Google LangExtract: The Ultimate AI Developer's Guide to Structured Information Extraction

Introduction
What is LangExtract?
How LangExtract Works
- Core Architecture
- Key Features
Installation and Setup
Complete Code Examples
Real-World Applications
Using Local Models with Ollama
Performance Optimization
Best Practices
Conclusion
Further Reading

Introduction

Google's LangExtract is a revolutionary open-source Python library that transforms unstructured text into structured, actionable data using large language models like Gemini and OpenAI's GPT models. Released in July 2025, this powerful tool addresses the critical challenge of extracting reliable, traceable information from complex documents—from clinical notes and legal contracts to research papers and customer feedback.

Unlike traditional Named Entity Recognition (NER) tools that require extensive training data and domain-specific fine-tuning, LangExtract leverages the natural language understanding capabilities of modern LLMs to adapt to any domain with just a few examples, achieving 99.9% accuracy while maintaining precise source grounding for every extraction.

The library transforms chaotic, free-form text into clean, structured data formats while maintaining precise source grounding—mapping every extraction back to its exact location in the original document. This ensures transparency, traceability, and verification of extracted information.

In this comprehensive guide, we'll explore how to implement LangExtract in production environments, optimize performance for large-scale deployments, and leverage its capabilities for various AI applications including knowledge graphs, RAG systems, and document processing pipelines.

What is LangExtract?

LangExtract is a Python library designed to programmatically extract structured information from unstructured text documents using LLMs. Unlike traditional Named Entity Recognition (NER) tools that require extensive training data and domain-specific fine-tuning, LangExtract leverages the natural language understanding capabilities of modern LLMs to adapt to any domain with just a few examples.

No training required - works with just 3-5 examples
Character-level source grounding for verification and visual highlighting
Supports 100+ languages out of the box
99.9% accuracy on industry-standard datasets
Interactive HTML visualizations for easy review and validation
Multi-model support - works with cloud and local models via Ollama

How LangExtract Works

LangExtract operates through a sophisticated pipeline that combines prompt engineering, few-shot learning, and controlled generation to extract structured information from text.

Core Architecture

The extraction pipeline consists of several key steps:

Input Processing: Accepts text documents, URLs, or file paths as input
Prompt Engineering: Uses developer-defined extraction prompts with clear instructions
Few-Shot Learning: Leverages example data to guide the model's understanding
LLM Processing: Employs advanced language models (Gemini, GPT, or local models via Ollama) for extraction
Source Grounding: Maps each extracted entity to its precise location in the source text
Structured Output: Generates JSONL format data with consistent schema

Key Features

LangExtract provides several powerful features that set it apart from traditional extraction tools:

Precise Source Grounding: Every extraction includes character-level mapping to the original text
Controlled Generation: Uses schema constraints and few-shot examples to ensure consistent outputs
Long Document Processing: Handles extensive documents through intelligent text chunking
Multi-Model Support: Works with cloud-based models (Gemini, OpenAI) and local models via Ollama

Installation and Setup

Getting started with LangExtract is straightforward. First, install the library using pip:

Installation

# Standard installation
pip install langextract

# For OpenAI models
pip install "langextract[openai]"

# For development
pip install -e ".[dev]"

For cloud-based models, you'll need to configure API access. Set up your API key using environment variables:

API Configuration

# Option 1: Environment variable
export LANGEXTRACT_API_KEY="your-api-key-here"

# Option 2: .env file (recommended)
echo "LANGEXTRACT_API_KEY=your-api-key-here" > .env
echo ".env" >> .gitignore

Complete Code Examples

Basic Entity Extraction

Here's a simple example of extracting entities from text using LangExtract:

import langextract as lx
import textwrap
import os

# Define extraction prompt
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.""")

# Provide few-shot examples
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"}
            ),
        ]
    )
]

# Input text to process
input_text = "Lady Juliet gazed longingly at the stars, her heart aching for Romeo"

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

# Display results
for extraction in result.extractions:
    print(f"Class: {extraction.extraction_class}")
    print(f"Text: {extraction.extraction_text}")
    print(f"Attributes: {extraction.attributes}")
    print(f"Source location: {extraction.start_char}-{extraction.end_char}")
    print("---")

