LangExtract: Mastering Structured Information Extraction from Unstructured Text Using LLMs

In the modern data-driven landscape, organizations are inundated with vast amounts of unstructured text—from clinical notes and legal contracts to literary works and customer feedback. The challenge is not just processing this text, but transforming it into actionable, structured data that can be analyzed, searched, and verified. This article explores LangExtract, a powerful Python library that leverages Large Language Models (LLMs) to perform precise, source-grounded information extraction from unstructured documents.

What is LangExtract and Why Does It Matter?

This section answers the core question: What makes LangExtract a distinct and essential tool for developers dealing with unstructured text data?
LangExtract is not just another text processing wrapper; it is a sophisticated framework designed to bridge the gap between raw human language and structured machine-readable data. Unlike traditional regex-based parsers that fail at understanding context, or generic LLM prompts that produce inconsistent outputs, LangExtract enforces structure, ensures traceability, and scales efficiently.
At its heart, LangExtract uses LLMs to extract information based on user-defined instructions. Whether you are processing clinical reports or organizing literary characters, the library identifies key details and organizes them while ensuring the extracted data faithfully corresponds to the source text.

The Seven Core Advantages

To understand its value, we must look at the specific problems LangExtract solves:
1. Precise Source Grounding
One of the biggest hurdles in AI extraction is the “black box” problem. LangExtract maps every extraction to its exact location in the source text. This allows for visual highlighting, making it easy to trace a data point back to its origin. For instance, if the system extracts a dosage from a medical note, you can immediately see where in the doctor’s notes that specific information appeared.
2. Reliable Structured Outputs
Consistency is key for data pipelines. LangExtract enforces a consistent output schema based on the few-shot examples you provide. By leveraging controlled generation in supported models like Gemini, it guarantees robust, structured results without the need for complex post-processing scripts.
3. Optimized for Long Documents
The “needle-in-a-haystack” problem is real when processing entire novels or long reports. LangExtract overcomes this using an optimized strategy of text chunking, parallel processing, and multiple extraction passes. This ensures high recall even in massive documents.
4. Interactive Visualization
Reviewing thousands of extracted entities can be overwhelming. LangExtract instantly generates a self-contained, interactive HTML file. This allows you to visualize and review entities within their original context, turning a daunting spreadsheet into an navigable interface.
5. Flexible LLM Support
Whether you prefer the power of cloud-based models like the Google Gemini family or the privacy of local open-source models via Ollama, LangExtract supports your preferred infrastructure without locking you in.
6. Adaptable to Any Domain
You do not need to fine-tune a model for every specific task. By defining extraction tasks with just a few high-quality examples, LangExtract adapts to your specific domain—be it radiology, finance, or literature—immediately.
7. Leveraging LLM World Knowledge
Unlike strict extractors, LangExtract can utilize the reasoning capabilities of LLMs. Through precise prompt wording, you can influence how the extraction task utilizes the model’s world knowledge to infer implicit details, provided you maintain a balance between text evidence and inference.

Author Reflection: Having worked with various NLP pipelines, I find the “Source Grounding” feature to be a game-changer. In enterprise settings, stakeholders often distrust AI outputs because they cannot verify them. The ability to click an extracted entity and see exactly where it came from in the source document builds a level of trust that is often missing in black-box AI solutions.

Image Source: Unsplash

How to Get Started: Your First Extraction Task

This section answers the core question: How can a developer set up and execute their first text extraction task using LangExtract with minimal code?
Getting started with LangExtract is designed to be intuitive. We will walk through a classic example: extracting characters, emotions, and relationships from a literary text. This demonstrates the library’s ability to handle nuanced, entity-rich content.

Step 1: Define Your Extraction Task

The first step is to create a prompt that clearly describes what you want to extract. More importantly, you must provide high-quality examples to guide the model. In the world of LLMs, examples are often more powerful than instructions.

import langextract as lx
import textwrap
# 1. Define the prompt and extraction rules
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.""")
# 2. Provide a high-quality example to guide the model
examples = [
    lx.data.ExampleData(
        text="ROMEO. But soft! What light through yonder window breaks? It is the east, and Juliet is the sun.",
        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"}
            ),
            lx.data.Extraction(
                extraction_class="relationship",
                extraction_text="Juliet is the sun",
                attributes={"type": "metaphor"}
            ),
        ]
    )
]

In this snippet, we define a schema that categorizes text into “character,” “emotion,” and “relationship.” We also teach the model to capture attributes, such as the emotional state of a character.
Note: Examples drive model behavior. The extraction_text should ideally be verbatim from the example’s text (no paraphrasing), listed in order of appearance. LangExtract raises Prompt alignment warnings by default if examples don’t follow this pattern—resolving these is crucial for best results.

Step 2: Run the Extraction

Once your prompt and examples are ready, you can pass your input text to the lx.extract function.

