Gemini

Google's flagship multimodal model family (text, image, audio, video, code) available via the Gemini API and Vertex AI. · 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 · Audio & Speech · Multimodal Live API · Image & Video Generation · Evaluation · Tuning & Customization · Open & Partner Models · Responsible AI · MLOps & Deployment · Applied Use Cases

Used in 245 notebooks

Agents & ADK (24)

• 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.
• Deploy your containerized agent on Agent Runtime (prev. Agent Engine) — Deploys a BYOC ADK weather agent to Agent Runtime and queries it through the Vertex AI API.
• Deploy your first agent to Vertex AI Agent Engine — Deploys ADK agents to Vertex AI Agent Engine with Express Mode, agent object, and inline source workflows.
• Get started with Agent Engine Terraform Deployment — Deploy Vertex AI Agent Engine agents with Terraform, cloudpickle packaging, and ADK tools.
• Get started with Cloud API Registry on Vertex AI Agent Engine — Builds and deploys an ADK BigQuery data analyst agent using Cloud API Registry on Vertex AI Agent Engine.
• 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.
• Gemini Data Analytics: A2A HTTP API Sample — Calls Gemini Data Analytics DataA2AService over HTTP for agent cards, messages, artifacts, and cancellation.
• Gemini Data Analytics: A2A SDK API Sample — Uses the A2A Python SDK to send synchronous-style requests to Gemini Data Analytics.
• Intro to Gemini Data Analytics — Shows REST-based Gemini Data Analytics agents over BigQuery, Looker, or Looker Studio data.
• Intro to Gemini Data Analytics — Shows how to create Gemini Data Analytics agents over BigQuery, Looker, or Looker Studio and chat with them.
• Get started with Sessions and Memory Bank for ADK agents in Google Kubernetes Engine — Builds an ADK weather agent with Vertex AI Sessions, Memory Bank, and GKE deployment.
• Managed Agents API - Analyzing the 2026 World Cup — Deploys a Managed Agents API World Cup analyst agent with GCS skills and multi-turn interactions.
• Intro to Skill Registry — Introduces Skill Registry for creating, ingesting, registering, and semantically retrieving agent skills.

Audio (4)

• Gemini 3.1 Flash Text-to-Speech Generation — Shows Gemini 3.1 Flash TTS generation with voices, languages, speakers, and audio tags.
• Get started with Gemini-TTS voices using Text-to-Speech — Synthesizes Gemini-TTS speech with Cloud Text-to-Speech and Agent Platform APIs.
• Create a Multi-Speaker Podcast with Gemini 2.0 & Text-to-Speech — Generates a two-speaker podcast from a PDF using Gemini JSON output and Text-to-Speech MP3 synthesis.
• Narrate a Multi-character Story with Gemini and Text-to-Speech — Generates a Gemini play script and narrates it with distinct Text-to-Speech voices per character.

Embeddings & Vector Search (9)

• 🛡️ AI Brand Safety: Three-Tier Agent Anomaly Detection — Builds ADK agent anomaly detection with Gemini baselines, Vector Search scoring, and tiered audits.
• Use Gemini and OSS Text-Embedding Models Against Your BigQuery Data — Generate BigQuery embeddings with Gemini and a deployed OSS E5 model.
• Visualizing embedding similarity from text documents using t-SNE plots — Embeds 20 Newsgroups text with gemini-embedding-001 and visualizes similarity clusters using t-SNE.
• 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.
• Get started with embeddings tuning on Agent Platform — Tunes text-embedding-005 for retrieval using synthetic Gemini queries and Document AI PDF chunks.
• Introduction to Gemini Multimodal Embeddings — Generates Gemini multimodal embeddings and applies them to search, similarity, PDFs, and RAG.
• Using “task type” embeddings for improving RAG search quality — Shows how task-type embeddings improve RAG Q&A retrieval quality and MRR.
• Anomaly Detection of Infrastructure Logs using Gemini and BigQuery Vector Search — Detects HDFS log anomalies with Gemini summaries, text embeddings, and BigQuery Vector Search.
• Introduction to Agent Platform Vector Search 2.0 — Builds an e-commerce product search demo with Agent Platform Vector Search 2.0 and auto-embeddings.

