欢迎来到 SuifengFlying!

1
# 创建环境
2
conda create -n lagent python=3.10 -y
3
# 激活环境
4
conda activate lagent
5
# 安装 torch
6
conda install pytorch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 pytorch-cuda=12.1 -c pytorch -c nvidia -y
7
# 安装其他依赖包
8
pip install termcolor==2.4.0
9
pip install streamlit==1.39.0
10
pip install class_registry==2.1.2

1
conda activate lagent
2
cd /root/agent_camp4/lagent/examples
3
touch agent_api_web_demo.py

1
import copy
2
import os
3
from typing import List
4
import streamlit as st
5
from lagent.actions import ArxivSearch
6
from lagent.prompts.parsers import PluginParser
7
from lagent.agents.stream import INTERPRETER_CN, META_CN, PLUGIN_CN, AgentForInternLM, get_plugin_prompt
8
from lagent.llms import GPTAPI
9

10
class SessionState:
11
    """管理会话状态的类。"""
12

13
    def init_state(self):
14
        """初始化会话状态变量。"""
15
        st.session_state['assistant'] = []  # 助手消息历史
16
        st.session_state['user'] = []  # 用户消息历史
17
        # 初始化插件列表
18
        action_list = [
19
            ArxivSearch(),
20
        ]
21
        st.session_state['plugin_map'] = {action.name: action for action in action_list}
22
        st.session_state['model_map'] = {}  # 存储模型实例
23
        st.session_state['model_selected'] = None  # 当前选定模型
24
        st.session_state['plugin_actions'] = set()  # 当前激活插件
25
        st.session_state['history'] = []  # 聊天历史
26
        st.session_state['api_base'] = None  # 初始化API base地址
27

28
    def clear_state(self):
29
        """清除当前会话状态。"""
30
        st.session_state['assistant'] = []
31
        st.session_state['user'] = []
32
        st.session_state['model_selected'] = None
33

34

35
class StreamlitUI:
36
    """管理 Streamlit 界面的类。"""
37

38
    def __init__(self, session_state: SessionState):
39
        self.session_state = session_state
40
        self.plugin_action = []  # 当前选定的插件
41
        # 初始化提示词
42
        self.meta_prompt = META_CN
43
        self.plugin_prompt = PLUGIN_CN
44
        self.init_streamlit()
45

46
    def init_streamlit(self):
47
        """初始化 Streamlit 的 UI 设置。"""
48
        st.set_page_config(
49
            layout='wide',
50
            page_title='lagent-web',
51
            page_icon='./docs/imgs/lagent_icon.png'
52
        )
53
        st.header(':robot_face: :blue[Lagent] Web Demo ', divider='rainbow')
54

55
    def setup_sidebar(self):
56
        """设置侧边栏，选择模型和插件。"""
57
        # 模型名称和 API Base 输入框
58
        model_name = st.sidebar.text_input('模型名称：', value='internlm2.5-latest')
59
        # ================================== 硅基流动的API ==================================
60
        # 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
61
        # api_base = st.sidebar.text_input(
62
        #     'API Base 地址：', value='https://api.siliconflow.cn/v1/chat/completions'
63
        # )
64
        # ================================== 浦语官方的API ==================================
65
        api_base = st.sidebar.text_input(
66
            'API Base 地址：', value='https://internlm-chat.intern-ai.org.cn/puyu/api/v1/chat/completions'
67
        )
68
        # ==================================================================================
69
        # 插件选择
70
        plugin_name = st.sidebar.multiselect(
71
            '插件选择',
72
            options=list(st.session_state['plugin_map'].keys()),
73
            default=[],
74
        )
75

76
        # 根据选择的插件生成插件操作列表
77
        self.plugin_action = [st.session_state['plugin_map'][name] for name in plugin_name]
78

79
        # 动态生成插件提示
80
        if self.plugin_action:
81
            self.plugin_prompt = get_plugin_prompt(self.plugin_action)
82

