并写入 user_id=user_id\n path=path
config_path = os.path.join(path_dir, '.config')
with open(config_path, 'w') as f:
f.write(f"user_id={user_id}\npath={path}")
return render_template('index.html', user_id=user_id, path=path)
@app.route('/vscode_data', methods=['POST'])
def vscode_data():
data = request.json
config = data['config']
realtime_response(config,data)
print(f"Received data from VSCode: {data}")
return jsonify({"status": "success", "received": data})
@app.route('/get_session')
def get_session():
user_session = session.get('user_id', 'default_session')
return jsonify({"session": user_session})
if __name__ == '__main__':
socketio.run(app, host='0.0.0.0', port=5500)

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Html/runs/run_20241123-164807_sy4ep8/code/backboardManager.py Normal file
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import time
class BackBoardManager:
def __init__(self):
self.backboards = {} # user_id: Backboard
def add_backboard(self, user_id, folder):
self.backboards[user_id] = Backboard(user_id, folder)
def get_backboard(self, user_id):
if user_id not in self.backboards:
return None
return self.backboards[user_id]
class Backboard:
def __init__(self, user_id, folder):
self.user_id = user_id
self.folder = folder
self.create_time = time.time()
self.enter_chapter_time = time.time()
def next_chapter(self):
self.enter_chapter_time = time.time()
def get_info_prompt(self):
return f"###Global Info:###\n\
User is {self.user_id}\n\
User's total study time is {self.get_deltatime_mmss(self.create_time, time.time())}\n\
User's current chapter study time is {self.get_deltatime_mmss(self.enter_chapter_time, time.time())}\n\
\n\n\
"
def get_deltatime_mmss(self, start_time, end_time):
elapsed_time = end_time - start_time
hours = int(elapsed_time // 3600)
minutes = int((elapsed_time % 3600) // 60)
seconds = int(elapsed_time % 60)
return f"{hours:02d}:{minutes:02d}:{seconds:02d}"

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{"id": "d67f8b09019c42ffb8fa5fba08af6719", "timestamp": "2024-11-23 16:49:09", "name": "assistant", "content": "##################### ITER 1, STEP 1: REASONING ######################", "role": "assistant", "url": null, "metadata": null}
{"id": "a0f6bd49028a49eca623c6b34c6923d9", "timestamp": "2024-11-23 16:49:09", "name": "system", "content": "## Tool Functions:\nThe following tool functions are available in the format of\n```\n{index}. {function name}: {function description}\n{argument1 name} ({argument type}): {argument description}\n{argument2 name} ({argument type}): {argument description}\n...\n```\n\n1. record: Call it to save some important info for other chapters. Like file path, function call example.\n\tcontent (string): The content to be recorded.\n2. next_chapter: Call it to go to next chapter.\n", "role": "system", "url": null, "metadata": null}
{"id": "1c556e54e5954fa8952ff8fc712ff31d", "timestamp": "2024-11-23 16:49:09", "name": "system", "content": "Respond a JSON dictionary in a markdown's fenced code block as follows:\n```json\n{\"thought\": \"what you thought\", \"speak\": \"what you speak\", \"function\": \"[{\\\"name\\\": \\\"{function name}\\\", \\\"arguments\\\": {\\\"{argument1 name}\\\": xxx, \\\"{argument2 name}\\\": xxx}}]\"}\n```\n ####", "role": "system", "url": null, "metadata": null}
{"id": "f461dc3e6ddd450e9b94e6cc2b0e7f5c", "timestamp": "2024-11-23 16:49:09", "name": "assistant", "content": "################################Prompt################################", "role": "assistant", "url": null, "metadata": null}
{"id": "9d37d51a26f44fe498f6de263fa92202", "timestamp": "2024-11-23 16:49:09", "name": "assistant", "content": "hi", "role": "assistant", "url": null, "metadata": null}
{"id": "60b441f75d854f50aad786839420e9c4", "timestamp": "2024-11-23 16:49:12", "name": "assistant", "content": "############################Result Parsed#############################", "role": "assistant", "url": null, "metadata": null}
