Langextract
A Python library for extracting structured information from unstructured text using LLMs with precise source grounding and interactive visualization.
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What is Langextract?
LangExtract is a Python library that uses large language models (LLMs) to turn unstructured text documents into structured, machine‑readable data with precise source grounding. Users define extraction tasks by writing prompts and providing a few example documents, then pass raw text into the library to obtain structured JSON‑like outputs that map each extracted field back to its original span in the source.
Key features include schema‑aware extraction, where you describe what entities or attributes to pull (for example diagnoses, entities in contracts, or sentiment markers), support for Gemini and other LLM backends, and built‑in visualization tools that generate interactive HTML views highlighting where each extracted element appears in the original text. The library also supports batch processing of multiple documents, saving results to JSONL, and is designed to integrate cleanly into existing Python data‑processing and MLOps pipelines.
LangExtract is aimed at data engineers, researchers, and domain specialists who need to go from clinical notes, legal contracts, customer reviews, or financial reports into structured datasets without manual labeling or model fine‑tuning. It is particularly useful in regulated or high‑stake settings where traceability and auditability of extracted values back to the source text are important, and it can be extended via plugins such as VLLM‑based providers for running open‑source models locally.
The project is open source under the Apache 2.0 license and is hosted on GitHub, with documentation showing how to install via pip, set up an API key for cloud models, and build end‑to‑end extraction workflows from text to visual inspection and downstream storage. Community contributions and plugin development are encouraged, allowing teams to adapt the library to their own modeling stacks and deployment environments while reusing the same extraction and visualization abstractions.
Langextract pricing
Pricing model: Freemium
LangExtract itself is open source and free to use under the Apache 2.0 license, with no separate product license fee. When using cloud models such as Gemini, users incur the standard API costs billed by the model provider according to their usage, while local models via Ollama or VLLM plugins have no direct library fees but require self‑hosted compute resources.
Langextract pros
- Extracts structured data from unstructured text using LLMs
- Supports Gemini and other LLM backends
- Provides precise source grounding for each extracted value
- Includes interactive HTML visualization of extracted spans
- Uses few‑shot learning via user‑provided examples
- Schema‑aware extraction with user‑defined prompts
- Batch processing of multiple documents
- Saves results to JSONL for easy downstream integration
- Open source under Apache 2.0 license
- Can run with local models via Ollama and similar backends
- Works with domain‑specific text like clinical notes and contracts
- Rich documentation and quick‑start examples
- Plugin architecture for custom model providers
- No need to fine‑tune models for new extraction tasks
- Easy installation via pip (pip install langextract)
Langextract cons
- Requires an internet connection and API keys for cloud models
- Costs accumulate quickly when using paid LLM APIs at scale
- Latency depends on the chosen model backend
- Complex prompts may require experimentation to stabilize
- Output quality is sensitive to example quality and task definition
- Limited by the context window of the underlying model
- May introduce hallucinated or inaccurate extractions if not carefully validated
- Advanced plugin or backend development requires Python and systems knowledge
Frequently asked questions about Langextract
What is LangExtract used for?
LangExtract is used to convert unstructured text documents—such as clinical notes, contracts, reports, and customer reviews—into structured, machine‑readable data by applying large language models with user‑defined prompts and examples, producing outputs that map each extracted field to its original location in the text.
How does LangExtract handle source grounding?
LangExtract associates each extracted entity or attribute with the exact text span in the original document that supports it, enabling precise source grounding and traceability for audit or validation purposes, and this information is preserved in the output and reflected in its interactive visualizations.
Can I run LangExtract with local models?
Yes, LangExtract can be used with local models such as Gemma or other open‑source LLMs via Ollama or custom VLLM‑based plugins, which allows teams to keep data on‑premises and avoid cloud API calls while still benefiting from the same extraction and visualization abstractions.
What file formats does LangExtract support?
LangExtract primarily works with plain text or text‑like inputs supplied as Python strings or lists of documents; users are responsible for pre‑processing common formats such as PDF or DOCX into text before passing them into the extraction pipeline, and results can be saved to JSONL files for structured storage and reuse.
How do I define an extraction task in LangExtract?
You define an extraction task by writing a prompt that describes what to extract and how to structure it, then providing a set of example ExampleData objects that show the expected input text and the corresponding Extraction spans, which the library uses in a few‑shot fashion to guide the LLM toward consistent outputs for new documents.
Does LangExtract require model fine‑tuning?
No, LangExtract does not require you to fine‑tune any model; instead, it relies on prompt engineering and example‑based learning to adapt the underlying LLM to your extraction task, making it faster to setup and modify without training cycles.
How can I visualize LangExtract results?
LangExtract includes a built‑in visualization function that reads saved JSONL results and generates an interactive HTML page that highlights the extracted spans within the original text, allowing users to inspect, validate, and share the extraction quality across documents.
Is LangExtract suitable for production workflows?
LangExtract is designed to plug into production data‑processing pipelines, offering batch extraction, JSONL output, and integration with cloud or local model backends, but production use requires careful validation of outputs, monitoring of model‑api costs, and handling of model‑specific rate limits and latency.
How do I install LangExtract?
LangExtract can be installed via pip with the command pip install langextract, and for development or plugin work you can clone the GitHub repository and install it in editable mode with pip install -e .[dev], after which you configure any required API keys for cloud models as environment variables.
What models and providers does LangExtract support?
LangExtract is built around pluggable model providers and natively supports Gemini‑series models, with community and plugin‑based extensions for other backends such as Ollama‑hosted models and VLLM‑based providers, enabling you to choose from cloud‑hosted, local, or self‑hosted LLMs while reusing the same extraction interface.