import operator
from typing import Annotated, TypedDict, List
from langchain_core.messages import BaseMessage, HumanMessage
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, END
from langgraph.prebuilt import ToolNode

# 1. 定義 Agent 狀態 (State)
class AgentState(TypedDict):
    messages: Annotated[List[BaseMessage], operator.add]

# 2. 定義模型與工具
model = ChatOpenAI(model="gpt-4o", temperature=0)
tools = [my_search_tool, my_calculator_tool]
model_with_tools = model.bind_tools(tools)

# 3. 定義節點邏輯
def call_agent(state: AgentState):
    response = model_with_tools.invoke(state["messages"])
    return {"messages": [response]}

def should_continue(state: AgentState):
    last_message = state["messages"][-1]
    if last_message.tool_calls:
        return "tools"
    return END

# 4. 構建狀態圖
workflow = StateGraph(AgentState)
workflow.add_node("agent", call_agent)
workflow.add_node("tools", ToolNode(tools))

workflow.set_entry_point("agent")
workflow.add_conditional_edges("agent", should_continue)
workflow.add_edge("tools", "agent")

app = workflow.compile()
result = app.invoke({"messages": [HumanMessage(content="分析這檔股票的本益比")]})

from crewai import Agent, Task, Crew, Process
from langchain_community.tools import DuckDuckGoSearchRun

search_tool = DuckDuckGoSearchRun()

# 定義專業角色
researcher = Agent(
    role='資深產業研究員',
    goal='深入探勘特定領域的最新技術突破與數據',
    backstory='擁有10年科技產業分析經驗，善於在海量資訊中去蕪存菁。',
    tools=[search_tool],
    verbose=True
)

writer = Agent(
    role='首席科技專欄作家',
    goal='將調研數據整理成結構清晰、引人入勝的白皮書',
    backstory='知名科技媒體專欄主筆，文筆精煉有力。',
    verbose=True
)

# 定義任務
task1 = Task(
    description='調研 2026 年最新 AI Agent 架構趨勢',
    expected_output='條列 5 大關鍵技術突破',
    agent=researcher
)
task2 = Task(
    description='根據調研產出 Markdown 格式的總結報告',
    expected_output='完整的 Markdown 研報',
    agent=writer
)

# 組成團隊並執行
tech_crew = Crew(
    agents=[researcher, writer],
    tasks=[task1, task2],
    process=Process.sequential
)

result = tech_crew.kickoff()

from openai import OpenAI
import json

client = OpenAI()

tools = [{
    "type": "function",
    "function": {
        "name": "get_stock_price",
        "description": "取得指定股票代碼的即時股價",
        "parameters": {
            "type": "object",
            "properties": {
                "ticker": {"type": "string", "description": "股票代碼，例如 2330.TW"}
            },
            "required": ["ticker"]
        }
    }
}]

messages = [{"role": "user", "content": "請問台積電現價多少？"}]
response = client.chat.completions.create(
    model="gpt-4o",
    messages=messages,
    tools=tools
)

tool_call = response.choices[0].message.tool_calls
if tool_call:
    fn_name = tool_call[0].function.name
    fn_args = json.loads(tool_call[0].function.arguments)
    print(f"調用工具名稱: {fn_name}, 參數: {fn_args}")