{"id": "5d8cc88aa1da4b3ea3c45a05535c24ed", "timestamp": "2024-11-23 16:49:12", "name": "assistant", "content": "{'thought': 'The student seems to be starting the learning process. I should guide them to focus on the current chapter about binary search basics.', 'speak': \"Hello! Let's focus on learning about binary search. Do you have any questions about how binary search works or the example of guessing a number?\", 'function': []}", "role": "assistant", "url": null, "metadata": null}
{"id": "976ac1e152da41e4ae97e3d1b3657721", "timestamp": "2024-11-23 16:49:12", "name": "assistant", "content": "################################Speak#################################", "role": "assistant", "url": null, "metadata": null}
{"id": "edbc1b75451a4364a48d6fff70c8df0c", "timestamp": "2024-11-23 16:49:12", "name": "assistant", "content": {"thought": "The student seems to be starting the learning process. I should guide them to focus on the current chapter about binary search basics.", "speak": "Hello! Let's focus on learning about binary search. Do you have any questions about how binary search works or the example of guessing a number?", "function": []}, "role": "assistant", "url": null, "metadata": null}
{"id": "512870cf575040b5bf8b76a1aacb758d", "timestamp": "2024-11-23 16:51:06", "name": "assistant", "content": "##################### ITER 1, STEP 1: REASONING ######################", "role": "assistant", "url": null, "metadata": null}
{"id": "b87eb15a615f4947b353621f04c270a2", "timestamp": "2024-11-23 16:51:06", "name": "system", "content": "## Tool Functions:\nThe following tool functions are available in the format of\n```\n{index}. {function name}: {function description}\n{argument1 name} ({argument type}): {argument description}\n{argument2 name} ({argument type}): {argument description}\n...\n```\n\n1. record: Call it to save some important info for other chapters. Like file path, function call example.\n\tcontent (string): The content to be recorded.\n2. next_chapter: Call it to go to next chapter.\n", "role": "system", "url": null, "metadata": null}
{"id": "f48f64fa131e488c9609db67abb0d82e", "timestamp": "2024-11-23 16:51:06", "name": "system", "content": "Respond a JSON dictionary in a markdown's fenced code block as follows:\n```json\n{\"thought\": \"what you thought\", \"speak\": \"what you speak\", \"function\": \"[{\\\"name\\\": \\\"{function name}\\\", \\\"arguments\\\": {\\\"{argument1 name}\\\": xxx, \\\"{argument2 name}\\\": xxx}}]\"}\n```\n ####", "role": "system", "url": null, "metadata": null}
{"id": "a208fb97c336454e9b6f93349b5eb4c7", "timestamp": "2024-11-23 16:51:06", "name": "assistant", "content": "################################Prompt################################", "role": "assistant", "url": null, "metadata": null}
{"id": "ebe720e5e2da4437a34f80bc6edbed20", "timestamp": "2024-11-23 16:51:06", "name": "assistant", "content": "第一章节的内容我已经会了，我已经看懂了引入部分的二分原理。对于思考题，答案是13。", "role": "assistant", "url": null, "metadata": null}
{"id": "e5b40f6ef106429fba71436fcb61c363", "timestamp": "2024-11-23 16:51:09", "name": "assistant", "content": "############################Result Parsed#############################", "role": "assistant", "url": null, "metadata": null}
{"id": "dae56f4d73b44af8ba7dfe6f15b6524a", "timestamp": "2024-11-23 16:51:09", "name": "assistant", "content": "{'thought': 'The student is confident in their understanding and offers an answer to the question, but it needs verification. I will confirm their answer and provide a bit of support to ensure they grasp the concept well.', 'speak': \"Your answer is close, but let's think through the binary search process together. For the range from 1 to 4000, how many divisions do you think it would take to reduce that range down to just one number?\", 'function': []}", "role": "assistant", "url": null, "metadata": null}
{"id": "c5ae521400304a55aa073e94caac520d", "timestamp": "2024-11-23 16:51:09", "name": "assistant", "content": "################################Speak#################################", "role": "assistant", "url": null, "metadata": null}
