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Wendong
2025-03-15 10:02:42 +08:00
parent a12047f299
commit c783c2c8bb
16 changed files with 1110 additions and 757 deletions
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18
owl/.env_template
View File

@@ -1,6 +1,9 @@
# MODEL & API (See https://docs.camel-ai.org/key_modules/models.html#)
#===========================================
# MODEL & API
# (See https://docs.camel-ai.org/key_modules/models.html#)
#===========================================
# OPENAI API
# OPENAI API (https://platform.openai.com/api-keys)
# OPENAI_API_KEY= ""
# OPENAI_API_BASE_URL=""
@@ -15,15 +18,12 @@
#===========================================
# Google Search API (https://developers.google.com/custom-search/v1/overview)
GOOGLE_API_KEY=""
SEARCH_ENGINE_ID=""
# Hugging Face API (https://huggingface.co/join)
HF_TOKEN=""
# GOOGLE_API_KEY=""
# SEARCH_ENGINE_ID=""
# Chunkr API (https://chunkr.ai/)
CHUNKR_API_KEY=""
# CHUNKR_API_KEY=""
# Firecrawl API (https://www.firecrawl.dev/)
FIRECRAWL_API_KEY=""
#FIRECRAWL_API_KEY=""
#FIRECRAWL_API_URL="https://api.firecrawl.dev"

9
owl/examples/run.py
View File

@@ -25,22 +25,23 @@ from camel.toolkits import (
)
from camel.types import ModelPlatformType, ModelType
from camel.logger import set_log_level
from camel.societies import RolePlaying
from owl.utils import OwlRolePlaying, run_society, DocumentProcessingToolkit
from owl.utils import run_society, DocumentProcessingToolkit
load_dotenv()
set_log_level(level="DEBUG")
def construct_society(question: str) -> OwlRolePlaying:
def construct_society(question: str) -> RolePlaying:
r"""Construct a society of agents based on the given question.
Args:
question (str): The task or question to be addressed by the society.
Returns:
OwlRolePlaying: A configured society of agents ready to address the question.
RolePlaying: A configured society of agents ready to address the question.
"""
# Create models for different components
@@ -112,7 +113,7 @@ def construct_society(question: str) -> OwlRolePlaying:
}
# Create and return the society
society = OwlRolePlaying(
society = RolePlaying(
**task_kwargs,
user_role_name="user",
user_agent_kwargs=user_agent_kwargs,

14
owl/examples/run_deepseek_zh.py
View File

@@ -31,7 +31,9 @@ from camel.toolkits import (
from camel.types import ModelPlatformType, ModelType
from owl.utils import OwlRolePlaying, run_society, DocumentProcessingToolkit
from owl.utils import run_society
from camel.societies import RolePlaying
from camel.logger import set_log_level
@@ -40,14 +42,14 @@ set_log_level(level="DEBUG")
load_dotenv()
def construct_society(question: str) -> OwlRolePlaying:
def construct_society(question: str) -> RolePlaying:
r"""Construct a society of agents based on the given question.
Args:
question (str): The task or question to be addressed by the society.
Returns:
OwlRolePlaying: A configured society of agents ready to address the question.
RolePlaying: A configured society of agents ready to address the question.
"""
# Create models for different components
@@ -84,7 +86,7 @@ def construct_society(question: str) -> OwlRolePlaying:
}
# Create and return the society
society = OwlRolePlaying(
society = RolePlaying(
**task_kwargs,
user_role_name="user",
user_agent_kwargs=user_agent_kwargs,
@@ -99,9 +101,7 @@ def construct_society(question: str) -> OwlRolePlaying:
def main():
r"""Main function to run the OWL system with an example question."""
# Example research question
question = (
"搜索OWL项目最近的新闻并生成一篇报告，最后保存到本地。"
)
question = "搜索OWL项目最近的新闻并生成一篇报告，最后保存到本地。"
# Construct and run the society
society = construct_society(question)

