Instructor & Pydantic Tooling
Structured data extraction & type-safe LLM function schemas
Instructor enforces strict Pydantic and Zod schemas on LLM outputs, turning unstructured web and API data into typed objects with automatic retries.
Kom hurtigt i gang
extract.py
Kom i gang med Instructor & Pydantic Tooling i dit projekt.
import instructor
from openai import OpenAI
from pydantic import BaseModel
class DanishAddress(BaseModel):
street: str
house_number: str
postal_code: int
city: str
client = instructor.from_openai(OpenAI())
addr = client.chat.completions.create(
model="gpt-4o",
response_model=DanishAddress,
messages=[{"role": "user", "content": "Rådhuspladsen 1, 1550 København V"}],
)
print(addr.city)Om Instructor & Pydantic Tooling
Instructor is the gold standard for getting structured, type-safe data out of LLMs. When parsing Danish government PDF reports, unstructured HTML, or legacy XML APIs, Instructor guarantees the response matches your schema with validation rules and automatic self-correction on error.
Danske data- og produkt-guides
Hvordan udviklere og agenter bruger Instructor & Pydantic Tooling til at tilgå dansk digital infrastruktur.
Extract Danish Company Accounting Metrics into Pydantic
Define a Pydantic schema for Danish solvency, EBITDA, and equity, and extract structured data directly from annual PDF reports.
from pydantic import BaseModel
import instructor
from openai import OpenAI
class DanishFinancials(BaseModel):
cvr: str
year: int
ebitda_dkk: float
equity_dkk: float
client = instructor.from_openai(OpenAI())
data = client.chat.completions.create(
model="gpt-4o",
response_model=DanishFinancials,
messages=[{"role": "user", "content": "Extract financials from this text..."}],
)