Vertex AI SDK

The vertexai / google-cloud-aiplatform SDK for the broader Vertex AI platform: training, tuning, evaluation, endpoints, and model registry. · Official docs

Related concepts

• Getting Started · Gemini Capabilities · Prompt Engineering · Function Calling & Tools · Agents & ADK · Agent Engine · RAG & Grounding · Vertex AI Search · Embeddings & Vector Search · Vision · Multimodal Live API · Image & Video Generation · Evaluation · Tuning & Customization · Open & Partner Models · Responsible AI · Translation · MLOps & Deployment · Applied Use Cases

Used in 145 notebooks

Agents & ADK (14)

• Get started with Vertex AI Memory Bank — Builds a Vertex AI Memory Bank hotel concierge that stores and retrieves guest preferences across sessions.
• Customizing Memory Topics — Customizes Vertex AI Memory Bank topics for a financial advisor assistant and compares default vs custom extraction.
• Governance with Vertex AI Memory Bank — Builds a governed Vertex AI Memory Bank with TTL, topics, revision history, rollback, and cleanup.
• Get started with Memory Bank on ADK — Build ADK agents that generate, retrieve, preload, and customize Agent Engine Memory Bank memories.
• Building a Multimodal Trip Planner with ADK on Vertex AI Agent Engine Memory Bank — Builds and deploys a multimodal ADK trip planner using Vertex AI Agent Engine Memory Bank.
• Get started with A2A on Agent Engine — Builds, deploys, and queries an A2A Q&A agent on Vertex AI Agent Engine.
• Getting Started with Bidirectional Streaming v2 on Agent Runtime — Builds and deploys Agent Runtime bidirectional streaming agents, including a Live API audio agent.
• Get started with Agent Engine Terraform Deployment — Deploy Vertex AI Agent Engine agents with Terraform, cloudpickle packaging, and ADK tools.
• Get started with Code Execution on Vertex AI Agent Engine — Runs LLM-generated Python securely with Vertex AI Agent Engine Sandbox and ADK agents.
• Getting Started with Live API on Agent Engine — Deploys bidirectional streaming agents on Vertex AI Agent Engine using Gemini Live API audio and ADK tools.
• MCP on Vertex AI Agent Engine with custom installation scripts — Deploys a Reddit MCP tool agent to Vertex AI Agent Engine with custom install scripts.
• Building multi-agent systems with Vertex AI and Claude — Builds a Vertex AI Agent Engine multi-agent market analysis system with Gemini, Claude, ADK, A2A, and MCP.
• Building Multi-Agent Systems with Vertex AI and Llama model — Builds a traced Vertex AI multi-agent trading analyst with Gemini, Llama, ADK, A2A, and MCP tools.
• Get started with Sessions and Memory Bank for ADK agents in Cloud Run — Builds an ADK weather agent with Vertex AI Sessions, Memory Bank, and Cloud Run deployment.

Embeddings & Vector Search (5)

• Use Gemini and OSS Text-Embedding Models Against Your BigQuery Data — Generate BigQuery embeddings with Gemini and a deployed OSS E5 model.
• Combining Semantic & Keyword Search: A Hybrid Search Tutorial with Agent Platform Vector Search — Builds sparse and hybrid Vector Search indexes for Google Merch Shop product search.
• Getting Started with Text Embeddings + Agent Platform Vector Search — Builds text embeddings from Stack Overflow titles and serves semantic search with Vector Search.
• Get started with embeddings tuning on Agent Platform — Tunes text-embedding-005 for retrieval using synthetic Gemini queries and Document AI PDF chunks.
• Agent Platform Vector Search Quickstart — Builds and queries a streaming-update Vector Search index from product embeddings.

