Introducing Automatic Metadata Extraction: Supercharge Your RAG Pipelines with Structured Information

Automatic Metadata Extraction is a new feature in Vectorize that significantly enhances the handling of unstructured documents in Retrieval Augmented Generation (RAG) pipelines by automatically extracting structured information. This feature uses the Iris model to analyze documents and apply predefined schemas, thereby improving retrieval capabilities, providing enhanced context for language models, and organizing documents more effectively. It supports two types of metadata: document metadata, which provides high-level information like title and author, and section metadata, which offers detailed data like part numbers and technical specifications. The feature is particularly beneficial in sectors such as financial services, manufacturing, and healthcare, where it aids in classifying documents and extracting specific data points. With a visual schema editor, users can easily create or generate schemas without needing to write JSON. By integrating extracted metadata into text chunks, the system improves retrieval quality and ensures consistent information availability. This advancement allows organizations to gain deeper insights and provide more precise information to users, enhancing the value derived from their document collections.