Real-World Examples

Practical examples of using Instructor for structured data extraction.

Data Extraction

class CompanyInfo(BaseModel):
    name: str
    founded: int
    industry: str
    employees: int

text = "Apple was founded in 1976 in the technology industry with 164,000 employees."

company = client.messages.create(
    model="claude-sonnet-4-5-20250929",
    max_tokens=1024,
    messages=[{"role": "user", "content": f"Extract: {text}"}],
    response_model=CompanyInfo
)

Classification

class Sentiment(str, Enum):
    POSITIVE = "positive"
    NEGATIVE = "negative"
    NEUTRAL = "neutral"

class Review(BaseModel):
    sentiment: Sentiment
    confidence: float = Field(ge=0.0, le=1.0)

review = client.messages.create(
    model="claude-sonnet-4-5-20250929",
    max_tokens=1024,
    messages=[{"role": "user", "content": "This product is amazing!"}],
    response_model=Review
)

Multi-Entity Extraction

class Person(BaseModel):
    name: str
    role: str

class Entities(BaseModel):
    people: list[Person]
    organizations: list[str]
    locations: list[str]

entities = client.messages.create(
    model="claude-sonnet-4-5-20250929",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Tim Cook, CEO of Apple, spoke in Cupertino..."}],
    response_model=Entities
)

Structured Analysis

class Analysis(BaseModel):
    summary: str
    key_points: list[str]
    sentiment: Sentiment
    actionable_items: list[str]

analysis = client.messages.create(
    model="claude-sonnet-4-5-20250929",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Analyze: [long text]"}],
    response_model=Analysis
)

Batch Processing

texts = ["text1", "text2", "text3"]
results = [
    client.messages.create(
        model="claude-sonnet-4-5-20250929",
        max_tokens=1024,
        messages=[{"role": "user", "content": text}],
        response_model=YourModel
    )
    for text in texts
]

Streaming

for partial in client.messages.create_partial(
    model="claude-sonnet-4-5-20250929",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Generate report..."}],
    response_model=Report
):
    print(f"Progress: {partial.title}")
    # Update UI in real-time