Gemini (99)

• Evaluate a CrewAI agent on Vertex AI Agent Engine (Customized template) — Evaluates a CrewAI Gemini agent on Vertex AI Agent Engine with tool, trajectory, and response metrics.
• Evaluating a LangChain Agent on Vertex AI Agent Engine (Prebuilt template) — Deploys and evaluates a LangChain Gemini agent on Vertex AI Agent Engine using Gen AI Evaluation.
• Evaluate a LangGraph agent on Vertex AI Agent Engine (Customized template) — Builds a Gemini LangGraph agent on Agent Engine and evaluates tools, trajectories, and responses.
• Intro to Building and Deploying an Agent with Agent Engine in Vertex AI — Builds, tests, deploys, streams, customizes, and deletes a LangChain Gemini agent on Vertex AI Agent Engine.
• Building and Deploying a Human-in-the-Loop LangGraph Application with Agent Engine on Vertex AI — Builds, tests, deploys, and resumes a human-in-the-loop LangGraph agent on Vertex AI Agent Engine.
• Get started with Vertex AI Memory Bank - ADK — Builds an ADK agent with Vertex AI Memory Bank for long-term user memory across sessions.
• Debugging and Optimizing Agents: A Guide to Tracing in Agent Engine — Builds, deploys, and traces a Gemini LangChain agent on Vertex AI Agent Engine.
• Building and Deploying a Google Maps API Agent with Agent Engine — Builds, tests, deploys, and queries a Gemini Google Maps agent on Vertex AI Agent Engine.
• Building a Multi-Agent RAG Application with LangGraph and Agent Engine — Builds and deploys a LangGraph multi-agent RAG app on Vertex AI Agent Engine with Cloud SQL vector stores.
• Building a Conversational Search Agent with Agent Engine and RAG on Vertex AI Search — Builds and deploys a Gemini movie-search RAG agent with LangChain, Agent Engine, and Vertex AI Search.
• Monitor batch prediction with Gemini API — Orchestrates and monitors Gemini batch predictions with Vertex AI Pipelines and BigQuery output.
• Create & Deploy Agent and Run Gen AI Agent Evaluation — Creates, deploys, runs, and evaluates an ecommerce ADK agent on Vertex AI Agent Engine.
• Create a Gen AI Agent Evaluation for a Deployed Agent — Runs inference and creates a persisted Gen AI Agent Evaluation for a deployed Vertex AI agent.
• Bring-Your-Own-Autorater using CustomMetric — Evaluates Gemini prompts with a client-side CustomMetric and a BYO Gemini autorater.
• Bring your own computation-based CustomMetric — Evaluates Gemini outputs with locally defined computation-based Vertex AI CustomMetric functions.
• Migrate from PaLM to Gemini model — Compares PaLM text-bison and gemini-2.5-flash with Vertex AI EvalTask for summarization migration.
• Compare Generative AI Models — Evaluates Gemini models on summarization with Vertex AI Gen AI Evaluation Service.
• Customize Model-based Metrics to Evaluate a Gen AI model — Shows how to customize Vertex AI Gen AI Evaluation model-based metrics for Gemini summarization outputs.
• Enhancing quality and explainability with Vertex AI Evaluation — Ranks Gemini answers with Vertex AI pairwise and pointwise evaluation explanations.
• Evaluate your autorater with meta-evaluation — Meta-evaluates Gemini autoraters on RewardBench with agreement and correlation metrics.
• Evaluate Generative Model Tool Use — Evaluates Gemini function calling and saved tool-call predictions with Vertex AI EvalTask metrics.
• Evaluate groundedness with custom parsing — Evaluates Gemini response groundedness with Vertex AI EvalTask and custom JSON parsing.
• Evaluate images with Gecko — Evaluates prompt-image alignment with Gecko-style rubric generation and VQA validation in Vertex AI.
• Evaluate LangChain — Evaluates a LangChain recipe chatbot with Vertex AI Rapid Evaluation and custom Gemini-based metrics.
• Evaluating multimodal task — Evaluates image-grounded car damage labels with Vertex AI EvalTask and a Gemini custom autorater.
