The unstructured library provides open-source components for ingesting and pre-processing images and text documents, such as PDFs, HTML, Word docs, and many more. The use cases of unstructured revolve around streamlining and optimizing the data processing workflow for LLMs. unstructured modular functions and connectors form a cohesive system that simplifies data ingestion and pre-processing, making it adaptable to different platforms and efficient in transforming unstructured data into structured outputs.
[!NOTE]
The open-source library is, and will stay, completely free. If you need higher-quality output, though, our latest models are available through the Transform v2 API. Compared with this library, Transform gets 2x table content accuracy, 58% less invented content, and 0.878 text accuracy (vs. 0.715):
| Model | Table Cell Content | Table Cell Spatial | Adjusted CCT (text accuracy) | Tokens Added (lower is better) |
|---|
| Transform (Best) | 0.866 | 0.853 | 0.878 | 0.050 |
| Unstructured OSS | 0.426 | 0.498 | 0.715 | 0.119 |
OSS: unstructured.io/benchmarks (1,000+ pages). Transform Best: SCOREBench, 224 pages, Sept 2026.
:eight_pointed_black_star: Quick Start
There are several ways to use the unstructured library:
Run the library in a container
The following instructions are intended to help you get up and running using Docker to interact with unstructured.
See here if you don't already have docker installed on your machine.
NOTE: we build multi-platform images to support both x86_64 and Apple silicon hardware. docker pull should download the corresponding image for your architecture, but you can specify with --platform (e.g. --platform linux/amd64) if needed.
We build Docker images for all pushes to main. We tag each image with the corresponding short commit hash (e.g. fbc7a69) and the application version (e.g. 0.5.5-dev1). We also tag the most recent image with latest. To leverage this, docker pull from our image repository.
docker pull downloads.unstructured.io/unstructured-io/unstructured:latest
Once pulled, you can create a container from this image and shell to it.
# create the container
docker run -dt --name unstructured downloads.unstructured.io/unstructured-io/unstructured:latest
# this will drop you into a bash shell where the Docker image is running
docker exec -it unstructured bash
You can also build your own Docker image. Note that the base image is wolfi-base, which is
updated regularly. If you are building the image locally, it is possible docker-build could
fail due to upstream changes in wolfi-base.
If you only plan on parsing one type of data you can speed up building the image by commenting out some
of the packages/requirements necessary for other data types. See Dockerfile to know which lines are necessary
for your use case.
make docker-build
# this will drop you into a bash shell where the Docker image is running
make docker-start-bash
Once in the running container, you can try things directly in Python interpreter's interactive mode.
# this will drop you into a python console so you can run the below partition functions
python3
>>> from unstructured.partition.pdf import partition_pdf
>>> elements = partition_pdf(filename="example-docs/layout-parser-paper-fast.pdf")
>>> from unstructured.partition.text import partition_text
>>> elements = partition_text(filename="example-docs/fake-text.txt")
Installing the library
Use the following instructions to get up and running with unstructured and test your
installation.
-
Install the Python SDK to support all document types with pip install "unstructured[all-docs]"
- For plain text files, HTML, XML, JSON and Emails that do not require any extra dependencies, you can run
pip install unstructured
- To process other doc types, you can install the extras required for those documents, such as
pip install "unstructured[docx,pptx]"
-
Install the following system dependencies if they are not already available on your system.
Depending on what document types you're parsing, you may not need all of these.
libmagic-dev (filetype detection)poppler-utils (images and PDFs)tesseract-ocr (images and PDFs, install tesseract-lang for additional language support)libreoffice (MS Office docs)pandoc is bundled automatically via the pypandoc-binary Python package (no system install needed)
-
For suggestions on how to install on the Windows and to learn about dependencies for other features, see the
installation documentation here.
At this point, you should be able to run the following code:
from unstructured.partition.auto import partition
elements = partition(filename="example-docs/eml/fake-email.eml")
print("\n\n".join([str(el) for el in elements]))
Installation Instructions for Local Development
The following instructions are intended to help you get up and running with unstructured
locally if you are planning to contribute to the project.
This is the beginning of the README. Read the rest at the original. Read the full README ↗