{"id": "0dea919cff08461493c20fe15e791151", "timestamp": "2024-11-23 16:51:09", "name": "assistant", "content": {"thought": "The student is confident in their understanding and offers an answer to the question, but it needs verification. I will confirm their answer and provide a bit of support to ensure they grasp the concept well.", "speak": "Your answer is close, but let's think through the binary search process together. For the range from 1 to 4000, how many divisions do you think it would take to reduce that range down to just one number?", "function": []}, "role": "assistant", "url": null, "metadata": null}
{"id": "a5d26f5cdd1d463d9f7e6bdd3e817011", "timestamp": "2024-11-23 16:52:03", "name": "assistant", "content": "##################### ITER 1, STEP 1: REASONING ######################", "role": "assistant", "url": null, "metadata": null}
{"id": "c28e054bc53f41da90da636c0f92c34a", "timestamp": "2024-11-23 16:52:03", "name": "system", "content": "## Tool Functions:\nThe following tool functions are available in the format of\n```\n{index}. {function name}: {function description}\n{argument1 name} ({argument type}): {argument description}\n{argument2 name} ({argument type}): {argument description}\n...\n```\n\n1. record: Call it to save some important info for other chapters. Like file path, function call example.\n\tcontent (string): The content to be recorded.\n2. next_chapter: Call it to go to next chapter.\n", "role": "system", "url": null, "metadata": null}
{"id": "30f533c16fca48b28d587f9c93d5b1b9", "timestamp": "2024-11-23 16:52:03", "name": "system", "content": "Respond a JSON dictionary in a markdown's fenced code block as follows:\n```json\n{\"thought\": \"what you thought\", \"speak\": \"what you speak\", \"function\": \"[{\\\"name\\\": \\\"{function name}\\\", \\\"arguments\\\": {\\\"{argument1 name}\\\": xxx, \\\"{argument2 name}\\\": xxx}}]\"}\n```\n ####", "role": "system", "url": null, "metadata": null}
{"id": "19321a0e961442d681e73059efb81168", "timestamp": "2024-11-23 16:52:03", "name": "assistant", "content": "################################Prompt################################", "role": "assistant", "url": null, "metadata": null}
{"id": "132417d8314d4027828ff8a45dd6d47b", "timestamp": "2024-11-23 16:52:03", "name": "assistant", "content": "13次中，12次是用于将答案的区间进行收缩的。因为每次二分区间会缩小一半。现在我可以进入下一章了嘛？\n", "role": "assistant", "url": null, "metadata": null}
{"id": "82fded1b3b1a43e891a74b26b3deeb3e", "timestamp": "2024-11-23 16:52:06", "name": "assistant", "content": "############################Result Parsed#############################", "role": "assistant", "url": null, "metadata": null}
{"id": "9e27912d563e4285b102711cd77b86cc", "timestamp": "2024-11-23 16:52:06", "name": "assistant", "content": "{'thought': 'The student has provided a correct understanding of the binary search process in terms of how many guesses are needed. They are ready to move on to the next chapter.', 'speak': \"Great job! Your understanding of the binary search concept is solid. Let's move on to the next chapter about implementing binary search in code. I'll take you to the next chapter now.\", 'function': '[{\"name\": \"next_chapter\", \"arguments\": {}}]'}", "role": "assistant", "url": null, "metadata": null}
{"id": "6c0cd502ff904a23836ca2355cc0d658", "timestamp": "2024-11-23 16:52:06", "name": "assistant", "content": "################################Speak#################################", "role": "assistant", "url": null, "metadata": null}
{"id": "429a6089d16142de93ccd56a3d9c6830", "timestamp": "2024-11-23 16:52:06", "name": "assistant", "content": {"thought": "The student has provided a correct understanding of the binary search process in terms of how many guesses are needed. They are ready to move on to the next chapter.", "speak": "Great job! Your understanding of the binary search concept is solid. Let's move on to the next chapter about implementing binary search in code. I'll take you to the next chapter now.", "function": "[{\"name\": \"next_chapter\", \"arguments\": {}}]"}, "role": "assistant", "url": null, "metadata": null}
{"id": "d16918b0419a48d7a05b5f690faba78b", "timestamp": "2024-11-23 16:53:00", "name": "assistant", "content": "##################### ITER 1, STEP 1: REASONING ######################", "role": "assistant", "url": null, "metadata": null}