• Evaluate generated answers from Retrieval-Augmented Generation (RAG) using Rapid Evaluation and Dataflow ML with Vertex AI pipelines — Builds a Vertex AI Pipeline to batch-evaluate RAG Q&A outputs with Rapid Eval API and Dataflow ML.
• Evaluate Generated Answers from Retrieval-Augmented Generation (RAG) for Question Answering with Gen AI Evaluation Service SDK — Evaluates BYO RAG QA answers with Vertex AI Gen AI Evaluation, custom metrics, and result visualizations.
• Evaluate a Translation Model — Evaluates stored translation responses with Vertex AI EvalTask using BLEU, COMET, and MetricX.
• Evaluate videos with Gecko — Uses Vertex AI evaluation to run Gecko-style rubric generation and video validation.
• Evaluating Agents - Evaluate a CrewAI agent with Vertex AI Gen AI Evaluation Service — Evaluates a CrewAI product agent with Vertex AI Gen AI Evaluation metrics and BYOD evaluation data.
• Evaluating Agents - Evaluate a LangGraph agent with Vertex AI Gen AI Evaluation Service — Evaluates a LangGraph Gemini agent with Vertex AI Gen AI Evaluation metrics and BYOD data.
• Evaluating prompts at scale with Gemini Batch Prediction API — Evaluates Gemini image-classification prompts at scale with Batch Prediction and BigQuery.
• Intro to Batch Evaluations with the Gemini API — Runs asynchronous batch evaluation of Gemini responses with Vertex AI and Cloud Storage.
• Getting Started with Vertex AI Python SDK for Gen AI Evaluation Service — Defines a custom pointwise metric and evaluates stored LLM responses with Vertex AI Gen AI Evaluation Service.
• Gen AI Evaluation Service SDK Preview-to-GA Migration Guide — Migrates Vertex AI Gen AI Evaluation SDK preview patterns to GA EvalTask, pointwise, and pairwise metrics.
• Rubric evaluation - Multimodal and Custom metric for text quality — Evaluates multimodal car-damage responses and text summaries with Vertex AI rubric-based metrics
• Evaluate and Optimize Prompt Template Design for Better Results — Compares Gemini prompt templates using Vertex AI EvalTask metrics to choose the best summarization prompt.
• Rubric-based instruction following evaluation using Gen AI Evaluation Service — Evaluates Gemini instruction following with rubric-based metrics in Vertex AI.
• Evaluate Gemini Structured Output — Evaluates Gemini structured JSON extraction from scanned order forms with Vertex AI Gen AI Evaluation.
• Evaluate images with predefined Gecko — Evaluates text-to-image outputs with Vertex AI predefined Gecko image rubrics.
• Use Gen AI Evaluation SDK to Evaluate Models in Vertex AI Studio, Model Garden, and Model Registry — Evaluates Gemini, Llama MaaS, Claude, and prompt templates with Vertex AI Gen AI Evaluation SDK.
• Evaluate videos with predefined Gecko — Evaluates generated videos with Vertex AI Gecko text-to-video rubrics.
• Evaluate Generative Model Tool Use with Custom Code Execution — Evaluates Gemini tool-use outputs with Vertex AI remote custom metrics.
• Using Gen AI Evaluation SDK for Google Observability Gen AI multi-modal datasets — Evaluates Google Observability multimodal Gen AI data from GCS using Vertex AI Gen AI Evaluation.
• Evaluating Third-Party LLMs with the Vertex AI Gen AI Evaluation SDK — Evaluates third-party, MaaS, BYOM, and Gemini models with Vertex AI Gen AI Evaluation.
• Migrating Foundation Models: A Practical Guide with Gen AI Evaluation Serivce — Compares model migration candidates with Vertex AI Gen AI Evaluation and prompt optimization workflows.
• Overview — Evaluates a CrewAI research crew with Phoenix tracing and Vertex AI trajectory metrics.
• Getting Started: Quick Gen AI Evaluation — Evaluates gemini-2.5-flash responses with Vertex AI Gen AI Eval Service using a default quality rubric.
• Intro to Request and Response Logging with Gemini — Shows how to enable Gemini request-response logging to BigQuery, query logs, and disable logging.
• Intro to Agent Platform Multimodal Datasets — Builds Agent Platform multimodal datasets for Gemini tuning validation, resource estimates, tuning, and batch prediction.
