Google's LangExtract: Structured Information Extraction with Source Grounding

Google has quietly released one of the most practical AI tools for production document processing in 2026: LangExtract, an open-source Python library that uses large language models to extract structured information from unstructured text—with a critical feature most extraction tools lack: precise source grounding.

While LLM-powered extraction isn’t new, LangExtract tackles the core problems that have kept teams from trusting AI for document workflows: hallucinated extractions, inconsistent schemas, and zero traceability back to source material. If you’ve ever tried to build a production system that processes contracts, medical reports, or legal documents with GPT-4, you know these pain points intimately.

What Makes LangExtract Different

The library’s standout feature is source grounding—every extraction maps to its exact location in the source document. Not approximate. Not “somewhere in paragraph 3.” Exact character offsets that enable visual highlighting for verification.

This matters because production systems can’t afford hallucinations. A medical report extraction that invents dosages or a contract parser that misses liability clauses isn’t just wrong—it’s dangerous. LangExtract’s grounding mechanism lets human reviewers instantly verify extractions against source text through an auto-generated interactive HTML visualization.

But source grounding is just the start. LangExtract enforces reliable structured outputs using JSON Schema with controlled generation. Define your schema once using few-shot examples, and the library guarantees outputs match that structure—no manual validation loops or retry logic.

Solving the “Needle in a Haystack” Problem

Long document extraction has plagued LLM applications since the beginning. Even with 200K+ context windows, models still struggle to extract every relevant entity from dense legal briefs or research papers.

LangExtract uses an optimized strategy: text chunking, parallel processing, and multiple extraction passes. The library can process entire novels from Project Gutenberg (147,000+ characters) and extract hundreds of entities while maintaining high accuracy. The documentation demonstrates this capability with a Romeo and Juliet full-text extraction, where the system identifies characters, emotions, and relationships across the entire play using 20 parallel workers and 3 sequential passes.

The key parameters:

result = lx.extract(
    text_or_documents="https://www.gutenberg.org/files/1513/1513-0.txt",
    prompt_description=prompt,
    examples=examples,
    model_id="gemini-2.5-flash",
    extraction_passes=3,      # Multiple passes improve recall
    max_workers=20,           # Parallel processing for speed
    max_char_buffer=1000      # Smaller contexts = better accuracy
)

The max_char_buffer parameter is particularly clever—smaller context windows paradoxically improve accuracy by reducing the surface area where models lose focus. This parameter lets you tune the trade-off between context size and extraction precision for your specific use case.

Quick Start: Building Your First Extractor

Getting started requires just three steps. First, define your extraction task with clear rules:

import langextract as lx
import textwrap

prompt = textwrap.dedent("""
    Extract characters, emotions, and relationships in order of appearance.
    Use exact text for extractions. Do not paraphrase or overlap entities.
    Provide meaningful attributes for each entity to add context.
""")

Second, provide a high-quality example to guide the model:

examples = [
    lx.data.ExampleData(
        text="ROMEO. But soft! What light through yonder window breaks?",
        extractions=[
            lx.data.Extraction(
                extraction_class="character",
                extraction_text="ROMEO",
                attributes={"emotional_state": "wonder"}
            ),
            lx.data.Extraction(
                extraction_class="emotion",
                extraction_text="But soft!",
                attributes={"feeling": "gentle awe"}
            )
        ]
    )
]

Third, run extraction and visualize results:

result = lx.extract(
    text_or_documents=input_text,
    prompt_description=prompt,
    examples=examples,
    model_id="gemini-2.5-flash"
)

# Save and generate interactive HTML visualization
lx.io.save_annotated_documents([result], "results.jsonl", ".")
html_content = lx.visualize("results.jsonl")

The visualization is production-ready—a self-contained HTML file that handles thousands of entities with highlighting and filtering. This makes human-in-the-loop verification workflows straightforward to implement.

