unstructured-ingest
Local ETL data pipeline to get data RAG ready
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.11 or later (supports 3.11, 3.12, 3.13)
- Low install friction with a pure-Python wheel distribution.
- Active maintenance with a release on 2026-08-14.
License · maintenance · safety
Apache-2.0 (permissive) — 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.
last release 2026-08-14 (0 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 620,054 downloads/mo, #5,727 on PyPI
Alternatives
Verify before relying
pip install unstructured-ingest
from unstructured_ingest import ...
# See documentation for specific ingestion and transformation workflows- 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?
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 on it.
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
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.
Requires Python 3.11 or later (supports 3.11, 3.12, 3.13)
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 workflowsVerify 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 packagescertificlickijsonopentelemetry-sdkpydanticpython-dateutiltqdm |
| 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 |
| Classifiers | 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 |
Evidence: unstructured_ingest-1.9.3-py3-none-any.whl
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