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Fix google-generativeai embedder validation error (issue #3741)

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This commit fixes the validation error that occurred when using the
google-generativeai embedder provider with a flat configuration format.

Changes:
1. Made the 'config' field optional in GenerativeAiProviderSpec by adding
   'total=False' and marking 'provider' as Required, consistent with other
   provider specs like VertexAIProviderSpec.

2. Added normalization in the Crew class to automatically convert flat
   embedder configs to nested format before validation. This allows users
   to use either format:
   - Flat: {'provider': 'google-generativeai', 'api_key': '...', 'model_name': '...'}
   - Nested: {'provider': 'google-generativeai', 'config': {'api_key': '...', 'model_name': '...'}}

3. Updated the embedder factory to support both flat and nested config
   formats by checking for the presence of 'config' key and extracting
   config fields accordingly.

4. Added comprehensive tests to verify both formats work correctly:
   - Test for flat config format (the issue reported in #3741)
   - Test for nested config format (recommended format)
   - Test for TypedDict validation

Fixes #3741

Co-Authored-By: João <joao@crewai.com>
This commit is contained in:
Devin AI
2025-10-20 19:26:08 +00:00
parent 42f2b4d551
commit 36673f89e7
6 changed files with 3570 additions and 3459 deletions
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58
tests/rag/embeddings/test_embedding_factory.py
View File

@@ -242,3 +242,61 @@ class TestEmbeddingFactory:
mock_build_from_provider.assert_called_once_with(mock_provider)
assert result == mock_embedding_function
mock_import.assert_not_called()
@patch("crewai.rag.embeddings.factory.import_and_validate_definition")
def test_build_embedder_google_generativeai_nested_config(self, mock_import):
"""Test building Google Generative AI embedder with nested config format."""
mock_provider_class = MagicMock()
mock_provider_instance = MagicMock()
mock_embedding_function = MagicMock()
mock_import.return_value = mock_provider_class
mock_provider_class.return_value = mock_provider_instance
mock_provider_instance.embedding_callable.return_value = mock_embedding_function
config = {
"provider": "google-generativeai",
"config": {
"api_key": "test-gemini-key",
"model_name": "models/text-embedding-004",
},
}
build_embedder(config)
mock_import.assert_called_once_with(
"crewai.rag.embeddings.providers.google.generative_ai.GenerativeAiProvider"
)
mock_provider_class.assert_called_once()
call_kwargs = mock_provider_class.call_args.kwargs
assert call_kwargs["api_key"] == "test-gemini-key"
assert call_kwargs["model_name"] == "models/text-embedding-004"
@patch("crewai.rag.embeddings.factory.import_and_validate_definition")
def test_build_embedder_google_generativeai_flat_config(self, mock_import):
"""Test building Google Generative AI embedder with flat config format (issue #3741)."""
mock_provider_class = MagicMock()
mock_provider_instance = MagicMock()
mock_embedding_function = MagicMock()
mock_import.return_value = mock_provider_class
mock_provider_class.return_value = mock_provider_instance
mock_provider_instance.embedding_callable.return_value = mock_embedding_function
config = {
"provider": "google-generativeai",
"api_key": "test-gemini-key",
"model_name": "models/text-embedding-004",
}
build_embedder(config)
mock_import.assert_called_once_with(
"crewai.rag.embeddings.providers.google.generative_ai.GenerativeAiProvider"
)
mock_provider_class.assert_called_once()
call_kwargs = mock_provider_class.call_args.kwargs
assert call_kwargs["api_key"] == "test-gemini-key"
assert call_kwargs["model_name"] == "models/text-embedding-004"