Advanced Document Processing

For more complex extraction tasks, you can optimize the extraction process with multiple passes and parallel processing:

import langextract as lx
import textwrap

# Complex extraction for business documents
prompt = textwrap.dedent("""\
    Extract companies, financial metrics, dates, and market sentiment.
    Use exact text for extractions. Include specific values and context.""")

examples = [
    lx.data.ExampleData(
        text="TechCorp reported Q3 revenue of $2.5B on October 15, 2024, exceeding analyst expectations and driving bullish market sentiment.",
        extractions=[
            lx.data.Extraction(
                extraction_class="company",
                extraction_text="TechCorp",
                attributes={"type": "public_company"}
            ),
            lx.data.Extraction(
                extraction_class="financial_metric",
                extraction_text="Q3 revenue of $2.5B",
                attributes={"metric_type": "revenue", "period": "Q3", "value": "$2.5B"}
            ),
            lx.data.Extraction(
                extraction_class="date",
                extraction_text="October 15, 2024",
                attributes={"event": "earnings_report"}
            ),
            lx.data.Extraction(
                extraction_class="sentiment",
                extraction_text="bullish market sentiment",
                attributes={"sentiment": "bullish", "context": "earnings_reaction"}
            ),
        ]
    )
]

# Process large document with optimization
result = lx.extract(
    text_or_documents="path/to/large_document.txt",  # Or URL
    prompt_description=prompt,
    examples=examples,
    model_id="gemini-2.5-flash",
    extraction_passes=3,        # Multiple passes for better recall
    max_workers=20,            # Parallel processing
    max_char_buffer=1000       # Optimal chunking size
)

print(f"Extracted {len(result.extractions)} entities")
print(f"Processing completed with {result.extraction_passes} passes")

Real-World Applications

LangExtract excels in various real-world applications where structured information extraction is critical. Here are some practical implementations:

Healthcare: Extract medications, dosages, symptoms, and diagnoses from clinical notes with precise accuracy.
Legal: Process contracts and legal documents to extract parties, terms, dates, and obligations.
Finance: Analyze financial reports to extract metrics, companies, and market sentiment for investment analysis.
Research: Extract findings, methodologies, and citations from academic papers for literature reviews.
Customer Intelligence: Process customer feedback to extract sentiment, product mentions, and feature requests.

Interactive HTML Visualization

One of LangExtract's most powerful features is its ability to generate interactive HTML visualizations that highlight extracted entities directly in the source text:

Generating Interactive Visualizations

import langextract as lx

# Run extraction
result = lx.extract(
    text_or_documents="path/to/document.txt",
    prompt_description="Extract key entities...",
    examples=examples,
    model_id="gemini-2.0-flash-exp"
)

# Generate interactive HTML visualization
result.to_html("extraction_visualization.html")

# The HTML file provides:
# - Color-coded entity highlighting
# - Hover tooltips with extraction details
# - Side panel with extraction list
# - Search and filter capabilities
# - Export options for further processing

# You can also get the HTML as a string
html_content = result.to_html()

# Or create a custom visualization
from langextract.visualization import create_custom_viz

custom_html = create_custom_viz(
    result,
    highlight_colors={"person": "#3B82F6", "location": "#10B981"},
    show_confidence_scores=True,
    enable_export=True
)

Medical Information Extraction

import langextract as lx
import textwrap

# Healthcare-specific extraction
prompt = textwrap.dedent("""\
    Extract medications, dosages, symptoms, and diagnoses from clinical notes.
    Include administration routes and frequencies where mentioned.
    Use exact medical terminology from the text.""")

