Turning free-form text into structured, actionable data is a persistent challenge across industries. Google’s open-source Python library, LangExtract, addresses this gap by harnessing the power of Gemini-based large language models (LLMs).
With LangExtract, developers can efficiently convert sources like clinical notes, legal contracts, or customer feedback into usable insights, streamlining workflows and amplifying the value of text data.
What Makes LangExtract Stand Out?
- Precise Source Grounding:
- Each extracted entity is linked directly to its location in the source text, ensuring data is both traceable and verifiable.
- Reliable Structured Outputs:
- Users define the schema and provide examples, allowing LangExtract to consistently generate structured outputs using Gemini’s Controlled Generation.
- Scalable for Long-Form Documents:
- Chunking, parallelization, and multi-pass extraction enable LangExtract to handle large documents and retrieve multiple facts, maintaining accuracy even in million-token contexts.
- Built-In Interactive Visualizations:
- Explore and review extractions in context with HTML visualization tools, ideal for presentations and deep dives alike.
- Flexible LLM Backend:
- Compatible with both Gemini in the cloud and open-source on-device models, giving teams the flexibility to choose their preferred backend.
- Domain Independence:
- Adaptable to any text-rich field (medical, financial, legal, and more) without the need for model fine-tuning. High-quality examples guide the extraction process, making customization straightforward.
- World Knowledge Integration:
- LangExtract leverages model knowledge to supplement extractions, inferring information that may not be explicitly stated in the text.
How to Get Started with LangExtract
LangExtract’s workflow is simple yet powerful. Install the library with pip, craft a focused extraction prompt, and supply a representative example.
The library applies your instructions to target text, generating structured outputs mapped to their exact positions in the document. Results can be exported as JSONL and explored through interactive HTML visualizations, streamlining review and collaboration.
This design enables seamless extraction of complex entities (like characters and relationships in literature or medications and dosages in clinical notes) by blending LLM reasoning with user-defined guidance and schema enforcement.
A Powerful Radiology Demo
Structured reporting is essential in radiology to enhance clarity, ensure completeness, and improve data interoperability for clinical care and research. To address this need, Google researchers developed RadExtract, an interactive demonstration on Hugging Face. RadExtract utilizes LangExtract to process free-text reports, automatically converting key findings into a structured format and highlighting important observations.
Industry Applications and Impact
LangExtract’s roots are in healthcare, where it excels at parsing intricate relationships in clinical documentation. A standout example is RadExtract above. By standardizing information, LangExtract improves clarity and interoperability, critical for sectors like medicine and finance. It’s important to note that these demos are intended for research and illustration only, not for direct clinical use or decision-making.
Ready to Unlock Your Text Data?
Developers can access detailed documentation, tutorials, and the full codebase on GitHub. With its robust feature set, lightweight interface, and adaptability, LangExtract is poised to transform how organizations extract value from unstructured text. Dive in, experiment with your data, and discover new insights today.
Transforming Unstructured Text: LangExtract Unlocks Data with Gemini-Powered LLMs