{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/5"}],"enrichment":{"capability":"LangExtract uses LLMs to extract and ground structured information from unstructured text, mapping each extraction to its exact source location and generating interactive visualizations of results.","skillfed_tags":["llm-extraction","document-parsing","data-annotation"],"use_cases":["Extract medications, diagnoses, and clinical findings from medical notes with exact source citations for audit and verification.","Parse radiology or pathology reports to structure unstructured narrative text into standardized data fields.","Identify characters, relationships, and emotions from literary texts with interactive visualization for literary analysis.","Annotate and label large document collections for machine learning training datasets with built-in grounding and conflict detection.","Extract key entities and attributes from legal contracts or regulatory documents while maintaining source traceability."],"what_it_does":"LangExtract is a Python library that uses large language models to extract structured information from unstructured text\u2014such as clinical notes, reports, or literary documents\u2014based on user-defined extraction rules and few-shot examples. It maps every extracted entity back to its exact location in the source text, enabling visual verification and traceability. The library handles long documents through optimized chunking and parallel processing, supports multiple LLM providers (Google Gemini, OpenAI, local Ollama), and generates interactive HTML visualizations to review thousands of extracted entities in their original context.\n\nThe core workflow involves defining a prompt that describes what to extract, providing high-quality examples to guide the model, running the extraction on your input text, and optionally visualizing results. LangExtract enforces consistent output schemas and detects when the model extracts content from examples rather than the source text. It is designed for domains where precise source grounding and schema-constrained outputs matter\u2014medical records, legal documents, research papers, or any scenario where traceability and structured output are critical.","worth_installing":"Yes. LangExtract is actively maintained, has low install friction, carries no known vulnerabilities, and offers a permissive Apache-2.0 license. It solves a concrete problem\u2014grounded structured extraction from unstructured text\u2014with built-in visualization and multi-provider LLM support. Install it if you need to extract and ground structured data from documents with traceability and schema enforcement. Requires Python 3.10+ and an LLM provider (cloud or local)."},"id":"langextract","links":{"html":"https://skillfed.io/packages/langextract","md":"https://skillfed.io/packages/langextract.md","pypi":"https://pypi.org/project/langextract/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-02","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"langextract","python_support":"supports_current","summary":"LangExtract: A library for extracting structured data from language models"},"popularity":{"monthly_downloads":407057,"position":6890,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.6.0"}