What is LangExtract?
LangExtract is a powerful Python library designed to help you extract structured information from unstructured text with precision and confidence. It addresses the critical challenge of turning raw documents, like reports or clinical notes, into organized, actionable data. By leveraging Large Language Models (LLMs), LangExtract ensures that every piece of extracted information is reliably structured and directly traceable to its source.
Key Features
🗺️ Achieve Precise Source Grounding LangExtract maps every extracted entity to its exact character-level location in the source text. This core feature allows you to instantly verify the origin of your data, building trust and ensuring accuracy by eliminating guesswork.
📊 Visualize Results Instantly Generate a self-contained, interactive HTML file to review thousands of extractions in their original context. This powerful visualization makes it easy to audit results, share findings with stakeholders, and gain insights at a glance by simply hovering over highlighted text.
📚 Process Long Documents with Ease Overcome the "needle-in-a-haystack" problem common with large texts. LangExtract uses an optimized strategy of intelligent text chunking, parallel processing, and multi-pass extraction to maintain high recall and speed, even when processing entire novels or extensive reports.
⚙️ Enforce Reliable Structured Outputs Define your desired data schema with just a few high-quality examples, and LangExtract will enforce it. For supported models like Google's Gemini, it uses controlled generation to guarantee consistent, predictable JSON outputs that you can depend on for downstream applications.
🔌 Use Your Preferred Language Models LangExtract is built for flexibility. Seamlessly integrate with popular cloud-based LLMs like the Google Gemini family and OpenAI models, or run extractions completely locally with open-source models through the built-in Ollama interface.
How LangExtract Solves Your Problems:
LangExtract is designed for practical, real-world applications where data quality and verifiability are paramount.
For Technical and Scientific Analysis: Imagine you need to extract all mentions of medications, dosages, and patient responses from thousands of clinical notes. You can provide LangExtract with a few examples, and it will systematically process the documents, structuring the information and linking each finding back to the exact sentence it came from.
For Research and Humanities: When analyzing literary texts like Romeo and Juliet, you can instruct LangExtract to identify all characters, their expressed emotions, and their relationships. It can process the entire book and produce a structured dataset, complete with an interactive visualization for exploring the character dynamics in their original context.
For Business and Operations: Automatically structure key information from inbound customer support tickets, legal contracts, or financial reports. By defining the entities you care about—such as product names, issue types, or contract clauses—you can build automated workflows that turn unstructured text into a queryable database.
Why Choose LangExtract?
Verifiable by Design: Unlike many extraction tools that return data without context, LangExtract’s tight integration of source grounding and interactive visualization is fundamental. This creates a transparent and auditable workflow, ensuring you can always trust and defend your results.
Adaptable with No Fine-Tuning: You can define complex, domain-specific extraction tasks using just a handful of clear examples. LangExtract adapts to your needs without the time and expense of fine-tuning a dedicated model, allowing you to get started in minutes.
Conclusion:
LangExtract provides the tools you need to move from messy, unstructured text to clean, reliable, and verifiable data. By combining the advanced reasoning of LLMs with a steadfast commitment to source-grounded accuracy, it empowers you to build more trustworthy and powerful data pipelines.
More information on LangExtract
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LangExtract was manually vetted by our editorial team and was first featured on 2025-08-05.
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