Update import paths of google genai modules to fix pyright errors

.github/workflows/pytest.yml

@@ -19,7 +19,7 @@ jobs:
   # This workflow contains a single job called "pytest"
   pytest:
     # The type of runner that the job will run on
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-latest-4-cores
 
     # Steps represent a sequence of tasks that will be executed as part of the job
     steps:

src/langcheck/metrics/eval_clients/_gemini.py

@@ -5,8 +5,10 @@
 from typing import Any
 
 import google.ai.generativelanguage as glm
-import google.generativeai as genai
 import torch
+from google.generativeai.client import configure
+from google.generativeai.embedding import embed_content
+from google.generativeai.generative_models import GenerativeModel
 
 from langcheck.utils.progress_bar import tqdm_wrapper
 
@@ -20,7 +22,7 @@ class GeminiEvalClient(EvalClient):
 
     def __init__(
         self,
-        model: genai.GenerativeModel | None = None,
+        model: GenerativeModel | None = None,
         model_args: dict[str, Any] | None = None,
         generate_content_args: dict[str, Any] | None = None,
         embed_model_name: str | None = None,
@@ -49,9 +51,9 @@ def __init__(
         if model:
             self._model = model
         else:
-            genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+            configure(api_key=os.getenv("GOOGLE_API_KEY"))
             model_args = model_args or {}
-            self._model = genai.GenerativeModel(**model_args)
+            self._model = GenerativeModel(**model_args)
 
         self._generate_content_args = generate_content_args or {}
         self._embed_model_name = embed_model_name
@@ -234,7 +236,7 @@ def __init__(self, embed_model_name: str | None):
     def _embed(self, inputs: list[str]) -> torch.Tensor:
         """Embed the inputs using the Gemini API."""
         # Embed the inputs
-        embed_response = genai.embed_content(
+        embed_response = embed_content(
             model=self.embed_model_name, content=inputs
         )
 

tests/metrics/eval_clients/test_gemini.py

@@ -17,9 +17,10 @@ def test_get_text_response_gemini():
     mock_response.candidates = [Mock(finish_reason=1)]
     # Calling the google.generativeai.GenerativeModel.generate_content method
     # requires a Google API key, so we mock the return value instead
-    with patch("google.generativeai.GenerativeModel.generate_content",
-               return_value=mock_response):
-
+    with patch(
+        "google.generativeai.GenerativeModel.generate_content",
+        return_value=mock_response,
+    ):
         # Set the necessary env vars for the GeminiEvalClient
         os.environ["GOOGLE_API_KEY"] = "dummy_key"
         client = GeminiEvalClient()
@@ -41,28 +42,30 @@ def test_get_float_score_gemini(language):
     mock_response.text = short_assessment_result
 
     class FunctionCallMock(Mock):
-
         @classmethod
         def to_dict(cls, instance):
             return {"args": {"assessment": short_assessment_result}}
 
     mock_response.candidates = [
-        Mock(finish_reason=1,
-             content=Mock(parts=[Mock(function_call=FunctionCallMock())]))
+        Mock(
+            finish_reason=1,
+            content=Mock(parts=[Mock(function_call=FunctionCallMock())]),
+        )
     ]
 
     # Calling the google.generativeai.GenerativeModel.generate_content method
     # requires a Google API key, so we mock the return value instead
-    with patch("google.generativeai.GenerativeModel.generate_content",
-               return_value=mock_response):
-
+    with patch(
+        "google.generativeai.GenerativeModel.generate_content",
+        return_value=mock_response,
+    ):
         # Set the necessary env vars for the GeminiEvalClient
         os.environ["GOOGLE_API_KEY"] = "dummy_key"
         client = GeminiEvalClient()
 
-        scores = client.get_float_score("dummy_metric", language,
-                                        unstructured_assessment_result,
-                                        score_map)
+        scores = client.get_float_score(
+            "dummy_metric", language, unstructured_assessment_result, score_map
+        )
         assert len(scores) == len(unstructured_assessment_result)
         for score in scores:
             assert score == 1.0
@@ -73,14 +76,17 @@ def test_similarity_scorer_gemini():
 
     # Calling the google.generativeai.embed_content method requires a Google
     # API key, so we mock the return value instead
-    with patch("google.generativeai.embed_content",
-               Mock(return_value=mock_embedding_response)):
+    with patch(
+        "langcheck.metrics.eval_clients._gemini.embed_content",
+        Mock(return_value=mock_embedding_response),
+    ):
         # Set the necessary env vars for the GeminiEvalClient
         os.environ["GOOGLE_API_KEY"] = "dummy_key"
         gemini_client = GeminiEvalClient()
         scorer = gemini_client.similarity_scorer()
         # Since the mock embeddings are the same for the generated and reference
         # outputs, the similarity score should be 1.
-        score = scorer.score(["The cat sat on the mat."],
-                             ["The cat sat on the mat."])
+        score = scorer.score(
+            ["The cat sat on the mat."], ["The cat sat on the mat."]
+        )
         assert 0.99 <= score[0] <= 1