Production Use Cases

Google showcases three compelling production applications:

Healthcare: The RadExtract demo on HuggingFace Spaces automatically structures radiology reports, extracting findings, anatomical locations, and clinical impressions. The library handles medical terminology extraction (medication names, dosages, routes) with relationship mapping between entities.

Legal & Contracts: Extract clauses, parties, dates, and obligations from contracts with source grounding for legal review workflows. The character-level grounding is particularly valuable for compliance and audit trails.

Research & Knowledge Management: Process academic papers, technical documentation, or internal knowledge bases to build structured knowledge graphs with traceable citations. Every extracted fact links back to its source location.

The JSONL output format is intentional—it’s the lingua franca of LLM data pipelines, making LangExtract outputs compatible with vector databases, fine-tuning workflows, and downstream analytics.

Model Flexibility: Cloud to Local

LangExtract supports Gemini (recommended), OpenAI (GPT-4o), and local models via Ollama—no vendor lock-in.

The recommended default is gemini-2.5-flash, offering the best balance of speed, cost, and quality. For complex reasoning tasks, gemini-2.5-pro provides superior results. Gemini models support controlled generation natively for guaranteed schema adherence.

OpenAI models are also supported but require specific parameter configurations:

result = lx.extract(
    text_or_documents=input_text,
    prompt_description=prompt,
    examples=examples,
    model_id="gpt-4o",
    fence_output=True,                # Required for OpenAI
    use_schema_constraints=False      # Required for OpenAI
)

These parameters work around differences in how OpenAI handles structured outputs compared to Gemini’s native controlled generation.

For privacy-sensitive workloads or air-gapped environments, Ollama support enables fully local extraction:

result = lx.extract(
    text_or_documents=input_text,
    prompt_description=prompt,
    examples=examples,
    model_id="gemma2:2b",
    model_url="http://localhost:11434",
    fence_output=False,
    use_schema_constraints=False
)

Enterprise Considerations

For large-scale production, LangExtract integrates with Vertex AI Batch API to reduce costs:

language_model_params={
    "vertexai": True,
    "batch": {"enabled": True}
}

Batch processing is critical for high-volume document workflows—think processing thousands of legal briefs overnight or structuring entire medical record archives. The cost savings from batch processing can be substantial for workloads that don’t require real-time results.

The library also includes enterprise-grade CI/CD integration with pytest, tox, and pre-commit hooks. The plugin architecture lets teams add custom model providers without forking the core library, making it easier to integrate with internal LLM deployments or specialized models.

How It Compares

vs. Traditional NLP (spaCy, Stanford CoreNLP): Rule-based systems are faster and cheaper but require manual pattern engineering per domain. LangExtract uses few-shot learning—define 2-3 examples and the LLM generalizes. The trade-off is latency and cost, but the flexibility is unmatched.

vs. Unstructured.io: Both handle document parsing, but Unstructured focuses on pre-processing (PDF → text), while LangExtract focuses on post-processing (text → structured entities). They’re complementary tools in a document processing pipeline.

vs. OpenAI Structured Outputs: OpenAI’s approach is model-locked and doesn’t provide source grounding or long document optimization. LangExtract is model-agnostic and built specifically for document extraction workflows where traceability matters.

The Bottom Line

LangExtract is production-ready infrastructure for LLM-powered document processing. The source grounding feature alone justifies adoption—it transforms extraction from “AI magic” to auditable, verifiable data pipelines.

The library is Apache 2.0 licensed (with Health AI Developer Foundations Terms for medical use cases), maintained by Google Research, and actively developed with community provider plugins.

For teams building document workflows—contract analysis, medical record structuring, research paper parsing, or compliance automation—LangExtract provides the reliability and traceability that production systems demand.

Available now at github.com/google/langextract and pip install langextract.

The community is already building custom provider plugins—early examples include specialized medical extractors and legal document parsers that extend the base library without forking. This plugin architecture signals Google’s intent to make LangExtract a platform, not just a library.

Note: This is not an officially supported Google product, but it’s built by Google researchers and reflects the kind of practical AI tooling that actually ships in 2026—less hype, more reliability.