examples = [
    lx.data.ExampleData(
        text="Patient prescribed Metformin 500mg twice daily for Type 2 diabetes",
        extractions=[
            lx.data.Extraction(
                extraction_class="medication",
                extraction_text="Metformin",
                attributes={"dosage": "500mg", "frequency": "twice daily"}
            ),
            lx.data.Extraction(
                extraction_class="diagnosis",
                extraction_text="Type 2 diabetes",
                attributes={"status": "ongoing_management"}
            ),
        ]
    )
]

clinical_note = """
Patient presents with chest pain and shortness of breath. 
Prescribed Lisinopril 10mg once daily for hypertension.
Follow-up recommended in 2 weeks.
"""

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

# Process results
medications = [e for e in result.extractions if e.extraction_class == "medication"]
for med in medications:
    print(f"Medication: {med.extraction_text}")
    print(f"Details: {med.attributes}")

OpenAI Models Integration

LangExtract also supports OpenAI models like GPT-4o with specific configuration requirements:

Using OpenAI Models

import langextract as lx
import os

# Configure for OpenAI
result = lx.extract(
    text_or_documents=input_text,
    prompt_description=prompt,
    examples=examples,
    model_id="gpt-4o",
    api_key=os.environ.get('OPENAI_API_KEY'),
    fence_output=True,              # Required for OpenAI
    use_schema_constraints=False    # Required for OpenAI
)

# Alternative models
# model_id="gpt-4o-mini"  # Faster, cheaper option
# model_id="gpt-4-turbo"  # Balance of speed and capability

Batch Processing Pipeline

For processing multiple documents efficiently, implement a batch processing pipeline:

Batch Document Processing

import langextract as lx
import os
from pathlib import Path

def process_document_batch(file_paths, prompt, examples, output_dir="results"):
    """Process multiple documents efficiently"""

    Path(output_dir).mkdir(exist_ok=True)
    results = []

    for file_path in file_paths:
        print(f"Processing {file_path}...")

        result = lx.extract(
            text_or_documents=file_path,
            prompt_description=prompt,
            examples=examples,
            model_id="gemini-2.5-flash",
            extraction_passes=2,
            max_workers=10
        )

        results.append(result)

        # Save individual results
        filename = Path(file_path).stem
        lx.io.save_annotated_documents(
            [result],
            output_name=f"{filename}_extractions.jsonl",
            output_dir=output_dir
        )

    return results

# Example usage
document_files = [
    "contract1.pdf",
    "report2.docx",
    "notes3.txt"
]

batch_results = process_document_batch(document_files, prompt, examples)

Visualization and Output

Interactive HTML Visualization

One of LangExtract's most powerful features is its ability to generate interactive HTML visualizations that highlight extracted entities directly in the source text with precise character-level grounding:

Generating Interactive Visualizations

# Save results and create visualization
lx.io.save_annotated_documents(
    [result],
    output_name="extraction_results.jsonl",
    output_dir="."
)

# Generate interactive HTML
html_content = lx.visualize("extraction_results.jsonl")
with open("visualization.html", "w", encoding="utf-8") as f:
    if hasattr(html_content, 'data'):
        f.write(html_content.data)  # For Jupyter/Colab
    else:
        f.write(html_content)

print("Open visualization.html in your browser to review results")

# The HTML visualization provides:
# - Color-coded entity highlighting
# - Character-level source grounding
# - Hover tooltips with extraction details
# - Side panel with extraction list
# - Search and filter capabilities
# - Export options for further processing

JSONL Output Format

LangExtract outputs data in JSONL (JSON Lines) format, where each line represents an extracted document with its entities and precise source grounding:

JSONL Output Structure

{
  "document_id": "1",
  "text": "Original input text...",
  "extractions": [
    {
      "extraction_class": "character",
      "extraction_text": "ROMEO",
      "start_char": 0,
      "end_char": 5,
      "attributes": {
        "emotional_state": "wonder"
      }
    }
  ]
}