{"id": "b8642d6896eb472e919651dba5d812c3", "timestamp": "2024-11-23 16:53:00", "name": "system", "content": "## Tool Functions:\nThe following tool functions are available in the format of\n```\n{index}. {function name}: {function description}\n{argument1 name} ({argument type}): {argument description}\n{argument2 name} ({argument type}): {argument description}\n...\n```\n\n1. record: Call it to save some important info for other chapters. Like file path, function call example.\n\tcontent (string): The content to be recorded.\n2. next_chapter: Call it to go to next chapter.\n", "role": "system", "url": null, "metadata": null}
{"id": "4ccab5f64d064b999ec8c7af1febb553", "timestamp": "2024-11-23 16:53:00", "name": "system", "content": "Respond a JSON dictionary in a markdown's fenced code block as follows:\n```json\n{\"thought\": \"what you thought\", \"speak\": \"what you speak\", \"function\": \"[{\\\"name\\\": \\\"{function name}\\\", \\\"arguments\\\": {\\\"{argument1 name}\\\": xxx, \\\"{argument2 name}\\\": xxx}}]\"}\n```\n ####", "role": "system", "url": null, "metadata": null}
{"id": "60e291554aac4a6a8a7e435dc91bb32c", "timestamp": "2024-11-23 16:53:00", "name": "assistant", "content": "################################Prompt################################", "role": "assistant", "url": null, "metadata": null}
{"id": "67867ce0bc214090a774f2bb29095fde", "timestamp": "2024-11-23 16:53:00", "name": "assistant", "content": "对于这一章，我需要写的代码该取名叫什么呢？\n", "role": "assistant", "url": null, "metadata": null}
{"id": "092719ffdf214eb7b1a34b7abb6e76e4", "timestamp": "2024-11-23 16:53:02", "name": "assistant", "content": "############################Result Parsed#############################", "role": "assistant", "url": null, "metadata": null}
{"id": "1673342cd9064734979453acb5f82bb3", "timestamp": "2024-11-23 16:53:02", "name": "assistant", "content": "{'thought': \"The student is now trying to decide on a name for their binary search code. It's an important step in the coding process.\", 'speak': 'You can name your binary search function something descriptive like `binarySearch` or `findIndex`. Choose a name that clearly represents what the function does.', 'function': []}", "role": "assistant", "url": null, "metadata": null}
{"id": "db6f053c7a1e4e0380c086be502d1caa", "timestamp": "2024-11-23 16:53:02", "name": "assistant", "content": "################################Speak#################################", "role": "assistant", "url": null, "metadata": null}
{"id": "bfc9805a364e4b98a841f575a267603e", "timestamp": "2024-11-23 16:53:02", "name": "assistant", "content": {"thought": "The student is now trying to decide on a name for their binary search code. It's an important step in the coding process.", "speak": "You can name your binary search function something descriptive like `binarySearch` or `findIndex`. Choose a name that clearly represents what the function does.", "function": []}, "role": "assistant", "url": null, "metadata": null}
{"id": "5030e3e87b7f46c885c06cba0a38a271", "timestamp": "2024-11-23 16:54:11", "name": "assistant", "content": "##################### ITER 1, STEP 1: REASONING ######################", "role": "assistant", "url": null, "metadata": null}
{"id": "415416db2f1441188b971b6b5108715f", "timestamp": "2024-11-23 16:54:11", "name": "system", "content": "## Tool Functions:\nThe following tool functions are available in the format of\n```\n{index}. {function name}: {function description}\n{argument1 name} ({argument type}): {argument description}\n{argument2 name} ({argument type}): {argument description}\n...\n```\n\n1. record: Call it to save some important info for other chapters. Like file path, function call example.\n\tcontent (string): The content to be recorded.\n2. next_chapter: Call it to go to next chapter.\n", "role": "system", "url": null, "metadata": null}
{"id": "77151466db204504bc437fc5db464013", "timestamp": "2024-11-23 16:54:11", "name": "system", "content": "Respond a JSON dictionary in a markdown's fenced code block as follows:\n```json\n{\"thought\": \"what you thought\", \"speak\": \"what you speak\", \"function\": \"[{\\\"name\\\": \\\"{function name}\\\", \\\"arguments\\\": {\\\"{argument1 name}\\\": xxx, \\\"{argument2 name}\\\": xxx}}]\"}\n```\n ####", "role": "system", "url": null, "metadata": null}
