google/langextract

Project overview

This Python library extracts structured information from unstructured text documents such as clinical notes and reports. It provides source grounding for every extraction, processes large documents using chunking and parallel processing, and generates interactive HTML visualizations for review.

Project type

RAG · Document Processing

Use cases

Knowledge Q&A

Deployment

Refer to project documentation

License

Apache-2.0

Best for

• Developers and data teams who need to extract structured data from unstructured text with verifiable source traceability and interactive review capabilities.
• Teams processing large documents who can benefit from chunking and parallel processing to optimize extraction recall.

Key capabilities

• Maps every extraction to its exact location in the source text, enabling visual highlighting for traceability.
• Processes large documents using text chunking, parallel processing, and multiple extraction passes for higher recall.
• Generates a self-contained, interactive HTML file to visualize and review extracted entities in their original context.
• Supports adding custom LLM providers via a lightweight plugin system independently of the core library.
• Enables Vertex AI Batch API for saving costs on large-scale tasks.

Limitations and risks

• Ollama does not currently support output_schema, which may affect structured data formatting when using local models.

Getting started

• Install the library from PyPI using pip. Cloud-based models require an API key, while local models via Ollama can bypass this. Run extraction using the lx.extract() function.

Evidence and sources

• GitHub project description: A Python library for extracting structured information from unstructured text using LLMs with precise source grounding and interactive visualization.
• README: LangExtract is a Python library that uses LLMs to extract structured information from unstructured text documents based on user-defined instructions. It processes materials such a…
• README: 1. **Precise Source Grounding:** Maps every extraction to its exact location in the source text, enabling visual highlighting for easy traceability and verification. 2. **Reliable…
• README: 
• README: LangExtract supports custom LLM providers via a lightweight plugin system. You can add support for new models without changing core code. - Add new model support independently of…

Reviewed at: 2026-07-17T08:47:23Z