Add application file

app.py

@@ -0,0 +1,196 @@
+import copy
+import os
+from typing import List
+import streamlit as st
+from lagent.actions import ArxivSearch, WeatherQuery
+from lagent.prompts.parsers import PluginParser
+from lagent.agents.stream import INTERPRETER_CN, META_CN, PLUGIN_CN, AgentForInternLM, get_plugin_prompt
+from lagent.llms import GPTAPI
+
+class SessionState:
+    """管理会话状态的类。"""
+
+    def init_state(self):
+        """初始化会话状态变量。"""
+        st.session_state['assistant'] = []  # 助手消息历史
+        st.session_state['user'] = []  # 用户消息历史
+        # 初始化插件列表
+        action_list = [
+            ArxivSearch(),
+            WeatherQuery()
+        ]
+        st.session_state['plugin_map'] = {action.name: action for action in action_list}
+        st.session_state['model_map'] = {}  # 存储模型实例
+        st.session_state['model_selected'] = None  # 当前选定模型
+        st.session_state['plugin_actions'] = set()  # 当前激活插件
+        st.session_state['history'] = []  # 聊天历史
+        st.session_state['api_base'] = None  # 初始化API base地址
+
+    def clear_state(self):
+        """清除当前会话状态。"""
+        st.session_state['assistant'] = []
+        st.session_state['user'] = []
+        st.session_state['model_selected'] = None
+
+
+class StreamlitUI:
+    """管理 Streamlit 界面的类。"""
+
+    def __init__(self, session_state: SessionState):
+        self.session_state = session_state
+        self.plugin_action = []  # 当前选定的插件
+        # 初始化提示词
+        self.meta_prompt = META_CN
+        self.plugin_prompt = PLUGIN_CN
+        self.init_streamlit()
+
+    def init_streamlit(self):
+        """初始化 Streamlit 的 UI 设置。"""
+        st.set_page_config(
+            layout='wide',
+            page_title='lagent-web',
+            page_icon='./docs/imgs/lagent_icon.png'
+        )
+        st.header(':robot_face: :blue[Lagent] Web Demo ', divider='rainbow')
+
+    def setup_sidebar(self):
+        """设置侧边栏，选择模型和插件。"""
+        # 模型名称和 API Base 输入框
+        model_name = st.sidebar.text_input('模型名称：', value='internlm2.5-latest')
+        
+        # ================================== 硅基流动的API ==================================
+        # 注意，如果采用硅基流动API，模型名称需要更改为：internlm/internlm2_5-7b-chat 或者 internlm/internlm2_5-20b-chat
+        # api_base = st.sidebar.text_input(
+        #     'API Base 地址：', value='https://api.siliconflow.cn/v1/chat/completions'
+        # )
+        # ================================== 浦语官方的API ==================================
+        api_base = st.sidebar.text_input(
+            'API Base 地址：', value='https://internlm-chat.intern-ai.org.cn/puyu/api/v1/chat/completions'
+        )
+        # ==================================================================================
+        # 插件选择
+        plugin_name = st.sidebar.multiselect(
+            '插件选择',
+            options=list(st.session_state['plugin_map'].keys()),
+            default=[],
+        )
+
+        # 根据选择的插件生成插件操作列表
+        self.plugin_action = [st.session_state['plugin_map'][name] for name in plugin_name]
+
+        # 动态生成插件提示
+        if self.plugin_action:
+            self.plugin_prompt = get_plugin_prompt(self.plugin_action)
+
+        # 清空对话按钮
+        if st.sidebar.button('清空对话', key='clear'):
+            self.session_state.clear_state()
+
+        return model_name, api_base, self.plugin_action
+
+    def initialize_chatbot(self, model_name, api_base, plugin_action):
+        """初始化 GPTAPI 实例作为 chatbot。"""
+        token = os.getenv("INTERNLM_API_KEY")
+        if not token:
+            st.error("未检测到环境变量 `token`，请设置环境变量，例如 `export token='your_token_here'` 后重新运行 X﹏X")
+            st.stop()  # 停止运行应用
+            
+        # 创建完整的 meta_prompt，保留原始结构并动态插入侧边栏配置
+        meta_prompt = [
+            {"role": "system", "content": self.meta_prompt, "api_role": "system"},
+            {"role": "user", "content": "", "api_role": "user"},
+            {"role": "assistant", "content": self.plugin_prompt, "api_role": "assistant"},
+            {"role": "environment", "content": "", "api_role": "environment"}
+        ]
+
+        api_model = GPTAPI(
+            model_type=model_name,
+            api_base=api_base,
+            key=token,  # 从环境变量中获取授权令牌
+            meta_template=meta_prompt,
+            max_new_tokens=512,
+            temperature=0.8,
+            top_p=0.9
+        )
+        return api_model
+
+    def render_user(self, prompt: str):
+        """渲染用户输入内容。"""
+        with st.chat_message('user'):
+            st.markdown(prompt)
+
+    def render_assistant(self, agent_return):
+        """渲染助手响应内容。"""
+        with st.chat_message('assistant'):
+            content = getattr(agent_return, "content", str(agent_return))
+            st.markdown(content if isinstance(content, str) else str(content))
+
+
+def main():
+    """主函数，运行 Streamlit 应用。"""
+    if 'ui' not in st.session_state:
+        session_state = SessionState()
+        session_state.init_state()
+        st.session_state['ui'] = StreamlitUI(session_state)
+    else:
+        st.set_page_config(
+            layout='wide',
+            page_title='lagent-web',
+            page_icon='./docs/imgs/lagent_icon.png'
+        )
+        st.header(':robot_face: :blue[Lagent] Web Demo ', divider='rainbow')
+
+    # 设置侧边栏并获取模型和插件信息
+    model_name, api_base, plugin_action = st.session_state['ui'].setup_sidebar()
+    plugins = [dict(type=f"lagent.actions.{plugin.__class__.__name__}") for plugin in plugin_action]
+
+    if (
+        'chatbot' not in st.session_state or
+        model_name != st.session_state['chatbot'].model_type or
+        'last_plugin_action' not in st.session_state or
+        plugin_action != st.session_state['last_plugin_action'] or
+        api_base != st.session_state['api_base']    
+    ):
+        # 更新 Chatbot
+        st.session_state['chatbot'] = st.session_state['ui'].initialize_chatbot(model_name, api_base, plugin_action)
+        st.session_state['last_plugin_action'] = plugin_action  # 更新插件状态
+        st.session_state['api_base'] = api_base  # 更新 API Base 地址
+
+        # 初始化 AgentForInternLM
+        st.session_state['agent'] = AgentForInternLM(
+            llm=st.session_state['chatbot'],
+            plugins=plugins,
+            output_format=dict(
+                type=PluginParser,
+                template=PLUGIN_CN,
+                prompt=get_plugin_prompt(plugin_action)
+            )
+        )
+        # 清空对话历史
+        st.session_state['session_history'] = []
+
+    if 'agent' not in st.session_state:
+        st.session_state['agent'] = None
+
+    agent = st.session_state['agent']
+    for prompt, agent_return in zip(st.session_state['user'], st.session_state['assistant']):
+        st.session_state['ui'].render_user(prompt)
+        st.session_state['ui'].render_assistant(agent_return)
+
+    # 处理用户输入
+    if user_input := st.chat_input(''):
+        st.session_state['ui'].render_user(user_input)
+
+        # 调用模型时确保侧边栏的系统提示词和插件提示词生效
+        res = agent(user_input, session_id=0)
+        st.session_state['ui'].render_assistant(res)
+
+        # 更新会话状态
+        st.session_state['user'].append(user_input)
+        st.session_state['assistant'].append(copy.deepcopy(res))
+
+    st.session_state['last_status'] = None
+
+
+if __name__ == '__main__':
+    main()

examples/agent_api_web_demo.py

@@ -0,0 +1,196 @@
+import copy
+import os
+from typing import List
+import streamlit as st
+from lagent.actions import ArxivSearch, WeatherQuery
+from lagent.prompts.parsers import PluginParser
+from lagent.agents.stream import INTERPRETER_CN, META_CN, PLUGIN_CN, AgentForInternLM, get_plugin_prompt
+from lagent.llms import GPTAPI
+
+class SessionState:
+    """管理会话状态的类。"""
+
+    def init_state(self):
+        """初始化会话状态变量。"""
+        st.session_state['assistant'] = []  # 助手消息历史
+        st.session_state['user'] = []  # 用户消息历史
+        # 初始化插件列表
+        action_list = [
+            ArxivSearch(),
+            WeatherQuery()
+        ]
+        st.session_state['plugin_map'] = {action.name: action for action in action_list}
+        st.session_state['model_map'] = {}  # 存储模型实例
+        st.session_state['model_selected'] = None  # 当前选定模型
+        st.session_state['plugin_actions'] = set()  # 当前激活插件
+        st.session_state['history'] = []  # 聊天历史
+        st.session_state['api_base'] = None  # 初始化API base地址
+
+    def clear_state(self):
+        """清除当前会话状态。"""
+        st.session_state['assistant'] = []
+        st.session_state['user'] = []
+        st.session_state['model_selected'] = None
+
+
+class StreamlitUI:
+    """管理 Streamlit 界面的类。"""
+
+    def __init__(self, session_state: SessionState):
+        self.session_state = session_state
+        self.plugin_action = []  # 当前选定的插件
+        # 初始化提示词
+        self.meta_prompt = META_CN
+        self.plugin_prompt = PLUGIN_CN
+        self.init_streamlit()
+
+    def init_streamlit(self):
+        """初始化 Streamlit 的 UI 设置。"""
+        st.set_page_config(
+            layout='wide',
+            page_title='lagent-web',
+            page_icon='./docs/imgs/lagent_icon.png'
+        )
+        st.header(':robot_face: :blue[Lagent] Web Demo ', divider='rainbow')
+
+    def setup_sidebar(self):
+        """设置侧边栏，选择模型和插件。"""
+        # 模型名称和 API Base 输入框
+        model_name = st.sidebar.text_input('模型名称：', value='internlm2.5-latest')
+        
+        # ================================== 硅基流动的API ==================================
+        # 注意，如果采用硅基流动API，模型名称需要更改为：internlm/internlm2_5-7b-chat 或者 internlm/internlm2_5-20b-chat
+        # api_base = st.sidebar.text_input(
+        #     'API Base 地址：', value='https://api.siliconflow.cn/v1/chat/completions'
+        # )
+        # ================================== 浦语官方的API ==================================
+        api_base = st.sidebar.text_input(
+            'API Base 地址：', value='https://internlm-chat.intern-ai.org.cn/puyu/api/v1/chat/completions'
+        )
+        # ==================================================================================
+        # 插件选择
+        plugin_name = st.sidebar.multiselect(
+            '插件选择',
+            options=list(st.session_state['plugin_map'].keys()),
+            default=[],
+        )
+
+        # 根据选择的插件生成插件操作列表
+        self.plugin_action = [st.session_state['plugin_map'][name] for name in plugin_name]
+
+        # 动态生成插件提示
+        if self.plugin_action:
+            self.plugin_prompt = get_plugin_prompt(self.plugin_action)
+
+        # 清空对话按钮
+        if st.sidebar.button('清空对话', key='clear'):
+            self.session_state.clear_state()
+
+        return model_name, api_base, self.plugin_action
+
+    def initialize_chatbot(self, model_name, api_base, plugin_action):
+        """初始化 GPTAPI 实例作为 chatbot。"""
+        token = os.getenv("INTERNLM_API_KEY")
+        if not token:
+            st.error("未检测到环境变量 `token`，请设置环境变量，例如 `export token='your_token_here'` 后重新运行 X﹏X")
+            st.stop()  # 停止运行应用
+            
+        # 创建完整的 meta_prompt，保留原始结构并动态插入侧边栏配置
+        meta_prompt = [
+            {"role": "system", "content": self.meta_prompt, "api_role": "system"},
+            {"role": "user", "content": "", "api_role": "user"},
+            {"role": "assistant", "content": self.plugin_prompt, "api_role": "assistant"},
+            {"role": "environment", "content": "", "api_role": "environment"}
+        ]
+
+        api_model = GPTAPI(
+            model_type=model_name,
+            api_base=api_base,
+            key=token,  # 从环境变量中获取授权令牌
+            meta_template=meta_prompt,
+            max_new_tokens=512,
+            temperature=0.8,
+            top_p=0.9
+        )
+        return api_model
+
+    def render_user(self, prompt: str):
+        """渲染用户输入内容。"""
+        with st.chat_message('user'):
+            st.markdown(prompt)
+
+    def render_assistant(self, agent_return):
+        """渲染助手响应内容。"""
+        with st.chat_message('assistant'):
+            content = getattr(agent_return, "content", str(agent_return))
+            st.markdown(content if isinstance(content, str) else str(content))
+
+
+def main():
+    """主函数，运行 Streamlit 应用。"""
+    if 'ui' not in st.session_state:
+        session_state = SessionState()
+        session_state.init_state()
+        st.session_state['ui'] = StreamlitUI(session_state)
+    else:
+        st.set_page_config(
+            layout='wide',
+            page_title='lagent-web',
+            page_icon='./docs/imgs/lagent_icon.png'
+        )
+        st.header(':robot_face: :blue[Lagent] Web Demo ', divider='rainbow')
+
+    # 设置侧边栏并获取模型和插件信息
+    model_name, api_base, plugin_action = st.session_state['ui'].setup_sidebar()
+    plugins = [dict(type=f"lagent.actions.{plugin.__class__.__name__}") for plugin in plugin_action]
+
+    if (
+        'chatbot' not in st.session_state or
+        model_name != st.session_state['chatbot'].model_type or
+        'last_plugin_action' not in st.session_state or
+        plugin_action != st.session_state['last_plugin_action'] or
+        api_base != st.session_state['api_base']    
+    ):
+        # 更新 Chatbot
+        st.session_state['chatbot'] = st.session_state['ui'].initialize_chatbot(model_name, api_base, plugin_action)
+        st.session_state['last_plugin_action'] = plugin_action  # 更新插件状态
+        st.session_state['api_base'] = api_base  # 更新 API Base 地址
+
+        # 初始化 AgentForInternLM
+        st.session_state['agent'] = AgentForInternLM(
+            llm=st.session_state['chatbot'],
+            plugins=plugins,
+            output_format=dict(
+                type=PluginParser,
+                template=PLUGIN_CN,
+                prompt=get_plugin_prompt(plugin_action)
+            )
+        )
+        # 清空对话历史
+        st.session_state['session_history'] = []
+
+    if 'agent' not in st.session_state:
+        st.session_state['agent'] = None
+
+    agent = st.session_state['agent']
+    for prompt, agent_return in zip(st.session_state['user'], st.session_state['assistant']):
+        st.session_state['ui'].render_user(prompt)
+        st.session_state['ui'].render_assistant(agent_return)
+
+    # 处理用户输入
+    if user_input := st.chat_input(''):
+        st.session_state['ui'].render_user(user_input)
+
+        # 调用模型时确保侧边栏的系统提示词和插件提示词生效
+        res = agent(user_input, session_id=0)
+        st.session_state['ui'].render_assistant(res)
+
+        # 更新会话状态
+        st.session_state['user'].append(user_input)
+        st.session_state['assistant'].append(copy.deepcopy(res))
+
+    st.session_state['last_status'] = None
+
+
+if __name__ == '__main__':
+    main()

examples/multi_agents_api_web_demo.py

@@ -0,0 +1,198 @@
+import os
+import asyncio
+import json
+import re
+import requests
+import streamlit as st
+
+from lagent.agents import Agent
+from lagent.prompts.parsers import PluginParser
+from lagent.agents.stream import PLUGIN_CN, get_plugin_prompt
+from lagent.schema import AgentMessage
+from lagent.actions import ArxivSearch
+from lagent.hooks import Hook
+from lagent.llms import GPTAPI
+
+YOUR_TOKEN_HERE = os.getenv("INTERNLM_API_KEY")
+if not YOUR_TOKEN_HERE:
+    raise EnvironmentError("未找到环境变量 'token'，请设置后再运行程序。")
+
+# Hook类，用于对消息添加前缀
+class PrefixedMessageHook(Hook):
+    def __init__(self, prefix, senders=None):
+        """
+        初始化Hook
+        :param prefix: 消息前缀
+        :param senders: 指定发送者列表
+        """
+        self.prefix = prefix
+        self.senders = senders or []
+
+    def before_agent(self, agent, messages, session_id):
+        """
+        在代理处理消息前修改消息内容
+        :param agent: 当前代理
+        :param messages: 消息列表
+        :param session_id: 会话ID
+        """
+        for message in messages:
+            if message.sender in self.senders:
+                message.content = self.prefix + message.content
+
+class AsyncBlogger:
+    """博客生成类，整合写作者和批评者。"""
+
+    def __init__(self, model_type, api_base, writer_prompt, critic_prompt, critic_prefix='', max_turn=2):
+        """
+        初始化博客生成器
+        :param model_type: 模型类型
+        :param api_base: API 基地址
+        :param writer_prompt: 写作者提示词
+        :param critic_prompt: 批评者提示词
+        :param critic_prefix: 批评消息前缀
+        :param max_turn: 最大轮次
+        """
+        self.model_type = model_type
+        self.api_base = api_base
+        self.llm = GPTAPI(
+            model_type=model_type,
+            api_base=api_base,
+            key=YOUR_TOKEN_HERE,
+            max_new_tokens=4096,
+        )
+        self.plugins = [dict(type='lagent.actions.ArxivSearch')]
+        self.writer = Agent(
+            self.llm,
+            writer_prompt,
+            name='写作者',
+            output_format=dict(
+                type=PluginParser,
+                template=PLUGIN_CN,
+                prompt=get_plugin_prompt(self.plugins)
+            )
+        )
+        self.critic = Agent(
+            self.llm,
+            critic_prompt,
+            name='批评者',
+            hooks=[PrefixedMessageHook(critic_prefix, ['写作者'])]
+        )
+        self.max_turn = max_turn
+
+    async def forward(self, message: AgentMessage, update_placeholder):
+        """
+        执行多阶段博客生成流程
+        :param message: 初始消息
+        :param update_placeholder: Streamlit占位符
+        :return: 最终优化的博客内容
+        """
+        step1_placeholder = update_placeholder.container()
+        step2_placeholder = update_placeholder.container()
+        step3_placeholder = update_placeholder.container()
+
+        # 第一步：生成初始内容
+        step1_placeholder.markdown("**Step 1: 生成初始内容...**")
+        message = self.writer(message)
+        if message.content:
+            step1_placeholder.markdown(f"**生成的初始内容**:\n\n{message.content}")
+        else:
+            step1_placeholder.markdown("**生成的初始内容为空，请检查生成逻辑。**")
+
+        # 第二步：批评者提供反馈
+        step2_placeholder.markdown("**Step 2: 批评者正在提供反馈和文献推荐...**")
+        message = self.critic(message)
+        if message.content:
+            # 解析批评者反馈
+            suggestions = re.search(r"1\. 批评建议：\n(.*?)2\. 推荐的关键词：", message.content, re.S)
+            keywords = re.search(r"2\. 推荐的关键词：\n- (.*)", message.content)
+            feedback = suggestions.group(1).strip() if suggestions else "未提供批评建议"
+            keywords = keywords.group(1).strip() if keywords else "未提供关键词"
+
+            # Arxiv 文献查询
+            arxiv_search = ArxivSearch()
+            arxiv_results = arxiv_search.get_arxiv_article_information(keywords)
+
+            # 显示批评内容和文献推荐
+            message.content = f"**批评建议**:\n{feedback}\n\n**推荐的文献**:\n{arxiv_results}"
+            step2_placeholder.markdown(f"**批评和文献推荐**:\n\n{message.content}")
+        else:
+            step2_placeholder.markdown("**批评内容为空，请检查批评逻辑。**")
+
+        # 第三步：写作者根据反馈优化内容
+        step3_placeholder.markdown("**Step 3: 根据反馈改进内容...**")
+        improvement_prompt = AgentMessage(
+            sender="critic",
+            content=(
+                f"根据以下批评建议和推荐文献对内容进行改进：\n\n"
+                f"批评建议：\n{feedback}\n\n"
+                f"推荐文献：\n{arxiv_results}\n\n"
+                f"请优化初始内容，使其更加清晰、丰富，并符合专业水准。"
+            ),
+        )
+        message = self.writer(improvement_prompt)
+        if message.content:
+            step3_placeholder.markdown(f"**最终优化的博客内容**:\n\n{message.content}")
+        else:
+            step3_placeholder.markdown("**最终优化的博客内容为空，请检查生成逻辑。**")
+
+        return message
+
+def setup_sidebar():
+    """设置侧边栏，选择模型。"""
+    model_name = st.sidebar.text_input('模型名称：', value='internlm2.5-latest')
+    api_base = st.sidebar.text_input(
+        'API Base 地址：', value='https://internlm-chat.intern-ai.org.cn/puyu/api/v1/chat/completions'
+    )
+    
+    return model_name, api_base
+    
+def main():
+    """
+    主函数：构建Streamlit界面并处理用户交互
+    """
+    st.set_page_config(layout='wide', page_title='Lagent Web Demo', page_icon='🤖')
+    st.title("多代理博客优化助手")
+
+    model_type, api_base = setup_sidebar()
+    topic = st.text_input('输入一个话题：', 'Self-Supervised Learning')
+    generate_button = st.button('生成博客内容')
+
+    if (
+        'blogger' not in st.session_state or
+        st.session_state['model_type'] != model_type or
+        st.session_state['api_base'] != api_base
+    ):
+        st.session_state['blogger'] = AsyncBlogger(
+            model_type=model_type,
+            api_base=api_base,
+            writer_prompt="你是一位优秀的AI内容写作者，请撰写一篇有吸引力且信息丰富的博客内容。",
+            critic_prompt="""
+                作为一位严谨的批评者，请给出建设性的批评和改进建议，并基于相关主题使用已有的工具推荐一些参考文献，推荐的关键词应该是英语形式，简洁且切题。
+                请按照以下格式提供反馈：
+                1. 批评建议：
+                - （具体建议）
+                2. 推荐的关键词：
+                - （关键词1, 关键词2, ...）
+            """,
+            critic_prefix="请批评以下内容，并提供改进建议：\n\n"
+        )
+        st.session_state['model_type'] = model_type
+        st.session_state['api_base'] = api_base
+
+    if generate_button:
+        update_placeholder = st.empty()
+
+        async def run_async_blogger():
+            message = AgentMessage(
+                sender='user',
+                content=f"请撰写一篇关于{topic}的博客文章，要求表达专业，生动有趣，并且易于理解。"
+            )
+            result = await st.session_state['blogger'].forward(message, update_placeholder)
+            return result
+
+        loop = asyncio.new_event_loop()
+        asyncio.set_event_loop(loop)
+        loop.run_until_complete(run_async_blogger())
+
+if __name__ == '__main__':
+    main()

lagent/__init__.py

@@ -0,0 +1,4 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .version import __version__, version_info
+
+__all__ = ['__version__', 'version_info']

lagent/actions/__init__.py

@@ -0,0 +1,26 @@
+from .action_executor import ActionExecutor, AsyncActionExecutor
+from .arxiv_search import ArxivSearch, AsyncArxivSearch
+from .base_action import BaseAction, tool_api
+from .bing_map import AsyncBINGMap, BINGMap
+from .builtin_actions import FinishAction, InvalidAction, NoAction
+from .google_scholar_search import AsyncGoogleScholar, GoogleScholar
+from .google_search import AsyncGoogleSearch, GoogleSearch
+from .ipython_interactive import AsyncIPythonInteractive, IPythonInteractive
+from .ipython_interpreter import AsyncIPythonInterpreter, IPythonInterpreter
+from .ipython_manager import IPythonInteractiveManager
+from .parser import BaseParser, JsonParser, TupleParser
+from .ppt import PPT, AsyncPPT
+from .python_interpreter import AsyncPythonInterpreter, PythonInterpreter
+from .web_browser import AsyncWebBrowser, WebBrowser
+from .weather_query import WeatherQuery
+
+__all__ = [
+    'BaseAction', 'ActionExecutor', 'AsyncActionExecutor', 'InvalidAction',
+    'FinishAction', 'NoAction', 'BINGMap', 'AsyncBINGMap', 'ArxivSearch',
+    'AsyncArxivSearch', 'GoogleSearch', 'AsyncGoogleSearch', 'GoogleScholar',
+    'AsyncGoogleScholar', 'IPythonInterpreter', 'AsyncIPythonInterpreter',
+    'IPythonInteractive', 'AsyncIPythonInteractive',
+    'IPythonInteractiveManager', 'PythonInterpreter', 'AsyncPythonInterpreter',
+    'PPT', 'AsyncPPT', 'WebBrowser', 'AsyncWebBrowser', 'BaseParser',
+    'JsonParser', 'TupleParser', 'tool_api', 'WeatherQuery'
+]

lagent/actions/action_executor.py

@@ -0,0 +1,198 @@
+import inspect
+from collections import OrderedDict
+from typing import Callable, Dict, List, Union
+
+from lagent.actions.base_action import BaseAction
+from lagent.actions.builtin_actions import FinishAction, InvalidAction, NoAction
+from lagent.hooks import Hook, RemovableHandle
+from lagent.schema import ActionReturn, ActionValidCode, AgentMessage, FunctionCall
+from lagent.utils import create_object
+
+
+class ActionExecutor:
+    """The action executor class.
+
+    Args:
+        actions (Union[BaseAction, List[BaseAction]]): The action or actions.
+        invalid_action (BaseAction, optional): The invalid action. Defaults to
+            InvalidAction().
+        no_action (BaseAction, optional): The no action.
+            Defaults to NoAction().
+        finish_action (BaseAction, optional): The finish action. Defaults to
+            FinishAction().
+        finish_in_action (bool, optional): Whether the finish action is in the
+            action list. Defaults to False.
+    """
+
+    def __init__(
+        self,
+        actions: Union[BaseAction, List[BaseAction], Dict, List[Dict]],
+        invalid_action: BaseAction = dict(type=InvalidAction),
+        no_action: BaseAction = dict(type=NoAction),
+        finish_action: BaseAction = dict(type=FinishAction),
+        finish_in_action: bool = False,
+        hooks: List[Dict] = None,
+    ):
+
+        if not isinstance(actions, list):
+            actions = [actions]
+        finish_action = create_object(finish_action)
+        if finish_in_action:
+            actions.append(finish_action)
+        for i, action in enumerate(actions):
+            actions[i] = create_object(action)
+        self.actions = {action.name: action for action in actions}
+
+        self.invalid_action = create_object(invalid_action)
+        self.no_action = create_object(no_action)
+        self.finish_action = finish_action
+        self._hooks: Dict[int, Hook] = OrderedDict()
+        if hooks:
+            for hook in hooks:
+                hook = create_object(hook)
+                self.register_hook(hook)
+
+    def description(self) -> List[Dict]:
+        actions = []
+        for action_name, action in self.actions.items():
+            if action.is_toolkit:
+                for api in action.description['api_list']:
+                    api_desc = api.copy()
+                    api_desc['name'] = f"{action_name}.{api_desc['name']}"
+                    actions.append(api_desc)
+            else:
+                action_desc = action.description.copy()
+                actions.append(action_desc)
+        return actions
+
+    def __contains__(self, name: str):
+        return name in self.actions
+
+    def keys(self):
+        return list(self.actions.keys())
+
+    def __setitem__(self, name: str, action: Union[BaseAction, Dict]):
+        action = create_object(action)
+        self.actions[action.name] = action
+
+    def __delitem__(self, name: str):
+        del self.actions[name]
+
+    def forward(self, name, parameters, **kwargs) -> ActionReturn:
+        action_name, api_name = (
+            name.split('.') if '.' in name else (name, 'run'))
+        action_return: ActionReturn = ActionReturn()
+        if action_name not in self:
+            if name == self.no_action.name:
+                action_return = self.no_action(parameters)
+            elif name == self.finish_action.name:
+                action_return = self.finish_action(parameters)
+            else:
+                action_return = self.invalid_action(parameters)
+        else:
+            action_return = self.actions[action_name](parameters, api_name)
+            action_return.valid = ActionValidCode.OPEN
+        return action_return
+
+    def __call__(self,
+                 message: AgentMessage,
+                 session_id=0,
+                 **kwargs) -> AgentMessage:
+        # message.receiver = self.name
+        for hook in self._hooks.values():
+            result = hook.before_action(self, message, session_id)
+            if result:
+                message = result
+
+        assert isinstance(message.content, FunctionCall) or (
+            isinstance(message.content, dict) and 'name' in message.content
+            and 'parameters' in message.content)
+        if isinstance(message.content, dict):
+            name = message.content.get('name')
+            parameters = message.content.get('parameters')
+        else:
+            name = message.content.name
+            parameters = message.content.parameters
+
+        response_message = self.forward(
+            name=name, parameters=parameters, **kwargs)
+        if not isinstance(response_message, AgentMessage):
+            response_message = AgentMessage(
+                sender=self.__class__.__name__,
+                content=response_message,
+            )
+
+        for hook in self._hooks.values():
+            result = hook.after_action(self, response_message, session_id)
+            if result:
+                response_message = result
+        return response_message
+
+    def register_hook(self, hook: Callable):
+        handle = RemovableHandle(self._hooks)
+        self._hooks[handle.id] = hook
+        return handle
+
+
+class AsyncActionExecutor(ActionExecutor):
+
+    async def forward(self, name, parameters, **kwargs) -> ActionReturn:
+        action_name, api_name = (
+            name.split('.') if '.' in name else (name, 'run'))
+        action_return: ActionReturn = ActionReturn()
+        if action_name not in self:
+            if name == self.no_action.name:
+                action_return = self.no_action(parameters)
+            elif name == self.finish_action.name:
+                action_return = self.finish_action(parameters)
+            else:
+                action_return = self.invalid_action(parameters)
+        else:
+            action = self.actions[action_name]
+            if inspect.iscoroutinefunction(action.__call__):
+                action_return = await action(parameters, api_name)
+            else:
+                action_return = action(parameters, api_name)
+            action_return.valid = ActionValidCode.OPEN
+        return action_return
+
+    async def __call__(self,
+                       message: AgentMessage,
+                       session_id=0,
+                       **kwargs) -> AgentMessage:
+        # message.receiver = self.name
+        for hook in self._hooks.values():
+            if inspect.iscoroutinefunction(hook.before_action):
+                result = await hook.before_action(self, message, session_id)
+            else:
+                result = hook.before_action(self, message, session_id)
+            if result:
+                message = result
+
+        assert isinstance(message.content, FunctionCall) or (
+            isinstance(message.content, dict) and 'name' in message.content
+            and 'parameters' in message.content)
+        if isinstance(message.content, dict):
+            name = message.content.get('name')
+            parameters = message.content.get('parameters')
+        else:
+            name = message.content.name
+            parameters = message.content.parameters
+
+        response_message = await self.forward(
+            name=name, parameters=parameters, **kwargs)
+        if not isinstance(response_message, AgentMessage):
+            response_message = AgentMessage(
+                sender=self.__class__.__name__,
+                content=response_message,
+            )
+
+        for hook in self._hooks.values():
+            if inspect.iscoroutinefunction(hook.after_action):
+                result = await hook.after_action(self, response_message,
+                                                 session_id)
+            else:
+                result = hook.after_action(self, response_message, session_id)
+            if result:
+                response_message = result
+        return response_message

lagent/actions/arxiv_search.py

@@ -0,0 +1,79 @@
+from typing import Optional, Type
+
+from asyncer import asyncify
+
+from lagent.actions.base_action import AsyncActionMixin, BaseAction, tool_api
+from lagent.actions.parser import BaseParser, JsonParser
+from lagent.schema import ActionReturn, ActionStatusCode
+
+
+class ArxivSearch(BaseAction):
+    """Search information from Arxiv.org. \
+Useful for when you need to answer questions about Physics, Mathematics, \
+Computer Science, Quantitative Biology, Quantitative Finance, Statistics, \
+Electrical Engineering, and Economics from scientific articles on arxiv.org.
+    """
+
+    def __init__(
+        self,
+        top_k_results: int = 3,
+        max_query_len: int = 300,
+        doc_content_chars_max: int = 1500,
+        description: Optional[dict] = None,
+        parser: Type[BaseParser] = JsonParser,
+    ):
+        super().__init__(description, parser)
+        self.top_k_results = top_k_results
+        self.max_query_len = max_query_len
+        self.doc_content_chars_max = doc_content_chars_max
+
+    @tool_api(explode_return=True)
+    def get_arxiv_article_information(self, query: str) -> dict:
+        """Run Arxiv search and get the article meta information.
+
+        Args:
+            query (:class:`str`): the content of search query
+
+        Returns:
+            :class:`dict`: article information
+                * content (str): a list of 3 arxiv search papers
+        """
+        import arxiv
+
+        try:
+            results = arxiv.Search(  # type: ignore
+                query[: self.max_query_len], max_results=self.top_k_results
+            ).results()
+        except Exception as exc:
+            return ActionReturn(errmsg=f'Arxiv exception: {exc}', state=ActionStatusCode.HTTP_ERROR)
+        docs = [
+            f'Published: {result.updated.date()}\nTitle: {result.title}\n'
+            f'Authors: {", ".join(a.name for a in result.authors)}\n'
+            f'Summary: {result.summary[:self.doc_content_chars_max]}'
+            for result in results
+        ]
+        if docs:
+            return {'content': '\n\n'.join(docs)}
+        return {'content': 'No good Arxiv Result was found'}
+
+
+class AsyncArxivSearch(AsyncActionMixin, ArxivSearch):
+    """Search information from Arxiv.org. \
+Useful for when you need to answer questions about Physics, Mathematics, \
+Computer Science, Quantitative Biology, Quantitative Finance, Statistics, \
+Electrical Engineering, and Economics from scientific articles on arxiv.org.
+    """
+
+    @tool_api(explode_return=True)
+    @asyncify
+    def get_arxiv_article_information(self, query: str) -> dict:
+        """Run Arxiv search and get the article meta information.
+
+        Args:
+            query (:class:`str`): the content of search query
+
+        Returns:
+            :class:`dict`: article information
+                * content (str): a list of 3 arxiv search papers
+        """
+        return super().get_arxiv_article_information(query)

lagent/actions/base_action.py

@@ -0,0 +1,434 @@
+import inspect
+import logging
+import re
+from abc import ABCMeta
+from copy import deepcopy
+from functools import wraps
+from typing import Callable, Optional, Type, get_args, get_origin
+
+try:
+    from typing import Annotated
+except ImportError:
+    from typing_extensions import Annotated
+
+from griffe import Docstring
+
+try:
+    from griffe import DocstringSectionKind
+except ImportError:
+    from griffe.enumerations import DocstringSectionKind
+
+from ..schema import ActionReturn, ActionStatusCode
+from .parser import BaseParser, JsonParser, ParseError
+
+logging.getLogger('griffe').setLevel(logging.ERROR)
+
+
+def tool_api(func: Optional[Callable] = None,
+             *,
+             explode_return: bool = False,
+             returns_named_value: bool = False,
+             **kwargs):
+    """Turn functions into tools. It will parse typehints as well as docstrings
+    to build the tool description and attach it to functions via an attribute
+    ``api_description``.
+
+    Examples:
+
+        .. code-block:: python
+
+            # typehints has higher priority than docstrings
+            from typing import Annotated
+
+            @tool_api
+            def add(a: Annotated[int, 'augend'], b: Annotated[int, 'addend'] = 1):
+                '''Add operation
+
+                Args:
+                    x (int): a
+                    y (int): b
+                '''
+                return a + b
+
+            print(add.api_description)
+
+    Args:
+        func (Optional[Callable]): function to decorate. Defaults to ``None``.
+        explode_return (bool): whether to flatten the dictionary or tuple return
+            as the ``return_data`` field. When enabled, it is recommended to
+            annotate the member in docstrings. Defaults to ``False``.
+
+            .. code-block:: python
+
+                @tool_api(explode_return=True)
+                def foo(a, b):
+                    '''A simple function
+
+                    Args:
+                        a (int): a
+                        b (int): b
+
+                    Returns:
+                        dict: information of inputs
+                            * x: value of a
+                            * y: value of b
+                    '''
+                    return {'x': a, 'y': b}
+
+                print(foo.api_description)
+
+        returns_named_value (bool): whether to parse ``thing: Description`` in
+            returns sections as a name and description, rather than a type and
+            description. When true, type must be wrapped in parentheses:
+            ``(int): Description``. When false, parentheses are optional but
+            the items cannot be named: ``int: Description``. Defaults to ``False``.
+
+    Returns:
+        Callable: wrapped function or partial decorator
+
+    Important:
+        ``return_data`` field will be added to ``api_description`` only
+        when ``explode_return`` or ``returns_named_value`` is enabled.
+    """
+
+    def _detect_type(string):
+        field_type = 'STRING'
+        if 'list' in string:
+            field_type = 'Array'
+        elif 'str' not in string:
+            if 'float' in string:
+                field_type = 'FLOAT'
+            elif 'int' in string:
+                field_type = 'NUMBER'
+            elif 'bool' in string:
+                field_type = 'BOOLEAN'
+        return field_type
+
+    def _explode(desc):
+        kvs = []
+        desc = '\nArgs:\n' + '\n'.join([
+            '    ' + item.lstrip(' -+*#.')
+            for item in desc.split('\n')[1:] if item.strip()
+        ])
+        docs = Docstring(desc).parse('google')
+        if not docs:
+            return kvs
+        if docs[0].kind is DocstringSectionKind.parameters:
+            for d in docs[0].value:
+                d = d.as_dict()
+                if not d['annotation']:
+                    d.pop('annotation')
+                else:
+                    d['type'] = _detect_type(d.pop('annotation').lower())
+                kvs.append(d)
+        return kvs
+
+    def _parse_tool(function):
+        # remove rst syntax
+        docs = Docstring(
+            re.sub(':(.+?):`(.+?)`', '\\2', function.__doc__ or '')).parse(
+                'google', returns_named_value=returns_named_value, **kwargs)
+        desc = dict(
+            name=function.__name__,
+            description=docs[0].value
+            if docs[0].kind is DocstringSectionKind.text else '',
+            parameters=[],
+            required=[],
+        )
+        args_doc, returns_doc = {}, []
+        for doc in docs:
+            if doc.kind is DocstringSectionKind.parameters:
+                for d in doc.value:
+                    d = d.as_dict()
+                    d['type'] = _detect_type(d.pop('annotation').lower())
+                    args_doc[d['name']] = d
+            if doc.kind is DocstringSectionKind.returns:
+                for d in doc.value:
+                    d = d.as_dict()
+                    if not d['name']:
+                        d.pop('name')
+                    if not d['annotation']:
+                        d.pop('annotation')
+                    else:
+                        d['type'] = _detect_type(d.pop('annotation').lower())
+                    returns_doc.append(d)
+
+        sig = inspect.signature(function)
+        for name, param in sig.parameters.items():
+            if name == 'self':
+                continue
+            parameter = dict(
+                name=param.name,
+                type='STRING',
+                description=args_doc.get(param.name,
+                                         {}).get('description', ''))
+            annotation = param.annotation
+            if annotation is inspect.Signature.empty:
+                parameter['type'] = args_doc.get(param.name,
+                                                 {}).get('type', 'STRING')
+            else:
+                if get_origin(annotation) is Annotated:
+                    annotation, info = get_args(annotation)
+                    if info:
+                        parameter['description'] = info
+                while get_origin(annotation):
+                    annotation = get_args(annotation)
+                parameter['type'] = _detect_type(str(annotation))
+            desc['parameters'].append(parameter)
+            if param.default is inspect.Signature.empty:
+                desc['required'].append(param.name)
+
+        return_data = []
+        if explode_return:
+            return_data = _explode(returns_doc[0]['description'])
+        elif returns_named_value:
+            return_data = returns_doc
+        if return_data:
+            desc['return_data'] = return_data
+        return desc
+
+    if callable(func):
+
+        if inspect.iscoroutinefunction(func):
+
+            @wraps(func)
+            async def wrapper(self, *args, **kwargs):
+                return await func(self, *args, **kwargs)
+
+        else:
+
+            @wraps(func)
+            def wrapper(self, *args, **kwargs):
+                return func(self, *args, **kwargs)
+
+        wrapper.api_description = _parse_tool(func)
+        return wrapper
+
+    def decorate(func):
+
+        if inspect.iscoroutinefunction(func):
+
+            @wraps(func)
+            async def wrapper(self, *args, **kwargs):
+                return await func(self, *args, **kwargs)
+
+        else:
+
+            @wraps(func)
+            def wrapper(self, *args, **kwargs):
+                return func(self, *args, **kwargs)
+
+        wrapper.api_description = _parse_tool(func)
+        return wrapper
+
+    return decorate
+
+
+class ToolMeta(ABCMeta):
+    """Metaclass of tools."""
+
+    def __new__(mcs, name, base, attrs):
+        is_toolkit, tool_desc = True, dict(
+            name=name,
+            description=Docstring(attrs.get('__doc__',
+                                            '')).parse('google')[0].value)
+        for key, value in attrs.items():
+            if callable(value) and hasattr(value, 'api_description'):
+                api_desc = getattr(value, 'api_description')
+                if key == 'run':
+                    tool_desc['parameters'] = api_desc['parameters']
+                    tool_desc['required'] = api_desc['required']
+                    if api_desc['description']:
+                        tool_desc['description'] = api_desc['description']
+                    if api_desc.get('return_data'):
+                        tool_desc['return_data'] = api_desc['return_data']
+                    is_toolkit = False
+                else:
+                    tool_desc.setdefault('api_list', []).append(api_desc)
+        if not is_toolkit and 'api_list' in tool_desc:
+            raise KeyError('`run` and other tool APIs can not be implemented '
+                           'at the same time')
+        if is_toolkit and 'api_list' not in tool_desc:
+            is_toolkit = False
+            if callable(attrs.get('run')):
+                run_api = tool_api(attrs['run'])
+                api_desc = run_api.api_description
+                tool_desc['parameters'] = api_desc['parameters']
+                tool_desc['required'] = api_desc['required']
+                if api_desc['description']:
+                    tool_desc['description'] = api_desc['description']
+                if api_desc.get('return_data'):
+                    tool_desc['return_data'] = api_desc['return_data']
+                attrs['run'] = run_api
+            else:
+                tool_desc['parameters'], tool_desc['required'] = [], []
+        attrs['_is_toolkit'] = is_toolkit
+        attrs['__tool_description__'] = tool_desc
+        return super().__new__(mcs, name, base, attrs)
+
+
+class BaseAction(metaclass=ToolMeta):
+    """Base class for all actions.
+
+    Args:
+        description (:class:`Optional[dict]`): The description of the action.
+            Defaults to ``None``.
+        parser (:class:`Type[BaseParser]`): The parser class to process the
+            action's inputs and outputs. Defaults to :class:`JsonParser`.
+
+    Examples:
+
+        * simple tool
+
+        .. code-block:: python
+
+            class Bold(BaseAction):
+                '''Make text bold'''
+
+                def run(self, text: str):
+                    '''
+                    Args:
+                        text (str): input text
+
+                    Returns:
+                        str: bold text
+                    '''
+                    return '**' + text + '**'
+
+            action = Bold()
+
+        * toolkit with multiple APIs
+
+        .. code-block:: python
+
+            class Calculator(BaseAction):
+                '''Calculator'''
+
+                @tool_api
+                def add(self, a, b):
+                    '''Add operation
+
+                    Args:
+                        a (int): augend
+                        b (int): addend
+
+                    Returns:
+                        int: sum
+                    '''
+                    return a + b
+
+                @tool_api
+                def sub(self, a, b):
+                    '''Subtraction operation
+
+                    Args:
+                        a (int): minuend
+                        b (int): subtrahend
+
+                    Returns:
+                        int: difference
+                    '''
+                    return a - b
+
+            action = Calculator()
+    """
+
+    def __init__(
+        self,
+        description: Optional[dict] = None,
+        parser: Type[BaseParser] = JsonParser,
+    ):
+        self._description = deepcopy(description or self.__tool_description__)
+        self._name = self._description['name']
+        self._parser = parser(self)
+
+    def __call__(self, inputs: str, name='run') -> ActionReturn:
+        fallback_args = {'inputs': inputs, 'name': name}
+        if not hasattr(self, name):
+            return ActionReturn(
+                fallback_args,
+                type=self.name,
+                errmsg=f'invalid API: {name}',
+                state=ActionStatusCode.API_ERROR)
+        try:
+            inputs = self._parser.parse_inputs(inputs, name)
+        except ParseError as exc:
+            return ActionReturn(
+                fallback_args,
+                type=self.name,
+                errmsg=exc.err_msg,
+                state=ActionStatusCode.ARGS_ERROR)
+        try:
+            outputs = getattr(self, name)(**inputs)
+        except Exception as exc:
+            return ActionReturn(
+                inputs,
+                type=self.name,
+                errmsg=str(exc),
+                state=ActionStatusCode.API_ERROR)
+        if isinstance(outputs, ActionReturn):
+            action_return = outputs
+            if not action_return.args:
+                action_return.args = inputs
+            if not action_return.type:
+                action_return.type = self.name
+        else:
+            result = self._parser.parse_outputs(outputs)
+            action_return = ActionReturn(inputs, type=self.name, result=result)
+        return action_return
+
+    @property
+    def name(self):
+        return self._name
+
+    @property
+    def is_toolkit(self):
+        return self._is_toolkit
+
+    @property
+    def description(self) -> dict:
+        """Description of the tool."""
+        return self._description
+
+    def __repr__(self):
+        return f'{self.description}'
+
+    __str__ = __repr__
+
+
+class AsyncActionMixin:
+
+    async def __call__(self, inputs: str, name='run') -> ActionReturn:
+        fallback_args = {'inputs': inputs, 'name': name}
+        if not hasattr(self, name):
+            return ActionReturn(
+                fallback_args,
+                type=self.name,
+                errmsg=f'invalid API: {name}',
+                state=ActionStatusCode.API_ERROR)
+        try:
+            inputs = self._parser.parse_inputs(inputs, name)
+        except ParseError as exc:
+            return ActionReturn(
+                fallback_args,
+                type=self.name,
+                errmsg=exc.err_msg,
+                state=ActionStatusCode.ARGS_ERROR)
+        try:
+            outputs = await getattr(self, name)(**inputs)
+        except Exception as exc:
+            return ActionReturn(
+                inputs,
+                type=self.name,
+                errmsg=str(exc),
+                state=ActionStatusCode.API_ERROR)
+        if isinstance(outputs, ActionReturn):
+            action_return = outputs
+            if not action_return.args:
+                action_return.args = inputs
+            if not action_return.type:
+                action_return.type = self.name
+        else:
+            result = self._parser.parse_outputs(outputs)
+            action_return = ActionReturn(inputs, type=self.name, result=result)
+        return action_return

