מבוא לבקשות להפעלת פונקציות

PROJECT_ID=myproject
LOCATION=us-central1
MODEL_ID=gemini-2.5-flash

curl -X POST \
  -H "Authorization: Bearer $(gcloud auth print-access-token)" \
  -H "Content-Type: application/json" \
  https://${LOCATION}-aiplatform.googleapis.com/v1/projects/${PROJECT_ID}/locations/${LOCATION}/publishers/google/models/${MODEL_ID}:generateContent \
  -d '{
    "contents": [{
      "role": "user",
      "parts": [{
        "text": "What is the weather in Boston?"
      }]
    }],
    "tools": [{
      "functionDeclarations": [
        {
          "name": "get_current_weather",
          "description": "Get the current weather in a given location",
          "parameters": {
            "type": "object",
            "properties": {
              "location": {
                "type": "string",
                "description": "The city name of the location for which to get the weather.",
                "default": {
                  "string_value": "Boston, MA"
                }
              }
            },
            "required": [
              "location"
            ]
          }
        }
      ]
    }]
  }'

from google import genai
from google.genai.types import GenerateContentConfig, Part

# The project and location are passed directly to the client,
# and vertexai=True is added to specify the use of the Agent Platform backend.
client = genai.Client(vertexai=True, project="PROJECT_ID", location="global")

def get_current_weather(location: str) -> str:
  """Returns the current weather.

  Args:
    location: The city and state, e.g. San Francisco, CA
  """
  return 'sunny'

response = client.models.generate_content(
    model="gemini-2.5-flash",
    contents="What is the weather in boston?",
    config=GenerateContentConfig(tools=[get_current_weather]),
)

import vertexai
from vertexai.generative_models import (
    Content,
    FunctionDeclaration,
    GenerationConfig,
    GenerativeModel,
    Part,
    Tool,
    ToolConfig
)

# Initialize Agent Platform
# TODO(developer): Update the project
vertexai.init(project="PROJECT_ID", location="us-central1")

# Initialize Gemini model
model = GenerativeModel(model_name="gemini-2.5-flash")

# Manual function declaration
get_current_weather_func = FunctionDeclaration(
    name="get_current_weather",
    description="Get the current weather in a given location",
    # Function parameters are specified in JSON schema format
    parameters={
        "type": "object",
        "properties": {
            "location": {
              "type": "string",
              "description": "The city name of the location for which to get the weather.",
              "default": {
                "string_value": "Boston, MA"
              }
           }
        },
    },
)

response = model.generate_content(
    contents = [
      Content(
        role="user",
          parts=[
              Part.from_text("What is the weather like in Boston?"),
          ],
      )
    ],
    generation_config = GenerationConfig(temperature=0),
    tools = [
      Tool(
        function_declarations=[get_current_weather_func],
      )
    ]
)

def get_current_weather(location: str = "Boston, MA"):
  """
  Get the current weather in a given location

  Args:
      location: The city name of the location for which to get the weather.

  """
  # This example uses a mock implementation.
  # You can define a local function or import the requests library to call an API
  return {
    "location": "Boston, MA",
    "temperature": 38,
    "description": "Partly Cloudy",
    "icon": "partly-cloudy",
    "humidity": 65,
    "wind": {
        "speed": 10,
        "direction": "NW"
    }
  }
get_current_weather_func = FunctionDeclaration.from_func(get_current_weather)

PROJECT_ID=myproject
MODEL_ID=gemini-2.5-flash
LOCATION="us-central1"

curl -X POST \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
https://${LOCATION}-aiplatform.googleapis.com/v1/projects/${PROJECT_ID}/locations/${LOCATION}/publishers/google/models/${MODEL_ID}:generateContent \
-d '{
"contents": [
{
  "role": "user",
  "parts": {
    "text": "What is the weather in Boston?"
  }
},
{
  "role": "model",
  "parts": [
    {
      "functionCall": {
        "name": "get_current_weather",
        "args": {
          "location": "Boston, MA"
        }
      }
    }
  ]
},
{
  "role": "user",
  "parts": [
    {
      "functionResponse": {
        "name": "get_current_weather",
        "response": {
          "temperature": 20,
          "unit": "C"
        }
      }
    }
  ]
}
],
"tools": [
{
  "function_declarations": [
    {
      "name": "get_current_weather",
      "description": "Get the current weather in a specific location",
      "parameters": {
        "type": "object",
        "properties": {
          "location": {
            "type": "string",
            "description": "The city name of the location for which to get the weather."
          }
        },
        "required": [
          "location"
        ]
      }
    }
  ]
}
]
}'

function_response_contents = []
function_response_parts = []