10
owl/examples/run_mini.py
View File

@@ -22,20 +22,22 @@ from camel.toolkits import (
from camel.types import ModelPlatformType, ModelType
from camel.logger import set_log_level
from owl.utils import OwlRolePlaying, run_society
from owl.utils import run_society
from camel.societies import RolePlaying
load_dotenv()
set_log_level(level="DEBUG")
def construct_society(question: str) -> OwlRolePlaying:
def construct_society(question: str) -> RolePlaying:
r"""Construct a society of agents based on the given question.
Args:
question (str): The task or question to be addressed by the society.
Returns:
OwlRolePlaying: A configured society of agents ready to address the
RolePlaying: A configured society of agents ready to address the
question.
"""
@@ -86,7 +88,7 @@ def construct_society(question: str) -> OwlRolePlaying:
}
# Create and return the society
society = OwlRolePlaying(
society = RolePlaying(
**task_kwargs,
user_role_name="user",
user_agent_kwargs=user_agent_kwargs,

10
owl/examples/run_ollama.py
View File

@@ -25,7 +25,9 @@ from camel.toolkits import (
)
from camel.types import ModelPlatformType
from owl.utils import OwlRolePlaying, run_society
from owl.utils import run_society
from camel.societies import RolePlaying
from camel.logger import set_log_level
@@ -34,14 +36,14 @@ set_log_level(level="DEBUG")
load_dotenv()
def construct_society(question: str) -> OwlRolePlaying:
def construct_society(question: str) -> RolePlaying:
r"""Construct a society of agents based on the given question.
Args:
question (str): The task or question to be addressed by the society.
Returns:
OwlRolePlaying: A configured society of agents ready to address the question.
RolePlaying: A configured society of agents ready to address the question.
"""
# Create models for different components
@@ -105,7 +107,7 @@ def construct_society(question: str) -> OwlRolePlaying:
}
# Create and return the society
society = OwlRolePlaying(
society = RolePlaying(
**task_kwargs,
user_role_name="user",
user_agent_kwargs=user_agent_kwargs,

10
owl/examples/run_openai_compatiable_model.py
View File

@@ -25,8 +25,8 @@ from camel.toolkits import (
)
from camel.types import ModelPlatformType
from owl.utils import OwlRolePlaying, run_society
from owl.utils import run_society
from camel.societies import RolePlaying
from camel.logger import set_log_level
set_log_level(level="DEBUG")
@@ -34,14 +34,14 @@ set_log_level(level="DEBUG")
load_dotenv()
def construct_society(question: str) -> OwlRolePlaying:
def construct_society(question: str) -> RolePlaying:
r"""Construct a society of agents based on the given question.
Args:
question (str): The task or question to be addressed by the society.
Returns:
OwlRolePlaying: A configured society of agents ready to address the question.
RolePlaying: A configured society of agents ready to address the question.
"""
# Create models for different components
@@ -110,7 +110,7 @@ def construct_society(question: str) -> OwlRolePlaying:
}
# Create and return the society
society = OwlRolePlaying(
society = RolePlaying(
**task_kwargs,
user_role_name="user",
user_agent_kwargs=user_agent_kwargs,

8
owl/examples/run_qwen_mini_zh.py
View File

@@ -22,7 +22,9 @@ from camel.models import ModelFactory
from camel.toolkits import BrowserToolkit, SearchToolkit, FileWriteToolkit
from camel.types import ModelPlatformType, ModelType
from owl.utils import OwlRolePlaying, run_society
from owl.utils import run_society
from camel.societies import RolePlaying
from camel.logger import set_log_level
@@ -31,7 +33,7 @@ set_log_level(level="DEBUG")
load_dotenv()
def construct_society(question: str) -> OwlRolePlaying:
def construct_society(question: str) -> RolePlaying:
r"""Construct the society based on the question."""
user_role_name = "user"
@@ -82,7 +84,7 @@ def construct_society(question: str) -> OwlRolePlaying:
"with_task_specify": False,
}
society = OwlRolePlaying(
society = RolePlaying(
**task_kwargs,
user_role_name=user_role_name,
user_agent_kwargs=user_agent_kwargs,

9
owl/examples/run_qwen_zh.py
View File

@@ -28,8 +28,9 @@ from camel.toolkits import (
FileWriteToolkit,
)
from camel.types import ModelPlatformType, ModelType
from camel.societies import RolePlaying
from owl.utils import OwlRolePlaying, run_society, DocumentProcessingToolkit
from owl.utils import run_society, DocumentProcessingToolkit
from camel.logger import set_log_level
@@ -38,7 +39,7 @@ set_log_level(level="DEBUG")
load_dotenv()
def construct_society(question: str) -> OwlRolePlaying:
def construct_society(question: str) -> RolePlaying:
"""
Construct a society of agents based on the given question.
@@ -46,7 +47,7 @@ def construct_society(question: str) -> OwlRolePlaying:
question (str): The task or question to be addressed by the society.
Returns:
OwlRolePlaying: A configured society of agents ready to address the question.
RolePlaying: A configured society of agents ready to address the question.
"""
# Create models for different components
@@ -118,7 +119,7 @@ def construct_society(question: str) -> OwlRolePlaying:
}
# Create and return the society
society = OwlRolePlaying(
society = RolePlaying(
**task_kwargs,
user_role_name="user",
user_agent_kwargs=user_agent_kwargs,

