Hermes Agent从入门到AI数字员工实战指南

# 创建虚拟环境
python -m venv hermes_env
source hermes_env/bin/activate  # Linux/Mac
hermes_env\Scripts\activate     # Windows

# 安装核心包
pip install hermes-agent
pip install openai==0.28

llm_provider: "openai"
api_key: "your_api_key"
model: "gpt-4-1106-preview"
tools:
  - web_search
  - code_interpreter
  - file_editor

from hermes import HermesAgent

agent = HermesAgent(config_path="config.yaml")

response = agent.run(
    "查询2023年全球AI市场规模并生成分析报告",
    tools=["web_search", "report_generator"]
)

print(response)

from hermes import BaseTool
import requests

class WeatherTool(BaseTool):
    name = "weather_checker"
    description = "查询指定城市的实时天气情况"

    def __init__(self, api_key):
        self.api_key = api_key

    def run(self, city: str):
        url = f"https://api.weatherapi.com/v1/current.json?key={self.api_key}&q={city}"
        response = requests.get(url)
        return response.json()

# 注册自定义工具
agent.register_tool(WeatherTool("your_weather_api_key"))

from hermes import MultiAgentSystem

team = MultiAgentSystem(
    roles=["研究员", "分析师", "编辑"],
    config="team_config.yaml"
)

report = team.execute(
    "制作一份关于生成式AI在金融领域应用的10页行业报告",
    steps=["数据收集", "分析", "撰写", "校对"]
)

workflow = {
    "name": "每日市场简报",
    "steps": [
        {
            "task": "收集最新金融新闻",
            "agent": "数据收集员",
            "tools": ["web_scraper"]
        },
        {
            "task": "生成趋势分析",
            "agent": "分析师",
            "tools": ["data_analyzer"]
        },
        {
            "task": "制作可视化图表",
            "agent": "设计师",
            "tools": ["chart_generator"]
        }
    ],
    "schedule": "0 9 * * 1-5"  # 工作日早上9点
}

agent.create_workflow(workflow)

# 启用长期记忆
agent.enable_memory(
    memory_type="vector_db",
    config={
        "db_path": "./memory_db",
        "embedding_model": "text-embedding-ada-002"
    }
)

# 记忆存取示例
agent.store_memory(
    key="client_preferences",
    value={"format": "PDF", "detail_level": "high"}
)

prefs = agent.recall_memory("client_preferences")

security_policy = {
    "data_handling": {
        "encryption": True,
        "pii_redaction": True
    },
    "tool_permissions": {
        "financial_data": ["L3_analysts"],
        "admin_tools": ["management"]
    }
}

agent.set_security_policy(security_policy)

# 启用缓存
agent.enable_cache(
    backend="redis",
    config={"host": "localhost", "port": 6379}
)

# 设置速率限制
agent.set_rate_limit(
    requests_per_minute=30,
    llm_calls_per_hour=500
)

agent.enable_monitoring(
    metrics=["latency", "accuracy", "cost"],
    alert_rules={
        "high_latency": ">5s",
        "high_cost": ">$0.50/task"
    },
    log_file="./agent_activity.log"
)

FROM python:3.9-slim

WORKDIR /app
COPY . .

RUN pip install --no-cache-dir hermes-agent gunicorn
ENV PORT 8000

CMD ["gunicorn", "hermes.wsgi:app", "--bind", "0.0.0.0:${PORT}"]

def handle_customer_query(query):
    context = agent.recall_memory(f"customer_{customer_id}_history")
    response = agent.run(
        f"作为客服代表回复以下客户问题: {query}",
        context=context,
        tools=["knowledge_base", "ticket_system"]
    )
    agent.store_memory(
        f"customer_{customer_id}_history",
        f"Q: {query}\nA: {response}"
    )
    return response

report = agent.run(
    "分析sales_data.csv中的销售趋势，识别top 3产品和最需要关注的区域",
    tools=["pandas_analyzer", "matplotlib"],
    files=["sales_data.csv"]
)

report.save("sales_analysis.docx")

contract_analysis = agent.run(
    "分析contract.docx中的关键条款，识别潜在风险点并与标准条款对比",
    tools=["doc_parser", "legal_knowledge"],
    files=["contract.docx"]
)