Generate text responses using Gemini API with external function calls in a chat scenario

const{
VertexAI,
FunctionDeclarationSchemaType,
}=require('@google-cloud/vertexai');
constfunctionDeclarations=[
{
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'],
},
},
],
},
];
constfunctionResponseParts=[
{
functionResponse:{
name:'get_current_weather',
response:{name:'get_current_weather',content:{weather:'super nice'}},
},
},
];
/**
 * TODO(developer): Update these variables before running the sample.
 */
asyncfunctionfunctionCallingStreamChat(
projectId='PROJECT_ID',
location='us-central1',
model='gemini-2.0-flash-001'
){
// Initialize Vertex with your Cloud project and location
constvertexAI=newVertexAI ({project:projectId,location:location});
// Instantiate the model
constgenerativeModel=vertexAI.getGenerativeModel ({
model:model,
});
// Create a chat session and pass your function declarations
constchat=generativeModel.startChat({
tools:functionDeclarations,
});
constchatInput1='What is the weather in Boston?';
// This should include a functionCall response from the model
constresult1=awaitchat.sendMessageStream(chatInput1);
forawait(constitemofresult1.stream ){
console.log(item.candidates [0]);
}
awaitresult1.response;
// Send a follow up message with a FunctionResponse
constresult2=awaitchat.sendMessageStream(functionResponseParts);
forawait(constitemofresult2.stream ){
console.log(item.candidates [0]);
}
// This should include a text response from the model using the response content
// provided above
constresponse2=awaitresult2.response;
console.log(response2.candidates [0].content .parts [0].text);
}