LangExtract

Google's open-source Python library for extracting structured information from unstructured text using LLMs with precise source grounding and interactive visualization.

Overview

LangExtract emerged from Google's research into medical information extraction, addressing the challenge of programmatically extracting structured data from unstructured text while maintaining traceability to source content. Released under Apache 2.0 license in July 2025, the library represents Google's approach to making LLM-powered document processing accessible to developers without requiring model fine-tuning.

Unlike traditional OCR-focused IDP platforms like ABBYY or Hyperscience, LangExtract operates on text that has already been extracted, focusing on the intelligence layer that transforms unstructured narratives into structured data. The library's core innovation lies in its "precise source grounding" — mapping every extracted entity back to its exact location in the original text for verification and compliance requirements.

Google positioned LangExtract as a free alternative to $50K enterprise solutions in the $1.5-5 billion data extraction market, directly challenging commercial platforms through open-source accessibility. The platform supports multiple LLM providers including Google Gemini, Microsoft OpenAI, and local models through Ollama integration.

Technical Architecture

Multi-Pass Processing Pipeline

LangExtract addresses the "needle-in-a-haystack" challenge through optimized text chunking, parallel processing with up to 20 workers, and multiple extraction passes for higher recall. This approach enables processing of lengthy documents like clinical notes, legal contracts, and financial reports that exceed typical LLM context windows of 128K tokens.

The library successfully processed a 147,843-character Romeo and Juliet text from Project Gutenberg, demonstrating scalability for enterprise document workflows. Performance testing shows the platform handled a 15,000-character video meeting transcript, extracting 67 distinct entities with exact character positioning.

Source Grounding Technology

Every extraction maps to precise character positions in source documents, enabling visual highlighting for audit trails and compliance verification. This addresses a critical gap in traditional document processing where extracted data loses connection to its origin, making regulatory compliance difficult for industries like healthcare and financial services.

The grounding capability supports relationship extraction through attribute grouping, connecting related entities like employee names with departments and contact information while maintaining source traceability for each data point.

Model Integration Strategy

LangExtract supports vendor-agnostic LLM deployment through standardized interfaces. Gemini 2.5 Flash provides the optimal speed-cost balance for most extraction tasks, while Gemini 2.5 Pro handles complex reasoning requirements. Local model support via Ollama enables offline processing for sensitive documents requiring air-gapped deployment.

Akshay Goel, a key contributor, emphasized community development focus, leading to a TypeScript port with OpenAI model support created by developer Kyle Brown. The open-source approach enables domain-specific extensions and custom model integration.

Healthcare Applications

Google demonstrated clinical capabilities through RadExtract, an interactive demo for structured radiology reporting hosted on Hugging Face Spaces. The platform converts free-text findings into standardized formats required for research and clinical care while maintaining source references for regulatory compliance.

The library processes clinical notes and medical reports with source traceability meeting HIPAA requirements, positioning it as an alternative to specialized healthcare platforms like Nuance or clinical-focused solutions from Microsoft.

Competitive Positioning

LangExtract occupies a unique position by focusing on the semantic intelligence layer rather than competing with full-stack platforms. Its open-source nature and Google backing provide cost advantages over proprietary solutions like Docugami or Sensible.so, while its emphasis on source grounding addresses compliance requirements that generic LLM APIs cannot satisfy.

Unlike general-purpose orchestration frameworks, LangExtract specializes in structured extraction with built-in schema enforcement and audit trails. The library complements Google's Docling tool, which handles document parsing and layout recovery, by focusing on semantic entity extraction from clean text.

A data scientist reviewing the platform noted: "LangExtract is the most production-ready unstructured to structured extraction library I've used. It treats extraction as an engineering problem, not just an AI problem."

Implementation Examples

Basic Extraction Workflow

import langextract as lx

## Define extraction task with examples
prompt = "Extract characters, emotions, and relationships in order of appearance."
examples = [
    lx.data.ExampleData(
        text="ROMEO. But soft! What light through yonder window breaks?",
        extractions=[
            lx.data.Extraction(
                extraction_class="character",
                extraction_text="ROMEO",
                attributes={"emotional_state": "wonder"}
            )
        ]
    )
]

## Process document
result = lx.extract(
    text_or_documents=input_text,
    prompt_description=prompt,
    examples=examples,
    model_id="gemini-2.5-flash"
)

Interactive Visualization

LangExtract generates self-contained HTML files for reviewing thousands of extracted entities in their original context. The visualization works seamlessly in Google Colab or as standalone files, providing immediate feedback on extraction quality without additional tooling.

## Save results and generate interactive HTML
lx.io.save_annotated_documents([result], output_name="results.jsonl")
html_content = lx.visualize("results.jsonl")
with open("visualization.html", "w") as f:
    f.write(html_content)

Technical Specifications

Feature	Specification
License	Apache 2.0 (Open Source)
Primary Language	Python
Recommended Model	Gemini 2.5 Flash (speed/cost balance)
Advanced Model	Gemini 2.5 Pro (complex reasoning)
Local Model Support	Ollama integration for offline processing
Output Format	Structured JSON with source mapping
Visualization	Interactive HTML with entity highlighting
Processing Architecture	Chunking, parallel processing, multiple passes
Context Handling	Optimized for documents exceeding LLM limits
Maximum Document Size	147,843+ characters (demonstrated)
Parallel Workers	Up to 20 configurable workers