lagent/actions/bing_map.py

@@ -0,0 +1,268 @@
+# flake8: noqa: E501
+import json
+import os
+from typing import Optional, Type
+
+import aiohttp
+import requests
+
+from lagent.actions.base_action import AsyncActionMixin, BaseAction, tool_api
+from lagent.actions.parser import BaseParser, JsonParser
+
+
+class BINGMap(BaseAction):
+    """BING Map plugin for looking up map information."""
+
+    def __init__(
+        self,
+        key: Optional[str] = None,
+        description: Optional[dict] = None,
+        parser: Type[BaseParser] = JsonParser,
+    ) -> None:
+        super().__init__(description, parser)
+        key = os.environ.get('BING_MAP_KEY', key)
+        if key is None:
+            raise ValueError(
+                'Please set BING Map API key either in the environment '
+                'as BING_MAP_KEY or pass it as `key` parameter.')
+        self.key = key
+        self.base_url = 'http://dev.virtualearth.net/REST/V1/'
+
+    @tool_api(explode_return=True)
+    def get_distance(self, start: str, end: str) -> dict:
+        """Get the distance between two locations in km.
+
+        Args:
+            start (:class:`str`): The start location
+            end (:class:`str`): The end location
+
+        Returns:
+            :class:`dict`: distance information
+                * distance (str): the distance in km.
+        """
+        # Request URL
+        url = self.base_url + 'Routes/Driving?o=json&wp.0=' + start + '&wp.1=' + end + '&key=' + self.key
+        # GET request
+        r = requests.get(url)
+        # TODO check request status?
+        data = json.loads(r.text)
+        # Extract route information
+        route = data['resourceSets'][0]['resources'][0]
+        # Extract distance in miles
+        distance = route['travelDistance']
+        return dict(distance=distance)
+
+    @tool_api(explode_return=True)
+    def get_route(self, start: str, end: str) -> dict:
+        """Get the route between two locations in km.
+
+        Args:
+            start (:class:`str`): The start location
+            end (:class:`str`): The end location
+
+        Returns:
+            :class:`dict`: route information
+                * route (list): the route, a list of actions.
+        """
+        # Request URL
+        url = self.base_url + 'Routes/Driving?o=json&wp.0=' + start + '&wp.1=' + end + '&key=' + self.key
+        # GET request
+        r = requests.get(url)
+        data = json.loads(r.text)
+        # Extract route information
+        route = data['resourceSets'][0]['resources'][0]
+        itinerary = route['routeLegs'][0]['itineraryItems']
+        # Extract route text information
+        route_text = []
+        for item in itinerary:
+            if 'instruction' in item:
+                route_text.append(item['instruction']['text'])
+        return dict(route=route_text)
+
+    @tool_api(explode_return=True)
+    def get_coordinates(self, location: str) -> dict:
+        """Get the coordinates of a location.
+
+        Args:
+            location (:class:`str`): the location need to get coordinates.
+
+        Returns:
+            :class:`dict`: coordinates information
+                * latitude (float): the latitude of the location.
+                * longitude (float): the longitude of the location.
+        """
+        url = self.base_url + 'Locations'
+        params = {'query': location, 'key': self.key}
+        response = requests.get(url, params=params)
+        json_data = response.json()
+        coordinates = json_data['resourceSets'][0]['resources'][0]['point'][
+            'coordinates']
+        return dict(latitude=coordinates[0], longitude=coordinates[1])
+
+    @tool_api(explode_return=True)
+    def search_nearby(self,
+                      search_term: str,
+                      places: str = 'unknown',
+                      latitude: float = 0.0,
+                      longitude: float = 0.0,
+                      radius: int = 5000) -> dict:
+        """Search for places nearby a location, within a given radius, and return the results into a list. You can use either the places name or the latitude and longitude.
+
+        Args:
+            search_term (:class:`str`): the place name.
+            places (:class:`str`): the name of the location. Defaults to ``'unknown'``.
+            latitude (:class:`float`): the latitude of the location. Defaults to ``0.0``.
+            longitude (:class:`float`): the longitude of the location. Defaults to ``0.0``.
+            radius (:class:`int`): radius in meters. Defaults to ``5000``.
+
+        Returns:
+            :class:`dict`: places information
+                * places (list): the list of places, each place is a dict with name and address, at most 5 places.
+        """
+        url = self.base_url + 'LocalSearch'
+        if places != 'unknown':
+            pos = self.get_coordinates(**{'location': places})
+            latitude, longitude = pos[1]['latitude'], pos[1]['longitude']
+        # Build the request query string
+        params = {
+            'query': search_term,
+            'userLocation': f'{latitude},{longitude}',
+            'radius': radius,
+            'key': self.key
+        }
+        # Make the request
+        response = requests.get(url, params=params)
+        # Parse the response
+        response_data = json.loads(response.content)
+        # Get the results
+        results = response_data['resourceSets'][0]['resources']
+        addresses = []
+        for result in results:
+            name = result['name']
+            address = result['Address']['formattedAddress']
+            addresses.append(dict(name=name, address=address))
+            if len(addresses) == 5:
+                break
+        return dict(place=addresses)
+
+
+class AsyncBINGMap(AsyncActionMixin, BINGMap):
+    """BING Map plugin for looking up map information."""
+
+    @tool_api(explode_return=True)
+    async def get_distance(self, start: str, end: str) -> dict:
+        """Get the distance between two locations in km.
+
+        Args:
+            start (:class:`str`): The start location
+            end (:class:`str`): The end location
+
+        Returns:
+            :class:`dict`: distance information
+                * distance (str): the distance in km.
+        """
+        # Request URL
+        url = self.base_url + 'Routes/Driving?o=json&wp.0=' + start + '&wp.1=' + end + '&key=' + self.key
+        # GET request
+        async with aiohttp.ClientSession() as session:
+            async with session.get(url) as resp:
+                # TODO check request status?
+                data = await resp.json()
+        # Extract route information
+        route = data['resourceSets'][0]['resources'][0]
+        # Extract distance in miles
+        distance = route['travelDistance']
+        return dict(distance=distance)
+
+    @tool_api(explode_return=True)
+    async def get_route(self, start: str, end: str) -> dict:
+        """Get the route between two locations in km.
+
+        Args:
+            start (:class:`str`): The start location
+            end (:class:`str`): The end location
+
+        Returns:
+            :class:`dict`: route information
+                * route (list): the route, a list of actions.
+        """
+        # Request URL
+        url = self.base_url + 'Routes/Driving?o=json&wp.0=' + start + '&wp.1=' + end + '&key=' + self.key
+        # GET request
+        async with aiohttp.ClientSession() as session:
+            async with session.get(url) as resp:
+                data = await resp.json()
+        # Extract route information
+        route = data['resourceSets'][0]['resources'][0]
+        itinerary = route['routeLegs'][0]['itineraryItems']
+        # Extract route text information
+        route_text = []
+        for item in itinerary:
+            if 'instruction' in item:
+                route_text.append(item['instruction']['text'])
+        return dict(route=route_text)
+
+    @tool_api(explode_return=True)
+    async def get_coordinates(self, location: str) -> dict:
+        """Get the coordinates of a location.
+
+        Args:
+            location (:class:`str`): the location need to get coordinates.
+
+        Returns:
+            :class:`dict`: coordinates information
+                * latitude (float): the latitude of the location.
+                * longitude (float): the longitude of the location.
+        """
+        url = self.base_url + 'Locations'
+        params = {'query': location, 'key': self.key}
+        async with aiohttp.ClientSession() as session:
+            async with session.get(url, params=params) as resp:
+                data = await resp.json()
+        coordinates = data['resourceSets'][0]['resources'][0]['point'][
+            'coordinates']
+        return dict(latitude=coordinates[0], longitude=coordinates[1])
+
+    @tool_api(explode_return=True)
+    async def search_nearby(self,
+                            search_term: str,
+                            places: str = 'unknown',
+                            latitude: float = 0.0,
+                            longitude: float = 0.0,
+                            radius: int = 5000) -> dict:
+        """Search for places nearby a location, within a given radius, and return the results into a list. You can use either the places name or the latitude and longitude.
+
+        Args:
+            search_term (:class:`str`): the place name.
+            places (:class:`str`): the name of the location. Defaults to ``'unknown'``.
+            latitude (:class:`float`): the latitude of the location. Defaults to ``0.0``.
+            longitude (:class:`float`): the longitude of the location. Defaults to ``0.0``.
+            radius (:class:`int`): radius in meters. Defaults to ``5000``.
+
+        Returns:
+            :class:`dict`: places information
+                * places (list): the list of places, each place is a dict with name and address, at most 5 places.
+        """
+        url = self.base_url + 'LocalSearch'
+        if places != 'unknown':
+            pos = self.get_coordinates(**{'location': places})
+            latitude, longitude = pos[1]['latitude'], pos[1]['longitude']
+        # Build the request query string
+        params = {
+            'query': search_term,
+            'userLocation': f'{latitude},{longitude}',
+            'radius': radius,
+            'key': self.key
+        }
+        async with aiohttp.ClientSession() as session:
+            async with session.get(url, params=params) as resp:
+                data = await resp.json()
+        results = data['resourceSets'][0]['resources']
+        addresses = []
+        for result in results:
+            name = result['name']
+            address = result['Address']['formattedAddress']
+            addresses.append(dict(name=name, address=address))
+            if len(addresses) == 5:
+                break
+        return dict(place=addresses)

lagent/actions/builtin_actions.py

@@ -0,0 +1,109 @@
+from typing import Optional
+
+from lagent.actions.base_action import BaseAction, tool_api
+from lagent.actions.parser import BaseParser
+from lagent.schema import ActionReturn, ActionStatusCode, ActionValidCode
+
+
+class InvalidAction(BaseAction):
+    """This is a invalid action class, which is used to return error message
+    when the action is invalid.
+
+    Args:
+        err_msg (str): The error message. Defaults to 'The action is invalid,
+            please check the action name'.
+
+    Returns:
+        ActionReturn: The action return.
+    """
+
+    def __init__(self,
+                 err_msg:
+                 str = 'The action is invalid, please check the action name.',
+                 description: Optional[dict] = None,
+                 parser=BaseParser) -> None:
+        super().__init__(description, parser)
+        self._err_msg = err_msg
+
+    @tool_api
+    def run(self, err_msg: Optional[str] = None) -> ActionReturn:
+        """Return the error message.
+
+        Args:
+            err_msg (str, optional): The error message. If err_msg is not None,
+                it will be returned, otherwise the default error message will
+                be returned. Defaults to None.
+        """
+        action_return = ActionReturn(
+            url=None,
+            args=dict(text=err_msg),
+            errmsg=err_msg or self._err_msg,
+            type=self.name,
+            valid=ActionValidCode.INVALID,
+            state=ActionStatusCode.API_ERROR)
+        return action_return
+
+
+class NoAction(BaseAction):
+    """This is a no action class, which is used to return error message when
+    the response does not follow the format.
+
+    Args:
+        err_msg (str): The error message. Defaults to
+            'Please follow the format'.
+    """
+
+    def __init__(self,
+                 err_msg: str = 'Please follow the format',
+                 description: Optional[dict] = None,
+                 parser=BaseParser):
+        super().__init__(description, parser)
+        self._err_msg = err_msg
+
+    @tool_api
+    def run(self, err_msg: Optional[str] = None) -> ActionReturn:
+        """Return the error message.
+
+        Args:
+            err_msg (str, optional): The error message. If err_msg is not None,
+                it will be returned, otherwise the default error message will
+                be returned. Defaults to None.
+
+        Returns:
+            ActionReturn: The action return.
+        """
+        action_return = ActionReturn(
+            url=None,
+            args=dict(text=err_msg),
+            type=self.name,
+            errmsg=err_msg or self._err_msg,
+            valid=ActionValidCode.INVALID,
+            state=ActionStatusCode.API_ERROR)
+        return action_return
+
+
+class FinishAction(BaseAction):
+    """This is a finish action class, which is used to return the final
+    result."""
+
+    def __init__(self, description: Optional[dict] = None, parser=BaseParser):
+        super().__init__(description, parser)
+
+    @tool_api
+    def run(self, response: str) -> ActionReturn:
+        """Return the final result.
+
+        Args:
+            response (str): The final result.
+
+        Returns:
+            ActionReturn: The action return.
+        """
+        action_return = ActionReturn(
+            url=None,
+            args=dict(text=response),
+            result=[dict(type='text', content=response)],
+            type=self.name,
+            valid=ActionValidCode.FINISH,
+            state=ActionStatusCode.SUCCESS)
+        return action_return

lagent/actions/google_scholar_search.py

@@ -0,0 +1,438 @@
+# flake8: noqa: E501
+import os
+from typing import Optional, Type
+
+from asyncer import asyncify
+
+from lagent.actions.base_action import AsyncActionMixin, BaseAction, tool_api
+from lagent.schema import ActionReturn, ActionStatusCode
+from .parser import BaseParser, JsonParser
+
+
+class GoogleScholar(BaseAction):
+    """Plugin for google scholar search.
+
+    Args:
+        api_key (str): API KEY to use serper google search API,
+            You can create a free API key at https://serper.dev.
+        description (dict): The description of the action. Defaults to ``None``.
+        parser (Type[BaseParser]): The parser class to process the
+            action's inputs and outputs. Defaults to :class:`JsonParser`.
+    """
+
+    def __init__(
+        self,
+        api_key: Optional[str] = None,
+        description: Optional[dict] = None,
+        parser: Type[BaseParser] = JsonParser,
+    ):
+        super().__init__(description, parser)
+        api_key = os.environ.get('SERPER_API_KEY', api_key)
+        if api_key is None:
+            raise ValueError(
+                'Please set Serper API key either in the environment '
+                'as SERPER_API_KEY or pass it as `api_key` parameter.'
+            )
+        self.api_key = api_key
+
+    @tool_api(explode_return=True)
+    def search_google_scholar(
+        self,
+        query: str,
+        cites: Optional[str] = None,
+        as_ylo: Optional[int] = None,
+        as_yhi: Optional[int] = None,
+        scisbd: Optional[int] = None,
+        cluster: Optional[str] = None,
+        hl: Optional[str] = None,
+        lr: Optional[str] = None,
+        start: Optional[int] = None,
+        num: Optional[int] = None,
+        as_sdt: Optional[str] = None,
+        safe: Optional[str] = None,
+        filter: Optional[str] = None,
+        as_vis: Optional[str] = None,
+    ) -> dict:
+        """Search for scholarly articles based on a query according to the google scholar.
+
+        Args:
+            query (str): The query to search for.
+            cites (Optional[str]): The unique ID of an article for triggering "Cited By" searches.
+            as_ylo (Optional[int]): The starting year for results (e.g., if as_ylo=2018, results before this year will be omitted).
+            as_yhi (Optional[int]): The ending year for results (e.g., if as_yhi=2018, results after this year will be omitted).
+            scisbd (Optional[int]): Defines articles added in the last year, sorted by date. It can be set to 1 to include only abstracts, or 2 to include everything.
+            cluster (Optional[str]): The unique ID of an article for triggering "All Versions" searches.
+            hl (Optional[str]): The language to use for the Google Scholar search.
+            lr (Optional[str]): One or multiple languages to limit the search to.
+            start (Optional[int]): The result offset for pagination (0 is the first page of results, 10 is the 2nd page, etc.)
+            num (Optional[int]): The maximum number of results to return, limited to 20.
+            as_sdt (Optional[str]): Can be used either as a search type or a filter.
+            safe (Optional[str]): The level of filtering for adult content.
+            filter (Optional[str]): Defines if the filters for 'Similar Results' and 'Omitted Results' are on or off.
+            as_vis (Optional[str]): Defines whether to include citations or not.
+
+        Returns:
+            :class:`dict`: article information
+                - title: a list of the titles of the three selected papers
+                - cited_by: a list of the citation numbers of the three selected papers
+                - organic_id: a list of the organic results' ids of the three selected papers
+                - pub_info: publication information of selected papers
+        """
+        from serpapi import GoogleSearch
+
+        params = {
+            'q': query,
+            'engine': 'google_scholar',
+            'api_key': self.api_key,
+            'cites': cites,
+            'as_ylo': as_ylo,
+            'as_yhi': as_yhi,
+            'scisbd': scisbd,
+            'cluster': cluster,
+            'hl': hl,
+            'lr': lr,
+            'start': start,
+            'num': num,
+            'as_sdt': as_sdt,
+            'safe': safe,
+            'filter': filter,
+            'as_vis': as_vis,
+        }
+        search = GoogleSearch(params)
+        try:
+            r = search.get_dict()
+            results = r['organic_results']
+            title = []
+            snippets = []
+            cited_by = []
+            organic_id = []
+            pub_info = []
+            for item in results[:3]:
+                title.append(item['title'])
+                pub_info.append(item['publication_info']['summary'])
+                citation = item['inline_links'].get('cited_by', {'total': ''})
+                cited_by.append(citation['total'])
+                snippets.append(item['snippet'])
+                organic_id.append(item['result_id'])
+            return dict(title=title, cited_by=cited_by, organic_id=organic_id, snippets=snippets)
+        except Exception as e:
+            return ActionReturn(errmsg=str(e), state=ActionStatusCode.HTTP_ERROR)
+
+    @tool_api(explode_return=True)
+    def get_author_information(
+        self,
+        author_id: str,
+        hl: Optional[str] = None,
+        view_op: Optional[str] = None,
+        sort: Optional[str] = None,
+        citation_id: Optional[str] = None,
+        start: Optional[int] = None,
+        num: Optional[int] = None,
+        no_cache: Optional[bool] = None,
+        async_req: Optional[bool] = None,
+        output: Optional[str] = None,
+    ) -> dict:
+        """Search for an author's information by author's id provided by get_author_id.
+
+        Args:
+            author_id (str): Required. The ID of an author.
+            hl (Optional[str]): The language to use for the Google Scholar Author search. Default is 'en'.
+            view_op (Optional[str]): Used for viewing specific parts of a page.
+            sort (Optional[str]): Used for sorting and refining articles.
+            citation_id (Optional[str]): Used for retrieving individual article citation.
+            start (Optional[int]): Defines the result offset. Default is 0.
+            num (Optional[int]): Defines the number of results to return. Default is 20.
+            no_cache (Optional[bool]): Forces SerpApi to fetch the results even if a cached version is already present. Default is False.
+            async_req (Optional[bool]): Defines the way you want to submit your search to SerpApi. Default is False.
+            output (Optional[str]): Defines the final output you want. Default is 'json'.
+
+        Returns:
+            :class:`dict`: author information
+                * name: author's name
+                * affliation: the affliation of the author
+                * articles: at most 3 articles by the author
+                * website: the author's homepage url
+        """
+        from serpapi import GoogleSearch
+
+        params = {
+            'engine': 'google_scholar_author',
+            'author_id': author_id,
+            'api_key': self.api_key,
+            'hl': hl,
+            'view_op': view_op,
+            'sort': sort,
+            'citation_id': citation_id,
+            'start': start,
+            'num': num,
+            'no_cache': no_cache,
+            'async': async_req,
+            'output': output,
+        }
+        try:
+            search = GoogleSearch(params)
+            results = search.get_dict()
+            author = results['author']
+            articles = results.get('articles', [])
+            return dict(
+                name=author['name'],
+                affiliations=author.get('affiliations', ''),
+                website=author.get('website', ''),
+                articles=[dict(title=article['title'], authors=article['authors']) for article in articles[:3]],
+            )
+        except Exception as e:
+            return ActionReturn(errmsg=str(e), state=ActionStatusCode.HTTP_ERROR)
+
+    @tool_api(explode_return=True)
+    def get_citation_format(
+        self,
+        q: str,
+        no_cache: Optional[bool] = None,
+        async_: Optional[bool] = None,
+        output: Optional[str] = 'json',
+    ) -> dict:
+        """Function to get MLA citation format by an identification of organic_result's id provided by search_google_scholar.
+
+        Args:
+            q (str): ID of an individual Google Scholar organic search result.
+            no_cache (Optional[bool]): If set to True, will force SerpApi to fetch the Google Scholar Cite results even if a cached version is already present. Defaults to None.
+            async_ (Optional[bool]): If set to True, will submit search to SerpApi and retrieve results later. Defaults to None.
+            output (Optional[str]): Final output format. Set to 'json' to get a structured JSON of the results, or 'html' to get the raw html retrieved. Defaults to 'json'.
+
+        Returns:
+            :class:`dict`: citation format
+                * authors: the authors of the article
+                * citation: the citation format of the article
+        """
+        from serpapi import GoogleSearch
+
+        params = {
+            'q': q,
+            'engine': 'google_scholar_cite',
+            'api_key': self.api_key,
+            'no_cache': no_cache,
+            'async': async_,
+            'output': output,
+        }
+        try:
+            search = GoogleSearch(params)
+            results = search.get_dict()
+            citation = results['citations']
+            citation_info = citation[0]['snippet']
+            return citation_info
+        except Exception as e:
+            return ActionReturn(errmsg=str(e), state=ActionStatusCode.HTTP_ERROR)
+
+    @tool_api(explode_return=True)
+    def get_author_id(
+        self,
+        mauthors: str,
+        hl: Optional[str] = 'en',
+        after_author: Optional[str] = None,
+        before_author: Optional[str] = None,
+        no_cache: Optional[bool] = False,
+        _async: Optional[bool] = False,
+        output: Optional[str] = 'json',
+    ) -> dict:
+        """The getAuthorId function is used to get the author's id by his or her name.
+
+        Args:
+            mauthors (str): Defines the author you want to search for.
+            hl (Optional[str]): Defines the language to use for the Google Scholar Profiles search. It's a two-letter language code. (e.g., 'en' for English, 'es' for Spanish, or 'fr' for French). Defaults to 'en'.
+            after_author (Optional[str]): Defines the next page token. It is used for retrieving the next page results. The parameter has the precedence over before_author parameter. Defaults to None.
+            before_author (Optional[str]): Defines the previous page token. It is used for retrieving the previous page results. Defaults to None.
+            no_cache (Optional[bool]): Will force SerpApi to fetch the Google Scholar Profiles results even if a cached version is already present. Defaults to False.
+            _async (Optional[bool]): Defines the way you want to submit your search to SerpApi. Defaults to False.
+            output (Optional[str]): Defines the final output you want. It can be set to 'json' (default) to get a structured JSON of the results, or 'html' to get the raw html retrieved. Defaults to 'json'.
+
+        Returns:
+            :class:`dict`: author id
+                * author_id: the author_id of the author
+        """
+        from serpapi import GoogleSearch
+
+        params = {
+            'mauthors': mauthors,
+            'engine': 'google_scholar_profiles',
+            'api_key': self.api_key,
+            'hl': hl,
+            'after_author': after_author,
+            'before_author': before_author,
+            'no_cache': no_cache,
+            'async': _async,
+            'output': output,
+        }
+        try:
+            search = GoogleSearch(params)
+            results = search.get_dict()
+            profile = results['profiles']
+            author_info = dict(author_id=profile[0]['author_id'])
+            return author_info
+        except Exception as e:
+            return ActionReturn(errmsg=str(e), state=ActionStatusCode.HTTP_ERROR)
+
+
+class AsyncGoogleScholar(AsyncActionMixin, GoogleScholar):
+    """Plugin for google scholar search.
+
+    Args:
+        api_key (str): API KEY to use serper google search API,
+            You can create a free API key at https://serper.dev.
+        description (dict): The description of the action. Defaults to ``None``.
+        parser (Type[BaseParser]): The parser class to process the
+            action's inputs and outputs. Defaults to :class:`JsonParser`.
+    """
+
+    @tool_api(explode_return=True)
+    @asyncify
+    def search_google_scholar(
+        self,
+        query: str,
+        cites: Optional[str] = None,
+        as_ylo: Optional[int] = None,
+        as_yhi: Optional[int] = None,
+        scisbd: Optional[int] = None,
+        cluster: Optional[str] = None,
+        hl: Optional[str] = None,
+        lr: Optional[str] = None,
+        start: Optional[int] = None,
+        num: Optional[int] = None,
+        as_sdt: Optional[str] = None,
+        safe: Optional[str] = None,
+        filter: Optional[str] = None,
+        as_vis: Optional[str] = None,
+    ) -> dict:
+        """Search for scholarly articles based on a query according to the google scholar.
+
+        Args:
+            query (str): The query to search for.
+            cites (Optional[str]): The unique ID of an article for triggering "Cited By" searches.
+            as_ylo (Optional[int]): The starting year for results (e.g., if as_ylo=2018, results before this year will be omitted).
+            as_yhi (Optional[int]): The ending year for results (e.g., if as_yhi=2018, results after this year will be omitted).
+            scisbd (Optional[int]): Defines articles added in the last year, sorted by date. It can be set to 1 to include only abstracts, or 2 to include everything.
+            cluster (Optional[str]): The unique ID of an article for triggering "All Versions" searches.
+            hl (Optional[str]): The language to use for the Google Scholar search.
+            lr (Optional[str]): One or multiple languages to limit the search to.
+            start (Optional[int]): The result offset for pagination (0 is the first page of results, 10 is the 2nd page, etc.)
+            num (Optional[int]): The maximum number of results to return, limited to 20.
+            as_sdt (Optional[str]): Can be used either as a search type or a filter.
+            safe (Optional[str]): The level of filtering for adult content.
+            filter (Optional[str]): Defines if the filters for 'Similar Results' and 'Omitted Results' are on or off.
+            as_vis (Optional[str]): Defines whether to include citations or not.
+
+        Returns:
+            :class:`dict`: article information
+                - title: a list of the titles of the three selected papers
+                - cited_by: a list of the citation numbers of the three selected papers
+                - organic_id: a list of the organic results' ids of the three selected papers
+                - pub_info: publication information of selected papers
+        """
+        return super().search_google_scholar(
+            query,
+            cites,
+            as_ylo,
+            as_yhi,
+            scisbd,
+            cluster,
+            hl,
+            lr,
+            start,
+            num,
+            as_sdt,
+            safe,
+            filter,
+            as_vis,
+        )
+
+    @tool_api(explode_return=True)
+    @asyncify
+    def get_author_information(
+        self,
+        author_id: str,
+        hl: Optional[str] = None,
+        view_op: Optional[str] = None,
+        sort: Optional[str] = None,
+        citation_id: Optional[str] = None,
+        start: Optional[int] = None,
+        num: Optional[int] = None,
+        no_cache: Optional[bool] = None,
+        async_req: Optional[bool] = None,
+        output: Optional[str] = None,
+    ) -> dict:
+        """Search for an author's information by author's id provided by get_author_id.
+
+        Args:
+            author_id (str): Required. The ID of an author.
+            hl (Optional[str]): The language to use for the Google Scholar Author search. Default is 'en'.
+            view_op (Optional[str]): Used for viewing specific parts of a page.
+            sort (Optional[str]): Used for sorting and refining articles.
+            citation_id (Optional[str]): Used for retrieving individual article citation.
+            start (Optional[int]): Defines the result offset. Default is 0.
+            num (Optional[int]): Defines the number of results to return. Default is 20.
+            no_cache (Optional[bool]): Forces SerpApi to fetch the results even if a cached version is already present. Default is False.
+            async_req (Optional[bool]): Defines the way you want to submit your search to SerpApi. Default is False.
+            output (Optional[str]): Defines the final output you want. Default is 'json'.
+
+        Returns:
+            :class:`dict`: author information
+                * name: author's name
+                * affliation: the affliation of the author
+                * articles: at most 3 articles by the author
+                * website: the author's homepage url
+        """
+        return super().get_author_information(
+            author_id, hl, view_op, sort, citation_id, start, num, no_cache, async_req, output
+        )
+
+    @tool_api(explode_return=True)
+    @asyncify
+    def get_citation_format(
+        self,
+        q: str,
+        no_cache: Optional[bool] = None,
+        async_: Optional[bool] = None,
+        output: Optional[str] = 'json',
+    ) -> dict:
+        """Function to get MLA citation format by an identification of organic_result's id provided by search_google_scholar.
+
+        Args:
+            q (str): ID of an individual Google Scholar organic search result.
+            no_cache (Optional[bool]): If set to True, will force SerpApi to fetch the Google Scholar Cite results even if a cached version is already present. Defaults to None.
+            async_ (Optional[bool]): If set to True, will submit search to SerpApi and retrieve results later. Defaults to None.
+            output (Optional[str]): Final output format. Set to 'json' to get a structured JSON of the results, or 'html' to get the raw html retrieved. Defaults to 'json'.
+
+        Returns:
+            :class:`dict`: citation format
+                * authors: the authors of the article
+                * citation: the citation format of the article
+        """
+        return super().get_citation_format(q, no_cache, async_, output)
+
+    @tool_api(explode_return=True)
+    @asyncify
+    def get_author_id(
+        self,
+        mauthors: str,
+        hl: Optional[str] = 'en',
+        after_author: Optional[str] = None,
+        before_author: Optional[str] = None,
+        no_cache: Optional[bool] = False,
+        _async: Optional[bool] = False,
+        output: Optional[str] = 'json',
+    ) -> dict:
+        """The getAuthorId function is used to get the author's id by his or her name.
+
+        Args:
+            mauthors (str): Defines the author you want to search for.
+            hl (Optional[str]): Defines the language to use for the Google Scholar Profiles search. It's a two-letter language code. (e.g., 'en' for English, 'es' for Spanish, or 'fr' for French). Defaults to 'en'.
+            after_author (Optional[str]): Defines the next page token. It is used for retrieving the next page results. The parameter has the precedence over before_author parameter. Defaults to None.
+            before_author (Optional[str]): Defines the previous page token. It is used for retrieving the previous page results. Defaults to None.
+            no_cache (Optional[bool]): Will force SerpApi to fetch the Google Scholar Profiles results even if a cached version is already present. Defaults to False.
+            _async (Optional[bool]): Defines the way you want to submit your search to SerpApi. Defaults to False.
+            output (Optional[str]): Defines the final output you want. It can be set to 'json' (default) to get a structured JSON of the results, or 'html' to get the raw html retrieved. Defaults to 'json'.
+
+        Returns:
+            :class:`dict`: author id
+                * author_id: the author_id of the author
+        """
+        return super().get_author_id(mauthors, hl, after_author, before_author, no_cache, _async, output)

lagent/actions/google_search.py

@@ -0,0 +1,244 @@
+import os
+from typing import List, Optional, Tuple, Type, Union
+
+import aiohttp
+import requests
+
+from lagent.schema import ActionReturn, ActionStatusCode
+from .base_action import AsyncActionMixin, BaseAction, tool_api
+from .parser import BaseParser, JsonParser
+
+
+class GoogleSearch(BaseAction):
+    """Wrapper around the Serper.dev Google Search API.
+
+    To use, you should pass your serper API key to the constructor.
+
+    Code is modified from lang-chain GoogleSerperAPIWrapper
+    (https://github.com/langchain-ai/langchain/blob/ba5f
+    baba704a2d729a4b8f568ed70d7c53e799bb/libs/langchain/
+    langchain/utilities/google_serper.py)
+
+    Args:
+        api_key (str): API KEY to use serper google search API,
+            You can create a free API key at https://serper.dev.
+        timeout (int): Upper bound of waiting time for a serper request.
+        search_type (str): Serper API support ['search', 'images', 'news',
+            'places'] types of search, currently we only support 'search'.
+        description (dict): The description of the action. Defaults to ``None``.
+        parser (Type[BaseParser]): The parser class to process the
+            action's inputs and outputs. Defaults to :class:`JsonParser`.
+    """
+    result_key_for_type = {
+        'news': 'news',
+        'places': 'places',
+        'images': 'images',
+        'search': 'organic',
+    }
+
+    def __init__(
+        self,
+        api_key: Optional[str] = None,
+        timeout: int = 5,
+        search_type: str = 'search',
+        description: Optional[dict] = None,
+        parser: Type[BaseParser] = JsonParser,
+    ):
+        super().__init__(description, parser)
+        api_key = os.environ.get('SERPER_API_KEY', api_key)
+        if api_key is None:
+            raise ValueError(
+                'Please set Serper API key either in the environment '
+                'as SERPER_API_KEY or pass it as `api_key` parameter.')
+        self.api_key = api_key
+        self.timeout = timeout
+        self.search_type = search_type
+
+    @tool_api
+    def run(self, query: str, k: int = 10) -> ActionReturn:
+        """一个可以从谷歌搜索结果的API。当你需要对于一个特定问题找到简短明了的回答时，可以使用它。输入应该是一个搜索查询。
+
+        Args:
+            query (str): the search content
+            k (int): select first k results in the search results as response
+        """
+        tool_return = ActionReturn(type=self.name)
+        status_code, response = self._search(query, k=k)
+        # convert search results to ToolReturn format
+        if status_code == -1:
+            tool_return.errmsg = response
+            tool_return.state = ActionStatusCode.HTTP_ERROR
+        elif status_code == 200:
+            parsed_res = self._parse_results(response, k)
+            tool_return.result = [dict(type='text', content=str(parsed_res))]
+            tool_return.state = ActionStatusCode.SUCCESS
+        else:
+            tool_return.errmsg = str(status_code)
+            tool_return.state = ActionStatusCode.API_ERROR
+        return tool_return
+
+    def _parse_results(self, results: dict, k: int) -> Union[str, List[str]]:
+        """Parse the search results from Serper API.
+
+        Args:
+            results (dict): The search content from Serper API
+                in json format.
+
+        Returns:
+            List[str]: The parsed search results.
+        """
+
+        snippets = []
+
+        if results.get('answerBox'):
+            answer_box = results.get('answerBox', {})
+            if answer_box.get('answer'):
+                return [answer_box.get('answer')]
+            elif answer_box.get('snippet'):
+                return [answer_box.get('snippet').replace('\n', ' ')]
+            elif answer_box.get('snippetHighlighted'):
+                return answer_box.get('snippetHighlighted')
+
+        if results.get('knowledgeGraph'):
+            kg = results.get('knowledgeGraph', {})
+            title = kg.get('title')
+            entity_type = kg.get('type')
+            if entity_type:
+                snippets.append(f'{title}: {entity_type}.')
+            description = kg.get('description')
+            if description:
+                snippets.append(description)
+            for attribute, value in kg.get('attributes', {}).items():
+                snippets.append(f'{title} {attribute}: {value}.')
+
+        for result in results[self.result_key_for_type[
+                self.search_type]][:k]:
+            if 'snippet' in result:
+                snippets.append(result['snippet'])
+            for attribute, value in result.get('attributes', {}).items():
+                snippets.append(f'{attribute}: {value}.')
+
+        if len(snippets) == 0:
+            return ['No good Google Search Result was found']
+        return snippets
+
+    def _search(self,
+                search_term: str,
+                search_type: Optional[str] = None,
+                **kwargs) -> Tuple[int, Union[dict, str]]:
+        """HTTP requests to Serper API.
+
+        Args:
+            search_term (str): The search query.
+            search_type (str): search type supported by Serper API,
+                default to 'search'.
+
+        Returns:
+            tuple: the return value is a tuple contains:
+                - status_code (int): HTTP status code from Serper API.
+                - response (dict): response context with json format.
+        """
+        headers = {
+            'X-API-KEY': self.api_key or '',
+            'Content-Type': 'application/json',
+        }
+        params = {
+            'q': search_term,
+            **{
+                key: value
+                for key, value in kwargs.items() if value is not None
+            },
+        }
+        try:
+            response = requests.post(
+                f'https://google.serper.dev/{search_type or self.search_type}',
+                headers=headers,
+                params=params,
+                timeout=self.timeout)
+        except Exception as e:
+            return -1, str(e)
+        return response.status_code, response.json()
+
+
+class AsyncGoogleSearch(AsyncActionMixin, GoogleSearch):
+    """Wrapper around the Serper.dev Google Search API.
+
+    To use, you should pass your serper API key to the constructor.
+
+    Code is modified from lang-chain GoogleSerperAPIWrapper
+    (https://github.com/langchain-ai/langchain/blob/ba5f
+    baba704a2d729a4b8f568ed70d7c53e799bb/libs/langchain/
+    langchain/utilities/google_serper.py)
+
+    Args:
+        api_key (str): API KEY to use serper google search API,
+            You can create a free API key at https://serper.dev.
+        timeout (int): Upper bound of waiting time for a serper request.
+        search_type (str): Serper API support ['search', 'images', 'news',
+            'places'] types of search, currently we only support 'search'.
+        description (dict): The description of the action. Defaults to ``None``.
+        parser (Type[BaseParser]): The parser class to process the
+            action's inputs and outputs. Defaults to :class:`JsonParser`.
+    """
+
+    @tool_api
+    async def run(self, query: str, k: int = 10) -> ActionReturn:
+        """一个可以从谷歌搜索结果的API。当你需要对于一个特定问题找到简短明了的回答时，可以使用它。输入应该是一个搜索查询。
+
+        Args:
+            query (str): the search content
+            k (int): select first k results in the search results as response
+        """
+        tool_return = ActionReturn(type=self.name)
+        status_code, response = await self._search(query, k=k)
+        # convert search results to ToolReturn format
+        if status_code == -1:
+            tool_return.errmsg = response
+            tool_return.state = ActionStatusCode.HTTP_ERROR
+        elif status_code == 200:
+            parsed_res = self._parse_results(response)
+            tool_return.result = [dict(type='text', content=str(parsed_res))]
+            tool_return.state = ActionStatusCode.SUCCESS
+        else:
+            tool_return.errmsg = str(status_code)
+            tool_return.state = ActionStatusCode.API_ERROR
+        return tool_return
+
+    async def _search(self,
+                      search_term: str,
+                      search_type: Optional[str] = None,
+                      **kwargs) -> Tuple[int, Union[dict, str]]:
+        """HTTP requests to Serper API.
+
+        Args:
+            search_term (str): The search query.
+            search_type (str): search type supported by Serper API,
+                default to 'search'.
+
+        Returns:
+            tuple: the return value is a tuple contains:
+                - status_code (int): HTTP status code from Serper API.
+                - response (dict): response context with json format.
+        """
+        headers = {
+            'X-API-KEY': self.api_key or '',
+            'Content-Type': 'application/json',
+        }
+        params = {
+            'q': search_term,
+            **{
+                key: value
+                for key, value in kwargs.items() if value is not None
+            },
+        }
+        timeout = aiohttp.ClientTimeout(total=self.timeout)
+        async with aiohttp.ClientSession(timeout=timeout) as session:
+            try:
+                async with session.post(
+                        f'https://google.serper.dev/{search_type or self.search_type}',
+                        headers=headers,
+                        params=params) as resp:
+                    code, ret = resp.status, await resp.json()
+            except aiohttp.ClientError as e:
+                code, ret = -1, str(e)
+        return code, ret