83
        # 清空对话按钮
84
        if st.sidebar.button('清空对话', key='clear'):
85
            self.session_state.clear_state()
86

87
        return model_name, api_base, self.plugin_action
88

89
    def initialize_chatbot(self, model_name, api_base, plugin_action):
90
        """初始化 GPTAPI 实例作为 chatbot。"""
91
        token = os.getenv("token")
92
        if not token:
93
            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")
94
            st.stop()  # 停止运行应用
95
        # 创建完整的 meta_prompt，保留原始结构并动态插入侧边栏配置
96
        meta_prompt = [
97
            {"role": "system", "content": self.meta_prompt, "api_role": "system"},
98
            {"role": "user", "content": "", "api_role": "user"},
99
            {"role": "assistant", "content": self.plugin_prompt, "api_role": "assistant"},
100
            {"role": "environment", "content": "", "api_role": "environment"}
101
        ]
102

103
        api_model = GPTAPI(
104
            model_type=model_name,
105
            api_base=api_base,
106
            key=token,  # 从环境变量中获取授权令牌
107
            meta_template=meta_prompt,
108
            max_new_tokens=512,
109
            temperature=0.8,
110
            top_p=0.9
111
        )
112
        return api_model
113

114
    def render_user(self, prompt: str):
115
        """渲染用户输入内容。"""
116
        with st.chat_message('user'):
117
            st.markdown(prompt)
118

119
    def render_assistant(self, agent_return):
120
        """渲染助手响应内容。"""
121
        with st.chat_message('assistant'):
122
            content = getattr(agent_return, "content", str(agent_return))
123
            st.markdown(content if isinstance(content, str) else str(content))
124

125

126
def main():
127
    """主函数，运行 Streamlit 应用。"""
128
    if 'ui' not in st.session_state:
129
        session_state = SessionState()
130
        session_state.init_state()
131
        st.session_state['ui'] = StreamlitUI(session_state)
132
    else:
133
        st.set_page_config(
134
            layout='wide',
135
            page_title='lagent-web',
136
            page_icon='./docs/imgs/lagent_icon.png'
137
        )
138
        st.header(':robot_face: :blue[Lagent] Web Demo ', divider='rainbow')
139

140
    # 设置侧边栏并获取模型和插件信息
141
    model_name, api_base, plugin_action = st.session_state['ui'].setup_sidebar()
142
    plugins = [dict(type=f"lagent.actions.{plugin.__class__.__name__}") for plugin in plugin_action]
143

144
    if (
145
        'chatbot' not in st.session_state or
146
        model_name != st.session_state['chatbot'].model_type or
147
        'last_plugin_action' not in st.session_state or
148
        plugin_action != st.session_state['last_plugin_action'] or
149
        api_base != st.session_state['api_base']    ):
150
        # 更新 Chatbot
151
        st.session_state['chatbot'] = st.session_state['ui'].initialize_chatbot(model_name, api_base, plugin_action)
152
        st.session_state['last_plugin_action'] = plugin_action  # 更新插件状态
153
        st.session_state['api_base'] = api_base  # 更新 API Base 地址
154

155
        # 初始化 AgentForInternLM
156
        st.session_state['agent'] = AgentForInternLM(
157
            llm=st.session_state['chatbot'],
158
            plugins=plugins,
159
            output_format=dict(
160
                type=PluginParser,
161
                template=PLUGIN_CN,
162
                prompt=get_plugin_prompt(plugin_action)
163
            )
164
        )
165
        # 清空对话历史
166
        st.session_state['session_history'] = []
167

168
    if 'agent' not in st.session_state:
169
        st.session_state['agent'] = None
170

171
    agent = st.session_state['agent']
172
    for prompt, agent_return in zip(st.session_state['user'], st.session_state['assistant']):
173
        st.session_state['ui'].render_user(prompt)
174
        st.session_state['ui'].render_assistant(agent_return)
175

