Langextract

LangExtract - Structured Information Extraction

Expert assistance for extracting structured, source-grounded information from unstructured text using large language models.

When to Use This Skill

Use this skill when you need to:

• Extract structured entities from unstructured text (medical notes, reports, documents)
• Maintain precise source grounding (map extracted data to original text locations)
• Process long documents beyond LLM token limits
• Visualize extraction results with interactive HTML highlighting
• Extract clinical information from medical records
• Structure radiology or pathology reports
• Extract medications, diagnoses, or symptoms from clinical notes
• Analyze literary texts for characters, emotions, relationships
• Build domain-specific extraction pipelines
• Work with Gemini, OpenAI, or local models (Ollama)
• Generate schema-compliant outputs without fine-tuning

Overview

LangExtract is a Python library by Google for extracting structured information from unstructured text using large language models. It emphasizes:

• Source Grounding: Every extraction maps to its exact location in source text
• Structured Outputs: Schema-compliant results with controlled generation
• Long Document Processing: Intelligent chunking and multi-pass extraction
• Interactive Visualization: Self-contained HTML for reviewing extractions in context
• Flexible LLM Support: Works with Gemini, OpenAI, and local models
• Few-Shot Learning: Requires only quality examples, no expensive fine-tuning

Key Resources:

• GitHub: https://github.com/google/langextract
• Examples: https://github.com/google/langextract/tree/main/examples
• Documentation: https://github.com/google/langextract/tree/main/docs/examples

Installation

Prerequisites

• Python 3.8 or higher
• API key for Gemini (AI Studio), OpenAI, or local Ollama setup

Basic Installation

# Install from PyPI (recommended)
pip install langextract

# Install with OpenAI support
pip install langextract[openai]

# Install with development tools
pip install langextract[dev]

Install from Source

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

# For development with testing
pip install -e ".[test]"

Docker Installation

# Build Docker image
docker build -t langextract .

# Run with API key
docker run --rm \
  -e LANGEXTRACT_API_KEY="your-api-key" \
  langextract python your_script.py

API Key Setup

Gemini (Google AI Studio):

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

Get keys from: https://ai.google.dev/

OpenAI:

export OPENAI_API_KEY="your-openai-api-key"

Vertex AI (Enterprise):

# Use service account authentication
# Set project in language_model_params

.env File (Development):

# Create .env file
echo "LANGEXTRACT_API_KEY=your-key-here" > .env

Quick Start

Basic Extraction Example

import langextract as lx
import textwrap

# 1. Define extraction task
prompt = textwrap.dedent("""\
    Extract all medications mentioned in the clinical note.
    Include medication name, dosage, and frequency.
    Use exact text from the document.""")

# 2. Provide examples (few-shot learning)
examples = [
    lx.data.ExampleData(
        text="Patient prescribed Lisinopril 10mg daily for hypertension.",
        extractions=[
            lx.data.Extraction(
                extraction_class="medication",
                extraction_text="Lisinopril 10mg daily",
                attributes={
                    "name": "Lisinopril",
                    "dosage": "10mg",
                    "frequency": "daily",
                    "indication": "hypertension"
                }
            )
        ]
    )
]

# 3. Input text to extract from
input_text = """
Patient continues on Metformin 500mg twice daily for diabetes management.
Started on Amlodipine 5mg once daily for blood pressure control.
Discontinued Aspirin 81mg due to side effects.
"""

# 4. Run extraction
result = lx.extract(
    text_or_documents=input_text,
    prompt_description=prompt,
    examples=examples,
    model_id="gemini-2.0-flash-exp"
)

# 5. Access results
for extraction in result.extractions:
    print(f"Medication: {extraction.extraction_text}")
    print(f"  Name: {extraction.attributes.get('name')}")
    print(f"  Dosage: {extraction.attributes.get('dosage')}")
    print(f"  Frequency: {extraction.attributes.get('frequency')}")
    print(f"  Location: {extraction.start_char}-{extraction.end_char}")
    print()

