feat: GenAI - Added support for Grounding

PiperOrigin-RevId: 607165697

tests/system/vertexai/test_generative_models.py

@@ -24,6 +24,7 @@
 from google.cloud import aiplatform
 from tests.system.aiplatform import e2e_base
 from vertexai import generative_models
+from vertexai.preview import generative_models as preview_generative_models
 
 
 class TestGenerativeModels(e2e_base.TestEndToEnd):
@@ -134,6 +135,20 @@ def test_generate_content_from_text_and_remote_video(self):
         assert response.text
         assert "Zootopia" in response.text
 
+    def test_grounding_google_search_retriever(self):
+        model = preview_generative_models.GenerativeModel("gemini-pro")
+        google_search_retriever_tool = (
+            preview_generative_models.Tool.from_google_search_retrieval(
+                preview_generative_models.grounding.GoogleSearchRetrieval(
+                    disable_attribution=False
+                )
+            )
+        )
+        response = model.generate_content(
+            "Why is sky blue?", tools=[google_search_retriever_tool]
+        )
+        assert response.text
+
     # Chat
 
     def test_send_message_from_text(self):

tests/unit/vertexai/test_generative_models.py

@@ -121,12 +121,27 @@ def mock_generate_content(
     contents: Optional[MutableSequence[gapic_content_types.Content]] = None,
 ) -> Iterable[gapic_prediction_service_types.GenerateContentResponse]:
     is_continued_chat = len(request.contents) > 1
-    has_tools = bool(request.tools)
+    has_retrieval = any(
+        tool.retrieval or tool.google_search_retrieval for tool in request.tools
+    )
+    has_function_declarations = any(
+        tool.function_declarations for tool in request.tools
+    )
+    has_function_request = any(
+        content.parts[0].function_call for content in request.contents
+    )
+    has_function_response = any(
+        content.parts[0].function_response for content in request.contents
+    )
 
-    if has_tools:
-        has_function_response = any(
-            "function_response" in content.parts[0] for content in request.contents
-        )
+    if has_function_request:
+        assert has_function_response
+
+    if has_function_response:
+        assert has_function_request
+        assert has_function_declarations
+
+    if has_function_declarations:
         needs_function_call = not has_function_response
         if needs_function_call:
             response_part_struct = _RESPONSE_FUNCTION_CALL_PART_STRUCT
@@ -158,6 +173,24 @@ def mock_generate_content(
                         gapic_content_types.Citation(_RESPONSE_CITATION_STRUCT),
                     ]
                 ),
+                grounding_metadata=gapic_content_types.GroundingMetadata(
+                    web_search_queries=[request.contents[0].parts[0].text],
+                    grounding_attributions=[
+                        gapic_content_types.GroundingAttribution(
+                            segment=gapic_content_types.Segment(
+                                start_index=0,
+                                end_index=67,
+                            ),
+                            confidence_score=0.69857746,
+                            web=gapic_content_types.GroundingAttribution.Web(
+                                uri="https://math.ucr.edu/home/baez/physics/General/BlueSky/blue_sky.html",
+                                title="Why is the sky blue? - UCR Math",
+                            ),
+                        ),
+                    ],
+                )
+                if has_retrieval and request.contents[0].parts[0].text
+                else None,
             ),
         ],
     )
@@ -288,3 +321,41 @@ def test_chat_function_calling(self, generative_models: generative_models):
             ),
         )
         assert response2.text == "The weather in Boston is super nice!"
+
+    @mock.patch.object(
+        target=prediction_service.PredictionServiceClient,
+        attribute="generate_content",
+        new=mock_generate_content,
+    )
+    def test_generate_content_grounding_google_search_retriever(self):
+        model = preview_generative_models.GenerativeModel("gemini-pro")
+        google_search_retriever_tool = (
+            preview_generative_models.Tool.from_google_search_retrieval(
+                preview_generative_models.grounding.GoogleSearchRetrieval(
+                    disable_attribution=False
+                )
+            )
+        )
+        response = model.generate_content(
+            "Why is sky blue?", tools=[google_search_retriever_tool]
+        )
+        assert response.text
+
+    @mock.patch.object(
+        target=prediction_service.PredictionServiceClient,
+        attribute="generate_content",
+        new=mock_generate_content,
+    )
+    def test_generate_content_grounding_vertex_ai_search_retriever(self):
+        model = preview_generative_models.GenerativeModel("gemini-pro")
+        google_search_retriever_tool = preview_generative_models.Tool.from_retrieval(
+            retrieval=preview_generative_models.grounding.Retrieval(
+                source=preview_generative_models.grounding.VertexAISearch(
+                    datastore=f"projects/{_TEST_PROJECT}/locations/global/collections/default_collection/dataStores/test-datastore",
+                )
+            )
+        )
+        response = model.generate_content(
+            "Why is sky blue?", tools=[google_search_retriever_tool]
+        )
+        assert response.text

vertexai/generative_models/_generative_models.py

@@ -1132,6 +1132,40 @@ def __init__(
             function_declarations=gapic_function_declarations
         )
 