# The input text to be processed
input_text = "Lady Juliet gazed longingly at the stars, her heart aching for Romeo"
# Run the extraction
result = lx.extract(
    text_or_documents=input_text,
    prompt_description=prompt,
    examples=examples,
    model_id="gemini-2.5-flash",
)

Model Selection: The gemini-2.5-flash model is the recommended default, offering an excellent balance of speed, cost, and quality. For highly complex tasks requiring deeper reasoning, gemini-2.5-pro may provide superior results.

Step 3: Visualize the Results

Data extraction is useless if you cannot verify it. LangExtract saves results to a .jsonl file and generates an interactive HTML visualization.

# Save the results to a JSONL file
lx.io.save_annotated_documents([result], output_name="extraction_results.jsonl", output_dir=".")
# Generate the visualization from the file
html_content = lx.visualize("extraction_results.jsonl")
with open("visualization.html", "w") as f:
    if hasattr(html_content, 'data'):
        f.write(html_content.data)  # For Jupyter/Colab
    else:
        f.write(html_content)

This creates an animated HTML file where you can see the extracted entities highlighted in their original context.

Author Reflection: I often see teams skip the visualization step, moving straight to JSON processing. However, spending five minutes clicking through the HTML output can save hours of debugging later. It allows you to spot edge cases where the model might be “hallucinating” or misinterpreting a nuance that a simple JSON validation would miss.

Handling Long Documents: Scaling Your Extraction

This section answers the core question: What strategies does LangExtract employ to maintain high accuracy and recall when processing full-length books or extensive reports?
Processing a single paragraph is easy; processing a 147,000-character novel is a different challenge. LangExtract includes specific features to handle long documents without sacrificing quality.
For larger texts, you can process entire documents directly from URLs. The library handles the downloading and chunking automatically.

# Process Romeo & Juliet directly from Project Gutenberg
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,    # Improves recall through multiple passes
    max_workers=20,         # Parallel processing for speed
    max_char_buffer=1000    # Smaller contexts for better accuracy
)

Key Parameters for Long Texts

• extraction_passes: Setting this to 3 means the system scans the text multiple times. This is crucial for recall. A character mentioned in Chapter 1 might be referenced again in Chapter 10; multiple passes help link these references consistently.
• max_workers: With a value of 20, LangExtract processes 20 chunks of text simultaneously. This parallelization turns a task that could take hours into one that takes minutes.
• max_char_buffer: This controls the context window size. A smaller buffer ensures the model focuses sharply on the immediate context of the text chunk, reducing noise and improving extraction precision.
  Vertex AI Batch Processing: For massive scale, you can also save costs by enabling the Vertex AI Batch API via language_model_params={"vertexai": True, "batch": {"enabled": True}}.

Installation and Environment Configuration

This section answers the core question: How do I install LangExtract and configure API keys across different development environments?
LangExtract is designed for flexibility in installation. You can install it via PyPI, from source, or using Docker.

Installation Methods

From PyPI (Recommended)

pip install langextract

From Source
For developers who want to modify the code or contribute:

git clone https://github.com/google/langextract.git
cd langextract
pip install -e .

Docker
For containerized deployments:

docker build -t langextract .
docker run --rm -e LANGEXTRACT_API_KEY="your-api-key" langextract python your_script.py

API Key Configuration

When using cloud-hosted models like Gemini or OpenAI, an API key is required. Local models via Ollama do not require one.
Option 1: Environment Variable

export LANGEXTRACT_API_KEY="your-api-key-here"

Option 2: .env File (Recommended)
Create a .env file in your project root:

LANGEXTRACT_API_KEY=your-api-key-here

Remember to add .env to your .gitignore file to keep credentials secure.
Option 3: Vertex AI (Enterprise)
For enterprise users leveraging Google Cloud:

result = lx.extract(
    ...,
    language_model_params={
        "vertexai": True,
        "project": "your-project-id",
        "location": "global"
    }
)

Expanding Model Support: OpenAI and Local LLMs

This section answers the core question: Can I use LangExtract with models other than Gemini, and can I run extractions completely offline?
Yes, LangExtract is built to be model-agnostic. While Gemini offers advanced features like controlled generation, you can easily integrate OpenAI or run local models.

Using OpenAI Models

To use OpenAI models like GPT-4o, you must install the optional dependency: pip install langextract[openai].

import langextract as lx
result = lx.extract(
    text_or_documents=input_text,
    prompt_description=prompt,
    examples=examples,
    model_id="gpt-4o",  # Automatically selects OpenAI provider
    api_key=os.environ.get('OPENAI_API_KEY'),
    fence_output=True,
    use_schema_constraints=False
)

Note: Since LangExtract currently implements schema constraints primarily for Gemini, OpenAI models require fence_output=True to ensure clean formatting and use_schema_constraints=False.

Using Local LLMs with Ollama

For privacy, cost savings, or offline capabilities, LangExtract supports local inference via Ollama. This is ideal for sensitive data that cannot leave your local machine.

import langextract as lx
result = lx.extract(
    text_or_documents=input_text,
    prompt_description=prompt,
    examples=examples,
    model_id="gemma2:2b",  # Automatically selects Ollama provider
    model_url="http://localhost:11434",
    fence_output=False,
    use_schema_constraints=False
)

Quick Setup:

1. Install Ollama from ollama.com.
2. Run ollama pull gemma2:2b to download the model.
3. Run ollama serve to start the local server.
   
