ExtractThinker

An intelligent document processing framework designed for LLMs.

CommonProductProductivity\Document Processing\\LLM Integration\

ExtractThinker is a flexible intelligent document framework that helps users extract and classify structured data from various documents, akin to an ORM for document processing workflows. It is referred to as the 'Document Intelligence for LLMs' or the 'LangChain of Intelligent Document Processing.' The framework aims to provide specific functionalities required for document processing, such as splitting large documents and advanced classification.

\Data extraction with Pydantic: Extract structured data from any document type
and utilize Pydantic models for validation
custom functionalities
and prompt engineering capabilities.\
\Intelligent document classification and segmentation: Supports consensus strategies
eager/lazy segmentation
and confidence thresholds for intelligent document categorization and splitting.\
\PII detection: Automatically detect and handle sensitive personal information in documents
adopting a privacy-first approach with advanced validation practices.\
\LLM and OCR neutrality: Freely choose and switch between different LLM providers and OCR engines based on requirements and cost considerations.\

\Target audience includes businesses or individuals who need to process large volumes of documents and extract structured data
such as financial analysts
data scientists
and legal professionals. ExtractThinker is ideal for them because it offers a flexible and powerful tool to automate document processing tasks
enhancing efficiency and reducing manual errors.\

\Extract invoice data from PDF: Use ExtractThinker to extract invoice numbers
dates
and total amounts from PDF files.\
\Intelligent document classification: Classify large volumes of documents
identifying different document types and processing them accordingly.\

\1. Install ExtractThinker: Use pip to install extract_thinker.\
\2. Define the data to be extracted: Create a class that inherits from Contract and define the necessary data fields to extract.\
\3. Initialize the extractor: Create an Extractor instance and load the document loader and LLM model.\
\4. Extract data from documents: Use the extract method of the Extractor to extract data from the specified document
passing in the Contract class.\

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