Zipstack/unstract

Features

• LLM-Integrated Extraction Pipelines - Orchestrates complex data pipelines that chain file ingestion, LLM-based extraction, and database loading.
• Document-to-JSON Converters - Converts unstructured files such as PDFs, images, and scans into structured JSON using natural language prompts.
• Extraction Endpoints - Enables converting defined extraction workflows into RESTful endpoints that process documents and return structured JSON.
• Human-in-the-Loop Systems - Provides a user interface for manual verification of extracted data with coordinate-based document highlighting.
• Cross-Model Consistency Checks - Validates extraction accuracy by comparing the outputs of two separate language model passes.
• Document Layout Analysis - Uses LLMs to analyze inconsistent document layouts and parse structural information into structured JSON.
• Extraction Field Specifications - Allows specifying required fields and formatting for converting unstructured documents into structured data using natural language prompts.
• Document Automation Pipelines - Implements automated pipelines to parse, transform, and manipulate document formats programmatically.
• Data Pipeline Orchestration - Provides a workflow engine for defining, scheduling, and monitoring document extraction and loading sequences.
• ETL Workflows - Orchestrates the full lifecycle of extracting document data and loading it into databases or data warehouses.
• Document and Unstructured Extraction - Converts unstructured PDFs, images, and scans into structured JSON using large language models.
• Prompt-Based Schema Mapping - Uses natural language prompts and LLMs to map unstructured document content into predefined JSON schemas.
• Structured Data Extraction - Reads unstructured files from a filesystem and converts them into structured formats based on defined extraction schemas.
• Unstructured Data Transformation Tools - Uses large language models to convert narrative, unstructured documents into structured JSON schemas.
• Document Processing APIs - Provides RESTful endpoints that accept unstructured documents and return structured data for external integration.
• Model Context Protocol - Integrates extraction capabilities with external AI agents through the standardized Model Context Protocol.
• Model Context Protocol Integrations - Integrates extraction capabilities with AI agents using the Model Context Protocol for structured information retrieval from documents.
• Model Context Protocol Servers - Implements a Model Context Protocol server to connect AI agents to structured document retrieval tools.
• Extraction Verification Tools - Provides visual utilities with coordinate-based highlighting to verify the accuracy of extracted document data.
• Model Context Protocol Servers - Implements a Model Context Protocol server that allows AI agents to process documents and receive structured results.
• Cron Trigger Management - Automates recurring ETL tasks by using a cron-based scheduling mechanism to trigger data extraction workflows.
• Workflow Execution Triggers - Provides capabilities to trigger data workflows manually, via REST API, or on a recurring basis using cron triggers.
• Batch Document Processing - Allows submitting multiple files in a single API call for independent, high-throughput automated data extraction.
• Long-Running Operation Polling - Utilizes a polling architecture to handle long-running document extractions in the background via status endpoints.

Star history