@llm_function(llm_interface=llm)
async def extract_entities(text: str) -> list[Entity]:
    """从文本中提取命名实体。包括人物、组织和地点类型。"""
    pass

class Analysis(BaseModel):
    sentiment: Literal["positive", "negative", "neutral"]
    confidence: float = Field(ge=0.0, le=1.0)
    reasoning: str

result: Analysis = await analyze(text)  # 类型检查，结构保证

async def full_pipeline(document: str) -> Report:
    entities = await extract_entities(document)
    summary = await summarize(document)
    sentiment = await analyze_sentiment(document)
    return Report(entities=entities, summary=summary, sentiment=sentiment)

async def test_pipeline():
    with mock.patch("my_module.extract_entities") as mock_extract:
        mock_extract.return_value = [Entity(name="Alice", type="person")]
        result = await full_pipeline("Alice went to Paris.")
        assert result.entities[0].name == "Alice"

@llm_chat(llm_interface=llm, toolkit=[search, calculate], stream=True)
async def research_agent(question: str, history: list | None = None):
    """研究问题，使用可用工具。引用你的来源。"""
    pass