Google’s LangExtract: From Raw Text to Structured Insights
From clinical notes to contracts, extract what matters with just a few lines of code.
Google has released LangExtract, a new open-source Python library that turns messy, unstructured text into clean, structured data using LLMs like Gemini.
Why It Matters
Most real-world data lives in free-form text—clinical notes, contracts, support tickets. LangExtract helps extract entities, attributes, and relationships with few-shot prompts, no retraining required.
Key Features
Model flexibility: Works with Gemini, Ollama, or other local/open-source models.
Handles long docs: Smart chunking + parallel runs for scale.
Source traceability: Every extraction maps back to its exact text span.
Interactive review: Auto-generates HTML visualizations to inspect results.
Schema-driven: Guarantees structured JSON outputs that fit your workflow.
Real-World Use Cases
Healthcare: Extract medications, diagnoses, lab results.
Legal: Identify clauses, obligations, risks.
Research & Literature: Analyze character relationships, emotions, themes.
Quick Start
pip install langextract
import langextract as lx
result = lx.extract(
text_or_documents="Engineer Alice Williams designed the software architecture.",
prompt_description="Extract person names and roles.",
model_id="gemini-2.5-flash"
)
Bottom Line
LangExtract makes information extraction easy, scalable, and trustworthy. If you work with large amounts of text, this is a tool worth exploring.