# 6. Save and visualize
lx.io.save_annotated_documents(
    [result],
    output_name="medications.jsonl",
    output_dir="."
)

html_content = lx.visualize("medications.jsonl")
with open("medications.html", "w") as f:
    f.write(html_content)

Literary Text Example

import langextract as lx

prompt = """Extract characters, emotions, and relationships in order of appearance.
Use exact text for extractions. Do not paraphrase or overlap entities."""

examples = [
    lx.data.ExampleData(
        text="ROMEO entered the garden, filled with wonder at JULIET's beauty.",
        extractions=[
            lx.data.Extraction(
                extraction_class="character",
                extraction_text="ROMEO",
                attributes={"emotional_state": "wonder"}
            ),
            lx.data.Extraction(
                extraction_class="character",
                extraction_text="JULIET",
                attributes={}
            ),
            lx.data.Extraction(
                extraction_class="relationship",
                extraction_text="ROMEO ... JULIET's beauty",
                attributes={
                    "subject": "ROMEO",
                    "relation": "admires",
                    "object": "JULIET"
                }
            )
        ]
    )
]

text = """Act 2, Scene 2: The Capulet's orchard.
ROMEO appears beneath JULIET's balcony, gazing upward with longing.
JULIET steps onto the balcony, unaware of ROMEO's presence below."""

result = lx.extract(
    text_or_documents=text,
    prompt_description=prompt,
    examples=examples,
    model_id="gemini-2.0-flash-exp"
)

Core Concepts

1. Extraction Classes

Define categories of entities to extract:

# Single class
extraction_class="medication"

# Multiple classes via examples
examples = [
    lx.data.ExampleData(
        text="...",
        extractions=[
            lx.data.Extraction(extraction_class="diagnosis", ...),
            lx.data.Extraction(extraction_class="symptom", ...),
            lx.data.Extraction(extraction_class="medication", ...)
        ]
    )
]

2. Source Grounding

Every extraction includes precise text location:

extraction = result.extractions[0]
print(f"Text: {extraction.extraction_text}")
print(f"Start: {extraction.start_char}")
print(f"End: {extraction.end_char}")

# Extract from original document
original_text = input_text[extraction.start_char:extraction.end_char]

3. Attributes

Add structured metadata to extractions:

lx.data.Extraction(
    extraction_class="medication",
    extraction_text="Lisinopril 10mg daily",
    attributes={
        "name": "Lisinopril",
        "dosage": "10mg",
        "frequency": "daily",
        "route": "oral",
        "indication": "hypertension"
    }
)

4. Few-Shot Learning

Provide 1-5 quality examples instead of fine-tuning:

# Minimal examples (1-2) for simple tasks
examples = [example1]

# More examples (3-5) for complex schemas
examples = [example1, example2, example3, example4, example5]

5. Long Document Processing

Automatic chunking for documents beyond token limits:

result = lx.extract(
    text_or_documents=long_document,  # Any length
    prompt_description=prompt,
    examples=examples,
    model_id="gemini-2.0-flash-exp",
    extraction_passes=3,  # Multiple passes for better recall
    max_workers=20,        # Parallel processing
    max_char_buffer=1000   # Chunk overlap for continuity
)

Configuration

Model Selection

# Gemini models (recommended)
model_id="gemini-2.0-flash-exp"       # Fast, cost-effective
model_id="gemini-2.0-flash-thinking-exp"  # Complex reasoning
model_id="gemini-1.5-pro"             # Legacy

# OpenAI models
model_id="gpt-4o"                     # GPT-4 Optimized
model_id="gpt-4o-mini"                # Smaller, faster

# Local models via Ollama
model_id="gemma2:2b"                  # Local inference
model_url="http://localhost:11434"

Scaling Parameters

result = lx.extract(
    text_or_documents=documents,
    prompt_description=prompt,
    examples=examples,

