skillfed

unstructured-ingest

Local ETL data pipeline to get data RAG ready

unstructured-ingest v1.9.3 620.1K downloads/30d#5,727 on PyPI
Permissive license Apache-2.0 Active released

What it is and what it does

Unstructured Ingest is a Python package that runs as a local ETL pipeline designed to take raw, unstructured documents and prepare them for use in AI systems, particularly retrieval-augmented generation (RAG) applications. It handles the work of ingesting documents and transforming them into clean, structured formats that downstream AI models can consume.

The package depends on a lean set of runtime libraries: pydantic for data validation, click for CLI support, tqdm for progress indication, opentelemetry-sdk for observability, ijson for JSON streaming, certifi for SSL certificates, and python-dateutil for date handling. It supports Python 3.11, 3.12, and 3.13, and is currently in Beta status with active maintenance.

Use it for:

  • Ingest a folder of documents and transform them into text chunks for a vector database.
  • Build a preprocessing step in a RAG pipeline that normalizes documents from multiple sources.
  • Extract structured content from unstructured documents before feeding them to a language model.
  • Automate local document processing workflows without relying on external APIs.
  • Prepare datasets by cleaning and standardizing raw document collections.

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

Unstructured Ingest is a local ETL pipeline that prepares unstructured data for retrieval-augmented generation (RAG) and other AI workflows by ingesting and transforming raw documents into structured, ready-to-use formats.

Yes, if you need to prepare unstructured documents for AI/RAG workflows locally. The package is actively maintained, has low install friction, carries a permissive Apache-2.0 license, and depends on stable, lightweight libraries. No known vulnerabilities as of 2026-08-14. The main uncertainty is whether its supported document formats and transformation capabilities match your specific use case.

Install

unstructured-ingest on PyPI

pip

pip install unstructured-ingest

uv

uv add unstructured-ingest

poetry

poetry add unstructured-ingest

Installing unstructured-ingest

Before you install

Low install friction with a pure-Python wheel distribution. Active maintenance with a release on 2026-08-14. Runtime dependencies are all well-established libraries, suggesting a stable, dependency-light setup.

License in practice

Apache-2.0 is a permissive open-source license. You can use, modify, and distribute this package freely in commercial and private projects, provided you include a copy of the license and state significant changes.

Quickstart

pip install unstructured-ingest

from unstructured_ingest import ...
# See documentation for specific ingestion and transformation workflows

Requires Python 3.11 or later (supports 3.11, 3.12, 3.13)

Verify before relying

  • What document formats (PDF, Word, HTML, etc.) does the ingestion pipeline actually support?
  • Does the package require external services or APIs, or does it run entirely locally?
  • What is the typical performance or throughput for document ingestion and transformation?

Package facts

License Apache-2.0 (permissive)
Python support supports the current Python release (<3.14,>=3.11)
Install friction low — pure-Python wheel
Runtime dependencies 7 — certifi, click, ijson, opentelemetry-sdk, pydantic, python-dateutil, tqdm
Maintenance actively maintained — 0 days since the last release
First released
Downloads 620,054/month — #5,727 on PyPI (30-day window, as of 2026-08-14)
Known vulnerabilities none known (OSV.dev, checked 2026-08-14)

Evidence: unstructured_ingest-1.9.3-py3-none-any.whl

Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Topic :: Scientific/Engineering :: Artificial Intelligence

Tags

ETL pipeline for document ingestionprepare data for RAGunstructured data processingdocument extraction and transformationdata pipeline for AIingest raw documentsstructured data preparation
rag-pipelinedocument-processingetl

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