langextract-openai 0.0.2

OpenAI and Azure OpenAI provider plugin for LangExtract

LangExtract OpenAI Plugin

OpenAI and Azure OpenAI provider plugin for LangExtract that enables structured data extraction using OpenAI's language models.

Features

• OpenAI Support: Direct integration with OpenAI's API
• Azure OpenAI Support: Full support for Azure OpenAI deployments
• Structured Output: JSON and YAML format support
• Parallel Processing: Efficient batch processing with configurable concurrency
• Plugin System: Seamless integration with LangExtract's provider system

Structure

langextract-openai/
├── pyproject.toml                    # Package configuration and metadata
├── README.md                         # This file
├── langextract_openai/              # Package directory
│   ├── __init__.py                  # Package initialization and exports
│   └── openai_providers.py         # OpenAI and Azure OpenAI providers
├── examples/                        # Usage examples
│   └── usage_examples.ipynb        # Jupyter notebook with examples
└── LICENSE

Provider Implementations

• OpenAI (OpenAILanguageModel): Direct OpenAI API integration
• Azure OpenAI (AzureOpenAILanguageModel): Azure OpenAI service integration (inherits OpenAI implementation)

Package Configuration (pyproject.toml)

[project.entry-points."langextract.providers"]
openai = "langextract_openai:OpenAILanguageModel"
azure_openai = "langextract_openai:AzureOpenAILanguageModel"

This entry point allows LangExtract to automatically discover your provider.

Installation

Prerequisites

First install the latest LangExtract from source:

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

Install Plugin

# Clone this plugin
git clone <this-repo-url>
cd langextract-openai

# Install in development mode
pip install -e .

# Run the example notebook
# (ensure you have Jupyter installed: pip install jupyter)
jupyter notebook examples/usage_examples.ipynb

Quick Start

OpenAI

import langextract as lx

# Extract structured data with OpenAI
result = lx.extract(
    text_or_documents="John Smith is a software engineer at Tech Corp.",
    model_id="gpt-4o-mini",
    api_key="your-openai-api-key",
    prompt_description="Extract person's name, job title, and company",
    examples=[{
        "input": "Jane Doe works as a data scientist at DataCorp.",
        "output": {"name": "Jane Doe", "job_title": "data scientist", "company": "DataCorp"}
    }]
)

# Tip: If there are multiple providers matching your model_id in your environment,
# you can disambiguate by explicitly specifying the provider name:
# result = lx.extract(
#     text_or_documents="...",
#     model_id="gpt-4o-mini",
#     api_key="...",
#     provider="OpenAILanguageModel",
#     prompt_description="...",
# )

Azure OpenAI

import langextract as lx

# Extract with Azure OpenAI
result = lx.extract(
    text_or_documents="John Smith is a software engineer at Tech Corp.",
    model_id="azure:your-deployment-name",
    api_key="your-azure-api-key",
    azure_endpoint="https://your-resource.openai.azure.com",
    prompt_description="Extract person's name, job title, and company",
)

Environment Variables

Set these environment variables for the examples and tests:

• OPENAI_API_KEY: Your OpenAI API key
• AZURE_OPENAI_API_KEY: Your Azure OpenAI API key
• AZURE_OPENAI_ENDPOINT: Your Azure OpenAI endpoint URL
• AZURE_OPENAI_DEPLOYMENT: Your Azure deployment name (optional, defaults to 'gpt-4o-mini')

Release

1. Bump version in pyproject.toml under [project] version.

2. Build and upload to PyPI:

python -m pip install --upgrade build twine
rm -rf dist build *.egg-info
python -m build
twine upload dist/*

Optional: Upload to TestPyPI first:

twine upload --repository testpypi dist/*

Optional: Tag the release in git:

git tag vX.Y.Z
git push origin vX.Y.Z

Notes:

• Use a PyPI API token (username: __token__, password: your token), or configure ~/.pypirc.
• Ensure you have a clean tree and tests/examples pass before publishing.

License

Apache License 2.0