# enoch3712/ExtractThinker

ExtractThinker is a Document Intelligence library for LLMs, offering ORM-style interaction for flexible and powerful document workflows.

Repository: https://github.com/enoch3712/ExtractThinker
Canonical: https://ross.abutalabs.com/products/extractthinker
Homepage: https://enoch3712.github.io/ExtractThinker
Language: Python
License: Apache-2.0
License Family: permissive
Topics: ai, llm, nlp, ocr, openai, python, document-image-analysis, document-intelligence, document-parsing, document-processing, langchain, machine-learning, pdf, pdf-to-text
Last push: 2025-08-27T23:06:09+00:00

## Health v2 (maintenance only)
Score: 45/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 39, release rhythm 40, longevity 67
- inputs: {"age_days": 944, "days_push": 371, "days_rel": 450, "gap_med": 5, "n_releases_24m": 34}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1595, forks 154 (observed 2026-08-28T04:05:09.091152+00:00)

## What it is
ExtractThinker is a Python document intelligence library that uses LLMs to extract and classify structured data from documents like PDFs, images, and spreadsheets. It provides ORM-style interaction with Pydantic contracts and pluggable document loaders (Tesseract OCR, Azure Form Recognizer, AWS Textract, Google Document AI).

## Use cases
- extract structured fields from invoices and receipts with LLMs
- convert pdf documents to structured  using pydantic contracts
- classify documents or document sections automatically
- process large multi-page pdfs page by page with splitting strategies
- run ocr on scanned documents and feed results to an llm
- build document processing pipelines with multiple cloud ocr providers
- extract data from spreadsheets and images into typed python objects

## When to choose
- you need LLM-powered structured extraction from documents with typed contracts
- you want to swap between OCR providers (Azure, AWS Textract, Google Document AI, Tesseract) behind one interface
- you prefer an ORM-like API for document workflows with async support
- you need document classification and splitting strategies for large files

## When to avoid
- you need simple deterministic pdf text extraction without LLMs
- you want a ready-made GUI application rather than a Python library
- your documents contain highly sensitive data you cannot send to external LLM providers
- you need a fully managed document intelligence service with SLAs

## Facets
- artifact type: library
- maturity: active
- function: ocr, nlp, llm-inference, rag, pdf, parser, machine-learning, data-science
- domain: artificial-intelligence, large-language-models, pdf, data-science, developer-tools
- platform: python, cross-platform
- tags: document-intelligence, document-processing, pydantic-contracts, structured-extraction, langchain-inspired, document-classification, invoice-parsing, natural-language-processing

## Member repositories
- enoch3712/ExtractThinker (main) score 45

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:09.091152+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:54:06.585776+00:00, confidence not recorded.
  - readme: https://github.com/enoch3712/ExtractThinker (fetched 2026-08-28T04:05:09.091152+00:00, sha 283af60d69c7)
  - homepage: https://enoch3712.github.io/ExtractThinker (fetched 2026-08-29T11:24:50.933344+00:00, sha c88905d4e266)
- Data as of 2026-08-30T08:39:29.467469+00:00.