• LlamaIndex RAG Workflows using Gemini and Firestore — Builds a LlamaIndex RAG workflow with Gemini, Vertex embeddings, and Firestore storage.
• Chain of Thought & ReAct — Demonstrates CoT prompting and ReAct agents with Vertex AI, LangChain, Wikipedia, and BigQuery.
• Question Answering with Generative Models on Vertex AI — Shows prompt patterns for Gemini question answering on Vertex AI, including simple fuzzy evaluation.
• Text Classification with Generative Models on Vertex AI — Classifies text with Gemini on Vertex AI using zero-shot, few-shot, and evaluation workflows.
• Text Extraction with Generative Models on Vertex AI — Uses Gemini on Vertex AI to extract structured facts from text with constrained and few-shot prompts.
• Text Summarization with Generative Models on Vertex AI — Demonstrates Gemini text summarization prompts on Vertex AI and evaluates summaries with ROUGE.
• Get Started with Vertex AI Prompt Optimizer — Shows zero-shot and data-driven prompt optimization with Vertex AI Prompt Optimizer.
• Get started with Vertex Prompt Optimizer - Custom metric — Optimizes a Gemini prompt with Vertex AI Prompt Optimizer using a custom Cloud Function metric.
• Get Started with Vertex AI Prompt Optimizer - Long prompt — Runs Vertex AI Prompt Optimizer to improve a long Gemini prompt using data-driven evaluation.
• Get Started with Vertex AI Prompt Optimizer - Multimodality — Optimizes a multimodal Gemini prompt with Vertex AI Prompt Optimizer on MathVista image QA.
• Get Started with Vertex AI Prompt Optimizer - Tool usage — Optimizes a Gemini tool-calling system instruction with Vertex AI Prompt Optimizer data-driven mode.
• Intro to Building a Scalable and Modular RAG System with RAG Engine in Vertex AI — Builds a Vertex AI RAG Engine corpus, imports files, retrieves context, and grounds Gemini or Llama responses.
• Vertex AI Rag: Cross-Corpus Retrieval with AskContexts and AsyncRetrieveContexts Demo — Demonstrates cross-corpus Vertex AI RAG retrieval with ask_contexts and async_retrieve_contexts.
• Evaluating Vertex RAG Engine Generation with Vertex AI Python SDK for Gen AI Evaluation Service — Evaluates Vertex AI RAG Engine responses with a custom Gen AI Evaluation Service metric.
• Advanced RAG Techniques - Vertex RAG Engine Retrieval Quality Evaluation and Hyperparameters Tuning — Evaluates Vertex AI RAG Engine retrieval quality and tunes chunking, top-k, threshold, and embedding settings.
• Vertex AI RAG Engine with Vertex AI Feature Store — Builds a Vertex AI RAG Engine corpus backed by Vertex AI Feature Store and queries it with Gemini.
• Vertex AI RAG Engine with Pinecone — Shows how to use Vertex AI RAG Engine with Pinecone as the vector database for Gemini retrieval.
• Vertex AI RAG Engine with Vertex AI Vector Search — Builds a Vertex AI RAG Engine corpus backed by Vertex AI Vector Search and queries it with Gemini.
• Vertex AI RAG Engine with Vertex AI Search — Build a Vertex AI RAG Engine corpus backed by Vertex AI Search and query it with Gemini.
• Vertex AI RAG Engine with Weaviate — Creates a Vertex AI RAG Engine corpus backed by Weaviate and queries it with Gemini.
• Gen AI & LLM Security for developers — Shows prompt injection attacks against Gemini and layered mitigations with DLP, NL API, safety filters, and embeddings.
• Gen AI and LLM Security - ReAct and RAG attacks & mitigations — Demonstrates ReAct and RAG prompt-injection attacks with Gemini and simple mitigations.
• Building a photo recognition agent: Agent Engine setup — Sets up and deploys a Gemini LangChain agent on Agent Engine with Wikipedia and Vertex AI Search tools.
• Prepare High-Quality Preference Data for Gemini 2.5 — Prepares and filters Gemini preference data with Vertex AI Gen AI Evaluation SDK for DPO and SFT.
• Get Started with Gemini Preference Optimization — Tunes Gemini 2.5 Flash with human preference data using Vertex AI preference optimization.
