---
id: unstructured-ingest
version: "1.9.3"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# unstructured-ingest — Local ETL data pipeline to get data RAG ready

License: permissive · Maintenance: active · Downloads: 620.1K/mo

## 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 above — 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

pip install unstructured-ingest
uv add unstructured-ingest
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_current
- Install friction: low
- Maintenance: active
- Downloads: 620.1K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

ETL pipeline for document ingestion, prepare data for RAG, unstructured data processing, document extraction and transformation, data pipeline for AI, ingest raw documents, structured data preparation, rag-pipeline, document-processing, etl

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