lagent/actions/ipython_interactive.py

@@ -0,0 +1,273 @@
+import re
+import signal
+from contextlib import contextmanager, redirect_stdout
+from dataclasses import dataclass
+from enum import Enum
+from io import StringIO
+from typing import Optional, Type
+
+from ..schema import ActionReturn, ActionStatusCode
+from .base_action import AsyncActionMixin, BaseAction, tool_api
+from .parser import BaseParser, JsonParser
+
+
+class Status(str, Enum):
+    """Execution status."""
+    SUCCESS = 'success'
+    FAILURE = 'failure'
+
+
+@dataclass
+class ExecutionResult:
+    """Execution result."""
+    status: Status
+    value: Optional[str] = None
+    msg: Optional[str] = None
+
+
+@contextmanager
+def _raise_timeout(timeout):
+
+    def _handler(signum, frame):
+        raise TimeoutError()
+
+    signal.signal(signal.SIGALRM, _handler)
+    signal.alarm(timeout)
+
+    try:
+        yield
+    finally:
+        signal.alarm(0)
+
+
+class IPythonInteractive(BaseAction):
+    """An interactive IPython shell for code execution.
+
+    Args:
+        timeout (int): Upper bound of waiting time for Python script execution.
+            Defaults to ``20``.
+        max_out_len (int): maximum output length. No truncation occurs if negative.
+            Defaults to ``2048``.
+        use_signals (bool): whether signals should be used for timing function out
+            or the multiprocessing. Set to ``False`` when not running in the main
+            thread, e.g. web applications. Defaults to ``True``
+        description (dict): The description of the action. Defaults to ``None``.
+        parser (Type[BaseParser]): The parser class to process the
+            action's inputs and outputs. Defaults to :class:`JsonParser`.
+    """
+
+    def __init__(
+        self,
+        timeout: int = 30,
+        max_out_len: int = 8192,
+        use_signals: bool = True,
+        description: Optional[dict] = None,
+        parser: Type[BaseParser] = JsonParser,
+    ):
+        super().__init__(description, parser)
+        self.timeout = timeout
+        self._executor = self.create_shell()
+        self._highlighting = re.compile(
+            r'(?:\x1B[@-_]|[\x80-\x9F])[0-?]*[ -/]*[@-~]')
+        self._max_out_len = max_out_len if max_out_len >= 0 else None
+        self._use_signals = use_signals
+
+    def reset(self):
+        """Clear the context."""
+        self._executor.reset()
+
+    @tool_api
+    def run(self, command: str, timeout: Optional[int] = None) -> ActionReturn:
+        """Launch an IPython Interactive Shell to execute code.
+
+        Args:
+            command (:class:`str`): Python code snippet
+            timeout (:class:`Optional[int]`): timeout for execution.
+                This argument only works in the main thread. Defaults to ``None``.
+        """
+        from timeout_decorator import timeout as timer
+        tool_return = ActionReturn(args={'text': command}, type=self.name)
+        ret = (
+            timer(timeout or self.timeout)(self.exec)(command)
+            if self._use_signals else self.exec(command))
+        if ret.status is Status.SUCCESS:
+            tool_return.result = [{'type': 'text', 'content': ret.value}]
+            tool_return.state = ActionStatusCode.SUCCESS
+        else:
+            tool_return.errmsg = ret.msg
+            tool_return.state = ActionStatusCode.API_ERROR
+        return tool_return
+
+    def exec(self, code: str) -> ExecutionResult:
+        """Run Python scripts in IPython shell.
+
+        Args:
+            code (:class:`str`): code block
+
+        Returns:
+            :py:class:`ExecutionResult`: execution result
+        """
+        with StringIO() as io:
+            with redirect_stdout(io):
+                ret = self._executor.run_cell(self.extract_code(code))
+                result = ret.result
+                if result is not None:
+                    return ExecutionResult(Status.SUCCESS,
+                                           str(result)[:self._max_out_len])
+            outs = io.getvalue().strip().split('\n')
+        if not outs:
+            return ExecutionResult(Status.SUCCESS, '')
+        for i, out in enumerate(outs):
+            if re.search('Error|Traceback', out, re.S):
+                if 'TimeoutError' in out:
+                    return ExecutionResult(
+                        Status.FAILURE,
+                        msg=('The code interpreter encountered '
+                             'a timeout error.'))
+                err_idx = i
+                break
+        else:
+            return ExecutionResult(Status.SUCCESS,
+                                   outs[-1].strip()[:self._max_out_len])
+        return ExecutionResult(
+            Status.FAILURE,
+            msg=self._highlighting.sub(
+                '', '\n'.join(outs[err_idx:])[:self._max_out_len]),
+        )
+
+    @staticmethod
+    def create_shell():
+        from IPython import InteractiveShell
+        from traitlets.config import Config
+
+        c = Config()
+        c.HistoryManager.enabled = False
+        c.HistoryManager.hist_file = ':memory:'
+        return InteractiveShell(
+            user_ns={'_raise_timeout': _raise_timeout}, config=c)
+
+    @staticmethod
+    def extract_code(text: str) -> str:
+        """Extract Python code from markup languages.
+
+        Args:
+            text (:class:`str`): Markdown-formatted text
+
+        Returns:
+            :class:`str`: Python code
+        """
+        import json5
+
+        # Match triple backtick blocks first
+        triple_match = re.search(r'```[^\n]*\n(.+?)```', text, re.DOTALL)
+        # Match single backtick blocks second
+        single_match = re.search(r'`([^`]*)`', text, re.DOTALL)
+        if triple_match:
+            text = triple_match.group(1)
+        elif single_match:
+            text = single_match.group(1)
+        else:
+            try:
+                text = json5.loads(text)['code']
+            except Exception:
+                pass
+        # If no code blocks found, return original text
+        return text
+
+    @staticmethod
+    def wrap_code_with_timeout(code: str, timeout: int) -> str:
+        if not code.strip():
+            return code
+        code = code.strip('\n').rstrip()
+        indent = len(code) - len(code.lstrip())
+        handle = ' ' * indent + f'with _raise_timeout({timeout}):\n'
+        block = '\n'.join(['    ' + line for line in code.split('\n')])
+        wrapped_code = handle + block
+        last_line = code.split('\n')[-1]
+        is_expression = True
+        try:
+            compile(last_line.lstrip(), '<stdin>', 'eval')
+        except SyntaxError:
+            is_expression = False
+        if is_expression:
+            wrapped_code += '\n' * 5 + last_line
+        return wrapped_code
+
+
+class AsyncIPythonInteractive(AsyncActionMixin, IPythonInteractive):
+    """An interactive IPython shell for code execution.
+
+    Args:
+        timeout (int): Upper bound of waiting time for Python script execution.
+            Defaults to ``20``.
+        max_out_len (int): maximum output length. No truncation occurs if negative.
+            Defaults to ``2048``.
+        use_signals (bool): whether signals should be used for timing function out
+            or the multiprocessing. Set to ``False`` when not running in the main
+            thread, e.g. web applications. Defaults to ``True``
+        description (dict): The description of the action. Defaults to ``None``.
+        parser (Type[BaseParser]): The parser class to process the
+            action's inputs and outputs. Defaults to :class:`JsonParser`.
+    """
+
+    @tool_api
+    async def run(self,
+                  command: str,
+                  timeout: Optional[int] = None) -> ActionReturn:
+        """Launch an IPython Interactive Shell to execute code.
+
+        Args:
+            command (:class:`str`): Python code snippet
+            timeout (:class:`Optional[int]`): timeout for execution.
+                This argument only works in the main thread. Defaults to ``None``.
+        """
+        tool_return = ActionReturn(args={'text': command}, type=self.name)
+        ret = await self.exec(command, timeout)
+        if ret.status is Status.SUCCESS:
+            tool_return.result = [{'type': 'text', 'content': ret.value}]
+            tool_return.state = ActionStatusCode.SUCCESS
+        else:
+            tool_return.errmsg = ret.msg
+            tool_return.state = ActionStatusCode.API_ERROR
+        return tool_return
+
+    async def exec(self, code: str, timeout: int = None) -> ExecutionResult:
+        """Asynchronously run Python scripts in IPython shell.
+
+        Args:
+            code (:class:`str`): code block
+            timeout (:class:`int`): max waiting time for code execution
+
+        Returns:
+            :py:class:`ExecutionResult`: execution result
+        """
+        with StringIO() as io:
+            with redirect_stdout(io):
+                ret = await self._executor.run_cell_async(
+                    # ret = await self.create_shell().run_cell_async(
+                    self.wrap_code_with_timeout(
+                        self.extract_code(code), timeout or self.timeout))
+                result = ret.result
+                if result is not None:
+                    return ExecutionResult(Status.SUCCESS,
+                                           str(result)[:self._max_out_len])
+            outs = io.getvalue().strip().split('\n')
+        if not outs:
+            return ExecutionResult(Status.SUCCESS, '')
+        for i, out in enumerate(outs):
+            if re.search('Error|Traceback', out, re.S):
+                if 'TimeoutError' in out:
+                    return ExecutionResult(
+                        Status.FAILURE,
+                        msg=('The code interpreter encountered a '
+                             'timeout error.'))
+                err_idx = i
+                break
+        else:
+            return ExecutionResult(Status.SUCCESS,
+                                   outs[-1].strip()[:self._max_out_len])
+        return ExecutionResult(
+            Status.FAILURE,
+            msg=self._highlighting.sub(
+                '', '\n'.join(outs[err_idx:])[:self._max_out_len]),
+        )

lagent/actions/ipython_interpreter.py

@@ -0,0 +1,584 @@
+# flake8: noqa: E501
+import asyncio
+import base64
+import io
+import json
+import logging
+import os
+import queue
+import re
+import signal
+import sys
+import tempfile
+import traceback
+import uuid
+from typing import Optional, Tuple, Type
+
+from jupyter_client import AsyncKernelClient, AsyncKernelManager, AsyncMultiKernelManager
+from tenacity import retry, retry_if_result, stop_after_attempt, wait_fixed
+
+from lagent.actions.base_action import AsyncActionMixin, BaseAction, tool_api
+from lagent.actions.parser import BaseParser, JsonParser
+from lagent.schema import ActionReturn, ActionStatusCode
+
+logger = logging.getLogger(__name__)
+
+START_CODE = """
+def input(*args, **kwargs):
+    raise NotImplementedError('Python input() function is disabled.')
+
+get_ipython().system = lambda *args: print('Assume we have this package, ! is disabled!')
+{}
+"""  # noqa
+
+
+class TimeoutError(Exception):
+    pass
+
+
+class KernelDeath(Exception):
+    pass
+
+
+async def async_run_code(
+    km: AsyncKernelManager,
+    code,
+    *,
+    interrupt_after=30,
+    iopub_timeout=40,
+    wait_for_ready_timeout=60,
+    shutdown_kernel=True,
+):
+    assert iopub_timeout > interrupt_after
+    try:
+
+        async def get_iopub_msg_with_death_detection(kc: AsyncKernelClient,
+                                                     *,
+                                                     timeout=None):
+            loop = asyncio.get_running_loop()
+            dead_fut = loop.create_future()
+
+            def restarting():
+                assert (
+                    False
+                ), "Restart shouldn't happen because config.KernelRestarter.restart_limit is expected to be set to 0"
+
+            def dead():
+                logger.info("Kernel has died, will NOT restart")
+                dead_fut.set_result(None)
+
+            msg_task = asyncio.create_task(kc.get_iopub_msg(timeout=timeout))
+            km.add_restart_callback(restarting, "restart")
+            km.add_restart_callback(dead, "dead")
+            try:
+                done, _ = await asyncio.wait(
+                    [dead_fut, msg_task], return_when=asyncio.FIRST_COMPLETED)
+                if dead_fut in done:
+                    raise KernelDeath()
+                assert msg_task in done
+                return await msg_task
+            finally:
+                msg_task.cancel()
+                km.remove_restart_callback(restarting, "restart")
+                km.remove_restart_callback(dead, "dead")
+
+        async def send_interrupt():
+            await asyncio.sleep(interrupt_after)
+            logger.info("Sending interrupt to kernel")
+            await km.interrupt_kernel()
+
+        @retry(
+            retry=retry_if_result(lambda ret: ret[-1].strip() in [
+                'KeyboardInterrupt',
+                f"Kernel didn't respond in {wait_for_ready_timeout} seconds",
+            ] if isinstance(ret, tuple) else False),
+            stop=stop_after_attempt(3),
+            wait=wait_fixed(1),
+            retry_error_callback=lambda state: state.outcome.result())
+        async def run():
+            execute_result = None
+            error_traceback = None
+            stream_text_list = []
+            kc = km.client()
+            assert isinstance(kc, AsyncKernelClient)
+            kc.start_channels()
+            try:
+                await kc.wait_for_ready(timeout=wait_for_ready_timeout)
+                msg_id = kc.execute(code)
+                while True:
+                    message = await get_iopub_msg_with_death_detection(
+                        kc, timeout=iopub_timeout)
+                    if logger.isEnabledFor(logging.DEBUG):
+                        logger.debug(
+                            json.dumps(message, indent=2, default=str))
+                    assert message["parent_header"]["msg_id"] == msg_id
+                    msg_type = message["msg_type"]
+                    if msg_type == "status":
+                        if message["content"]["execution_state"] == "idle":
+                            break
+                    elif msg_type == "stream":
+                        stream_name = message["content"]["name"]
+                        stream_text = message["content"]["text"]
+                        stream_text_list.append(stream_text)
+                    elif msg_type == "execute_result":
+                        execute_result = message["content"]["data"]
+                    elif msg_type == "error":
+                        error_traceback_lines = message["content"]["traceback"]
+                        error_traceback = "\n".join(error_traceback_lines)
+                    elif msg_type == "execute_input":
+                        pass
+                    else:
+                        assert False, f"Unknown message_type: {msg_type}"
+            finally:
+                kc.stop_channels()
+            return execute_result, error_traceback, "".join(stream_text_list)
+
+        if interrupt_after:
+            run_task = asyncio.create_task(run())
+            send_interrupt_task = asyncio.create_task(send_interrupt())
+            done, _ = await asyncio.wait([run_task, send_interrupt_task],
+                                         return_when=asyncio.FIRST_COMPLETED)
+            if run_task in done:
+                send_interrupt_task.cancel()
+            else:
+                assert send_interrupt_task in done
+            result = await run_task
+        else:
+            result = await run()
+        return result
+    finally:
+        if shutdown_kernel:
+            await km.shutdown_kernel()
+
+
+class IPythonInterpreter(BaseAction):
+    """A IPython executor that can execute Python scripts in a jupyter manner.
+
+    Args:
+        timeout (int): Upper bound of waiting time for Python script execution.
+            Defaults to 20.
+        user_data_dir (str, optional): Specified the user data directory for files
+            loading. If set to `ENV`, use `USER_DATA_DIR` environment variable.
+            Defaults to `ENV`.
+        work_dir (str, optional): Specify which directory to save output images to.
+            Defaults to ``'./work_dir/tmp_dir'``.
+        description (dict): The description of the action. Defaults to ``None``.
+        parser (Type[BaseParser]): The parser class to process the
+            action's inputs and outputs. Defaults to :class:`JsonParser`.
+    """
+
+    _KERNEL_CLIENTS = {}
+
+    def __init__(
+        self,
+        timeout: int = 20,
+        user_data_dir: str = 'ENV',
+        work_dir='./work_dir/tmp_dir',
+        description: Optional[dict] = None,
+        parser: Type[BaseParser] = JsonParser,
+    ):
+        super().__init__(description, parser)
+
+        self.timeout = timeout
+        if user_data_dir == 'ENV':
+            user_data_dir = os.environ.get('USER_DATA_DIR', '')
+
+        if user_data_dir:
+            user_data_dir = os.path.dirname(user_data_dir)
+            user_data_dir = f"import os\nos.chdir('{user_data_dir}')"
+        self.user_data_dir = user_data_dir
+        self._initialized = False
+        self.work_dir = work_dir
+        if not os.path.exists(self.work_dir):
+            os.makedirs(self.work_dir, exist_ok=True)
+
+    @staticmethod
+    def start_kernel():
+        from jupyter_client import KernelManager
+
+        # start the kernel and manager
+        km = KernelManager()
+        km.start_kernel()
+        kc = km.client()
+        return km, kc
+
+    def initialize(self):
+        if self._initialized:
+            return
+        pid = os.getpid()
+        if pid not in self._KERNEL_CLIENTS:
+            self._KERNEL_CLIENTS[pid] = self.start_kernel()
+        self.kernel_manager, self.kernel_client = self._KERNEL_CLIENTS[pid]
+        self._initialized = True
+        self._call(START_CODE.format(self.user_data_dir), None)
+
+    def reset(self):
+        if not self._initialized:
+            self.initialize()
+        else:
+            code = "get_ipython().run_line_magic('reset', '-f')\n" + \
+                START_CODE.format(self.user_data_dir)
+            self._call(code, None)
+
+    def _call(self,
+              command: str,
+              timeout: Optional[int] = None) -> Tuple[str, bool]:
+        self.initialize()
+        command = extract_code(command)
+
+        # check previous remaining result
+        while True:
+            try:
+                msg = self.kernel_client.get_iopub_msg(timeout=5)
+                msg_type = msg['msg_type']
+                if msg_type == 'status':
+                    if msg['content'].get('execution_state') == 'idle':
+                        break
+            except queue.Empty:
+                # assume no result
+                break
+
+        self.kernel_client.execute(command)
+
+        def _inner_call():
+            result = ''
+            images = []
+            succeed = True
+            image_idx = 0
+
+            while True:
+                text = ''
+                image = ''
+                finished = False
+                msg_type = 'error'
+                try:
+                    msg = self.kernel_client.get_iopub_msg(timeout=20)
+                    msg_type = msg['msg_type']
+                    if msg_type == 'status':
+                        if msg['content'].get('execution_state') == 'idle':
+                            finished = True
+                    elif msg_type == 'execute_result':
+                        text = msg['content']['data'].get('text/plain', '')
+                        if 'image/png' in msg['content']['data']:
+                            image_b64 = msg['content']['data']['image/png']
+                            image_url = publish_image_to_local(
+                                image_b64, self.work_dir)
+                            image_idx += 1
+                            image = '![fig-%03d](%s)' % (image_idx, image_url)
+
+                    elif msg_type == 'display_data':
+                        if 'image/png' in msg['content']['data']:
+                            image_b64 = msg['content']['data']['image/png']
+                            image_url = publish_image_to_local(
+                                image_b64, self.work_dir)
+                            image_idx += 1
+                            image = '![fig-%03d](%s)' % (image_idx, image_url)
+
+                        else:
+                            text = msg['content']['data'].get('text/plain', '')
+                    elif msg_type == 'stream':
+                        msg_type = msg['content']['name']  # stdout, stderr
+                        text = msg['content']['text']
+                    elif msg_type == 'error':
+                        succeed = False
+                        text = escape_ansi('\n'.join(
+                            msg['content']['traceback']))
+                        if 'M6_CODE_INTERPRETER_TIMEOUT' in text:
+                            text = f'Timeout. No response after {timeout} seconds.'  # noqa
+                except queue.Empty:
+                    # stop current task in case break next input.
+                    self.kernel_manager.interrupt_kernel()
+                    succeed = False
+                    text = f'Timeout. No response after {timeout} seconds.'
+                    finished = True
+                except Exception:
+                    succeed = False
+                    msg = ''.join(traceback.format_exception(*sys.exc_info()))
+                    # text = 'The code interpreter encountered an unexpected error.'  # noqa
+                    text = msg
+                    logging.warning(msg)
+                    finished = True
+                if text:
+                    # result += f'\n\n{msg_type}:\n\n```\n{text}\n```'
+                    result += f'{text}'
+
+                if image:
+                    images.append(image_url)
+                if finished:
+                    return succeed, dict(text=result, image=images)
+
+        try:
+            if timeout:
+
+                def handler(signum, frame):
+                    raise TimeoutError()
+
+                signal.signal(signal.SIGALRM, handler)
+                signal.alarm(timeout)
+            succeed, result = _inner_call()
+        except TimeoutError:
+            succeed = False
+            text = 'The code interpreter encountered an unexpected error.'
+            result = f'\n\nerror:\n\n```\n{text}\n```'
+        finally:
+            if timeout:
+                signal.alarm(0)
+
+        # result = result.strip('\n')
+        return succeed, result
+
+    @tool_api
+    def run(self, command: str, timeout: Optional[int] = None) -> ActionReturn:
+        r"""When you send a message containing Python code to python, it will be executed in a stateful Jupyter notebook environment. python will respond with the output of the execution or time out after 60.0 seconds. The drive at '/mnt/data' can be used to save and persist user files. Internet access for this session is disabled. Do not make external web requests or API calls as they will fail.
+
+        Args:
+            command (:class:`str`): Python code
+            timeout (:class:`Optional[int]`): Upper bound of waiting time for Python script execution.
+        """
+        tool_return = ActionReturn(url=None, args=None, type=self.name)
+        tool_return.args = dict(text=command)
+        succeed, result = self._call(command, timeout)
+        if succeed:
+            text = result['text']
+            image = result.get('image', [])
+            resp = [dict(type='text', content=text)]
+            if image:
+                resp.extend([dict(type='image', content=im) for im in image])
+            tool_return.result = resp
+            # tool_return.result = dict(
+            #     text=result['text'], image=result.get('image', [])[0])
+            tool_return.state = ActionStatusCode.SUCCESS
+        else:
+            tool_return.errmsg = result.get('text', '') if isinstance(
+                result, dict) else result
+            tool_return.state = ActionStatusCode.API_ERROR
+        return tool_return
+
+
+class AsyncIPythonInterpreter(AsyncActionMixin, IPythonInterpreter):
+    """A IPython executor that can execute Python scripts in a jupyter manner.
+
+    Args:
+        timeout (int): Upper bound of waiting time for Python script execution.
+            Defaults to 20.
+        user_data_dir (str, optional): Specified the user data directory for files
+            loading. If set to `ENV`, use `USER_DATA_DIR` environment variable.
+            Defaults to `ENV`.
+        work_dir (str, optional): Specify which directory to save output images to.
+            Defaults to ``'./work_dir/tmp_dir'``.
+        description (dict): The description of the action. Defaults to ``None``.
+        parser (Type[BaseParser]): The parser class to process the
+            action's inputs and outputs. Defaults to :class:`JsonParser`.
+    """
+
+    _UNBOUND_KERNEL_CLIENTS = asyncio.Queue()
+
+    def __init__(
+        self,
+        timeout: int = 20,
+        user_data_dir: str = 'ENV',
+        work_dir=os.path.join(tempfile.gettempdir(), 'tmp_dir'),
+        max_kernels: Optional[int] = None,
+        reuse_kernel: bool = True,
+        startup_rate: bool = 32,
+        connection_dir: str = tempfile.gettempdir(),
+        description: Optional[dict] = None,
+        parser: Type[BaseParser] = JsonParser,
+    ):
+        super().__init__(timeout, user_data_dir, work_dir, description, parser)
+        from traitlets.config import Config
+
+        c = Config()
+        c.KernelManager.transport = 'ipc'
+        self._amkm = AsyncMultiKernelManager(
+            config=c, connection_dir=connection_dir)
+        self._max_kernels = max_kernels
+        self._reuse_kernel = reuse_kernel
+        self._sem = asyncio.Semaphore(startup_rate)
+        self._lock = asyncio.Lock()
+
+    async def initialize(self, session_id: str):
+        session_id = str(session_id)
+        while True:
+            if session_id in self._KERNEL_CLIENTS:
+                return self._KERNEL_CLIENTS[session_id]
+            if self._reuse_kernel and not self._UNBOUND_KERNEL_CLIENTS.empty():
+                self._KERNEL_CLIENTS[
+                    session_id] = await self._UNBOUND_KERNEL_CLIENTS.get()
+                return self._KERNEL_CLIENTS[session_id]
+            async with self._sem:
+                if self._max_kernels is None or len(
+                        self._KERNEL_CLIENTS
+                ) + self._UNBOUND_KERNEL_CLIENTS.qsize() < self._max_kernels:
+                    kernel_id = None
+                    try:
+                        kernel_id = await self._amkm.start_kernel()
+                        kernel = self._amkm.get_kernel(kernel_id)
+                        client = kernel.client()
+                        _, error_stacktrace, stream_text = await async_run_code(
+                            kernel,
+                            START_CODE.format(self.user_data_dir),
+                            shutdown_kernel=False)
+                        # check if the output of START_CODE meets expectations
+                        if not (error_stacktrace is None
+                                and stream_text == ''):
+                            raise RuntimeError
+                    except Exception as e:
+                        print(f'Starting kernel error: {e}')
+                        if kernel_id:
+                            await self._amkm.shutdown_kernel(kernel_id)
+                            self._amkm.remove_kernel(kernel_id)
+                        await asyncio.sleep(1)
+                        continue
+                    if self._max_kernels is None:
+                        self._KERNEL_CLIENTS[session_id] = (kernel_id, kernel,
+                                                            client)
+                        return kernel_id, kernel, client
+                    async with self._lock:
+                        if len(self._KERNEL_CLIENTS
+                               ) + self._UNBOUND_KERNEL_CLIENTS.qsize(
+                               ) < self._max_kernels:
+                            self._KERNEL_CLIENTS[session_id] = (kernel_id,
+                                                                kernel, client)
+                            return kernel_id, kernel, client
+                    await self._amkm.shutdown_kernel(kernel_id)
+                    self._amkm.remove_kernel(kernel_id)
+            await asyncio.sleep(1)
+
+    async def reset(self, session_id: str):
+        session_id = str(session_id)
+        if session_id not in self._KERNEL_CLIENTS:
+            return
+        _, kernel, _ = self._KERNEL_CLIENTS[session_id]
+        code = "get_ipython().run_line_magic('reset', '-f')\n" + \
+            START_CODE.format(self.user_data_dir)
+        await async_run_code(kernel, code, shutdown_kernel=False)
+
+    async def shutdown(self, session_id: str):
+        session_id = str(session_id)
+        if session_id in self._KERNEL_CLIENTS:
+            kernel_id, _, _ = self._KERNEL_CLIENTS.get(session_id)
+            await self._amkm.shutdown_kernel(kernel_id)
+            self._amkm.remove_kernel(kernel_id)
+            del self._KERNEL_CLIENTS[session_id]
+
+    async def close_session(self, session_id: str):
+        session_id = str(session_id)
+        if self._reuse_kernel:
+            if session_id in self._KERNEL_CLIENTS:
+                await self.reset(session_id)
+                await self._UNBOUND_KERNEL_CLIENTS.put(
+                    self._KERNEL_CLIENTS.pop(session_id))
+        else:
+            await self.shutdown(session_id)
+
+    async def _call(self, command, timeout=None, session_id=None):
+        _, kernel, _ = await self.initialize(str(session_id))
+        result = await async_run_code(
+            kernel,
+            extract_code(command),
+            interrupt_after=timeout or self.timeout,
+            shutdown_kernel=False)
+        execute_result, error_stacktrace, stream_text = result
+        if error_stacktrace is not None:
+            ret = re.sub('^-*\n', '', escape_ansi(error_stacktrace))
+            if ret.endswith('KeyboardInterrupt: '):
+                ret = 'The code interpreter encountered a timeout error.'
+            status, ret = False, ret.strip()
+        elif execute_result is not None:
+            status, ret = True, dict(text=execute_result.get('text/plain', ''))
+        else:
+            status, ret = True, dict(text=stream_text.strip())
+        return status, ret
+
+    @tool_api
+    async def run(self,
+                  command: str,
+                  timeout: Optional[int] = None,
+                  session_id: Optional[str] = None) -> ActionReturn:
+        r"""When you send a message containing Python code to python, it will be executed in a stateful Jupyter notebook environment. python will respond with the output of the execution or time out after 60.0 seconds. The drive at '/mnt/data' can be used to save and persist user files. Internet access for this session is disabled. Do not make external web requests or API calls as they will fail.
+
+        Args:
+            command (:class:`str`): Python code
+            timeout (:class:`Optional[int]`): Upper bound of waiting time for Python script execution.
+        """
+        tool_return = ActionReturn(url=None, args=None, type=self.name)
+        tool_return.args = dict(text=command)
+        succeed, result = await self._call(command, timeout, session_id)
+        if succeed:
+            text = result['text']
+            image = result.get('image', [])
+            resp = [dict(type='text', content=text)]
+            if image:
+                resp.extend([dict(type='image', content=im) for im in image])
+            tool_return.result = resp
+            # tool_return.result = dict(
+            #     text=result['text'], image=result.get('image', [])[0])
+            tool_return.state = ActionStatusCode.SUCCESS
+        else:
+            tool_return.errmsg = result.get('text', '') if isinstance(
+                result, dict) else result
+            tool_return.state = ActionStatusCode.API_ERROR
+        return tool_return
+
+
+def extract_code(text):
+    import json5
+
+    # Match triple backtick blocks first
+    triple_match = re.search(r'```[^\n]*\n(.+?)```', text, re.DOTALL)
+    # Match single backtick blocks second
+    single_match = re.search(r'`([^`]*)`', text, re.DOTALL)
+    if triple_match:
+        text = triple_match.group(1)
+    elif single_match:
+        text = single_match.group(1)
+    else:
+        try:
+            text = json5.loads(text)['code']
+        except Exception:
+            pass
+    # If no code blocks found, return original text
+    return text
+
+
+def escape_ansi(line):
+    ansi_escape = re.compile(r'(?:\x1B[@-_]|[\x80-\x9F])[0-?]*[ -/]*[@-~]')
+    return ansi_escape.sub('', line)
+
+
+def publish_image_to_local(image_base64: str, work_dir='./work_dir/tmp_dir'):
+    import PIL.Image
+    image_file = str(uuid.uuid4()) + '.png'
+    local_image_file = os.path.join(work_dir, image_file)
+
+    png_bytes = base64.b64decode(image_base64)
+    assert isinstance(png_bytes, bytes)
+    bytes_io = io.BytesIO(png_bytes)
+    PIL.Image.open(bytes_io).save(local_image_file, 'png')
+
+    return local_image_file
+
+
+# local test for code interpreter
+def get_multiline_input(hint):
+    print(hint)
+    print('// Press ENTER to make a new line. Press CTRL-D to end input.')
+    lines = []
+    while True:
+        try:
+            line = input()
+        except EOFError:  # CTRL-D
+            break
+        lines.append(line)
+    print('// Input received.')
+    if lines:
+        return '\n'.join(lines)
+    else:
+        return ''
+
+
+if __name__ == '__main__':
+    code_interpreter = IPythonInterpreter()
+    while True:
+        print(code_interpreter(get_multiline_input('Enter python code:')))

lagent/actions/ipython_manager.py

@@ -0,0 +1,220 @@
+import re
+import sys
+from collections import defaultdict
+from contextlib import nullcontext
+from io import StringIO
+from multiprocessing import Process, Queue
+from typing import List, Optional, Type, Union
+
+from filelock import FileLock
+from timeout_decorator import timeout as tm
+
+from ..schema import ActionReturn, ActionStatusCode
+from .base_action import BaseAction
+from .parser import BaseParser, JsonParser
+
+
+class IPythonProcess(Process):
+
+    def __init__(self,
+                 in_q: Queue,
+                 out_q: Queue,
+                 timeout: int = 20,
+                 ci_lock: str = None,
+                 daemon: bool = True):
+        super().__init__(daemon=daemon)
+        self.in_q = in_q
+        self.out_q = out_q
+        self.timeout = timeout
+        self.session_id2shell = defaultdict(self.create_shell)
+        self.ci_lock = FileLock(
+            ci_lock) if ci_lock else nullcontext()  # avoid core corruption
+        self._highlighting = re.compile(r'\x1b\[\d{,3}(;\d{,3}){,3}m')
+
+    def run(self):
+        while True:
+            msg = self.in_q.get()
+            if msg == 'reset':
+                for session_id, shell in self.session_id2shell.items():
+                    with self.ci_lock:
+                        try:
+                            shell.reset(new_session=False)
+                            # shell.run_line_magic('reset', '-sf')
+                        except Exception:
+                            self.session_id2shell[
+                                session_id] = self.create_shell()
+                self.out_q.put('ok')
+            elif isinstance(msg, tuple) and len(msg) == 3:
+                i, session_id, code = msg
+                res = self.exec(session_id, code)
+                self.out_q.put((i, session_id, res))
+
+    def exec(self, session_id, code):
+        try:
+            shell = self.session_id2shell[session_id]
+            with StringIO() as io:
+                old_stdout = sys.stdout
+                sys.stdout = io
+                if self.timeout is False or self.timeout < 0:
+                    shell.run_cell(self.extract_code(code))
+                else:
+                    tm(self.timeout)(shell.run_cell)(self.extract_code(code))
+                sys.stdout = old_stdout
+                output = self._highlighting.sub('', io.getvalue().strip())
+                output = re.sub(r'^Out\[\d+\]: ', '', output)
+            if 'Error' in output or 'Traceback' in output:
+                output = output.lstrip('-').strip()
+                if output.startswith('TimeoutError'):
+                    output = 'The code interpreter encountered a timeout error.'
+                return {'status': 'FAILURE', 'msg': output, 'code': code}
+            return {'status': 'SUCCESS', 'value': output, 'code': code}
+        except Exception as e:
+            return {'status': 'FAILURE', 'msg': str(e), 'code': code}
+
+    @staticmethod
+    def create_shell(enable_history: bool = False, in_memory: bool = True):
+        from IPython import InteractiveShell
+        from traitlets.config import Config
+
+        c = Config()
+        c.HistoryManager.enabled = enable_history
+        if in_memory:
+            c.HistoryManager.hist_file = ':memory:'
+        shell = InteractiveShell(config=c)
+        return shell
+
+    @staticmethod
+    def extract_code(text: str) -> str:
+        """Extract Python code from markup languages.
+
+        Args:
+            text (:class:`str`): Markdown-formatted text
+
+        Returns:
+            :class:`str`: Python code
+        """
+        import json5
+
+        # Match triple backtick blocks first
+        triple_match = re.search(r'```[^\n]*\n(.+?)```', text, re.DOTALL)
+        # Match single backtick blocks second
+        single_match = re.search(r'`([^`]*)`', text, re.DOTALL)
+        if triple_match:
+            text = triple_match.group(1)
+        elif single_match:
+            text = single_match.group(1)
+        else:
+            try:
+                text = json5.loads(text)['code']
+            except Exception:
+                pass
+        # If no code blocks found, return original text
+        return text
+
+
+class IPythonInteractiveManager(BaseAction):
+    """An interactive IPython shell manager for code execution"""
+
+    def __init__(
+        self,
+        max_workers: int = 50,
+        timeout: int = 20,
+        ci_lock: str = None,
+        description: Optional[dict] = None,
+        parser: Type[BaseParser] = JsonParser,
+    ):
+        super().__init__(description, parser)
+        self.max_workers = max_workers
+        self.timeout = timeout
+        self.ci_lock = ci_lock
+        self.id2queue = defaultdict(Queue)
+        self.id2process = {}
+        self.out_queue = Queue()
+
+    def __call__(self,
+                 commands: Union[str, List[str]],
+                 session_ids: Union[int, List[int]] = None):
+        if isinstance(commands, list):
+            batch_size = len(commands)
+            is_batch = True
+        else:
+            batch_size = 1
+            commands = [commands]
+            is_batch = False
+        if session_ids is None:
+            session_ids = range(batch_size)
+        elif isinstance(session_ids, int):
+            session_ids = [session_ids]
+        if len(session_ids) != batch_size or len(session_ids) != len(
+                set(session_ids)):
+            raise ValueError(
+                'the size of `session_ids` must equal that of `commands`')
+        try:
+            exec_results = self.run_code_blocks([
+                (session_id, command)
+                for session_id, command in zip(session_ids, commands)
+            ])
+        except KeyboardInterrupt:
+            self.clear()
+            exit(1)
+        action_returns = []
+        for result, code in zip(exec_results, commands):
+            action_return = ActionReturn({'command': code}, type=self.name)
+            if result['status'] == 'SUCCESS':
+                action_return.result = [
+                    dict(type='text', content=result['value'])
+                ]
+                action_return.state = ActionStatusCode.SUCCESS
+            else:
+                action_return.errmsg = result['msg']
+                action_return.state = ActionStatusCode.API_ERROR
+            action_returns.append(action_return)
+        if not is_batch:
+            return action_returns[0]
+        return action_returns
+
+    def process_code(self, index, session_id, code):
+        ipy_id = session_id % self.max_workers
+        input_queue = self.id2queue[ipy_id]
+        proc = self.id2process.setdefault(
+            ipy_id,
+            IPythonProcess(
+                input_queue,
+                self.out_queue,
+                self.timeout,
+                self.ci_lock,
+                daemon=True))
+        if not proc.is_alive():
+            proc.start()
+        input_queue.put((index, session_id, code))
+
+    def run_code_blocks(self, session_code_pairs):
+        size = len(session_code_pairs)
+        for index, (session_id, code) in enumerate(session_code_pairs):
+            self.process_code(index, session_id, code)
+        results = []
+        while len(results) < size:
+            msg = self.out_queue.get()
+            if isinstance(msg, tuple) and len(msg) == 3:
+                index, _, result = msg
+                results.append((index, result))
+        results.sort()
+        return [item[1] for item in results]
+
+    def clear(self):
+        self.id2queue.clear()
+        for proc in self.id2process.values():
+            proc.terminate()
+        self.id2process.clear()
+        while not self.out_queue.empty():
+            self.out_queue.get()
+
+    def reset(self):
+        cnt = 0
+        for q in self.id2queue.values():
+            q.put('reset')
+            cnt += 1
+        while cnt > 0:
+            msg = self.out_queue.get()
+            if msg == 'ok':
+                cnt -= 1

lagent/actions/parser.py

@@ -0,0 +1,146 @@
+import json
+import re
+from ast import literal_eval
+from typing import Any, List, Union
+
+
+class ParseError(Exception):
+    """Parsing exception class."""
+
+    def __init__(self, err_msg: str):
+        self.err_msg = err_msg
+
+
+class BaseParser:
+    """Base parser to process inputs and outputs of actions.
+
+    Args:
+        action (:class:`BaseAction`): action to validate
+
+    Attributes:
+        PARAMETER_DESCRIPTION (:class:`str`): declare the input format which
+            LLMs should follow when generating arguments for decided tools.
+    """
+
+    PARAMETER_DESCRIPTION: str = ''
+
+    def __init__(self, action):
+        self.action = action
+        self._api2param = {}
+        self._api2required = {}
+        # perform basic argument validation
+        if action.description:
+            for api in action.description.get('api_list',
+                                              [action.description]):
+                name = (f'{action.name}.{api["name"]}'
+                        if self.action.is_toolkit else api['name'])
+                required_parameters = set(api['required'])
+                all_parameters = {j['name'] for j in api['parameters']}
+                if not required_parameters.issubset(all_parameters):
+                    raise ValueError(
+                        f'unknown parameters for function "{name}": '
+                        f'{required_parameters - all_parameters}')
+                if self.PARAMETER_DESCRIPTION:
+                    api['parameter_description'] = self.PARAMETER_DESCRIPTION
+                api_name = api['name'] if self.action.is_toolkit else 'run'
+                self._api2param[api_name] = api['parameters']
+                self._api2required[api_name] = api['required']
+
+    def parse_inputs(self, inputs: str, name: str = 'run') -> dict:
+        """Parse inputs LLMs generate for the action.
+
+        Args:
+            inputs (:class:`str`): input string extracted from responses
+
+        Returns:
+            :class:`dict`: processed input
+        """
+        inputs = {self._api2param[name][0]['name']: inputs}
+        return inputs
+
+    def parse_outputs(self, outputs: Any) -> List[dict]:
+        """Parser outputs returned by the action.
+
+        Args:
+            outputs (:class:`Any`): raw output of the action
+
+        Returns:
+            :class:`List[dict]`: processed output of which each member is a
+                dictionary with two keys - 'type' and 'content'.
+        """
+        if isinstance(outputs, dict):
+            outputs = json.dumps(outputs, ensure_ascii=False)
+        elif not isinstance(outputs, str):
+            outputs = str(outputs)
+        return [{
+            'type': 'text',
+            'content': outputs.encode('gbk', 'ignore').decode('gbk')
+        }]
+
+
+class JsonParser(BaseParser):
+    """Json parser to convert input string into a dictionary.
+
+    Args:
+        action (:class:`BaseAction`): action to validate
+    """
+
+    PARAMETER_DESCRIPTION = (
+        'If you call this tool, you must pass arguments in '
+        'the JSON format {key: value}, where the key is the parameter name.')
+
+    def parse_inputs(self,
+                     inputs: Union[str, dict],
+                     name: str = 'run') -> dict:
+        if not isinstance(inputs, dict):
+            try:
+                match = re.search(r'^\s*(```json\n)?(.*)\n```\s*$', inputs,
+                                  re.S)
+                if match:
+                    inputs = match.group(2).strip()
+                inputs = json.loads(inputs)
+            except json.JSONDecodeError as exc:
+                raise ParseError(f'invalid json format: {inputs}') from exc
+        input_keys = set(inputs)
+        all_keys = {param['name'] for param in self._api2param[name]}
+        if not input_keys.issubset(all_keys):
+            raise ParseError(f'unknown arguments: {input_keys - all_keys}')
+        required_keys = set(self._api2required[name])
+        if not input_keys.issuperset(required_keys):
+            raise ParseError(
+                f'missing required arguments: {required_keys - input_keys}')
+        return inputs
+
+
+class TupleParser(BaseParser):
+    """Tuple parser to convert input string into a tuple.
+
+    Args:
+        action (:class:`BaseAction`): action to validate
+    """
+
+    PARAMETER_DESCRIPTION = (
+        'If you call this tool, you must pass arguments in the tuple format '
+        'like (arg1, arg2, arg3), and the arguments are ordered.')
+
+    def parse_inputs(self,
+                     inputs: Union[str, tuple],
+                     name: str = 'run') -> dict:
+        if not isinstance(inputs, tuple):
+            try:
+                inputs = literal_eval(inputs)
+            except Exception as exc:
+                raise ParseError(f'invalid tuple format: {inputs}') from exc
+        if len(inputs) < len(self._api2required[name]):
+            raise ParseError(
+                f'API takes {len(self._api2required[name])} required positional '
+                f'arguments but {len(inputs)} were given')
+        if len(inputs) > len(self._api2param[name]):
+            raise ParseError(
+                f'API takes {len(self._api2param[name])} positional arguments '
+                f'but {len(inputs)} were given')
+        inputs = {
+            self._api2param[name][i]['name']: item
+            for i, item in enumerate(inputs)
+        }
+        return inputs

