Document intelligence library for LLMs, ORM-style interaction
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ExtractThinker is a Python library designed for document intelligence, enabling users to extract and classify structured data from various document formats using Large Language Models (LLMs). It targets developers and researchers working with document processing workflows, offering an ORM-like interface for simplified interaction with LLMs and document data.
How It Works
ExtractThinker employs a modular architecture, inspired by LangChain, comprising Document Loaders for data ingestion, Extractors for LLM orchestration, Splitters for chunking, Contracts (Pydantic models) for defining output schemas, Classifications for routing, and Processes for managing workflows. This design facilitates specialized document processing, aiming for higher accuracy and ease of use compared to general-purpose frameworks.
Quick Start & Requirements
pip install extract_thinker
dotenv
for environment variables. LLM API keys (e.g., OpenAI) are required for most functionalities. Tesseract OCR path may be needed for image processing.Highlighted Details
Maintenance & Community
Licensing & Compatibility
Limitations & Caveats
The README mentions "path_to_your_files" and "path_to_your_document.pdf" as placeholders, indicating that users must configure these paths correctly. Some advanced features like OCR or specific LLM integrations may require additional setup or dependencies not detailed in the basic installation.
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