In modern enterprises and research settings, raw text from clinical records, contracts, or customer feedback hides insights that could guide critical decisions. Traditional methods rely on bespoke parsers or manual review, which slows teams down and makes traceability difficult. LangExtract, an open-source Python library from Google AI, takes a different approach. It employs large language models such as Gemini to convert unstructured text into structured, verifiable information without losing sight of the original source.
Users interact with LangExtract by providing plain-English instructions paired with a handful of examples—following a few-shot method—to define the exact entities, relationships, or facts they need. Once configured, the library applies LLM reasoning to retrieve the requested details and formats the output according to any user-defined schema. Each item remains linked to its exact location in the text, creating an audit trail for validation and compliance.
LangExtract has delivered results across multiple sectors. In healthcare, it pulls medication names, dosages, and administration schedules from clinical notes to support interoperability and patient-safety workflows. In finance and law, analysts extract contract clauses, risk indicators, and summary points from dense documents for rapid due diligence. Academic teams survey thousands of research papers to gather key findings and metadata. Literary scholars mine themes, character sentiments, and dialogue networks from Shakespeare’s works.
Under the hood, LangExtract handles large files by breaking them into manageable chunks, running tasks in parallel, and merging the outputs. Developers can enforce strict result formats—JSON, CSV, or custom structures—so that the library’s output feeds straight into data warehouses, analytics systems, or AI pipelines. Grounding every response in the source text dramatically cuts down on hallucinated data or schema drift.
LangExtract works out of the box with Gemini and remains compatible with other LLM services. Teams can switch models or adjust parameters to balance throughput, cost, or domain-specific accuracy.
- Processes lengthy documents by chunking, parallelizing work, and aggregating final results.
- Generates interactive HTML reports that highlight each extracted item within its original context for easy auditing.
- Integrates with Jupyter notebooks, Google Colab environments, and standalone HTML layouts to accelerate developer feedback cycles.
An example use case shows how LangExtract parses a Shakespeare play to list characters, their traits, and interactions. The workflow produces a structured JSON file containing each character’s attributes and references to their lines, plus an HTML view that highlights those passages within the play.
The project team offers RadExtract, a demonstration module for organizing radiology reports. It identifies findings, measurements, and recommendations and ties them back to the source sentences in the original report.
This setup helps reduce manual review time, accelerates data processing, and provides clear audit trails for regulated industries.
LangExtract offers:
- Declarative, explainable extraction driven by natural-language instructions.
- Traceable results anchored in original text segments.
- Immediate HTML visualization for rapid iteration.
- Seamless integration into existing Python workflows.