    # Multi-pass extraction for better recall
    extraction_passes=3,

    # Parallel processing
    max_workers=20,

    # Chunk size tuning
    max_char_buffer=1000,

    # Model configuration
    model_id="gemini-2.0-flash-exp"
)

Backend Configuration

Vertex AI:

result = lx.extract(
    text_or_documents=text,
    prompt_description=prompt,
    examples=examples,
    model_id="gemini-2.0-flash-exp",
    language_model_params={
        "vertexai": True,
        "project": "your-gcp-project-id",
        "location": "us-central1"
    }
)

Batch Processing:

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

OpenAI Configuration:

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

Local Ollama:

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

Environment Variables

# API Keys
LANGEXTRACT_API_KEY="gemini-api-key"
OPENAI_API_KEY="openai-api-key"

# Vertex AI
GOOGLE_APPLICATION_CREDENTIALS="/path/to/service-account.json"

# Model configuration
LANGEXTRACT_MODEL_ID="gemini-2.0-flash-exp"
LANGEXTRACT_MODEL_URL="http://localhost:11434"

Common Patterns

Pattern 1: Clinical Note Extraction

import langextract as lx

prompt = """Extract diagnoses, symptoms, and medications from clinical notes.
Include ICD-10 codes when available. Use exact medical terminology."""

examples = [
    lx.data.ExampleData(
        text="Patient presents with Type 2 Diabetes Mellitus (E11.9). Started on Metformin 500mg BID. Reports fatigue and increased thirst.",
        extractions=[
            lx.data.Extraction(
                extraction_class="diagnosis",
                extraction_text="Type 2 Diabetes Mellitus (E11.9)",
                attributes={"condition": "Type 2 Diabetes Mellitus", "icd10": "E11.9"}
            ),
            lx.data.Extraction(
                extraction_class="medication",
                extraction_text="Metformin 500mg BID",
                attributes={"name": "Metformin", "dosage": "500mg", "frequency": "BID"}
            ),
            lx.data.Extraction(
                extraction_class="symptom",
                extraction_text="fatigue",
                attributes={"symptom": "fatigue"}
            ),
            lx.data.Extraction(
                extraction_class="symptom",
                extraction_text="increased thirst",
                attributes={"symptom": "polydipsia"}
            )
        ]
    )
]

# Process multiple clinical notes
clinical_notes = [
    "Note 1: Patient presents with...",
    "Note 2: Follow-up visit for...",
    "Note 3: New onset chest pain..."
]

results = lx.extract(
    text_or_documents=clinical_notes,
    prompt_description=prompt,
    examples=examples,
    model_id="gemini-2.0-flash-exp",
    extraction_passes=2,
    max_workers=10
)

# Save structured output
lx.io.save_annotated_documents(
    results,
    output_name="clinical_extractions.jsonl",
    output_dir="./output"
)

Pattern 2: Radiology Report Structuring

prompt = """Extract findings, impressions, and recommendations from radiology reports.
Include anatomical location, abnormality type, and severity."""

examples = [
    lx.data.ExampleData(
        text="FINDINGS: 3.2cm mass in right upper lobe. IMPRESSION: Suspicious for malignancy. RECOMMENDATION: Biopsy recommended.",
        extractions=[
            lx.data.Extraction(
                extraction_class="finding",
                extraction_text="3.2cm mass in right upper lobe",
                attributes={
                    "location": "right upper lobe",
                    "type": "mass",
                    "size": "3.2cm"
                }
            ),
            lx.data.Extraction(
                extraction_class="impression",
                extraction_text="Suspicious for malignancy",
                attributes={"diagnosis": "possible malignancy", "certainty": "suspicious"}
            ),
            lx.data.Extraction(
                extraction_class="recommendation",
                extraction_text="Biopsy recommended",
                attributes={"action": "biopsy"}
            )
        ]
    )
]

Pattern 3: Multi-Document Processing

import langextract as lx
from pathlib import Path

# Load multiple documents
documents = []
for file_path in Path("./documents").glob("*.txt"):
    with open(file_path, "r") as f:
        documents.append(f.read())

# Extract from all documents
results = lx.extract(
    text_or_documents=documents,
    prompt_description=prompt,
    examples=examples,
    model_id="gemini-2.0-flash-exp",
    extraction_passes=3,
    max_workers=20
)