Gemini (189)

• Vertex AI Agent Engine in Express Mode — Build, test, deploy, and query a Gemini ADK currency agent on Vertex AI Agent Engine Express Mode.
• 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.
• Persisting LangChain History with Vertex AI Session Service — Persists LangChain chat and tool history in Vertex AI Agent Engine Session Service.
• 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.
• Get started with Vertex AI Memory Bank - CrewAI — Integrates Vertex AI Memory Bank with CrewAI agents for persistent long-term conversational memory.
• Get started with Vertex AI Memory Bank - LangGraph — Builds a LangGraph chatbot with Vertex AI Memory Bank for long-term personalized memory.
• Debugging and Optimizing Agents: A Guide to Tracing in Agent Engine — Builds, deploys, and traces a Gemini LangChain agent on Vertex AI Agent Engine.
• AG2 (formerly Autogen) Multi-Agents Example on Vertex AI Agent Engine — Builds and deploys an AG2 multi-agent research app to 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 and Deploying a LangGraph Agent with Agent Engine in Vertex AI — Builds, tests, deploys, and deletes a Gemini LangGraph 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.
• Deploying an Agent with Agent Engine and MCP Toolbox for Databases — Deploys a Gemini hotel-booking agent on Agent Engine using Cloud SQL, Cloud Run Toolbox, and LangGraph.
• 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.
• Function Calling Agent — Builds a Gemini function-calling retail assistant over Cymbal Retail data in BigQuery.
• Guardrail Classifier Agent — Builds a LangGraph guardrail classifier agent for Cymbal retail chat using Gemini on Vertex AI.
• Semantic Router Agent — Builds a LangGraph semantic router that sends retail queries to mocked Gemini expert assistants.
• Task Planner Agent — Builds a LangGraph task-planner agent that plans, searches with Gemini, reflects, and responds.
• Query a Remote LangGraph Agent Server — Queries a local or Cloud Run LangGraph RemoteGraph agent and displays streamed agent outputs and state.
• Intro to Batch Inference with the Gemini API — Runs Gemini batch inference jobs using Cloud Storage and BigQuery inputs and outputs.
• Monitor batch prediction with Gemini API — Orchestrates and monitors Gemini batch predictions with Vertex AI Pipelines and BigQuery output.
• Using OpenAI libraries with Gemini on Vertex AI — Calls Gemini on Vertex AI through OpenAI Chat Completions with streaming, tools, schemas, caching, and safety.
• Intro to Gemini Agentic Vision — Uses Gemini code execution for agentic vision tasks: zooming, plotting, and image annotation.
• Intro to Generating and Executing Python Code with Gemini 3 — Uses Gemini 3.5 Flash code execution to generate, run, inspect, and stream Python results via Vertex AI.
• Intro to Computer Use with Gemini — Builds a Gemini Computer Use browser agent loop with Playwright and screenshots.
• Intro to Context Caching with the Gemini API — Shows implicit and explicit context caching for Gemini in Vertex AI with the Google Gen AI SDK.
• Intro to Structured Output with the Gemini API — Shows how to make Gemini 2.5 Flash return JSON, enums, and schema-constrained multimodal outputs.
• Introduction to Gemini Deep Research Agent — Shows how to run Gemini Deep Research Agent with streaming, multimodal input/output, and grounding tools.
• Create & Deploy Agent and Run Gen AI Agent Evaluation — Creates, deploys, runs, and evaluates an ecommerce ADK agent on Vertex AI Agent Engine.
• 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 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.
• 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.
• 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.
• Evaluate your ADK agent using Vertex AI Gen AI Evaluation service — Evaluates an ADK product research agent with Vertex AI Gen AI Evaluation metrics.
• 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.
• Gen AI Eval - Multi-turn Agent Eval, User Simulation, Metric Registration, Auto-Loss Analysis — Evaluates ADK travel agents with Vertex Gen AI Eval multi-turn simulation, custom metrics, and loss analysis.
• Getting Started: Quick Gen AI Evaluation — Evaluates gemini-2.5-flash responses with Vertex AI Gen AI Eval Service using a default quality rubric.
• Forced Function Calling with Tool Configurations in Gemini — Shows how to force, allow, or disable Gemini function calls with tool configurations.
• Working with Data Structures and Schemas in Gemini Function Calling — Shows how to extract structured function-call arguments from Gemini using simple, array, and nested schemas.
• Intro to Function Calling with the Gemini API & Python SDK — Introduces Gemini function calling in Vertex AI with chat tools, mock API responses, and live geocoding.
• Multimodal Function Calling with the Gemini API & Python SDK — Shows Gemini multimodal function calling with images, video, audio, PDFs, and chat using Google Gen AI SDK.
• Working with Parallel Function Calls and Multiple Function Responses in Gemini — Shows how Gemini returns and handles parallel function calls with Wikipedia lookup tools.
• Intro to Gemini 2.5 Flash — Introduces Gemini 2.5 Flash on Vertex AI with text, thinking, multimodal, tools, and structured output.
• Intro to Gemini 2.5 Flash-Lite — Introduces Gemini 2.5 Flash-Lite on Vertex AI with prompting, thinking, structured output, search, and code tools.
• Gemini 2.5 Flash Image (Nano Banana 🍌) Generation — Generates and edits images with Gemini 2.5 Flash Image using the Google Gen AI SDK on Agent Platform.
• Intro to Gemini 2.5 Pro — Introduces Gemini 2.5 Pro on Vertex AI using the Google Gen AI SDK for text, multimodal, tools, and reasoning.
