feat(vertex-ai): function calling: convert markdown 'samples' to python

generative_ai/function_calling/basic_example.py

@@ -39,7 +39,7 @@ def generate_function_call() -> GenerationResponse:
     vertexai.init(project=PROJECT_ID, location="us-central1")
 
     # Initialize Gemini model
-    model = GenerativeModel("gemini-1.5-flash-002")
+    model = GenerativeModel("gemini-1.5-pro-002")
 
     # Define the user's prompt in a Content object that we can reuse in model calls
     user_prompt_content = Content(

generative_ai/function_calling/declare_function_from_function.py

@@ -0,0 +1,74 @@
+# 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
+
+from typing import List
+
+from vertexai.generative_models import FunctionDeclaration
+
+
+PROJECT_ID = os.getenv("GOOGLE_CLOUD_PROJECT")
+
+
+def prototype():
+    # [START generativeaionvertexai_gemini_function_calling_declare_from_function]
+    # Define a function. Could be a local function or you can import the requests library to call an API
+    def multiply_numbers(numbers: List[int]) -> int:
+        """
+        Calculates the product of all numbers in an array.
+
+        Args:
+            numbers: An array of numbers to be multiplied.
+
+        Returns:
+            The product of all the numbers. If the array is empty, returns 1.
+        """
+
+        if not numbers:  # Handle empty array
+            return 1
+
+        product = 1
+        for num in numbers:
+            product *= num
+
+        return product
+
+    multiply_number_func = FunctionDeclaration.from_func(multiply_numbers)
+
+    '''
+    multiply_number_func contains the following schema:
+
+    name: "multiply_numbers"
+    description: "Calculates the product of all numbers in an array."
+    parameters {
+    type_: OBJECT
+    properties {
+        key: "numbers"
+        value {
+        description: "An array of numbers to be multiplied."
+        title: "Numbers"
+        }
+    }
+    required: "numbers"
+    description: "Calculates the product of all numbers in an array."
+    title: "multiply_numbers"
+    }
+    '''
+    # [END generativeaionvertexai_gemini_function_calling_declare_from_function]
+    return multiply_number_func
+
+
+if __name__ == "__main__":
+    prototype()

generative_ai/function_calling/test_function_calling.py

@@ -15,13 +15,15 @@
 import backoff
 
 from google.api_core.exceptions import ResourceExhausted
+from vertexai.generative_models import GenerativeModel, Tool
 
 import advanced_example
 import basic_example
 import chat_example
 import chat_function_calling_basic
 import chat_function_calling_config
 import parallel_function_calling_example
+import declare_function_from_function
 
 
 @backoff.on_exception(backoff.expo, ResourceExhausted, max_time=10)
@@ -85,3 +87,13 @@ def test_function_calling_chat() -> None:
 def test_parallel_function_calling() -> None:
     response = parallel_function_calling_example.parallel_function_calling_example()
     assert response is not None
+
+
+def test_prototype() -> None:
+    func_declaration = declare_function_from_function.prototype()
+    tools = Tool(function_declarations=[func_declaration])
+    model = GenerativeModel(model_name="gemini-1.5-pro-002", tools=[tools])
+    chat_session = model.start_chat()
+    response = chat_session.send_message("What will be 1 multiplied by 2?")
+
+    assert response is not None