Extret

Extret is a hypothetical information processing framework designed to combine the extraction of structured data from unstructured text with the retrieval of relevant documents. The name extret is a portmanteau of extract and retrieve, used in theoretical discussions of end-to-end knowledge extraction pipelines.

Architecture and workflow: Extret envisions a modular pipeline with input adapters, extractor modules, a retriever, a re-ranker, and a data store. Input adapters normalize sources such as text streams, PDFs, and web content. Extractor modules perform tasks including named entity recognition, relation extraction, and event extraction to produce structured records. The retriever uses vector embeddings to fetch relevant documents, which are then re-ranked and merged with the extracted data. A downstream connector stores results in a knowledge graph or database and exposes an API for applications.

Applications: The concept appears in discussions of enterprise search, legal discovery, scientific literature review, and research intelligence, where combined extraction and retrieval can improve coverage, traceability, and justification for conclusions.

Limitations: As a hypothetical framework, extret does not define a universal standard for interfaces or evaluation. Real-world implementations depend on model quality, data governance, interoperability with existing data stacks, and latency constraints.

Variants: Some proposals distinguish a core extraction layer (extret-core) from enterprise-oriented deployments (extret-enterprise), with optional plug-ins for multi-modal data and governance features.