lagent/actions/ppt.py

@@ -0,0 +1,233 @@
+from typing import Dict, Optional, Type
+
+from asyncer import asyncify
+
+from lagent.actions.base_action import AsyncActionMixin, BaseAction, tool_api
+from lagent.actions.parser import BaseParser, JsonParser
+
+THEME_MAPPING = {
+    'Default': {
+        'template': None,
+        'title': 'Title Slide',
+        'single': 'Title and Content',
+        'two': 'Two Content',
+    }
+}
+
+
+class PPT(BaseAction):
+    """Plugin to create ppt slides with text, paragraph, images in good looking styles."""
+
+    def __init__(
+        self,
+        theme_mapping: Optional[Dict[str, dict]] = None,
+        description: Optional[dict] = None,
+        parser: Type[BaseParser] = JsonParser,
+    ):
+        super().__init__(description, parser)
+        self.theme_mapping = theme_mapping or THEME_MAPPING
+        self.pointer = None
+        self.location = None
+
+    @tool_api(explode_return=True)
+    def create_file(self, theme: str, abs_location: str) -> dict:
+        """Create a pptx file with specific themes.
+
+        Args:
+            theme (:class:`str`): the theme used. The value should be one of ['Default'].
+            abs_location (:class:`str`): the ppt file's absolute location
+
+        Returns:
+            :class:`dict`: operation status
+                * status: the result of the execution
+        """
+        from pptx import Presentation
+
+        self.location = abs_location
+        try:
+            self.pointer = Presentation(self.theme_mapping[theme]['template'])
+            self.pointer.slide_master.name = theme
+            # print('created')
+        except Exception as e:
+            print(e)
+        return dict(status='created a ppt file.')
+
+    @tool_api(explode_return=True)
+    def add_first_page(self, title: str, subtitle: str) -> dict:
+        """Add the first page of ppt.
+
+        Args:
+            title (:class:`str`): the title of ppt
+            subtitle (:class:`str`): the subtitle of ppt
+
+        Returns:
+            :class:`dict`: operation status
+                * status: the result of the execution
+        """
+        layout_name = self.theme_mapping[self.pointer.slide_master.name]['title']
+        layout = next(i for i in self.pointer.slide_master.slide_layouts if i.name == layout_name)
+        slide = self.pointer.slides.add_slide(layout)
+        ph_title, ph_subtitle = slide.placeholders
+        ph_title.text = title
+        if subtitle:
+            ph_subtitle.text = subtitle
+        return dict(status='added page')
+
+    @tool_api(explode_return=True)
+    def add_text_page(self, title: str, bullet_items: str) -> dict:
+        """Add text page of ppt.
+
+        Args:
+            title (:class:`str`): the title of the page
+            bullet_items (:class:`str`): bullet_items should be string, for multiple bullet items, please use [SPAN] to separate them.
+
+        Returns:
+            :class:`dict`: operation status
+                * status: the result of the execution
+        """  # noqa: E501
+        layout_name = self.theme_mapping[self.pointer.slide_master.name]['single']
+        layout = next(i for i in self.pointer.slide_master.slide_layouts if i.name == layout_name)
+        slide = self.pointer.slides.add_slide(layout)
+        ph_title, ph_body = slide.placeholders
+        ph_title.text = title
+        ph = ph_body
+        tf = ph.text_frame
+        for i, item in enumerate(bullet_items.split('[SPAN]')):
+            if i == 0:
+                p = tf.paragraphs[0]
+            else:
+                p = tf.add_paragraph()
+            p.text = item.strip()
+            p.level = 0
+        return dict(status='added page')
+
+    @tool_api(explode_return=True)
+    def add_text_image_page(self, title: str, bullet_items: str, image: str) -> dict:
+        """Add a text page with one image. Image should be a path.
+
+        Args:
+            title (:class:`str`): the title of the page
+            bullet_items (:class:`str`): bullet_items should be string, for multiple bullet items, please use [SPAN] to separate them.
+            image (:class:`str`): the path of the image
+
+        Returns:
+            :class:`dict`: operation status
+                * status: the result of the execution
+        """  # noqa: E501
+        from PIL import Image
+
+        layout_name = self.theme_mapping[self.pointer.slide_master.name]['two']
+        layout = next(i for i in self.pointer.slide_master.slide_layouts if i.name == layout_name)
+        slide = self.pointer.slides.add_slide(layout)
+        ph_title, ph_body1, ph_body2 = slide.placeholders
+        ph_title.text = title
+        ph = ph_body2
+        image = Image.open(image)
+        image_pil = image.to_pil()
+        left = ph.left
+        width = ph.width
+        height = int(width / image_pil.width * image_pil.height)
+        top = (ph.top + (ph.top + ph.height)) // 2 - height // 2
+        slide.shapes.add_picture(image.to_path(), left, top, width, height)
+
+        ph = ph_body1
+        tf = ph.text_frame
+        for i, item in enumerate(bullet_items.split('[SPAN]')):
+            if i == 0:
+                p = tf.paragraphs[0]
+            else:
+                p = tf.add_paragraph()
+            p.text = item.strip()
+            p.level = 0
+
+        return dict(status='added page')
+
+    @tool_api(explode_return=True)
+    def submit_file(self) -> dict:
+        """When all steps done, YOU MUST use submit_file() to submit your work.
+
+        Returns:
+            :class:`dict`: operation status
+                * status: the result of the execution
+        """
+        # file_path = os.path.join(self.CACHE_DIR, f'{self._return_timestamp()}.pptx')
+        # self.pointer.save(file_path)
+        # retreival_url = upload_file(file_path)
+        self.pointer.save(self.location)
+        return dict(status=f'submitted. view ppt at {self.location}')
+
+
+class AsyncPPT(AsyncActionMixin, PPT):
+    """Plugin to create ppt slides with text, paragraph, images in good looking styles."""
+
+    @tool_api(explode_return=True)
+    @asyncify
+    def create_file(self, theme: str, abs_location: str) -> dict:
+        """Create a pptx file with specific themes.
+
+        Args:
+            theme (:class:`str`): the theme used. The value should be one of ['Default'].
+            abs_location (:class:`str`): the ppt file's absolute location
+
+        Returns:
+            :class:`dict`: operation status
+                * status: the result of the execution
+        """
+        return super().create_file(theme, abs_location)
+
+    @tool_api(explode_return=True)
+    @asyncify
+    def add_first_page(self, title: str, subtitle: str) -> dict:
+        """Add the first page of ppt.
+
+        Args:
+            title (:class:`str`): the title of ppt
+            subtitle (:class:`str`): the subtitle of ppt
+
+        Returns:
+            :class:`dict`: operation status
+                * status: the result of the execution
+        """
+        return super().add_first_page(title, subtitle)
+
+    @tool_api(explode_return=True)
+    @asyncify
+    def add_text_page(self, title: str, bullet_items: str) -> dict:
+        """Add text page of ppt.
+
+        Args:
+            title (:class:`str`): the title of the page
+            bullet_items (:class:`str`): bullet_items should be string, for multiple bullet items, please use [SPAN] to separate them.
+
+        Returns:
+            :class:`dict`: operation status
+                * status: the result of the execution
+        """  # noqa: E501
+        return super().add_text_page(title, bullet_items)
+
+    @tool_api(explode_return=True)
+    @asyncify
+    def add_text_image_page(self, title: str, bullet_items: str, image: str) -> dict:
+        """Add a text page with one image. Image should be a path.
+
+        Args:
+            title (:class:`str`): the title of the page
+            bullet_items (:class:`str`): bullet_items should be string, for multiple bullet items, please use [SPAN] to separate them.
+            image (:class:`str`): the path of the image
+
+        Returns:
+            :class:`dict`: operation status
+                * status: the result of the execution
+        """  # noqa: E501
+        return super().add_text_image_page(title, bullet_items, image)
+
+    @tool_api(explode_return=True)
+    @asyncify
+    def submit_file(self) -> dict:
+        """When all steps done, YOU MUST use submit_file() to submit your work.
+
+        Returns:
+            :class:`dict`: operation status
+                * status: the result of the execution
+        """
+        return super().submit_file()

lagent/actions/python_interpreter.py

@@ -0,0 +1,176 @@
+# flake8: noqa: E501
+import copy
+import io
+from contextlib import redirect_stdout
+from typing import Any, Optional, Type
+
+from asyncer import asyncify
+
+from lagent.actions.base_action import AsyncActionMixin, BaseAction, tool_api
+from lagent.actions.parser import BaseParser, JsonParser
+from lagent.schema import ActionReturn, ActionStatusCode
+
+
+class GenericRuntime:
+    GLOBAL_DICT = {}
+    LOCAL_DICT = None
+    HEADERS = []
+
+    def __init__(self):
+        self._global_vars = copy.copy(self.GLOBAL_DICT)
+        self._local_vars = copy.copy(self.LOCAL_DICT) if self.LOCAL_DICT else None
+
+        for c in self.HEADERS:
+            self.exec_code(c)
+
+    def exec_code(self, code_piece: str) -> None:
+        exec(code_piece, self._global_vars)
+
+    def eval_code(self, expr: str) -> Any:
+        return eval(expr, self._global_vars)
+
+
+class PythonInterpreter(BaseAction):
+    """A Python executor that can execute Python scripts.
+
+    Args:
+        answer_symbol (str, Optional): the answer symbol from LLM. Defaults to ``None``.
+        answer_expr (str, Optional): the answer function name of the Python
+            script. Defaults to ``'solution()'``.
+        answer_from_stdout (boolean, Optional): whether the execution results is from
+            stdout. Defaults to ``False``.
+        timeout (int, Optional): Upper bound of waiting time for Python script execution.
+            Defaults to ``20``.
+        description (dict, Optional): The description of the action. Defaults to ``None``.
+        parser (Type[BaseParser]): The parser class to process the
+            action's inputs and outputs. Defaults to :class:`JsonParser`.
+    """
+
+    def __init__(
+        self,
+        answer_symbol: Optional[str] = None,
+        answer_expr: Optional[str] = 'solution()',
+        answer_from_stdout: bool = False,
+        timeout: int = 20,
+        description: Optional[dict] = None,
+        parser: Type[BaseParser] = JsonParser,
+    ) -> None:
+        super().__init__(description, parser)
+        self.answer_symbol = answer_symbol
+        self.answer_expr = answer_expr
+        self.answer_from_stdout = answer_from_stdout
+        self.timeout = timeout
+
+    @tool_api
+    def run(self, command: str) -> ActionReturn:
+        """用来执行Python代码。代码必须是一个函数，函数名必须得是 'solution'，代码对应你的思考过程。代码实例格式如下：
+
+        ```python
+        # import 依赖包
+        import xxx
+        def solution():
+            # 初始化一些变量
+            variable_names_with_real_meaning = xxx
+            # 步骤一
+            mid_variable = func(variable_names_with_real_meaning)
+            # 步骤 x
+            mid_variable = func(mid_variable)
+            # 最后结果
+            final_answer =  func(mid_variable)
+            return final_answer
+        ```
+
+        Args:
+            command (:class:`str`): Python code snippet
+        """
+        from func_timeout import FunctionTimedOut, func_set_timeout
+
+        self.runtime = GenericRuntime()
+        try:
+            tool_return = func_set_timeout(self.timeout)(self._call)(command)
+        except FunctionTimedOut as e:
+            tool_return = ActionReturn(type=self.name)
+            tool_return.errmsg = repr(e)
+            tool_return.state = ActionStatusCode.API_ERROR
+        return tool_return
+
+    def _call(self, command: str) -> ActionReturn:
+        tool_return = ActionReturn(type=self.name)
+        try:
+            if '```python' in command:
+                command = command.split('```python')[1].split('```')[0]
+            elif '```' in command:
+                command = command.split('```')[1].split('```')[0]
+            tool_return.args = dict(text='```python\n' + command + '\n```')
+            command = command.split('\n')
+
+            if self.answer_from_stdout:
+                program_io = io.StringIO()
+                with redirect_stdout(program_io):
+                    self.runtime.exec_code('\n'.join(command))
+                program_io.seek(0)
+                res = program_io.readlines()[-1]
+            elif self.answer_symbol:
+                self.runtime.exec_code('\n'.join(command))
+                res = self.runtime._global_vars[self.answer_symbol]
+            elif self.answer_expr:
+                self.runtime.exec_code('\n'.join(command))
+                res = self.runtime.eval_code(self.answer_expr)
+            else:
+                self.runtime.exec_code('\n'.join(command[:-1]))
+                res = self.runtime.eval_code(command[-1])
+        except Exception as e:
+            tool_return.errmsg = repr(e)
+            tool_return.type = self.name
+            tool_return.state = ActionStatusCode.API_ERROR
+            return tool_return
+        try:
+            tool_return.result = [dict(type='text', content=str(res))]
+            tool_return.state = ActionStatusCode.SUCCESS
+        except Exception as e:
+            tool_return.errmsg = repr(e)
+            tool_return.type = self.name
+            tool_return.state = ActionStatusCode.API_ERROR
+        return tool_return
+
+
+class AsyncPythonInterpreter(AsyncActionMixin, PythonInterpreter):
+    """A Python executor that can execute Python scripts.
+
+    Args:
+        answer_symbol (str, Optional): the answer symbol from LLM. Defaults to ``None``.
+        answer_expr (str, Optional): the answer function name of the Python
+            script. Defaults to ``'solution()'``.
+        answer_from_stdout (boolean, Optional): whether the execution results is from
+            stdout. Defaults to ``False``.
+        timeout (int, Optional): Upper bound of waiting time for Python script execution.
+            Defaults to ``20``.
+        description (dict, Optional): The description of the action. Defaults to ``None``.
+        parser (Type[BaseParser]): The parser class to process the
+            action's inputs and outputs. Defaults to :class:`JsonParser`.
+    """
+
+    @tool_api
+    @asyncify
+    def run(self, command: str) -> ActionReturn:
+        """用来执行Python代码。代码必须是一个函数，函数名必须得是 'solution'，代码对应你的思考过程。代码实例格式如下：
+
+        ```python
+        # import 依赖包
+        import xxx
+        def solution():
+            # 初始化一些变量
+            variable_names_with_real_meaning = xxx
+            # 步骤一
+            mid_variable = func(variable_names_with_real_meaning)
+            # 步骤 x
+            mid_variable = func(mid_variable)
+            # 最后结果
+            final_answer =  func(mid_variable)
+            return final_answer
+        ```
+
+        Args:
+            command (:class:`str`): Python code snippet
+        """
+        return super().run(command)

lagent/actions/weather_query.py

@@ -0,0 +1,71 @@
+import os
+import requests
+from lagent.actions.base_action import BaseAction, tool_api
+from lagent.schema import ActionReturn, ActionStatusCode
+
+class WeatherQuery(BaseAction):
+    def __init__(self):
+        super().__init__()
+        self.api_key = os.getenv("weather_token")
+        print(self.api_key)
+        if not self.api_key:
+            raise EnvironmentError("未找到环境变量 'token'。请设置你的和风天气 API Key 到 'weather_token' 环境变量中，比如export weather_token='xxx' ")
+
+    @tool_api
+    def run(self, location: str) -> dict:
+        """
+        查询实时天气信息。
+
+        Args:
+            location (str): 要查询的地点名称、LocationID 或经纬度坐标（如 "101010100" 或 "116.41,39.92"）。
+
+        Returns:
+            dict: 包含天气信息的字典
+                * location: 地点名称
+                * weather: 天气状况
+                * temperature: 当前温度
+                * wind_direction: 风向
+                * wind_speed: 风速（公里/小时）
+                * humidity: 相对湿度（%）
+                * report_time: 数据报告时间
+        """
+        try:
+            # 如果 location 不是坐标格式（例如 "116.41,39.92"），则调用 GeoAPI 获取 LocationID
+            if not ("," in location and location.replace(",", "").replace(".", "").isdigit()):
+                # 使用 GeoAPI 获取 LocationID
+                geo_url = f"https://geoapi.qweather.com/v2/city/lookup?location={location}&key={self.api_key}"
+                geo_response = requests.get(geo_url)
+                geo_data = geo_response.json()
+
+                if geo_data.get("code") != "200" or not geo_data.get("location"):
+                    raise Exception(f"GeoAPI 返回错误码：{geo_data.get('code')} 或未找到位置")
+
+                location = geo_data["location"][0]["id"]
+
+            # 构建天气查询的 API 请求 URL
+            weather_url = f"https://devapi.qweather.com/v7/weather/now?location={location}&key={self.api_key}"
+            response = requests.get(weather_url)
+            data = response.json()
+
+            # 检查 API 响应码
+            if data.get("code") != "200":
+                raise Exception(f"Weather API 返回错误码：{data.get('code')}")
+
+            # 解析和组织天气信息
+            weather_info = {
+                "location": location,
+                "weather": data["now"]["text"],
+                "temperature": data["now"]["temp"] + "°C", 
+                "wind_direction": data["now"]["windDir"],
+                "wind_speed": data["now"]["windSpeed"] + " km/h",  
+                "humidity": data["now"]["humidity"] + "%",
+                "report_time": data["updateTime"]
+            }
+
+            return {"result": weather_info}
+
+        except Exception as exc:
+            return ActionReturn(
+                errmsg=f"WeatherQuery 异常：{exc}",
+                state=ActionStatusCode.HTTP_ERROR
+            )

lagent/actions/web_browser.py

@@ -0,0 +1,908 @@
+import asyncio
+import hashlib
+import hmac
+import json
+import logging
+import random
+import re
+import time
+import warnings
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from datetime import datetime
+from http.client import HTTPSConnection
+from typing import List, Optional, Tuple, Type, Union
+
+import aiohttp
+import aiohttp.client_exceptions
+import requests
+from asyncache import cached as acached
+from bs4 import BeautifulSoup
+from cachetools import TTLCache, cached
+from duckduckgo_search import DDGS, AsyncDDGS
+
+from lagent.actions.base_action import AsyncActionMixin, BaseAction, tool_api
+from lagent.actions.parser import BaseParser, JsonParser
+from lagent.utils import async_as_completed
+
+
+class BaseSearch:
+
+    def __init__(self, topk: int = 3, black_list: List[str] = None):
+        self.topk = topk
+        self.black_list = black_list
+
+    def _filter_results(self, results: List[tuple]) -> dict:
+        filtered_results = {}
+        count = 0
+        for url, snippet, title in results:
+            if all(domain not in url
+                   for domain in self.black_list) and not url.endswith('.pdf'):
+                filtered_results[count] = {
+                    'url': url,
+                    'summ': json.dumps(snippet, ensure_ascii=False)[1:-1],
+                    'title': title
+                }
+                count += 1
+                if count >= self.topk:
+                    break
+        return filtered_results
+
+
+class DuckDuckGoSearch(BaseSearch):
+
+    def __init__(self,
+                 topk: int = 3,
+                 black_list: List[str] = [
+                     'enoN',
+                     'youtube.com',
+                     'bilibili.com',
+                     'researchgate.net',
+                 ],
+                 **kwargs):
+        self.proxy = kwargs.get('proxy')
+        self.timeout = kwargs.get('timeout', 30)
+        super().__init__(topk, black_list)
+
+    @cached(cache=TTLCache(maxsize=100, ttl=600))
+    def search(self, query: str, max_retry: int = 3) -> dict:
+        for attempt in range(max_retry):
+            try:
+                response = self._call_ddgs(
+                    query, timeout=self.timeout, proxy=self.proxy)
+                return self._parse_response(response)
+            except Exception as e:
+                logging.exception(str(e))
+                warnings.warn(
+                    f'Retry {attempt + 1}/{max_retry} due to error: {e}')
+                time.sleep(random.randint(2, 5))
+        raise Exception(
+            'Failed to get search results from DuckDuckGo after retries.')
+
+    @acached(cache=TTLCache(maxsize=100, ttl=600))
+    async def asearch(self, query: str, max_retry: int = 3) -> dict:
+        for attempt in range(max_retry):
+            try:
+                ddgs = AsyncDDGS(timeout=self.timeout, proxy=self.proxy)
+                response = await ddgs.atext(query.strip("'"), max_results=10)
+                return self._parse_response(response)
+            except Exception as e:
+                if isinstance(e, asyncio.TimeoutError):
+                    logging.exception('Request to DDGS timed out.')
+                logging.exception(str(e))
+                warnings.warn(
+                    f'Retry {attempt + 1}/{max_retry} due to error: {e}')
+                await asyncio.sleep(random.randint(2, 5))
+        raise Exception(
+            'Failed to get search results from DuckDuckGo after retries.')
+
+    async def _async_call_ddgs(self, query: str, **kwargs) -> dict:
+        ddgs = DDGS(**kwargs)
+        try:
+            response = await asyncio.wait_for(
+                asyncio.to_thread(ddgs.text, query.strip("'"), max_results=10),
+                timeout=self.timeout)
+            return response
+        except asyncio.TimeoutError:
+            logging.exception('Request to DDGS timed out.')
+            raise
+
+    def _call_ddgs(self, query: str, **kwargs) -> dict:
+        loop = asyncio.new_event_loop()
+        asyncio.set_event_loop(loop)
+        try:
+            response = loop.run_until_complete(
+                self._async_call_ddgs(query, **kwargs))
+            return response
+        finally:
+            loop.close()
+
+    def _parse_response(self, response: dict) -> dict:
+        raw_results = []
+        for item in response:
+            raw_results.append(
+                (item['href'], item['description']
+                 if 'description' in item else item['body'], item['title']))
+        return self._filter_results(raw_results)
+
+
+class BingSearch(BaseSearch):
+
+    def __init__(self,
+                 api_key: str,
+                 region: str = 'zh-CN',
+                 topk: int = 3,
+                 black_list: List[str] = [
+                     'enoN',
+                     'youtube.com',
+                     'bilibili.com',
+                     'researchgate.net',
+                 ],
+                 **kwargs):
+        self.api_key = api_key
+        self.market = region
+        self.proxy = kwargs.get('proxy')
+        super().__init__(topk, black_list)
+
+    @cached(cache=TTLCache(maxsize=100, ttl=600))
+    def search(self, query: str, max_retry: int = 3) -> dict:
+        for attempt in range(max_retry):
+            try:
+                response = self._call_bing_api(query)
+                return self._parse_response(response)
+            except Exception as e:
+                logging.exception(str(e))
+                warnings.warn(
+                    f'Retry {attempt + 1}/{max_retry} due to error: {e}')
+                time.sleep(random.randint(2, 5))
+        raise Exception(
+            'Failed to get search results from Bing Search after retries.')
+
+    @acached(cache=TTLCache(maxsize=100, ttl=600))
+    async def asearch(self, query: str, max_retry: int = 3) -> dict:
+        for attempt in range(max_retry):
+            try:
+                response = await self._async_call_bing_api(query)
+                return self._parse_response(response)
+            except Exception as e:
+                logging.exception(str(e))
+                warnings.warn(
+                    f'Retry {attempt + 1}/{max_retry} due to error: {e}')
+                await asyncio.sleep(random.randint(2, 5))
+        raise Exception(
+            'Failed to get search results from Bing Search after retries.')
+
+    def _call_bing_api(self, query: str) -> dict:
+        endpoint = 'https://api.bing.microsoft.com/v7.0/search'
+        params = {'q': query, 'mkt': self.market, 'count': f'{self.topk * 2}'}
+        headers = {'Ocp-Apim-Subscription-Key': self.api_key}
+        response = requests.get(
+            endpoint, headers=headers, params=params, proxies=self.proxy)
+        response.raise_for_status()
+        return response.json()
+
+    async def _async_call_bing_api(self, query: str) -> dict:
+        endpoint = 'https://api.bing.microsoft.com/v7.0/search'
+        params = {'q': query, 'mkt': self.market, 'count': f'{self.topk * 2}'}
+        headers = {'Ocp-Apim-Subscription-Key': self.api_key}
+        async with aiohttp.ClientSession(raise_for_status=True) as session:
+            async with session.get(
+                    endpoint,
+                    headers=headers,
+                    params=params,
+                    proxy=self.proxy and
+                (self.proxy.get('http') or self.proxy.get('https'))) as resp:
+                return await resp.json()
+
+    def _parse_response(self, response: dict) -> dict:
+        webpages = {
+            w['id']: w
+            for w in response.get('webPages', {}).get('value', [])
+        }
+        raw_results = []
+
+        for item in response.get('rankingResponse',
+                                 {}).get('mainline', {}).get('items', []):
+            if item['answerType'] == 'WebPages':
+                webpage = webpages.get(item['value']['id'])
+                if webpage:
+                    raw_results.append(
+                        (webpage['url'], webpage['snippet'], webpage['name']))
+            elif item['answerType'] == 'News' and item['value'][
+                    'id'] == response.get('news', {}).get('id'):
+                for news in response.get('news', {}).get('value', []):
+                    raw_results.append(
+                        (news['url'], news['description'], news['name']))
+
+        return self._filter_results(raw_results)
+
+
+class BraveSearch(BaseSearch):
+    """
+    Wrapper around the Brave Search API.
+
+    To use, you should pass your Brave Search API key to the constructor.
+
+    Args:
+        api_key (str): API KEY to use Brave Search API.
+            You can create a free API key at https://api.search.brave.com/app/keys.
+        search_type (str): Brave Search API supports ['web', 'news', 'images', 'videos'],
+            currently only supports 'news' and 'web'.
+        topk (int): The number of search results returned in response from API search results.
+        region (str): The country code string. Specifies the country where the search results come from.
+        language (str): The language code string. Specifies the preferred language for the search results.
+        extra_snippets (bool): Allows retrieving up to 5 additional snippets, which are alternative excerpts from the search results.
+        **kwargs: Any other parameters related to the Brave Search API. Find more details at
+            https://api.search.brave.com/app/documentation/web-search/get-started.
+    """
+
+    def __init__(self,
+                 api_key: str,
+                 region: str = 'ALL',
+                 language: str = 'zh-hans',
+                 extra_snippests: bool = True,
+                 topk: int = 3,
+                 black_list: List[str] = [
+                     'enoN',
+                     'youtube.com',
+                     'bilibili.com',
+                     'researchgate.net',
+                 ],
+                 **kwargs):
+        self.api_key = api_key
+        self.market = region
+        self.proxy = kwargs.get('proxy')
+        self.language = language
+        self.extra_snippests = extra_snippests
+        self.search_type = kwargs.get('search_type', 'web')
+        self.kwargs = kwargs
+        super().__init__(topk, black_list)
+
+    @cached(cache=TTLCache(maxsize=100, ttl=600))
+    def search(self, query: str, max_retry: int = 3) -> dict:
+        for attempt in range(max_retry):
+            try:
+                response = self._call_brave_api(query)
+                return self._parse_response(response)
+            except Exception as e:
+                logging.exception(str(e))
+                warnings.warn(
+                    f'Retry {attempt + 1}/{max_retry} due to error: {e}')
+                time.sleep(random.randint(2, 5))
+        raise Exception(
+            'Failed to get search results from Brave Search after retries.')
+
+    @acached(cache=TTLCache(maxsize=100, ttl=600))
+    async def asearch(self, query: str, max_retry: int = 3) -> dict:
+        for attempt in range(max_retry):
+            try:
+                response = await self._async_call_brave_api(query)
+                return self._parse_response(response)
+            except Exception as e:
+                logging.exception(str(e))
+                warnings.warn(
+                    f'Retry {attempt + 1}/{max_retry} due to error: {e}')
+                await asyncio.sleep(random.randint(2, 5))
+        raise Exception(
+            'Failed to get search results from Brave Search after retries.')
+
+    def _call_brave_api(self, query: str) -> dict:
+        endpoint = f'https://api.search.brave.com/res/v1/{self.search_type}/search'
+        params = {
+            'q': query,
+            'country': self.market,
+            'search_lang': self.language,
+            'extra_snippets': self.extra_snippests,
+            'count': self.topk,
+            **{
+                key: value
+                for key, value in self.kwargs.items() if value is not None
+            },
+        }
+        headers = {
+            'X-Subscription-Token': self.api_key or '',
+            'Accept': 'application/json'
+        }
+        response = requests.get(
+            endpoint, headers=headers, params=params, proxies=self.proxy)
+        response.raise_for_status()
+        return response.json()
+
+    async def _async_call_brave_api(self, query: str) -> dict:
+        endpoint = f'https://api.search.brave.com/res/v1/{self.search_type}/search'
+        params = {
+            'q': query,
+            'country': self.market,
+            'search_lang': self.language,
+            'extra_snippets': self.extra_snippests,
+            'count': self.topk,
+            **{
+                key: value
+                for key, value in self.kwargs.items() if value is not None
+            },
+        }
+        headers = {
+            'X-Subscription-Token': self.api_key or '',
+            'Accept': 'application/json'
+        }
+        async with aiohttp.ClientSession(raise_for_status=True) as session:
+            async with session.get(
+                    endpoint,
+                    headers=headers,
+                    params=params,
+                    proxy=self.proxy and
+                (self.proxy.get('http') or self.proxy.get('https'))) as resp:
+                return await resp.json()
+
+    def _parse_response(self, response: dict) -> dict:
+        if self.search_type == 'web':
+            filtered_result = response.get('web', {}).get('results', [])
+        else:
+            filtered_result = response.get('results', {})
+        raw_results = []
+
+        for item in filtered_result:
+            raw_results.append((
+                item.get('url', ''),
+                ' '.join(
+                    filter(None, [
+                        item.get('description'),
+                        *item.get('extra_snippets', [])
+                    ])),
+                item.get('title', ''),
+            ))
+        return self._filter_results(raw_results)
+
+
+class GoogleSearch(BaseSearch):
+    """
+    Wrapper around the Serper.dev Google Search API.
+
+    To use, you should pass your serper API key to the constructor.
+
+    Args:
+        api_key (str): API KEY to use serper google search API.
+            You can create a free API key at https://serper.dev.
+        search_type (str): Serper API supports ['search', 'images', 'news',
+            'places'] types of search, currently we only support 'search' and 'news'.
+        topk (int): The number of search results returned in response from api search results.
+        **kwargs: Any other parameters related to the Serper API. Find more details at
+            https://serper.dev/playground
+    """
+
+    result_key_for_type = {
+        'news': 'news',
+        'places': 'places',
+        'images': 'images',
+        'search': 'organic',
+    }
+
+    def __init__(self,
+                 api_key: str,
+                 topk: int = 3,
+                 black_list: List[str] = [
+                     'enoN',
+                     'youtube.com',
+                     'bilibili.com',
+                     'researchgate.net',
+                 ],
+                 **kwargs):
+        self.api_key = api_key
+        self.proxy = kwargs.get('proxy')
+        self.search_type = kwargs.get('search_type', 'search')
+        self.kwargs = kwargs
+        super().__init__(topk, black_list)
+
+    @cached(cache=TTLCache(maxsize=100, ttl=600))
+    def search(self, query: str, max_retry: int = 3) -> dict:
+        for attempt in range(max_retry):
+            try:
+                response = self._call_serper_api(query)
+                return self._parse_response(response)
+            except Exception as e:
+                logging.exception(str(e))
+                warnings.warn(
+                    f'Retry {attempt + 1}/{max_retry} due to error: {e}')
+                time.sleep(random.randint(2, 5))
+        raise Exception(
+            'Failed to get search results from Google Serper Search after retries.'
+        )
+
+    @acached(cache=TTLCache(maxsize=100, ttl=600))
+    async def asearch(self, query: str, max_retry: int = 3) -> dict:
+        for attempt in range(max_retry):
+            try:
+                response = await self._async_call_serper_api(query)
+                return self._parse_response(response)
+            except Exception as e:
+                logging.exception(str(e))
+                warnings.warn(
+                    f'Retry {attempt + 1}/{max_retry} due to error: {e}')
+                await asyncio.sleep(random.randint(2, 5))
+        raise Exception(
+            'Failed to get search results from Google Serper Search after retries.'
+        )
+
+    def _call_serper_api(self, query: str) -> dict:
+        endpoint = f'https://google.serper.dev/{self.search_type}'
+        params = {
+            'q': query,
+            'num': self.topk,
+            **{
+                key: value
+                for key, value in self.kwargs.items() if value is not None
+            },
+        }
+        headers = {
+            'X-API-KEY': self.api_key or '',
+            'Content-Type': 'application/json'
+        }
+        response = requests.get(
+            endpoint, headers=headers, params=params, proxies=self.proxy)
+        response.raise_for_status()
+        return response.json()
+
+    async def _async_call_serper_api(self, query: str) -> dict:
+        endpoint = f'https://google.serper.dev/{self.search_type}'
+        params = {
+            'q': query,
+            'num': self.topk,
+            **{
+                key: value
+                for key, value in self.kwargs.items() if value is not None
+            },
+        }
+        headers = {
+            'X-API-KEY': self.api_key or '',
+            'Content-Type': 'application/json'
+        }
+        async with aiohttp.ClientSession(raise_for_status=True) as session:
+            async with session.get(
+                    endpoint,
+                    headers=headers,
+                    params=params,
+                    proxy=self.proxy and
+                (self.proxy.get('http') or self.proxy.get('https'))) as resp:
+                return await resp.json()
+
+    def _parse_response(self, response: dict) -> dict:
+        raw_results = []
+
+        if response.get('answerBox'):
+            answer_box = response.get('answerBox', {})
+            if answer_box.get('answer'):
+                raw_results.append(('', answer_box.get('answer'), ''))
+            elif answer_box.get('snippet'):
+                raw_results.append(
+                    ('', answer_box.get('snippet').replace('\n', ' '), ''))
+            elif answer_box.get('snippetHighlighted'):
+                raw_results.append(
+                    ('', answer_box.get('snippetHighlighted'), ''))
+
+        if response.get('knowledgeGraph'):
+            kg = response.get('knowledgeGraph', {})
+            description = kg.get('description', '')
+            attributes = '. '.join(
+                f'{attribute}: {value}'
+                for attribute, value in kg.get('attributes', {}).items())
+            raw_results.append(
+                (kg.get('descriptionLink', ''),
+                 f'{description}. {attributes}' if attributes else description,
+                 f"{kg.get('title', '')}: {kg.get('type', '')}."))
+
+        for result in response[self.result_key_for_type[
+                self.search_type]][:self.topk]:
+            description = result.get('snippet', '')
+            attributes = '. '.join(
+                f'{attribute}: {value}'
+                for attribute, value in result.get('attributes', {}).items())
+            raw_results.append(
+                (result.get('link', ''),
+                 f'{description}. {attributes}' if attributes else description,
+                 result.get('title', '')))
+
+        return self._filter_results(raw_results)
+
+
+class TencentSearch(BaseSearch):
+    """Wrapper around the tencentclound Search API.
+
+    To use, you should pass your secret_id and secret_key to the constructor.
+
+    Args:
+        secret_id (str): Your Tencent Cloud secret ID for accessing the API.
+            For more details, refer to the documentation: https://cloud.tencent.com/document/product/598/40488.
+        secret_key (str): Your Tencent Cloud secret key for accessing the API.
+        api_key (str, optional): Additional API key, if required.
+        action (str): The action for this interface, use `SearchCommon`.
+        version (str): The API version, use `2020-12-29`.
+        service (str): The service name, use `tms`.
+        host (str): The API host, use `tms.tencentcloudapi.com`.
+        topk (int): The maximum number of search results to return.
+        tsn (int): Time filter for search results. Valid values:
+            1 (within 1 day), 2 (within 1 week), 3 (within 1 month),
+            4 (within 1 year), 5 (within 6 months), 6 (within 3 years).
+        insite (str): Specify a site to search within (supports only a single site).
+            If not specified, the entire web is searched. Example: `zhihu.com`.
+        category (str): Vertical category for filtering results. Optional values include:
+            `baike` (encyclopedia), `weather`, `calendar`, `medical`, `news`, `train`, `star` (horoscope).
+        vrid (str): Result card type(s). Different `vrid` values represent different types of result cards.
+            Supports multiple values separated by commas. Example: `30010255`.
+    """
+
+    def __init__(self,
+                 secret_id: str = 'Your SecretId',
+                 secret_key: str = 'Your SecretKey',
+                 api_key: str = '',
+                 action: str = 'SearchCommon',
+                 version: str = '2020-12-29',
+                 service: str = 'tms',
+                 host: str = 'tms.tencentcloudapi.com',
+                 topk: int = 3,
+                 tsn: int = None,
+                 insite: str = None,
+                 category: str = None,
+                 vrid: str = None,
+                 black_list: List[str] = [
+                     'enoN',
+                     'youtube.com',
+                     'bilibili.com',
+                     'researchgate.net',
+                 ]):
+        self.secret_id = secret_id
+        self.secret_key = secret_key
+        self.api_key = api_key
+        self.action = action
+        self.version = version
+        self.service = service
+        self.host = host
+        self.tsn = tsn
+        self.insite = insite
+        self.category = category
+        self.vrid = vrid
+        super().__init__(topk, black_list=black_list)
+
+    @cached(cache=TTLCache(maxsize=100, ttl=600))
+    def search(self, query: str, max_retry: int = 3) -> dict:
+        for attempt in range(max_retry):
+            try:
+                response = self._call_tencent_api(query)
+                return self._parse_response(response)
+            except Exception as e:
+                logging.exception(str(e))
+                warnings.warn(
+                    f'Retry {attempt + 1}/{max_retry} due to error: {e}')
+                time.sleep(random.randint(2, 5))
+        raise Exception(
+            'Failed to get search results from Bing Search after retries.')
+
+    @acached(cache=TTLCache(maxsize=100, ttl=600))
+    async def asearch(self, query: str, max_retry: int = 3) -> dict:
+        for attempt in range(max_retry):
+            try:
+                response = await self._async_call_tencent_api(query)
+                return self._parse_response(response)
+            except Exception as e:
+                logging.exception(str(e))
+                warnings.warn(
+                    f'Retry {attempt + 1}/{max_retry} due to error: {e}')
+                await asyncio.sleep(random.randint(2, 5))
+        raise Exception(
+            'Failed to get search results from Bing Search after retries.')
+
+    def _get_headers_and_payload(self, query: str) -> tuple:
+
+        def sign(key, msg):
+            return hmac.new(key, msg.encode('utf-8'), hashlib.sha256).digest()
+
+        params = dict(Query=query)
+        # if self.topk:
+        #     params['Cnt'] = self.topk
+        if self.tsn:
+            params['Tsn'] = self.tsn
+        if self.insite:
+            params['Insite'] = self.insite
+        if self.category:
+            params['Category'] = self.category
+        if self.vrid:
+            params['Vrid'] = self.vrid
+        payload = json.dumps(params)
+        algorithm = 'TC3-HMAC-SHA256'
+        timestamp = int(time.time())
+        date = datetime.utcfromtimestamp(timestamp).strftime('%Y-%m-%d')
+
+        # ************* 步骤 1：拼接规范请求串 *************
+        http_request_method = 'POST'
+        canonical_uri = '/'
+        canonical_querystring = ''
+        ct = 'application/json; charset=utf-8'
+        canonical_headers = f'content-type:{ct}\nhost:{self.host}\nx-tc-action:{self.action.lower()}\n'
+        signed_headers = 'content-type;host;x-tc-action'
+        hashed_request_payload = hashlib.sha256(
+            payload.encode('utf-8')).hexdigest()
+        canonical_request = (
+            http_request_method + '\n' + canonical_uri + '\n' +
+            canonical_querystring + '\n' + canonical_headers + '\n' +
+            signed_headers + '\n' + hashed_request_payload)
+
+        # ************* 步骤 2：拼接待签名字符串 *************
+        credential_scope = date + '/' + self.service + '/' + 'tc3_request'
+        hashed_canonical_request = hashlib.sha256(
+            canonical_request.encode('utf-8')).hexdigest()
+        string_to_sign = (
+            algorithm + '\n' + str(timestamp) + '\n' + credential_scope +
+            '\n' + hashed_canonical_request)
+
+        # ************* 步骤 3：计算签名 *************
+        secret_date = sign(('TC3' + self.secret_key).encode('utf-8'), date)
+        secret_service = sign(secret_date, self.service)
+        secret_signing = sign(secret_service, 'tc3_request')
+        signature = hmac.new(secret_signing, string_to_sign.encode('utf-8'),
+                             hashlib.sha256).hexdigest()
+
+        # ************* 步骤 4：拼接 Authorization *************
+        authorization = (
+            algorithm + ' ' + 'Credential=' + self.secret_id + '/' +
+            credential_scope + ', ' + 'SignedHeaders=' + signed_headers +
+            ', ' + 'Signature=' + signature)
+
+        # ************* 步骤 5：构造并发起请求 *************
+        headers = {
+            'Authorization': authorization,
+            'Content-Type': 'application/json; charset=utf-8',
+            'Host': self.host,
+            'X-TC-Action': self.action,
+            'X-TC-Timestamp': str(timestamp),
+            'X-TC-Version': self.version
+        }
+        # if self.region:
+        #     headers["X-TC-Region"] = self.region
+        if self.api_key:
+            headers['X-TC-Token'] = self.api_key
+        return headers, payload
+
+    def _call_tencent_api(self, query: str) -> dict:
+        headers, payload = self._get_headers_and_payload(query)
+        req = HTTPSConnection(self.host)
+        req.request('POST', '/', headers=headers, body=payload.encode('utf-8'))
+        resp = req.getresponse()
+        try:
+            resp = json.loads(resp.read().decode('utf-8'))
+        except Exception as e:
+            logging.warning(str(e))
+            import ast
+            resp = ast.literal_eval(resp)
+        return resp.get('Response', dict())
+
+    async def _async_call_tencent_api(self, query: str):
+        headers, payload = self._get_headers_and_payload(query)
+        async with aiohttp.ClientSession(raise_for_status=True) as session:
+            async with session.post(
+                    'https://' + self.host.lstrip('/'),
+                    headers=headers,
+                    data=payload) as resp:
+                return (await resp.json()).get('Response', {})
+
+    def _parse_response(self, response: dict) -> dict:
+        raw_results = []
+        for item in response.get('Pages', []):
+            display = json.loads(item['Display'])
+            if not display['url']:
+                continue
+            raw_results.append((display['url'], display['content']
+                                or display['abstract_info'], display['title']))
+        return self._filter_results(raw_results)
+
+
+class ContentFetcher:
+
+    def __init__(self, timeout: int = 5):
+        self.timeout = timeout
+
+    @cached(cache=TTLCache(maxsize=100, ttl=600))
+    def fetch(self, url: str) -> Tuple[bool, str]:
+        try:
+            response = requests.get(url, timeout=self.timeout)
+            response.raise_for_status()
+            html = response.content
+        except requests.RequestException as e:
+            return False, str(e)
+
+        text = BeautifulSoup(html, 'html.parser').get_text()
+        cleaned_text = re.sub(r'\n+', '\n', text)
+        return True, cleaned_text
+
+    @acached(cache=TTLCache(maxsize=100, ttl=600))
+    async def afetch(self, url: str) -> Tuple[bool, str]:
+        try:
+            async with aiohttp.ClientSession(
+                    raise_for_status=True,
+                    timeout=aiohttp.ClientTimeout(self.timeout)) as session:
+                async with session.get(url) as resp:
+                    html = await resp.text(errors='ignore')
+                    text = BeautifulSoup(html, 'html.parser').get_text()
+                    cleaned_text = re.sub(r'\n+', '\n', text)
+                    return True, cleaned_text
+        except Exception as e:
+            return False, str(e)
+
+
+class WebBrowser(BaseAction):
+    """Wrapper around the Web Browser Tool.
+    """
+
+    def __init__(self,
+                 searcher_type: str = 'DuckDuckGoSearch',
+                 timeout: int = 5,
+                 black_list: Optional[List[str]] = [
+                     'enoN',
+                     'youtube.com',
+                     'bilibili.com',
+                     'researchgate.net',
+                 ],
+                 topk: int = 20,
+                 description: Optional[dict] = None,
+                 parser: Type[BaseParser] = JsonParser,
+                 **kwargs):
+        self.searcher = eval(searcher_type)(
+            black_list=black_list, topk=topk, **kwargs)
+        self.fetcher = ContentFetcher(timeout=timeout)
+        self.search_results = None
+        super().__init__(description, parser)
+
+    @tool_api
+    def search(self, query: Union[str, List[str]]) -> dict:
+        """BING search API
+        Args:
+            query (List[str]): list of search query strings
+        """
+        queries = query if isinstance(query, list) else [query]
+        search_results = {}
+
+        with ThreadPoolExecutor() as executor:
+            future_to_query = {
+                executor.submit(self.searcher.search, q): q
+                for q in queries
+            }
+
+            for future in as_completed(future_to_query):
+                query = future_to_query[future]
+                try:
+                    results = future.result()
+                except Exception as exc:
+                    warnings.warn(f'{query} generated an exception: {exc}')
+                else:
+                    for result in results.values():
+                        if result['url'] not in search_results:
+                            search_results[result['url']] = result
+                        else:
+                            search_results[
+                                result['url']]['summ'] += f"\n{result['summ']}"
+
+        self.search_results = {
+            idx: result
+            for idx, result in enumerate(search_results.values())
+        }
+        return self.search_results
+
+    @tool_api
+    def select(self, select_ids: List[int]) -> dict:
+        """get the detailed content on the selected pages.
+
+        Args:
+            select_ids (List[int]): list of index to select. Max number of index to be selected is no more than 4.
+        """
+        if not self.search_results:
+            raise ValueError('No search results to select from.')
+
+        new_search_results = {}
+        with ThreadPoolExecutor() as executor:
+            future_to_id = {
+                executor.submit(self.fetcher.fetch, self.search_results[select_id]['url']): select_id
+                for select_id in select_ids if select_id in self.search_results
+            }
+            for future in as_completed(future_to_id):
+                select_id = future_to_id[future]
+                try:
+                    web_success, web_content = future.result()
+                except Exception as exc:
+                    warnings.warn(f'{select_id} generated an exception: {exc}')
+                else:
+                    if web_success:
+                        self.search_results[select_id][
+                            'content'] = web_content[:8192]
+                        new_search_results[select_id] = self.search_results[
+                            select_id].copy()
+                        new_search_results[select_id].pop('summ')
+
+        return new_search_results
+
+    @tool_api
+    def open_url(self, url: str) -> dict:
+        print(f'Start Browsing: {url}')
+        web_success, web_content = self.fetcher.fetch(url)
+        if web_success:
+            return {'type': 'text', 'content': web_content}
+        else:
+            return {'error': web_content}
+
+
+class AsyncWebBrowser(AsyncActionMixin, WebBrowser):
+    """Wrapper around the Web Browser Tool.
+    """
+
+    @tool_api
+    async def search(self, query: Union[str, List[str]]) -> dict:
+        """BING search API
+        
+        Args:
+            query (List[str]): list of search query strings
+        """
+        queries = query if isinstance(query, list) else [query]
+        search_results = {}
+
+        tasks = []
+        for q in queries:
+            task = asyncio.create_task(self.searcher.asearch(q))
+            task.query = q
+            tasks.append(task)
+        async for future in async_as_completed(tasks):
+            query = future.query
+            try:
+                results = await future
+            except Exception as exc:
+                warnings.warn(f'{query} generated an exception: {exc}')
+            else:
+                for result in results.values():
+                    if result['url'] not in search_results:
+                        search_results[result['url']] = result
+                    else:
+                        search_results[
+                            result['url']]['summ'] += f"\n{result['summ']}"
+
+        self.search_results = {
+            idx: result
+            for idx, result in enumerate(search_results.values())
+        }
+        return self.search_results
+
+    @tool_api
+    async def select(self, select_ids: List[int]) -> dict:
+        """get the detailed content on the selected pages.
+
+        Args:
+            select_ids (List[int]): list of index to select. Max number of index to be selected is no more than 4.
+        """
+        if not self.search_results:
+            raise ValueError('No search results to select from.')
+
+        new_search_results = {}
+        tasks = []
+        for select_id in select_ids:
+            if select_id in self.search_results:
+                task = asyncio.create_task(
+                    self.fetcher.afetch(self.search_results[select_id]['url']))
+                task.select_id = select_id
+                tasks.append(task)
+        async for future in async_as_completed(tasks):
+            select_id = future.select_id
+            try:
+                web_success, web_content = await future
+            except Exception as exc:
+                warnings.warn(f'{select_id} generated an exception: {exc}')
+            else:
+                if web_success:
+                    self.search_results[select_id][
+                        'content'] = web_content[:8192]
+                    new_search_results[select_id] = self.search_results[
+                        select_id].copy()
+                    new_search_results[select_id].pop('summ')
+        return new_search_results
+
+    @tool_api
+    async def open_url(self, url: str) -> dict:
+        print(f'Start Browsing: {url}')
+        web_success, web_content = await self.fetcher.afetch(url)
+        if web_success:
+            return {'type': 'text', 'content': web_content}
+        else:
+            return {'error': web_content}

