# emcf/thepipe

Get clean data from tricky documents, powered by vision-language models ⚡

Repository: https://github.com/emcf/thepipe
Canonical: https://ross.abutalabs.com/products/thepipe
Homepage: https://thepi.pe
Language: Python
License: MIT
License Family: permissive
Topics: multimodal, pdf, vision-transformer, large-language-models, web, scrapers, document, openai, python, scraping, vision-language-model, structured-data, unstructured-data
Last push: 2026-03-25T21:17:38+00:00

## Health v2 (maintenance only)
Score: 58/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 74, release rhythm 35, longevity 63
- inputs: {"age_days": 894, "days_push": 161, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1524, forks 99 (observed 2026-08-28T04:04:58.156025+00:00)

## What it is
thepipe is a Python package and API that extracts clean markdown, multimodal media, and structured data from tricky documents like PDFs, Word docs, webpages, videos, and audio, using vision-language models under the hood. It integrates out-of-the-box with LLMs, vector databases, and RAG frameworks, and can be self-hosted or used via a hosted cloud API.

## Use cases
- extract clean markdown from complex pdfs
- parse tables and figures from word documents for llms
- scrape webpages into structured data for rag pipelines
- chunk documents for vector databases like chromadb
- convert pdfs to  or csv with vision-language models
- transcribe and extract data from audio and video files
- build an ai document etl pipeline for ingestion

## When to choose
- you need high-quality extraction from messy or complex document layouts
- you want multimodal extraction including images, tables, video, and audio
- you need output ready for LLMs, vector databases, or RAG frameworks
- you want a self-hosted, on-prem or air-gapped document pipeline

## When to avoid
- you only need simple plain-text extraction from well-structured documents without AI overhead
- you cannot use external VLM APIs and lack GPU hardware for local models
- you need a lightweight CPU-only parser with no optional heavy dependencies

## Facets
- artifact type: library
- maturity: active
- function: web-scraping, ocr, pdf, etl, rag, nlp, machine-learning, parser, data-generation
- domain: large-language-models, pdf, artificial-intelligence, developer-tools, files
- platform: python, cross-platform, self-hosted
- tags: vision-language-models, document-extraction, multimodal, markdown-conversion, structured-data-extraction, chunking, vector-database-integration, document-parsing, retrieval-augmented-generation, natural-language-processing, data-engineering, web-server

## Member repositories
- emcf/thepipe (main) score 58

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:58.156025+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 2026-08-30T04:31:42.401572+00:00, confidence not recorded.
  - readme: https://github.com/emcf/thepipe (fetched 2026-08-28T04:04:58.156025+00:00, sha 285ad1e8a763)
  - homepage: https://thepi.pe (fetched 2026-08-29T11:34:32.543238+00:00, sha fbbbf61cda6d)
- Data as of 2026-08-30T08:39:29.467469+00:00.