Using Local Models with Ollama

For privacy-sensitive applications or when you need to process data offline, LangExtract supports running local models through Ollama integration:

Setting up Ollama

# Install Ollama from ollama.com
ollama pull gemma2:2b
ollama serve

Local Model Configuration

import langextract as lx

# Use local models (no API key required)
result = lx.extract(
    text_or_documents=input_text,
    prompt_description=prompt,
    examples=examples,
    model_id="gemma2:2b",              # Ollama model
    model_url="http://localhost:11434", # Ollama server
    fence_output=False,
    use_schema_constraints=False
)

# Alternative local models:
# model_id="llama3.2"     # Larger model for better accuracy
# model_id="mistral"      # Good balance of speed and quality

# Benefits of local models:
# - Complete data privacy - no data leaves your infrastructure
# - No API costs or rate limits
# - Consistent latency without network dependencies
# - Compliance with strict data residency requirements

# Trade-offs:
# - Requires local compute resources
# - Model management and updates are manual
# - May have lower accuracy than cloud models

Performance Optimization

Optimize LangExtract performance for large-scale deployments with these proven strategies. LangExtract with Gemini achieves 99.9% accuracy and can process 100+ documents per second with proper configuration:

Model Selection Guidelines

gemini-2.5-flash: Recommended default, excellent balance of speed, cost, and quality
gemini-2.5-pro: Superior reasoning for complex extraction tasks
gpt-4o-mini: Fast OpenAI alternative for cost optimization
gemma2:2b: Lightweight local model via Ollama for privacy

Performance Optimization Strategies

import langextract as lx
from concurrent.futures import ThreadPoolExecutor
import time
import logging

# 1. Optimize for Large Documents
result = lx.extract(
    text_or_documents=large_document,
    prompt_description=prompt,
    examples=examples,
    model_id="gemini-2.5-flash",
    extraction_passes=3,        # Multiple passes improve recall
    max_workers=20,            # Parallel processing
    max_char_buffer=800,       # Smaller chunks for accuracy
    # Consider reducing for cost optimization
)

# 2. Smart Model Selection
def smart_model_selection(text_length, complexity):
    """Choose optimal model based on task requirements"""
    if text_length < 1000 and complexity == "simple":
        return "gemini-2.5-flash"  # Fastest, cheapest
    elif complexity == "complex":
        return "gemini-2.5-pro"  # Best accuracy
    else:
        return "gemma2:2b"  # Local processing

# 3. Batch Processing with Rate Limiting
from functools import wraps

def rate_limited(max_per_minute=60):
    min_interval = 60.0 / max_per_minute
    last_called = [0.0]

    def decorator(func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            elapsed = time.time() - last_called[0]
            left_to_wait = min_interval - elapsed
            if left_to_wait > 0:
                time.sleep(left_to_wait)
            ret = func(*args, **kwargs)
            last_called[0] = time.time()
            return ret
        return wrapper
    return decorator

@rate_limited(max_per_minute=30)
def controlled_extraction(text, prompt, examples):
    return lx.extract(text, prompt, examples, "gemini-2.5-flash")

# 4. Performance Monitoring
logger = logging.getLogger(__name__)

def timed_extraction(text, prompt, examples):
    """Extract with performance monitoring"""
    start_time = time.time()

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

    elapsed = time.time() - start_time
    tokens_processed = len(text.split())

    logger.info(f"Extraction completed in {elapsed:.2f}s")
    logger.info(f"Tokens/sec: {tokens_processed/elapsed:.0f}")
    logger.info(f"Entities extracted: {len(result.extractions)}")

    return result

Cost Management

Implement cost-effective strategies for production environments:

Cost Optimization

def cost_optimized_extraction(documents, prompt, examples):
    """Optimize for cost in production environments"""

    results = []
    for doc in documents:
        # Use faster, cheaper model for initial processing
        result = lx.extract(
            doc, prompt, examples,
            model_id="gemini-2.5-flash",  # Cost-effective choice
            extraction_passes=1,          # Reduce passes for speed
            max_workers=5                 # Limit parallelism
        )

        # Only use expensive model for complex cases
        if len(result.extractions) < expected_minimum:
            result = lx.extract(
                doc, prompt, examples,
                model_id="gemini-2.5-pro",  # More expensive but accurate
                extraction_passes=2
            )

        results.append(result)

    return results

Advanced Use Cases

LangExtract excels in sophisticated AI applications beyond basic entity extraction:

Building Knowledge Graphs

Knowledge Graph Construction

def build_knowledge_graph(documents):
    """Extract entities and relationships for knowledge graph construction"""

    prompt = textwrap.dedent("""\
        Extract entities and their relationships.
        Focus on connections between people, organizations, and concepts.""")

    examples = [
        lx.data.ExampleData(
            text="Apple Inc. was founded by Steve Jobs in Cupertino.",
            extractions=[
                lx.data.Extraction("organization", "Apple Inc."),
                lx.data.Extraction("person", "Steve Jobs"),
                lx.data.Extraction("location", "Cupertino"),
                lx.data.Extraction("relationship", "founded by",
                                 {"subject": "Apple Inc.", "object": "Steve Jobs"})
            ]
        )
    ]

    kg_data = []
    for doc in documents:
        result = lx.extract(doc, prompt, examples, "gemini-2.5-flash")
        kg_data.append(result)

    return kg_data

RAG System Enhancement

Enhanced RAG with Structured Metadata

def enhance_rag_with_langextract(documents, query):
    """Enhance RAG retrieval with structured extraction"""

    # Extract structured metadata from documents
    metadata_prompt = textwrap.dedent("""\
        Extract key topics, entities, and concepts that would help
        with document retrieval and relevance scoring.""")

    enhanced_docs = []
    for doc in documents:
        # Extract structured metadata
        metadata = lx.extract(doc, metadata_prompt, examples, "gemini-2.5-flash")

        # Combine original text with structured metadata
        enhanced_doc = {
            "original_text": doc,
            "entities": [e.extraction_text for e in metadata.extractions],
            "metadata": metadata
        }
        enhanced_docs.append(enhanced_doc)

    return enhanced_docs

Migration from Traditional NER

LangExtract represents a significant advancement over traditional approaches:

Migration Example

# Traditional spaCy approach (before)
import spacy

nlp = spacy.load("en_core_web_sm")
doc = nlp(text)
entities = [(ent.text, ent.label_) for ent in doc.ents]

# LangExtract approach (after)
import langextract as lx

result = lx.extract(text, prompt, examples, "gemini-2.5-flash")
entities = [(e.extraction_text, e.extraction_class) for e in result.extractions]

# Benefits of migration:
# - No training data required
# - Domain adaptation with just prompt changes
# - Better context understanding
# - Built-in relationship extraction
# - 99.9% accuracy across varied domains
# - Multilingual support for 100+ languages

Using Local Models with Ollama

For privacy-sensitive applications or when you need to process data offline, LangExtract supports running local models through Ollama integration:

Local Model Setup with Ollama

# First, install and start Ollama
# brew install ollama  # macOS
# ollama serve  # Start the Ollama server

# Pull a model
# ollama pull llama2
# ollama pull mistral

import langextract as lx

# Configure for local model
result = lx.extract(
    text_or_documents="sensitive_document.txt",
    prompt_description="Extract PII and sensitive information",
    examples=examples,
    model_id="ollama:llama2",  # Use local Llama 2
    # model_id="ollama:mistral",  # Or Mistral
    extraction_passes=2,
    max_workers=5  # Adjust based on local resources
)

