Gemini Notebooks

205 notebooks from the gemini/ area of GoogleCloudPlatform/generative-ai.

Agent Engine (17)

• 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.

Agents (6)

• 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.
• Constructing a LangGraphAgent — Wraps a simple LangGraph StateGraph in LangGraphAgent and tests stateful/stateless streaming.
• Query a Remote LangGraph Agent Server — Queries a local or Cloud Run LangGraph RemoteGraph agent and displays streamed agent outputs and state.

Batch Prediction (2)

• 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.

Chat Completions (1)

• Using OpenAI libraries with Gemini on Vertex AI — Calls Gemini on Vertex AI through OpenAI Chat Completions with streaming, tools, schemas, caching, and safety.

Code Execution (2)

• 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.

Computer Use (1)

• Intro to Computer Use with Gemini — Builds a Gemini Computer Use browser agent loop with Playwright and screenshots.

Context Caching (1)

• 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.

Controlled Generation (1)

• Intro to Structured Output with the Gemini API — Shows how to make Gemini 2.5 Flash return JSON, enums, and schema-constrained multimodal outputs.

Deep Research (1)

• Introduction to Gemini Deep Research Agent — Shows how to run Gemini Deep Research Agent with streaming, multimodal input/output, and grounding tools.

Evaluation (41)

• 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 agent final answer with custom parsing — Evaluates agent final answers using Vertex AI EvalTask with a custom JSON parser for autorater output.
• 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.
• Evaluate your ADK agent using Vertex AI Gen AI Evaluation service — Evaluates an ADK product research agent with Vertex AI Gen AI Evaluation 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.
• 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.
• View Gen AI Agent Evaluation Run Results — Retrieves a Vertex AI Gen AI evaluation run and displays its embedded report.

Function Calling (5)

• 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.

Getting Started (13)

• 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 (2)

• 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.

Logging (1)

• Intro to Request and Response Logging with Gemini — Shows how to enable Gemini request-response logging to BigQuery, query logs, and disable logging.

Logprobs (1)

• Intro to Logprobs — Introduces Gemini logprobs on Vertex AI for confidence, autocomplete, and RAG grounding analysis.

Long Context (1)

• 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.

Mcp (1)

• Intro to Model Context Protocol (MCP) integration with Vertex AI — Shows how to connect Gemini on Vertex AI to custom and prebuilt MCP servers.

Model Optimizer (1)

• Getting Started with Model Optimizer — Introduces Vertex AI Model Optimizer routing with Google Gen AI SDK prompts and function calls.

Multimodal Dataset (1)

• Intro to Agent Platform Multimodal Datasets — Builds Agent Platform multimodal datasets for Gemini tuning validation, resource estimates, tuning, and batch prediction.

Multimodal Live API (6)

• 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 (1)

• Nano Banana 🍌: Gemini 2.5 Flash Image Recipes — Demonstrates Gemini 2.5 Flash image generation and editing recipes on Vertex AI with google-genai.

Orchestration (4)

• 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.

Prompts (12)

• 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.

RAG Engine (9)

• 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.

Responsible AI (3)

• 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.

Sample Apps (1)

• 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.

Thinking (2)

• 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.

Tuning (11)

• 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.

URL Context (1)

• Intro to Url Context — Shows how to use Gemini URL context to summarize, compare, and analyze web pages with optional Google Search grounding.

Use Cases (56)

• 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.
• Text + multimodal embedding generation and vector search in BigQuery — Builds text and image embeddings in BigQuery for semantic product search.
• 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.
• Performing Semantic Search in BigQuery — Builds semantic search over Stack Overflow questions in BigQuery with Vertex AI text embeddings.
• 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.
• Data Curation Pipeline: Splitting and Transcoding — Deduplicates video clips using video embeddings and BigQuery vector search.
• Data Curation Pipeline: Splitting and Transcoding — Builds a video curation pipeline for metadata filtering, splitting, scene detection, and transcoding.
• 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.

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