自定义工作流

from typing import TypedDict
from pydantic import BaseModel
from langgraph.graph import StateGraph, START, END
from langchain.agents import create_agent
from langchain.tools import tool
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_core.vectorstores import InMemoryVectorStore

class State(TypedDict):
    question: str
    rewritten_query: str
    documents: list[str]
    answer: str

# WNBA 知识库，包含阵容、比赛结果和球员统计数据
embeddings = OpenAIEmbeddings()
vector_store = InMemoryVectorStore(embeddings)
vector_store.add_texts([
    # 阵容
    "New York Liberty 2024 roster: Breanna Stewart, Sabrina Ionescu, Jonquel Jones, Courtney Vandersloot.",
    "Las Vegas Aces 2024 roster: A'ja Wilson, Kelsey Plum, Jackie Young, Chelsea Gray.",
    "Indiana Fever 2024 roster: Caitlin Clark, Aliyah Boston, Kelsey Mitchell, NaLyssa Smith.",
    # 比赛结果
    "2024 WNBA Finals: New York Liberty defeated Minnesota Lynx 3-2 to win the championship.",
    "June 15, 2024: Indiana Fever 85, Chicago Sky 79. Caitlin Clark had 23 points and 8 assists.",
    "August 20, 2024: Las Vegas Aces 92, Phoenix Mercury 84. A'ja Wilson scored 35 points.",
    # 球员统计数据
    "A'ja Wilson 2024 season stats: 26.9 PPG, 11.9 RPG, 2.6 BPG. Won MVP award.",
    "Caitlin Clark 2024 rookie stats: 19.2 PPG, 8.4 APG, 5.7 RPG. Won Rookie of the Year.",
    "Breanna Stewart 2024 stats: 20.4 PPG, 8.5 RPG, 3.5 APG.",
])
retriever = vector_store.as_retriever(search_kwargs={"k": 5})

@tool
def get_latest_news(query: str) -> str:
    """获取最新的 WNBA 新闻和更新。"""
    # 您的新闻 API 在这里
    return "Latest: The WNBA announced expanded playoff format for 2025..."

agent = create_agent(
    model="openai:gpt-5.4",
    tools=[get_latest_news],
)

model = ChatOpenAI(model="gpt-5.4")

class RewrittenQuery(BaseModel):
    query: str

def rewrite_query(state: State) -> dict:
    """重写用户查询以获得更好的检索效果。"""
    system_prompt = """Rewrite this query to retrieve relevant WNBA information.
The knowledge base contains: team rosters, game results with scores, and player statistics (PPG, RPG, APG).
Focus on specific player names, team names, or stat categories mentioned."""
    response = model.with_structured_output(RewrittenQuery).invoke([
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": state["question"]}
    ])
    return {"rewritten_query": response.query}

def retrieve(state: State) -> dict:
    """基于重写的查询检索文档。"""
    docs = retriever.invoke(state["rewritten_query"])
    return {"documents": [doc.page_content for doc in docs]}

def call_agent(state: State) -> dict:
    """使用检索到的上下文生成答案。"""
    context = "\n\n".join(state["documents"])
    prompt = f"Context:\n{context}\n\nQuestion: {state['question']}"
    response = agent.invoke({"messages": [{"role": "user", "content": prompt}]})
    return {"answer": response["messages"][-1].content_blocks}

workflow = (
    StateGraph(State)
    .add_node("rewrite", rewrite_query)
    .add_node("retrieve", retrieve)
    .add_node("agent", call_agent)
    .add_edge(START, "rewrite")
    .add_edge("rewrite", "retrieve")
    .add_edge("retrieve", "agent")
    .add_edge("agent", END)
    .compile()
)

result = workflow.invoke({"question": "Who won the 2024 WNBA Championship?"})
print(result["answer"])