The unstructured library offers an open-source toolkit designed to simplify the ingestion and pre-processing of diverse data formats, including images and text-based documents such as PDFs, HTML files, Word documents, and more. With a focus on optimizing data workflows for Large Language Models (LLMs), unstructured provides modular functions and connectors that work seamlessly together. This cohesive system ensures efficient transformation of unstructured data into structured formats, while also offering adaptability to various platforms and use cases.
The unstructured library is designed as a starting point for quick prototyping. For production scenarios, check out the Unstructured API Services and Unstructured Platform. These solutions include the library’s functionality, and offer:
  • Security and SOC2 compliance
  • High performance table extraction
  • Access to our latest and continuously improving visitation transformer models
  • Authentication and identity management
  • Incremental data loading
  • ETL job scheduling and monitoring
  • Image extraction from documents
  • Improved document hierarchy detection
  • and more!

​
Key functionality

  • Precise Document Extraction: Unstructured offers advanced capabilities in extracting elements and metadata from documents. This includes a variety of document element types and metadata. Learn more about Document elements and metadata.
  • Extensive File Support: The platform supports a wide array of file types, ensuring versatility in handling different document formats from PDF, Images, HTML, and many more. Detailed information on supported file types can be found here.
  • Robust Core Functionality: Unstructured provides a suite of core functionalities critical for efficient data processing. This includes:
    • Partitioning: The partitioning functions in Unstructured enable the extraction of structured content from raw, unstructured documents. This feature is crucial for transforming unorganized data into usable formats, aiding in efficient data processing and analysis.
    • Cleaning: Data preparation for NLP models often requires cleaning to ensure quality. The Unstructured library includes cleaning functions that assist in sanitizing output, removing unwanted content, and improving the performance of NLP models. This step is essential for maintaining the integrity of data before it is passed to downstream applications.
    • Extracting: This functionality allows for the extraction of specific entities within documents. It is designed to identify and isolate relevant pieces of information, making it easier for users to focus on the most pertinent data in their documents.
    • Staging: Staging functions help prepare your data for ingestion into downstream systems. Please note that this functionality is being deprecated in favor of Destination Connectors.
    • Chunking: The chunking process in Unstructured is distinct from conventional methods. Instead of relying solely on text-based features to form chunks, Unstructured uses a deep understanding of document formats to partition documents into semantic units (document elements).
    • Embedding: The embedding encoder classes in Unstructured leverage document elements detected through partitioning or grouped via chunking to obtain embeddings for each element. This is particularly useful for applications like Retrieval Augmented Generation (RAG), where precise and contextually relevant embeddings are crucial.
  • High-performant Connectors: The platform includes optimized connectors for efficient data ingestion and output. These comprise Source Connectors for data input and Destination Connectors for data export.

​
Common Use Cases

  • Pretraining Models
  • Fine-tuning Models
  • Retrieval Augmented Generation (RAG)
  • Traditional ETL