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

Using the Gemini API, you can build freeform conversations across multiple turns. The Vertex AI for 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.

Before you begin

If you haven't already, work through the getting started guide for the Vertex AI for Firebase SDKs. Make sure that you've done all of the following:

  • Set up a new or existing Firebase project, including using the Blaze pricing plan and enabling the required APIs.

  • Connect your app to Firebase, including registering your app and adding your Firebase config to your app.

  • Add the SDK and initialize the Vertex AI service and the generative model in your app.

After you've connected your app to Firebase, added the SDK, and initialized the Vertex AI service and the generative model, you're ready to call the Gemini API.

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).


You can achieve faster interactions by not waiting for the entire result from the model generation, and instead use streaming to handle partial results.

This example shows how to use sendMessageStream() to stream responses from the model:

Without streaming

Alternatively, you can wait for the entire result instead of streaming; the result is only returned after the model completes the entire generation process.

This example shows how to use sendMessage() to send a new user message:

Learn how to choose a Gemini 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 using Cloud Storage URLs. 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.

Try out other capabilities of the Gemini API

Learn how to control content generation

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

Learn more about the Gemini models

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

Give feedback about your experience with Vertex AI for Firebase