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


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.

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).

Streaming

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

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.

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

Learn how to control content generation

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.


Give feedback about your experience with Vertex AI in Firebase