# Results is a list of AnnotatedDocument objects
for i, result in enumerate(results):
    print(f"\nDocument {i+1}: {len(result.extractions)} extractions")
    for extraction in result.extractions:
        print(f"  - {extraction.extraction_class}: {extraction.extraction_text}")

Pattern 4: Interactive Visualization

# Generate interactive HTML
html_content = lx.visualize("extractions.jsonl")

# Save to file
with open("interactive_results.html", "w") as f:
    f.write(html_content)

# Open in browser (optional)
import webbrowser
webbrowser.open("interactive_results.html")

Pattern 5: Custom Provider Plugin

# See examples/custom_provider_plugin/ for full implementation
from langextract.providers import ProviderPlugin

class CustomProvider(ProviderPlugin):
    def extract(self, text, prompt, examples, **kwargs):
        # Custom extraction logic
        return extractions

    def supports_schema_constraints(self):
        return False

# Register custom provider
lx.register_provider("custom", CustomProvider())

# Use custom provider
result = lx.extract(
    text_or_documents=text,
    prompt_description=prompt,
    examples=examples,
    model_id="custom",
    provider="custom"
)

API Reference

Core Functions

lx.extract()

Main extraction function.

result = lx.extract(
    text_or_documents,           # str or list of str
    prompt_description,          # str: extraction instructions
    examples,                    # list of ExampleData
    model_id="gemini-2.0-flash-exp",  # str: model identifier
    extraction_passes=1,         # int: number of passes
    max_workers=None,            # int: parallel workers
    max_char_buffer=1000,        # int: chunk overlap
    language_model_params=None,  # dict: model config
    fence_output=False,          # bool: required for OpenAI
    use_schema_constraints=True, # bool: use schema enforcement
    model_url=None,              # str: custom model endpoint
    api_key=None                 # str: API key (prefer env var)
)

Returns: AnnotatedDocument or list[AnnotatedDocument]

lx.visualize()

Generate interactive HTML visualization.

html_content = lx.visualize(
    jsonl_file_path,           # str: path to JSONL file
    title="Extraction Results", # str: HTML page title
    show_attributes=True       # bool: display attributes
)

Returns: str (HTML content)

lx.io.save_annotated_documents()

Save results to JSONL format.

lx.io.save_annotated_documents(
    annotated_documents,  # list of AnnotatedDocument
    output_name,          # str: filename (e.g., "results.jsonl")
    output_dir="."        # str: output directory
)

Data Classes

ExampleData

Few-shot example definition.

example = lx.data.ExampleData(
    text="Example text here",
    extractions=[
        lx.data.Extraction(...)
    ]
)

Extraction

Single extraction definition.

extraction = lx.data.Extraction(
    extraction_class="medication",  # str: entity type
    extraction_text="Aspirin 81mg",  # str: exact text
    attributes={                     # dict: metadata
        "name": "Aspirin",
        "dosage": "81mg"
    },
    start_char=0,                    # int: start position (auto-set)
    end_char=13                      # int: end position (auto-set)
)

AnnotatedDocument

Extraction results for a document.

result.text                  # str: original text
result.extractions          # list of Extraction
result.metadata             # dict: additional info

Best Practices

Extraction Design

1. Write Clear Prompts: Be specific about what to extract and how

   # Good
   prompt = "Extract medications with dosage, frequency, and route of administration. Use exact medical terminology."
   
   # Avoid
   prompt = "Extract medications."
   

2. Provide Quality Examples: 1-5 well-crafted examples beat many poor ones

   # Include edge cases in examples
   examples = [
       normal_case_example,
       edge_case_example,
       complex_case_example
   ]
   

3. Use Exact Text: Extract verbatim from source for accurate grounding

   # Good
   extraction_text="Lisinopril 10mg daily"
   
   # Avoid paraphrasing
   extraction_text="10mg lisinopril taken once per day"
   

4. Define Attributes Clearly: Structure metadata consistently

   attributes={
       "name": "Lisinopril",        # Drug name
       "dosage": "10mg",             # Amount
       "frequency": "daily",         # How often
       "route": "oral"               # How taken
   }
   

