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instructor

by davila7

Extract structured data from LLM responses with Pydantic validation, retry failed extractions automatically, parse complex JSON with type safety, and stream partial results with Instructor - battle-tested structured output library

Installation

Pick a client and clone the repository into its skills directory.

Installation

Quick info

Author
davila7
Category
Security
Views
1
GitHub repo

About this skill

Extract structured data from LLM responses with Pydantic validation, retry failed extractions automatically, parse complex JSON with type safety, and stream partial results with Instructor - battle-tested structured output library

How to use

  1. Zainstaluj Instructor za pomocą pip install instructor. Jeśli pracujesz z Claude, uruchom pip install "instructor[anthropic]", a dla OpenAI — pip install "instructor[openai]".

  2. Zdefiniuj strukturę danych, którą chcesz otrzymać, używając klasy Pydantic. Na przykład stwórz klasę User z polami name (tekst), age (liczba) i email (tekst).

  3. Utwórz klienta Instructor, łącząc go z dostawcą LLM. Dla Claude użyj instructor.from_anthropic(Anthropic()), dla OpenAI — instructor.from_openai(OpenAI()).

  4. Wyślij wiadomość do modelu, przekazując response_model z definicją Pydantic. Model automatycznie zwróci dane w oczekiwanym formacie.

  5. Otrzymane dane są już zwalidowane i typizowane — możesz bezpośrednio odwoływać się do pól obiektu, np. user.name lub user.age.

  6. Jeśli chcesz przetwarzać wyniki w czasie rzeczywistym, włącz streaming, aby otrzymywać częściowe rezultaty w miarę generowania odpowiedzi.

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