{"id": "b833f4f8790f4bd688917b6be0496288", "timestamp": "2024-11-23 16:54:11", "name": "assistant", "content": "################################Prompt################################", "role": "assistant", "url": null, "metadata": null}
{"id": "b3e1299e8c7a4983a4cb306c9020efd8", "timestamp": "2024-11-23 16:54:11", "name": "assistant", "content": "很不好意思我是pyhon的初学者，不过我学过不少算法内容 ，我相信我能进行实现。 这个题目的输入改怎么读入呢？\n", "role": "assistant", "url": null, "metadata": null}
{"id": "5af2893e413c46bb86469092a1734356", "timestamp": "2024-11-23 16:54:14", "name": "assistant", "content": "############################Result Parsed#############################", "role": "assistant", "url": null, "metadata": null}
{"id": "0ff6aff1bcd44ff9a9b92acd154ad997", "timestamp": "2024-11-23 16:54:14", "name": "assistant", "content": "{'thought': 'The student is learning how to read input in Python, which is crucial for implementing the binary search algorithm.', 'speak': 'In Python, you can read the input using the `input()` function. For the first line, use `n = int(input())` to get the number of elements. For the second line, use `array = list(map(int, input().split()))` to read the sorted array. After that, read the number of queries with `q = int(input())` and loop through the next `q` lines for the queries.', 'function': []}", "role": "assistant", "url": null, "metadata": null}
{"id": "f70cd80c16cf4ba6af335a4517becfe2", "timestamp": "2024-11-23 16:54:14", "name": "assistant", "content": "################################Speak#################################", "role": "assistant", "url": null, "metadata": null}
{"id": "c58e29327f9643738be6c78028236dd3", "timestamp": "2024-11-23 16:54:14", "name": "assistant", "content": {"thought": "The student is learning how to read input in Python, which is crucial for implementing the binary search algorithm.", "speak": "In Python, you can read the input using the `input()` function. For the first line, use `n = int(input())` to get the number of elements. For the second line, use `array = list(map(int, input().split()))` to read the sorted array. After that, read the number of queries with `q = int(input())` and loop through the next `q` lines for the queries.", "function": []}, "role": "assistant", "url": null, "metadata": null}

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2024-11-23 16:48:19.264 | INFO | agentscope.models:read_model_configs:186 - Load configs for model wrapper: openai_cfg
2024-11-23 16:48:26.652 | INFO | agentscope.models.model:__init__:201 - Initialize model by configuration [openai_cfg]
2024-11-23 16:48:26.666 | WARNING | agentscope.models.openai_model:__init__:83 - fail to get max_length for gpt-4o-mini: 'Model [gpt-4o-mini] not found in OPENAI_MAX_LENGTH. The last updated date is 20231212'
2024-11-23 16:48:26.667 | INFO | agentscope.utils.monitor:register_budget:609 - set budget None to gpt-4o-mini
2024-11-23 16:48:26.667 | WARNING | agentscope.utils.monitor:register_budget:639 - Calculate budgets for model [gpt-4o-mini] is not supported
2024-11-23 16:48:26.679 | INFO | agentscope.utils.monitor:register:417 - Register metric [gpt-4o-mini.call_counter] to SqliteMonitor with unit [times] and quota [None]
2024-11-23 16:48:26.703 | INFO | agentscope.utils.monitor:register:417 - Register metric [gpt-4o-mini.prompt_tokens] to SqliteMonitor with unit [token] and quota [None]
2024-11-23 16:48:26.724 | INFO | agentscope.utils.monitor:register:417 - Register metric [gpt-4o-mini.completion_tokens] to SqliteMonitor with unit [token] and quota [None]
2024-11-23 16:48:26.746 | INFO | agentscope.utils.monitor:register:417 - Register metric [gpt-4o-mini.total_tokens] to SqliteMonitor with unit [token] and quota [None]
assistant: ##################### ITER 1, STEP 1: REASONING ######################
system: ## Tool Functions:
The following tool functions are available in the format of
```
{index}. {function name}: {function description}
{argument1 name} ({argument type}): {argument description}
{argument2 name} ({argument type}): {argument description}
...