• Gemini 3.1 Flash Image (Nano Banana 2 🍌) Generation — Shows Gemini 3.1 Flash Image generation, grounding, video input, and image editing with Google Gen AI SDK.
• Intro to Gemini 3.1 Flash-Lite — Quickstart for Gemini 3.1 Flash-Lite with Google Gen AI SDK on Google Cloud.
• Intro to Gemini 3.1 Pro — Quickstart for Gemini 3.1 Pro on Vertex AI with Google Gen AI SDK API features.
• Intro to Gemini 3.5 Flash — Quickstart for Gemini 3.5 Flash generation, multimodal inputs, tools, grounding, and safety with Google GenAI SDK.
• Intro to Gemini 3 Flash (Preview) — Quickstart for Gemini 3 Flash Preview on Vertex AI with Gen AI SDK features and multimodal/tool examples.
• Gemini 3 Pro Image (Nano Banana Pro 🍌) Generation — Shows how to generate and edit images with Gemini 3 Pro Image using the Google GenAI SDK.
• Getting Started with Chat with Gemini — Shows stateful Gemini chat with Google GenAI SDK and LangChain on Vertex AI.
• REST API — Uses cURL to call Gemini 3.5 Flash on Vertex AI for text, chat, tools, multimodal, and JSON output.
• Getting started with Gemini using Vertex AI in Express Mode — Introduces Gemini on Vertex AI Express Mode using the Google Gen AI SDK.
• Grounding with Vertex AI Search — Creates a Vertex AI Search engine and uses it to ground a Gemini response with retrieved context.
• Intro to Grounding with Gemini in Vertex AI — Shows how to ground Gemini 3.5 Flash responses with Search, Enterprise Web Search, Maps, and Vertex AI Search.
• 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 Logprobs — Introduces Gemini logprobs on Vertex AI for confidence, autocomplete, and RAG grounding analysis.
• Introduction to Long Context Window with Gemini on Vertex AI — Demonstrates Gemini long-context text, video, and audio prompts on Vertex AI with token counting.
• Intro to Model Context Protocol (MCP) integration with Vertex AI — Shows how to connect Gemini on Vertex AI to custom and prebuilt MCP servers.
• Getting Started with Model Optimizer — Introduces Vertex AI Model Optimizer routing with Google Gen AI SDK prompts and function calls.
• Intro to Agent Platform Multimodal Datasets — Builds Agent Platform multimodal datasets for Gemini tuning validation, resource estimates, tuning, and batch prediction.
• Getting Started with the Live API Native Audio — Connects to Gemini Live API native audio with proactive chime-in and affective dialog examples.
• Getting Started with Gemini Live API using WebSocket — Uses Gemini Live API over WebSocket for text, audio, video, tools, and native audio features.
• Getting Started with Gemini Live API using Gen AI SDK — Introduces Gemini Live API audio sessions, tools, transcription, and VAD with the Google Gen AI SDK.
• Gemini Live API Quickstart — Connects to Gemini Live API for bidirectional audio streaming with Gen AI SDK and raw WebSockets.
• Interactive Loan Application Assistant (Financial Services) — Builds a Gemini 2.0 loan document assistant with RAG, large context, audio, Vertex AI Search, and Vector Search.
• Real-time Retrieval Augmented Generation (RAG) using the Multimodal Live API with Gemini 2.0 — Builds a retail RAG pipeline with Gemini Multimodal Live API for grounded text and audio answers.
• Nano Banana 🍌: Gemini 2.5 Flash Image Recipes — Demonstrates Gemini 2.5 Flash image generation and editing recipes on Vertex AI with google-genai.
• Getting Started with LangChain 🦜️🔗 + Gemini API in Vertex AI — Introduces LangChain with Gemini on Vertex AI for prompts, embeddings, retrieval, memory, and chains.
• From API to Report: Building a Currency Analysis Agent with LangGraph and Gemini — Builds a LangGraph currency analysis agent using Gemini on Vertex AI and an exchange-rate API.
• Build Your Own AI Podcasting Agent with LangGraph, Gemini, and Chirp 3 — Builds a LangGraph podcast agent using Gemini research loops and Chirp 3 text-to-speech audio.
• 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.
• Prompt Design - Best Practices — Demonstrates prompt design best practices for Gemini on Vertex AI with google-genai.
• 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.
• Vertex Prompt Optimizer Notebook UI (Preview) — Shows how to configure, run, and inspect Vertex AI Prompt Optimizer jobs from a notebook UI.
• 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.
• 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.
• 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.
• Responsible AI with Gemini API in Vertex AI: Safety ratings and thresholds — Shows how to inspect Gemini safety ratings and set stricter safety thresholds in Vertex AI.
• 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.
• Intro to thought signatures — Shows how Gemini thought signatures preserve reasoning context across multi-turn function calling.
• Intro to thought signatures with REST API — Shows how to pass Gemini thought signatures through REST function-calling turns.
• 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.
• Integrate Custom Metrics into Gemini Supervised Fine-Tuning — Adds a Python custom metric to Gemini SFT using Vertex AI REST tuning jobs and GCS outputs.
• Vertex AI SFT Gemini Migration Recipe — Migrates explicit SFT hyperparameters from gemini-1.5-flash-002 tuning jobs to gemini-2.5-flash.
• 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.
• Intro to Url Context — Shows how to use Gemini URL context to summarize, compare, and analyze web pages with optional Google Search grounding.
• AI-Assisted Data Science Workflows in BigQuery — Builds a BigQuery multimodal housing workflow with Gemini enrichment, BQML clustering, and vector search.
• Analyzing movie posters in BigQuery with Gemini — Analyzes movie poster images in BigQuery with Gemini, embeddings, and vector search.
• Semantic Analysis in BigQuery with AI Functions — Uses BigQuery AI functions with Gemini to rank, classify, filter, join, and enrich pet product data.