16
owl/examples/run_terminal.py
View File

@@ -18,26 +18,28 @@ from camel.toolkits import (
SearchToolkit,
BrowserToolkit,
FileWriteToolkit,
TerminalToolkit
TerminalToolkit,
)
from camel.types import ModelPlatformType, ModelType
from camel.logger import set_log_level
from owl.utils import OwlRolePlaying, run_society
from owl.utils import run_society
from camel.societies import RolePlaying
load_dotenv()
set_log_level(level="DEBUG")
# Get current script directory
base_dir = os.path.dirname(os.path.abspath(__file__))
def construct_society(question: str) -> OwlRolePlaying:
def construct_society(question: str) -> RolePlaying:
r"""Construct a society of agents based on the given question.
Args:
question (str): The task or question to be addressed by the society.
Returns:
OwlRolePlaying: A configured society of agents ready to address the
RolePlaying: A configured society of agents ready to address the
question.
"""
@@ -89,7 +91,7 @@ def construct_society(question: str) -> OwlRolePlaying:
}
# Create and return the society
society = OwlRolePlaying(
society = RolePlaying(
**task_kwargs,
user_role_name="user",
user_agent_kwargs=user_agent_kwargs,
@@ -113,7 +115,9 @@ def main():
answer, chat_history, token_count = run_society(society)
# Output the result
print(f"\033[94mAnswer: {answer}\nChat History: {chat_history}\ntoken_count:{token_count}\033[0m")
print(
f"\033[94mAnswer: {answer}\nChat History: {chat_history}\ntoken_count:{token_count}\033[0m"
)
if __name__ == "__main__":

20
owl/examples/run_terminal_zh.py
View File

@@ -18,27 +18,31 @@ from camel.toolkits import (
SearchToolkit,
BrowserToolkit,
FileWriteToolkit,
TerminalToolkit
TerminalToolkit,
)
from camel.types import ModelPlatformType, ModelType
from camel.logger import set_log_level
from owl.utils import OwlRolePlaying, run_society
from owl.utils import run_society
from camel.societies import RolePlaying
import os
load_dotenv()
set_log_level(level="DEBUG")
import os
# Get current script directory
base_dir = os.path.dirname(os.path.abspath(__file__))
def construct_society(question: str) -> OwlRolePlaying:
def construct_society(question: str) -> RolePlaying:
r"""Construct a society of agents based on the given question.
Args:
question (str): The task or question to be addressed by the society.
Returns:
OwlRolePlaying: A configured society of agents ready to address the
RolePlaying: A configured society of agents ready to address the
question.
"""
@@ -90,7 +94,7 @@ def construct_society(question: str) -> OwlRolePlaying:
}
# Create and return the society
society = OwlRolePlaying(
society = RolePlaying(
**task_kwargs,
user_role_name="user",
user_agent_kwargs=user_agent_kwargs,
@@ -112,7 +116,9 @@ def main():
answer, chat_history, token_count = run_society(society)
# Output the result
print(f"\033[94mAnswer: {answer}\nChat History: {chat_history}\ntoken_count:{token_count}\033[0m")
print(
f"\033[94mAnswer: {answer}\nChat History: {chat_history}\ntoken_count:{token_count}\033[0m"
)
if __name__ == "__main__":

