Genkit includes a plugin that helps you deploy your flows to Cloud Functions for Firebase. Flows, once deployed, are available as HTTPS endpoints and accessible as callable functions using the Cloud Functions client libraries.
Before you begin
- Install the Firebase CLI.
- You should be familiar with Genkit's concept of flows, and how to write them. The instructions on this page assume that you already have some flows defined, which you want to deploy.
- It would be helpful, but not required, if you've already used Cloud Functions for Firebase before.
1. Set up a Firebase project
If you don't already have a Firebase project with TypeScript Cloud Functions set up, follow these steps:
Create a new Firebase project using the Firebase console or choose an existing one.
Upgrade the project to the Blaze plan, which is required to deploy Cloud Functions.
Log in with the Firebase CLI:
firebase login
firebase login --reauth # alternative, if necessary
firebase login --no-localhost # if running in a remote shell
Create a new project directory:
export PROJECT_ROOT=~/tmp/genkit-firebase-project1
mkdir -p $PROJECT_ROOT
Initialize a Firebase project in the directory:
cd $PROJECT_ROOT
firebase init genkit
The rest of this page assumes that you've selected to write your functions in TypeScript, but you can also deploy your Genkit flows if you're using JavaScript.
2. Update flow definitions
After you've set up a Firebase project with Cloud Functions, you can copy or
write flow definitions in the project’s functions/src
directory, and export
them in index.ts
.
For your flows to be deployable, you will need to make some small changes to how you define them. The core logic will remain the same, but you will add some additional information to make them smoothly deployable and more secure once deployed.
Suppose you have the following flow:
const generatePoemFlow = ai.defineFlow(
{
name: "generatePoem",
inputSchema: z.string(),
outputSchema: z.string(),
},
async (subject: string) => {
const { text } = await ai.generate(`Compose a poem about ${subject}.`);
return text;
}
);
The following sections describe the changes you need to make before you can deploy it.
Define flows with onFlow
Instead of defining your flow with Genkit.defineFlow()
, use the Firebase
plugin's onFlow()
function. Using this function wraps your flow logic in a
Cloud Functions request handler, similar to
onCall
.
import { onFlow } from "@genkit-ai/firebase/functions";
export const generatePoem = onFlow(
ai,
{
// ...
},
async (subject: string) => {
// ...
}
);
Note that onFlow
isn't a method of Genkit
, but rather a function that takes
a Genkit
instance as its first parameter. Otherwise, the syntax is similar to
defineFlow
.
Define an authorization policy
All deployed flows, whether deployed to Firebase or not, should have an
authorization policy; without one, your potentially-expensive generative AI
flows would be invocable by anyone. To define an authorization policy, use the
authPolicy
parameter in the flow definition:
import { firebaseAuth } from "@genkit-ai/firebase/auth";
export const generatePoem = onFlow(
ai,
{
name: "generatePoem",
// ...
authPolicy: firebaseAuth((user, input) => {
if (!user.email_verified) {
throw new Error("Verified email required to run flow");
}
}),
},
async (subject: string) => {
// ...
}
);
This policy uses the firebaseAuth()
helper to allow access only to registered
users of your app with verfied email addresses. On the client side, you need to
set the Authorization: Bearer
header to a Firebase ID token that satisfies
your policy. The Cloud Functions client SDKs provide callable function methods
that automate this; see the section Try the deployed flow for
an example.
Make API credentials available to deployed flows
Once deployed, your flows need some way to authenticate with any remote services they rely on. Most flows will at a minimum need credentials for accessing the model API service they use.
For this example, do one of the following, depending on the model provider you chose:
Gemini (Google AI)
Make sure Google AI is available in your region.
Generate an API key for the Gemini API using Google AI Studio.
Store your API key in Cloud Secret Manager:
firebase functions:secrets:set GOOGLE_GENAI_API_KEY
This step is important to prevent accidentally leaking your API key, which grants access to a potentially metered service.
See Store and access sensitive configuration information for more information on managing secrets.
Edit
src/index.ts
and add the following after the existing imports:import {defineSecret} from "firebase-functions/params"; const googleAIapiKey = defineSecret("GOOGLE_GENAI_API_KEY");
Then, in the flow definition, declare that the cloud function needs access to this secret value:
export const generatePoem = onFlow( { name: "generatePoem", // ... httpsOptions: { secrets: [googleAIapiKey], // Add this line. }, }, async (subject) => { // ... } );
Now, when you deploy this function, your API key will be stored in Cloud Secret Manager, and available from the Cloud Functions environment.
Gemini (Vertex AI)
In the Cloud console, Enable the Vertex AI API for your Firebase project.
On the IAM page, ensure that the Default compute service account is granted the Vertex AI User role.
The only secret you need to set up for this tutorial is for the model provider, but in general, you must do something similar for each service your flow uses.
