Project Title
langextract — Python Library for Extracting Structured Information from Unstructured Text Using LLMs
Overview
LangExtract is a Python library designed to extract structured information from unstructured text documents using Large Language Models (LLMs). It stands out for its precise source grounding, which maps every extraction to its exact location in the source text, and its interactive visualization feature that allows users to review thousands of extracted entities in their original context. The library is adaptable to any domain and leverages LLM world knowledge to influence the extraction task.
Key Features
- Precise Source Grounding: Maps every extraction to its exact location in the source text.
- Reliable Structured Outputs: Enforces a consistent output schema based on few-shot examples.
- Optimized for Long Documents: Uses text chunking, parallel processing, and multiple passes for higher recall.
- Interactive Visualization: Generates a self-contained, interactive HTML file for visualizing extracted entities.
- Flexible LLM Support: Supports cloud-based LLMs like Google Gemini and local open-source models via Ollama.
Use Cases
- Clinical Notes Extraction: Extracting and organizing key details from clinical notes or reports.
- Domain-Specific Information Extraction: Defining extraction tasks for any domain using just a few examples.
- Data Structuring: Structuring unstructured text data for further analysis and processing.
Advantages
- Traceability and Verification: Enables easy traceability and verification through visual highlighting.
- Consistent Output Schema: Leverages controlled generation for robust, structured results.
- Adaptable to Any Domain: No model fine-tuning required; adapts to user-defined extraction tasks.
Limitations / Considerations
- API Key Requirement: Using cloud-hosted models like Gemini requires an API key setup.
- Complexity of Task Specification: The accuracy of inferred information depends on the clarity of prompt instructions and the nature of prompt examples.
Similar / Related Projects
- spaCy: An open-source natural language processing library that offers a range of tools for text processing, differing in its focus on general NLP tasks rather than LLM-based extraction.
- Hugging Face Transformers: A library of pre-trained models for NLP, offering a different approach by providing a wide range of models rather than focusing on structured extraction.
- GEM: A benchmark for evaluating the social biases of NLP models, highlighting a different aspect of model evaluation compared to LangExtract's extraction capabilities.
Basic Information
- GitHub: https://github.com/google/langextract
- Stars: 15,057
- License: Unknown
- Last Commit: 2025-09-15
📊 Project Information
- Project Name: langextract
- GitHub URL: https://github.com/google/langextract
- Programming Language: Python
- ⭐ Stars: 15,057
- 🍴 Forks: 1,028
- 📅 Created: 2025-07-08
- 🔄 Last Updated: 2025-09-15
🏷️ Project Topics
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