LangExtract is a Python library that leverages Large Language Models (LLMs) to extract structured information from unstructured text. It is optimized for long documents, features precise source grounding to map extractions back to their origin, and generates interactive visualizations for easy verification.
One-Minute Overview#
LangExtract is a Python library designed for extracting structured information from long documents using LLMs. It addresses the "needle-in-a-haystack" problem often faced when processing large texts with LLMs. Its standout feature is source grounding, which maps extracted data back to specific locations in the source text for visual verification.
Core Value: It combines the reasoning power of LLMs with rigorous traceability, ensuring that extracted data is not only accurate but also verifiable against the original text.
Quick Start#
Installation Difficulty: Low - Simple pip install, but requires API key configuration for cloud models.
pip install langextract
Is this suitable for me?
- ✅ Document Analysis: Ideal for extracting entities/relationships from novels, clinical reports, or legal contracts.
- ✅ High Verification Needs: Essential when you need to prove exactly where the LLM found the information.
- ❌ Simple Regex Tasks: If you just need basic keyword matching, traditional regex is lighter and cheaper.
Core Capabilities#
1. Precise Source Grounding#
LangExtract maps every extracted entity to its exact location in the source text. This facilitates human auditing and allows for highlighted visualizations in the generated HTML output, solving the "black box" issue typical of LLM extractions.
2. Optimized for Long Documents#
For long texts (e.g., full novels), LangExtract employs intelligent chunking and multi-pass extraction strategies. This significantly improves recall rates, preventing the LLM from missing key information due to context window limitations.
3. Interactive Visualization#
The library automatically generates a standalone HTML file containing all extracted entities. Users can interactively browse hundreds or thousands of entries in their browser, viewing them within the context of the original text, which greatly streamlines data review and cleaning.
Tech Stack & Integration#
Development Language: Python Key Dependencies: Google Gemini (Default), also supports OpenAI, Ollama. Integration Method: Python SDK / API
LangExtract features a modular design that supports custom model providers via a plugin system. It supports high-performance cloud models (like Gemini 2.5) as well as local open-source models (via Ollama), offering flexibility between data privacy and processing costs.
Maintenance Status#
- Development Activity: Active. Maintained by Google, updated to support the latest Gemini models (e.g., gemini-2.5-flash).
- Community Support: Comprehensive documentation and examples are provided, covering basic usage to advanced Vertex AI batch processing.