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Sources

Destinations

Supported connectors

Unstructured

file-certificate

microchip-ai

Overview

Supported file types

Quickstart

Unstructured UI quickstart

Workflows

Jobs

Billing

Document elements and metadata

Partitioning

Chunking

Embedding

POST request

Process an individual file by making a direct POST request

Python SDK

JavaScript/TypeScript SDK

Endpoint parameters

Platform Endpoint parameters

This section details the structure of HTTP validation errors returned by the Unstructured Partition Endpoint.

Endpoint validation errors

This page provides some examples of accessing Unstructured Partition Endpoint via different methods.

Examples

Partitioning strategies

Chunking strategies

Speed up processing of large files and batches

Get element contents

Extract tables as HTML

Extract images and tables from documents

Get chunked elements

Transform a JSON file into a different schema

Generate a JSON schema for a file

Pipeline

Free Unstructured API

Follow these steps to deploy the Unstructured API service into your AWS account.

Unstructured API on AWS

Unstructured API on Azure

API keys and URLs

Troubleshooting Unstructured API keys and URLs

Notebooks contain complete working sample code for end-to-end solutions.

Notebooks

Vector database ingestion

Delta Table source connector

Multi-file API processing

This section describes two methods for extracting tables from PDF files.

Table extraction from PDF

Query processed PDF with HuggingChat

Langflow

VectorShift

Ingest CLI

Process files in batches by using the Unstructured Ingest CLI

Ingest Python library

Process files in batches by using the Unstructured Ingest Python library

Ingest dependencies

Unstructured Open Source

The `unstructured` library includes functions to partition, chunk, clean, and stage raw source documents. These functions serve as the primary public interfaces within the library.

Core Functionality

Partitioning functions in `unstructured` allow users to extract structured content from a raw unstructured document. These functions break a document down into elements such as `Title`, `NarrativeText`, and `ListItem`, enabling users to decide what content they’d like to keep for their particular application. If you’re training a summarization model, for example, you may only be interested in `NarrativeText`.

As part of data preparation for an NLP model, it’s common to need to clean up your data prior to passing it into the model. If there’s unwanted content in your output, for example, it could impact the quality of your NLP model. To help with this, the `unstructured` library includes cleaning functions to help users sanitize output before sending it to downstream applications.

Cleaning

Extracting

Staging

Chunking functions in `unstructured` use metadata and document elements detected with `partition` functions to post-process elements into more useful "chunks" for uses cases such as Retrieval Augmented Generation (RAG).

Set the OCR agent

Depending on your need, `Unstructured` provides OCR-based and Transformer-based models to detect elements in the documents. The models are useful to detect the complex layout in the documents and predict the element types.

Models

Integrate your model development pipeline with your favorite machine learning frameworks and libraries, and prepare your data for ingestion into downstream systems. Most of our integrations come in the form of [staging functions](/open-source/core-functionality/staging), which take a list of `Element` objects as input and return formatted dictionaries as output.

Integrations

Frequently asked questions

Contact Us

Community

Feedback

Blog

Product

Trust Portal

Meta-Prompt (Titles)

Meta-Prompt (Full)

Welcome

Example code

Ingest

Open source

Unstructured open source library offers flexible options for installation.

Full Installation

The instructions below guide you on how to use the unstructured library inside a Docker container.

Docker Installation

Source connectors are essential components in data integration systems that establish a link between your files and the data ingestion process. They facilitate the batch processing of files, allowing for the systematic retrieval and ingestion of data stored in various file formats.

Azure

Confluence

Couchbase

Databricks Volumes

Dropbox

Elasticsearch

Google Cloud Storage

Google Drive

Kafka

MongoDB

OneDrive

Outlook

PostgreSQL

Salesforce

SharePoint

Snowflake

Destination connectors in Unstructured are designed to specify the endpoint for data processed within the platform. These connectors ensure that the transformed and analyzed data is securely and efficiently transferred to a storage system for future use, often to a vector database for tasks that involve high-speed retrieval and advanced data analytics operations.

Astra DB

Azure AI Search

Delta Tables in Amazon S3

Delta Tables in Databricks

Milvus

MotherDuck

Neo4j

Pinecone

Qdrant

Redis

Weaviate

Image descriptions

Table descriptions

Tables to HTML

Named entity recognition (NER)

Processor configuration

Read configuration

Partition configuration

Permissions configuration

Retry strategy configuration

Chunking configuration

Embedding configuration

Fsspec configuration

Airtable

Biomed

Delta Table

Discord

GitHub

GitLab

HubSpot

Jira

Local

Notion

OpenSearch

Reddit

SFTP

SingleStore

Slack

SQLite

Wikipedia

Zendesk

Chroma

Clarifai

DuckDB

KDB.AI

LanceDB

Vectara

This page provides some examples of accessing Unstructured by using the Unstructured Ingest CLI and the Unstructured Ingest Python library.

Process a subset of files

Set embedding behavior

Unstructured UI

Getting started with the UI

Using the UI

Concepts

Supported connectors

Sources

Destinations

Unstructured UI

Getting started with the UI

Using the UI

Concepts

​Sources

​Destinations

Sources

Destinations