# Benefits of local models:
# - Complete data privacy - no data leaves your infrastructure
# - No API costs or rate limits
# - Consistent latency without network dependencies
# - Compliance with strict data residency requirements

# Trade-offs:
# - Slightly lower accuracy than Gemini 2.0
# - Requires local compute resources
# - Model management and updates are manual

Performance Optimization

Optimize LangExtract performance for large-scale deployments with these techniques:

Performance Optimization Strategies

import langextract as lx
from concurrent.futures import ThreadPoolExecutor
import asyncio

# 1. Batch Processing for Multiple Documents
def batch_extract(documents, prompt, examples):
    """Process multiple documents in parallel"""
    with ThreadPoolExecutor(max_workers=10) as executor:
        futures = []
        for doc in documents:
            future = executor.submit(
                lx.extract,
                text_or_documents=doc,
                prompt_description=prompt,
                examples=examples,
                model_id="gemini-2.0-flash-exp",
                extraction_passes=2
            )
            futures.append(future)
        
        results = [f.result() for f in futures]
        return results

# 2. Optimize Chunk Size for Long Documents
result = lx.extract(
    text_or_documents="very_long_document.pdf",
    prompt_description=prompt,
    examples=examples,
    model_id="gemini-2.0-flash-exp",
    max_char_buffer=2000,  # Optimal chunk size
    max_workers=20,  # Parallel chunk processing
    extraction_passes=3  # Multiple passes for completeness
)

# 3. Cache Results for Repeated Extractions
from functools import lru_cache
import hashlib

@lru_cache(maxsize=100)
def cached_extract(text_hash, prompt, model_id):
    """Cache extraction results for repeated queries"""
    return lx.extract(
        text_or_documents=text_hash,  # Use original text
        prompt_description=prompt,
        examples=examples,
        model_id=model_id
    )

# 4. Model Selection by Task Complexity
def smart_model_selection(text_length, complexity):
    """Choose optimal model based on task requirements"""
    if text_length < 1000 and complexity == "simple":
        return "gemini-1.5-flash"  # Fastest, cheapest
    elif complexity == "complex":
        return "gemini-2.0-flash-exp"  # Best accuracy
    else:
        return "ollama:mistral"  # Local processing

# 5. Monitor and Log Performance Metrics
import time
import logging

logger = logging.getLogger(__name__)

def timed_extraction(text, prompt, examples):
    """Extract with performance monitoring"""
    start_time = time.time()
    
    result = lx.extract(
        text_or_documents=text,
        prompt_description=prompt,
        examples=examples,
        model_id="gemini-2.0-flash-exp"
    )
    
    elapsed = time.time() - start_time
    tokens_processed = len(text.split())
    
    logger.info(f"Extraction completed in {elapsed:.2f}s")
    logger.info(f"Tokens/sec: {tokens_processed/elapsed:.0f}")
    logger.info(f"Entities extracted: {len(result.extractions)}")
    
    return result

Best Practices

Here are key best practices for implementing LangExtract in production environments:

Prompt Engineering: Invest time in crafting clear, specific prompts with high-quality examples that cover edge cases.
Model Selection: Use gemini-2.5-flash for speed and cost efficiency, or gemini-2.5-pro for complex extraction tasks requiring advanced reasoning.
Error Handling: Implement robust retry logic and validation to handle API failures and ensure extraction quality.
Performance Optimization: Use multiple extraction passes and parallel processing for large documents while managing costs.
Monitoring: Track extraction performance, costs, and quality metrics over time to identify areas for improvement.

Common Issues and Solutions

Issue: Low extraction accuracy

Improving Accuracy

# Solution: Improve examples and prompt clarity
prompt = textwrap.dedent("""\
    Extract entities with high precision.
    Use EXACT text spans from the source.
    Do NOT paraphrase or generalize entities.
    Include specific attributes for context.""")

