讲解器

将 LiteLLM 与 jxnl 的 instructor 库 结合使用，以获得更健壮的结构化输出。输出会自动验证为 Pydantic 类型，并将验证错误提供回模型，以增加重试时成功响应的机会。

使用方法 (同步)

import instructor
from litellm import completion
from pydantic import BaseModel


client = instructor.from_litellm(completion)


class User(BaseModel):
    name: str
    age: int


def extract_user(text: str):
    return client.chat.completions.create(
        model="gpt-4o-mini",
        response_model=User,
        messages=[
            {"role": "user", "content": text},
        ],
        max_retries=3,
    )

user = extract_user("Jason is 25 years old")

assert isinstance(user, User)
assert user.name == "Jason"
assert user.age == 25
print(f"{user=}")

使用方法 (异步)

import asyncio

import instructor
from litellm import acompletion
from pydantic import BaseModel


client = instructor.from_litellm(acompletion)


class User(BaseModel):
    name: str
    age: int


async def extract(text: str) -> User:
    return await client.chat.completions.create(
        model="gpt-4o-mini",
        response_model=User,
        messages=[
            {"role": "user", "content": text},
        ],
        max_retries=3,
    )

user = asyncio.run(extract("Alice is 30 years old"))

assert isinstance(user, User)
assert user.name == "Alice"
assert user.age == 30
print(f"{user=}")