395
owl/nextwebapp.py
View File

@@ -1,14 +1,25 @@
# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
# Import from the correct module path
from owl.utils import run_society
import os
import gradio as gr
import time
import json
from typing import Tuple, List, Dict, Any
from typing import Tuple, List, Dict
import importlib
from dotenv import load_dotenv, set_key, find_dotenv, unset_key
os.environ['PYTHONIOENCODING'] = 'utf-8'
os.environ["PYTHONIOENCODING"] = "utf-8"
# Enhanced CSS with navigation bar and additional styling
custom_css = """
@@ -250,14 +261,14 @@ button.primary:hover {
# Dictionary containing module descriptions
MODULE_DESCRIPTIONS = {
"run": "默认模式：使用OpenAI模型的默认的智能体协作模式，适合大多数任务。",
"run_mini":"使用使用OpenAI模型最小化配置处理任务",
"run_deepseek_zh":"使用deepseek模型处理中文任务",
"run_mini": "使用使用OpenAI模型最小化配置处理任务",
"run_deepseek_zh": "使用deepseek模型处理中文任务",
"run_terminal_zh": "终端模式：可执行命令行操作，支持网络搜索、文件处理等功能。适合需要系统交互的任务，使用OpenAI模型",
"run_gaia_roleplaying":"GAIA基准测试实现，用于评估Agent能力",
"run_openai_compatiable_model":"使用openai兼容模型处理任务",
"run_ollama":"使用本地ollama模型处理任务",
"run_qwen_mini_zh":"使用qwen模型最小化配置处理任务",
"run_qwen_zh":"使用qwen模型处理任务",
"run_gaia_roleplaying": "GAIA基准测试实现，用于评估Agent能力",
"run_openai_compatiable_model": "使用openai兼容模型处理任务",
"run_ollama": "使用本地ollama模型处理任务",
"run_qwen_mini_zh": "使用qwen模型最小化配置处理任务",
"run_qwen_zh": "使用qwen模型处理任务",
}
# 默认环境变量模板
@@ -292,12 +303,13 @@ FIRECRAWL_API_KEY=""
#FIRECRAWL_API_URL="https://api.firecrawl.dev"
"""
def format_chat_history(chat_history: List[Dict[str, str]]) -> List[List[str]]:
"""将聊天历史格式化为Gradio聊天组件可接受的格式
Args:
chat_history: 原始聊天历史
Returns:
List[List[str]]: 格式化后的聊天历史
"""
@@ -305,22 +317,23 @@ def format_chat_history(chat_history: List[Dict[str, str]]) -> List[List[str]]:
for message in chat_history:
user_msg = message.get("user", "")
assistant_msg = message.get("assistant", "")
if user_msg:
formatted_history.append([user_msg, None])
if assistant_msg and formatted_history:
formatted_history[-1][1] = assistant_msg
elif assistant_msg:
formatted_history.append([None, assistant_msg])
return formatted_history
def validate_input(question: str) -> bool:
"""验证用户输入是否有效
Args:
question: 用户问题
Returns:
bool: 输入是否有效
"""
@@ -329,121 +342,111 @@ def validate_input(question: str) -> bool:
return False
return True
def run_owl(question: str, example_module: str) -> Tuple[str, List[List[str]], str, str]:
def run_owl(
question: str, example_module: str
) -> Tuple[str, List[List[str]], str, str]:
"""运行OWL系统并返回结果
Args:
question: 用户问题
example_module: 要导入的示例模块名（如 "run_terminal_zh" 或 "run_deep"）
Returns:
Tuple[...]: 回答、聊天历史、令牌计数、状态
"""
# 验证输入
if not validate_input(question):
return (
"请输入有效的问题",
[],
"0",
"❌ 错误: 输入无效"
)
return ("请输入有效的问题", [], "0", "❌ 错误: 输入无效")
try:
# 确保环境变量已加载
load_dotenv(find_dotenv(), override=True)
# 检查模块是否在MODULE_DESCRIPTIONS中
if example_module not in MODULE_DESCRIPTIONS:
return (
f"所选模块 '{example_module}' 不受支持",
[],
"0",
f"❌ 错误: 不支持的模块"
f"所选模块 '{example_module}' 不受支持",
[],
"0",
"❌ 错误: 不支持的模块",
)
# 动态导入目标模块
module_path = f"owl.examples.{example_module}"
try:
module = importlib.import_module(module_path)
except ImportError as ie:
return (
f"无法导入模块: {module_path}",
[],
"0",
f"❌ 错误: 模块 {example_module} 不存在或无法加载 - {str(ie)}"
f"无法导入模块: {module_path}",
[],
"0",
f"❌ 错误: 模块 {example_module} 不存在或无法加载 - {str(ie)}",
)
except Exception as e:
return (
