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Official skill for integrating Firebase AI Logic (Gemini API) into web applications. Covers setup, multimodal inference, structured output, and security.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
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| name | firebase-ai-logic |
| description | Official skill for integrating Firebase AI Logic (Gemini API) into web applications. Covers setup, multimodal inference, structured output, and security. |
| version | 1.0.0 |
Firebase AI Logic is a product of Firebase that allows developers to add gen AI to their mobile and web apps using client-side SDKs. You can call Gemini models directly from your app without managing a dedicated backend. Firebase AI Logic, which was previously known as "Vertex AI for Firebase", represents the evolution of Google's AI integration platform for mobile and web developers.
It supports the two Gemini API providers:
Use the Gemini Developer API as a default, and only Vertex AI Gemini API if the application requires it.
The library is part of the standard Firebase Web SDK.
npm install -g firebase@latest
If you're in a firebase directory (with a firebase.json) the currently selected project will be marked with "current" using this command:
npx -y firebase-tools@latest projects:list
Ensure there's at least one app associated with the current project
npx -y firebase-tools@latest apps:list
Initialize AI logic SDK with the init command
npx -y firebase-tools@latest init # Choose AI logic
This will automatically enable the Gemini Developer API in the Firebase console.
More info in Firebase AI Logic Getting Started
Firebase AI Logic allows Gemini models to analyze image files directly from your app. This enables features like creating captions, answering questions about images, detecting objects, and categorizing images. Beyond images, Gemini can analyze other media types like audio, video, and PDFs by passing them as inline data with their MIME type. For files larger than 20 megabytes (which can cause HTTP 413 errors as inline data), store them in Cloud Storage for Firebase and pass their URLs to the Gemini Developer API.
Maintain history automatically using startChat.
To improve the user experience by showing partial results as they arrive (like a typing effect), use generateContentStream instead of generateContent for faster display of results.
Supported Platforms and Frameworks include Kotlin and Java for Android, Swift for iOS, JavaScript for web apps, Dart for Flutter, and C Sharp for Unity.
Enforce a specific JSON schema for the response.
Hybrid on-device inference for web apps, where the Firebase Javascript SDK automatically checks for Gemini Nano's availability (after installation) and switches between on-device or cloud-hosted prompt execution. This requires specific steps to enable model usage in the Chrome browser, more info in the hybrid-on-device-inference documentation.
Recommended: The developer must enable Firebase App Check to prevent unauthorized clients from using their API quota. see App-check recaptcha enterprise.
Consider that you do not need to hardcode model names (e.g., gemini-flash-lite-latest). Use Firebase Remote Config to update model versions dynamically without deploying new client code. See Changing model names remotely
| Language, Framework, Platform | Gemini API provider | Context URL |
|---|---|---|
| Web Modular API | Gemini Developer API (Developer API) | firebase://docs/ai-logic/get-started |
Always use the most recent version of Gemini (gemini-flash-latest) unless another model is requested by the docs or the user. DO NOT USE gemini-1.5-flash
