Langextract
Langextract is an open-source Python library developed by Google that leverages large language models (LLMs) to extract structured information from unstructured text with precise source grounding. It enables users to define extraction schemas and then processes input documents, using LLMs to identify and pull relevant entities, relations, or facts while providing verifiable provenance by linking each extracted piece back to its original location in the text. This grounding ensures transparency and trust in the extracted data, making it suitable for applications where auditability is critical. The library includes an interactive visualization component that allows users to explore extracted results directly within the source documents, facilitating validation and refinement of extraction schemas. Langextract supports multiple LLM backends, including commercial APIs like Gemini and open-source models via Ollama, giving users flexibility in model choice. It is designed with performance in mind, featuring optimized algorithms for speed and accuracy, as demonstrated by recent improvements to its fuzzy aligner that drastically reduced processing times for long documents. Langextract is designed to be modular and extensible, with a focus on ease of integration into existing data processing pipelines. It can be installed via pip and used programmatically or through its command-line interface. The library aims to bridge the gap between raw text and structured databases, empowering developers and data scientists to build reliable extraction workflows with minimal effort. Key features include schema-driven extraction, multi-provider LLM support, efficient source grounding using a novel alignment algorithm, and built-in visualization tools for iterative development. The project is actively maintained with a growing community and extensive documentation.
- Category
- LLM
- Pricing
- Open Source
Problem solved
Organizations often have vast amounts of unstructured text data—such as reports, articles, or logs—from which they need to extract structured information for analysis, databases, or automation. Manual extraction is slow, and existing solutions lack reliable source verification, leading to errors and hallucinated data. Langextract solves this by using LLMs to automatically pull structured data while anchoring each extracted field to its exact text fragment, ensuring accuracy and traceability. This enables teams to rapidly convert free-form text into verified, structured records without extensive custom development.
Use cases
- Extracting key entities (e.g., people, organizations, dates) from news articles with clickable links back to the source text.
- Building a knowledge graph from research papers by pulling relationships and claims with in-text citations.
- Processing customer feedback to identify sentiments and product mentions, then visualizing which parts of the review triggered each extraction.
- Automating report generation by parsing financial documents to populate standardized tables with grounded numbers and terms.
- Creating structured training data for ML models by extracting labeled spans from raw corpora with human-in-the-loop verification.
Audience
- Developers
- Data Scientists
- ML Engineers
- Researchers
Pros
- Precise source grounding provides verifiable extractions, reducing hallucination risks.
- Interactive visualization accelerates schema design and debugging.
- Multi-provider LLM support offers flexibility and avoids vendor lock-in.
- Open-source and actively maintained with strong community engagement.
- Performance-optimized for long documents, with fast alignment algorithms.
Cons
- Dependency on external LLM APIs may incur costs and introduce latency.
- Advanced use cases may require careful schema design and prompt engineering.
- Limited to text data; no native support for image or table extraction.
Official website: https://github.com/google/langextract
Canonical LambdaBase page: https://www.lambdabase.com/tools/langextract