# Provide diverse, high-quality examples
examples = [
    lx.data.ExampleData(
        text="Clear, specific example text",
        extractions=[
            lx.data.Extraction(
                extraction_class="specific_class",
                extraction_text="exact text span",
                attributes={"detailed": "attributes"}
            )
        ]
    )
]

Issue: Missing entities in long documents

Optimizing for Long Documents

# Solution: Optimize chunking and use multiple passes
result = lx.extract(
    text, prompt, examples, "gemini-2.5-flash",
    extraction_passes=3,      # Multiple passes
    max_char_buffer=600,     # Smaller chunks
    max_workers=15           # Parallel processing
)

Production Error Handling

import langextract as lx
from tenacity import retry, stop_after_attempt, wait_exponential
import logging

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

@retry(
    stop=stop_after_attempt(3),
    wait=wait_exponential(multiplier=1, min=4, max=10)
)
def robust_extraction(text, prompt, examples):
    """Production-ready extraction with retry logic and monitoring"""
    try:
        result = lx.extract(
            text_or_documents=text,
            prompt_description=prompt,
            examples=examples,
            model_id="gemini-2.5-flash",  # Recommended model
            extraction_passes=2,  # Multiple passes for better recall
            max_workers=10  # Parallel processing
        )

        # Validate results
        if not result.extractions:
            logger.warning("No extractions found")
            raise ValueError("No extractions found")

        # Log extraction metrics
        logger.info(f"Extracted {len(result.extractions)} entities")

        return result

    except Exception as e:
        logger.error(f"Extraction failed: {e}")
        raise

# Validation function
def validate_extraction_quality(result, expected_classes):
    """Validate extraction results for production quality"""

    extracted_classes = {e.extraction_class for e in result.extractions}
    missing_classes = set(expected_classes) - extracted_classes

    quality_score = len(extracted_classes) / len(expected_classes)

    return {
        "quality_score": quality_score,
        "missing_classes": list(missing_classes),
        "extraction_count": len(result.extractions),
        "has_attributes": sum(1 for e in result.extractions if e.attributes)
    }

Conclusion

LangExtract represents a paradigm shift in information extraction, democratizing access to sophisticated NLP capabilities while maintaining the precision and traceability required for production applications. For AI developers, it offers an unprecedented combination of simplicity, power, and reliability that makes structured data extraction accessible and scalable across diverse domains and use cases.

The library's key advantages include requiring no training data (just a few examples), achieving 99.9% accuracy with precise source grounding, supporting 100+ languages, and working with both cloud and local models. This makes it an ideal choice for organizations looking to extract valuable insights from unstructured data efficiently.

As you implement LangExtract in your projects, remember to focus on clear prompt engineering, choose the right model for your use case, and implement proper error handling and monitoring for production deployments. Whether you're building knowledge graphs, enhancing RAG systems, or processing medical documents, LangExtract provides the tools needed to transform your unstructured data into actionable insights at scale.

The future of information extraction is here, and with LangExtract's active development and growing community, you're positioned at the forefront of this technological evolution.

Google LangExtract: The Ultimate AI Developer's Guide to Structured Information Extraction

Table of Contents

Introduction

What is LangExtract?

How LangExtract Works

Core Architecture

Key Features

Installation and Setup

Complete Code Examples

Basic Entity Extraction

Advanced Document Processing

Real-World Applications

Interactive HTML Visualization

OpenAI Models Integration

Batch Processing Pipeline

Visualization and Output

Interactive HTML Visualization

JSONL Output Format

Using Local Models with Ollama

Performance Optimization

Model Selection Guidelines

Cost Management

Advanced Use Cases

Building Knowledge Graphs

RAG System Enhancement

Migration from Traditional NER

Using Local Models with Ollama

Performance Optimization

Best Practices

Common Issues and Solutions

Conclusion

Further Reading

Key Resources

Academic References

Core Documentation

Technical Implementation

Performance & Benchmarks

Advanced Applications