   Image Source: Unsplash

The Ecosystem: Community Provider Plugins

This section answers the core question: What third-party integrations are available to extend LangExtract’s capabilities to other backends like AWS Bedrock or vLLM?
LangExtract supports a lightweight plugin system that allows the community to add support for new model backends without modifying the core library. This ecosystem is rapidly growing.

Available Plugins

Here is a registry of community-developed provider plugins:

Plugin Name	PyPI Package	Maintainer	Description
AWS Bedrock	langextract-bedrock	andyxhadji	AWS Bedrock provider for LangExtract, supports all models & inference profiles
LiteLLM	langextract-litellm	JustStas	LiteLLM provider for LangExtract, supports all models covered in LiteLLM, including OpenAI, Azure, Anthropic, etc.
Llama.cpp	langextract-llamacpp	fgarnadi	Llama.cpp provider for LangExtract, supports GGUF models from HuggingFace and local files
Outlines	langextract-outlines	RobinPicard	Outlines provider for LangExtract, supports structured generation for various local and API-based models
vLLM	langextract-vllm	wuli666	vLLM provider for LangExtract, supports local and distributed model serving

Safety and Usage Guidelines

Since community plugins are independently developed and maintained, the LangExtract team cannot guarantee their safety or functionality. Before installing any plugin, it is recommended to:

• Review the code: Examine the source code and dependencies on GitHub.
• Check community feedback: Read issues and discussions for user experiences.
• Test safely: Try plugins in isolated environments before production use.

How to Add Your Own Plugin

If you have developed a provider, you can add it to the registry by submitting a Pull Request. The checklist requires:

• PyPI package name starting with langextract-.
• A published package.
• A clear description of the provider’s functionality.
• A link to a tracking issue in the main LangExtract repository.

Conclusion

LangExtract represents a significant step forward in the practical application of Large Language Models for data engineering. By combining the reasoning power of LLMs with strict schema enforcement and precise source grounding, it solves the “trust” and “consistency” problems that often hinder AI adoption in production environments.
Whether you are analyzing radiology reports, structuring legal contracts, or mining literature for data, LangExtract provides the tools to do so efficiently, scalably, and verifiably.

Practical Summary / Action Checklist

• Install: Run pip install langextract to get the core library.
• Configure: Set up your LANGEXTRACT_API_KEY (Gemini) or configure OPENAI_API_KEY if using OpenAI.
• Define: Write a clear prompt_description and create 1-3 high-quality examples that show exact text extraction.
• Extract: Run lx.extract() with your text, prompt, examples, and chosen model_id.
• Scale: For long documents, enable extraction_passes=3 and set max_workers for parallelism.
• Visualize: Always generate the HTML visualization to verify results before moving to production.
• Extend: Explore community plugins for Bedrock, vLLM, or LiteLLM if you need specific backends.

One-page Summary

• What is it? A Python library using LLMs to extract structured data from unstructured text with source grounding.
• Key Feature: “Source Grounding” maps every extracted entity back to the original text for verification.
• Best For: Clinical notes, reports, literature analysis, and any domain requiring structured data from text.
• Models: Supports Gemini (default, best features), OpenAI, and local models via Ollama.
• Scalability: Handles long documents via chunking and parallel processing (e.g., full novels).
• Community: Extensible via plugins for AWS Bedrock, vLLM, LiteLLM, and more.

Frequently Asked Questions (FAQ)

1. Does LangExtract work with languages other than English?
Yes, LangExtract is language-agnostic. As long as the underlying LLM you select (e.g., Gemini or a local model) supports the language, LangExtract can extract structured information from it.
2. Can I use LangExtract without an internet connection?
Yes, if you use local models via the Ollama provider. You would need to download the model files first, but the actual extraction process runs entirely offline on your machine.
3. How does LangExtract handle data privacy?
When using cloud providers like Gemini or OpenAI, data is sent to their servers. For strict privacy requirements, use the Ollama provider to run inference locally, ensuring data never leaves your infrastructure.
4. What is the difference between gemini-2.5-flash and gemini-2.5-pro?
The flash model is optimized for speed and cost-effectiveness, making it ideal for high-volume tasks. The pro model is designed for deeper reasoning and higher complexity, suitable for nuanced extraction tasks where accuracy is paramount.
5. Do I need to fine-tune a model to use LangExtract?
No. LangExtract is designed to work with pre-trained models. You define the extraction logic and examples, and the library adapts the model’s behavior to your specific task without requiring fine-tuning.
6. Is there a limit to the length of documents I can process?
There is no hard-coded limit in LangExtract. It automatically handles document chunking. However, processing extremely large documents (e.g., millions of words) will take longer and incur higher API costs if using cloud models.
7. How can I contribute to the LangExtract project?
Contributions are welcome! You can contribute by developing new provider plugins, improving documentation, or submitting code patches. You will need to sign a Contributor License Agreement (CLA) before submitting patches.