Unstructured
AIOpen-source ETL platform that converts complex documents into structured data for LLMs and GenAI workflows.
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Free Available
Starting from Free
Premium plans available
Unstructured
AI
Open-source ETL platform that converts complex documents into structured data for LLMs and GenAI workflows.
-(0 Reviews)
Free Available
Starting from Free
Premium plans available
About Unstructured
Unstructured provides an open-source library and hosted platform to ingest, parse, enrich, chunk, and embed documents so they are ready for large language models and GenAI applications. It supports a wide range of document formats (PDF, HTML, Word, images, spreadsheets, email formats, etc.) and exposes modular building blocks (bricks) and SDKs to assemble transformation pipelines. The project includes both local libraries for preprocessing and a hosted Unstructured API; the company also offers an enterprise Platform for production-grade continuous workflows with partitioning, enrichments, and monitoring. Its value lies in automating complex document ETL to deliver higher-quality, LLM-ready structured outputs at scale.
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Key Features
Multi-format Ingestion: Supports a broad set of input types (PDF, HTML, DOCX, PPTX, XLSX, EPUB, images, emails, CSV/TSV, compressed archives) to ingest documents from varied sources and normalize them for downstream processing.
Modular Bricks and SDKs: Provides reusable, open-source building blocks (bricks) and language SDKs to assemble custom preprocessing pipelines for parsing, cleaning, and transforming document content.
Pipeline Orchestration & Enrichments: Routes data through dynamic transformation pipelines that perform partitioning, enrichment, metadata extraction, and content normalization to produce structured outputs tailored for LLMs.
Layout Parsing & Chipper Model: Includes layout and document structure analysis (layout parsing) to extract tables, figures, headings, and positional context from complex page layouts for accurate content segmentation.
Chunking & Embedding Preparation: Implements intelligent chunking and embedding generation workflows to create LLM-friendly segments and vectors, improving retrieval, RAG, and semantic search performance.
Hosted API & Local Libraries: Offers a hosted Unstructured API (API keys required) for cloud-based processing alongside open-source local libraries for on-prem or custom deployments, enabling flexible integration models.
Enterprise Platform Capabilities: Provides production-grade Platform features—continuous ingestion, monitoring, partitioning strategies, and scalability—targeted at enterprise workflows and compliance needs.
File-type Analytics & Metrics: Collects analytics on processed document types and transformation success to help operators measure ingestion quality and pipeline performance.
Convert documents to structured data (supports PDFs, HTML, Word, images, tables, graphs)
Modular components ("bricks") for building custom preprocessing pipelines
Dynamic transformation and enrichment pipelines for routing and improving data quality
Partitioning and chunking to prepare content for LLM consumption
Embedding support and integration points for vectorization
Layout parsing and inference models (separate inference repository)
Python SDK and libraries (unstructured, unstructured-api, unstructured-inference)
Containerized deployment options (Dockerfile present in repo) and Makefile-driven install
Apache-2.0 open-source licensing for core libraries
Enterprise Platform for production-grade workflows, continuous automated processing and scaling
Use Cases
Preparing LLM Training & RAG Corpora: Clean, partition, and chunk large collections of PDFs, manuals, and reports into semantically coherent passages and embeddings for retrieval-augmented generation and model fine-tuning.
Automated Document Ingestion for Knowledge Bases: Continuously ingest and transform new documents (contracts, policies, manuals) into structured records for searchable knowledge bases and Q&A assistants.
Table and Figure Extraction for Data Pipelines: Parse complex tables, figures, and embedded images from financial reports or scientific papers to convert them into structured datasets for analytics or downstream models.
Compliance and Contract Analysis: Extract clauses, metadata, and named entities from legal and regulatory documents to populate contract management systems and support compliance workflows.
Invoice/Receipt Processing: Normalize and extract line-items, totals, dates, and vendor information from invoices and receipts to automate AP workflows and accounting ingestion.
Migration of Legacy Documents: Convert large legacy document collections (scanned PDFs, archived emails, disparate formats) into structured, searchable formats to modernize enterprise data stores.
Prototype to Production Pipelines: Use open-source bricks to prototype document parsing locally, then scale to the Unstructured Platform for continuous, monitored production processing with enterprise controls.
Preprocessing document corpora to create high-quality input for retrieval-augmented generation (RAG) pipelines
Extracting tables, figures, and structured fields from PDFs and scanned documents
Continuous ingestion and enrichment of enterprise documents for knowledge bases
Generating embeddings and chunked passages for semantic search over documents
Receipt, invoice, and financial filings parsing (example pipelines and archived repos exist)
Building document Q&A or chatbot applications using cleaned, structured document content