# Iterates through the function calls in the response in case there are parallel function call requests
for function_call in response.candidates[0].function_calls:
    print(f"Function call: {function_call.name}")

    # In this example, we'll use synthetic data to simulate a response payload from an external API
    if (function_call.args['location'] == "Boston, MA"):
      api_response = { "location": "Boston, MA", "temperature": 38, "description": "Partly Cloudy" }
    if (function_call.args['location'] == "San Francisco, CA"):
      api_response = { "location": "San Francisco, CA", "temperature": 58, "description": "Sunny" }

    function_response_parts.append(
        Part.from_function_response(
            name=function_call.name,
            response={"contents": api_response}
        )
    )
    # Add the function call response to the contents
    function_response_contents = Content(role="user", parts=function_response_parts)

# Submit the User's prompt, model's response, and API output back to the model
response = model.generate_content(
  [
    Content( # User prompt
      role="user",
      parts=[
          Part.from_text("What is the weather like in Boston?"),
      ],
    ),
    response.candidates[0].content,  # Function call response
    function_response_contents   # API output
  ],
  tools=[
    Tool(
      function_declarations=[get_current_weather_func],
    )
  ],
)
# Get the model summary response
print(response.text)

# Call the model with function declarations
# ...Generation config, Configure the client, and Define user prompt (No changes)

# Send request with declarations (using a thinking model)
response = client.models.generate_content(
  model="gemini-2.5-flash", config=config, contents=contents)

# See thought signatures
for part in response.candidates[0].content.parts:
  if not part.text:
    continue
  if part.thought and part.thought_signature:
    print("Thought signature:")
    print(part.thought_signature)

# Create user friendly response with function result and call the model again
# ...Create a function response part (No change)

# Append thought signatures, function call and result of the function execution to contents
function_call_content = response.candidates[0].content
# Append the model's function call message, which includes thought signatures
contents.append(function_call_content)
contents.append(types.Content(role="user", parts=[function_response_part])) # Append the function response

final_response = client.models.generate_content(
    model="gemini-2.5-flash",
    config=config,
    contents=contents,
)

print(final_response.text)

{
"candidates": [
  {
    "content": {
      "role": "model",
      "parts": [
        {
          "functionCall": {
            "name": "get_current_weather",
            "args": {
              "location": "Boston"
            }
          }
        },
        {
          "functionCall": {
            "name": "get_current_weather",
            "args": {
              "location": "San Francisco"
            }
          }
        }
      ]
    },
    ...
  }
],
...
}

import vertexai
from vertexai.generative_models import (
    Content,
    FunctionDeclaration,
    GenerationConfig,
    GenerativeModel,
    Part,
    Tool,
    ToolConfig
)

# Initialize Agent Platform
# TODO(developer): Update the project
vertexai.init(project="my-project", location="us-central1")

# Initialize Gemini model
model = GenerativeModel(model_name="gemini-2.5-flash")

# Manual function declaration
get_current_weather_func = FunctionDeclaration(
    name="get_current_weather",
    description="Get the current weather in a given location",
    # Function parameters are specified in JSON schema format
    parameters={
        "type": "object",
        "properties": {
            "location": {
              "type": "string",
              "description": "The city name of the location for which to get the weather.",
              "default": {
                "string_value": "Boston, MA"
              }
          }
        },
    },
)

response = model.generate_content(
    contents = [
      Content(
        role="user",
          parts=[
              Part.from_text("What is the weather like in Boston and San Francisco?"),
          ],
      )
    ],
    generation_config = GenerationConfig(temperature=0),
    tools = [
      Tool(
        function_declarations=[get_current_weather_func],
      )
    ]
)

function_response_contents = []
function_response_parts = []

# You can have parallel function call requests for the same function type.
# For example, 'location_to_lat_long("London")' and 'location_to_lat_long("Paris")'
# In that case, collect API responses in parts and send them back to the model

for function_call in response.candidates[0].function_calls:
    print(f"Function call: {function_call.name}")

    # In this example, we'll use synthetic data to simulate a response payload from an external API
    if (function_call.args['location'] == "Boston, MA"):
      api_response = { "location": "Boston, MA", "temperature": 38, "description": "Partly Cloudy" }
    if (function_call.args['location'] == "San Francisco, CA"):
      api_response = { "location": "San Francisco, CA", "temperature": 58, "description": "Sunny" }

    function_response_parts.append(
        Part.from_function_response(
            name=function_call.name,
            response={"contents": api_response}
        )
    )
    # Add the function call response to the contents
    function_response_contents = Content(role="user", parts=function_response_parts)

function_response_contents

response = model.generate_content(
    contents = [
        Content(
        role="user",
          parts=[
              Part.from_text("What is the weather like in Boston and San Francisco?"),
          ],
        ),  # User prompt
        response.candidates[0].content,  # Function call response
        function_response_contents,  # Function response
    ],
    tools = [
      Tool(
        function_declarations=[get_current_weather_func],
      )
    ]
)
# Get the model summary response
print(response.text)

from google import genai
from google.genai import types

client = genai.Client()

