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Commit a78dae5

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koverholtmsampathkumargcf-owl-bot[bot]
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feat(generative-ai): Add function calling example for multimodal snippet (GoogleCloudPlatform#11015)
* Add function calling example for multimodal snippet * Update generative_ai/function_calling.py Co-authored-by: Sampath Kumar <sampathm@google.com> * Remove unused import * 🦉 Updates from OwlBot post-processor See https://github.com/googleapis/repo-automation-bots/blob/main/packages/owl-bot/README.md --------- Co-authored-by: Sampath Kumar <sampathm@google.com> Co-authored-by: Owl Bot <gcf-owl-bot[bot]@users.noreply.github.com>
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‎generative_ai/function_calling.py

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# Copyright 2023 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# [START aiplatform_function_calling]
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from vertexai.preview.generative_models import (
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FunctionDeclaration,
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GenerativeModel,
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Tool,
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)
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# Load the Vertex AI Gemini API to use function calling
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model = GenerativeModel("gemini-pro")
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# Specify a function declaration and parameters for an API request
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get_current_weather_func = FunctionDeclaration(
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name="get_current_weather",
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description="Get the current weather in a given location",
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# Function parameters are specified in OpenAPI JSON schema format
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parameters={
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"type": "object",
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"properties": {"location": {"type": "string", "description": "Location"}},
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},
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)
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# Define a tool that includes the above get_current_weather_func
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weather_tool = Tool(
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function_declarations=[get_current_weather_func],
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)
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# Prompt to ask the model about weather, which will invoke the Tool
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prompt = "What is the weather like in Boston?"
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# Instruct the model to generate content using the Tool that you just created:
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response = model.generate_content(
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prompt,
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generation_config={"temperature": 0},
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tools=[weather_tool],
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)
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# Print the entire response
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print(response)
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# Print the part of the response that contains info about the function call
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print(response.candidates[0].content.parts[0].function_call)
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# [END aiplatform_function_calling]

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