feat(vertex_httpx.py): add support for gemini 'grounding'

Adds support for https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/grounding#rest

litellm/llms/vertex_httpx.py

@@ -391,16 +391,23 @@ class VertexGeminiConfig:
                 optional_params["presence_penalty"] = value
             if param == "tools" and isinstance(value, list):
                 gtool_func_declarations = []
+                google_search_tool: Optional[dict] = None
                 for tool in value:
-                    gtool_func_declaration = FunctionDeclaration(
-                        name=tool["function"]["name"],
-                        description=tool["function"].get("description", ""),
-                        parameters=tool["function"].get("parameters", {}),
-                    )
-                    gtool_func_declarations.append(gtool_func_declaration)
-                optional_params["tools"] = [
-                    Tools(function_declarations=gtool_func_declarations)
-                ]
+                    # check if grounding
+                    _search_tool = tool.get("googleSearchRetrieval", None)
+                    if google_search_tool is not None:
+                        google_search_tool = _search_tool
+                    else:
+                        gtool_func_declaration = FunctionDeclaration(
+                            name=tool["function"]["name"],
+                            description=tool["function"].get("description", ""),
+                            parameters=tool["function"].get("parameters", {}),
+                        )
+                        gtool_func_declarations.append(gtool_func_declaration)
+                _tools = Tools(function_declarations=gtool_func_declarations)
+                if google_search_tool is not None:
+                    _tools["googleSearchRetrieval"] = google_search_tool
+                optional_params["tools"] = [_tools]
             if param == "tool_choice" and (
                 isinstance(value, str) or isinstance(value, dict)
             ):

litellm/tests/test_amazing_vertex_completion.py

@@ -677,6 +677,57 @@ def test_gemini_pro_vision_base64():
             pytest.fail(f"An exception occurred - {str(e)}")
 
 
+def test_gemini_pro_grounding():
+    try:
+        load_vertex_ai_credentials()
+        litellm.set_verbose = True
+
+        messages = [
+            {
+                "role": "system",
+                "content": "Your name is Litellm Bot, you are a helpful assistant",
+            },
+            # User asks for their name and weather in San Francisco
+            {
+                "role": "user",
+                "content": "Hello, what is your name and can you tell me the weather?",
+            },
+        ]
+
+        tools = [{"googleSearchRetrieval": {}}]
+
+        litellm.set_verbose = True
+
+        from litellm.llms.custom_httpx.http_handler import HTTPHandler
+
+        client = HTTPHandler()
+
+        with patch.object(client, "post", new=MagicMock()) as mock_call:
+            try:
+                resp = litellm.completion(
+                    model="vertex_ai_beta/gemini-1.0-pro-001",
+                    messages=[{"role": "user", "content": "Who won the world cup?"}],
+                    tools=tools,
+                    client=client,
+                )
+            except Exception:
+                pass
+
+            mock_call.assert_called_once()
+
+            print(mock_call.call_args.kwargs["json"]["tools"][0])
+
+            assert (
+                "googleSearchRetrieval"
+                in mock_call.call_args.kwargs["json"]["tools"][0]
+            )
+
+    except litellm.InternalServerError:
+        pass
+    except litellm.RateLimitError:
+        pass
+
+
 # @pytest.mark.skip(reason="exhausted vertex quota. need to refactor to mock the call")
 @pytest.mark.parametrize(
     "model", ["vertex_ai_beta/gemini-1.5-pro", "vertex_ai/claude-3-sonnet@20240229"]

litellm/types/llms/vertex_ai.py

@@ -94,6 +94,14 @@ class FunctionDeclaration(TypedDict, total=False):
     response: Schema
 
 
+class VertexAISearch(TypedDict, total=False):
+    datastore: Required[str]
+
+
+class Retrieval(TypedDict):
+    source: VertexAISearch
+
+
 class FunctionCallingConfig(TypedDict, total=False):
     mode: Literal["ANY", "AUTO", "NONE"]
     allowed_function_names: List[str]
@@ -147,8 +155,10 @@ class GenerationConfig(TypedDict, total=False):
     response_mime_type: Literal["text/plain", "application/json"]
 
 
-class Tools(TypedDict):
+class Tools(TypedDict, total=False):
     function_declarations: List[FunctionDeclaration]
+    googleSearchRetrieval: dict
+    retrieval: Retrieval
 
 
 class ToolConfig(TypedDict):