• BigQuery DataFrames ML: Prescription Drug Name Generation — Generates pharmaceutical brand name ideas with BigQuery DataFrames ML and Gemini.
• Introduction to Generative AI functions in BigQuery — Introduces BigQuery generative AI functions for SQL-based text analysis and forecasting.
• Test Document AI Gemini — Compares Document AI entity extraction with Gemini-based extraction on a PDF.
• Analyze Multimodal Data in BigQuery — Shows BigQuery multimodal analysis over structured tables and GCS media using ObjectRefs and Gemini.
• Analyze a codebase with Gemini in Vertex AI — Uses Gemini 3.5 Flash on Vertex AI to analyze, document, debug, and extend a GitHub codebase.
• 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.
• Reduce Tech Debt with Gemini 3 Pro — Uses Gemini 3.1 Pro on Vertex AI to refactor legacy Python code through tests, specs, design docs, and verification.
• Get hands-on with a customer support use case using Gemini and Gen AI SDK — Builds a Gemini customer-support flow for retail product matching, room-fit reasoning, tools, search, and Live API audio.
• 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.
• Document Processing with Gemini — Processes PDFs with Gemini on Vertex AI for extraction, classification, QA, summarization, translation, and page selection.
• Patents Document Understanding with Gemini — Uses Gemini batch prediction on Vertex AI to extract structured fields and figure boxes from patent PDFs.
• Sheet Music Analysis with Gemini — Uses Gemini on Vertex AI to extract and edit sheet music PDF metadata and identify a song from audio.
• Text Summarization of Large Documents using LangChain 🦜🔗 — Summarizes large PDFs with LangChain and Gemini using stuff, map-reduce, and refine chains.
• Automating Income Taxes with Gemini — Classifies tax PDFs and extracts structured tax fields with Gemini on Vertex AI.
• Using Gemini in Education — Demonstrates Gemini education prompts across text, math, images, multiple images, and video.
• Evaluate — Evaluates Gemini-based document classification on image samples and visualizes confusion matrices.
• 🛡️ Agentic GraphRAG: Cybersecurity Threat Intelligence — Builds and deploys an ADK GraphRAG threat-intel agent using Neo4j and Vertex AI Agent Engine.
• 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.
• 🌿 Eco-Nomad Swarm: 100% Real-Data Sustainable Travel Orchestration — Builds a Vertex AI ADK travel swarm using Neo4j GraphRAG and live APIs for sustainable trip briefs.
• ReAct (Reasoning + Acting) + Custom tool for Healthcare NL API + Gemini 2.0 + LangChain — Builds a LangChain ReAct agent using Gemini 2.0 and Healthcare NLP to suggest medical billing codes.
• Gemini: An Overview of Multimodal Use Cases — Shows Gemini multimodal prompts for text, PDFs, images, video, audio, code, retail, diagrams, and comparisons.
• Building Knowledge Graphs with Gemini — Extracts knowledge graphs from text and PDF documents with Gemini using deterministic prompts and TSV outputs.
• Know Your Customer Use Case - Gemini Grounding with Google Search — Builds grounded Gemini KYC negative-news reports and evaluates response quality with custom Vertex AI metrics.
• Creative Content Generation with Gemini in Vertex AI and Imagen — Generates and personalizes GShoe marketing copy with Gemini, then outpaints product images with Imagen.
• Generating Consistent Imagery with Gemini 🍌 — Generates a consistent robot image story from one archive image using Gemini 2.5 Flash Image.
• Video Captioning with Gemini — Uses Gemini on Vertex AI to generate rich multimodal captions from a video in Cloud Storage.
• Video Data Curation - Video Quality Filtering — Filters video datasets by metadata, OCR/watermarks, aesthetics, and motion scores for curation.
• Multimodal Sentiment Analysis with Gemini — Compares Gemini sentiment analysis on audio versus a transcript of the same conversation.
• Productivity Coaching with Gemini and Google Calendar — Uses Gemini 2.5 Flash with Google Calendar data to provide productivity coaching and schedule recommendations.
• Slide Generation with Gemini and Marp — Uses Gemini and Marp to turn a blog post into a Markdown slide deck and PDF.
• Multimodal retail recommendation: using Gemini to recommend items based on images and image reasoning — Builds a Gemini multimodal retail recommender that explains chair fit for a room image.
• 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.
• Retail AI Location Strategy: Autonomous Site Selection & Market Analysis — Builds a Gemini 3 retail site-selection pipeline using search, Maps, code execution, reasoning, and JSON output.
• 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.
• Run RAG Pipelines in BigQuery with BQML and Vector Search — Builds a BigQuery RAG pipeline over a PDF using Document AI, embeddings, vector search, and Gemini.
• 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.
• Detecting and Editing Visual Objects with Gemini — Detects visual objects with Gemini, crops them, and edits them with Gemini image models.
• Spatial understanding with Gemini 3 — Uses Gemini 2.5 Flash on Vertex AI to detect objects and draw normalized bounding boxes on images.
• Unlocking Multimodal Video Transcription with Gemini — Uses Gemini to build multimodal video transcripts with voice IDs, speaker names, and structured JSON output.
• Identifcation of Scene Transitions in Movies Using Gemini — Uses Gemini to detect movie scene transitions from a video and matching VTT subtitles.
• YouTube Video Analysis with Gemini — Analyzes public YouTube videos with Gemini for summaries, structured JSON extraction, and cross-video insights.
• Enhanced Vision Assistant with Gemini — Builds a Gemini vision assistant that detects objects, assesses hazards, and speaks navigation guidance.

