Add provider registry infrastructure and custom provider plugin example #97
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Introduces a provider registry system enabling third-party providers to be dynamically registered and discovered through a plugin architecture. Users can now integrate custom LLM backends (Azure OpenAI, AWS Bedrock, custom inference servers) without modifying core LangExtract code. Fixes #80, #67, #54, #49, #48, #53 Key Changes: **Provider Registry** (`langextract/providers/registry.py`) - Pattern-based registration with priority resolution - Automatic discovery via Python entry points - Lazy loading for performance **Factory Enhancements** (`langextract/factory.py`) - `ModelConfig` dataclass for structured configuration - Explicit provider selection when patterns overlap - Full backward compatibility maintained **Plugin Example** (`examples/custom_provider_plugin/`) - Complete working example with entry point configuration - Shows how to create custom providers for any backend **Documentation** - Comprehensive provider system README with architecture diagrams - Step-by-step plugin creation guide **Dependencies** - Move openai to optional dependencies - Update tox.ini to include openai in test environments **Lint Fixes** - Add appropriate pylint suppressions for legitimate patterns - Fix unused variable warnings in tests - Address import and global statement warnings No anticipated breakage - full backward compatibility maintained. Given significant internal changes to provider loading, issues should be reported if unexpected behavior is encountered.
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…le (#97) Introduces a provider registry system enabling third-party providers to be dynamically registered and discovered through a plugin architecture. Users can now integrate custom LLM backends (Azure OpenAI, AWS Bedrock, custom inference servers) without modifying core LangExtract code. Fixes #80, #67, #54, #49, #48, #53 Key Changes: **Provider Registry** (`langextract/providers/registry.py`) - Pattern-based registration with priority resolution - Automatic discovery via Python entry points - Lazy loading for performance **Factory Enhancements** (`langextract/factory.py`) - `ModelConfig` dataclass for structured configuration - Explicit provider selection when patterns overlap - Full backward compatibility maintained **Plugin Example** (`examples/custom_provider_plugin/`) - Complete working example with entry point configuration - Shows how to create custom providers for any backend **Documentation** - Comprehensive provider system README with architecture diagrams - Step-by-step plugin creation guide **Dependencies** - Move openai to optional dependencies - Update tox.ini to include openai in test environments **Lint Fixes** - Add appropriate pylint suppressions for legitimate patterns - Fix unused variable warnings in tests - Address import and global statement warnings No anticipated breakage - full backward compatibility maintained. Given significant internal changes to provider loading, issues should be reported if unexpected behavior is encountered.
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…le (google#97) Introduces a provider registry system enabling third-party providers to be dynamically registered and discovered through a plugin architecture. Users can now integrate custom LLM backends (Azure OpenAI, AWS Bedrock, custom inference servers) without modifying core LangExtract code. Fixes google#80, #67, #54, #49, #48, #53 Key Changes: **Provider Registry** (`langextract/providers/registry.py`) - Pattern-based registration with priority resolution - Automatic discovery via Python entry points - Lazy loading for performance **Factory Enhancements** (`langextract/factory.py`) - `ModelConfig` dataclass for structured configuration - Explicit provider selection when patterns overlap - Full backward compatibility maintained **Plugin Example** (`examples/custom_provider_plugin/`) - Complete working example with entry point configuration - Shows how to create custom providers for any backend **Documentation** - Comprehensive provider system README with architecture diagrams - Step-by-step plugin creation guide **Dependencies** - Move openai to optional dependencies - Update tox.ini to include openai in test environments **Lint Fixes** - Add appropriate pylint suppressions for legitimate patterns - Fix unused variable warnings in tests - Address import and global statement warnings No anticipated breakage - full backward compatibility maintained. Given significant internal changes to provider loading, issues should be reported if unexpected behavior is encountered.
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…le (#97) Introduces a provider registry system enabling third-party providers to be dynamically registered and discovered through a plugin architecture. Users can now integrate custom LLM backends (Azure OpenAI, AWS Bedrock, custom inference servers) without modifying core LangExtract code. Fixes #80, #67, #54, #49, #48, #53 Key Changes: **Provider Registry** (`langextract/providers/registry.py`) - Pattern-based registration with priority resolution - Automatic discovery via Python entry points - Lazy loading for performance **Factory Enhancements** (`langextract/factory.py`) - `ModelConfig` dataclass for structured configuration - Explicit provider selection when patterns overlap - Full backward compatibility maintained **Plugin Example** (`examples/custom_provider_plugin/`) - Complete working example with entry point configuration - Shows how to create custom providers for any backend **Documentation** - Comprehensive provider system README with architecture diagrams - Step-by-step plugin creation guide **Dependencies** - Move openai to optional dependencies - Update tox.ini to include openai in test environments **Lint Fixes** - Add appropriate pylint suppressions for legitimate patterns - Fix unused variable warnings in tests - Address import and global statement warnings No anticipated breakage - full backward compatibility maintained. Given significant internal changes to provider loading, issues should be reported if unexpected behavior is encountered.
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Description
Introduces a provider registry system enabling third-party providers to be dynamically registered and discovered through a plugin architecture. Users can now integrate custom LLM backends (Azure OpenAI, AWS Bedrock, custom inference servers) without modifying core LangExtract code.
Feature
How Has This Been Tested?
Verified backward compatibility with existing providers and tested custom plugin with entry points.
Checklist:
Code of conduct.
Contributing
page, and I either signed the Google
Individual CLA
or am covered by my company's
Corporate CLA.
issue(s) and we have agreed upon the general approach.
issue(s) that documentation elsewhere needs updating.
Google's Python Style Guide
and ran `pylint` over the affected code.
Key Changes
Provider Registry (`langextract/providers/registry.py`)
Factory Enhancements (`langextract/factory.py`)
Plugin Example (`examples/custom_provider_plugin/`)
Documentation
Breaking Changes
No anticipated breakage - full backward compatibility maintained. Given significant internal changes to provider loading, issues should be reported if unexpected behavior is encountered.