```
1. record: Call it to save some important info for other chapters. Like file path, function call example.
content (string): The content to be recorded.
2. next_chapter: Call it to go to next chapter.
system: Respond a JSON dictionary in a markdown's fenced code block as follows:
```json
{"thought": "what you thought", "speak": "what you speak", "function": "[{\"name\": \"{function name}\", \"arguments\": {\"{argument1 name}\": xxx, \"{argument2 name}\": xxx}}]"}
```
####
assistant: ################################Prompt################################
assistant: hi
assistant: ############################Result Parsed#############################
assistant: {'thought': 'The student seems to be starting the learning process. I should guide them to focus on the current chapter about binary search basics.', 'speak': "Hello! Let's focus on learning about binary search. Do you have any questions about how binary search works or the example of guessing a number?", 'function': []}
assistant: ################################Speak#################################
assistant: {'thought': 'The student seems to be starting the learning process. I should guide them to focus on the current chapter about binary search basics.', 'speak': "Hello! Let's focus on learning about binary search. Do you have any questions about how binary search works or the example of guessing a number?", 'function': []}
assistant: ##################### ITER 1, STEP 1: REASONING ######################
system: ## Tool Functions:
The following tool functions are available in the format of
```
{index}. {function name}: {function description}
{argument1 name} ({argument type}): {argument description}
{argument2 name} ({argument type}): {argument description}
...
```
1. record: Call it to save some important info for other chapters. Like file path, function call example.
content (string): The content to be recorded.
2. next_chapter: Call it to go to next chapter.
system: Respond a JSON dictionary in a markdown's fenced code block as follows:
```json
{"thought": "what you thought", "speak": "what you speak", "function": "[{\"name\": \"{function name}\", \"arguments\": {\"{argument1 name}\": xxx, \"{argument2 name}\": xxx}}]"}
```
####
assistant: ################################Prompt################################
assistant: 第一章节的内容我已经会了，我已经看懂了引入部分的二分原理。对于思考题，答案是13。
assistant: ############################Result Parsed#############################
assistant: {'thought': 'The student is confident in their understanding and offers an answer to the question, but it needs verification. I will confirm their answer and provide a bit of support to ensure they grasp the concept well.', 'speak': "Your answer is close, but let's think through the binary search process together. For the range from 1 to 4000, how many divisions do you think it would take to reduce that range down to just one number?", 'function': []}
assistant: ################################Speak#################################
assistant: {'thought': 'The student is confident in their understanding and offers an answer to the question, but it needs verification. I will confirm their answer and provide a bit of support to ensure they grasp the concept well.', 'speak': "Your answer is close, but let's think through the binary search process together. For the range from 1 to 4000, how many divisions do you think it would take to reduce that range down to just one number?", 'function': []}
assistant: ##################### ITER 1, STEP 1: REASONING ######################
system: ## Tool Functions:
The following tool functions are available in the format of
```
{index}. {function name}: {function description}
{argument1 name} ({argument type}): {argument description}
{argument2 name} ({argument type}): {argument description}
...
```
1. record: Call it to save some important info for other chapters. Like file path, function call example.
content (string): The content to be recorded.
2. next_chapter: Call it to go to next chapter.
system: Respond a JSON dictionary in a markdown's fenced code block as follows:
```json
{"thought": "what you thought", "speak": "what you speak", "function": "[{\"name\": \"{function name}\", \"arguments\": {\"{argument1 name}\": xxx, \"{argument2 name}\": xxx}}]"}
```
####
assistant: ################################Prompt################################
assistant: 13次中，12次是用于将答案的区间进行收缩的。因为每次二分区间会缩小一半。现在我可以进入下一章了嘛？
assistant: ############################Result Parsed#############################
assistant: {'thought': 'The student has provided a correct understanding of the binary search process in terms of how many guesses are needed. They are ready to move on to the next chapter.', 'speak': "Great job! Your understanding of the binary search concept is solid. Let's move on to the next chapter about implementing binary search in code. I'll take you to the next chapter now.", 'function': '[{"name": "next_chapter", "arguments": {}}]'}
assistant: ################################Speak#################################
assistant: {'thought': 'The student has provided a correct understanding of the binary search process in terms of how many guesses are needed. They are ready to move on to the next chapter.', 'speak': "Great job! Your understanding of the binary search concept is solid. Let's move on to the next chapter about implementing binary search in code. I'll take you to the next chapter now.", 'function': '[{"name": "next_chapter", "arguments": {}}]'}
assistant: ##################### ITER 1, STEP 1: REASONING ######################
system: ## Tool Functions:
The following tool functions are available in the format of
```
{index}. {function name}: {function description}
{argument1 name} ({argument type}): {argument description}
{argument2 name} ({argument type}): {argument description}
...