Performance Optimization

1. Multi-Pass for Long Documents: Improves recall

   extraction_passes=3  # 2-3 passes recommended for thorough extraction
   

2. Parallel Processing: Speed up batch operations

   max_workers=20  # Adjust based on API rate limits
   

3. Chunk Size Tuning: Balance accuracy and context

   max_char_buffer=1000  # Larger for context, smaller for speed
   

4. Model Selection: Choose based on task complexity

   # Simple extraction
   model_id="gemini-2.0-flash-exp"
   
   # Complex reasoning
   model_id="gemini-2.0-flash-thinking-exp"
   

Production Deployment

1. API Key Security: Never hardcode keys

   # Good: Use environment variables
   import os
   api_key = os.getenv("LANGEXTRACT_API_KEY")
   
   # Avoid: Hardcoding
   api_key = "AIza..."  # Never do this
   

2. Error Handling: Handle API failures gracefully

   try:
       result = lx.extract(...)
   except Exception as e:
       logger.error(f"Extraction failed: {e}")
       # Implement retry logic or fallback
   

3. Cost Management: Monitor API usage

   # Use cheaper models for bulk processing
   model_id="gemini-2.0-flash-exp"  # vs "gemini-1.5-pro"
   
   # Batch processing for cost efficiency
   language_model_params={"batch": {"enabled": True}}
   

4. Validation: Verify extraction quality

   for extraction in result.extractions:
       # Validate extraction is within document bounds
       assert 0 <= extraction.start_char < len(result.text)
       assert extraction.end_char <= len(result.text)
   
       # Verify text matches
       extracted = result.text[extraction.start_char:extraction.end_char]
       assert extracted == extraction.extraction_text
   

Common Pitfalls

1. Overlapping Extractions

   • Issue: Extractions overlap or duplicate
   • Solution: Specify in prompt "Do not overlap entities"

2. Paraphrasing Instead of Exact Text

   • Issue: Extracted text doesn't match original
   • Solution: Prompt "Use exact text from document. Do not paraphrase."

3. Insufficient Examples

   • Issue: Poor extraction quality
   • Solution: Provide 3-5 diverse examples covering edge cases

4. Model Limitations

   • Issue: Schema constraints not supported on all models
   • Solution: Set use_schema_constraints=False for OpenAI/Ollama

Troubleshooting

Common Issues

Issue 1: API Authentication Failed

Symptoms:

• AuthenticationError: Invalid API key
• Permission denied errors

Solution:

# Verify API key is set
echo $LANGEXTRACT_API_KEY

# Set API key
export LANGEXTRACT_API_KEY="your-key-here"

# For OpenAI
export OPENAI_API_KEY="your-openai-key"

# Verify key works
python -c "import os; print(os.getenv('LANGEXTRACT_API_KEY'))"

Issue 2: Schema Constraints Error

Symptoms:

• Schema constraints not supported error
• Malformed output with OpenAI or Ollama

Solution:

# Disable schema constraints for non-Gemini models
result = lx.extract(
    text_or_documents=text,
    prompt_description=prompt,
    examples=examples,
    model_id="gpt-4o",
    use_schema_constraints=False,  # Disable for OpenAI
    fence_output=True               # Enable for OpenAI
)

Issue 3: Token Limit Exceeded

Symptoms:

• Token limit exceeded error
• Truncated results

Solution:

# Use multi-pass extraction
result = lx.extract(
    text_or_documents=long_text,
    prompt_description=prompt,
    examples=examples,
    extraction_passes=3,      # Multiple passes
    max_char_buffer=1000,     # Adjust chunk size
    max_workers=10            # Parallel processing
)

Issue 4: Poor Extraction Quality

Symptoms:

• Missing entities
• Incorrect extractions
• Paraphrased text

Solution:

# Improve prompt specificity
prompt = """Extract medications with exact dosage and frequency.
Use exact text from document. Do not paraphrase.
Include generic and brand names.
Extract discontinued medications as well."""