f"导入模块时发生错误: {module_path}",
[],
"0",
f"❌ 错误: {str(e)}"
)
return (f"导入模块时发生错误: {module_path}", [], "0", f"❌ 错误: {str(e)}")
# 检查是否包含construct_society函数
if not hasattr(module, "construct_society"):
return (
f"模块 {module_path} 中未找到 construct_society 函数",
[],
"0",
f"❌ 错误: 模块接口不兼容"
f"模块 {module_path} 中未找到 construct_society 函数",
[],
"0",
"❌ 错误: 模块接口不兼容",
)
# 构建社会模拟
try:
society = module.construct_society(question)
except Exception as e:
return (
f"构建社会模拟时发生错误: {str(e)}",
[],
"0",
f"❌ 错误: 构建失败 - {str(e)}"
f"构建社会模拟时发生错误: {str(e)}",
[],
"0",
f"❌ 错误: 构建失败 - {str(e)}",
)
# 运行社会模拟
try:
answer, chat_history, token_info = run_society(society)
except Exception as e:
return (
f"运行社会模拟时发生错误: {str(e)}",
[],
"0",
f"❌ 错误: 运行失败 - {str(e)}"
f"运行社会模拟时发生错误: {str(e)}",
[],
"0",
f"❌ 错误: 运行失败 - {str(e)}",
)
# 格式化聊天历史
try:
formatted_chat_history = format_chat_history(chat_history)
except Exception as e:
except Exception:
# 如果格式化失败，返回空历史记录但继续处理
formatted_chat_history = []
# 安全地获取令牌计数
if not isinstance(token_info, dict):
token_info = {}
completion_tokens = token_info.get("completion_token_count", 0)
prompt_tokens = token_info.get("prompt_token_count", 0)
total_tokens = completion_tokens + prompt_tokens
return (
answer,
formatted_chat_history,
f"完成令牌: {completion_tokens:,} | 提示令牌: {prompt_tokens:,} | 总计: {total_tokens:,}",
"✅ 成功完成"
answer,
formatted_chat_history,
f"完成令牌: {completion_tokens:,} | 提示令牌: {prompt_tokens:,} | 总计: {total_tokens:,}",
"✅ 成功完成",
)
except Exception as e:
return (
f"发生错误: {str(e)}",
[],
"0",
f"❌ 错误: {str(e)}"
)
return (f"发生错误: {str(e)}", [], "0", f"❌ 错误: {str(e)}")
def update_module_description(module_name: str) -> str:
"""返回所选模块的描述"""
return MODULE_DESCRIPTIONS.get(module_name, "无可用描述")
# 环境变量管理功能
def init_env_file():
"""初始化.env文件如果不存在"""
@@ -454,11 +457,12 @@ def init_env_file():
dotenv_path = find_dotenv()
return dotenv_path
def load_env_vars():
"""加载环境变量并返回字典格式"""
dotenv_path = init_env_file()
load_dotenv(dotenv_path, override=True)
env_vars = {}
with open(dotenv_path, "r") as f:
for line in f:
@@ -466,79 +470,84 @@ def load_env_vars():
if line and not line.startswith("#"):
if "=" in line:
key, value = line.split("=", 1)
env_vars[key.strip()] = value.strip().strip('"\'')
env_vars[key.strip()] = value.strip().strip("\"'")
return env_vars
def save_env_vars(env_vars):
"""保存环境变量到.env文件"""
try:
dotenv_path = init_env_file()
# 保存每个环境变量
for key, value in env_vars.items():
if key and key.strip(): # 确保键不为空
set_key(dotenv_path, key.strip(), value.strip())
# 重新加载环境变量以确保生效
load_dotenv(dotenv_path, override=True)
return True, "环境变量已成功保存！"
except Exception as e:
return False, f"保存环境变量时出错: {str(e)}"
def add_env_var(key, value):
"""添加或更新单个环境变量"""
try:
if not key or not key.strip():
return False, "变量名不能为空"
dotenv_path = init_env_file()
set_key(dotenv_path, key.strip(), value.strip())
load_dotenv(dotenv_path, override=True)
return True, f"环境变量 {key} 已成功添加/更新！"
except Exception as e:
return False, f"添加环境变量时出错: {str(e)}"
def delete_env_var(key):
"""删除环境变量"""
try:
if not key or not key.strip():
return False, "变量名不能为空"
dotenv_path = init_env_file()
unset_key(dotenv_path, key.strip())
# 从当前进程环境中也删除
if key in os.environ:
del os.environ[key]
return True, f"环境变量 {key} 已成功删除！"
except Exception as e:
return False, f"删除环境变量时出错: {str(e)}"
def mask_sensitive_value(key: str, value: str) -> str:
"""对敏感信息进行掩码处理
Args:
key: 环境变量名
