feat: Add support for output_dimensionality parameter through get_embeddings.

vertex-sdk-bot · copybara-github · commit b1cab3fca34e · 2024-03-19T12:07:09.000-07:00
PiperOrigin-RevId: 617251035
diff --git a/tests/unit/aiplatform/test_language_models.py b/tests/unit/aiplatform/test_language_models.py
@@ -4164,6 +4164,7 @@ def test_text_embedding(self):
                     ),
                 ],
                 auto_truncate=False,
+                output_dimensionality=3,
             )
             prediction_instances = mock_predict.call_args[1]["instances"]
             assert prediction_instances == [
@@ -4180,6 +4181,7 @@ def test_text_embedding(self):
             ]
             prediction_parameters = mock_predict.call_args[1]["parameters"]
             assert not prediction_parameters["autoTruncate"]
+            assert prediction_parameters["outputDimensionality"] == 3
             assert embeddings
             for embedding in embeddings:
                 vector = embedding.values
diff --git a/vertexai/language_models/_language_models.py b/vertexai/language_models/_language_models.py
@@ -2003,12 +2003,14 @@ def _prepare_text_embedding_request(
         texts: List[Union[str, TextEmbeddingInput]],
         *,
         auto_truncate: bool = True,
+        output_dimensionality: Optional[int] = None,
     ) -> _MultiInstancePredictionRequest:
         """Asynchronously calculates embeddings for the given texts.
 
         Args:
             texts(str): A list of texts or `TextEmbeddingInput` objects to embed.
             auto_truncate(bool): Whether to automatically truncate long texts. Default: True.
+            output_dimensionality: Optional dimensions of embeddings. Range: [1, 768]. Default: None.
 
         Returns:
             A `_MultiInstancePredictionRequest` object.
@@ -2029,6 +2031,8 @@ def _prepare_text_embedding_request(
                 raise TypeError(f"Unsupported text embedding input type: {text}.")
             instances.append(instance)
         parameters = {"autoTruncate": auto_truncate}
+        if output_dimensionality is not None:
+            parameters["outputDimensionality"] = output_dimensionality
         return _MultiInstancePredictionRequest(
             instances=instances,
             parameters=parameters,
@@ -2057,19 +2061,22 @@ def get_embeddings(
         texts: List[Union[str, TextEmbeddingInput]],
         *,
         auto_truncate: bool = True,
+        output_dimensionality: Optional[int] = None
     ) -> List["TextEmbedding"]:
         """Calculates embeddings for the given texts.
 
         Args:
-            texts(str): A list of texts or `TextEmbeddingInput` objects to embed.
-            auto_truncate(bool): Whether to automatically truncate long texts. Default: True.
+            texts: A list of texts or `TextEmbeddingInput` objects to embed.
+            auto_truncate: Whether to automatically truncate long texts. Default: True.
+            output_dimensionality: Optional dimensions of embeddings. Range: [1, 768]. Default: None.
 
         Returns:
             A list of `TextEmbedding` objects.
         """
         prediction_request = self._prepare_text_embedding_request(
             texts=texts,
             auto_truncate=auto_truncate,
+            output_dimensionality=output_dimensionality,
         )
 
         prediction_response = self._endpoint.predict(
@@ -2092,19 +2099,22 @@ async def get_embeddings_async(
         texts: List[Union[str, TextEmbeddingInput]],
         *,
         auto_truncate: bool = True,
+        output_dimensionality: Optional[int] = None,
     ) -> List["TextEmbedding"]:
         """Asynchronously calculates embeddings for the given texts.
 
         Args:
-            texts(str): A list of texts or `TextEmbeddingInput` objects to embed.
-            auto_truncate(bool): Whether to automatically truncate long texts. Default: True.
+            texts: A list of texts or `TextEmbeddingInput` objects to embed.
+            auto_truncate: Whether to automatically truncate long texts. Default: True.
+            output_dimensionality: Optional dimensions of embeddings. Range: [1, 768]. Default: None.
 
         Returns:
             A list of `TextEmbedding` objects.
         """
         prediction_request = self._prepare_text_embedding_request(
             texts=texts,
             auto_truncate=auto_truncate,
+            output_dimensionality=output_dimensionality
         )
 
         prediction_response = await self._endpoint.predict_async(