lagent/agents/__init__.py

@@ -0,0 +1,9 @@
+from .agent import Agent, AgentDict, AgentList, AsyncAgent, AsyncSequential, Sequential
+from .react import AsyncReAct, ReAct
+from .stream import AgentForInternLM, AsyncAgentForInternLM, AsyncMathCoder, MathCoder
+
+__all__ = [
+    'Agent', 'AgentDict', 'AgentList', 'AsyncAgent', 'AgentForInternLM',
+    'AsyncAgentForInternLM', 'MathCoder', 'AsyncMathCoder', 'ReAct',
+    'AsyncReAct', 'Sequential', 'AsyncSequential'
+]

lagent/agents/agent.py

@@ -0,0 +1,400 @@
+import copy
+import warnings
+from collections import OrderedDict, UserDict, UserList, abc
+from functools import wraps
+from itertools import chain, repeat
+from typing import Any, Callable, Dict, Iterable, List, Mapping, Optional, Union
+
+from lagent.agents.aggregator import DefaultAggregator
+from lagent.hooks import Hook, RemovableHandle
+from lagent.llms import BaseLLM
+from lagent.memory import Memory, MemoryManager
+from lagent.prompts.parsers import StrParser
+from lagent.prompts.prompt_template import PromptTemplate
+from lagent.schema import AgentMessage
+from lagent.utils import create_object
+
+
+class Agent:
+    """Agent is the basic unit of the system. It is responsible for
+    communicating with the LLM, managing the memory, and handling the
+    message aggregation and parsing. It can also be extended with hooks
+
+    Args:
+        llm (Union[BaseLLM, Dict]): The language model used by the agent.
+        template (Union[PromptTemplate, str]): The template used to format the
+            messages.
+        memory (Dict): The memory used by the agent.
+        output_format (Dict): The output format used by the agent.
+        aggregator (Dict): The aggregator used by the agent.
+        name (Optional[str]): The name of the agent.
+        description (Optional[str]): The description of the agent.
+        hooks (Optional[Union[List[Dict], Dict]]): The hooks used by the agent.
+
+    Returns:
+        AgentMessage: The response message.
+    """
+
+    def __init__(
+        self,
+        llm: Union[BaseLLM, Dict] = None,
+        template: Union[PromptTemplate, str, dict, List[dict]] = None,
+        memory: Dict = dict(type=Memory),
+        output_format: Optional[Dict] = None,
+        aggregator: Dict = dict(type=DefaultAggregator),
+        name: Optional[str] = None,
+        description: Optional[str] = None,
+        hooks: Optional[Union[List[Dict], Dict]] = None,
+    ):
+        self.name = name or self.__class__.__name__
+        self.llm: BaseLLM = create_object(llm)
+        self.memory: MemoryManager = MemoryManager(memory) if memory else None
+        self.output_format: StrParser = create_object(output_format)
+        self.template = template
+        self.description = description
+        self.aggregator: DefaultAggregator = create_object(aggregator)
+        self._hooks: Dict[int, Hook] = OrderedDict()
+        if hooks:
+            for hook in hooks:
+                hook = create_object(hook)
+                self.register_hook(hook)
+
+    def update_memory(self, message, session_id=0):
+        if self.memory:
+            self.memory.add(message, session_id=session_id)
+
+    def __call__(
+        self,
+        *message: Union[str, AgentMessage, List[AgentMessage]],
+        session_id=0,
+        **kwargs,
+    ) -> AgentMessage:
+        # message.receiver = self.name
+        message = [
+            AgentMessage(sender='user', content=m)
+            if isinstance(m, str) else copy.deepcopy(m) for m in message
+        ]
+        for hook in self._hooks.values():
+            result = hook.before_agent(self, message, session_id)
+            if result:
+                message = result
+        self.update_memory(message, session_id=session_id)
+        response_message = self.forward(
+            *message, session_id=session_id, **kwargs)
+        if not isinstance(response_message, AgentMessage):
+            response_message = AgentMessage(
+                sender=self.name,
+                content=response_message,
+            )
+        self.update_memory(response_message, session_id=session_id)
+        response_message = copy.deepcopy(response_message)
+        for hook in self._hooks.values():
+            result = hook.after_agent(self, response_message, session_id)
+            if result:
+                response_message = result
+        return response_message
+
+    def forward(self,
+                *message: AgentMessage,
+                session_id=0,
+                **kwargs) -> Union[AgentMessage, str]:
+        formatted_messages = self.aggregator.aggregate(
+            self.memory.get(session_id),
+            self.name,
+            self.output_format,
+            self.template,
+        )
+        llm_response = self.llm.chat(formatted_messages, **kwargs)
+        if self.output_format:
+            formatted_messages = self.output_format.parse_response(
+                llm_response)
+            return AgentMessage(
+                sender=self.name,
+                content=llm_response,
+                formatted=formatted_messages,
+            )
+        return llm_response
+
+    def __setattr__(self, __name: str, __value: Any) -> None:
+        if isinstance(__value, Agent):
+            _agents = getattr(self, '_agents', OrderedDict())
+            _agents[__name] = __value
+            super().__setattr__('_agents', _agents)
+        super().__setattr__(__name, __value)
+
+    def state_dict(self, session_id=0):
+        state_dict, stack = {}, [('', self)]
+        while stack:
+            prefix, node = stack.pop()
+            key = prefix + 'memory'
+            if node.memory is not None:
+                if session_id not in node.memory.memory_map:
+                    warnings.warn(f'No session id {session_id} in {key}')
+                memory = node.memory.get(session_id)
+                state_dict[key] = memory and memory.save() or []
+            if hasattr(node, '_agents'):
+                for name, value in reversed(node._agents.items()):
+                    stack.append((prefix + name + '.', value))
+        return state_dict
+
+    def load_state_dict(self, state_dict: Dict, session_id=0):
+        _state_dict = self.state_dict()
+        missing_keys = set(_state_dict) - set(state_dict)
+        if missing_keys:
+            raise KeyError(f'Missing keys: {missing_keys}')
+        extra_keys = set(state_dict) - set(_state_dict)
+        if extra_keys:
+            warnings.warn(f'Mismatch keys which are not used: {extra_keys}')
+        for key in _state_dict:
+            obj = self
+            for attr in key.split('.')[:-1]:
+                if isinstance(obj, AgentList):
+                    assert attr.isdigit()
+                    obj = obj[int(attr)]
+                elif isinstance(obj, AgentDict):
+                    obj = obj[attr]
+                else:
+                    obj = getattr(obj, attr)
+            if obj.memory is not None:
+                if session_id not in obj.memory.memory_map:
+                    obj.memory.create_instance(session_id)
+                obj.memory.memory_map[session_id].load(state_dict[key] or [])
+
+    def register_hook(self, hook: Callable):
+        handle = RemovableHandle(self._hooks)
+        self._hooks[handle.id] = hook
+        return handle
+
+    def reset(self,
+              session_id=0,
+              keypath: Optional[str] = None,
+              recursive: bool = False):
+        assert not (keypath and
+                    recursive), 'keypath and recursive can\'t be used together'
+        if keypath:
+            keys, agent = keypath.split('.'), self
+            for key in keys:
+                agents = getattr(agent, '_agents', {})
+                if key not in agents:
+                    raise KeyError(f'No sub-agent named {key} in {agent}')
+                agent = agents[key]
+            agent.reset(session_id, recursive=False)
+        else:
+            if self.memory:
+                self.memory.reset(session_id=session_id)
+            if recursive:
+                for agent in getattr(self, '_agents', {}).values():
+                    agent.reset(session_id, recursive=True)
+
+    def __repr__(self):
+
+        def _rcsv_repr(agent, n_indent=1):
+            res = agent.__class__.__name__ + (f"(name='{agent.name}')"
+                                              if agent.name else '')
+            modules = [
+                f"{n_indent * '  '}({name}): {_rcsv_repr(agent, n_indent + 1)}"
+                for name, agent in getattr(agent, '_agents', {}).items()
+            ]
+            if modules:
+                res += '(\n' + '\n'.join(
+                    modules) + f'\n{(n_indent - 1) * "  "})'
+            elif not res.endswith(')'):
+                res += '()'
+            return res
+
+        return _rcsv_repr(self)
+
+
+class AsyncAgent(Agent):
+
+    async def __call__(self,
+                       *message: AgentMessage | List[AgentMessage],
+                       session_id=0,
+                       **kwargs) -> AgentMessage:
+        message = [
+            AgentMessage(sender='user', content=m)
+            if isinstance(m, str) else copy.deepcopy(m) for m in message
+        ]
+        for hook in self._hooks.values():
+            result = hook.before_agent(self, message, session_id)
+            if result:
+                message = result
+        self.update_memory(message, session_id=session_id)
+        response_message = await self.forward(
+            *message, session_id=session_id, **kwargs)
+        if not isinstance(response_message, AgentMessage):
+            response_message = AgentMessage(
+                sender=self.name,
+                content=response_message,
+            )
+        self.update_memory(response_message, session_id=session_id)
+        response_message = copy.deepcopy(response_message)
+        for hook in self._hooks.values():
+            result = hook.after_agent(self, response_message, session_id)
+            if result:
+                response_message = result
+        return response_message
+
+    async def forward(self,
+                      *message: AgentMessage,
+                      session_id=0,
+                      **kwargs) -> Union[AgentMessage, str]:
+        formatted_messages = self.aggregator.aggregate(
+            self.memory.get(session_id),
+            self.name,
+            self.output_format,
+            self.template,
+        )
+        llm_response = await self.llm.chat(formatted_messages, session_id,
+                                           **kwargs)
+        if self.output_format:
+            formatted_messages = self.output_format.parse_response(
+                llm_response)
+            return AgentMessage(
+                sender=self.name,
+                content=llm_response,
+                formatted=formatted_messages,
+            )
+        return llm_response
+
+
+class Sequential(Agent):
+    """Sequential is an agent container that forwards messages to each agent 
+    in the order they are added."""
+
+    def __init__(self, *agents: Union[Agent, AsyncAgent, Iterable], **kwargs):
+        super().__init__(**kwargs)
+        self._agents = OrderedDict()
+        if not agents:
+            raise ValueError('At least one agent should be provided')
+        if isinstance(agents[0],
+                      Iterable) and not isinstance(agents[0], Agent):
+            if not agents[0]:
+                raise ValueError('At least one agent should be provided')
+            agents = agents[0]
+        for key, agent in enumerate(agents):
+            if isinstance(agents, Mapping):
+                key, agent = agent, agents[agent]
+            elif isinstance(agent, tuple):
+                key, agent = agent
+            self.add_agent(key, agent)
+
+    def add_agent(self, name: str, agent: Union[Agent, AsyncAgent]):
+        assert isinstance(
+            agent, (Agent, AsyncAgent
+                    )), f'{type(agent)} is not an Agent or AsyncAgent subclass'
+        self._agents[str(name)] = agent
+
+    def forward(self,
+                *message: AgentMessage,
+                session_id=0,
+                exit_at: Optional[int] = None,
+                **kwargs) -> AgentMessage:
+        assert exit_at is None or exit_at >= 0, 'exit_at should be greater than or equal to 0'
+        if exit_at is None:
+            exit_at = len(self) - 1
+        iterator = chain.from_iterable(repeat(self._agents.values()))
+        for _ in range(exit_at + 1):
+            agent = next(iterator)
+            if isinstance(message, AgentMessage):
+                message = (message, )
+            message = agent(*message, session_id=session_id, **kwargs)
+        return message
+
+    def __getitem__(self, key):
+        if isinstance(key, int) and key < 0:
+            assert key >= -len(self), 'index out of range'
+            key = len(self) + key
+        return self._agents[str(key)]
+
+    def __len__(self):
+        return len(self._agents)
+
+
+class AsyncSequential(Sequential, AsyncAgent):
+
+    async def forward(self,
+                      *message: AgentMessage,
+                      session_id=0,
+                      exit_at: Optional[int] = None,
+                      **kwargs) -> AgentMessage:
+        assert exit_at is None or exit_at >= 0, 'exit_at should be greater than or equal to 0'
+        if exit_at is None:
+            exit_at = len(self) - 1
+        iterator = chain.from_iterable(repeat(self._agents.values()))
+        for _ in range(exit_at + 1):
+            agent = next(iterator)
+            if isinstance(message, AgentMessage):
+                message = (message, )
+            message = await agent(*message, session_id=session_id, **kwargs)
+        return message
+
+
+class AgentContainerMixin:
+
+    def __init_subclass__(cls):
+        super().__init_subclass__()
+
+        def wrap_api(func):
+
+            @wraps(func)
+            def wrapped_func(self, *args, **kwargs):
+                data = self.data.copy() if hasattr(self, 'data') else None
+
+                def _backup(d):
+                    if d is None:
+                        self.data.clear()
+                    else:
+                        self.data = d
+
+                ret = func(self, *args, **kwargs)
+                agents = OrderedDict()
+                for k, item in (self.data.items() if isinstance(
+                        self.data, abc.Mapping) else enumerate(self.data)):
+                    if isinstance(self.data,
+                                  abc.Mapping) and not isinstance(k, str):
+                        _backup(data)
+                        raise KeyError(
+                            f'agent name should be a string, got {type(k)}')
+                    if isinstance(k, str) and '.' in k:
+                        _backup(data)
+                        raise KeyError(
+                            f'agent name can\'t contain ".", got {k}')
+                    if not isinstance(item, (Agent, AsyncAgent)):
+                        _backup(data)
+                        raise TypeError(
+                            f'{type(item)} is not an Agent or AsyncAgent subclass'
+                        )
+                    agents[str(k)] = item
+                self._agents = agents
+                return ret
+
+            return wrapped_func
+
+        for method in [
+                'append', 'sort', 'reverse', 'pop', 'clear', 'update',
+                'insert', 'extend', 'remove', '__init__', '__setitem__',
+                '__delitem__', '__add__', '__iadd__', '__radd__', '__mul__',
+                '__imul__', '__rmul__'
+        ]:
+            if hasattr(cls, method):
+                setattr(cls, method, wrap_api(getattr(cls, method)))
+
+
+class AgentList(Agent, UserList, AgentContainerMixin):
+
+    def __init__(self,
+                 agents: Optional[Iterable[Union[Agent, AsyncAgent]]] = None):
+        Agent.__init__(self, memory=None)
+        UserList.__init__(self, agents)
+        self.name = None
+
+
+class AgentDict(Agent, UserDict, AgentContainerMixin):
+
+    def __init__(self,
+                 agents: Optional[Mapping[str, Union[Agent,
+                                                     AsyncAgent]]] = None):
+        Agent.__init__(self, memory=None)
+        UserDict.__init__(self, agents)
+        self.name = None

lagent/agents/aggregator/__init__.py

@@ -0,0 +1,4 @@
+from .default_aggregator import DefaultAggregator
+from .tool_aggregator import InternLMToolAggregator
+
+__all__ = ['DefaultAggregator', 'InternLMToolAggregator']

lagent/agents/aggregator/default_aggregator.py

@@ -0,0 +1,44 @@
+from typing import Dict, List
+
+from lagent.memory import Memory
+from lagent.prompts import StrParser
+
+
+class DefaultAggregator:
+
+    def aggregate(self,
+                  messages: Memory,
+                  name: str,
+                  parser: StrParser = None,
+                  system_instruction: str = None) -> List[Dict[str, str]]:
+        _message = []
+        messages = messages.get_memory()
+        if system_instruction:
+            _message.extend(
+                self.aggregate_system_intruction(system_instruction))
+        for message in messages:
+            if message.sender == name:
+                _message.append(
+                    dict(role='assistant', content=str(message.content)))
+            else:
+                user_message = message.content
+                if len(_message) > 0 and _message[-1]['role'] == 'user':
+                    _message[-1]['content'] += user_message
+                else:
+                    _message.append(dict(role='user', content=user_message))
+        return _message
+
+    @staticmethod
+    def aggregate_system_intruction(system_intruction) -> List[dict]:
+        if isinstance(system_intruction, str):
+            system_intruction = dict(role='system', content=system_intruction)
+        if isinstance(system_intruction, dict):
+            system_intruction = [system_intruction]
+        if isinstance(system_intruction, list):
+            for msg in system_intruction:
+                if not isinstance(msg, dict):
+                    raise TypeError(f'Unsupported message type: {type(msg)}')
+                if not ('role' in msg and 'content' in msg):
+                    raise KeyError(
+                        f"Missing required key 'role' or 'content': {msg}")
+        return system_intruction

lagent/agents/aggregator/tool_aggregator.py

@@ -0,0 +1,106 @@
+from typing import Dict, List, Optional, Union
+
+from lagent.agents.aggregator.default_aggregator import DefaultAggregator
+from lagent.memory.base_memory import Memory
+from lagent.prompts.parsers.tool_parser import MixedToolParser, ToolParser, ToolStatusCode
+
+
+class InternLMToolAggregator(DefaultAggregator):
+
+    def __init__(self,
+                 environment_role='environment',
+                 environment_begin='',
+                 environment_end='',
+                 user_names: Optional[List[str]] = None,
+                 few_shot: Optional[List[List[dict]]] = None):
+        self.environment_role = environment_role
+        self.environment_begin = environment_begin
+        self.environment_end = environment_end
+        self.user_names = user_names or ['user']
+        self.few_shot = few_shot or []
+
+    def aggregate(self,
+                  messages: Memory,
+                  name: str,
+                  parser: Union[ToolParser, MixedToolParser],
+                  system_instruction: str = None) -> List[Dict[str, str]]:
+        _message = []
+        messages = messages.get_memory()
+        if system_instruction:
+            _message.extend(
+                self.aggregate_system_intruction(system_instruction))
+        tool_instruction = parser.format_instruction()
+        if tool_instruction:
+            if isinstance(tool_instruction, str):
+                tool_instruction = dict(
+                    role='system', content=tool_instruction)
+                if parser.tool_type:
+                    tool_instruction['name'] = parser.tool_type
+            if isinstance(tool_instruction, dict):
+                tool_instruction = [tool_instruction]
+            _message.extend(tool_instruction)
+
+        for shot in self.few_shot:
+            i = 0
+            while i < len(shot):
+                msg = shot[i]
+                if msg['role'] in ['assistant', 'user', 'system']:
+                    _message.append(msg)
+                elif msg['role'] == self.environment_role:
+                    if not msg['content'].startswith(self.environment_begin):
+                        msg['content'] = self.environment_begin + msg['content']
+                    if not msg['content'].endswith(self.environment_end):
+                        msg['content'] += self.environment_end
+                    _message.append(msg)
+                elif msg['role'] in ['thought', 'language']:
+                    if i < len(shot) - 1 and shot[i + 1]['role'] == 'tool':
+                        _message.append(
+                            dict(
+                                role='assistant',
+                                content=parser.format_response(
+                                    dict(
+                                        tool_type=shot[i + 1]['name'],
+                                        thought=msg['content'],
+                                        action=shot[i + 1]['content'],
+                                        status=None))))
+                        i += 1
+                    else:
+                        _message.append(
+                            dict(
+                                role='assistant',
+                                content=parser.format_response(
+                                    dict(
+                                        tool_type=None,
+                                        thought=msg['content'],
+                                        action=None,
+                                        status=None))))
+                else:
+                    raise KeyError(f'Unkown role: {msg["role"]}')
+                i += 1
+
+        tool_type = None
+        for message in messages:
+            if message.sender == name:
+                if isinstance(message.formatted, dict):
+                    parsed = message.formatted
+                    if parsed['status'] == ToolStatusCode.PARSING_ERROR:
+                        continue
+                    _message.append(
+                        dict(
+                            role='assistant',
+                            content=parser.format_response(parsed)))
+                    tool_type = parsed['tool_type']
+                else:
+                    _message.append(
+                        dict(role='assistant', content=str(message.content)))
+            elif message.sender in self.user_names:
+                _message.append(dict(role='user', content=message.content))
+            else:
+                msg = dict(
+                    role=self.environment_role,
+                    content=self.environment_begin + str(message.content) +
+                    self.environment_end)
+                if tool_type:
+                    msg['name'] = tool_type
+                _message.append(msg)
+        return _message

lagent/agents/react.py

@@ -0,0 +1,161 @@
+import json
+from typing import Callable, Dict, List, Union
+
+from pydantic import BaseModel, Field
+
+from lagent.actions import ActionExecutor, AsyncActionExecutor, BaseAction
+from lagent.agents.agent import Agent, AsyncAgent
+from lagent.agents.aggregator import DefaultAggregator
+from lagent.hooks import ActionPreprocessor
+from lagent.llms import BaseLLM
+from lagent.memory import Memory
+from lagent.prompts.parsers.json_parser import JSONParser
+from lagent.prompts.prompt_template import PromptTemplate
+from lagent.schema import AgentMessage
+from lagent.utils import create_object
+
+select_action_template = """你是一个可以调用外部工具的助手，可以使用的工具包括：
+{action_info}
+{output_format}
+开始!"""
+
+output_format_template = """如果使用工具请遵循以下格式回复：
+{function_format}
+
+如果你已经知道了答案，或者你不需要工具，请遵循以下格式回复
+{finish_format}"""
+
+
+class ReAct(Agent):
+
+    def __init__(self,
+                 llm: Union[BaseLLM, Dict],
+                 actions: Union[BaseAction, List[BaseAction]],
+                 template: Union[PromptTemplate, str] = None,
+                 memory: Dict = dict(type=Memory),
+                 output_format: Dict = dict(type=JSONParser),
+                 aggregator: Dict = dict(type=DefaultAggregator),
+                 hooks: List = [dict(type=ActionPreprocessor)],
+                 finish_condition: Callable[[AgentMessage], bool] = lambda m:
+                 'conclusion' in m.content or 'conclusion' in m.formatted,
+                 max_turn: int = 5,
+                 **kwargs):
+        self.max_turn = max_turn
+        self.finish_condition = finish_condition
+        actions = dict(
+            type=ActionExecutor,
+            actions=actions,
+            hooks=hooks,
+        )
+        self.actions: ActionExecutor = create_object(actions)
+        select_agent = dict(
+            type=Agent,
+            llm=llm,
+            template=template.format(
+                action_info=json.dumps(self.actions.description()),
+                output_format=output_format.format_instruction()),
+            output_format=output_format,
+            memory=memory,
+            aggregator=aggregator,
+            hooks=hooks,
+        )
+        self.select_agent = create_object(select_agent)
+        super().__init__(**kwargs)
+
+    def forward(self, message: AgentMessage, **kwargs) -> AgentMessage:
+        for _ in range(self.max_turn):
+            message = self.select_agent(message)
+            if self.finish_condition(message):
+                return message
+            message = self.actions(message)
+        return message
+
+
+class AsyncReAct(AsyncAgent):
+
+    def __init__(self,
+                 llm: Union[BaseLLM, Dict],
+                 actions: Union[BaseAction, List[BaseAction]],
+                 template: Union[PromptTemplate, str] = None,
+                 memory: Dict = dict(type=Memory),
+                 output_format: Dict = dict(type=JSONParser),
+                 aggregator: Dict = dict(type=DefaultAggregator),
+                 hooks: List = [dict(type=ActionPreprocessor)],
+                 finish_condition: Callable[[AgentMessage], bool] = lambda m:
+                 'conclusion' in m.content or 'conclusion' in m.formatted,
+                 max_turn: int = 5,
+                 **kwargs):
+        self.max_turn = max_turn
+        self.finish_condition = finish_condition
+        actions = dict(
+            type=AsyncActionExecutor,
+            actions=actions,
+            hooks=hooks,
+        )
+        self.actions: AsyncActionExecutor = create_object(actions)
+        select_agent = dict(
+            type=AsyncAgent,
+            llm=llm,
+            template=template.format(
+                action_info=json.dumps(self.actions.description()),
+                output_format=output_format.format_instruction()),
+            output_format=output_format,
+            memory=memory,
+            aggregator=aggregator,
+            hooks=hooks,
+        )
+        self.select_agent = create_object(select_agent)
+        super().__init__(**kwargs)
+
+    async def forward(self, message: AgentMessage, **kwargs) -> AgentMessage:
+        for _ in range(self.max_turn):
+            message = await self.select_agent(message)
+            if self.finish_condition(message):
+                return message
+            message = await self.actions(message)
+        return message
+
+
+if __name__ == '__main__':
+    from lagent.llms import GPTAPI
+
+    class ActionCall(BaseModel):
+        name: str = Field(description='调用的函数名称')
+        parameters: Dict = Field(description='调用函数的参数')
+
+    class ActionFormat(BaseModel):
+        thought_process: str = Field(
+            description='描述当前所处的状态和已知信息。这有助于明确目前所掌握的信息和接下来的搜索方向。')
+        action: ActionCall = Field(description='当前步骤需要执行的操作，包括函数名��和参数。')
+
+    class FinishFormat(BaseModel):
+        thought_process: str = Field(
+            description='描述当前所处的状态和已知信息。这有助于明确目前所掌握的信息和接下来的搜索方向。')
+        conclusion: str = Field(description='总结当前的搜索结果，回答问题。')
+
+    prompt_template = PromptTemplate(select_action_template)
+    output_format = JSONParser(
+        output_format_template,
+        function_format=ActionFormat,
+        finish_format=FinishFormat)
+
+    llm = dict(
+        type=GPTAPI,
+        model_type='gpt-4o-2024-05-13',
+        key=None,
+        max_new_tokens=4096,
+        proxies=dict(),
+        retry=1000)
+
+    agent = ReAct(
+        llm=llm,
+        template=prompt_template,
+        output_format=output_format,
+        aggregator=dict(type='DefaultAggregator'),
+        actions=[dict(type='PythonInterpreter')],
+    )
+    response = agent(
+        AgentMessage(sender='user', content='用 Python 计算一下 3 ** 5'))
+    print(response)
+    response = agent(AgentMessage(sender='user', content=' 2 ** 5 呢'))
+    print(response)

lagent/agents/stream.py

@@ -0,0 +1,316 @@
+import json
+import warnings
+from copy import deepcopy
+from typing import Callable, Dict, List, Union
+
+from lagent.actions import ActionExecutor, AsyncActionExecutor, AsyncIPythonInterpreter, IPythonInteractive
+from lagent.agents.agent import Agent, AsyncAgent
+from lagent.agents.aggregator import InternLMToolAggregator
+from lagent.hooks import InternLMActionProcessor
+from lagent.llms import BaseLLM
+from lagent.memory import Memory
+from lagent.prompts.parsers import InterpreterParser, MixedToolParser, PluginParser, ToolStatusCode
+from lagent.schema import AgentMessage
+from lagent.utils import create_object
+
+API_PREFIX = (
+    "This is the subfunction for tool '{tool_name}', you can use this tool. "
+    'The description of this function is: \n{description}')
+
+META_CN = ('当开启工具以及代码时，根据需求选择合适的工具进行调用')
+
+INTERPRETER_CN = ('你现在已经能够在一个有状态的 Jupyter 笔记本环境中运行 Python 代码。'
+                  '当你向 python 发送含有 Python 代码的消息时，它将在该环境中执行。'
+                  '这个工具适用于多种场景，如数据分析或处理（包括数据操作、统计分析、图表绘制），'
+                  '复杂的计算问题（解决数学和物理难题），编程示例（理解编程概念或特性），'
+                  '文本处理和分析（比如文本解析和自然语言处理），'
+                  '机器学习和数据科学（用于展示模型训练和数据可视化），'
+                  '以及文件操作和数据导入（处理CSV、JSON等格式的文件）。')
+
+PLUGIN_CN = ('你可以使用如下工具：'
+             '\n{prompt}\n'
+             '如果你已经获得足够信息，请直接给出答案. 避免不必要的工具调用! '
+             '同时注意你可以使用的工具，不要随意捏造！')
+
+
+def get_plugin_prompt(actions, api_desc_template=API_PREFIX):
+    plugin_descriptions = []
+    for action in actions if isinstance(actions, list) else [actions]:
+        action = create_object(action)
+        action_desc = deepcopy(action.description)
+        if action.is_toolkit:
+            for api in action_desc['api_list']:
+                api['name'] = f"{action.name}.{api['name']}"
+                api['description'] = api_desc_template.format(
+                    tool_name=action.name, description=api['description'])
+                api['parameters'] = [
+                    param for param in api['parameters']
+                    if param['name'] in api['required']
+                ]
+                plugin_descriptions.append(api)
+        else:
+            action_desc['description'] = api_desc_template.format(
+                tool_name=action.name, description=action_desc['description'])
+            action_desc['parameters'] = [
+                param for param in action_desc['parameters']
+                if param['name'] in action_desc['required']
+            ]
+            plugin_descriptions.append(action_desc)
+    return json.dumps(plugin_descriptions, ensure_ascii=False, indent=4)
+
+
+class AgentForInternLM(Agent):
+
+    _INTERNAL_AGENT_CLS = Agent
+
+    def __init__(
+        self,
+        llm: Union[BaseLLM, Dict],
+        plugins: Union[dict, List[dict]] = None,
+        interpreter: dict = None,
+        template: Union[str, dict, List[dict]] = None,
+        memory: Dict = dict(type=Memory),
+        output_format: Dict = dict(
+            type=MixedToolParser,
+            template=META_CN,
+            parsers=[
+                dict(type=PluginParser, template=PLUGIN_CN),
+                dict(type=InterpreterParser, template=INTERPRETER_CN),
+            ]),
+        aggregator: Dict = dict(type=InternLMToolAggregator),
+        action_hooks: List = [dict(type=InternLMActionProcessor)],
+        finish_condition: Callable[
+            [AgentMessage],
+            bool] = lambda m: m.formatted['status'] == ToolStatusCode.NO_TOOL,
+        max_turn: int = 4,
+        **kwargs,
+    ):
+        agent = dict(
+            type=self._INTERNAL_AGENT_CLS,
+            llm=llm,
+            template=template,
+            output_format=output_format,
+            memory=memory,
+            aggregator=aggregator,
+            hooks=kwargs.pop('hooks', None),
+        )
+        self.agent = create_object(agent)
+        self.plugin_executor = plugins and ActionExecutor(
+            plugins, hooks=action_hooks)
+        self.interpreter_executor = interpreter and ActionExecutor(
+            interpreter, hooks=action_hooks)
+        if not (self.plugin_executor or self.interpreter_executor):
+            warnings.warn(
+                'Neither plugin nor interpreter executor is initialized. '
+                'An exception will be thrown when the agent call a tool.')
+        self.finish_condition = finish_condition
+        self.max_turn = max_turn
+        super().__init__(**kwargs)
+
+    def forward(self, message: AgentMessage, session_id=0, **kwargs):
+        if isinstance(message, str):
+            message = AgentMessage(sender='user', content=message)
+        for _ in range(self.max_turn):
+            message = self.agent(message, session_id=session_id, **kwargs)
+            assert isinstance(message.formatted, dict)
+            if self.finish_condition(message):
+                return message
+            if message.formatted['tool_type']:
+                tool_type = message.formatted["tool_type"]
+                executor = getattr(self, f'{tool_type}_executor', None)
+                if not executor:
+                    raise RuntimeError(f'No available {tool_type} executor')
+                message = executor(message, session_id=session_id)
+        return message
+
+    def get_steps(self, session_id=0):
+        steps, tool_type = [], None
+        for msg in self.agent.memory.get_memory(session_id):
+            if msg.sender == self.agent.name:
+                steps.append(
+                    dict(role='thought', content=msg.formatted['thought']))
+                if msg.formatted['tool_type']:
+                    tool_type = msg.formatted['tool_type']
+                    steps.append(
+                        dict(
+                            role='tool',
+                            content=msg.formatted['action'],
+                            name=tool_type))
+            elif msg.sender != 'user':
+                feedback = dict(role='environment', content=msg.content)
+                if tool_type:
+                    feedback['name'] = tool_type
+                steps.append(feedback)
+        return steps
+
+
+class MathCoder(AgentForInternLM):
+
+    def __init__(
+        self,
+        llm: Union[BaseLLM, Dict],
+        interpreter: dict = dict(
+            type=IPythonInteractive, timeout=20, max_out_len=8192),
+        template: Union[str, dict, List[dict]] = None,
+        memory: Dict = dict(type=Memory),
+        output_format: Dict = dict(
+            type=InterpreterParser,
+            template=
+            ('Integrate step-by-step reasoning and Python code to solve math problems '
+             'using the following guidelines:\n'
+             '- Analyze the question and write jupyter code to solve the problem;\n'
+             r"- Present the final result in LaTeX using a '\boxed{{}}' without any "
+             'units. \n')),
+        aggregator: Dict = dict(type=InternLMToolAggregator),
+        action_hooks: List = [dict(type=InternLMActionProcessor)],
+        finish_condition: Callable[
+            [AgentMessage],
+            bool] = lambda m: m.formatted['status'] == ToolStatusCode.NO_TOOL,
+        max_turn: int = 6,
+        **kwargs,
+    ):
+        kwargs.pop('plugins', None)
+        super().__init__(
+            llm=llm,
+            interpreter=interpreter,
+            template=template,
+            memory=memory,
+            output_format=output_format,
+            aggregator=aggregator,
+            action_hooks=action_hooks,
+            finish_condition=finish_condition,
+            max_turn=max_turn,
+            **kwargs)
+
+
+class AsyncAgentForInternLM(AsyncAgent):
+
+    _INTERNAL_AGENT_CLS = AsyncAgent
+
+    def __init__(
+        self,
+        llm: Union[BaseLLM, Dict],
+        plugins: Union[dict, List[dict]] = None,
+        interpreter: dict = None,
+        template: Union[str, dict, List[dict]] = None,
+        memory: Dict = dict(type=Memory),
+        output_format: Dict = dict(
+            type=MixedToolParser,
+            template=META_CN,
+            parsers=[
+                dict(type=PluginParser, template=PLUGIN_CN),
+                dict(type=InterpreterParser, template=INTERPRETER_CN),
+            ]),
+        aggregator: Dict = dict(type=InternLMToolAggregator),
+        action_hooks: List = [dict(type=InternLMActionProcessor)],
+        finish_condition: Callable[
+            [AgentMessage],
+            bool] = lambda m: m.formatted['status'] == ToolStatusCode.NO_TOOL,
+        max_turn: int = 4,
+        **kwargs,
+    ):
+        agent = dict(
+            type=self._INTERNAL_AGENT_CLS,
+            llm=llm,
+            template=template,
+            output_format=output_format,
+            memory=memory,
+            aggregator=aggregator,
+            hooks=kwargs.pop('hooks', None),
+        )
+        self.agent = create_object(agent)
+        self.plugin_executor = plugins and AsyncActionExecutor(
+            plugins, hooks=action_hooks)
+        self.interpreter_executor = interpreter and AsyncActionExecutor(
+            interpreter, hooks=action_hooks)
+        if not (self.plugin_executor or self.interpreter_executor):
+            warnings.warn(
+                'Neither plugin nor interpreter executor is initialized. '
+                'An exception will be thrown when the agent call a tool.')
+        self.finish_condition = finish_condition
+        self.max_turn = max_turn
+        super().__init__(**kwargs)
+
+    async def forward(self, message: AgentMessage, session_id=0, **kwargs):
+        if isinstance(message, str):
+            message = AgentMessage(sender='user', content=message)
+        for _ in range(self.max_turn):
+            message = await self.agent(
+                message, session_id=session_id, **kwargs)
+            assert isinstance(message.formatted, dict)
+            if self.finish_condition(message):
+                return message
+            if message.formatted['tool_type']:
+                tool_type = message.formatted["tool_type"]
+                executor = getattr(self, f'{tool_type}_executor', None)
+                if not executor:
+                    raise RuntimeError(f'No available {tool_type} executor')
+                message = await executor(message, session_id=session_id)
+        return message
+
+    def get_steps(self, session_id=0):
+        steps, tool_type = [], None
+        for msg in self.agent.memory.get_memory(session_id):
+            if msg.sender == self.agent.name:
+                steps.append(
+                    dict(role='thought', content=msg.formatted['thought']))
+                if msg.formatted['tool_type']:
+                    tool_type = msg.formatted['tool_type']
+                    steps.append(
+                        dict(
+                            role='tool',
+                            content=msg.formatted['action'],
+                            name=tool_type))
+            elif msg.sender != 'user':
+                feedback = dict(role='environment', content=msg.content)
+                if tool_type:
+                    feedback['name'] = tool_type
+                steps.append(feedback)
+        return steps
+
+
+class AsyncMathCoder(AsyncAgentForInternLM):
+
+    def __init__(
+        self,
+        llm: Union[BaseLLM, Dict],
+        interpreter: dict = dict(type=AsyncIPythonInterpreter),
+        template: Union[str, dict, List[dict]] = None,
+        memory: Dict = dict(type=Memory),
+        output_format: Dict = dict(
+            type=InterpreterParser,
+            template=
+            ('Integrate step-by-step reasoning and Python code to solve math problems '
+             'using the following guidelines:\n'
+             '- Analyze the question and write jupyter code to solve the problem;\n'
+             r"- Present the final result in LaTeX using a '\boxed{{}}' without any "
+             'units. \n')),
+        aggregator: Dict = dict(type=InternLMToolAggregator),
+        action_hooks: List = [dict(type=InternLMActionProcessor)],
+        finish_condition: Callable[
+            [AgentMessage],
+            bool] = lambda m: m.formatted['status'] == ToolStatusCode.NO_TOOL,
+        max_turn: int = 6,
+        **kwargs,
+    ):
+        kwargs.pop('plugins', None)
+        super().__init__(
+            llm=llm,
+            interpreter=interpreter,
+            template=template,
+            memory=memory,
+            output_format=output_format,
+            aggregator=aggregator,
+            action_hooks=action_hooks,
+            finish_condition=finish_condition,
+            max_turn=max_turn,
+            **kwargs)
+
+    async def forward(self, message: AgentMessage, session_id=0, **kwargs):
+        try:
+            return await super().forward(message, session_id, **kwargs)
+        finally:
+            interpreter = next(
+                iter(self.interpreter_executor.actions.values()))
+            if interpreter.name == 'AsyncIPythonInterpreter':
+                await interpreter.close_session(session_id)

