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Creating GCP Vertex AI/BQML/ML Agent Kit With Jetbrains IDE

When We are deploying one SDK through Kotlin/Kotlinscript/JSON CLI in a K8S Node,the exact API for a compatible Cloud Storage bucket gets connected with enabled Port address of the IDE for regions

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Inspiration :

We have deployed Front End Application(Kotlin):

data class zs(val th:Int) interface gu{ } fun main(){

val y = zs(67).to("") - (Connect with one Jetbrains IDE with fixed GCP CLI function in a browser's Cloud Shell K8S to detect similar AI API)

 val tq=  {
    buildString { get(3).inc().plus(21)}.length
    buildString { length }     - (ADK n-1 nodes connection)
    val gq = buildString{ try{} catch(cf:Exception) } - (Here 1 AI/ML ADK API will be deployed by building atomic String function at one enabled Cloud Shell)
    try{

        buildString { length.plus(3) }   -(Here,Try to deploy one API from base Cloud Shell node Docker compose with mapped JSON function for one atomic Jetbrains IDE with one AI/ML API)
    }
    catch (t: Exception){       


    }
}                  -

}

Front End Application(Dart/Flutter):

import 'dart:typed_data';

import 'package:firebase_ai/firebase_ai.dart'; - (ADK n nodes connection)

Future generateContent() async { throw """ Your prompt includes the seed parameter, which is not currently supported by the Firebase AI SDK. If it's OK to not have the seed as part of your request, you can remove this exception. """;

final generationConfig = GenerationConfig( maxOutputTokens: 65535, - (Connect with distributed Cloud Shell function for a data length API in App that can retrieve exact AI API) temperature: 1, topP: 1, ); What it does :

Now We have to inject one Vertex AI API through atomic Jetbrains IDE with a connected GCP Cloud Shell with Flutter Template Application and test its AI/ML File type feasibility: cat << EOF > request.json { "contents": [ { "role": "user", "parts": [ { "fileData": { "mimeType": "video/mp4", "fileUri": "gs://cloud-samples-data/generative-ai/video/rio_de_janeiro_beyond_the_map_rio.mp4" } }, { "text": "Chapterize the video content by grouping the video content into chapters and providing a summary for each chapter. Please only capture key events and highlights. If you are not sure about any info, please do not make it up. Return the result in the JSON format with keys as follows : \"timecode\", \"chapterSummary\"" } ] } ] , "generationConfig": { "temperature": 1 ,"maxOutputTokens": 65535 ,"topP": 1 - (Scenario 1)Here,For Gemini 2.5 Pro model Vertex AI API is tested for in the midst Kotlinscript function run in Jetbrains IDE ,"seed": 0 ,"responseMimeType": "application/json" - The Future identifier in Dart function is enabled with only one JSON file type for topP attribute running inline for uniform Cloud Shell OS ,"responseSchema": {"type":"OBJECT","properties":{"response":{"type":"STRING"}}} - Here,We have to analyze a partly enabled Kotlinscript/Dart API scalable for seeded object in enabled Cloud Shell region running distributively },

How we built it: Now,Let us deploy one Firebase Application for Flutter template where ADK stage will be developed for a particular AI/ML App

ADK1: final ai = await FirebaseAI.vertexAI.instanceFor(location: 'global'); final model = ai.generativeModel( model: 'gemini-2.0-flash-preview-image-generation', -Here,Testing will be done based on the feasibility of Cloud Shell browser with Dart's Final identifier for one HTTP session generationConfig: generationConfig, safetySettings: safetySettings, ); final image1 = FileData( 'image/jpeg', 'gs://cloud-samples-data/generative-ai/image/croissant.jpeg', -Here,File type will be scaled for connected App in a browser );

ADK2:

GCP Landing Page URL Resource- https://console.cloud.google.com/vertex-ai/studio/multimodal/compare?inv=1&invt=Ab0Eiw&project=f-f-d-g25-b-b-s-r-anshu-0ywidu

Here,We will be populating one GCP AI/ML API where Dart's atomic Future identifier function is called with a single File type where interconnected Jetbrains IDE gets tested for a compatible GCP region:

Future generateContent() async { throw """ Your prompt includes the seed parameter, which is not currently supported by the Firebase AI SDK. If it's OK to not have the seed as part of your request, you can remove this exception. """;

Here,One Future identifier in Dart/Flutter CLI is not getting populated in Compare method when connected to IDE,so we have to add parallel ADK Cloud Shell where Future Identifier will call exact HTTP servlet request

import 'dart:typed_data';

import 'package:firebase_ai/firebase_ai.dart';

Future generateContent() async { throw """ Your prompt includes the seed parameter, which is not currently supported by the Firebase AI SDK. If it's OK to not have the seed as part of your request, you can remove this exception. """;

Challenges we ran into: One of the challenge We faced is the unavailability of arbitrary Kotlinscript's function for dual Cloud Shell where similar AI/ML API can be deployed

internal fun eq(){ fun tq() { try { val rq = eq().equals("").not() - Here,Atomic GCP Cloud Shell will detect not() function for one enabled Cloud Shell } catch (tq: Exception){

       }

    }

Accomplishments that we're proud of

What we learned:

Here,the Compare method in standalone GCP Cloud Shell is deployed for one AI/ML API with related Dart function in App that can run with a deployed Jetbrains IDE

Gemini 2.5-pro-preview model gets deployed with one GCP CLI in Jetbrains IntelliJ IDEA IDE for a base Dart function's curl attribute,where Compare method was not being enabled because topP parameter's API was not being developed in a small OS's Cloud Shell region and is now deployed within it parallely

"generationConfig": { "temperature": 1 , "maxOutputTokens" : 65535 - A text data length API was compared uniformly from standalone Cloud Shell to newer scalable Cloud Shell node , "topP": 1 ,"seed": 0

Now,The "Compare" method for topP parameter is deployed with each atomic Kotlin/Dart function for enabled Cloud Shell in Jetbrains IDE for one OS

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