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Toolofficial publisher

Unstructured

Tool : A precise action the AI can call · requires unstructured

Document chunking and extraction across all formats.

The Swiss army knife of document ingestion.

Docs
Overall score
7.7 /10
Security: nothing to flag
Utility
7.0
Reliability
6.0
Security
No caveat on record
Ease
5 min · Comfortable

Security reflects where data goes, requested permissions and incidents on record at the verification date. It is not a guarantee of zero risk: check the deployment mode and the connections you enable.

What it does, concretely

Many organizations accumulate files in various formats (PDF, Word, HTML, images, tables...) that remain unusable by AI systems until they are converted into structured data. Unstructured solves this problem by transforming raw files into clean JSON enriched with metadata.

In practice, the tool splits documents into structured elements (headings, paragraphs, tables, images), enriches them using AI models (image descriptions, named entity recognition, generative OCR), rechunks them into pieces sized for embedding models, and generates vectors ready to be loaded into a vector store to power RAG applications.

Unstructured is aimed at technical teams building document ingestion pipelines: Python developers, data engineers, and AI teams who want to connect more than 35 data sources and process more than 65 file types without maintaining in-house infrastructure.

It is not suited for non-technical users looking for a consumer-facing interface, nor for those whose needs are limited to a few occasional documents with no intention of automation.

What it offers

  • ·Partitioning: converts unstructured files into JSON document elements with contextual metadata.
  • ·Targeted extraction: define a custom JSON schema to extract desired values directly from source files.
  • ·AI enrichment: automatically adds image descriptions, table descriptions, table-to-HTML conversion, named entity recognition, and generative OCR.
  • ·Chunking: reorganizes partitioned output into pieces sized for embedding models and optimized for retrieval precision.
  • ·Embedding: converts text into numeric vectors stored alongside the text to power similarity search.
  • ·Connectors: access to more than 35 data sources and compatibility with more than 65 file types to unify data silos.
  • ·No-code or Python pipelines: build and preview pipelines from source to vector database in minutes.

In practice

Price
Freemium
Commercial use
Commercial use: yes
Available on
api, sdk-python
Host
unstructured
Licence
Apache-2.0
FR interface
No

Security and data

Rating
Security: nothing to flag
Capabilities
readswritesexecutesnetwork
Your data
Local or cloud depending on use
Maintenance
Active

Links and repository checked on 25 October 2026 · added on 1 September 2022

How to install Unstructured

1. Create an account on the Unstructured website to obtain your API key. 2. Note that your documents are processed in read-only mode: Unstructured does not write to your source files. 3. Install the Python SDK in your virtual environment using the command below. 4. Store your API key in an environment variable rather than hardcoding it in your source code. 5. Follow the Python quickstart in the documentation to send your first file and retrieve the structured JSON output.
pip install unstructured-client

Official documentation ↗

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