# Add more diverse examples
examples = [
    normal_case,
    edge_case_1,
    edge_case_2,
    complex_case
]

# Increase extraction passes
extraction_passes=3

# Try more capable model
model_id="gemini-2.0-flash-thinking-exp"

Issue 5: Ollama Connection Failed

Symptoms:

• Connection refused to localhost:11434
• Ollama model not found

Solution:

# Start Ollama server
ollama serve

# Pull required model
ollama pull gemma2:2b

# Verify Ollama is running
curl http://localhost:11434/api/tags

# Use in langextract
python -c "
import langextract as lx
result = lx.extract(
    text_or_documents='test',
    prompt_description='Extract entities',
    examples=[],
    model_id='gemma2:2b',
    model_url='http://localhost:11434',
    use_schema_constraints=False
)
"

Debugging Tips

1. Enable Verbose Logging

   import logging
   logging.basicConfig(level=logging.DEBUG)
   

2. Inspect Intermediate Results

   # Save each pass separately
   for i, result in enumerate(results):
       lx.io.save_annotated_documents(
           [result],
           output_name=f"pass_{i}.jsonl",
           output_dir="./debug"
       )
   

3. Validate Examples

   # Check examples match expected format
   for example in examples:
       for extraction in example.extractions:
           # Verify text is in example text
           assert extraction.extraction_text in example.text
           print(f"✓ {extraction.extraction_class}: {extraction.extraction_text}")
   

4. Test with Simple Input First

   # Start with minimal test
   test_result = lx.extract(
       text_or_documents="Patient on Aspirin 81mg daily.",
       prompt_description="Extract medications.",
       examples=[simple_example],
       model_id="gemini-2.0-flash-exp"
   )
   print(f"Extractions: {len(test_result.extractions)}")
   

Advanced Topics

Custom Extraction Schemas

Define complex nested structures:

examples = [
    lx.data.ExampleData(
        text="Patient presents with chest pain. ECG shows ST elevation. Diagnosed with STEMI.",
        extractions=[
            lx.data.Extraction(
                extraction_class="clinical_event",
                extraction_text="Patient presents with chest pain. ECG shows ST elevation. Diagnosed with STEMI.",
                attributes={
                    "symptom": "chest pain",
                    "diagnostic_test": "ECG",
                    "finding": "ST elevation",
                    "diagnosis": "STEMI",
                    "severity": "severe",
                    "timeline": [
                        {"event": "symptom_onset", "description": "chest pain"},
                        {"event": "diagnostic", "description": "ECG shows ST elevation"},
                        {"event": "diagnosis", "description": "STEMI"}
                    ]
                }
            )
        ]
    )
]

Batch Processing with Progress Tracking

from tqdm import tqdm
import langextract as lx

documents = load_documents()  # List of documents
results = []

for i, doc in enumerate(tqdm(documents)):
    try:
        result = lx.extract(
            text_or_documents=doc,
            prompt_description=prompt,
            examples=examples,
            model_id="gemini-2.0-flash-exp"
        )
        results.append(result)

        # Save incrementally
        if (i + 1) % 100 == 0:
            lx.io.save_annotated_documents(
                results,
                output_name=f"batch_{i+1}.jsonl",
                output_dir="./batches"
            )
            results = []  # Clear for next batch
    except Exception as e:
        print(f"Failed on document {i}: {e}")
        continue

Integration with Data Pipelines

import langextract as lx
import pandas as pd

# Load data
df = pd.read_csv("clinical_notes.csv")

# Extract from each note
extractions_data = []

for idx, row in df.iterrows():
    result = lx.extract(
        text_or_documents=row['note_text'],
        prompt_description=prompt,
        examples=examples,
        model_id="gemini-2.0-flash-exp"
    )

    for extraction in result.extractions:
        extractions_data.append({
            'patient_id': row['patient_id'],
            'note_date': row['note_date'],
            'extraction_class': extraction.extraction_class,
            'extraction_text': extraction.extraction_text,
            **extraction.attributes
        })

# Create structured DataFrame
extractions_df = pd.DataFrame(extractions_data)
extractions_df.to_csv("structured_extractions.csv", index=False)

