# Structured Extraction

Source: https://staging-docs.aiand.com/cookbook/extraction/

Extract structured records from free-form text — invoices, emails, transcripts, scraped pages. Use a strict JSON Schema so the result parses cleanly every time.

## Example: parse contact info

```python
from openai import OpenAI

client = OpenAI(base_url="https://api.aiand.com/v1", api_key="sk-...")

text = """
From: Jane Doe <jane@example.com>
Re: Coffee next week
Phone: +1 555-123-4567
"""

response = client.chat.completions.create(
    model="openai/gpt-oss-120b",
    messages=[
        {"role": "system", "content": "Extract contact info from the user's text."},
        {"role": "user", "content": text},
    ],
    response_format={
        "type": "json_schema",
        "json_schema": {
            "name": "contact",
            "strict": True,
            "schema": {
                "type": "object",
                "properties": {
                    "name": {"type": "string"},
                    "email": {"type": "string"},
                    "phone": {"type": ["string", "null"]},
                },
                "required": ["name", "email", "phone"],
                "additionalProperties": False,
            },
        },
    },
)

import json
contact = json.loads(response.choices[0].message.content)
print(contact)
# {'name': 'Jane Doe', 'email': 'jane@example.com', 'phone': '+1 555-123-4567'}
```

## With Pydantic (Python)

The OpenAI Python SDK can derive the schema from a Pydantic model:

```python
from pydantic import BaseModel
from typing import Optional

class Contact(BaseModel):
    name: str
    email: str
    phone: Optional[str]

response = client.chat.completions.parse(
    model="openai/gpt-oss-120b",
    messages=[
        {"role": "system", "content": "Extract contact info."},
        {"role": "user", "content": text},
    ],
    response_format=Contact,
)

contact = response.choices[0].message.parsed
print(contact.name, contact.email)
```

## Tips

- Make every field `required` and use `["string", "null"]` for optional values — `strict` mode rejects missing keys, not nullable ones.
- Set `additionalProperties: false` to prevent the model from sneaking in extra fields.
- For nested arrays (multiple contacts), wrap in `{"type": "array", "items": {...}}` inside an outer object.

<Aside type="note">
  `response_format: json_schema` is honored at the model layer. Models that advertise `tool_calling` on the [Catalog](/models/catalog/) generally accept strict JSON schemas; if a model rejects it, fall back to `{"type": "json_object"}` and validate client-side.
</Aside>