lagent/distributed/__init__.py

@@ -0,0 +1,8 @@
+from .http_serve import AgentAPIServer, AsyncHTTPAgentClient, AsyncHTTPAgentServer, HTTPAgentClient, HTTPAgentServer
+from .ray_serve import AgentRayActor, AsyncAgentRayActor
+
+__all__ = [
+    'AsyncAgentRayActor', 'AgentRayActor', 'HTTPAgentServer',
+    'HTTPAgentClient', 'AsyncHTTPAgentServer', 'AsyncHTTPAgentClient',
+    'AgentAPIServer'
+]

lagent/distributed/http_serve/__init__.py

@@ -0,0 +1,7 @@
+from .api_server import AsyncHTTPAgentClient, AsyncHTTPAgentServer, HTTPAgentClient, HTTPAgentServer
+from .app import AgentAPIServer
+
+__all__ = [
+    'HTTPAgentServer', 'HTTPAgentClient', 'AsyncHTTPAgentClient',
+    'AsyncHTTPAgentServer', 'AgentAPIServer'
+]

lagent/distributed/http_serve/api_server.py

@@ -0,0 +1,123 @@
+import json
+import os
+import subprocess
+import sys
+import time
+
+import aiohttp
+import requests
+
+from lagent.schema import AgentMessage
+
+
+class HTTPAgentClient:
+
+    def __init__(self, host='127.0.0.1', port=8090, timeout=None):
+        self.host = host
+        self.port = port
+        self.timeout = timeout
+
+    @property
+    def is_alive(self):
+        try:
+            resp = requests.get(
+                f'http://{self.host}:{self.port}/health_check',
+                timeout=self.timeout)
+            return resp.status_code == 200
+        except:
+            return False
+
+    def __call__(self, *message, session_id: int = 0, **kwargs):
+        response = requests.post(
+            f'http://{self.host}:{self.port}/chat_completion',
+            json={
+                'message': [
+                    m if isinstance(m, str) else m.model_dump()
+                    for m in message
+                ],
+                'session_id': session_id,
+                **kwargs,
+            },
+            headers={'Content-Type': 'application/json'},
+            timeout=self.timeout)
+        resp = response.json()
+        if response.status_code != 200:
+            return resp
+        return AgentMessage.model_validate(resp)
+
+    def state_dict(self, session_id: int = 0):
+        resp = requests.get(
+            f'http://{self.host}:{self.port}/memory/{session_id}',
+            timeout=self.timeout)
+        return resp.json()
+
+
+class HTTPAgentServer(HTTPAgentClient):
+
+    def __init__(self, gpu_id, config, host='127.0.0.1', port=8090):
+        super().__init__(host, port)
+        self.gpu_id = gpu_id
+        self.config = config
+        self.start_server()
+
+    def start_server(self):
+        # set CUDA_VISIBLE_DEVICES in subprocess
+        env = os.environ.copy()
+        env['CUDA_VISIBLE_DEVICES'] = self.gpu_id
+        cmds = [
+            sys.executable, 'lagent/distributed/http_serve/app.py', '--host',
+            self.host, '--port',
+            str(self.port), '--config',
+            json.dumps(self.config)
+        ]
+        self.process = subprocess.Popen(
+            cmds,
+            env=env,
+            stdout=subprocess.PIPE,
+            stderr=subprocess.STDOUT,
+            text=True)
+
+        while True:
+            output = self.process.stdout.readline()
+            if not output:  # 如果读到 EOF，跳出循环
+                break
+            sys.stdout.write(output)  # 打印到标准输出
+            sys.stdout.flush()
+            if 'Uvicorn running on' in output:  # 根据实际输出调整
+                break
+            time.sleep(0.1)
+
+    def shutdown(self):
+        self.process.terminate()
+        self.process.wait()
+
+
+class AsyncHTTPAgentMixin:
+
+    async def __call__(self, *message, session_id: int = 0, **kwargs):
+        async with aiohttp.ClientSession(
+                timeout=aiohttp.ClientTimeout(self.timeout)) as session:
+            async with session.post(
+                    f'http://{self.host}:{self.port}/chat_completion',
+                    json={
+                        'message': [
+                            m if isinstance(m, str) else m.model_dump()
+                            for m in message
+                        ],
+                        'session_id': session_id,
+                        **kwargs,
+                    },
+                    headers={'Content-Type': 'application/json'},
+            ) as response:
+                resp = await response.json()
+                if response.status != 200:
+                    return resp
+                return AgentMessage.model_validate(resp)
+
+
+class AsyncHTTPAgentClient(AsyncHTTPAgentMixin, HTTPAgentClient):
+    pass
+
+
+class AsyncHTTPAgentServer(AsyncHTTPAgentMixin, HTTPAgentServer):
+    pass

lagent/distributed/http_serve/app.py

@@ -0,0 +1,96 @@
+import argparse
+import json
+import logging
+import time
+
+import uvicorn
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.requests import Request
+
+from lagent.schema import AgentMessage
+from lagent.utils import load_class_from_string
+
+
+class AgentAPIServer:
+
+    def __init__(self,
+                 config: dict,
+                 host: str = '127.0.0.1',
+                 port: int = 8090):
+        self.app = FastAPI(docs_url='/')
+        self.app.add_middleware(
+            CORSMiddleware,
+            allow_origins=['*'],
+            allow_credentials=True,
+            allow_methods=['*'],
+            allow_headers=['*'],
+        )
+        cls_name = config.pop('type')
+        python_path = config.pop('python_path', None)
+        cls_name = load_class_from_string(cls_name, python_path) if isinstance(
+            cls_name, str) else cls_name
+        self.agent = cls_name(**config)
+        self.setup_routes()
+        self.run(host, port)
+
+    def setup_routes(self):
+
+        def heartbeat():
+            return {'status': 'success', 'timestamp': time.time()}
+
+        async def process_message(request: Request):
+            try:
+                body = await request.json()
+                message = [
+                    m if isinstance(m, str) else AgentMessage.model_validate(m)
+                    for m in body.pop('message')
+                ]
+                result = await self.agent(*message, **body)
+                return result
+            except Exception as e:
+                logging.error(f'Error processing message: {str(e)}')
+                raise HTTPException(
+                    status_code=500, detail='Internal Server Error')
+
+        def get_memory(session_id: int = 0):
+            try:
+                result = self.agent.state_dict(session_id)
+                return result
+            except KeyError:
+                raise HTTPException(
+                    status_code=404, detail="Session ID not found")
+            except Exception as e:
+                logging.error(f'Error processing message: {str(e)}')
+                raise HTTPException(
+                    status_code=500, detail='Internal Server Error')
+
+        self.app.add_api_route('/health_check', heartbeat, methods=['GET'])
+        self.app.add_api_route(
+            '/chat_completion', process_message, methods=['POST'])
+        self.app.add_api_route(
+            '/memory/{session_id}', get_memory, methods=['GET'])
+
+    def run(self, host='127.0.0.1', port=8090):
+        logging.info(f'Starting server at {host}:{port}')
+        uvicorn.run(self.app, host=host, port=port)
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Async Agent API Server')
+    parser.add_argument('--host', type=str, default='127.0.0.1')
+    parser.add_argument('--port', type=int, default=8090)
+    parser.add_argument(
+        '--config',
+        type=json.loads,
+        required=True,
+        help='JSON configuration for the agent')
+    args = parser.parse_args()
+
+    return args
+
+
+if __name__ == '__main__':
+    logging.basicConfig(level=logging.INFO)
+    args = parse_args()
+    AgentAPIServer(args.config, host=args.host, port=args.port)

lagent/distributed/ray_serve/__init__.py

@@ -0,0 +1,3 @@
+from .ray_warpper import AgentRayActor, AsyncAgentRayActor
+
+__all__ = ['AsyncAgentRayActor', 'AgentRayActor']

lagent/distributed/ray_serve/ray_warpper.py

@@ -0,0 +1,48 @@
+import importlib
+import sys
+from typing import Dict
+
+import ray
+
+from lagent.schema import AgentMessage
+from lagent.utils import load_class_from_string
+
+
+class AsyncAgentRayActor:
+
+    def __init__(
+        self,
+        config: Dict,
+        num_gpus: int,
+    ):
+        cls_name = config.pop('type')
+        python_path = config.pop('python_path', None)
+        cls_name = load_class_from_string(cls_name, python_path) if isinstance(
+            cls_name, str) else cls_name
+        AsyncAgentActor = ray.remote(num_gpus=num_gpus)(cls_name)
+        self.agent_actor = AsyncAgentActor.remote(**config)
+
+    async def __call__(self, *message: AgentMessage, session_id=0, **kwargs):
+        response = await self.agent_actor.__call__.remote(
+            *message, session_id=session_id, **kwargs)
+        return response
+
+
+class AgentRayActor:
+
+    def __init__(
+        self,
+        config: Dict,
+        num_gpus: int,
+    ):
+        cls_name = config.pop('type')
+        python_path = config.pop('python_path', None)
+        cls_name = load_class_from_string(cls_name, python_path) if isinstance(
+            cls_name, str) else cls_name
+        AgentActor = ray.remote(num_gpus=num_gpus)(cls_name)
+        self.agent_actor = AgentActor.remote(**config)
+
+    def __call__(self, *message: AgentMessage, session_id=0, **kwargs):
+        response = self.agent_actor.__call__.remote(
+            *message, session_id=session_id, **kwargs)
+        return ray.get(response)

lagent/hooks/__init__.py

@@ -0,0 +1,8 @@
+from .action_preprocessor import ActionPreprocessor, InternLMActionProcessor
+from .hook import Hook, RemovableHandle
+from .logger import MessageLogger
+
+__all__ = [
+    'Hook', 'RemovableHandle', 'ActionPreprocessor', 'InternLMActionProcessor',
+    'MessageLogger'
+]

lagent/hooks/action_preprocessor.py

@@ -0,0 +1,62 @@
+from copy import deepcopy
+
+from lagent.schema import ActionReturn, ActionStatusCode, FunctionCall
+from .hook import Hook
+
+
+class ActionPreprocessor(Hook):
+    """The ActionPreprocessor is a hook that preprocesses the action message
+    and postprocesses the action return message.
+
+    """
+
+    def before_action(self, executor, message, session_id):
+        assert isinstance(message.formatted, FunctionCall) or (
+            isinstance(message.formatted, dict) and 'name' in message.content
+            and 'parameters' in message.formatted) or (
+                'action' in message.formatted
+                and 'parameters' in message.formatted['action']
+                and 'name' in message.formatted['action'])
+        if isinstance(message.formatted, dict):
+            name = message.formatted.get('name',
+                                         message.formatted['action']['name'])
+            parameters = message.formatted.get(
+                'parameters', message.formatted['action']['parameters'])
+        else:
+            name = message.formatted.name
+            parameters = message.formatted.parameters
+        message.content = dict(name=name, parameters=parameters)
+        return message
+
+    def after_action(self, executor, message, session_id):
+        action_return = message.content
+        if isinstance(action_return, ActionReturn):
+            if action_return.state == ActionStatusCode.SUCCESS:
+                response = action_return.format_result()
+            else:
+                response = action_return.errmsg
+        else:
+            response = action_return
+        message.content = response
+        return message
+
+
+class InternLMActionProcessor(ActionPreprocessor):
+
+    def __init__(self, code_parameter: str = 'command'):
+        self.code_parameter = code_parameter
+
+    def before_action(self, executor, message, session_id):
+        message = deepcopy(message)
+        assert isinstance(message.formatted, dict) and set(
+            message.formatted).issuperset(
+                {'tool_type', 'thought', 'action', 'status'})
+        if isinstance(message.formatted['action'], str):
+            # encapsulate code interpreter arguments
+            action_name = next(iter(executor.actions))
+            parameters = {self.code_parameter: message.formatted['action']}
+            if action_name in ['AsyncIPythonInterpreter']:
+                parameters['session_id'] = session_id
+            message.formatted['action'] = dict(
+                name=action_name, parameters=parameters)
+        return super().before_action(executor, message, session_id)

lagent/hooks/hook.py

@@ -0,0 +1,50 @@
+from itertools import count
+from typing import Tuple
+
+from lagent.schema import AgentMessage
+
+
+class Hook:
+
+    def before_agent(
+        self,
+        agent,
+        message: Tuple[AgentMessage],
+        session_id: int,
+    ):
+        pass
+
+    def after_agent(
+        self,
+        agent,
+        message: AgentMessage,
+        session_id: int,
+    ):
+        pass
+
+    def before_action(
+        self,
+        executor,
+        message: AgentMessage,
+        session_id: int,
+    ):
+        pass
+
+    def after_action(
+        self,
+        executor,
+        message: AgentMessage,
+        session_id: int,
+    ):
+        pass
+
+
+class RemovableHandle:
+    _id_iter = count(0)
+
+    def __init__(self, hooks_dict):
+        self.hooks_dict = hooks_dict
+        self.id = next(self._id_iter)
+
+    def remove(self):
+        del self.hooks_dict[self.id]

lagent/hooks/logger.py

@@ -0,0 +1,37 @@
+import random
+from typing import Optional
+
+from termcolor import COLORS, colored
+
+from lagent.utils import get_logger
+from .hook import Hook
+
+
+class MessageLogger(Hook):
+
+    def __init__(self, name: str = 'lagent'):
+        self.logger = get_logger(
+            name, 'info', '%(asctime)s %(levelname)8s %(name)8s - %(message)s')
+        self.sender2color = {}
+
+    def before_agent(self, agent, messages, session_id):
+        for message in messages:
+            self._process_message(message, session_id)
+
+    def after_agent(self, agent, message, session_id):
+        self._process_message(message, session_id)
+
+    def before_action(self, executor, message, session_id):
+        self._process_message(message, session_id)
+
+    def after_action(self, executor, message, session_id):
+        self._process_message(message, session_id)
+
+    def _process_message(self, message, session_id):
+        sender = message.sender
+        color = self.sender2color.setdefault(sender,
+                                             random.choice(list(COLORS)))
+        self.logger.info(
+            colored(
+                f'session id: {session_id}, message sender: {sender}\n'
+                f'{message.content}', color))

lagent/llms/__init__.py

@@ -0,0 +1,32 @@
+from .base_api import AsyncBaseAPILLM, BaseAPILLM
+from .base_llm import AsyncBaseLLM, BaseLLM
+from .huggingface import HFTransformer, HFTransformerCasualLM, HFTransformerChat
+from .lmdeploy_wrapper import (AsyncLMDeployClient, AsyncLMDeployPipeline,
+                               AsyncLMDeployServer, LMDeployClient,
+                               LMDeployPipeline, LMDeployServer)
+from .meta_template import INTERNLM2_META
+from .openai import GPTAPI, AsyncGPTAPI
+from .sensenova import SensenovaAPI
+from .vllm_wrapper import AsyncVllmModel, VllmModel
+
+__all__ = [
+    'AsyncBaseLLM',
+    'BaseLLM',
+    'AsyncBaseAPILLM',
+    'BaseAPILLM',
+    'AsyncGPTAPI',
+    'GPTAPI',
+    'LMDeployClient',
+    'AsyncLMDeployClient',
+    'LMDeployPipeline',
+    'AsyncLMDeployPipeline',
+    'LMDeployServer',
+    'AsyncLMDeployServer',
+    'HFTransformer',
+    'HFTransformerCasualLM',
+    'INTERNLM2_META',
+    'HFTransformerChat',
+    'VllmModel',
+    'AsyncVllmModel',
+    'SensenovaAPI',
+]

lagent/llms/base_api.py

@@ -0,0 +1,175 @@
+import warnings
+from typing import Dict, List, Optional, Tuple, Union
+
+from lagent.llms.base_llm import AsyncLLMMixin, BaseLLM
+
+
+class APITemplateParser:
+    """Intermidate prompt template parser, specifically for API models.
+
+    Args:
+        meta_template (Dict): The meta template for the model.
+    """
+
+    def __init__(self, meta_template: Optional[Dict] = None):
+        self.meta_template = meta_template
+        # Check meta template
+        if meta_template:
+            assert isinstance(meta_template, list)
+            self.roles: Dict[str, dict] = dict()  # maps role name to config
+            for item in meta_template:
+                assert isinstance(item, dict)
+                assert item['role'] not in self.roles, \
+                    'role in meta prompt must be unique!'
+                self.roles[item['role']] = item.copy()
+
+    def __call__(self, dialog: List[Union[str, List]]):
+        """Parse the intermidate prompt template, and wrap it with meta
+        template if applicable. When the meta template is set and the input is
+        a list, the return value will be a list containing the full
+        conversation history. Each item looks like:
+
+        .. code-block:: python
+
+            {'role': 'user', 'content': '...'}).
+
+        Args:
+            dialog (List[str or list]): An intermidate prompt
+                template (potentially before being wrapped by meta template).
+
+        Returns:
+            List[str or list]: The finalized prompt or a conversation.
+        """
+        assert isinstance(dialog, (str, list))
+        if isinstance(dialog, str):
+            return dialog
+        if self.meta_template:
+
+            prompt = list()
+            # Whether to keep generating the prompt
+            generate = True
+            for i, item in enumerate(dialog):
+                if not generate:
+                    break
+                if isinstance(item, str):
+                    if item.strip():
+                        # TODO: logger
+                        warnings.warn('Non-empty string in prompt template '
+                                      'will be ignored in API models.')
+                else:
+                    api_prompts = self._prompt2api(item)
+                    prompt.append(api_prompts)
+
+            # merge the consecutive prompts assigned to the same role
+            new_prompt = list([prompt[0]])
+            last_role = prompt[0]['role']
+            for item in prompt[1:]:
+                if item['role'] == last_role:
+                    new_prompt[-1]['content'] += '\n' + item['content']
+                else:
+                    last_role = item['role']
+                    new_prompt.append(item)
+            prompt = new_prompt
+
+        else:
+            # in case the model does not have any meta template
+            prompt = ''
+            last_sep = ''
+            for item in dialog:
+                if isinstance(item, str):
+                    if item:
+                        prompt += last_sep + item
+                elif item.get('content', ''):
+                    prompt += last_sep + item.get('content', '')
+                last_sep = '\n'
+        return prompt
+
+    def _prompt2api(self, prompts: Union[List, str]) -> Tuple[str, bool]:
+        """Convert the prompts to a API-style prompts, given an updated
+        role_dict.
+
+        Args:
+            prompts (Union[List, str]): The prompts to be converted.
+            role_dict (Dict[str, Dict]): The updated role dict.
+            for_gen (bool): If True, the prompts will be converted for
+                generation tasks. The conversion stops before the first
+                role whose "generate" is set to True.
+
+        Returns:
+            Tuple[str, bool]: The converted string, and whether the follow-up
+            conversion should be proceeded.
+        """
+        if isinstance(prompts, str):
+            return prompts
+        elif isinstance(prompts, dict):
+            api_role = self._role2api_role(prompts)
+            return api_role
+
+        res = []
+        for prompt in prompts:
+            if isinstance(prompt, str):
+                raise TypeError('Mixing str without explicit role is not '
+                                'allowed in API models!')
+            else:
+                api_role = self._role2api_role(prompt)
+                res.append(api_role)
+        return res
+
+    def _role2api_role(self, role_prompt: Dict) -> Tuple[str, bool]:
+        merged_prompt = self.roles[role_prompt['role']]
+        if merged_prompt.get('fallback_role'):
+            merged_prompt = self.roles[self.roles[
+                merged_prompt['fallback_role']]]
+        res = role_prompt.copy()
+        res['role'] = merged_prompt['api_role']
+        res['content'] = merged_prompt.get('begin', '')
+        res['content'] += role_prompt.get('content', '')
+        res['content'] += merged_prompt.get('end', '')
+        return res
+
+
+class BaseAPILLM(BaseLLM):
+    """Base class for API model wrapper.
+
+    Args:
+        model_type (str): The type of model.
+        retry (int): Number of retires if the API call fails. Defaults to 2.
+        meta_template (Dict, optional): The model's meta prompt
+            template if needed, in case the requirement of injecting or
+            wrapping of any meta instructions.
+    """
+
+    is_api: bool = True
+
+    def __init__(self,
+                 model_type: str,
+                 retry: int = 2,
+                 template_parser: 'APITemplateParser' = APITemplateParser,
+                 meta_template: Optional[Dict] = None,
+                 *,
+                 max_new_tokens: int = 512,
+                 top_p: float = 0.8,
+                 top_k: int = 40,
+                 temperature: float = 0.8,
+                 repetition_penalty: float = 0.0,
+                 stop_words: Union[List[str], str] = None):
+        self.model_type = model_type
+        self.meta_template = meta_template
+        self.retry = retry
+        if template_parser:
+            self.template_parser = template_parser(meta_template)
+
+        if isinstance(stop_words, str):
+            stop_words = [stop_words]
+        self.gen_params = dict(
+            max_new_tokens=max_new_tokens,
+            top_p=top_p,
+            top_k=top_k,
+            temperature=temperature,
+            repetition_penalty=repetition_penalty,
+            stop_words=stop_words,
+            skip_special_tokens=False)
+
+
+class AsyncBaseAPILLM(AsyncLLMMixin, BaseAPILLM):
+    pass

lagent/llms/base_llm.py

@@ -0,0 +1,305 @@
+from copy import copy
+from typing import Dict, List, Optional, Tuple, Union
+
+
+class LMTemplateParser:
+    """Intermidate prompt template parser, specifically for language models.
+
+    Args:
+        meta_template (list of dict, optional): The meta template for the
+            model.
+    """
+
+    def __init__(self, meta_template: Optional[List[Dict]] = None):
+        self.meta_template = meta_template
+        if meta_template:
+            assert isinstance(meta_template, list)
+            self.roles: Dict[str, dict] = dict()  # maps role name to config
+            for item in meta_template:
+                assert isinstance(item, dict)
+                assert item['role'] not in self.roles, \
+                    'role in meta prompt must be unique!'
+                self.roles[item['role']] = item.copy()
+
+    def __call__(self, dialog) -> str:
+        """Parse a prompt template, and wrap it with meta template if
+        applicable.
+
+        Args:
+            dialog (List[str or PromptList]): A prompt
+                template (potentially before being wrapped by meta template).
+
+        Returns:
+            str: The final string.
+        """
+        assert isinstance(dialog, (str, list))
+        if isinstance(dialog, str):
+            return dialog
+        if self.meta_template:
+
+            prompt = ''
+            for index, item in enumerate(dialog):
+                if isinstance(item, str):
+                    prompt += item
+                else:
+                    new_str = self._prompt2str(item, index == len(dialog) - 1)
+                    prompt += new_str
+        else:
+            # in case the model does not have any meta template
+            prompt = ''
+            last_sep = ''
+            for item in dialog:
+                if isinstance(item, str):
+                    if item:
+                        prompt += last_sep + item
+                elif item.get('content', ''):
+                    prompt += last_sep + item.get('prompt', '')
+                last_sep = '\n'
+        return prompt
+
+    def _format_begin(self, role_cfg, message):
+        name = message.get('name', None)
+        if name is not None:
+            begin = role_cfg['begin'].get('with_name', '')
+            if name in role_cfg['begin'].get('name', {}):
+                begin = begin.format(name=role_cfg['begin']['name'][name])
+            else:
+                begin = begin.format(name=name)
+        else:
+            if isinstance(role_cfg.get('begin', ''), str):
+                begin = role_cfg.get('begin', '')
+            elif isinstance(role_cfg['begin'], dict):
+                begin = role_cfg['begin'].get('without_name', '')
+        return begin
+
+    def _prompt2str(self,
+                    prompt: Union[str, Dict],
+                    last: bool = False) -> Tuple[str, bool]:
+        if isinstance(prompt, str):
+            return prompt
+        merged_prompt = self.roles.get(prompt['role'])
+
+        if merged_prompt.get('fallback_role'):
+            merged_prompt = self.roles.get(merged_prompt['fallback_role'])
+        begin = self._format_begin(merged_prompt, prompt)
+        res = begin
+        if last and merged_prompt.get('generate', False):
+            res += prompt.get('content', '')
+            return res
+        res += prompt.get('content', '') + merged_prompt.get('end', '')
+        if last and merged_prompt['role'] != 'assistant':
+            res += self._format_begin(self.roles['assistant'], {})
+            return res
+        return res
+
+
+class BaseLLM:
+    """Base class for model wrapper.
+
+    Args:
+        path (str): The path to the model.
+        max_new_tokens (int): Maximum length of output expected to be generated by the model. Defaults
+            to 512.
+        tokenizer_only (bool): If True, only the tokenizer will be initialized.
+            Defaults to False.
+        meta_template (list of dict, optional): The model's meta prompt
+            template if needed, in case the requirement of injecting or
+            wrapping of any meta instructions.
+    """
+
+    def __init__(self,
+                 path: str,
+                 tokenizer_only: bool = False,
+                 template_parser: 'LMTemplateParser' = LMTemplateParser,
+                 meta_template: Optional[List[Dict]] = None,
+                 *,
+                 max_new_tokens: int = 512,
+                 top_p: float = 0.8,
+                 top_k: float = 40,
+                 temperature: float = 0.8,
+                 repetition_penalty: float = 1.0,
+                 stop_words: Union[List[str], str] = None):
+        self.path = path
+        self.tokenizer_only = tokenizer_only
+        # meta template
+        self.template_parser = template_parser(meta_template)
+        self.eos_token_id = None
+        if meta_template and 'eos_token_id' in meta_template:
+            self.eos_token_id = meta_template['eos_token_id']
+
+        if isinstance(stop_words, str):
+            stop_words = [stop_words]
+        self.gen_params = dict(
+            max_new_tokens=max_new_tokens,
+            top_p=top_p,
+            top_k=top_k,
+            temperature=temperature,
+            repetition_penalty=repetition_penalty,
+            stop_words=stop_words)
+
+    def generate(self, inputs: Union[str, List[str]], **gen_params) -> str:
+        """Generate results given a str (or list of) inputs.
+
+        Args:
+            inputs (Union[str, List[str]]):
+            gen_params (dict): The input params for generation.
+
+        Returns:
+            Union[str, List[str]]: A (list of) generated strings.
+
+        eg.
+            batched = True
+            if isinstance(inputs, str):
+                inputs = [inputs]
+                batched = False
+            response = ['']
+            if batched:
+                return response
+            return response[0]
+        """
+        raise NotImplementedError
+
+    def stream_generate(self, inputs: str, **gen_params) -> List[str]:
+        """Generate results as streaming given a str inputs.
+
+        Args:
+            inputs (str):
+            gen_params (dict): The input params for generation.
+
+        Returns:
+            str: A generated string.
+        """
+        raise NotImplementedError
+
+    def chat(self,
+             inputs: Union[List[dict], List[List[dict]]],
+             session_ids: Union[int, List[int]] = None,
+             **gen_params):
+        """Generate completion from a list of templates.
+
+        Args:
+            inputs (Union[List[dict], List[List[dict]]]):
+            gen_params (dict): The input params for generation.
+        Returns:
+        """
+        if isinstance(inputs[0], list):
+            _inputs = list()
+            for msg in inputs:
+                _inputs.append(self.template_parser(msg))
+        else:
+            _inputs = self.template_parser(inputs)
+        return self.generate(_inputs, **gen_params)
+
+    def stream_chat(self, inputs: List[dict], **gen_params):
+        """Generate results as streaming given a list of templates.
+
+        Args:
+            inputs (Union[List[dict]):
+            gen_params (dict): The input params for generation.
+        Returns:
+        """
+        raise NotImplementedError
+
+    def tokenize(self, prompts: Union[str, List[str], List[dict],
+                                      List[List[dict]]]):
+        """Tokenize the input prompts.
+
+        Args:
+            prompts(str | List[str]): user's prompt, or a batch prompts
+
+        Returns:
+            Tuple(numpy.ndarray, numpy.ndarray, numpy.ndarray): prompt's token
+            ids, ids' length and requested output length
+        """
+        raise NotImplementedError
+
+    def update_gen_params(self, **kwargs):
+        gen_params = copy(self.gen_params)
+        gen_params.update(kwargs)
+        return gen_params
+
+
+class AsyncLLMMixin:
+
+    async def generate(self,
+                       inputs: Union[str, List[str]],
+                       session_ids: Union[int, List[int]] = None,
+                       **gen_params) -> str:
+        """Generate results given a str (or list of) inputs.
+
+        Args:
+            inputs (Union[str, List[str]]):
+            gen_params (dict): The input params for generation.
+
+        Returns:
+            Union[str, List[str]]: A (list of) generated strings.
+
+        eg.
+            batched = True
+            if isinstance(inputs, str):
+                inputs = [inputs]
+                batched = False
+            response = ['']
+            if batched:
+                return response
+            return response[0]
+        """
+        raise NotImplementedError
+
+    async def stream_generate(self, inputs: str, **gen_params) -> List[str]:
+        """Generate results as streaming given a str inputs.
+
+        Args:
+            inputs (str):
+            gen_params (dict): The input params for generation.
+
+        Returns:
+            str: A generated string.
+        """
+        raise NotImplementedError
+
+    async def chat(self,
+                   inputs: Union[List[dict], List[List[dict]]],
+                   session_ids: Union[int, List[int]] = None,
+                   **gen_params):
+        """Generate completion from a list of templates.
+
+        Args:
+            inputs (Union[List[dict], List[List[dict]]]):
+            gen_params (dict): The input params for generation.
+        Returns:
+        """
+        if isinstance(inputs[0], list):
+            _inputs = list()
+            for msg in inputs:
+                _inputs.append(self.template_parser(msg))
+        else:
+            _inputs = self.template_parser(inputs)
+        return await self.generate(_inputs, session_ids, **gen_params)
+
+    async def stream_chat(self, inputs: List[dict], **gen_params):
+        """Generate results as streaming given a list of templates.
+
+        Args:
+            inputs (Union[List[dict]):
+            gen_params (dict): The input params for generation.
+        Returns:
+        """
+        raise NotImplementedError
+
+    async def tokenize(self, prompts: Union[str, List[str], List[dict],
+                                            List[List[dict]]]):
+        """Tokenize the input prompts.
+
+        Args:
+            prompts(str | List[str]): user's prompt, or a batch prompts
+
+        Returns:
+            Tuple(numpy.ndarray, numpy.ndarray, numpy.ndarray): prompt's token
+            ids, ids' length and requested output length
+        """
+        raise NotImplementedError
+
+
+class AsyncBaseLLM(AsyncLLMMixin, BaseLLM):
+    pass

lagent/llms/huggingface.py

@@ -0,0 +1,337 @@
+import copy
+import logging
+from typing import Dict, List, Optional, Union
+
+from lagent.schema import ModelStatusCode
+from .base_api import APITemplateParser
+from .base_llm import BaseLLM
+
+logger = logging.getLogger(__name__)
+
+
+class HFTransformer(BaseLLM):
+    """Model wrapper around HuggingFace general models.
+
+    Adapted from Internlm (https://github.com/InternLM/InternLM/blob/main/
+        chat/web_demo.py)
+
+    Args:
+        path (str): The name or path to HuggingFace's model.
+        tokenizer_path (str): The path to the tokenizer. Defaults to None.
+        tokenizer_kwargs (dict): Keyword arguments for the tokenizer.
+            Defaults to {}.
+        tokenizer_only (bool): If True, only the tokenizer will be initialized.
+            Defaults to False.
+        model_kwargs (dict): Keyword arguments for the model, used in loader.
+            Defaults to dict(device_map='auto').
+        meta_template (Dict, optional): The model's meta prompt
+            template if needed, in case the requirement of injecting or
+            wrapping of any meta instructions.
+    """
+
+    def __init__(self,
+                 path: str,
+                 tokenizer_path: Optional[str] = None,
+                 tokenizer_kwargs: dict = dict(),
+                 tokenizer_only: bool = False,
+                 model_kwargs: dict = dict(device_map='auto'),
+                 meta_template: Optional[Dict] = None,
+                 stop_words_id: Union[List[int], int] = None,
+                 **kwargs):
+        super().__init__(
+            path=path,
+            tokenizer_only=tokenizer_only,
+            meta_template=meta_template,
+            **kwargs)
+        if isinstance(stop_words_id, int):
+            stop_words_id = [stop_words_id]
+        self.gen_params.update(stop_words_id=stop_words_id)
+        if self.gen_params['stop_words'] is not None and \
+                self.gen_params['stop_words_id'] is not None:
+            logger.warning('Both stop_words and stop_words_id are specified,'
+                           'only stop_words_id will be used.')
+
+        self._load_tokenizer(
+            path=path,
+            tokenizer_path=tokenizer_path,
+            tokenizer_kwargs=tokenizer_kwargs)
+        if not tokenizer_only:
+            self._load_model(path=path, model_kwargs=model_kwargs)
+
+        from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList  # noqa: E501
+        self.logits_processor = LogitsProcessorList()
+        self.stopping_criteria = StoppingCriteriaList()
+        self.prefix_allowed_tokens_fn = None
+
+        stop_words_id = []
+        if self.gen_params.get('stop_words_id'):
+            stop_words_id = self.gen_params.get('stop_words_id')
+        elif self.gen_params.get('stop_words'):
+            for sw in self.gen_params.get('stop_words'):
+                stop_words_id.append(self.tokenizer(sw)['input_ids'][-1])
+        self.additional_eos_token_id = stop_words_id
+
+    def _load_tokenizer(self, path: str, tokenizer_path: Optional[str],
+                        tokenizer_kwargs: dict):
+        from transformers import AutoTokenizer
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            tokenizer_path if tokenizer_path else path,
+            trust_remote_code=True,
+            **tokenizer_kwargs)
+
+        if self.tokenizer.pad_token_id is None:
+            if self.tokenizer.eos_token is not None:
+                logger.warning(
+                    f'Using eos_token_id {self.tokenizer.eos_token} '
+                    'as pad_token_id.')
+                self.tokenizer.pad_token = self.tokenizer.eos_token
+            else:
+                from transformers.generation import GenerationConfig
+                self.gcfg = GenerationConfig.from_pretrained(path)
+
+                if self.gcfg.pad_token_id is not None:
+                    logger.warning(
+                        f'Using pad_token_id {self.gcfg.pad_token_id} '
+                        'as pad_token_id.')
+                    self.tokenizer.pad_token_id = self.gcfg.pad_token_id
+                else:
+                    raise ValueError(
+                        'pad_token_id is not set for this tokenizer. Try to '
+                        'set pad_token_id via passing '
+                        '`pad_token_id={PAD_TOKEN_ID}` in model_cfg.')
+
+    def _load_model(self, path: str, model_kwargs: dict):
+        import torch
+        from transformers import AutoModel
+        model_kwargs.setdefault('torch_dtype', torch.float16)
+        self.model = AutoModel.from_pretrained(
+            path, trust_remote_code=True, **model_kwargs)
+        self.model.eval()
+
+    def tokenize(self, inputs: str):
+        assert isinstance(inputs, str)
+        inputs = self.tokenizer(
+            inputs, return_tensors='pt', return_length=True)
+        return inputs['input_ids'].tolist()
+
+    def generate(
+        self,
+        inputs: Union[str, List[str]],
+        do_sample: bool = True,
+        **kwargs,
+    ):
+        """Return the chat completions in non-stream mode.
+
+        Args:
+            inputs (Union[str, List[str]]): input texts to be completed.
+            do_sample (bool): do sampling if enabled
+        Returns:
+            (a list of/batched) text/chat completion
+        """
+        for status, chunk, _ in self.stream_generate(inputs, do_sample,
+                                                     **kwargs):
+            response = chunk
+        return response
+
+    def stream_generate(
+        self,
+        inputs: List[str],
+        do_sample: bool = True,
+        **kwargs,
+    ):
+        """Return the chat completions in stream mode.
+
+        Args:
+            inputs (Union[str, List[str]]): input texts to be completed.
+            do_sample (bool): do sampling if enabled
+        Returns:
+            tuple(Status, str, int): status, text/chat completion,
+            generated token number
+        """
+        import torch
+        from torch import nn
+        with torch.no_grad():
+            batched = True
+            if isinstance(inputs, str):
+                inputs = [inputs]
+                batched = False
+            inputs = self.tokenizer(
+                inputs, padding=True, return_tensors='pt', return_length=True)
+            input_length = inputs['length']
+            for k, v in inputs.items():
+                inputs[k] = v.cuda()
+            input_ids = inputs['input_ids']
+            attention_mask = inputs['attention_mask']
+            batch_size = input_ids.shape[0]
+            input_ids_seq_length = input_ids.shape[-1]
+            generation_config = self.model.generation_config
+            generation_config = copy.deepcopy(generation_config)
+            new_gen_params = self.update_gen_params(**kwargs)
+            generation_config.update(**new_gen_params)
+            generation_config.update(**kwargs)
+            model_kwargs = generation_config.to_dict()
+            model_kwargs['attention_mask'] = attention_mask
+            _, eos_token_id = (  # noqa: F841  # pylint: disable=W0612
+                generation_config.bos_token_id,
+                generation_config.eos_token_id,
+            )
+            if eos_token_id is None:
+                if self.gcfg.eos_token_id is not None:
+                    eos_token_id = self.gcfg.eos_token_id
+                else:
+                    eos_token_id = []
+            if isinstance(eos_token_id, int):
+                eos_token_id = [eos_token_id]
+            if self.additional_eos_token_id is not None:
+                eos_token_id.extend(self.additional_eos_token_id)
+            eos_token_id_tensor = torch.tensor(eos_token_id).to(
+                input_ids.device) if eos_token_id is not None else None
+            generation_config.max_length = (
+                generation_config.max_new_tokens + input_ids_seq_length)
+            # Set generation parameters if not already defined
+            logits_processor = self.logits_processor
+            stopping_criteria = self.stopping_criteria
+
+            logits_processor = self.model._get_logits_processor(
+                generation_config=generation_config,
+                input_ids_seq_length=input_ids_seq_length,
+                encoder_input_ids=input_ids,
+                prefix_allowed_tokens_fn=self.prefix_allowed_tokens_fn,
+                logits_processor=logits_processor,
+            )
+
+            stopping_criteria = self.model._get_stopping_criteria(
+                generation_config=generation_config,
+                stopping_criteria=stopping_criteria)
+            logits_warper = self.model._get_logits_warper(generation_config)
+
+            unfinished_sequences = input_ids.new(batch_size).fill_(1)
+            scores = None
+            while True:
+                model_inputs = self.model.prepare_inputs_for_generation(
+                    input_ids, **model_kwargs)
+                # forward pass to get next token
+                outputs = self.model(
+                    **model_inputs,
+                    return_dict=True,
+                    output_attentions=False,
+                    output_hidden_states=False,
+                )
+
+                next_token_logits = outputs.logits[:, -1, :]
+
+                # pre-process distribution
+                next_token_scores = logits_processor(input_ids,
+                                                     next_token_logits)
+                next_token_scores = logits_warper(input_ids, next_token_scores)
+
+                # sample
+                probs = nn.functional.softmax(next_token_scores, dim=-1)
+                if do_sample:
+                    next_tokens = torch.multinomial(
+                        probs, num_samples=1).squeeze(1)
+                else:
+                    next_tokens = torch.argmax(probs, dim=-1)
+
+                # update generated ids, model inputs,
+                # and length for next step
+                input_ids = torch.cat([input_ids, next_tokens[:, None]],
+                                      dim=-1)
+                model_kwargs = self.model._update_model_kwargs_for_generation(  # noqa: E501
+                    outputs,
+                    model_kwargs,
+                    is_encoder_decoder=False)
+                unfinished_sequences = unfinished_sequences.mul(
+                    next_tokens.tile(eos_token_id_tensor.shape[0], 1).ne(
+                        eos_token_id_tensor.unsqueeze(1)).prod(dim=0))
+                output_token_ids = input_ids.cpu().tolist()
+                for i in range(len(output_token_ids)):
+                    output_token_ids[i] = output_token_ids[i][:][
+                        input_length[i]:]
+                    # Find the first occurrence of
+                    # an EOS token in the sequence
+                    first_eos_idx = next(
+                        (idx
+                         for idx, token_id in enumerate(output_token_ids[i])
+                         if token_id in eos_token_id), None)
+                    # If an EOS token is found, only the previous
+                    # part of it is retained
+                    if first_eos_idx is not None:
+                        output_token_ids[i] = output_token_ids[
+                            i][:first_eos_idx]
+
+                response = self.tokenizer.batch_decode(output_token_ids)
+                # print(response)
+                if not batched:
+                    response = response[0]
+                yield ModelStatusCode.STREAM_ING, response, None
+                # stop when each sentence is finished,
+                # or if we exceed the maximum length
+                if (unfinished_sequences.max() == 0
+                        or stopping_criteria(input_ids, scores)):
+                    break
+            yield ModelStatusCode.END, response, None
+
+    def stream_chat(
+        self,
+        inputs: List[dict],
+        do_sample: bool = True,
+        **kwargs,
+    ):
+        """Return the chat completions in stream mode.
+
+        Args:
+            inputs (List[dict]): input messages to be completed.
+            do_sample (bool): do sampling if enabled
+        Returns:
+            the text/chat completion
+        """
+        prompt = self.template_parser(inputs)
+        yield from self.stream_generate(prompt, do_sample, **kwargs)
+
+
+class HFTransformerCasualLM(HFTransformer):
+
+    def _load_model(self, path: str, model_kwargs: dict):
+        import torch
+        from transformers import AutoModelForCausalLM
+        model_kwargs.setdefault('torch_dtype', torch.float16)
+        self.model = AutoModelForCausalLM.from_pretrained(
+            path, trust_remote_code=True, **model_kwargs)
+        self.model.eval()
+
+
+class HFTransformerChat(HFTransformerCasualLM):
+
+    def __init__(self, template_parser=APITemplateParser, **kwargs):
+        super().__init__(template_parser=template_parser, **kwargs)
+
+    def chat(self,
+             inputs: Union[List[dict], List[List[dict]]],
+             do_sample: bool = True,
+             **kwargs):
+        """Return the chat completions in stream mode.
+
+        Args:
+            inputs (Union[List[dict], List[List[dict]]]): input messages to be completed.
+            do_sample (bool): do sampling if enabled
+        Returns:
+            the text/chat completion
+        """
+        # handle batch inference with vanilla for loop
+        if isinstance(inputs[0], list):
+            resps = []
+            for input in inputs:
+                resps.append(self.chat(input, do_sample, **kwargs))
+            return resps
+        prompt = self.template_parser(inputs)
+        query = prompt[-1]['content']
+        history = prompt[:-1]
+        try:
+            response, history = self.model.chat(
+                self.tokenizer, query, history=history)
+        except Exception as e:
+            # handle over-length input error
+            logger.warning(str(e))
+            response = ''
+        return response