• Supervised Fine-Tuning with integrated Gen AI Evaluation — Fine-tunes gemini-2.5-flash with automatic Gen AI Evaluation metrics at each checkpoint.
• Supervised Fine Tuning with Gemini 2.5 Flash for Image Captioning — Fine-tunes Gemini 2.5 Flash on GCS image-caption pairs and evaluates ROUGE before and after tuning.
• Supervised Fine Tuning with Gemini 2.0 Flash for change detection using the Google Gen AI SDK — Fine-tunes Gemini 2.0 Flash on paired images for spot-the-difference change detection.
• Supervised Fine-tuning Gemini 2.5 Flash for Predictive Maintenance — Fine-tunes Gemini 2.5 Flash on simulated sensor data to classify equipment maintenance status.
• Supervised fine-tuning with Gemini 2.0 Flash for Q&A using the Google Gen AI SDK — Fine-tunes Gemini 2.0 Flash on SQuAD Q&A with Vertex AI supervised tuning and evaluates EM/F1 gains.
• Supervised Fine Tuning with Gemini 2.5 Flash for Article Summarization — Fine-tunes Gemini 2.5 Flash on WikiLingua article summaries and evaluates ROUGE before and after tuning.
• Supervised Fine-tuning Gemini 2.5 Flash for Visual Defect Detection — Fine-tunes Gemini 2.5 Flash on image-based manufacturing defect classification.
• Test Document AI Gemini — Compares Document AI entity extraction with Gemini-based extraction on a PDF.
• Use Retrieval Augmented Generation (RAG) with Gemini API — Builds a LangChain RAG pipeline over GitHub code notebooks using Gemini and Vertex AI embeddings.
• Code Vulnerability Scanning & Automated Remediation using Gemini API in Vertex AI (Gemini 2.0) — Scans Python files from GCS with Gemini 2.0 Flash and exports vulnerability reports to CSV and JSON.
• Comparing LlamaIndex and LlamaParse for Dense Document Questioning Answering on Vertex AI — Compares LlamaIndex and LlamaParse RAG parsing methods for dense 10-Q document QA on Vertex AI.
• Text Summarization of Large Documents using LangChain 🦜🔗 — Summarizes large PDFs with LangChain and Gemini using stuff, map-reduce, and refine chains.
• GraphRAG on Google Cloud With Spanner and Vertex AI Agent Engine — Builds a GraphRAG Q&A agent with Spanner Graph, Gemini, ADK, and Vertex AI Agent Engine.
• Creative Content Generation with Gemini in Vertex AI and Imagen — Generates and personalizes GShoe marketing copy with Gemini, then outpaints product images with Imagen.
• Data Curation Pipeline: Splitting and Transcoding — Deduplicates video clips using video embeddings and BigQuery vector search.
• Product attributes extraction and detailed descriptions from images using Gemini 2.0 — Extract product attributes and detailed retail descriptions from images with Gemini 2.0 on Vertex AI.
• RAG Based on Sensitive Data Protection using Faker — Builds a RAG flow that anonymizes PII with Cloud DLP, Faker, Firestore, Chroma, and Gemini.
• Intra Knowledge QnA — Builds a Vertex AI and LangChain RAG Q&A app over an IRS PDF using Chroma embeddings.
• Leverage LlamaIndex with Vertex AI Vector Search to perform question answering RAG — Builds LlamaIndex RAG on Vertex AI Vector Search, compares prompts, and adds multi-document agents.
• Retrieval Augmented Generation(RAG) with AlloyDB — Builds a RAG workflow over patent abstracts using AlloyDB vector search and Gemini on Vertex AI.
• Building a Gen AI RAG application with Vertex AI Feature Store and BigQuery — Builds a LangChain RAG Q&A app using BigQuery Vector Search and Vertex AI Feature Store.
• Augment Gemini Output with Vector Embeddings from BigQuery — Builds BigQuery vector-search RAG over patent abstracts and uses Gemini to generate project ideas.
• Building a Multimodal Chatbot for Warranty Claims using Gemini and Vector Search in Vertex AI — Builds a multimodal warranty-claims chatbot with Gemini, RAG, Vector Search, and function calling.
• Production & Scalable RAG Pipeline Using BigFrames — Builds a scalable BigFrames RAG pipeline over Stack Overflow data with BigQuery, Vertex AI, and LangChain.

