diff --git a/generative_ai/function_calling.py b/generative_ai/function_calling.py
index aae65a2cc5d..de1b09cc0f4 100644
--- a/generative_ai/function_calling.py
+++ b/generative_ai/function_calling.py
@@ -23,7 +23,7 @@
 )
 
 
-def generate_function_call(prompt: str, project_id: str, location: str) -> str:
+def generate_function_call(prompt: str, project_id: str, location: str) -> tuple:
     # Initialize Vertex AI
     vertexai.init(project=project_id, location=location)
 
diff --git a/generative_ai/function_calling_chat.py b/generative_ai/function_calling_chat.py
new file mode 100644
index 00000000000..3bc99d5d14d
--- /dev/null
+++ b/generative_ai/function_calling_chat.py
@@ -0,0 +1,144 @@
+# Copyright 2024 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# [START aiplatform_gemini_function_calling_chat]
+import vertexai
+from vertexai.generative_models import (
+    FunctionDeclaration,
+    GenerativeModel,
+    Part,
+    Tool,
+)
+
+
+def generate_function_call_chat(project_id: str, location: str) -> tuple:
+    prompts = []
+    summaries = []
+
+    # Initialize Vertex AI
+    vertexai.init(project=project_id, location=location)
+
+    # Specify a function declaration and parameters for an API request
+    get_product_info_func = FunctionDeclaration(
+        name="get_product_sku",
+        description="Get the SKU for a product",
+        # Function parameters are specified in OpenAPI JSON schema format
+        parameters={
+            "type": "object",
+            "properties": {
+                "product_name": {"type": "string", "description": "Product name"}
+            },
+        },
+    )
+
+    # Specify another function declaration and parameters for an API request
+    get_store_location_func = FunctionDeclaration(
+        name="get_store_location",
+        description="Get the location of the closest store",
+        # Function parameters are specified in OpenAPI JSON schema format
+        parameters={
+            "type": "object",
+            "properties": {"location": {"type": "string", "description": "Location"}},
+        },
+    )
+
+    # Define a tool that includes the above functions
+    retail_tool = Tool(
+        function_declarations=[
+            get_product_info_func,
+            get_store_location_func,
+        ],
+    )
+
+    # Initialize Gemini model
+    model = GenerativeModel(
+        "gemini-1.0-pro", generation_config={"temperature": 0}, tools=[retail_tool]
+    )
+
+    # Start a chat session
+    chat = model.start_chat()
+
+    # Send a prompt for the first conversation turn that should invoke the get_product_sku function
+    prompt = "Do you have the Pixel 8 Pro in stock?"
+    response = chat.send_message(prompt)
+    prompts.append(prompt)
+
+    # Check the function name that the model responded with, and make an API call to an external system
+    if response.candidates[0].content.parts[0].function_call.name == "get_product_sku":
+        # Extract the arguments to use in your API call
+        product_name = (
+            response.candidates[0].content.parts[0].function_call.args["product_name"]
+        )
+        product_name
+
+        # Here you can use your preferred method to make an API request to retrieve the product SKU, as in:
+        # api_response = requests.post(product_api_url, data={"product_name": product_name})
+
+        # In this example, we'll use synthetic data to simulate a response payload from an external API
+        api_response = {"sku": "GA04834-US", "in_stock": "yes"}
+
+    # Return the API response to Gemini so it can generate a model response or request another function call
+    response = chat.send_message(
+        Part.from_function_response(
+            name="get_product_sku",
+            response={
+                "content": api_response,
+            },
+        ),
+    )
+
+    # Extract the text from the summary response
+    summary = response.candidates[0].content.parts[0].text
+    summaries.append(summary)
+
+    # Send a prompt for the second conversation turn that should invoke the get_store_location function
+    prompt = "Is there a store in Mountain View, CA that I can visit to try it out?"
+    response = chat.send_message(prompt)
+    prompts.append(prompt)
+
+    # Check the function name that the model responded with, and make an API call to an external system
+    if (
+        response.candidates[0].content.parts[0].function_call.name
+        == "get_store_location"
+    ):
+        # Extract the arguments to use in your API call
+        location = (
+            response.candidates[0].content.parts[0].function_call.args["location"]
+        )
+        location
+
+        # Here you can use your preferred method to make an API request to retrieve store location closest to the user, as in:
+        # api_response = requests.post(store_api_url, data={"location": location})
+
+        # In this example, we'll use synthetic data to simulate a response payload from an external API
+        api_response = {"store": "2000 N Shoreline Blvd, Mountain View, CA 94043, US"}
+
+    # Return the API response to Gemini so it can generate a model response or request another function call
+    response = chat.send_message(
+        Part.from_function_response(
+            name="get_store_location",
+            response={
+                "content": api_response,
+            },
+        ),
+    )
+
+    # Extract the text from the summary response
+    summary = response.candidates[0].content.parts[0].text
+    summaries.append(summary)
+
+    return prompts, summaries
+
+
+# [END aiplatform_gemini_function_calling_chat]
diff --git a/generative_ai/function_calling_chat_test.py b/generative_ai/function_calling_chat_test.py
new file mode 100644
index 00000000000..2c523005533
--- /dev/null
+++ b/generative_ai/function_calling_chat_test.py
@@ -0,0 +1,42 @@
+# Copyright 2024 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+
+import backoff
+from google.api_core.exceptions import ResourceExhausted
+
+import function_calling_chat
+
+
+_PROJECT_ID = os.getenv("GOOGLE_CLOUD_PROJECT")
+_LOCATION = "us-central1"
+
+
+summaries_expected = [
+    "Pixel 8 Pro",
+    "stock",
+    "store",
+    "2000 N Shoreline Blvd",
+    "Mountain View",
+]
+
+
+@backoff.on_exception(backoff.expo, ResourceExhausted, max_time=10)
+def test_function_calling_chat() -> None:
+    prompts, summaries = function_calling_chat.generate_function_call_chat(
+        project_id=_PROJECT_ID,
+        location=_LOCATION,
+    )
+    assert all(x in str(summaries) for x in summaries_expected)