value: 环境变量值
Returns:
str: 处理后的值
"""
# 定义需要掩码的敏感关键词
sensitive_keywords = ['key', 'token', 'secret', 'password', 'api']
sensitive_keywords = ["key", "token", "secret", "password", "api"]
# 检查是否包含敏感关键词（不区分大小写）
is_sensitive = any(keyword in key.lower() for keyword in sensitive_keywords)
if is_sensitive and value:
# 如果是敏感信息且有值，则显示掩码
return '*' * 8
return "*" * 8
return value
def update_env_table():
"""更新环境变量表格显示，对敏感信息进行掩码处理"""
env_vars = load_env_vars()
@@ -546,6 +555,7 @@ def update_env_table():
masked_env_vars = [[k, mask_sensitive_value(k, v)] for k, v in env_vars.items()]
return masked_env_vars
def create_ui():
"""创建增强版Gradio界面"""
with gr.Blocks(css=custom_css, theme=gr.themes.Soft(primary_hue="blue")) as app:
@@ -569,7 +579,7 @@ def create_ui():
<p>我们的愿景是彻底改变AI代理协作解决现实世界任务的方式。通过利用动态代理交互，OWL能够在多个领域实现更自然、高效和稳健的任务自动化。</p>
</div>
""")
with gr.Row(elem_id="features"):
gr.HTML("""
<div class="features-section">
@@ -610,7 +620,7 @@ def create_ui():
</div>
</div>
""")
with gr.Row():
with gr.Column(scale=2):
question_input = gr.Textbox(
@@ -620,26 +630,28 @@ def create_ui():
elem_id="question_input",
show_copy_button=True,
)
# 增强版模块选择下拉菜单
# 只包含MODULE_DESCRIPTIONS中定义的模块
module_dropdown = gr.Dropdown(
choices=list(MODULE_DESCRIPTIONS.keys()),
value="run_terminal_zh",
label="选择功能模块",
interactive=True
interactive=True,
)
# 模块描述文本框
module_description = gr.Textbox(
value=MODULE_DESCRIPTIONS["run_terminal_zh"],
label="模块描述",
interactive=False,
elem_classes="module-info"
elem_classes="module-info",
)
run_button = gr.Button("运行", variant="primary", elem_classes="primary")
run_button = gr.Button(
"运行", variant="primary", elem_classes="primary"
)
with gr.Column(scale=1):
gr.Markdown("""
### 使用指南
@@ -651,127 +663,103 @@ def create_ui():
> **高级提示**: 对于复杂任务，可以尝试指定具体步骤和预期结果
""")
status_output = gr.Textbox(label="状态", interactive=False)
with gr.Tabs():
with gr.TabItem("回答"):
answer_output = gr.Textbox(
label="回答",
lines=10,
elem_classes="answer-box"
label="回答", lines=10, elem_classes="answer-box"
)
with gr.TabItem("对话历史"):
chat_output = gr.Chatbot(
label="完整对话记录",
elem_classes="chat-container",
height=500
label="完整对话记录", elem_classes="chat-container", height=500
)
token_count_output = gr.Textbox(
label="令牌计数",
interactive=False,
elem_classes="token-count"
label="令牌计数", interactive=False, elem_classes="token-count"
)
# 示例问题
examples = [
"打开百度搜索，总结一下camel-ai的camel框架的github star、fork数目等，并把数字用plot包写成python文件保存到本地，用本地终端执行python文件显示图出来给我",
"请分析GitHub上CAMEL-AI项目的最新统计数据。找出该项目的星标数量、贡献者数量和最近的活跃度。",
"浏览亚马逊并找出一款对程序员有吸引力的产品。请提供产品名称和价格",
"写一个hello world的python文件，保存到本地",
]
gr.Examples(
examples=examples,
inputs=question_input
)
gr.Examples(examples=examples, inputs=question_input)
# 新增: 环境变量管理选项卡
with gr.TabItem("环境变量管理", id="env-settings"):
gr.Markdown("""
gr.Markdown("""
## 环境变量管理
在此处设置模型API密钥和其他服务凭证。这些信息将保存在本地的`.env`文件中，确保您的API密钥安全存储且不会上传到网络。
""")
# 环境变量表格
env_table = gr.Dataframe(
headers=["变量名", "值"],
datatype=["str", "str"],
row_count=10,
col_count=(2, "fixed"),
value=update_env_table,
label="当前环境变量",
interactive=False
)
with gr.Row():
with gr.Column(scale=1):
new_env_key = gr.Textbox(label="变量名", placeholder="例如: OPENAI_API_KEY")
with gr.Column(scale=2):
new_env_value = gr.Textbox(label="值", placeholder="输入API密钥或其他配置值")
with gr.Row():
add_env_button = gr.Button("添加/更新变量", variant="primary")