+    @classmethod
+    def from_function_declarations(
+        cls,
+        function_declarations: List["FunctionDeclaration"],
+    ):
+        gapic_function_declarations = [
+            function_declaration._raw_function_declaration
+            for function_declaration in function_declarations
+        ]
+        raw_tool = gapic_tool_types.Tool(
+            function_declarations=gapic_function_declarations
+        )
+        return cls._from_gapic(raw_tool=raw_tool)
+
+    @classmethod
+    def from_retrieval(
+        cls,
+        retrieval: "Retrieval",
+    ):
+        raw_tool = gapic_tool_types.Tool(
+            retrieval=retrieval._raw_retrieval
+        )
+        return cls._from_gapic(raw_tool=raw_tool)
+
+    @classmethod
+    def from_google_search_retrieval(
+        cls,
+        google_search_retrieval: "GoogleSearchRetrieval",
+    ):
+        raw_tool = gapic_tool_types.Tool(
+            google_search_retrieval=google_search_retrieval._raw_google_search_retrieval
+        )
+        return cls._from_gapic(raw_tool=raw_tool)
+
     @classmethod
     def _from_gapic(
         cls,
@@ -1520,6 +1554,87 @@ def _image(self) -> "Image":
         return Image.from_bytes(data=self._raw_part.inline_data.data)
 
 
+class grounding:  # pylint: disable=invalid-name
+    """Grounding namespace."""
+
+    def __init__(self):
+        raise RuntimeError("This class must not be instantiated.")
+
+    class Retrieval:
+        """Defines a retrieval tool that model can call to access external knowledge."""
+
+        def __init__(
+            self,
+            source: Union["grounding.VertexAISearch"],
+            disable_attribution: Optional[bool] = None,
+        ):
+            """Initializes a Retrieval tool.
+
+            Args:
+                source (VertexAISearch):
+                    Set to use data source powered by Vertex AI Search.
+                disable_attribution (bool):
+                    Optional. Disable using the result from this
+                    tool in detecting grounding attribution. This
+                    does not affect how the result is given to the
+                    model for generation.
+            """
+            self._raw_retrieval = gapic_tool_types.Retrieval(
+                vertex_ai_search=source._raw_vertex_ai_search,
+                disable_attribution=disable_attribution,
+            )
+
+    class VertexAISearch:
+        r"""Retrieve from Vertex AI Search datastore for grounding.
+        See https://cloud.google.com/vertex-ai-search-and-conversation
+        """
+
+        def __init__(
+            self,
+            datastore: str,
+        ):
+            """Initializes a Vertex AI Search tool.
+
+            Args:
+                datastore (str):
+                    Required. Fully-qualified Vertex AI Search's
+                    datastore resource ID.
+                    projects/<>/locations/<>/collections/<>/dataStores/<>
+            """
+            self._raw_vertex_ai_search = gapic_tool_types.VertexAISearch(
+                datastore=datastore,
+            )
+
+    class GoogleSearchRetrieval:
+        r"""Tool to retrieve public web data for grounding, powered by
+        Google Search.
+
+        Attributes:
+            disable_attribution (bool):
+                Optional. Disable using the result from this
+                tool in detecting grounding attribution. This
+                does not affect how the result is given to the
+                model for generation.
+        """
+
+        def __init__(
+            self,
+            disable_attribution: Optional[bool] = None,
+        ):
+            """Initializes a Google Search Retrieval tool.
+
+            Args:
+                disable_attribution (bool):
+                    Optional. Disable using the result from this
+                    tool in detecting grounding attribution. This
+                    does not affect how the result is given to the
+                    model for generation.
+            """
+            self._raw_google_search_retrieval = gapic_tool_types.GoogleSearchRetrieval(
+                disable_attribution=disable_attribution,
+            )
+
+
 def _to_content(
     value: Union[
         gapic_content_types.Content,

vertexai/preview/generative_models.py

@@ -17,6 +17,7 @@
 # We just want to re-export certain classes
 # pylint: disable=g-multiple-import,g-importing-member
 from vertexai.generative_models._generative_models import (
+    grounding,
     _PreviewGenerativeModel,
     GenerationConfig,
     GenerationResponse,
@@ -39,6 +40,7 @@ class GenerativeModel(_PreviewGenerativeModel):
 
 
 __all__ = [
+    "grounding",
     "GenerationConfig",
     "GenerativeModel",
     "GenerationResponse",