# Gemini API using Firebase AI Logic Build AI-powered mobile and web apps and features with the Gemini models using Firebase AI Logic [Video](https://www.youtube.com/watch?v=EpRSIFVtMng) Firebase AI Logic gives you access to the latest generative AI models from Google: the Gemini models. If you need to call the Gemini API directly from your mobile or web app --- rather than server-side --- you can use the Firebase AI Logic client SDKs. These client SDKs are built specifically for use with mobile and web apps, offering security options against unauthorized clients as well as integrations with other Firebase services. **These client SDKs are available in Swift for Apple platforms, Kotlin \& Java for Android, JavaScript for web, Dart for Flutter, and Unity.** With these client SDKs, you can add AI personalization to apps, build an AI chat experience, create AI-powered optimizations and automation, and much more! [Get started](https://firebase.google.com/docs/ai-logic/get-started.md.txt) ## Key capabilities |---|---| | Multimodal and natural language input | The [Gemini models](https://firebase.google.com/docs/ai-logic/models.md.txt) are multimodal, so prompts sent to the Gemini API can include text, images, PDFs, video, and audio. Some Gemini models can also generate multimodal *output* . The Gemini models can be prompted with natural language input. | | Growing suite of capabilities | With the SDKs, you can call the Gemini API directly from your mobile or web app to [build AI chat experiences](https://firebase.google.com/docs/ai-logic/chat.md.txt), [generate images](https://firebase.google.com/docs/ai-logic/generate-images-gemini.md.txt), [generate speech from text (TTS)](https://firebase.google.com/docs/ai-logic/generate-speech.md.txt), use tools (like [function calling](https://firebase.google.com/docs/ai-logic/function-calling.md.txt) and Grounding with [Google Search](https://firebase.google.com/docs/ai-logic/grounding-google-search.md.txt) or [Google Maps](https://firebase.google.com/docs/ai-logic/grounding-google-maps.md.txt)), [stream multimodal input and output (including audio)](https://firebase.google.com/docs/ai-logic/live-api.md.txt), and more. | | Security and abuse prevention for production apps | Use [Firebase App Check](https://firebase.google.com/docs/ai-logic/app-check.md.txt) to help protect the APIs that access the Gemini models from abuse by unauthorized clients. Firebase AI Logic also has [rate limits per user](https://firebase.google.com/docs/ai-logic/faq-and-troubleshooting.md.txt#rate-limits-per-user) *by default*, and these per-user rate limits are fully configurable. | | Robust infrastructure | Take advantage of scalable infrastructure that's built for use with mobile and web apps, like [managing files with Cloud Storage for Firebase](https://firebase.google.com/docs/ai-logic/solutions/cloud-storage.md.txt) and dynamically controlling run-time configurations with [Firebase Remote Config](https://firebase.google.com/docs/ai-logic/solutions/remote-config.md.txt) or [server prompt templates](https://firebase.google.com/docs/ai-logic/server-prompt-templates/get-started.md.txt). | ## How does it work? Firebase AI Logic provides client SDKs, a proxy service, and other features that let you access Google's generative AI models to build AI features in your mobile and web apps. #### Support for Google models and "Gemini API" providers We support all the latest Gemini models, and you choose your preferred "Gemini API" provider to access these models. We support both the Gemini Developer API and Agent Platform Gemini API (formerly Vertex AI). Learn about the [differences between using the two API providers](https://firebase.google.com/docs/ai-logic/faq-and-troubleshooting.md.txt#differences-between-gemini-api-providers). And if you choose to use the Gemini Developer API, you can take advantage of their "free tier" to get you up and running fast. #### Mobile \& web client SDKs You send requests to the models directly from your mobile or web app using our Firebase AI Logic client SDKs, available in Swift for Apple platforms, Kotlin \& Java for Android, JavaScript for Web, Dart for Flutter, and Unity. If you have both of the Gemini API providers set up in your Firebase project, then you can switch between API providers just by enabling the other API and changing a few lines of initialization code. Additionally, several of our client SDKs offer access to [hybrid and on-device inference](https://firebase.google.com/docs/ai-logic/hybrid.md.txt). This configuration lets your app use the on-device model when it's available, but fall back seamlessly to the cloud-hosted model when needed (and vice-versa). #### Proxy service Our proxy service acts as a gateway between the client and your chosen Gemini API provider (and Google's models). It provides services and integrations that are important for mobile and web apps. For example, you can [set up Firebase App Check](https://firebase.google.com/docs/ai-logic/app-check.md.txt) to help protect your chosen API provider and your backend resources from abuse by unauthorized clients. This is particularly critical if you chose to use the Gemini Developer API because our proxy service and this App Check integration make sure that your Gemini API key stays on the server and is *not* embedded in your apps' codebase. ## Implementation path |---|---|---| | | Set up your Firebase project and connect your app to Firebase | Use the guided workflow in the [**Firebase AI Logic** page](https://console.firebase.google.com/project/_/ailogic/?useAutoProject=true) of the Firebase console to set up your project (including enabling the required APIs for your chosen Gemini API provider), register your app with your Firebase project, and then add your Firebase configuration to your app. | | | Install the SDK and initialize | Install the Firebase AI Logic SDK that's specific to your app's platform, and then initialize the service and create a model instance in your app. | | | Send prompt requests to the Gemini models | Use the SDKs to send text-only or multimodal prompts to a Gemini model to generate [text and code](https://firebase.google.com/docs/ai-logic/generate-text.md.txt), [structured output (like JSON)](https://firebase.google.com/docs/ai-logic/generate-structured-output.md.txt), [images](https://firebase.google.com/docs/ai-logic/generate-images-gemini.md.txt), and [speech (TTS)](https://firebase.google.com/docs/ai-logic/generate-speech.md.txt). Build richer experiences with [multi-turn chat](https://firebase.google.com/docs/ai-logic/chat.md.txt), [bidirectional streaming (including audio)](https://firebase.google.com/docs/ai-logic/live-api.md.txt), and [function calling](https://firebase.google.com/docs/ai-logic/function-calling.md.txt). | | | Prepare for production | Implement important integrations for mobile and web apps, like protecting the API from abuse with [Firebase App Check](https://firebase.google.com/docs/ai-logic/app-check.md.txt) and using [Firebase Remote Config](https://firebase.google.com/docs/ai-logic/solutions/remote-config.md.txt) or [server prompt templates](https://firebase.google.com/docs/ai-logic/server-prompt-templates/get-started.md.txt) so that you can make on-demand changes to configurations for your AI feature (like model name) without releasing a new version of your app. | ## Next steps #### Get started with accessing a model from your mobile or web app [Go to Getting Started guide](https://firebase.google.com/docs/ai-logic/get-started.md.txt) #### Learn more about the supported models Learn about the [models available for various use cases](https://firebase.google.com/docs/ai-logic/models.md.txt) and their [quotas](https://firebase.google.com/docs/ai-logic/quotas.md.txt) and [pricing](https://firebase.google.com/docs/ai-logic/pricing.md.txt).