176
    # 处理用户输入
177
    if user_input := st.chat_input(''):
178
        st.session_state['ui'].render_user(user_input)
179

180
        # 调用模型时确保侧边栏的系统提示词和插件提示词生效
181
        res = agent(user_input, session_id=0)
182
        st.session_state['ui'].render_assistant(res)
183

184
        # 更新会话状态
185
        st.session_state['user'].append(user_input)
186
        st.session_state['assistant'].append(copy.deepcopy(res))
187

188
    st.session_state['last_status'] = None
189

190

191
if name == '__main__':
192
    main()

1
export token='your_token_here'
2
streamlit run agent_api_web_demo.py

1
conda activate lagent
2
cd /root/agent_camp4/lagent/lagent/actions
3
touch weather_query.py

1
export weather_token='your_token_here'

1
import os
2
import requests
3
from lagent.actions.base_action import BaseAction, tool_api
4
from lagent.schema import ActionReturn, ActionStatusCode
5

6
class WeatherQuery(BaseAction):
7
    def __init__(self):
8
        super().__init__()
9
        self.api_key = os.getenv("weather_token")
10
        print(self.api_key)
11
        if not self.api_key:
12
            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' ")
13

14
    @tool_api
15
    def run(self, location: str) -> dict:
16
        """
17
        查询实时天气信息。
18

19
        Args:
20
            location (str): 要查询的地点名称、LocationID 或经纬度坐标（如 "101010100" 或 "116.41,39.92"）。
21

22
        Returns:
23
            dict: 包含天气信息的字典
24
                * location: 地点名称
25
                * weather: 天气状况
26
                * temperature: 当前温度
27
                * wind_direction: 风向
28
                * wind_speed: 风速（公里/小时）
29
                * humidity: 相对湿度（%）
30
                * report_time: 数据报告时间
31
        """
32
        try:
33
            # 如果 location 不是坐标格式（例如 "116.41,39.92"），则调用 GeoAPI 获取 LocationID
34
            if not ("," in location and location.replace(",", "").replace(".", "").isdigit()):
35
                # 使用 GeoAPI 获取 LocationID
36
                geo_url = f"https://geoapi.qweather.com/v2/city/lookup?location={location}&key={self.api_key}"
37
                geo_response = requests.get(geo_url)
38
                geo_data = geo_response.json()
39

40
                if geo_data.get("code") != "200" or not geo_data.get("location"):
41
                    raise Exception(f"GeoAPI 返回错误码：{geo_data.get('code')} 或未找到位置")
42

43
                location = geo_data["location"][0]["id"]
44

45
            # 构建天气查询的 API 请求 URL
46
            weather_url = f"https://devapi.qweather.com/v7/weather/now?location={location}&key={self.api_key}"
47
            response = requests.get(weather_url)
48
            data = response.json()
49

50
            # 检查 API 响应码
51
            if data.get("code") != "200":
52
                raise Exception(f"Weather API 返回错误码：{data.get('code')}")
53

54
            # 解析和组织天气信息
55
            weather_info = {
56
                "location": location,
57
                "weather": data["now"]["text"],
58
                "temperature": data["now"]["temp"] + "°C",
59
                "wind_direction": data["now"]["windDir"],
60
                "wind_speed": data["now"]["windSpeed"] + " km/h",                "humidity": data["now"]["humidity"] + "%",
61
                "report_time": data["updateTime"]
62
            }
63

64
            return {"result": weather_info}
65

66
        except Exception as exc:
67
            return ActionReturn(
68
                errmsg=f"WeatherQuery 异常：{exc}",
69
                state=ActionStatusCode.HTTP_ERROR
70
            )

1
conda activate lagent
2
cd /root/agent_camp4/lagent/examples
3
touch multi_agents_api_web_demo.py

1
import os
2
import asyncio
3
import json
4
import re
5
import requests
6
import streamlit as st
7

