把 Answers 用作工具

from agents import Agent, Runner, function_tool
from openai import OpenAI

mirobody = OpenAI(
    api_key="mb_live_...",
    base_url="https://test-mirobody-api.thetahealth.ai/v1",
)

@function_tool
def health_answers(question: str, user_id: str) -> str:
    """Answer a question from this end user's REAL health records
    (labs, vitals, reports). Grounded and citation-backed — use it for
    anything about the user's own health data."""
    resp = mirobody.chat.completions.create(
        model="mirobody-flash",
        messages=[{"role": "user", "content": question}],
        user=user_id,                    # 终端用户的稳定 id（Subject）
    )
    return resp.choices[0].message.content

coach = Agent(
    name="Wellness coach",
    model="gpt-4.1",                     # 你的模型、你的编排
    instructions=(
        "You are a wellness coach. For anything about the user's own labs, "
        "vitals or reports, call health_answers with their user_id — never guess."
    ),
    tools=[health_answers],
)

result = Runner.run_sync(coach, "user_id=alice — Should I be worried about my recent glucose?")
print(result.final_output)

from langchain_core.tools import tool
from langchain.agents import create_agent          # LangChain v1 agent API
from openai import OpenAI

mirobody = OpenAI(
    api_key="mb_live_...",
    base_url="https://test-mirobody-api.thetahealth.ai/v1",
)

@tool
def health_answers(question: str, user_id: str) -> str:
    """Answer a question from this end user's real health records
    (labs, vitals, reports). Grounded, with traceable evidence."""
    resp = mirobody.chat.completions.create(
        model="mirobody-flash",
        messages=[{"role": "user", "content": question}],
        user=user_id,
    )
    return resp.choices[0].message.content

agent = create_agent(model="openai:gpt-4.1", tools=[health_answers])
out = agent.invoke({"messages": [
    {"role": "user", "content": "user_id=alice — how did my LDL respond to the diet change?"}
]})
print(out["messages"][-1].content)