refresh_button = gr.Button("刷新变量列表")
delete_env_button = gr.Button("删除选定变量", variant="stop")
env_status = gr.Textbox(label="状态", interactive=False)
# 变量选择器（用于删除）
env_var_to_delete = gr.Dropdown(
choices=[],
label="选择要删除的变量",
interactive=True
)
# 更新变量选择器的选项
def update_delete_dropdown():
env_vars = load_env_vars()
return gr.Dropdown.update(choices=list(env_vars.keys()))
# 连接事件处理函数
add_env_button.click(
fn=lambda k, v: add_env_var(k, v),
inputs=[new_env_key, new_env_value],
outputs=[env_status]
).then(
fn=update_env_table,
outputs=[env_table]
).then(
fn=update_delete_dropdown,
outputs=[env_var_to_delete]
).then(
fn=lambda: ("", ""), # 修改为返回两个空字符串的元组
outputs=[new_env_key, new_env_value]
)
refresh_button.click(
fn=update_env_table,
outputs=[env_table]
).then(
fn=update_delete_dropdown,
outputs=[env_var_to_delete]
)
delete_env_button.click(
fn=lambda k: delete_env_var(k),
inputs=[env_var_to_delete],
outputs=[env_status]
).then(
fn=update_env_table,
outputs=[env_table]
).then(
fn=update_delete_dropdown,
outputs=[env_var_to_delete]
)
# 环境变量表格
env_table = gr.Dataframe(
headers=["变量名", "值"],
datatype=["str", "str"],
row_count=10,
col_count=(2, "fixed"),
value=update_env_table,
label="当前环境变量",
interactive=False,
)
with gr.Row():
with gr.Column(scale=1):
new_env_key = gr.Textbox(
label="变量名", placeholder="例如: OPENAI_API_KEY"
)
with gr.Column(scale=2):
new_env_value = gr.Textbox(
label="值", placeholder="输入API密钥或其他配置值"
)
with gr.Row():
add_env_button = gr.Button("添加/更新变量", variant="primary")
refresh_button = gr.Button("刷新变量列表")
delete_env_button = gr.Button("删除选定变量", variant="stop")
env_status = gr.Textbox(label="状态", interactive=False)
# 变量选择器（用于删除）
env_var_to_delete = gr.Dropdown(
choices=[], label="选择要删除的变量", interactive=True
)
# 更新变量选择器的选项
def update_delete_dropdown():
env_vars = load_env_vars()
return gr.Dropdown.update(choices=list(env_vars.keys()))
# 连接事件处理函数
add_env_button.click(
fn=lambda k, v: add_env_var(k, v),
inputs=[new_env_key, new_env_value],
outputs=[env_status],
).then(fn=update_env_table, outputs=[env_table]).then(
fn=update_delete_dropdown, outputs=[env_var_to_delete]
).then(
fn=lambda: ("", ""), # 修改为返回两个空字符串的元组
outputs=[new_env_key, new_env_value],
)
refresh_button.click(fn=update_env_table, outputs=[env_table]).then(
fn=update_delete_dropdown, outputs=[env_var_to_delete]
)
delete_env_button.click(
fn=lambda k: delete_env_var(k),
inputs=[env_var_to_delete],
outputs=[env_status],
).then(fn=update_env_table, outputs=[env_table]).then(
fn=update_delete_dropdown, outputs=[env_var_to_delete]
)
gr.HTML("""
<div class="footer" id="about">
<h3>关于 OWL 多智能体协作系统</h3>
@@ -780,23 +768,24 @@ def create_ui():
<p><a href="https://github.com/camel-ai/owl" target="_blank">GitHub</a></p>
</div>
""")
# 设置事件处理
run_button.click(
fn=run_owl,
inputs=[question_input, module_dropdown],
outputs=[answer_output, chat_output, token_count_output, status_output]
inputs=[question_input, module_dropdown],
outputs=[answer_output, chat_output, token_count_output, status_output],
)
# 模块选择更新描述
module_dropdown.change(
fn=update_module_description,
inputs=module_dropdown,
outputs=module_description
outputs=module_description,
)
return app
# 主函数
def main():
try:
@@ -807,7 +796,9 @@ def main():
except Exception as e:
print(f"启动应用程序时发生错误: {str(e)}")
import traceback
traceback.print_exc()
if __name__ == "__main__":
main()
main()

