Langextract
FreeA Python library for extracting structured information from unstructured text using LLMs with precise source grounding and interactive visualization.
About Langextract
LangExtract is a Python library that uses large language models (LLMs) to extract structured information from unstructured text documents based on user-defined instructions. It is designed to process materials such as clinical notes or reports, identifying and organizing key details while ensuring the extracted data corresponds precisely to the source text. Key capabilities include precise source grounding that maps extractions to their exact location, reliable structured outputs enforced via few-shot examples and controlled generation, optimization for long documents through chunking and parallel processing, interactive HTML visualization of extractions, and flexible support for both cloud-based LLMs (e.g., Google Gemini) and local models via Ollama. The library is open source under the Apache 2.0 license and requires Python 3.10 or later.
Key Features
Pros & Cons
- Open source and free to use under Apache 2.0 license
- Precise source grounding enables verification and traceability of extractions
- Handles long documents effectively through chunking and parallel processing
- Generates interactive visualizations for reviewing extracted entities in context
- Supports both cloud and local LLMs, offering flexibility in model choice
- No need for model fine-tuning; adaptable using few-shot examples
- Requires an API key for cloud-based models like Google Gemini
- Accuracy depends on model selection, task complexity, and prompt clarity
- May require careful prompt engineering and high-quality examples for optimal results
- Only supports Python 3.10 and later