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functionCallingStreamContent.js
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// Copyright 2023 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_content]
const {
VertexAI,
FunctionDeclarationSchemaType,
} = require('@google-cloud/vertexai');
const functionDeclarations = [
{
function_declarations: [
{
name: 'get_current_weather',
description: 'get weather in a given location',
parameters: {
type: FunctionDeclarationSchemaType.OBJECT,
properties: {
location: {type: FunctionDeclarationSchemaType.STRING},
unit: {
type: FunctionDeclarationSchemaType.STRING,
enum: ['celsius', 'fahrenheit'],
},
},
required: ['location'],
},
},
],
},
];
const functionResponseParts = [
{
functionResponse: {
name: 'get_current_weather',
response: {name: 'get_current_weather', content: {weather: 'super nice'}},
},
},
];
/**
* TODO(developer): Update these variables before running the sample.
*/
async function functionCallingStreamChat(
projectId = 'PROJECT_ID',
location = 'us-central1',
model = 'gemini-1.0-pro'
) {
// Initialize Vertex with your Cloud project and location
const vertexAI = new VertexAI({project: projectId, location: location});
// Instantiate the model
const generativeModel = vertexAI.preview.getGenerativeModel({
model: model,
});
const request = {
contents: [
{role: 'user', parts: [{text: 'What is the weather in Boston?'}]},
{
role: 'model',
parts: [
{
functionCall: {
name: 'get_current_weather',
args: {location: 'Boston'},
},
},
],
},
{role: 'function', parts: functionResponseParts},
],
tools: functionDeclarations,
};
const streamingResp = await generativeModel.generateContentStream(request);
for await (const item of streamingResp.stream) {
console.log(item.candidates[0].content.parts[0].text);
}
}
// [END aiplatform_gemini_function_calling_content]
functionCallingStreamChat(...process.argv.slice(2)).catch(err => {
console.error(err.message);
process.exitCode = 1;
});