lagent/llms/lmdeploy_wrapper.py

@@ -0,0 +1,790 @@
+import asyncio
+import copy
+import logging
+from dataclasses import asdict
+from typing import List, Optional, Union
+
+import aiohttp
+
+from lagent.llms.base_llm import AsyncLLMMixin, BaseLLM
+from lagent.schema import ModelStatusCode
+from lagent.utils.util import filter_suffix
+
+
+class TritonClient(BaseLLM):
+    """TritonClient is a wrapper of TritonClient for LLM.
+
+    Args:
+        tritonserver_addr (str): the address in format "ip:port" of
+            triton inference server
+        model_name (str): the name of the model
+        session_len (int): the context size
+        max_tokens (int): the expected generated token numbers
+    """
+
+    def __init__(self,
+                 tritonserver_addr: str,
+                 model_name: str,
+                 session_len: int = 32768,
+                 log_level: str = 'WARNING',
+                 **kwargs):
+        super().__init__(path=None, **kwargs)
+        try:
+            from lmdeploy.serve.turbomind.chatbot import Chatbot, StatusCode
+        except Exception as e:
+            logging.error(f'{e}')
+            raise RuntimeError('DO NOT use turbomind.chatbot since it has '
+                               'been removed by lmdeploy since v0.5.2')
+        self.state_map = {
+            StatusCode.TRITON_STREAM_END: ModelStatusCode.END,
+            StatusCode.TRITON_SERVER_ERR: ModelStatusCode.SERVER_ERR,
+            StatusCode.TRITON_SESSION_CLOSED: ModelStatusCode.SESSION_CLOSED,
+            StatusCode.TRITON_STREAM_ING: ModelStatusCode.STREAM_ING,
+            StatusCode.TRITON_SESSION_OUT_OF_LIMIT:
+            ModelStatusCode.SESSION_OUT_OF_LIMIT,
+            StatusCode.TRITON_SESSION_INVALID_ARG:
+            ModelStatusCode.SESSION_INVALID_ARG,
+            StatusCode.TRITON_SESSION_READY: ModelStatusCode.SESSION_READY
+        }
+        self.chatbot = Chatbot(
+            tritonserver_addr=tritonserver_addr,
+            model_name=model_name,
+            session_len=session_len,
+            log_level=log_level,
+            **kwargs)
+
+    def generate(self,
+                 inputs: Union[str, List[str]],
+                 session_id: int = 2967,
+                 request_id: str = '',
+                 sequence_start: bool = True,
+                 sequence_end: bool = True,
+                 skip_special_tokens: bool = False,
+                 **kwargs):
+        """Start a new round conversation of a session. Return the chat
+        completions in non-stream mode.
+
+        Args:
+            inputs (str, List[str]): user's prompt(s) in this round
+            session_id (int): the identical id of a session
+            request_id (str): the identical id of this round conversation
+            sequence_start (bool): start flag of a session
+            sequence_end (bool): end flag of a session
+            skip_special_tokens (bool): Whether or not to remove special tokens
+                in the decoding. Default to be False.
+        Returns:
+            (a list of/batched) text/chat completion
+        """
+        from lmdeploy.serve.turbomind.chatbot import Session, get_logger
+        if isinstance(inputs, str):
+            inputs = [inputs]
+        prompt = inputs
+
+        assert isinstance(session_id, int), \
+            f'INT session id is required, but got {type(session_id)}'
+
+        self.chatbot.cfg = self._update_gen_params(**kwargs)
+        max_new_tokens = self.chatbot.cfg.max_new_tokens
+
+        logger = get_logger('service.ft', log_level=self.chatbot.log_level)
+        logger.info(f'session {session_id}, request_id {request_id}, '
+                    f'max_out_len {max_new_tokens}')
+
+        if self.chatbot._session is None:
+            sequence_start = True
+            self.chatbot._session = Session(session_id=session_id)
+        elif self.chatbot._session.status == 0:
+            logger.error(f'session {session_id} has been ended. Please set '
+                         f'`sequence_start` be True if you want to restart it')
+            return ''
+
+        self.chatbot._session.status = 1
+        self.chatbot._session.request_id = request_id
+        self.chatbot._session.response = ''
+
+        status, res, _ = None, '', 0
+        for status, res, _ in self.chatbot._stream_infer(
+                self.chatbot._session,
+                prompt,
+                max_new_tokens,
+                sequence_start,
+                sequence_end,
+                skip_special_tokens=skip_special_tokens):
+            status = self.state_map.get(status)
+            if status < ModelStatusCode.END:
+                return ''
+            elif status == ModelStatusCode.END:
+                self.chatbot._session.histories = (
+                    self.chatbot._session.histories +
+                    self.chatbot._session.prompt +
+                    self.chatbot._session.response)
+                # remove stop_words
+                res = filter_suffix(res, self.gen_params.get('stop_words'))
+                return res
+
+    def stream_chat(self,
+                    inputs: List[dict],
+                    session_id: int = 2967,
+                    request_id: str = '',
+                    sequence_start: bool = True,
+                    sequence_end: bool = True,
+                    skip_special_tokens: bool = False,
+                    **kwargs):
+        """Start a new round conversation of a session. Return the chat
+        completions in stream mode.
+
+        Args:
+            session_id (int): the identical id of a session
+            inputs (List[dict]): user's inputs in this round conversation
+            request_id (str): the identical id of this round conversation
+            sequence_start (bool): start flag of a session
+            sequence_end (bool): end flag of a session
+            skip_special_tokens (bool): Whether or not to remove special tokens
+                in the decoding. Default to be False.
+        Returns:
+            tuple(Status, str, int): status, text/chat completion,
+            generated token number
+        """
+        from lmdeploy.serve.turbomind.chatbot import Session, get_logger
+        assert isinstance(session_id, int), \
+            f'INT session id is required, but got {type(session_id)}'
+
+        self.chatbot.cfg = self._update_gen_params(**kwargs)
+        max_new_tokens = self.chatbot.cfg.max_new_tokens
+
+        logger = get_logger('service.ft', log_level=self.chatbot.log_level)
+        logger.info(f'session {session_id}, request_id {request_id}, '
+                    f'max_out_len {max_new_tokens}')
+
+        if self.chatbot._session is None:
+            sequence_start = True
+            self.chatbot._session = Session(session_id=session_id)
+        elif self.chatbot._session.status == 0:
+            logger.error(f'session {session_id} has been ended. Please set '
+                         f'`sequence_start` be True if you want to restart it')
+            return ModelStatusCode.SESSION_CLOSED, '', 0
+
+        self.chatbot._session.status = 1
+        self.chatbot._session.request_id = request_id
+        self.chatbot._session.response = ''
+
+        prompt = self.template_parser(inputs)
+        status, res, _ = None, '', 0
+        for status, res, _ in self.chatbot._stream_infer(
+                self.chatbot._session,
+                prompt,
+                max_new_tokens,
+                sequence_start,
+                sequence_end,
+                skip_special_tokens=skip_special_tokens):
+            status = self.state_map.get(status)
+            # The stop symbol also appears in the output of the last STREAM_ING state.
+            res = filter_suffix(res, self.gen_params.get('stop_words'))
+            if status < ModelStatusCode.END:
+                return status, res, _
+            elif status == ModelStatusCode.END:  # remove stop_words
+                self.chatbot._session.histories = (
+                    self.chatbot._session.histories +
+                    self.chatbot._session.prompt +
+                    self.chatbot._session.response)
+                yield status, res, _
+                break
+            else:
+                yield status, res, _
+
+    def _update_gen_params(self, **kwargs):
+        import mmengine
+        new_gen_params = self.update_gen_params(**kwargs)
+        self.gen_params['stop_words'] = new_gen_params.pop('stop_words')
+        stop_words = self.chatbot._stop_words(
+            self.gen_params.get('stop_words'))
+        cfg = mmengine.Config(
+            dict(
+                session_len=self.chatbot.model.session_len,
+                stop_words=stop_words,
+                bad_words=self.chatbot.cfg.bad_words,
+                **new_gen_params))
+        return cfg
+
+
+class LMDeployPipeline(BaseLLM):
+    """
+
+    Args:
+        path (str): The path to the model.
+            It could be one of the following options:
+                    - i) A local directory path of a turbomind model which is
+                        converted by `lmdeploy convert` command or download
+                        from ii) and iii).
+                    - ii) The model_id of a lmdeploy-quantized model hosted
+                        inside a model repo on huggingface.co, such as
+                        "InternLM/internlm-chat-20b-4bit",
+                        "lmdeploy/llama2-chat-70b-4bit", etc.
+                    - iii) The model_id of a model hosted inside a model repo
+                        on huggingface.co, such as "internlm/internlm-chat-7b",
+                        "Qwen/Qwen-7B-Chat ", "baichuan-inc/Baichuan2-7B-Chat"
+                        and so on.
+        model_name (str): needed when model_path is a pytorch model on
+            huggingface.co, such as "internlm-chat-7b",
+            "Qwen-7B-Chat ", "Baichuan2-7B-Chat" and so on.
+        tp (int): tensor parallel
+        pipeline_cfg (dict): config of pipeline
+    """
+
+    def __init__(self,
+                 path: str,
+                 model_name: Optional[str] = None,
+                 tp: int = 1,
+                 pipeline_cfg=dict(),
+                 **kwargs):
+        import lmdeploy
+        from lmdeploy import ChatTemplateConfig, TurbomindEngineConfig, pipeline, version_info
+
+        self.str_version = lmdeploy.__version__
+        self.version = version_info
+        self.do_sample = kwargs.pop('do_sample', None)
+        if self.do_sample is not None and self.version < (0, 6, 0):
+            raise RuntimeError(
+                '`do_sample` parameter is not supported by lmdeploy until '
+                f'v0.6.0, but currently using lmdeloy {self.str_version}')
+        super().__init__(path=path, **kwargs)
+        backend_config = copy.deepcopy(pipeline_cfg)
+        backend_config.update(tp=tp)
+        backend_config = {
+            k: v
+            for k, v in backend_config.items()
+            if hasattr(TurbomindEngineConfig, k)
+        }
+        backend_config = TurbomindEngineConfig(**backend_config)
+        chat_template_config = ChatTemplateConfig(
+            model_name=model_name) if model_name else None
+        self.model = pipeline(
+            model_path=self.path,
+            backend_config=backend_config,
+            chat_template_config=chat_template_config,
+            log_level='WARNING')
+
+    def generate(self,
+                 inputs: Union[str, List[str]],
+                 do_preprocess: bool = None,
+                 skip_special_tokens: bool = False,
+                 return_dict: bool = False,
+                 **kwargs):
+        """Return the chat completions in non-stream mode.
+
+        Args:
+            inputs (Union[str, List[str]]): input texts to be completed.
+            do_preprocess (bool): whether pre-process the messages. Default to
+                True, which means chat_template will be applied.
+            skip_special_tokens (bool): Whether or not to remove special tokens
+                in the decoding. Default to be False.
+        Returns:
+            (a list of/batched) text/chat completion
+        """
+        from lmdeploy.messages import GenerationConfig
+        batched = True
+        if isinstance(inputs, str):
+            inputs = [inputs]
+            batched = False
+        prompt = inputs
+        do_sample = kwargs.pop('do_sample', None)
+        gen_params = self.update_gen_params(**kwargs)
+
+        if do_sample is None:
+            do_sample = self.do_sample
+        if do_sample is not None and self.version < (0, 6, 0):
+            raise RuntimeError(
+                '`do_sample` parameter is not supported by lmdeploy until '
+                f'v0.6.0, but currently using lmdeloy {self.str_version}')
+        if self.version >= (0, 6, 0):
+            if do_sample is None:
+                do_sample = gen_params['top_k'] > 1 or gen_params[
+                    'temperature'] > 0
+            gen_params.update(do_sample=do_sample)
+
+        gen_config = GenerationConfig(
+            skip_special_tokens=skip_special_tokens, **gen_params)
+        response = self.model.batch_infer(
+            prompt, gen_config=gen_config, do_preprocess=do_preprocess)
+        texts = [resp.text for resp in response]
+        # remove stop_words
+        texts = filter_suffix(texts, self.gen_params.get('stop_words'))
+        for resp, text in zip(response, texts):
+            resp.text = text
+        if batched:
+            return [asdict(resp)
+                    for resp in response] if return_dict else texts
+        return asdict(response[0]) if return_dict else texts[0]
+
+
+class LMDeployServer(BaseLLM):
+    """
+
+    Args:
+        path (str): The path to the model.
+            It could be one of the following options:
+                - i) A local directory path of a turbomind model which is
+                    converted by `lmdeploy convert` command or download from
+                    ii) and iii).
+                - ii) The model_id of a lmdeploy-quantized model hosted
+                    inside a model repo on huggingface.co, such as
+                    "InternLM/internlm-chat-20b-4bit",
+                    "lmdeploy/llama2-chat-70b-4bit", etc.
+                - iii) The model_id of a model hosted inside a model repo
+                    on huggingface.co, such as "internlm/internlm-chat-7b",
+                    "Qwen/Qwen-7B-Chat ", "baichuan-inc/Baichuan2-7B-Chat"
+                    and so on.
+        model_name (str): needed when model_path is a pytorch model on
+            huggingface.co, such as "internlm-chat-7b",
+            "Qwen-7B-Chat ", "Baichuan2-7B-Chat" and so on.
+        server_name (str): host ip for serving
+        server_port (int): server port
+        tp (int): tensor parallel
+        log_level (str): set log level whose value among
+            [CRITICAL, ERROR, WARNING, INFO, DEBUG]
+    """
+
+    def __init__(self,
+                 path: str,
+                 model_name: Optional[str] = None,
+                 server_name: str = '0.0.0.0',
+                 server_port: int = 23333,
+                 tp: int = 1,
+                 log_level: str = 'WARNING',
+                 serve_cfg=dict(),
+                 **kwargs):
+        super().__init__(path=path, **kwargs)
+        self.model_name = model_name
+        # TODO get_logger issue in multi processing
+        import lmdeploy
+        self.client = lmdeploy.serve(
+            model_path=self.path,
+            model_name=model_name,
+            server_name=server_name,
+            server_port=server_port,
+            tp=tp,
+            log_level=log_level,
+            **serve_cfg)
+
+    def generate(self,
+                 inputs: Union[str, List[str]],
+                 session_id: int = 2967,
+                 sequence_start: bool = True,
+                 sequence_end: bool = True,
+                 ignore_eos: bool = False,
+                 skip_special_tokens: Optional[bool] = False,
+                 timeout: int = 30,
+                 **kwargs) -> List[str]:
+        """Start a new round conversation of a session. Return the chat
+        completions in non-stream mode.
+
+        Args:
+            inputs (str, List[str]): user's prompt(s) in this round
+            session_id (int): the identical id of a session
+            sequence_start (bool): start flag of a session
+            sequence_end (bool): end flag of a session
+            ignore_eos (bool): indicator for ignoring eos
+            skip_special_tokens (bool): Whether or not to remove special tokens
+                in the decoding. Default to be False.
+            timeout (int): max time to wait for response
+        Returns:
+            (a list of/batched) text/chat completion
+        """
+
+        batched = True
+        if isinstance(inputs, str):
+            inputs = [inputs]
+            batched = False
+
+        gen_params = self.update_gen_params(**kwargs)
+        max_new_tokens = gen_params.pop('max_new_tokens')
+        gen_params.update(max_tokens=max_new_tokens)
+
+        resp = [''] * len(inputs)
+        for text in self.client.completions_v1(
+                self.model_name,
+                inputs,
+                session_id=session_id,
+                sequence_start=sequence_start,
+                sequence_end=sequence_end,
+                stream=False,
+                ignore_eos=ignore_eos,
+                skip_special_tokens=skip_special_tokens,
+                timeout=timeout,
+                **gen_params):
+            resp = [
+                resp[i] + item['text']
+                for i, item in enumerate(text['choices'])
+            ]
+        # remove stop_words
+        resp = filter_suffix(resp, self.gen_params.get('stop_words'))
+        if not batched:
+            return resp[0]
+        return resp
+
+    def stream_chat(self,
+                    inputs: List[dict],
+                    session_id=0,
+                    sequence_start: bool = True,
+                    sequence_end: bool = True,
+                    stream: bool = True,
+                    ignore_eos: bool = False,
+                    skip_special_tokens: Optional[bool] = False,
+                    timeout: int = 30,
+                    **kwargs):
+        """Start a new round conversation of a session. Return the chat
+        completions in stream mode.
+
+        Args:
+            session_id (int): the identical id of a session
+            inputs (List[dict]): user's inputs in this round conversation
+            sequence_start (bool): start flag of a session
+            sequence_end (bool): end flag of a session
+            stream (bool): return in a streaming format if enabled
+            ignore_eos (bool): indicator for ignoring eos
+            skip_special_tokens (bool): Whether or not to remove special tokens
+                in the decoding. Default to be False.
+            timeout (int): max time to wait for response
+        Returns:
+            tuple(Status, str, int): status, text/chat completion,
+            generated token number
+        """
+        gen_params = self.update_gen_params(**kwargs)
+        max_new_tokens = gen_params.pop('max_new_tokens')
+        gen_params.update(max_tokens=max_new_tokens)
+        prompt = self.template_parser(inputs)
+
+        resp = ''
+        finished = False
+        stop_words = self.gen_params.get('stop_words')
+        for text in self.client.completions_v1(
+                self.model_name,
+                prompt,
+                session_id=session_id,
+                sequence_start=sequence_start,
+                sequence_end=sequence_end,
+                stream=stream,
+                ignore_eos=ignore_eos,
+                skip_special_tokens=skip_special_tokens,
+                timeout=timeout,
+                **gen_params):
+            resp += text['choices'][0]['text']
+            if not resp:
+                continue
+            # remove stop_words
+            for sw in stop_words:
+                if sw in resp:
+                    resp = filter_suffix(resp, stop_words)
+                    finished = True
+                    break
+            yield ModelStatusCode.STREAM_ING, resp, None
+            if finished:
+                break
+        yield ModelStatusCode.END, resp, None
+
+
+class LMDeployClient(LMDeployServer):
+    """
+
+    Args:
+        url (str): communicating address 'http://<ip>:<port>' of
+            api_server
+        model_name (str): needed when model_path is a pytorch model on
+            huggingface.co, such as "internlm-chat-7b",
+            "Qwen-7B-Chat ", "Baichuan2-7B-Chat" and so on.
+    """
+
+    def __init__(self, url: str, model_name: str, **kwargs):
+        BaseLLM.__init__(self, path=url, **kwargs)
+        from lmdeploy.serve.openai.api_client import APIClient
+        self.client = APIClient(url)
+        self.model_name = model_name
+
+
+class AsyncLMDeployPipeline(AsyncLLMMixin, LMDeployPipeline):
+    """
+
+    Args:
+        path (str): The path to the model.
+            It could be one of the following options:
+                    - i) A local directory path of a turbomind model which is
+                        converted by `lmdeploy convert` command or download
+                        from ii) and iii).
+                    - ii) The model_id of a lmdeploy-quantized model hosted
+                        inside a model repo on huggingface.co, such as
+                        "InternLM/internlm-chat-20b-4bit",
+                        "lmdeploy/llama2-chat-70b-4bit", etc.
+                    - iii) The model_id of a model hosted inside a model repo
+                        on huggingface.co, such as "internlm/internlm-chat-7b",
+                        "Qwen/Qwen-7B-Chat ", "baichuan-inc/Baichuan2-7B-Chat"
+                        and so on.
+        model_name (str): needed when model_path is a pytorch model on
+            huggingface.co, such as "internlm-chat-7b",
+            "Qwen-7B-Chat ", "Baichuan2-7B-Chat" and so on.
+        tp (int): tensor parallel
+        pipeline_cfg (dict): config of pipeline
+    """
+
+    async def generate(self,
+                       inputs: Union[str, List[str]],
+                       session_ids: Union[int, List[int]] = None,
+                       do_preprocess: bool = None,
+                       skip_special_tokens: bool = False,
+                       return_dict: bool = False,
+                       **kwargs):
+        """Return the chat completions in non-stream mode.
+
+        Args:
+            inputs (Union[str, List[str]]): input texts to be completed.
+            do_preprocess (bool): whether pre-process the messages. Default to
+                True, which means chat_template will be applied.
+            skip_special_tokens (bool): Whether or not to remove special tokens
+                in the decoding. Default to be False.
+        Returns:
+            (a list of/batched) text/chat completion
+        """
+        from lmdeploy.messages import GenerationConfig, Response
+
+        batched = True
+        if isinstance(inputs, str):
+            inputs = [inputs]
+            batched = False
+        if session_ids is None:
+            session_ids = list(range(len(inputs)))
+        elif isinstance(session_ids, (int, str)):
+            session_ids = [session_ids]
+        assert len(inputs) == len(session_ids)
+
+        prompt = inputs
+        gen_params = self.update_gen_params(**kwargs)
+        gen_config = GenerationConfig(
+            skip_special_tokens=skip_special_tokens, **gen_params)
+
+        async def _inner_generate(uid, text):
+            resp = Response('', 0, 0, uid)
+            async for out in self.model.generate(
+                    text,
+                    uid,
+                    gen_config,
+                    stream_response=True,
+                    sequence_start=True,
+                    sequence_end=True,
+                    do_preprocess=do_preprocess,
+                    **kwargs):
+                resp.text += out.response
+                resp.generate_token_len = out.generate_token_len
+                resp.input_token_len = out.input_token_len
+                resp.finish_reason = out.finish_reason
+                if out.token_ids:
+                    resp.token_ids.extend(out.token_ids)
+                if out.logprobs:
+                    if resp.logprobs is None:
+                        resp.logprobs = []
+                    resp.logprobs.extend(out.logprobs)
+            return resp
+
+        response = await asyncio.gather(*[
+            _inner_generate(sid, inp) for sid, inp in zip(session_ids, prompt)
+        ])
+        texts = [resp.text for resp in response]
+        # remove stop_words
+        texts = filter_suffix(texts, self.gen_params.get('stop_words'))
+        for resp, text in zip(response, texts):
+            resp.text = text
+        if batched:
+            return [asdict(resp)
+                    for resp in response] if return_dict else texts
+        return asdict(response[0]) if return_dict else texts[0]
+
+
+class AsyncLMDeployServer(AsyncLLMMixin, LMDeployServer):
+    """
+
+    Args:
+        path (str): The path to the model.
+            It could be one of the following options:
+                - i) A local directory path of a turbomind model which is
+                    converted by `lmdeploy convert` command or download from
+                    ii) and iii).
+                - ii) The model_id of a lmdeploy-quantized model hosted
+                    inside a model repo on huggingface.co, such as
+                    "InternLM/internlm-chat-20b-4bit",
+                    "lmdeploy/llama2-chat-70b-4bit", etc.
+                - iii) The model_id of a model hosted inside a model repo
+                    on huggingface.co, such as "internlm/internlm-chat-7b",
+                    "Qwen/Qwen-7B-Chat ", "baichuan-inc/Baichuan2-7B-Chat"
+                    and so on.
+        model_name (str): needed when model_path is a pytorch model on
+            huggingface.co, such as "internlm-chat-7b",
+            "Qwen-7B-Chat ", "Baichuan2-7B-Chat" and so on.
+        server_name (str): host ip for serving
+        server_port (int): server port
+        tp (int): tensor parallel
+        log_level (str): set log level whose value among
+            [CRITICAL, ERROR, WARNING, INFO, DEBUG]
+    """
+
+    async def generate(
+        self,
+        inputs: Union[str, List[str]],
+        session_ids: Union[int, List[int]] = None,
+        sequence_start: bool = True,
+        sequence_end: bool = True,
+        ignore_eos: bool = False,
+        skip_special_tokens: Optional[bool] = False,
+        timeout: int = 30,
+        **kwargs,
+    ):
+        """Start a new round conversation of a session. Return the chat
+        completions in non-stream mode.
+
+        Args:
+            inputs (str, List[str]): user's prompt(s) in this round
+            session_ids (int, List[int]): session id(s)
+            sequence_start (bool): start flag of a session
+            sequence_end (bool): end flag of a session
+            ignore_eos (bool): indicator for ignoring eos
+            skip_special_tokens (bool): Whether or not to remove special tokens
+                in the decoding. Default to be False.
+            timeout (int): max time to wait for response
+        Returns:
+            (a list of/batched) text/chat completion
+        """
+        from lmdeploy.serve.openai.api_client import json_loads
+
+        batched = True
+        if isinstance(inputs, str):
+            inputs = [inputs]
+            batched = False
+
+        gen_params = self.update_gen_params(**kwargs)
+        max_new_tokens = gen_params.pop('max_new_tokens')
+        gen_params.update(max_tokens=max_new_tokens)
+
+        responses = [''] * len(inputs)
+        pload = dict(
+            model=self.model_name,
+            prompt=inputs,
+            sequence_start=sequence_start,
+            sequence_end=sequence_end,
+            stream=False,
+            ignore_eos=ignore_eos,
+            skip_special_tokens=skip_special_tokens,
+            timeout=timeout,
+            **gen_params)
+        async with aiohttp.ClientSession(
+                timeout=aiohttp.ClientTimeout(3 * 3600)) as session:
+            async with session.post(
+                    self.client.completions_v1_url,
+                    headers=self.client.headers,
+                    json=pload) as resp:
+                async for chunk in resp.content:
+                    if chunk:
+                        decoded = chunk.decode('utf-8')
+                        output = json_loads(decoded)
+                        responses = [
+                            response + item['text'] for response, item in zip(
+                                responses, output['choices'])
+                        ]
+        # remove stop_words
+        responses = filter_suffix(responses, self.gen_params.get('stop_words'))
+        if not batched:
+            return responses[0]
+        return responses
+
+    async def stream_chat(
+        self,
+        inputs: List[dict],
+        session_id: int = None,
+        sequence_start: bool = True,
+        sequence_end: bool = True,
+        stream: bool = True,
+        ignore_eos: bool = False,
+        skip_special_tokens: Optional[bool] = False,
+        timeout: int = 30,
+        **kwargs,
+    ):
+        """Start a new round conversation of a session. Return the chat
+        completions in stream mode.
+
+        Args:
+            inputs (List[dict]): user's inputs in this round conversation
+            session_id (int): session id
+            sequence_start (bool): start flag of a session
+            sequence_end (bool): end flag of a session
+            stream (bool): return in a streaming format if enabled
+            ignore_eos (bool): indicator for ignoring eos
+            skip_special_tokens (bool): Whether or not to remove special tokens
+                in the decoding. Default to be False.
+            timeout (int): max time to wait for response
+        Returns:
+            tuple(Status, str, int): status, text/chat completion,
+            generated token number
+        """
+        from lmdeploy.serve.openai.api_client import json_loads
+
+        gen_params = self.update_gen_params(**kwargs)
+        max_new_tokens = gen_params.pop('max_new_tokens')
+        gen_params.update(max_tokens=max_new_tokens)
+        prompt = self.template_parser(inputs)
+
+        response = ''
+        finished = False
+        stop_words = self.gen_params.get('stop_words')
+
+        pload = dict(
+            model=self.model_name,
+            prompt=prompt,
+            sequence_start=sequence_start,
+            sequence_end=sequence_end,
+            stream=stream,
+            ignore_eos=ignore_eos,
+            skip_special_tokens=skip_special_tokens,
+            timeout=timeout,
+            **gen_params)
+        async with aiohttp.ClientSession(
+                timeout=aiohttp.ClientTimeout(3 * 3600)) as session:
+            async with session.post(
+                    self.client.completions_v1_url,
+                    headers=self.client.headers,
+                    json=pload) as resp:
+                async for chunk in resp.content:
+                    if chunk:
+                        decoded = chunk.decode('utf-8')
+                        if not decoded.strip() or decoded.rstrip(
+                        ) == 'data: [DONE]':
+                            continue
+                        if decoded[:6] == 'data: ':
+                            decoded = decoded[6:]
+                        output = json_loads(decoded)
+                        response += output['choices'][0]['text']
+                        if not response:
+                            continue
+                        # remove stop_words
+                        for sw in stop_words:
+                            if sw in response:
+                                response = filter_suffix(response, stop_words)
+                                finished = True
+                                break
+                        yield ModelStatusCode.STREAM_ING, response, None
+                        if finished:
+                            break
+                yield ModelStatusCode.END, response, None
+
+
+class AsyncLMDeployClient(AsyncLMDeployServer):
+    """
+
+    Args:
+        url (str): communicating address 'http://<ip>:<port>' of
+            api_server
+        model_name (str): needed when model_path is a pytorch model on
+            huggingface.co, such as "internlm-chat-7b",
+            "Qwen-7B-Chat ", "Baichuan2-7B-Chat" and so on.
+    """
+
+    def __init__(self, url: str, model_name: str, **kwargs):
+        BaseLLM.__init__(self, path=url, **kwargs)
+        from lmdeploy.serve.openai.api_client import APIClient
+        self.client = APIClient(url)
+        self.model_name = model_name

lagent/llms/meta_template.py

@@ -0,0 +1,40 @@
+INTERNLM2_META = [
+    dict(
+        role='system',
+        begin=dict(
+            with_name='<|im_start|>system name={name}\n',
+            without_name='<|im_start|>system\n',
+            name={
+                'interpreter': '<|interpreter|>',
+                'plugin': '<|plugin|>',
+            }),
+        end='<|im_end|>\n',
+    ),
+    dict(
+        role='user',
+        begin=dict(
+            with_name='<|im_start|>user name={name}\n',
+            without_name='<|im_start|>user\n',
+        ),
+        end='<|im_end|>\n'),
+    dict(
+        role='assistant',
+        begin=dict(
+            with_name='<|im_start|>assistant name={name}\n',
+            without_name='<|im_start|>assistant\n',
+            name={
+                'interpreter': '<|interpreter|>',
+                'plugin': '<|plugin|>',
+            }),
+        end='<|im_end|>\n'),
+    dict(
+        role='environment',
+        begin=dict(
+            with_name='<|im_start|>environment name={name}\n',
+            without_name='<|im_start|>environment\n',
+            name={
+                'interpreter': '<|interpreter|>',
+                'plugin': '<|plugin|>',
+            }),
+        end='<|im_end|>\n'),
+]

