AI Agent

from langchain_openai import ChatOpenAI
from langchain.agents import AgentExecutor, create_react_agent
from langchain_core.prompts import PromptTemplate
from langchain_core.tools import tool

# 定义工具
@tool
def search_web(query: str) -> str:
"""搜索互联网获取信息"""
return f"搜索结果: 关于 '{query}' 的信息..."

@tool
def calculator(expression: str) -> str:
"""计算数学表达式"""
try:
    result = eval(expression)
    return str(result)
except Exception as e:
    return f"计算错误: {e}"

# 创建提示模板
prompt = PromptTemplate.from_template("""
你是一个有用的助手。使用以下工具回答问题。

工具:
{tools}

工具名称: {tool_names}

问题: {input}

思考过程:
{agent_scratchpad}
""")

# 创建 Agent
llm = ChatOpenAI(model="gpt-3.5-turbo")
tools = [search_web, calculator]

agent = create_react_agent(llm, tools, prompt)
executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

# 运行 Agent
result = executor.invoke({"input": "北京今天天气怎么样？"})
print(result["output"])

from openai import OpenAI
import json

client = OpenAI()

# 定义函数
tools = [{
"type": "function",
"function": {
    "name": "get_weather",
    "description": "获取指定城市的天气信息",
    "parameters": {
        "type": "object",
        "properties": {
            "city": {
                "type": "string",
                "description": "城市名称"
            }
        },
        "required": ["city"]
    }
}
}]

# 调用
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": "北京今天天气怎么样？"}],
tools=tools,
tool_choice="auto"
)

# 处理函数调用
message = response.choices[0].message

if message.tool_calls:
for tool_call in message.tool_calls:
    function_name = tool_call.function.name
    arguments = json.loads(tool_call.function.arguments)
      print("调用函数:", function_name, "参数:", arguments)

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

llm = ChatOpenAI(model="gpt-3.5-turbo")

# 定义不同角色的 Agent
researcher_prompt = ChatPromptTemplate.from_messages([
("system", "你是研究员。根据主题收集和整理相关信息。"),
("user", "{topic}")
])

writer_prompt = ChatPromptTemplate.from_messages([
("system", "你是作家。根据研究内容撰写文章。"),
("user", "{research}")
])

reviewer_prompt = ChatPromptTemplate.from_messages([
("system", "你是审稿人。审阅文章并提供改进建议。"),
("user", "{article}")
])

# 创建 Agent 链
researcher = researcher_prompt | llm | StrOutputParser()
writer = writer_prompt | llm | StrOutputParser()
reviewer = reviewer_prompt | llm | StrOutputParser()

# 协作流程
topic = "AI Agent 的发展趋势"

# 1. 研究
research = researcher.invoke({"topic": topic})
print("研究完成")

# 2. 写作
article = writer.invoke({"research": research})
print("文章完成")

# 3. 审稿
feedback = reviewer.invoke({"article": article})
print("审稿完成")