gemini-3.5-flash

Model gemini-3.5-flash is used in 48 notebook(s) in this brain.

Concepts: Agent Engine · Agents & ADK · Applied Use Cases · Audio & Speech · Embeddings & Vector Search · Evaluation · Function Calling & Tools · Gemini Capabilities · Getting Started · Image & Video Generation · Multimodal Live API · Prompt Engineering · RAG & Grounding · Vertex AI Search · Vision

Notebooks

• Gemini 3.1 Flash Text-to-Speech Generation — Shows Gemini 3.1 Flash TTS generation with voices, languages, speakers, and audio tags.
• 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.
• Introduction to Gemini Multimodal Embeddings — Generates Gemini multimodal embeddings and applies them to search, similarity, PDFs, and RAG.
• 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.
• 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.
• Intro to Batch Inference with the Gemini API — Runs Gemini batch inference jobs using Cloud Storage and BigQuery inputs and outputs.
• 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.
• 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.
• 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 3.5 Flash — Quickstart for Gemini 3.5 Flash generation, multimodal inputs, tools, grounding, and safety with 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 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.
• 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.
• 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 Engine with Vertex AI Feature Store — Builds a Vertex AI RAG Engine corpus backed by Vertex AI Feature Store and queries it with 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.
• 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.
• 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.
• 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.
• Detecting and Editing Visual Objects with Gemini — Detects visual objects with Gemini, crops them, and edits them with Gemini image models.
• YouTube Video Analysis with Gemini — Analyzes public YouTube videos with Gemini for summaries, structured JSON extraction, and cross-video insights.
• 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.
• Veo 3.1 Video Generation — Generates Veo 3.1 videos from text and images with optional Gemini prompt optimization.
• AI Agents for Engineers (Evolution of AI Agents) — Compares zero-shot, LangChain, and LangGraph essay generation with Gemini and Tavily search.

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