{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/5"}],"enrichment":{"capability":"Unstructured Ingest is a local ETL pipeline that prepares unstructured data for retrieval-augmented generation (RAG) and other AI workflows by ingesting and transforming raw documents into structured, ready-to-use formats.","skillfed_tags":["rag-pipeline","document-processing","etl"],"use_cases":["Ingest a folder of documents and transform them into text chunks for a vector database.","Build a preprocessing step in a RAG pipeline that normalizes documents from multiple sources.","Extract structured content from unstructured documents before feeding them to a language model.","Automate local document processing workflows without relying on external APIs.","Prepare datasets by cleaning and standardizing raw document collections."],"what_it_does":"Unstructured Ingest is a Python package that runs as a local ETL pipeline designed to take raw, unstructured documents and prepare them for use in AI systems, particularly retrieval-augmented generation (RAG) applications. It handles the work of ingesting documents and transforming them into clean, structured formats that downstream AI models can consume.\n\nThe package depends on a lean set of runtime libraries: pydantic for data validation, click for CLI support, tqdm for progress indication, opentelemetry-sdk for observability, ijson for JSON streaming, certifi for SSL certificates, and python-dateutil for date handling. It supports Python 3.11, 3.12, and 3.13, and is currently in Beta status with active maintenance.","worth_installing":"Yes, if you need to prepare unstructured documents for AI/RAG workflows locally. The package is actively maintained, has low install friction, carries a permissive Apache-2.0 license, and depends on stable, lightweight libraries. No known vulnerabilities as of 2026-08-14. The main uncertainty is whether its supported document formats and transformation capabilities match your specific use case."},"id":"unstructured-ingest","links":{"html":"https://skillfed.io/packages/unstructured-ingest","md":"https://skillfed.io/packages/unstructured-ingest.md","pypi":"https://pypi.org/project/unstructured-ingest/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-14","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"unstructured-ingest","python_support":"supports_current","summary":"Local ETL data pipeline to get data RAG ready"},"popularity":{"monthly_downloads":620054,"position":5727,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.9.3"}