Open Models (5)

• 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.
• 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 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.
• 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.
• 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.

SDK (2)

• Getting started with Google Generative AI using the Gen AI SDK — Introduces Google Gen AI SDK on Vertex AI for Gemini prompts, tools, caching, batches, and embeddings.
• Configuring Retries in the Gen AI SDK — Configures Google Gen AI SDK HTTP retries for Gemini calls at client and request level.

Vertex AI Search (9)

• 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.
• Gemini Enterprise custom agent with Vertex AI session — Builds a Gemini Enterprise travel agent using ADK sub-agents and persistent Vertex AI sessions.
• Intro to Gemini Enterprise — Shows how to call Gemini Enterprise search and answer APIs with the Discovery Engine Python SDK.
• MCP Server with Gemini Enterprise — Builds an MCP HR leave tool on Cloud Run, connects it to an ADK Gemini agent, and registers it with Gemini Enterprise.
• 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.

Tools (1)

• Tutorial for Running Prompt Management and Evaluation — Runs prompt management, MathVista evaluation, and Vertex AI prompt optimization in a Streamlit app.

Vision (1)

• Veo 3.1 Video Generation — Generates Veo 3.1 videos from text and images with optional Gemini prompt optimization.

Workshops (1)

• AI Agents for Engineers (Evolution of AI Agents) — Compares zero-shot, LangChain, and LangGraph essay generation with Gemini and Tavily search.

---

Entity · All entities · Home