8
owl/utils/enhanced_role_playing.py
View File

@@ -381,6 +381,12 @@ Now please give me instructions to solve over overall task step by step. If the
"""
input_msg = society.init_chat(init_prompt)
for _round in range(round_limit):
# Check if previous user response had TASK_DONE before getting next assistant response
if _round > 0 and (
"TASK_DONE" in input_msg.content or "任务已完成" in input_msg.content
):
break
assistant_response, user_response = society.step(input_msg)
overall_completion_token_count += (
assistant_response.info["usage"]["completion_tokens"]
@@ -408,10 +414,12 @@ Now please give me instructions to solve over overall task step by step. If the
f"Round #{_round} assistant_response:\n {assistant_response.msgs[0].content}"
)
# Check other termination conditions
if (
assistant_response.terminated
or user_response.terminated
or "TASK_DONE" in user_response.msg.content
or "任务已完成" in user_response.msg.content
):
break

1328
owl/webapp_zh.py
View File

File diff suppressed because it is too large Load Diff

2
pyproject.toml
View File

@@ -21,7 +21,7 @@ keywords = [
"learning-systems"
]
dependencies = [
"camel-ai[all]==0.2.27",
"camel-ai[all]==0.2.30",
"chunkr-ai>=0.0.41",
"docx2markdown>=0.1.1",
"gradio>=3.50.2",

2
requirements.txt
View File

@@ -1,4 +1,4 @@
camel-ai[all]==0.2.27
camel-ai[all]==0.2.30
chunkr-ai>=0.0.41
docx2markdown>=0.1.1
gradio>=3.50.2

8
uv.lock generated
View File

@@ -482,7 +482,7 @@ wheels = [
[[package]]
name = "camel-ai"
version = "0.2.27"
version = "0.2.30"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "colorama" },
@@ -499,9 +499,9 @@ dependencies = [
{ name = "pyyaml" },
{ name = "tiktoken" },
]
sdist = { url = "https://files.pythonhosted.org/packages/ff/27/2bce666ae7f7d0db276d037b3afe84a460e782438e5cacc08de20417233b/camel_ai-0.2.27.tar.gz", hash = "sha256:4689245ad48f51e5e602d2651cf463afe212bcf046633a19c2189574c1f3481a", size = 441363 }
sdist = { url = "https://files.pythonhosted.org/packages/ef/86/57cbcae86d2d60dab0aad31b5302525c75f45ff5edc3c3819a378fa9e12c/camel_ai-0.2.30.tar.gz", hash = "sha256:e1639376e70e9cf1477eca88d1bdc1813855cbd1db683528e1f93027b6aa0b0a", size = 442842 }
wheels = [
{ url = "https://files.pythonhosted.org/packages/b0/fa/94f5b41cb6babc81aac00494b170ec2bea058b6c00f477ceb3e886c49177/camel_ai-0.2.27-py3-none-any.whl", hash = "sha256:c4a6597791faf2f2161c56c2579e60850557b126135b29af77ebd08fa0774e0b", size = 746387 },
{ url = "https://files.pythonhosted.org/packages/85/fe/8f1d17896aedbc9e0dfa1bff40d560e5a6808d9b727e04c293be6be5954f/camel_ai-0.2.30-py3-none-any.whl", hash = "sha256:e09eec860331cdb4da4e49f46f5d45345a81820c5847556fdf9e7827dd9bbfa9", size = 752672 },
]
[package.optional-dependencies]
@@ -3575,7 +3575,7 @@ dependencies = [
[package.metadata]
requires-dist = [
{ name = "camel-ai", extras = ["all"], specifier = "==0.2.27" },
{ name = "camel-ai", extras = ["all"], specifier = "==0.2.30" },
{ name = "chunkr-ai", specifier = ">=0.0.41" },
{ name = "docx2markdown", specifier = ">=0.1.1" },
{ name = "gradio", specifier = ">=3.50.2" },