Performance Benchmarking

import time
import langextract as lx

def benchmark_extraction(documents, model_id, passes=1):
    start = time.time()

    results = lx.extract(
        text_or_documents=documents,
        prompt_description=prompt,
        examples=examples,
        model_id=model_id,
        extraction_passes=passes,
        max_workers=20
    )

    elapsed = time.time() - start
    total_extractions = sum(len(r.extractions) for r in results)

    print(f"Model: {model_id}")
    print(f"Passes: {passes}")
    print(f"Documents: {len(documents)}")
    print(f"Total extractions: {total_extractions}")
    print(f"Time: {elapsed:.2f}s")
    print(f"Throughput: {len(documents)/elapsed:.2f} docs/sec")
    print()

# Compare models
benchmark_extraction(docs, "gemini-2.0-flash-exp", passes=1)
benchmark_extraction(docs, "gemini-2.0-flash-exp", passes=3)
benchmark_extraction(docs, "gpt-4o", passes=1)

Examples

Example Projects

The repository includes several example implementations:

1. Custom Provider Plugin (examples/custom_provider_plugin/)

   • How to create custom extraction backends
   • Integration with proprietary models

2. Jupyter Notebooks (examples/notebooks/)

   • Interactive extraction workflows
   • Visualization and analysis

3. Ollama Integration (examples/ollama/)

   • Local model usage
   • Privacy-preserving extraction

Medical Use Case

See examples/clinical_extraction.py for a complete medical extraction pipeline.

Literary Analysis

See examples/literary_extraction.py for character and relationship extraction from novels.

Testing

Running Tests

# Install test dependencies
pip install -e ".[test]"

# Run all tests
pytest tests

# Run with coverage
pytest tests --cov=langextract

# Run specific test
pytest tests/test_extraction.py

# Run integration tests
pytest tests/integration/

Integration Testing with Ollama

# Install tox
pip install tox

# Run Ollama integration tests
tox -e ollama-integration

Writing Tests

import langextract as lx

def test_basic_extraction():
    prompt = "Extract names."
    examples = [
        lx.data.ExampleData(
            text="John Smith visited the clinic.",
            extractions=[
                lx.data.Extraction(
                    extraction_class="name",
                    extraction_text="John Smith"
                )
            ]
        )
    ]

    result = lx.extract(
        text_or_documents="Mary Johnson was the doctor.",
        prompt_description=prompt,
        examples=examples,
        model_id="gemini-2.0-flash-exp"
    )

    assert len(result.extractions) >= 1
    assert result.extractions[0].extraction_class == "name"

Resources

Official Documentation

• GitHub Repository: https://github.com/google/langextract
• Examples Directory: https://github.com/google/langextract/tree/main/examples
• Documentation: https://github.com/google/langextract/tree/main/docs/examples

Model Documentation

• Gemini API: https://ai.google.dev/
• Vertex AI: https://cloud.google.com/vertex-ai
• OpenAI API: https://platform.openai.com/
• Ollama: https://ollama.ai/

Related Tools

• Google AI Studio: Web interface for Gemini models
• Vertex AI Workbench: Enterprise AI development
• LangChain: LLM application framework
• Instructor: Structured outputs library

Use Case Examples

• Clinical information extraction
• Legal document analysis
• Scientific literature mining
• Customer feedback structuring
• Contract entity extraction

Contributing

Contributions welcome! See the official repository for guidelines: https://github.com/google/langextract

Development Setup

git clone https://github.com/google/langextract.git
cd langextract
pip install -e ".[dev]"
pre-commit install

Running CI Locally

# Full test matrix
tox

# Specific Python version
tox -e py310

# Code formatting
black langextract/
isort langextract/

# Linting
flake8 langextract/
mypy langextract/

Version Information

Last Updated: 2025-12-25 Skill Version: 1.0.0 LangExtract Version: Latest (check PyPI)

This skill provides comprehensive guidance for LangExtract based on official documentation and examples. For the latest updates, refer to the GitHub repository.