```
1. record: Call it to save some important info for other chapters. Like file path, function call example.
content (string): The content to be recorded.
2. next_chapter: Call it to go to next chapter.
system: Respond a JSON dictionary in a markdown's fenced code block as follows:
```json
{"thought": "what you thought", "speak": "what you speak", "function": "[{\"name\": \"{function name}\", \"arguments\": {\"{argument1 name}\": xxx, \"{argument2 name}\": xxx}}]"}
```
####
assistant: ################################Prompt################################
assistant: 对于这一章，我需要写的代码该取名叫什么呢？
assistant: ############################Result Parsed#############################
assistant: {'thought': "The student is now trying to decide on a name for their binary search code. It's an important step in the coding process.", 'speak': 'You can name your binary search function something descriptive like `binarySearch` or `findIndex`. Choose a name that clearly represents what the function does.', 'function': []}
assistant: ################################Speak#################################
assistant: {'thought': "The student is now trying to decide on a name for their binary search code. It's an important step in the coding process.", 'speak': 'You can name your binary search function something descriptive like `binarySearch` or `findIndex`. Choose a name that clearly represents what the function does.', 'function': []}
assistant: ##################### ITER 1, STEP 1: REASONING ######################
system: ## Tool Functions:
The following tool functions are available in the format of
```
{index}. {function name}: {function description}
{argument1 name} ({argument type}): {argument description}
{argument2 name} ({argument type}): {argument description}
...
```
1. record: Call it to save some important info for other chapters. Like file path, function call example.
content (string): The content to be recorded.
2. next_chapter: Call it to go to next chapter.
system: Respond a JSON dictionary in a markdown's fenced code block as follows:
```json
{"thought": "what you thought", "speak": "what you speak", "function": "[{\"name\": \"{function name}\", \"arguments\": {\"{argument1 name}\": xxx, \"{argument2 name}\": xxx}}]"}
```
####
assistant: ################################Prompt################################
assistant: 很不好意思我是pyhon的初学者，不过我学过不少算法内容 ，我相信我能进行实现。 这个题目的输入改怎么读入呢？
assistant: ############################Result Parsed#############################
assistant: {'thought': 'The student is learning how to read input in Python, which is crucial for implementing the binary search algorithm.', 'speak': 'In Python, you can read the input using the `input()` function. For the first line, use `n = int(input())` to get the number of elements. For the second line, use `array = list(map(int, input().split()))` to read the sorted array. After that, read the number of queries with `q = int(input())` and loop through the next `q` lines for the queries.', 'function': []}
assistant: ################################Speak#################################
assistant: {'thought': 'The student is learning how to read input in Python, which is crucial for implementing the binary search algorithm.', 'speak': 'In Python, you can read the input using the `input()` function. For the first line, use `n = int(input())` to get the number of elements. For the second line, use `array = list(map(int, input().split()))` to read the sorted array. After that, read the number of queries with `q = int(input())` and loop through the next `q` lines for the queries.', 'function': []}
2024-11-23 20:04:46.841 | INFO | agentscope.models.model:__init__:201 - Initialize model by configuration [openai_cfg]
2024-11-23 20:04:46.926 | WARNING | agentscope.models.openai_model:__init__:83 - fail to get max_length for gpt-4o-mini: 'Model [gpt-4o-mini] not found in OPENAI_MAX_LENGTH. The last updated date is 20231212'
2024-11-23 20:04:46.927 | INFO | agentscope.utils.monitor:register_budget:609 - set budget None to gpt-4o-mini
2024-11-23 20:04:46.927 | WARNING | agentscope.utils.monitor:register_budget:639 - Calculate budgets for model [gpt-4o-mini] is not supported