Classification Example

ExtractThinker allows you to classify documents or parts of documents using custom classifications:

import os
from dotenv import load_dotenv
from extract_thinker import (
    Extractor, Classification, Process, ClassificationStrategy,
    DocumentLoaderPyPdf, Contract
)

load_dotenv()

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

class DriverLicenseContract(Contract):
    name: str
    license_number: str

# Initialize the extractor and load the document loader
extractor = Extractor()
extractor.load_document_loader(DocumentLoaderPyPdf())
extractor.load_llm("gpt-4o-mini")

# Define classifications
classifications = [
    Classification(
        name="Invoice",
        description="An invoice document",
        contract=InvoiceContract,
        extractor=extractor,
    ),
    Classification(
        name="Driver License",
        description="A driver's license document",
        contract=DriverLicenseContract,
        extractor=extractor,
    ),
]

# Classify the document directly using the extractor
result = extractor.classify(
    "path_to_your_document.pdf",  # Can be a file path or IO stream
    classifications,
    image=True  # Set to True for image-based classification
)

# The result will be a ClassificationResponse object with 'name' and 'confidence' fields
print(f"Document classified as: {result.name}")
print(f"Confidence level: {result.confidence}")

Splitting Files Example

ExtractThinker allows you to split and process documents using different strategies. Here's how you can split a document and extract data based on classifications.

import os
from dotenv import load_dotenv
from extract_thinker import (
    Extractor,
    Process,
    Classification,
    ImageSplitter,
    DocumentLoaderTesseract,
    Contract,
    SplittingStrategy,
)

load_dotenv()

class DriverLicenseContract(Contract):
    name: str
    license_number: str

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

# Initialize the extractor and load the document loader
extractor = Extractor()
extractor.load_document_loader(DocumentLoaderPyPdf())
extractor.load_llm("gpt-4o-mini")

# Define classifications
classifications = [
    Classification(
        name="Driver License",
        description="A driver's license document",
        contract=DriverLicenseContract,
        extractor=extractor,
    ),
    Classification(
        name="Invoice",
        description="An invoice document",
        contract=InvoiceContract,
        extractor=extractor,
    ),
]

# Initialize the process and load the splitter
process = Process()
process.load_document_loader(DocumentLoaderPyPdf())
process.load_splitter(ImageSplitter(model="gpt-4o-mini"))

# Load and process the document
path_to_document = "path_to_your_multipage_document.pdf"
split_content = (
    process.load_file(path_to_document)
    .split(classifications, strategy=SplittingStrategy.LAZY)
    .extract()
)

# Process the extracted content as needed
for item in split_content:
    if isinstance(item, InvoiceContract):
        print("Extracted Invoice:")
        print("Invoice Number:", item.invoice_number)
        print("Invoice Date:", item.invoice_date)
    elif isinstance(item, DriverLicenseContract):
        print("Extracted Driver License:")
        print("Name:", item.name)
        print("License Number:", item.license_number)

Batch Processing Example

You can also perform batch processing of documents:

from extract_thinker import Extractor, Contract

class ReceiptContract(Contract):
    store_name: str
    total_amount: float

extractor = Extractor()
extractor.load_llm("gpt-4o-mini")

# List of file paths or streams
document = "receipt1.jpg"

batch_job = extractor.extract_batch(
    source=document,
    response_model=ReceiptContract,
    vision=True,
)

# Monitor the batch job status
print("Batch Job Status:", await batch_job.get_status())

# Retrieve results once processing is complete
results = await batch_job.get_result()
for result in results.parsed_results:
    print("Store Name:", result.store_name)
    print("Total Amount:", result.total_amount)

Local LLM Integration Example

ExtractThinker supports custom LLM integrations. Here's how you can use a custom LLM:

from extract_thinker import Extractor, LLM, DocumentLoaderTesseract, Contract

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

# Initialize the extractor
extractor = Extractor()
extractor.load_document_loader(DocumentLoaderTesseract(os.getenv("TESSERACT_PATH")))

# Load a custom LLM (e.g., Ollama)
os.environ['API_BASE'] = "http://localhost:11434"
llm = LLM('ollama/phi3')
extractor.load_llm(llm)

# Extract data
result = extractor.extract("invoice.png", InvoiceContract)
print("Invoice Number:", result.invoice_number)
print("Invoice Date:", result.invoice_date)

📚 Documentation and Resources

• Examples: Check out the examples directory for Jupyter notebooks and scripts demonstrating various use cases.
• Medium Articles: Read articles about ExtractThinker on the author's Medium page.
• Test Suite: Explore the test suite in the tests/ directory for more advanced usage examples and test cases.

🧩 Integration with LLM Providers

ExtractThinker supports integration with multiple LLM providers:

• OpenAI: Use models like gpt-3.5-turbo, gpt-4, etc.
• Anthropic: Integrate with Claude models.
• Cohere: Utilize Cohere's language models.
• Azure OpenAI: Connect with Azure's OpenAI services.
• Local Models: Ollama compatible models.

⚙️ How It Works

ExtractThinker uses a modular architecture inspired by the LangChain ecosystem:

• Document Loaders: Responsible for loading and preprocessing documents from various sources and formats.
• Extractors: Orchestrate the interaction between the document loaders and LLMs to extract structured data.
• Splitters: Implement strategies to split documents into manageable chunks for processing.
• Contracts: Define the expected structure of the extracted data using Pydantic models.
• Classifications: Classify documents or document sections to apply appropriate extraction contracts.
• Processes: Manage the workflow of loading, classifying, splitting, and extracting data from documents.

📝 Why Use ExtractThinker?

While general frameworks like LangChain offer a broad range of functionalities, ExtractThinker is specialized for Intelligent Document Processing (IDP). It simplifies the complexities associated with IDP by providing:

• Specialized Components: Tailored tools for document loading, splitting, and extraction.
• High Accuracy with LLMs: Leverages the power of LLMs to improve the accuracy of data extraction and classification.
• Ease of Use: Intuitive APIs and ORM-style interactions reduce the learning curve.
• Community Support: Active development and support from the community.

🤝 Contributing

We welcome contributions from the community! To contribute:

1. Fork the repository
2. Create a new branch for your feature or bugfix
3. Write tests for your changes
4. Run tests to ensure everything is working correctly
5. Submit a pull request with a description of your changes

🌟 Community and Support

Stay updated and connect with the community:

• Scaling Document Extraction with o1, GPT-4o & Mini
• Claude 3.5 — The King of Document Intelligence
• Classification Tree for LLMs
• Advanced Document Classification with LLMs
• Phi-3 and Azure: PDF Data Extraction | ExtractThinker
• ExtractThinker: Document Intelligence for LLMs

📄 License

This project is licensed under the Apache License 2.0. See the LICENSE file for more details.

Contact

For any questions or issues, please open an issue on the GitHub repository or reach out via email.