Open Models (20)

• Accelerate LLM Inference with EAGLE Speculative Decoding on Vertex AI — Benchmarks EAGLE speculative decoding for Llama 4 Scout on Vertex AI against a baseline endpoint.
• Qwen 3 evaluation - Bring your own data eval — Compares fine-tuned and base Qwen 3 medical summaries using Vertex AI pairwise evaluation.
• Using open autorater for running evaluations with Vertex AI Gen AI Evaluation — Deploys Selene as an open judge on Vertex AI and uses Gen AI Evaluation to score LLM responses.
• Hugging Face DLCs: Using Gemma for running evaluations with Vertex AI Gen AI Evaluation — Deploys Gemma 2 on Vertex AI TGI and evaluates summarization with Gen AI Evaluation.
• MetaMath with Vertex AI Open Source Model Tuning — Fine-tunes a Llama 3.1 8B model on MetaMathQA using Vertex AI managed tuning.
• Hugging Face DLCs: Fine-tuning Gemma with Transformer Reinforcement Learning (TRL) on Vertex AI — Fine-tunes google/gemma-2b with TRL SFT and LoRA in a Vertex AI custom container job.
• Get started with Vertex AI Model Garden SDK — Deploy and test open models on Vertex AI with the Model Garden SDK.
• Import, Deploy, and Serve custom open models on Vertex AI using Vertex AI Model Garden SDK. — Imports Hugging Face open-model weights to GCS, deploys them on Vertex AI, and serves predictions.
• Get started with Model Garden Terraform Deployment — Deploys Model Garden and Hugging Face open models to Vertex AI endpoints with Terraform.
• Deploying Multiple LoRA Adapters on Vertex AI with vLLM — Deploys Gemma 2 with multiple LoRA adapters on Vertex AI using a custom vLLM container.
• Running a Gemma 2-based agentic RAG with Ollama on Vertex AI and LangGraph — Deploys a Gemma 2 Ollama container on Vertex AI and uses it in a LangGraph SQL RAG agent.
• Hugging Face DLCs: Serving PaliGemma using Pytorch Inference on Vertex AI with Custom Handler — Deploys gated PaliGemma from Hugging Face to Vertex AI using a PyTorch DLC and custom handler.
• Hugging Face DLCs: Serving PLLuM using Pytorch Inference on Vertex AI with Custom Handler — Deploys CYFRAGOVPL/PLLuM-12B-chat to Vertex AI with a Hugging Face PyTorch custom handler.
• Hugging Face DLCs: Serving Gemma with Text Generation Inference (TGI) on Vertex AI — Deploys gated Gemma 7B IT from Hugging Face to Vertex AI using a TGI deep learning container.
• Hugging Face DLCs: Serving Gemma 2 with multiple LoRA adapters with Text Generation Inference (TGI) on Vertex AI — Deploys Gemma 2 with SQL and code LoRA adapters on Vertex AI using a Hugging Face TGI custom handler.
• Use Any OSS Gen AI Model Against Your BigQuery Data — Deploys Llama 3.3 70B on Vertex AI and calls it from BigQuery ML for medical transcript analytics.
• Cloud Run GPU Inference: Gemma 2 RAG Q&A with Ollama and LangChain — Deploys Gemma 2 on Cloud Run GPU with Ollama and builds a LangChain RAG Q&A chain.
• Guess who or what app using Hugging Face Deep Learning container model on Vertex AI — Builds a Gradio riddle game using Gemini and a Hugging Face FLUX model deployed on Vertex AI.
• Serving Open-Source LLMs on Vertex AI with LiteLLM and OpenAI-Compatible APIs — Deploys a Llama 3.1 Model Garden model on Vertex AI and calls it through LiteLLM OpenAI-style APIs.
• Build and deploy a Hugging Face smolagent using DeepSeek-r1 on Vertex AI — Deploys DeepSeek R1 Distill Qwen 7B on Vertex AI and wraps it in a smolagents math verifier agent.

Vertex AI Search (7)

• Custom Embeddings with Vertex AI Search — Builds a Vertex AI Search app using custom text-embedding-005 embeddings from Stack Overflow data.
• Gemini Enterprise answer eval using BLEU, ROUGE, BERT, Similarity Score — Evaluates Gemini Enterprise answers against a golden dataset with NLP metrics and saves results.
• Gemini Enterprise custom agent with prompt management — Builds a Gemini Enterprise ADK SQL agent using Vertex AI Prompt Management and schema file context.
• Open Source Models (Gemma) as a agent with Gemini Enterprise — Deploys Gemma on Cloud Run, wraps it with ADK, deploys to Agent Engine, and registers it in Gemini Enterprise.
• Search tuning in Vertex AI Search — Tunes Vertex AI Search with JSONL/TSV Q&A data and tests a search app over Cloud Storage PDFs.
• Q&A Chatbot with Vertex AI Search for summarized website results without advanced indexing — Builds a Q&A flow that searches a Vertex AI Search website data store, fetches the top page, and summarizes it with Gemini.
• Building Search Applications with Vertex AI Search — Builds Vertex AI Search workflows using Search API, Gemini grounding, and LangChain retrieval.

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