lagent/llms/openai.py

@@ -0,0 +1,924 @@
+import asyncio
+import json
+import os
+import time
+import traceback
+import warnings
+from concurrent.futures import ThreadPoolExecutor
+from logging import getLogger
+from threading import Lock
+from typing import AsyncGenerator, Dict, List, Optional, Union
+
+import aiohttp
+import requests
+
+from ..schema import ModelStatusCode
+from ..utils import filter_suffix
+from .base_api import AsyncBaseAPILLM, BaseAPILLM
+
+warnings.simplefilter('default')
+
+OPENAI_API_BASE = 'https://api.openai.com/v1/chat/completions'
+
+
+class GPTAPI(BaseAPILLM):
+    """Model wrapper around OpenAI's models.
+
+    Args:
+        model_type (str): The name of OpenAI's model.
+        retry (int): Number of retires if the API call fails. Defaults to 2.
+        key (str or List[str]): OpenAI key(s). In particular, when it
+            is set to "ENV", the key will be fetched from the environment
+            variable $OPENAI_API_KEY, as how openai defaults to be. If it's a
+            list, the keys will be used in round-robin manner. Defaults to
+            'ENV'.
+        org (str or List[str], optional): OpenAI organization(s). If not
+            specified, OpenAI uses the default organization bound to each API
+            key. If specified, the orgs will be posted with each request in
+            round-robin manner. Defaults to None.
+        meta_template (Dict, optional): The model's meta prompt
+            template if needed, in case the requirement of injecting or
+            wrapping of any meta instructions.
+        api_base (str): The base url of OpenAI's API. Defaults to
+            'https://api.openai.com/v1/chat/completions'.
+        gen_params: Default generation configuration which could be overridden
+            on the fly of generation.
+    """
+
+    is_api: bool = True
+
+    def __init__(self,
+                 model_type: str = 'gpt-3.5-turbo',
+                 retry: int = 2,
+                 json_mode: bool = False,
+                 key: Union[str, List[str]] = 'ENV',
+                 org: Optional[Union[str, List[str]]] = None,
+                 meta_template: Optional[Dict] = [
+                     dict(role='system', api_role='system'),
+                     dict(role='user', api_role='user'),
+                     dict(role='assistant', api_role='assistant'),
+                     dict(role='environment', api_role='system')
+                 ],
+                 api_base: str = OPENAI_API_BASE,
+                 proxies: Optional[Dict] = None,
+                 **gen_params):
+        if 'top_k' in gen_params:
+            warnings.warn('`top_k` parameter is deprecated in OpenAI APIs.',
+                          DeprecationWarning)
+            gen_params.pop('top_k')
+        super().__init__(
+            model_type=model_type,
+            meta_template=meta_template,
+            retry=retry,
+            **gen_params)
+        self.gen_params.pop('top_k')
+        self.logger = getLogger(__name__)
+
+        if isinstance(key, str):
+            self.keys = [os.getenv('OPENAI_API_KEY') if key == 'ENV' else key]
+        else:
+            self.keys = key
+
+        # record invalid keys and skip them when requesting API
+        # - keys have insufficient_quota
+        self.invalid_keys = set()
+
+        self.key_ctr = 0
+        if isinstance(org, str):
+            self.orgs = [org]
+        else:
+            self.orgs = org
+        self.org_ctr = 0
+        self.url = api_base
+        self.model_type = model_type
+        self.proxies = proxies
+        self.json_mode = json_mode
+
+    def chat(
+        self,
+        inputs: Union[List[dict], List[List[dict]]],
+        **gen_params,
+    ) -> Union[str, List[str]]:
+        """Generate responses given the contexts.
+
+        Args:
+            inputs (Union[List[dict], List[List[dict]]]): a list of messages
+                or list of lists of messages
+            gen_params: additional generation configuration
+
+        Returns:
+            Union[str, List[str]]: generated string(s)
+        """
+        assert isinstance(inputs, list)
+        if 'max_tokens' in gen_params:
+            raise NotImplementedError('unsupported parameter: max_tokens')
+        gen_params = {**self.gen_params, **gen_params}
+        with ThreadPoolExecutor(max_workers=20) as executor:
+            tasks = [
+                executor.submit(self._chat,
+                                self.template_parser._prompt2api(messages),
+                                **gen_params)
+                for messages in (
+                    [inputs] if isinstance(inputs[0], dict) else inputs)
+            ]
+        ret = [task.result() for task in tasks]
+        return ret[0] if isinstance(inputs[0], dict) else ret
+
+    def stream_chat(
+        self,
+        inputs: List[dict],
+        **gen_params,
+    ):
+        """Generate responses given the contexts.
+
+        Args:
+            inputs (List[dict]): a list of messages
+            gen_params: additional generation configuration
+
+        Returns:
+            str: generated string
+        """
+        assert isinstance(inputs, list)
+        if 'max_tokens' in gen_params:
+            raise NotImplementedError('unsupported parameter: max_tokens')
+        gen_params = self.update_gen_params(**gen_params)
+        gen_params['stream'] = True
+
+        resp = ''
+        finished = False
+        stop_words = gen_params.get('stop_words')
+        if stop_words is None:
+            stop_words = []
+        # mapping to role that openai supports
+        messages = self.template_parser._prompt2api(inputs)
+        for text in self._stream_chat(messages, **gen_params):
+            if self.model_type.lower().startswith('qwen'):
+                resp = text
+            else:
+                resp += text
+            if not resp:
+                continue
+            # remove stop_words
+            for sw in stop_words:
+                if sw in resp:
+                    resp = filter_suffix(resp, stop_words)
+                    finished = True
+                    break
+            yield ModelStatusCode.STREAM_ING, resp, None
+            if finished:
+                break
+        yield ModelStatusCode.END, resp, None
+
+    def _chat(self, messages: List[dict], **gen_params) -> str:
+        """Generate completion from a list of templates.
+
+        Args:
+            messages (List[dict]): a list of prompt dictionaries
+            gen_params: additional generation configuration
+
+        Returns:
+            str: The generated string.
+        """
+        assert isinstance(messages, list)
+
+        header, data = self.generate_request_data(
+            model_type=self.model_type,
+            messages=messages,
+            gen_params=gen_params,
+            json_mode=self.json_mode)
+
+        max_num_retries, errmsg = 0, ''
+        while max_num_retries < self.retry:
+            with Lock():
+                if len(self.invalid_keys) == len(self.keys):
+                    raise RuntimeError('All keys have insufficient quota.')
+
+                # find the next valid key
+                while True:
+                    self.key_ctr += 1
+                    if self.key_ctr == len(self.keys):
+                        self.key_ctr = 0
+
+                    if self.keys[self.key_ctr] not in self.invalid_keys:
+                        break
+
+                key = self.keys[self.key_ctr]
+                header['Authorization'] = f'Bearer {key}'
+
+            if self.orgs:
+                with Lock():
+                    self.org_ctr += 1
+                    if self.org_ctr == len(self.orgs):
+                        self.org_ctr = 0
+                header['OpenAI-Organization'] = self.orgs[self.org_ctr]
+
+            response = dict()
+            try:
+                raw_response = requests.post(
+                    self.url,
+                    headers=header,
+                    data=json.dumps(data),
+                    proxies=self.proxies)
+                response = raw_response.json()
+                return response['choices'][0]['message']['content'].strip()
+            except requests.ConnectionError:
+                errmsg = 'Got connection error ' + str(traceback.format_exc())
+                self.logger.error(errmsg)
+                continue
+            except requests.JSONDecodeError:
+                errmsg = 'JsonDecode error, got ' + str(raw_response.content)
+                self.logger.error(errmsg)
+                continue
+            except KeyError:
+                if 'error' in response:
+                    if response['error']['code'] == 'rate_limit_exceeded':
+                        time.sleep(1)
+                        continue
+                    elif response['error']['code'] == 'insufficient_quota':
+                        self.invalid_keys.add(key)
+                        self.logger.warn(f'insufficient_quota key: {key}')
+                        continue
+
+                    errmsg = 'Find error message in response: ' + str(
+                        response['error'])
+                    self.logger.error(errmsg)
+            except Exception as error:
+                errmsg = str(error) + '\n' + str(traceback.format_exc())
+                self.logger.error(errmsg)
+            max_num_retries += 1
+
+        raise RuntimeError('Calling OpenAI failed after retrying for '
+                           f'{max_num_retries} times. Check the logs for '
+                           f'details. errmsg: {errmsg}')
+
+    def _stream_chat(self, messages: List[dict], **gen_params) -> str:
+        """Generate completion from a list of templates.
+
+        Args:
+            messages (List[dict]): a list of prompt dictionaries
+            gen_params: additional generation configuration
+
+        Returns:
+            str: The generated string.
+        """
+
+        def streaming(raw_response):
+            for chunk in raw_response.iter_lines(
+                    chunk_size=8192, decode_unicode=False, delimiter=b'\n'):
+                if chunk:
+                    decoded = chunk.decode('utf-8')
+                    if decoded.startswith('data: [DONE]'):
+                        return
+                    if decoded[:5] == 'data:':
+                        decoded = decoded[5:]
+                        if decoded[0] == ' ':
+                            decoded = decoded[1:]
+                    else:
+                        print(decoded)
+                        continue
+                    try:
+                        response = json.loads(decoded)
+                        if 'code' in response and response['code'] == -20003:
+                            # Context exceeds maximum length
+                            yield ''
+                            return
+                        if self.model_type.lower().startswith('qwen'):
+                            choice = response['output']['choices'][0]
+                            yield choice['message']['content']
+                            if choice['finish_reason'] == 'stop':
+                                return
+                        else:
+                            choice = response['choices'][0]
+                            if choice['finish_reason'] == 'stop':
+                                return
+                            yield choice['delta'].get('content', '')
+                    except Exception as exc:
+                        msg = f'response {decoded} lead to exception of {str(exc)}'
+                        self.logger.error(msg)
+                        raise Exception(msg) from exc
+
+        assert isinstance(messages, list)
+
+        header, data = self.generate_request_data(
+            model_type=self.model_type,
+            messages=messages,
+            gen_params=gen_params,
+            json_mode=self.json_mode)
+
+        max_num_retries, errmsg = 0, ''
+        while max_num_retries < self.retry:
+            if len(self.invalid_keys) == len(self.keys):
+                raise RuntimeError('All keys have insufficient quota.')
+
+            # find the next valid key
+            while True:
+                self.key_ctr += 1
+                if self.key_ctr == len(self.keys):
+                    self.key_ctr = 0
+
+                if self.keys[self.key_ctr] not in self.invalid_keys:
+                    break
+
+            key = self.keys[self.key_ctr]
+            header['Authorization'] = f'Bearer {key}'
+
+            if self.orgs:
+                self.org_ctr += 1
+                if self.org_ctr == len(self.orgs):
+                    self.org_ctr = 0
+                header['OpenAI-Organization'] = self.orgs[self.org_ctr]
+
+            response = dict()
+            try:
+                raw_response = requests.post(
+                    self.url,
+                    headers=header,
+                    data=json.dumps(data),
+                    proxies=self.proxies)
+                return streaming(raw_response)
+            except requests.ConnectionError:
+                errmsg = 'Got connection error ' + str(traceback.format_exc())
+                self.logger.error(errmsg)
+                continue
+            except requests.JSONDecodeError:
+                errmsg = 'JsonDecode error, got ' + str(raw_response.content)
+                self.logger.error(errmsg)
+                continue
+            except KeyError:
+                if 'error' in response:
+                    if response['error']['code'] == 'rate_limit_exceeded':
+                        time.sleep(1)
+                        continue
+                    elif response['error']['code'] == 'insufficient_quota':
+                        self.invalid_keys.add(key)
+                        self.logger.warn(f'insufficient_quota key: {key}')
+                        continue
+
+                    errmsg = 'Find error message in response: ' + str(
+                        response['error'])
+                    self.logger.error(errmsg)
+            except Exception as error:
+                errmsg = str(error) + '\n' + str(traceback.format_exc())
+                self.logger.error(errmsg)
+            max_num_retries += 1
+
+        raise RuntimeError('Calling OpenAI failed after retrying for '
+                           f'{max_num_retries} times. Check the logs for '
+                           f'details. errmsg: {errmsg}')
+
+    def generate_request_data(self,
+                              model_type,
+                              messages,
+                              gen_params,
+                              json_mode=False):
+        """
+        Generates the request data for different model types.
+
+        Args:
+            model_type (str): The type of the model (e.g., 'gpt', 'internlm', 'qwen').
+            messages (list): The list of messages to be sent to the model.
+            gen_params (dict): The generation parameters.
+            json_mode (bool): Flag to determine if the response format should be JSON.
+
+        Returns:
+            tuple: A tuple containing the header and the request data.
+        """
+        # Copy generation parameters to avoid modifying the original dictionary
+        gen_params = gen_params.copy()
+
+        # Hold out 100 tokens due to potential errors in token calculation
+        max_tokens = min(gen_params.pop('max_new_tokens'), 4096)
+        if max_tokens <= 0:
+            return '', ''
+
+        # Initialize the header
+        header = {
+            'content-type': 'application/json',
+        }
+
+        # Common parameters processing
+        gen_params['max_tokens'] = max_tokens
+        if 'stop_words' in gen_params:
+            gen_params['stop'] = gen_params.pop('stop_words')
+        if 'repetition_penalty' in gen_params:
+            gen_params['frequency_penalty'] = gen_params.pop(
+                'repetition_penalty')
+
+        # Model-specific processing
+        data = {}
+        if model_type.lower().startswith('gpt'):
+            if 'top_k' in gen_params:
+                warnings.warn(
+                    '`top_k` parameter is deprecated in OpenAI APIs.',
+                    DeprecationWarning)
+                gen_params.pop('top_k')
+            gen_params.pop('skip_special_tokens', None)
+            gen_params.pop('session_id', None)
+            data = {
+                'model': model_type,
+                'messages': messages,
+                'n': 1,
+                **gen_params
+            }
+            if json_mode:
+                data['response_format'] = {'type': 'json_object'}
+        elif model_type.lower().startswith('internlm'):
+            data = {
+                'model': model_type,
+                'messages': messages,
+                'n': 1,
+                **gen_params
+            }
+            if json_mode:
+                data['response_format'] = {'type': 'json_object'}
+        elif model_type.lower().startswith('qwen'):
+            header['X-DashScope-SSE'] = 'enable'
+            gen_params.pop('skip_special_tokens', None)
+            gen_params.pop('session_id', None)
+            if 'frequency_penalty' in gen_params:
+                gen_params['repetition_penalty'] = gen_params.pop(
+                    'frequency_penalty')
+            gen_params['result_format'] = 'message'
+            data = {
+                'model': model_type,
+                'input': {
+                    'messages': messages
+                },
+                'parameters': {
+                    **gen_params
+                }
+            }
+        else:
+            raise NotImplementedError(
+                f'Model type {model_type} is not supported')
+
+        return header, data
+
+    def tokenize(self, prompt: str) -> list:
+        """Tokenize the input prompt.
+
+        Args:
+            prompt (str): Input string.
+
+        Returns:
+            list: token ids
+        """
+        import tiktoken
+        self.tiktoken = tiktoken
+        enc = self.tiktoken.encoding_for_model(self.model_type)
+        return enc.encode(prompt)
+
+
+class AsyncGPTAPI(AsyncBaseAPILLM):
+    """Model wrapper around OpenAI's models.
+
+    Args:
+        model_type (str): The name of OpenAI's model.
+        retry (int): Number of retires if the API call fails. Defaults to 2.
+        key (str or List[str]): OpenAI key(s). In particular, when it
+            is set to "ENV", the key will be fetched from the environment
+            variable $OPENAI_API_KEY, as how openai defaults to be. If it's a
+            list, the keys will be used in round-robin manner. Defaults to
+            'ENV'.
+        org (str or List[str], optional): OpenAI organization(s). If not
+            specified, OpenAI uses the default organization bound to each API
+            key. If specified, the orgs will be posted with each request in
+            round-robin manner. Defaults to None.
+        meta_template (Dict, optional): The model's meta prompt
+            template if needed, in case the requirement of injecting or
+            wrapping of any meta instructions.
+        api_base (str): The base url of OpenAI's API. Defaults to
+            'https://api.openai.com/v1/chat/completions'.
+        gen_params: Default generation configuration which could be overridden
+            on the fly of generation.
+    """
+
+    is_api: bool = True
+
+    def __init__(self,
+                 model_type: str = 'gpt-3.5-turbo',
+                 retry: int = 2,
+                 json_mode: bool = False,
+                 key: Union[str, List[str]] = 'ENV',
+                 org: Optional[Union[str, List[str]]] = None,
+                 meta_template: Optional[Dict] = [
+                     dict(role='system', api_role='system'),
+                     dict(role='user', api_role='user'),
+                     dict(role='assistant', api_role='assistant')
+                 ],
+                 api_base: str = OPENAI_API_BASE,
+                 proxies: Optional[Dict] = None,
+                 **gen_params):
+        if 'top_k' in gen_params:
+            warnings.warn('`top_k` parameter is deprecated in OpenAI APIs.',
+                          DeprecationWarning)
+            gen_params.pop('top_k')
+        super().__init__(
+            model_type=model_type,
+            meta_template=meta_template,
+            retry=retry,
+            **gen_params)
+        self.gen_params.pop('top_k')
+        self.logger = getLogger(__name__)
+
+        if isinstance(key, str):
+            self.keys = [os.getenv('OPENAI_API_KEY') if key == 'ENV' else key]
+        else:
+            self.keys = key
+
+        # record invalid keys and skip them when requesting API
+        # - keys have insufficient_quota
+        self.invalid_keys = set()
+
+        self.key_ctr = 0
+        if isinstance(org, str):
+            self.orgs = [org]
+        else:
+            self.orgs = org
+        self.org_ctr = 0
+        self.url = api_base
+        self.model_type = model_type
+        self.proxies = proxies or {}
+        self.json_mode = json_mode
+
+    async def chat(
+        self,
+        inputs: Union[List[dict], List[List[dict]]],
+        session_ids: Union[int, List[int]] = None,
+        **gen_params,
+    ) -> Union[str, List[str]]:
+        """Generate responses given the contexts.
+
+        Args:
+            inputs (Union[List[dict], List[List[dict]]]): a list of messages
+                or list of lists of messages
+            gen_params: additional generation configuration
+
+        Returns:
+            Union[str, List[str]]: generated string(s)
+        """
+        assert isinstance(inputs, list)
+        if 'max_tokens' in gen_params:
+            raise NotImplementedError('unsupported parameter: max_tokens')
+        gen_params = {**self.gen_params, **gen_params}
+        tasks = [
+            self._chat(messages, **gen_params) for messages in (
+                [inputs] if isinstance(inputs[0], dict) else inputs)
+        ]
+        ret = await asyncio.gather(*tasks)
+        return ret[0] if isinstance(inputs[0], dict) else ret
+
+    async def stream_chat(
+        self,
+        inputs: List[dict],
+        **gen_params,
+    ):
+        """Generate responses given the contexts.
+
+        Args:
+            inputs (List[dict]): a list of messages
+            gen_params: additional generation configuration
+
+        Returns:
+            str: generated string
+        """
+        assert isinstance(inputs, list)
+        if 'max_tokens' in gen_params:
+            raise NotImplementedError('unsupported parameter: max_tokens')
+        gen_params = self.update_gen_params(**gen_params)
+        gen_params['stream'] = True
+
+        resp = ''
+        finished = False
+        stop_words = gen_params.get('stop_words')
+        if stop_words is None:
+            stop_words = []
+        # mapping to role that openai supports
+        messages = self.template_parser._prompt2api(inputs)
+        async for text in self._stream_chat(messages, **gen_params):
+            if self.model_type.lower().startswith('qwen'):
+                resp = text
+            else:
+                resp += text
+            if not resp:
+                continue
+            # remove stop_words
+            for sw in stop_words:
+                if sw in resp:
+                    resp = filter_suffix(resp, stop_words)
+                    finished = True
+                    break
+            yield ModelStatusCode.STREAM_ING, resp, None
+            if finished:
+                break
+        yield ModelStatusCode.END, resp, None
+
+    async def _chat(self, messages: List[dict], **gen_params) -> str:
+        """Generate completion from a list of templates.
+
+        Args:
+            messages (List[dict]): a list of prompt dictionaries
+            gen_params: additional generation configuration
+
+        Returns:
+            str: The generated string.
+        """
+        assert isinstance(messages, list)
+
+        header, data = self.generate_request_data(
+            model_type=self.model_type,
+            messages=messages,
+            gen_params=gen_params,
+            json_mode=self.json_mode)
+
+        max_num_retries, errmsg = 0, ''
+        while max_num_retries < self.retry:
+            if len(self.invalid_keys) == len(self.keys):
+                raise RuntimeError('All keys have insufficient quota.')
+
+            # find the next valid key
+            while True:
+                self.key_ctr += 1
+                if self.key_ctr == len(self.keys):
+                    self.key_ctr = 0
+
+                if self.keys[self.key_ctr] not in self.invalid_keys:
+                    break
+
+            key = self.keys[self.key_ctr]
+            header['Authorization'] = f'Bearer {key}'
+
+            if self.orgs:
+                self.org_ctr += 1
+                if self.org_ctr == len(self.orgs):
+                    self.org_ctr = 0
+                header['OpenAI-Organization'] = self.orgs[self.org_ctr]
+
+            response = dict()
+            try:
+                async with aiohttp.ClientSession() as session:
+                    async with session.post(
+                            self.url,
+                            headers=header,
+                            json=data,
+                            proxy=self.proxies.get(
+                                'https', self.proxies.get('http'))) as resp:
+                        response = await resp.json()
+                        return response['choices'][0]['message'][
+                            'content'].strip()
+            except aiohttp.ClientConnectionError:
+                errmsg = 'Got connection error ' + str(traceback.format_exc())
+                self.logger.error(errmsg)
+                continue
+            except aiohttp.ClientResponseError as e:
+                errmsg = 'Response error, got ' + str(e)
+                self.logger.error(errmsg)
+                continue
+            except json.JSONDecodeError:
+                errmsg = 'JsonDecode error, got ' + (await resp.text(
+                    errors='replace'))
+                self.logger.error(errmsg)
+                continue
+            except KeyError:
+                if 'error' in response:
+                    if response['error']['code'] == 'rate_limit_exceeded':
+                        time.sleep(1)
+                        continue
+                    elif response['error']['code'] == 'insufficient_quota':
+                        self.invalid_keys.add(key)
+                        self.logger.warn(f'insufficient_quota key: {key}')
+                        continue
+
+                    errmsg = 'Find error message in response: ' + str(
+                        response['error'])
+                    self.logger.error(errmsg)
+            except Exception as error:
+                errmsg = str(error) + '\n' + str(traceback.format_exc())
+                self.logger.error(errmsg)
+            max_num_retries += 1
+
+        raise RuntimeError('Calling OpenAI failed after retrying for '
+                           f'{max_num_retries} times. Check the logs for '
+                           f'details. errmsg: {errmsg}')
+
+    async def _stream_chat(self, messages: List[dict],
+                           **gen_params) -> AsyncGenerator[str, None]:
+        """Generate completion from a list of templates.
+
+        Args:
+            messages (List[dict]): a list of prompt dictionaries
+            gen_params: additional generation configuration
+
+        Returns:
+            str: The generated string.
+        """
+
+        async def streaming(raw_response):
+            async for chunk in raw_response.content:
+                if chunk:
+                    decoded = chunk.decode('utf-8')
+                    if decoded.startswith('data: [DONE]'):
+                        return
+                    if decoded[:5] == 'data:':
+                        decoded = decoded[5:]
+                        if decoded[0] == ' ':
+                            decoded = decoded[1:]
+                    else:
+                        print(decoded)
+                        continue
+                    try:
+                        response = json.loads(decoded)
+                        if 'code' in response and response['code'] == -20003:
+                            # Context exceeds maximum length
+                            yield ''
+                            return
+                        if self.model_type.lower().startswith('qwen'):
+                            choice = response['output']['choices'][0]
+                            yield choice['message']['content']
+                            if choice['finish_reason'] == 'stop':
+                                return
+                        else:
+                            choice = response['choices'][0]
+                            if choice['finish_reason'] == 'stop':
+                                return
+                            yield choice['delta'].get('content', '')
+                    except Exception as exc:
+                        msg = f'response {decoded} lead to exception of {str(exc)}'
+                        self.logger.error(msg)
+                        raise Exception(msg) from exc
+
+        assert isinstance(messages, list)
+
+        header, data = self.generate_request_data(
+            model_type=self.model_type,
+            messages=messages,
+            gen_params=gen_params,
+            json_mode=self.json_mode)
+
+        max_num_retries, errmsg = 0, ''
+        while max_num_retries < self.retry:
+            if len(self.invalid_keys) == len(self.keys):
+                raise RuntimeError('All keys have insufficient quota.')
+
+            # find the next valid key
+            while True:
+                self.key_ctr += 1
+                if self.key_ctr == len(self.keys):
+                    self.key_ctr = 0
+
+                if self.keys[self.key_ctr] not in self.invalid_keys:
+                    break
+
+            key = self.keys[self.key_ctr]
+            header['Authorization'] = f'Bearer {key}'
+
+            if self.orgs:
+                self.org_ctr += 1
+                if self.org_ctr == len(self.orgs):
+                    self.org_ctr = 0
+                header['OpenAI-Organization'] = self.orgs[self.org_ctr]
+
+            response = dict()
+            try:
+                async with aiohttp.ClientSession() as session:
+                    async with session.post(
+                            self.url,
+                            headers=header,
+                            json=data,
+                            proxy=self.proxies.get(
+                                'https',
+                                self.proxies.get('http'))) as raw_response:
+                        async for msg in streaming(raw_response):
+                            yield msg
+                        return
+            except aiohttp.ClientConnectionError:
+                errmsg = 'Got connection error ' + str(traceback.format_exc())
+                self.logger.error(errmsg)
+                continue
+            except aiohttp.ClientResponseError as e:
+                errmsg = 'Response error, got ' + str(e)
+                self.logger.error(errmsg)
+                continue
+            except KeyError:
+                if 'error' in response:
+                    if response['error']['code'] == 'rate_limit_exceeded':
+                        time.sleep(1)
+                        continue
+                    elif response['error']['code'] == 'insufficient_quota':
+                        self.invalid_keys.add(key)
+                        self.logger.warn(f'insufficient_quota key: {key}')
+                        continue
+
+                    errmsg = 'Find error message in response: ' + str(
+                        response['error'])
+                    self.logger.error(errmsg)
+            except Exception as error:
+                errmsg = str(error) + '\n' + str(traceback.format_exc())
+                self.logger.error(errmsg)
+            max_num_retries += 1
+
+        raise RuntimeError('Calling OpenAI failed after retrying for '
+                           f'{max_num_retries} times. Check the logs for '
+                           f'details. errmsg: {errmsg}')
+
+    def generate_request_data(self,
+                              model_type,
+                              messages,
+                              gen_params,
+                              json_mode=False):
+        """
+        Generates the request data for different model types.
+
+        Args:
+            model_type (str): The type of the model (e.g., 'gpt', 'internlm', 'qwen').
+            messages (list): The list of messages to be sent to the model.
+            gen_params (dict): The generation parameters.
+            json_mode (bool): Flag to determine if the response format should be JSON.
+
+        Returns:
+            tuple: A tuple containing the header and the request data.
+        """
+        # Copy generation parameters to avoid modifying the original dictionary
+        gen_params = gen_params.copy()
+
+        # Hold out 100 tokens due to potential errors in token calculation
+        max_tokens = min(gen_params.pop('max_new_tokens'), 4096)
+        if max_tokens <= 0:
+            return '', ''
+
+        # Initialize the header
+        header = {
+            'content-type': 'application/json',
+        }
+
+        # Common parameters processing
+        gen_params['max_tokens'] = max_tokens
+        if 'stop_words' in gen_params:
+            gen_params['stop'] = gen_params.pop('stop_words')
+        if 'repetition_penalty' in gen_params:
+            gen_params['frequency_penalty'] = gen_params.pop(
+                'repetition_penalty')
+
+        # Model-specific processing
+        data = {}
+        if model_type.lower().startswith('gpt'):
+            if 'top_k' in gen_params:
+                warnings.warn(
+                    '`top_k` parameter is deprecated in OpenAI APIs.',
+                    DeprecationWarning)
+                gen_params.pop('top_k')
+            gen_params.pop('skip_special_tokens', None)
+            gen_params.pop('session_id', None)
+            data = {
+                'model': model_type,
+                'messages': messages,
+                'n': 1,
+                **gen_params
+            }
+            if json_mode:
+                data['response_format'] = {'type': 'json_object'}
+        elif model_type.lower().startswith('internlm'):
+            data = {
+                'model': model_type,
+                'messages': messages,
+                'n': 1,
+                **gen_params
+            }
+            if json_mode:
+                data['response_format'] = {'type': 'json_object'}
+        elif model_type.lower().startswith('qwen'):
+            header['X-DashScope-SSE'] = 'enable'
+            gen_params.pop('skip_special_tokens', None)
+            gen_params.pop('session_id', None)
+            if 'frequency_penalty' in gen_params:
+                gen_params['repetition_penalty'] = gen_params.pop(
+                    'frequency_penalty')
+            gen_params['result_format'] = 'message'
+            data = {
+                'model': model_type,
+                'input': {
+                    'messages': messages
+                },
+                'parameters': {
+                    **gen_params
+                }
+            }
+        else:
+            raise NotImplementedError(
+                f'Model type {model_type} is not supported')
+
+        return header, data
+
+    def tokenize(self, prompt: str) -> list:
+        """Tokenize the input prompt.
+
+        Args:
+            prompt (str): Input string.
+
+        Returns:
+            list: token ids
+        """
+        import tiktoken
+        self.tiktoken = tiktoken
+        enc = self.tiktoken.encoding_for_model(self.model_type)
+        return enc.encode(prompt)

lagent/llms/sensenova.py

@@ -0,0 +1,406 @@
+import json
+import os
+import time
+import warnings
+from concurrent.futures import ThreadPoolExecutor
+from logging import getLogger
+from threading import Lock
+from typing import Dict, Generator, List, Optional, Tuple, Union
+
+import requests
+
+from lagent.schema import ModelStatusCode
+from lagent.utils.util import filter_suffix
+from .base_api import BaseAPILLM
+
+warnings.simplefilter('default')
+
+SENSENOVA_API_BASE = 'https://api.sensenova.cn/v1/llm/chat-completions'
+
+sensechat_models = {'SenseChat-5': 131072, 'SenseChat-5-Cantonese': 32768}
+
+
+class SensenovaAPI(BaseAPILLM):
+    """Model wrapper around SenseTime's models.
+
+    Args:
+        model_type (str): The name of SenseTime's model.
+        retry (int): Number of retires if the API call fails. Defaults to 2.
+        key (str or List[str]): SenseTime key(s). In particular, when it
+            is set to "ENV", the key will be fetched from the environment
+            variable $SENSENOVA_API_KEY. If it's a list, the keys will be
+            used in round-robin manner. Defaults to 'ENV'.
+        meta_template (Dict, optional): The model's meta prompt
+            template if needed, in case the requirement of injecting or
+            wrapping of any meta instructions.
+        sensenova_api_base (str): The base url of SenseTime's API. Defaults to
+            'https://api.sensenova.cn/v1/llm/chat-completions'.
+        gen_params: Default generation configuration which could be overridden
+            on the fly of generation.
+    """
+
+    is_api: bool = True
+
+    def __init__(
+        self,
+        model_type: str = 'SenseChat-5-Cantonese',
+        retry: int = 2,
+        json_mode: bool = False,
+        key: Union[str, List[str]] = 'ENV',
+        meta_template: Optional[Dict] = [
+            dict(role='system', api_role='system'),
+            dict(role='user', api_role='user'),
+            dict(role='assistant', api_role='assistant'),
+            dict(role='environment', api_role='system'),
+        ],
+        sensenova_api_base: str = SENSENOVA_API_BASE,
+        proxies: Optional[Dict] = None,
+        **gen_params,
+    ):
+
+        super().__init__(
+            model_type=model_type,
+            meta_template=meta_template,
+            retry=retry,
+            **gen_params,
+        )
+        self.logger = getLogger(__name__)
+
+        if isinstance(key, str):
+            # First, apply for SenseNova's ak and sk from SenseTime staff
+            # Then, generated SENSENOVA_API_KEY using lagent.utils.gen_key.auto_gen_jwt_token(ak, sk)
+            self.keys = [
+                os.getenv('SENSENOVA_API_KEY') if key == 'ENV' else key
+            ]
+        else:
+            self.keys = key
+
+        # record invalid keys and skip them when requesting API
+        # - keys have insufficient_quota
+        self.invalid_keys = set()
+
+        self.key_ctr = 0
+        self.url = sensenova_api_base
+        self.model_type = model_type
+        self.proxies = proxies
+        self.json_mode = json_mode
+
+    def chat(
+        self,
+        inputs: Union[List[dict], List[List[dict]]],
+        **gen_params,
+    ) -> Union[str, List[str]]:
+        """Generate responses given the contexts.
+
+        Args:
+            inputs (Union[List[dict], List[List[dict]]]): a list of messages
+                or list of lists of messages
+            gen_params: additional generation configuration
+
+        Returns:
+            Union[str, List[str]]: generated string(s)
+        """
+        assert isinstance(inputs, list)
+        if 'max_tokens' in gen_params:
+            raise NotImplementedError('unsupported parameter: max_tokens')
+        gen_params = {**self.gen_params, **gen_params}
+        with ThreadPoolExecutor(max_workers=20) as executor:
+            tasks = [
+                executor.submit(self._chat,
+                                self.template_parser._prompt2api(messages),
+                                **gen_params)
+                for messages in (
+                    [inputs] if isinstance(inputs[0], dict) else inputs)
+            ]
+        ret = [task.result() for task in tasks]
+        return ret[0] if isinstance(inputs[0], dict) else ret
+
+    def stream_chat(
+        self,
+        inputs: List[dict],
+        **gen_params,
+    ) -> Generator[Tuple[ModelStatusCode, str, Optional[str]], None, None]:
+        """Generate responses given the contexts.
+
+        Args:
+            inputs (List[dict]): a list of messages
+            gen_params: additional generation configuration
+
+        Yields:
+            Tuple[ModelStatusCode, str, Optional[str]]: Status code, generated string, and optional metadata
+        """
+        assert isinstance(inputs, list)
+        if 'max_tokens' in gen_params:
+            raise NotImplementedError('unsupported parameter: max_tokens')
+        gen_params = self.update_gen_params(**gen_params)
+        gen_params['stream'] = True
+
+        resp = ''
+        finished = False
+        stop_words = gen_params.get('stop_words') or []
+        messages = self.template_parser._prompt2api(inputs)
+        for text in self._stream_chat(messages, **gen_params):
+            # TODO 测试 resp = text 还是 resp += text
+            resp += text
+            if not resp:
+                continue
+            # remove stop_words
+            for sw in stop_words:
+                if sw in resp:
+                    resp = filter_suffix(resp, stop_words)
+                    finished = True
+                    break
+            yield ModelStatusCode.STREAM_ING, resp, None
+            if finished:
+                break
+        yield ModelStatusCode.END, resp, None
+
+    def _chat(self, messages: List[dict], **gen_params) -> str:
+        """Generate completion from a list of templates.
+
+        Args:
+            messages (List[dict]): a list of prompt dictionaries
+            gen_params: additional generation configuration
+
+        Returns:
+            str: The generated string.
+        """
+        assert isinstance(messages, list)
+
+        header, data = self.generate_request_data(
+            model_type=self.model_type,
+            messages=messages,
+            gen_params=gen_params,
+            json_mode=self.json_mode,
+        )
+
+        max_num_retries = 0
+        while max_num_retries < self.retry:
+            self._wait()
+
+            with Lock():
+                if len(self.invalid_keys) == len(self.keys):
+                    raise RuntimeError('All keys have insufficient quota.')
+
+                # find the next valid key
+                while True:
+                    self.key_ctr += 1
+                    if self.key_ctr == len(self.keys):
+                        self.key_ctr = 0
+
+                    if self.keys[self.key_ctr] not in self.invalid_keys:
+                        break
+
+                key = self.keys[self.key_ctr]
+                header['Authorization'] = f'Bearer {key}'
+
+            response = dict()
+            try:
+                raw_response = requests.post(
+                    self.url,
+                    headers=header,
+                    data=json.dumps(data),
+                    proxies=self.proxies,
+                )
+                response = raw_response.json()
+                return response['choices'][0]['message']['content'].strip()
+            except requests.ConnectionError:
+                print('Got connection error, retrying...')
+                continue
+            except requests.JSONDecodeError:
+                print('JsonDecode error, got', str(raw_response.content))
+                continue
+            except KeyError:
+                if 'error' in response:
+                    if response['error']['code'] == 'rate_limit_exceeded':
+                        time.sleep(1)
+                        continue
+                    elif response['error']['code'] == 'insufficient_quota':
+                        self.invalid_keys.add(key)
+                        self.logger.warn(f'insufficient_quota key: {key}')
+                        continue
+
+                    print('Find error message in response: ',
+                          str(response['error']))
+            except Exception as error:
+                print(str(error))
+            max_num_retries += 1
+
+        raise RuntimeError('Calling SenseTime failed after retrying for '
+                           f'{max_num_retries} times. Check the logs for '
+                           'details.')
+
+    def _stream_chat(self, messages: List[dict], **gen_params) -> str:
+        """Generate completion from a list of templates.
+
+        Args:
+            messages (List[dict]): a list of prompt dictionaries
+            gen_params: additional generation configuration
+
+        Returns:
+            str: The generated string.
+        """
+
+        def streaming(raw_response):
+            for chunk in raw_response.iter_lines():
+                if chunk:
+                    try:
+                        decoded_chunk = chunk.decode('utf-8')
+                        # print(f"Decoded chunk: {decoded_chunk}")
+
+                        if decoded_chunk == 'data:[DONE]':
+                            # print("Stream ended")
+                            break
+
+                        if decoded_chunk.startswith('data:'):
+                            json_str = decoded_chunk[5:]
+                            chunk_data = json.loads(json_str)
+
+                            if 'data' in chunk_data and 'choices' in chunk_data[
+                                    'data']:
+                                choice = chunk_data['data']['choices'][0]
+                                if 'delta' in choice:
+                                    content = choice['delta']
+                                    yield content
+                        else:
+                            print(f'Unexpected format: {decoded_chunk}')
+
+                    except json.JSONDecodeError as e:
+                        print(f'JSON parsing error: {e}')
+                    except Exception as e:
+                        print(
+                            f'An error occurred while processing the chunk: {e}'
+                        )
+
+        assert isinstance(messages, list)
+
+        header, data = self.generate_request_data(
+            model_type=self.model_type,
+            messages=messages,
+            gen_params=gen_params,
+            json_mode=self.json_mode,
+        )
+
+        max_num_retries = 0
+        while max_num_retries < self.retry:
+            if len(self.invalid_keys) == len(self.keys):
+                raise RuntimeError('All keys have insufficient quota.')
+
+            # find the next valid key
+            while True:
+                self.key_ctr += 1
+                if self.key_ctr == len(self.keys):
+                    self.key_ctr = 0
+
+                if self.keys[self.key_ctr] not in self.invalid_keys:
+                    break
+
+            key = self.keys[self.key_ctr]
+            header['Authorization'] = f'Bearer {key}'
+
+            response = dict()
+            try:
+                raw_response = requests.post(
+                    self.url,
+                    headers=header,
+                    data=json.dumps(data),
+                    proxies=self.proxies,
+                )
+                return streaming(raw_response)
+            except requests.ConnectionError:
+                print('Got connection error, retrying...')
+                continue
+            except requests.JSONDecodeError:
+                print('JsonDecode error, got', str(raw_response.content))
+                continue
+            except KeyError:
+                if 'error' in response:
+                    if response['error']['code'] == 'rate_limit_exceeded':
+                        time.sleep(1)
+                        continue
+                    elif response['error']['code'] == 'insufficient_quota':
+                        self.invalid_keys.add(key)
+                        self.logger.warn(f'insufficient_quota key: {key}')
+                        continue
+
+                    print('Find error message in response: ',
+                          str(response['error']))
+            except Exception as error:
+                print(str(error))
+            max_num_retries += 1
+
+        raise RuntimeError('Calling SenseTime failed after retrying for '
+                           f'{max_num_retries} times. Check the logs for '
+                           'details.')
+
+    def generate_request_data(self,
+                              model_type,
+                              messages,
+                              gen_params,
+                              json_mode=False):
+        """
+        Generates the request data for different model types.
+
+        Args:
+            model_type (str): The type of the model (e.g., 'sense').
+            messages (list): The list of messages to be sent to the model.
+            gen_params (dict): The generation parameters.
+            json_mode (bool): Flag to determine if the response format should be JSON.
+
+        Returns:
+            tuple: A tuple containing the header and the request data.
+        """
+        # Copy generation parameters to avoid modifying the original dictionary
+        gen_params = gen_params.copy()
+
+        # Hold out 100 tokens due to potential errors in token calculation
+        max_tokens = min(gen_params.pop('max_new_tokens'), 4096)
+        if max_tokens <= 0:
+            return '', ''
+
+        # Initialize the header
+        header = {
+            'content-type': 'application/json',
+        }
+
+        # Common parameters processing
+        gen_params['max_tokens'] = max_tokens
+        if 'stop_words' in gen_params:
+            gen_params['stop'] = gen_params.pop('stop_words')
+        if 'repetition_penalty' in gen_params:
+            gen_params['frequency_penalty'] = gen_params.pop(
+                'repetition_penalty')
+
+        # Model-specific processing
+        data = {}
+        if model_type.lower().startswith('sense'):
+            gen_params.pop('skip_special_tokens', None)
+            gen_params.pop('session_id', None)
+            data = {
+                'model': model_type,
+                'messages': messages,
+                'n': 1,
+                **gen_params
+            }
+            if json_mode:
+                data['response_format'] = {'type': 'json_object'}
+        else:
+            raise NotImplementedError(
+                f'Model type {model_type} is not supported')
+
+        return header, data
+
+    def tokenize(self, prompt: str) -> list:
+        """Tokenize the input prompt.
+
+        Args:
+            prompt (str): Input string.
+
+        Returns:
+            list: token ids
+        """
+        import tiktoken
+
+        self.tiktoken = tiktoken
+        enc = self.tiktoken.encoding_for_model('gpt-4o')
+        return enc.encode(prompt)

lagent/llms/vllm_wrapper.py

@@ -0,0 +1,176 @@
+import asyncio
+from typing import List, Union
+
+from lagent.llms.base_llm import AsyncBaseLLM, BaseLLM
+from lagent.utils.util import filter_suffix
+
+
+def asdict_completion(output):
+    return {
+        key: getattr(output, key)
+        for key in [
+            'text', 'token_ids', 'cumulative_logprob', 'logprobs',
+            'finish_reason', 'stop_reason'
+        ]
+    }
+
+
+class VllmModel(BaseLLM):
+    """
+    A wrapper of vLLM model.
+
+    Args:
+        path (str): The path to the model.
+            It could be one of the following options:
+                    - i) A local directory path of a huggingface model.
+                    - ii) The model_id of a model hosted inside a model repo
+                        on huggingface.co, such as "internlm/internlm-chat-7b",
+                        "Qwen/Qwen-7B-Chat ", "baichuan-inc/Baichuan2-7B-Chat"
+                        and so on.
+        tp (int): tensor parallel
+        vllm_cfg (dict): Other kwargs for vllm model initialization.
+    """
+
+    def __init__(self, path: str, tp: int = 1, vllm_cfg=dict(), **kwargs):
+
+        super().__init__(path=path, **kwargs)
+        from vllm import LLM
+        self.model = LLM(
+            model=self.path,
+            trust_remote_code=True,
+            tensor_parallel_size=tp,
+            **vllm_cfg)
+
+    def generate(self,
+                 inputs: Union[str, List[str]],
+                 do_preprocess: bool = None,
+                 skip_special_tokens: bool = False,
+                 return_dict: bool = False,
+                 **kwargs):
+        """Return the chat completions in non-stream mode.
+
+        Args:
+            inputs (Union[str, List[str]]): input texts to be completed.
+            do_preprocess (bool): whether pre-process the messages. Default to
+                True, which means chat_template will be applied.
+            skip_special_tokens (bool): Whether or not to remove special tokens
+                in the decoding. Default to be False.
+        Returns:
+            (a list of/batched) text/chat completion
+        """
+        from vllm import SamplingParams
+
+        batched = True
+        if isinstance(inputs, str):
+            inputs = [inputs]
+            batched = False
+        prompt = inputs
+        gen_params = self.update_gen_params(**kwargs)
+        max_new_tokens = gen_params.pop('max_new_tokens')
+        stop_words = gen_params.pop('stop_words')
+
+        sampling_config = SamplingParams(
+            skip_special_tokens=skip_special_tokens,
+            max_tokens=max_new_tokens,
+            stop=stop_words,
+            **gen_params)
+        response = self.model.generate(prompt, sampling_params=sampling_config)
+        texts = [resp.outputs[0].text for resp in response]
+        # remove stop_words
+        texts = filter_suffix(texts, self.gen_params.get('stop_words'))
+        for resp, text in zip(response, texts):
+            resp.outputs[0].text = text
+        if batched:
+            return [asdict_completion(resp.outputs[0])
+                    for resp in response] if return_dict else texts
+        return asdict_completion(
+            response[0].outputs[0]) if return_dict else texts[0]
+
+
+class AsyncVllmModel(AsyncBaseLLM):
+    """
+    A asynchronous wrapper of vLLM model.
+
+    Args:
+        path (str): The path to the model.
+            It could be one of the following options:
+                    - i) A local directory path of a huggingface model.
+                    - ii) The model_id of a model hosted inside a model repo
+                        on huggingface.co, such as "internlm/internlm-chat-7b",
+                        "Qwen/Qwen-7B-Chat ", "baichuan-inc/Baichuan2-7B-Chat"
+                        and so on.
+        tp (int): tensor parallel
+        vllm_cfg (dict): Other kwargs for vllm model initialization.
+    """
+
+    def __init__(self, path: str, tp: int = 1, vllm_cfg=dict(), **kwargs):
+        super().__init__(path=path, **kwargs)
+        from vllm import AsyncEngineArgs, AsyncLLMEngine
+
+        engine_args = AsyncEngineArgs(
+            model=self.path,
+            trust_remote_code=True,
+            tensor_parallel_size=tp,
+            **vllm_cfg)
+        self.model = AsyncLLMEngine.from_engine_args(engine_args)
+
+    async def generate(self,
+                       inputs: Union[str, List[str]],
+                       session_ids: Union[int, List[int]] = None,
+                       do_preprocess: bool = None,
+                       skip_special_tokens: bool = False,
+                       return_dict: bool = False,
+                       **kwargs):
+        """Return the chat completions in non-stream mode.
+
+        Args:
+            inputs (Union[str, List[str]]): input texts to be completed.
+            do_preprocess (bool): whether pre-process the messages. Default to
+                True, which means chat_template will be applied.
+            skip_special_tokens (bool): Whether or not to remove special tokens
+                in the decoding. Default to be False.
+        Returns:
+            (a list of/batched) text/chat completion
+        """
+        from vllm import SamplingParams
+
+        batched = True
+        if isinstance(inputs, str):
+            inputs = [inputs]
+            batched = False
+        if session_ids is None:
+            session_ids = list(range(len(inputs)))
+        elif isinstance(session_ids, (int, str)):
+            session_ids = [session_ids]
+        assert len(inputs) == len(session_ids)
+
+        prompt = inputs
+        gen_params = self.update_gen_params(**kwargs)
+        max_new_tokens = gen_params.pop('max_new_tokens')
+        stop_words = gen_params.pop('stop_words')
+
+        sampling_config = SamplingParams(
+            skip_special_tokens=skip_special_tokens,
+            max_tokens=max_new_tokens,
+            stop=stop_words,
+            **gen_params)
+
+        async def _inner_generate(uid, text):
+            resp, generator = '', self.model.generate(
+                text, sampling_params=sampling_config, request_id=uid)
+            async for out in generator:
+                resp = out.outputs[0]
+            return resp
+
+        response = await asyncio.gather(*[
+            _inner_generate(sid, inp) for sid, inp in zip(session_ids, prompt)
+        ])
+        texts = [resp.text for resp in response]
+        # remove stop_words
+        texts = filter_suffix(texts, self.gen_params.get('stop_words'))
+        for resp, text in zip(response, texts):
+            resp.text = text
+        if batched:
+            return [asdict_completion(resp)
+                    for resp in response] if return_dict else texts
+        return asdict_completion(response[0]) if return_dict else texts[0]

lagent/memory/__init__.py

@@ -0,0 +1,4 @@
+from .base_memory import Memory
+from .manager import MemoryManager
+
+__all__ = ['Memory', 'MemoryManager']

lagent/memory/base_memory.py

@@ -0,0 +1,60 @@
+from typing import Callable, Dict, List, Optional, Union
+
+from lagent.schema import AgentMessage
+
+
+class Memory:
+
+    def __init__(self, recent_n=None) -> None:
+        self.memory: List[AgentMessage] = []
+        self.recent_n = recent_n
+
+    def get_memory(
+        self,
+        recent_n: Optional[int] = None,
+        filter_func: Optional[Callable[[int, dict], bool]] = None,
+    ) -> list:
+        recent_n = recent_n or self.recent_n
+        if recent_n is not None:
+            memory = self.memory[-recent_n:]
+        else:
+            memory = self.memory
+        if filter_func is not None:
+            memory = [m for i, m in enumerate(memory) if filter_func(i, m)]
+        return memory
+
+    def add(self, memories: Union[List[Dict], Dict, None]) -> None:
+        for memory in memories if isinstance(memories,
+                                             (list, tuple)) else [memories]:
+            if isinstance(memory, str):
+                memory = AgentMessage(sender='user', content=memory)
+            if isinstance(memory, AgentMessage):
+                self.memory.append(memory)
+
+    def delete(self, index: Union[List, int]) -> None:
+        if isinstance(index, int):
+            del self.memory[index]
+        else:
+            for i in index:
+                del self.memory[i]
+
+    def load(
+        self,
+        memories: Union[str, Dict, List],
+        overwrite: bool = True,
+    ) -> None:
+        if overwrite:
+            self.memory = []
+        if isinstance(memories, dict):
+            self.memory.append(AgentMessage(**memories))
+        elif isinstance(memories, list):
+            for m in memories:
+                self.memory.append(AgentMessage(**m))
+        else:
+            raise TypeError(f'{type(memories)} is not supported')
+
+    def save(self) -> List[dict]:
+        memory = []
+        for m in self.memory:
+            memory.append(m.model_dump())
+        return memory

lagent/memory/manager.py

@@ -0,0 +1,29 @@
+from typing import Dict
+
+from ..utils import create_object
+from .base_memory import Memory
+
+
+class MemoryManager:
+
+    def __init__(self, cfg) -> None:
+        self.cfg = cfg
+        self.memory_map: Dict[str, Memory] = {}
+
+    def create_instance(self, session_id):
+        self.memory_map[session_id] = create_object(self.cfg)
+
+    def get_memory(self, session_id=0, **kwargs) -> list:
+        return self.memory_map[session_id].get_memory(**kwargs)
+
+    def add(self, memory, session_id=0, **kwargs) -> None:
+        if session_id not in self.memory_map:
+            self.create_instance(session_id)
+        self.memory_map[session_id].add(memory, **kwargs)
+
+    def get(self, session_id=0) -> Memory:
+        return self.memory_map.get(session_id, None)
+
+    def reset(self, session_id=0) -> None:
+        if session_id in self.memory_map:
+            del self.memory_map[session_id]

lagent/prompts/__init__.py

@@ -0,0 +1,4 @@
+from .parsers import *  # noqa
+from .prompt_template import PromptTemplate
+
+__all__ = ['PromptTemplate']