8
from lagent.agents import Agent
9
from lagent.prompts.parsers import PluginParser
10
from lagent.agents.stream import PLUGIN_CN, get_plugin_prompt
11
from lagent.schema import AgentMessage
12
from lagent.actions import ArxivSearch
13
from lagent.hooks import Hook
14
from lagent.llms import GPTAPI
15

16
YOUR_TOKEN_HERE = os.getenv("token")
17
if not YOUR_TOKEN_HERE:
18
    raise EnvironmentError("未找到环境变量 'token'，请设置后再运行程序。")
19

20
# Hook类，用于对消息添加前缀
21
class PrefixedMessageHook(Hook):
22
    def __init__(self, prefix, senders=None):
23
        """
24
        初始化Hook
25
        :param prefix: 消息前缀
26
        :param senders: 指定发送者列表
27
        """
28
        self.prefix = prefix
29
        self.senders = senders or []
30

31
    def before_agent(self, agent, messages, session_id):
32
        """
33
        在代理处理消息前修改消息内容
34
        :param agent: 当前代理
35
        :param messages: 消息列表
36
        :param session_id: 会话ID
37
        """
38
        for message in messages:
39
            if message.sender in self.senders:
40
                message.content = self.prefix + message.content
41

42
class AsyncBlogger:
43
    """博客生成类，整合写作者和批评者。"""
44

45
    def __init__(self, model_type, api_base, writer_prompt, critic_prompt, critic_prefix='', max_turn=2):
46
        """
47
        初始化博客生成器
48
        :param model_type: 模型类型
49
        :param api_base: API 基地址
50
        :param writer_prompt: 写作者提示词
51
        :param critic_prompt: 批评者提示词
52
        :param critic_prefix: 批评消息前缀
53
        :param max_turn: 最大轮次
54
        """
55
        self.model_type = model_type
56
        self.api_base = api_base
57
        self.llm = GPTAPI(
58
            model_type=model_type,
59
            api_base=api_base,
60
            key=YOUR_TOKEN_HERE,
61
            max_new_tokens=4096,
62
        )
63
        self.plugins = [dict(type='lagent.actions.ArxivSearch')]
64
        self.writer = Agent(
65
            self.llm,
66
            writer_prompt,
67
            name='写作者',
68
            output_format=dict(
69
                type=PluginParser,
70
                template=PLUGIN_CN,
71
                prompt=get_plugin_prompt(self.plugins)
72
            )
73
        )
74
        self.critic = Agent(
75
            self.llm,
76
            critic_prompt,
77
            name='批评者',
78
            hooks=[PrefixedMessageHook(critic_prefix, ['写作者'])]
79
        )
80
        self.max_turn = max_turn
81

82
    async def forward(self, message: AgentMessage, update_placeholder):
83
        """
84
        执行多阶段博客生成流程
85
        :param message: 初始消息
86
        :param update_placeholder: Streamlit占位符
87
        :return: 最终优化的博客内容
88
        """
89
        step1_placeholder = update_placeholder.container()
90
        step2_placeholder = update_placeholder.container()
91
        step3_placeholder = update_placeholder.container()
92

93
        # 第一步：生成初始内容
94
        step1_placeholder.markdown("**Step 1: 生成初始内容...**")
95
        message = self.writer(message)
96
        if message.content:
97
            step1_placeholder.markdown(f"**生成的初始内容**:\n\n{message.content}")
98
        else:
99
            step1_placeholder.markdown("**生成的初始内容为空，请检查生成逻辑。**")
100

101
        # 第二步：批评者提供反馈
102
        step2_placeholder.markdown("**Step 2: 批评者正在提供反馈和文献推荐...**")
103
        message = self.critic(message)
104
        if message.content:
105
            # 解析批评者反馈
106
            suggestions = re.search(r"1\. 批评建议：\n(.?)2\. 推荐的关键词：", message.content, re.S)
107
            keywords = re.search(r"2\. 推荐的关键词：\n- (.)", message.content)
108
            feedback = suggestions.group(1).strip() if suggestions else "未提供批评建议"
109
            keywords = keywords.group(1).strip() if keywords else "未提供关键词"
110

