/ai-provider-google-gemini-sdk

name: ai-provider-google-gemini-sdk
description: Official TypeScript SDK for Google Gemini — client setup, text generation, multimodal input, function calling, structured output, streaming, embeddings, context caching, and chat sessions

Google Gemini SDK Patterns

> **Quick Guide:** Use the `@google/genai` package (the unified SDK, NOT the deprecated `@google/generative-ai`) for all Gemini API interactions. All operations flow through a central `GoogleGenAI` client with service accessors: `ai.models` for generation, `ai.chats` for multi-turn, `ai.files` for uploads, `ai.caches` for context caching. Use `responseMimeType: "application/json"` with `responseJsonSchema` for structured output. Access response text via `response.text` (property, not method). Streaming uses `generateContentStream` returning an async iterable -- iterate with `for await`.

---

<critical_requirements>

CRITICAL: Before Using This Skill

> **All code must follow project conventions in CLAUDE.md** (kebab-case, named exports, import ordering, `import type`, named constants)

**(You MUST use `@google/genai` (the new unified SDK) -- NOT the deprecated `@google/generative-ai` package)**

**(You MUST access response text via `response.text` (a property) -- NOT `response.text()` (the old SDK used a method call))**

**(You MUST pass `model` as a string parameter in every API call -- there is no `getGenerativeModel()` step)**

**(You MUST use `config` for all generation parameters (temperature, safetySettings, tools, systemInstruction) -- NOT top-level properties)**

**(You MUST never hardcode API keys -- use environment variables via `process.env.GEMINI_API_KEY` or `GOOGLE_API_KEY`)**

</critical_requirements>

---

**Auto-detection:** Gemini, gemini, GoogleGenAI, @google/genai, ai.models.generateContent, generateContentStream, ai.chats, ai.files, ai.caches, gemini-2.5-flash, gemini-2.5-pro, gemini-2.0-flash, gemini-3-flash, gemini-embedding, GEMINI_API_KEY, GOOGLE_API_KEY, FunctionCallingConfigMode, createUserContent, createPartFromUri, responseMimeType, responseJsonSchema

**When to use:**

• Building applications that call Google Gemini models directly (Gemini 2.x, 2.5, 3.x)
• Processing multimodal input: images, video, audio, PDFs
• Implementing function calling / tool use with custom functions or built-in tools (Google Search, code execution)
• Extracting structured JSON data from LLM responses using response schemas
• Streaming text generation for user-facing output
• Creating embeddings for RAG pipelines or semantic search (text and multimodal)
• Caching large context (documents, code) to reduce cost and latency across multiple requests
• Multi-turn chat sessions with automatic history management

**Key patterns covered:**

• Client initialization and environment-based configuration
• Text generation with `ai.models.generateContent()`
• Streaming with `ai.models.generateContentStream()` and `for await`
• Multimodal input (inline base64, file upload, URIs)
• Function calling with `FunctionDeclaration` and manual tool loops
• Structured output with `responseMimeType` + `responseJsonSchema` + Zod
• Chat sessions with `ai.chats.create()` and `sendMessage()`
• Embeddings with `ai.models.embedContent()` (text and multimodal)
• Context caching with `ai.caches.create()`
• Safety settings per-request via `config.safetySettings`

**When NOT to use:**

• Multi-provider applications requiring provider switching -- use a unified provider SDK
• React-specific chat UI hooks (`useChat`) -- use a framework-integrated AI SDK
• When you need features unique to another provider's API -- use that provider's SDK directly

---

Examples Index

• [Core: Setup & Configuration](examples/core.md) -- Client init, text generation, system instructions, error handling
• [Multimodal Input](examples/multimodal.md) -- Inline images, file upload, video, audio, PDF, `createPartFromUri`
• [Streaming](examples/streaming.md) -- `generateContentStream`, `sendMessageStream`, abort patterns
• [Function Calling / Tools](examples/tools.md) -- `FunctionDeclaration`, `FunctionCallingConfigMode`, manual tool loop, built-in tools
• [Structured Output](examples/structured-output.md) -- JSON mode, Zod schemas, `responseJsonSchema`, enum extraction
• [Chat Sessions](examples/chat.md) -- `ai.chats.create()`, multi-turn, streaming chat, history
• [Advanced: Embeddings, Caching & Safety](examples/advanced.md) -- Embeddings, context caching, safety settings, token counting
• [Quick API Reference](reference.md) -- Model IDs, method signatures, config parameters, safety enums

---

<philosophy>

Philosophy

The `@google/genai` SDK is Google's **unified client** for the Gemini API and Vertex AI. It replaces the deprecated `@google/generative-ai` package with a cleaner, centralized architecture.

**Core principles:**

1. **Centralized client** -- A single `GoogleGenAI` instance provides all API services via `ai.models`, `ai.chats`, `ai.files`, `ai.caches`. No scattered manager classes. 2. **Model-per-call** -- Pass the model ID string in every API call rather than binding to a model instance. This simplifies multi-model usage. 3. **Config object pattern** -- All generation parameters (`temperature`, `systemInstruction`, `tools`, `safetySettings`) go inside a `config` object, keeping the top-level call clean. 4. **Native multimodal** -- Images, video, audio, and PDFs are first-class inputs via inline data or file upload. Gemini models handle all modalities natively. 5. **Response as property** -- Access `response.text` as a property (not a method). Access `response.functionCalls` for tool calls.

**When to use the Gemini SDK directly:**

• You primarily use Google Gemini models
• You need multimodal input (images, video, audio, PDF) as a core feature
• You want built-in tools like Google Search and code execution
• You need context caching for large documents
• You want the simplest path to Gemini API features

**When NOT to use:**

• You need to switch