Set a CORS policy
If you'll access your flow from a web app (which you will do in the Try the
deployed flow section), in the httpsOptions
parameter, set a
CORS policy:
export const generatePoem = onFlow(
ai,
{
name: "generatePoem",
// ...
httpsOptions: {
cors: '*',
},
},
async (subject: string) => {
// ...
}
);
You will likely want a more restrictive policy for production apps, but this will do for this tutorial.
Complete example
After you've made all of the changes described above, your deployable flow will look something like the following example:
const googleAIapiKey = defineSecret("GOOGLE_GENAI_API_KEY");
export const generatePoem = onFlow(
ai,
{
name: "generatePoem",
inputSchema: z.string(),
outputSchema: z.string(),
authPolicy: firebaseAuth((user, input) => {
if (!user.email_verified) {
throw new Error("Verified email required to run flow");
}
}),
httpsOptions: {
secrets: [googleAIapiKey],
cors: '*',
},
},
async (subject: string) => {
const { text } = await ai.generate(`Compose a poem about ${subject}.`);
return text;
}
);
3. Deploy flows to Firebase
After you've defined flows using onFlow
, you can deploy them as you would
deploy other Cloud Functions:
cd $PROJECT_ROOT
firebase deploy --only functions
You've now deployed the flow as a Cloud Function! But, you won't be able to
access your deployed endpoint with curl
or similar, because of the flow's
authorization policy. Continue to the next section to learn how to securely
access the flow.
Optional: Try the deployed flow
To try out your flow endpoint, you can deploy the following minimal example web app:
In the Project settings section of the Firebase console, add a new web app, selecting the option to also set up Hosting.
In the Authentication section of the Firebase console, enable the Google provider, which you will use in this example.
In your project directory, set up Firebase Hosting, where you will deploy the sample app:
cd $PROJECT_ROOT
firebase init hosting
Accept the defaults for all of the prompts.
Replace
public/index.html
with the following:<!DOCTYPE html> <html> <head> <title>Genkit demo</title> </head> <body> <div id="signin" hidden> <button id="signinBtn">Sign in with Google</button> </div> <div id="callGenkit" hidden> Subject: <input type="text" id="subject" /> <button id="generatePoem">Compose a poem on this subject</button> <p id="generatedPoem"></p> </div> <script type="module"> import { initializeApp } from "https://www.gstatic.com/firebasejs/11.0.1/firebase-app.js"; import { getAuth, onAuthStateChanged, GoogleAuthProvider, signInWithPopup, } from "https://www.gstatic.com/firebasejs/11.0.1/firebase-auth.js"; import { getFunctions, httpsCallable, } from "https://www.gstatic.com/firebasejs/11.0.1/firebase-functions.js"; const firebaseConfig = await fetch("/__/firebase/init.json"); initializeApp(await firebaseConfig.json()); async function generatePoem() { const poemFlow = httpsCallable(getFunctions(), "generatePoem"); const subject = document.querySelector("#subject").value; const response = await poemFlow(subject); document.querySelector("#generatedPoem").innerText = response.data; } function signIn() { signInWithPopup(getAuth(), new GoogleAuthProvider()); } document.querySelector("#signinBtn").addEventListener("click", signIn); document .querySelector("#generatePoem") .addEventListener("click", generatePoem); const signinEl = document.querySelector("#signin"); const genkitEl = document.querySelector("#callGenkit"); onAuthStateChanged(getAuth(), (user) => { if (!user) { signinEl.hidden = false; genkitEl.hidden = true; } else { signinEl.hidden = true; genkitEl.hidden = false; } }); </script> </body> </html>
Deploy the web app and Cloud Function:
cd $PROJECT_ROOT
firebase deploy
Open the web app by visiting the URL printed by the deploy
command. The app
requires you to sign in with a Google account, after which you can initiate
endpoint requests.
Optional: Run flows in the developer UI
You can run flows defined using onFlow
in the developer UI, exactly the same
way as you run flows defined using defineFlow
, so there's no need to switch
between the two between deployment and development.
cd $PROJECT_ROOT/functions
npx genkit start -- npx tsx --watch src/index.ts
or
cd $PROJECT_ROOT/functions
npm run genkit:start
You can now navigate to the URL printed by the genkit start
command to access.
Optional: Developing using Firebase Local Emulator Suite
Firebase offers a suite of emulators for local development, which you can use with Genkit.
To use the Genkit Dev UI with the Firebase Emulator Suite, start the Firebase emulators like this:
npx genkit start -- firebase emulators:start --inspect-functions
This will run your code in the emulator and run the Genkit framework in development mode, which launches and exposes the Genkit reflection API (but not the Dev UI).
To see traces from Firestore in the Dev UI you can navigate to the Inspect tab and toggle the "Dev/Prod" switch. When toggled to "prod" it will be loading traces from firestore.