Extractor

Extractor is the component that coordinates the extraction from documents. Contains a group of features for document processing like classify. Can be used alone or in group, inside of a Process.

Basic Extraction

The simplest way to extract data is using the Extractor class with a defined contract:

from extract_thinker import Extractor, DocumentLoaderPyPdf, Contract

class InvoiceContract(Contract):
    invoice_number: str
    invoice_date: str
    total_amount: float

#Initialize the extractor
extractor = Extractor()
extractor.load_document_loader(DocumentLoaderPyPdf())
extractor.load_llm("gpt-4o-mini") # or any other supported model

#Extract data from your document
result = extractor.extract("invoice.pdf", InvoiceContract)

print(f"Invoice #{result.invoice_number}")
print(f"Date: {result.invoice_date}")
print(f"Total: ${result.total_amount}")

---

Choosing the Right Model

When performing extraction, selecting the appropriate model is crucial for balancing performance, accuracy, and cost:

• GPT-4o-mini: Best for basic text extraction tasks, similar to OCR. Cost-effective for high-volume processing.
• GPT-4o: Ideal for tasks requiring deeper understanding of document structure and content.
• o1 and o1-mini: Perfect for complex extraction requiring reasoning and calculations.

Advanced Extraction with Vision

For documents that contain images or require visual understanding, you can enable vision capabilities:

from extract_thinker import Extractor, Contract
from typing import List

class ChartData(Contract):
    title: str
    data_points: List[float]
    description: str

#Initialize with vision support
extractor = Extractor()
extractor.load_llm("gpt-4o")

#Extract with vision enabled
result = extractor.extract(
    "chart.png",
    ChartData,
    vision=True # Enable vision processing
)

Note: When using vision capabilities, ensure your documents are high quality images or PDFs for optimal results.

Adding Context to Extraction

You can provide additional context to help guide the extraction process:

from extract_thinker import Extractor, Contract
class ResumeContract(Contract):
    name: str
    skills: List[str]
    experience: List[dict]

#Add context about the job requirements
job_description = {
    "role": "Software Engineer",
    "required_skills": ["Python", "AWS", "Docker"]
}

result = extractor.extract(
    "resume.pdf",
    ResumeContract,
    content=job_description # Add extra context
)