fix(vertex_httpx.py): google search grounding fix

docs/my-website/docs/providers/vertex.md

@@ -10,7 +10,7 @@ import TabItem from '@theme/TabItem';
 
 ## 🆕 `vertex_ai_beta/` route 
 
-New `vertex_ai_beta/` route. Adds support for system messages, tool_choice params, etc. by moving to httpx client (instead of vertex sdk).
+New `vertex_ai_beta/` route. Adds support for system messages, tool_choice params, etc. by moving to httpx client (instead of vertex sdk). This implementation uses [VertexAI's REST API](https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/inference#syntax).
 
 ```python
 from litellm import completion
@@ -377,6 +377,54 @@ curl http://0.0.0.0:4000/v1/chat/completions \
 </TabItem>
 </Tabs>
 
+#### **Moving from Vertex AI SDK to LiteLLM (GROUNDING)**
+
+
+If this was your initial VertexAI Grounding code,
+
+```python
+import vertexai 
+
+vertexai.init(project=project_id, location="us-central1")
+
+model = GenerativeModel("gemini-1.5-flash-001")
+
+# Use Google Search for grounding
+tool = Tool.from_google_search_retrieval(grounding.GoogleSearchRetrieval(disable_attributon=False))
+
+prompt = "When is the next total solar eclipse in US?"
+response = model.generate_content(
+    prompt,
+    tools=[tool],
+    generation_config=GenerationConfig(
+        temperature=0.0,
+    ),
+)
+
+print(response)
+```
+
+then, this is what it looks like now
+
+```python
+from litellm import completion 
+
+
+# !gcloud auth application-default login - run this to add vertex credentials to your env
+
+tools = [{"googleSearchRetrieval": {"disable_attributon": False}}] # 👈 ADD GOOGLE SEARCH
+
+resp = litellm.completion(
+                    model="vertex_ai_beta/gemini-1.0-pro-001",
+                    messages=[{"role": "user", "content": "Who won the world cup?"}],
+                    tools=tools,
+                    vertex_project="project-id"
+                )
+
+print(resp)
+```
+
+
 ## Pre-requisites
 * `pip install google-cloud-aiplatform` (pre-installed on proxy docker image)
 * Authentication: 

litellm/llms/vertex_httpx.py

@@ -396,7 +396,8 @@ class VertexGeminiConfig:
                 optional_params["presence_penalty"] = value
             if param == "tools" and isinstance(value, list):
                 gtool_func_declarations = []
-                google_search_tool: Optional[dict] = None
+                googleSearchRetrieval: Optional[dict] = None
+                provider_specific_tools: List[dict] = []
                 for tool in value:
                     # check if grounding
                     try:
@@ -411,11 +412,14 @@ class VertexGeminiConfig:
                         verbose_logger.warning(
                             "Got KeyError parsing tool={}. Assuming it's a provider-specific param. Use `litellm.set_verbose` or `litellm --detailed_debug` to see raw request."
                         )
-                        google_search_tool = tool
+                        if tool.get("googleSearchRetrieval", None) is not None:
-                _tools = Tools(function_declarations=gtool_func_declarations)
+                            googleSearchRetrieval = tool["googleSearchRetrieval"]
-                if google_search_tool is not None:
+                _tools = Tools(
-                    _tools["googleSearchRetrieval"] = google_search_tool
+                    function_declarations=gtool_func_declarations,
-                optional_params["tools"] = [_tools]
+                )
+                if googleSearchRetrieval is not None:
+                    _tools["googleSearchRetrieval"] = googleSearchRetrieval
+                optional_params["tools"] = [_tools] + provider_specific_tools
             if param == "tool_choice" and (
                 isinstance(value, str) or isinstance(value, dict)
             ):

litellm/tests/test_amazing_vertex_completion.py

@@ -677,12 +677,13 @@ def test_gemini_pro_vision_base64():
             pytest.fail(f"An exception occurred - {str(e)}")
 
 
-def test_gemini_pro_grounding():
+@pytest.mark.parametrize("value_in_dict", [{}, {"disable_attribution": False}])  #
+def test_gemini_pro_grounding(value_in_dict):
     try:
         load_vertex_ai_credentials()
         litellm.set_verbose = True
 
-        tools = [{"googleSearchRetrieval": {}}]
+        tools = [{"googleSearchRetrieval": value_in_dict}]
 
         litellm.set_verbose = True
 
@@ -709,6 +710,10 @@ def test_gemini_pro_grounding():
                 "googleSearchRetrieval"
                 in mock_call.call_args.kwargs["json"]["tools"][0]
             )
+            assert (
+                mock_call.call_args.kwargs["json"]["tools"][0]["googleSearchRetrieval"]
+                == value_in_dict
+            )
 
     except litellm.InternalServerError:
         pass