Build multi-turn conversations (chat) with the Gemini API

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Using the Gemini API, you can build freeform conversations across multiple turns. The Vertex AI in Firebase SDK simplifies the process by managing the state of the conversation, so unlike with generateContentStream() or generateContent(), you don't have to store the conversation history yourself.

Other options for working with the Gemini API

• Optionally experiment with an alternative "Google AI" version of the Gemini API
  Get free-of-charge access (within limits and where available) using Google AI Studio and Google AI client SDKs. These SDKs should be used for prototyping only in mobile and web apps.

  After you're familiar with how a Gemini API works, migrate to our Vertex AI in Firebase SDKs (this documentation), which have many additional features important for mobile and web apps, like protecting the API from abuse using Firebase App Check and support for large media files in requests.

• Optionally call the Gemini API in Vertex AI server-side (like with Python, Node.js, or Go)
  Use the server-side Vertex AI SDKs, Genkit, or Firebase Extensions for the Gemini API.

Before you begin

If you haven't already, complete the getting started guide, which describes how to set up your Firebase project, connect your app to Firebase, add the SDK, initialize the Vertex AI service, and create a GenerativeModel instance.

Send a chat prompt request

To build a multi-turn conversation (like chat), start off by initializing the chat by calling startChat(). Then use sendMessageStream() (or sendMessage()) to send a new user message, which will also append the message and the response to the chat history.

There are two possible options for role associated with the content in a conversation:

• user: the role which provides the prompts. This value is the default for calls to sendMessageStream() (or sendMessage()), and the function throws an exception if a different role is passed.

• model: the role which provides the responses. This role can be used when calling startChat() with existing history.

Choose whether you want to stream the response (sendMessageStream) or wait for the response until the entire result is generated (sendMessage).

Learn how to choose a model and optionally a location appropriate for your use case and app.

What else can you do?

• Learn how to count tokens before sending long prompts to the model.
• Set up Cloud Storage for Firebase so that you can include large files in your multimodal requests and have a more managed solution for providing files in prompts. Files can include images, PDFs, video, and audio.
• Start thinking about preparing for production, including setting up Firebase App Check to protect the Gemini API from abuse by unauthorized clients. Also, make sure to review the production checklist.

Try out other capabilities

• Generate text from text-only prompts.
• Generate text from multimodal prompts (including text, images, PDFs, video, and audio).
• Generate structured output (like JSON) from both text and multimodal prompts.
• Generate images from text prompts.
• Use function calling to connect generative models to external systems and information.

Learn how to control content generation

• Understand prompt design, including best practices, strategies, and example prompts.
• Configure model parameters like temperature and maximum output tokens (for Gemini) or aspect ratio and person generation (for Imagen).
• Use safety settings to adjust the likelihood of getting responses that may be considered harmful.

You can also experiment with prompts and model configurations using Vertex AI Studio.

Learn more about the supported models

Learn about the models available for various use cases and their quotas and pricing.

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