111
            # Arxiv 文献查询
112
            arxiv_search = ArxivSearch()
113
            arxiv_results = arxiv_search.get_arxiv_article_information(keywords)
114

115
            # 显示批评内容和文献推荐
116
            message.content = f"**批评建议**:\n{feedback}\n\n**推荐的文献**:\n{arxiv_results}"
117
            step2_placeholder.markdown(f"**批评和文献推荐**:\n\n{message.content}")
118
        else:
119
            step2_placeholder.markdown("**批评内容为空，请检查批评逻辑。**")
120

121
        # 第三步：写作者根据反馈优化内容
122
        step3_placeholder.markdown("**Step 3: 根据反馈改进内容...**")
123
        improvement_prompt = AgentMessage(
124
            sender="critic",
125
            content=(
126
                f"根据以下批评建议和推荐文献对内容进行改进：\n\n"
127
                f"批评建议：\n{feedback}\n\n"
128
                f"推荐文献：\n{arxiv_results}\n\n"
129
                f"请优化初始内容，使其更加清晰、丰富，并符合专业水准。"
130
            ),
131
        )
132
        message = self.writer(improvement_prompt)
133
        if message.content:
134
            step3_placeholder.markdown(f"**最终优化的博客内容**:\n\n{message.content}")
135
        else:
136
            step3_placeholder.markdown("**最终优化的博客内容为空，请检查生成逻辑。**")
137

138
        return message
139

140
def setup_sidebar():
141
    """设置侧边栏，选择模型。"""
142
    model_name = st.sidebar.text_input('模型名称：', value='internlm2.5-latest')
143
    api_base = st.sidebar.text_input(
144
        'API Base 地址：', value='https://internlm-chat.intern-ai.org.cn/puyu/api/v1/chat/completions'
145
    )
146
    return model_name, api_base
147

148
def main():
149
    """
150
    主函数：构建Streamlit界面并处理用户交互
151
    """
152
    st.set_page_config(layout='wide', page_title='Lagent Web Demo', page_icon='🤖')
153
    st.title("多代理博客优化助手")
154

155
    model_type, api_base = setup_sidebar()
156
    topic = st.text_input('输入一个话题：', 'Self-Supervised Learning')
157
    generate_button = st.button('生成博客内容')
158

159
    if (
160
        'blogger' not in st.session_state or
161
        st.session_state['model_type'] != model_type or
162
        st.session_state['api_base'] != api_base
163
    ):
164
        st.session_state['blogger'] = AsyncBlogger(
165
            model_type=model_type,
166
            api_base=api_base,
167
            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。",
168
            critic_prompt="""
169
                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。
170
                请按照以下格式提供反馈：
171
                1. 批评建议：
172
                - （具体建议）
173
                2. 推荐的关键词：
174
                - （关键词1, 关键词2, ...）
175
            """,
176
            critic_prefix="请批评以下内容，并提供改进建议：\n\n"
177
        )
178
        st.session_state['model_type'] = model_type
179
        st.session_state['api_base'] = api_base
180

181
    if generate_button:
182
        update_placeholder = st.empty()
183

184
        async def run_async_blogger():
185
            message = AgentMessage(
186
                sender='user',
187
                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。"
188
            )
189
            result = await st.session_state['blogger'].forward(message, update_placeholder)
190
            return result
191

192
        loop = asyncio.new_event_loop()
193
        asyncio.set_event_loop(loop)
194
        loop.run_until_complete(run_async_blogger())
195

196
if name == '__main__':
197
    main()

1
# git remote set-url origin https://<user_name>:<token>@huggingface.co/<repo_path>
2

3
# 如 git remote set-url origin https://blank:hf_xxxxxxxxxxx@huggingface.co/blank/intern_study_L0_4
4

5
# 这里blank和hf_xxxxxxxxxxxx只是示例 请替换为你的username和之前申请的access token