Minimal type-checked LLM helper (experimental)
Project description
dariko
Minimal type-checked LLM helper (experimental)
インストール
pip install dariko
使用方法
基本的な使い方
from dariko import configure, ask
from pydantic import BaseModel
# APIキーを設定
configure("your-api-key") # または環境変数 DARIKO_API_KEY を設定
# 型定義
class Person(BaseModel):
name: str
age: int
# 型アノテーションから自動推論
result: Person = ask("名前と年齢を教えて")
print(result.name) # "Alice"
print(result.age) # 30
関数の戻り値型から推論
def get_person(prompt: str) -> Person:
return ask(prompt) # 戻り値型から自動推論
result = get_person("名前と年齢を教えて")
明示的な型指定
result = ask("名前と年齢を教えて", output_model=Person)
バッチ処理
from dariko import ask_batch
prompts = [
"名前と年齢を教えて",
"別の人の名前と年齢を教えて"
]
results: list[Person] = ask_batch(prompts)
エラーハンドリング
from dariko import ValidationError
try:
result: Person = ask("無効な応答を返して")
except ValidationError as e:
print(f"型検証エラー: {e}")
型ヒントのサポート
- Pydantic BaseModel
- 基本的な型(str, int, float, bool)
- コレクション型(list, dict)
- Optional, Union などの型
- ネストされた構造
- 再帰的な型
ライセンス
MIT
from pydantic import BaseModel
from dariko import ask
class Person(BaseModel):
name: str
age: int
result: Person = ask("次の JSON を返して: {name:'Alice', age:30}")
print(result)
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