# This is a manual, two turn multimodal function calling workflow:

# 1. Define the function tool
get_image_declaration = types.FunctionDeclaration(
  name="get_image",
  description="Retrieves the image file reference for a specific order item.",
  parameters={
      "type": "object",
      "properties": {
          "item_name": {
              "type": "string",
              "description": "The name or description of the item ordered (e.g., 'green shirt')."
          }
      },
      "required": ["item_name"],
  },
)
tool_config = types.Tool(function_declarations=[get_image_declaration])

# 2. Send a message that triggers the tool
prompt = "Show me the green shirt I ordered last month."
response_1 = client.models.generate_content(
  model="gemini-3.5-flash",
  contents=[prompt],
  config=types.GenerateContentConfig(
      tools=[tool_config],
  )
)

# 3. Handle the function call
function_call = response_1.function_calls[0]
requested_item = function_call.args["item_name"]
print(f"Model wants to call: {function_call.name}")

# Execute your tool (e.g., call an API)
# (This is a mock response for the example)
print(f"Calling external tool for: {requested_item}")

function_response_data = {
  "image_ref": {"$ref": "dress.jpg"},
}

function_response_multimodal_data = types.FunctionResponsePart(
  file_data=types.FunctionResponseFileData(
    mime_type="image/png",
    display_name="dress.jpg",
    file_uri="gs://cloud-samples-data/generative-ai/image/dress.jpg",
  )
)

# 4. Send the tool's result back
# Append this turn's messages to history for a final response.
history = [
  types.Content(role="user", parts=[types.Part(text=prompt)]),
  response_1.candidates[0].content,
  types.Content(
    role="tool",
    parts=[
        types.Part.from_function_response(
          name=function_call.name,
          response=function_response_data,
          parts=[function_response_multimodal_data]
        )
    ],
  )
]

response_2 = client.models.generate_content(
  model="gemini-3.5-flash",
  contents=history,
  config=types.GenerateContentConfig(
      tools=[tool_config],
      thinking_config=types.ThinkingConfig(include_thoughts=True)
  ),
)

print(f"\nFinal model response: {response_2.text}")

"contents": [
  ...,
  {
    "role": "user",
    "parts": [
      {
        "functionResponse": {
          "name": "get_image",
          "response": {
            "image_ref": {
              "$ref": "wakeupcat.jpg"
            }
          },
          "parts": [
            {
              "fileData": {
                "displayName": "wakeupcat.jpg",
                "mimeType": "image/jpeg",
                "fileUri": "gs://cloud-samples-data/vision/label/wakeupcat.jpg"
              }
            }
          ]
        }
      }
    ]
  }
]

from google import genai
from google.genai import types

client = genai.Client()

get_weather_declaration = types.FunctionDeclaration(
  name="get_weather",
  description="Gets the current weather temperature for a given location.",
  parameters={
      "type": "object",
      "properties": {"location": {"type": "string"}},
      "required": ["location"],
  },
)
get_weather_tool = types.Tool(function_declarations=[get_weather_declaration])

for chunk in client.models.generate_content_stream(
  model="gemini-3.5-flash",
  contents="What's the weather in London and New York?",
  config=types.GenerateContentConfig(
      tools=[get_weather_tool],
      tool_config = types.ToolConfig(
          function_calling_config=types.FunctionCallingConfig(
              mode=types.FunctionCallingConfigMode.AUTO,
              stream_function_call_arguments=True,
          )
      ),
  ),
):
  function_call = chunk.function_calls[0]
  if function_call and function_call.name:
      print(f"{function_call.name}")
      print(f"will_continue={function_call.will_continue}")

response = model.generate_content(
    contents = [
      Content(
        role="user",
          parts=[
              Part.from_text("What is the weather like in Boston?"),
          ],
      )
    ],
    generation_config = GenerationConfig(temperature=0),
    tools = [
      Tool(
        function_declarations=[get_weather_func, some_other_function],
      )
    ],
    tool_config=ToolConfig(
        function_calling_config=ToolConfig.FunctionCallingConfig(
            # ANY mode forces the model to predict only function calls
            mode=ToolConfig.FunctionCallingConfig.Mode.ANY,
            # Allowed function calls to predict when the mode is ANY. If empty, any of
            # the provided function calls will be predicted.
            allowed_function_names=["get_weather"],
        )
    )
)