Firebase plugin

The Firebase plugin provides several integrations with Firebase services:

  • Indexers and retrievers using Cloud Firestore vector store
  • Trace storage using Cloud Firestore
  • Flow deployment using Cloud Functions
  • Authorization policies for Firebase Authentication users


npm i --save @genkit-ai/firebase


  • All Firebase products require a Firebase project. You can create a new project or enable Firebase in an existing Google Cloud project using the Firebase console.
  • In addition, if you want to deploy flows to Cloud Functions, you must upgrade your project to the Blaze pay-as-you-go plan.


Project ID

To use this plugin, specify it when you call configureGenkit():

import {configureGenkit} from "@genkit-ai/core";
import {firebase} from "@genkit-ai/firebase";

  plugins: [firebase({projectId: "your-firebase-project"})],

The plugin requires you to specify your Firebase project ID. You can specify your Firebase project ID in either of the following ways:

  • Set projectId in the firebase() configuration object.

  • Set the GCLOUD_PROJECT environment variable. If you're running your flow from a Google Cloud environment (Cloud Functions, Cloud Run, and so on), GCLOUD_PROJECT is automatically set to the project ID of the environment.

    If you set GCLOUD_PROJECT, you can omit the configuration parameter: firebase()

To provide Firebase credentials, you also need to set up Google Cloud Application Default Credentials. To specify your credentials:

  • If you're running your flow from a Google Cloud environment (Cloud Functions, Cloud Run, and so on), this is set automatically.

  • For other environments:

    1. Generate service account credentials for your Firebase project and download the JSON key file. You can do so on the Service account page of the Firebase console.
    2. Set the environment variable GOOGLE_APPLICATION_CREDENTIALS to the file path of the JSON file that contains your service account key.


The plugin has a direct dependency on the Google Cloud plugin and thus has provisions to enable telemetry export to Google's Cloud operations suite. To enable telemetry export, set the enableTracingAndMetrics to true and add a telemetry section to the Genkit configuration:

import {configureGenkit} from "@genkit-ai/core";
import {firebase} from "@genkit-ai/firebase";

  plugins: [firebase()],
  enableTracingAndMetrics: true,
  telemetry: {
    instrumentation: 'firebase',
    logger: 'firebase',

Refer the the Google Cloud plugin documentation for all configuration options and the necessary APIs that need to be enabled on the project.


This plugin provides several integrations with Firebase services, which you can use together or individually.

Cloud Firestore vector store

You can use Cloud Firestore as a vector store for RAG indexing and retrieval.

This section contains information specific to the firebase plugin and Cloud Firestore's vector search feature. See the Retrieval-augmented generation page for a more detailed discussion on implementing RAG using Genkit.

The firebase plugin provides a convenience function for defining Firestore retrievers, defineFirestoreRetriever():

import {defineFirestoreRetriever} from "@genkit-ai/firebase";
import {retrieve} from "@genkit-ai/ai/retriever";

import {initializeApp} from "firebase-admin/app";
import {getFirestore} from "firebase-admin/firestore";

const app = initializeApp();
const firestore = getFirestore(app);

const yourRetrieverRef = defineFirestoreRetriever({
  name: "yourRetriever",
  firestore: getFirestore(app),
  collection: "yourCollection",
  contentField: "yourDataChunks",
  vectorField: "embedding",
  embedder: textEmbeddingGecko, // Import from '@genkit-ai/googleai' or '@genkit-ai/vertexai'
  distanceMeasure: "COSINE", // "EUCLIDEAN", "DOT_PRODUCT", or "COSINE" (default)

To use it, pass it to the retrieve() function:

const docs = await retrieve({
  retriever: yourRetrieverRef,
  query: "look for something",
  options: {limit: 5},

Available retrieval options include:

  • limit: Specify the number of matching results to return.
  • where: Field/value pairs to match (e.g. {category: 'food'}) in addition to vector search.
  • collection: Override the default collection to search for e.g. subcollection search.

To populate your Firestore collection, use an embedding generator along with the Admin SDK. For example, the menu ingestion script from the Retrieval-augmented generation page could be adapted for Firestore in the following way:

import { configureGenkit } from "@genkit-ai/core";
import { embed } from "@genkit-ai/ai/embedder";
import { defineFlow, run } from "@genkit-ai/flow";
import { textEmbeddingGecko, vertexAI } from "@genkit-ai/vertexai";

import { applicationDefault, initializeApp } from "firebase-admin/app";
import { FieldValue, getFirestore } from "firebase-admin/firestore";

import { chunk } from "llm-chunk";
import pdf from "pdf-parse";
import * as z from "zod";

import { readFile } from "fs/promises";
import path from "path";

// Change these values to match your Firestore config/schema
const indexConfig = {
  collection: "menuInfo",
  contentField: "text",
  vectorField: "embedding",
  embedder: textEmbeddingGecko,

  plugins: [vertexAI({ location: "us-central1" })],
  enableTracingAndMetrics: false,

const app = initializeApp({ credential: applicationDefault() });
const firestore = getFirestore(app);

export const indexMenu = defineFlow(
    name: "indexMenu",
    inputSchema: z.string().describe("PDF file path"),
    outputSchema: z.void(),
  async (filePath: string) => {
    filePath = path.resolve(filePath);

    // Read the PDF.
    const pdfTxt = await run("extract-text", () =>

    // Divide the PDF text into segments.
    const chunks = await run("chunk-it", async () => chunk(pdfTxt));

    // Add chunks to the index.
    await run("index-chunks", async () => indexToFirestore(chunks));

async function indexToFirestore(data: string[]) {
  for (const text of data) {
    const embedding = await embed({
      embedder: indexConfig.embedder,
      content: text,
    await firestore.collection(indexConfig.collection).add({
      [indexConfig.vectorField]: FieldValue.vector(embedding),
      [indexConfig.contentField]: text,

async function extractTextFromPdf(filePath: string) {
  const pdfFile = path.resolve(filePath);
  const dataBuffer = await readFile(pdfFile);
  const data = await pdf(dataBuffer);
  return data.text;

Firestore depends on indexes to provide fast and efficient querying on collections. (Note that "index" here refers to database indexes, and not Genkit's indexer and retriever abstractions.)

The prior example requires the embedding field to be indexed to work. To create the index:

  • Run the gcloud command described in the Create a single-field vector index section of the Firestore docs.

    The command looks like the following:

    gcloud alpha firestore indexes composite create --project=your-project-id \
      --collection-group=yourCollectionName --query-scope=COLLECTION \
      --field-config=vector-config='{"dimension":"768","flat": "{}"}',field-path=yourEmbeddingField

    However, the correct indexing configuration depends on the queries you will make and the embedding model you're using.

  • Alternatively, call retrieve() and Firestore will throw an error with the correct command to create the index.

Learn more

Cloud Firestore trace storage

You can use Cloud Firestore to store traces:

import {firebase} from "@genkit-ai/firebase";

  plugins: [firebase()],
  traceStore: "firebase",
  enableTracingAndMetrics: true,

By default, the plugin stores traces in a collection called genkit-traces in the project's default database. To change either setting:

  traceStore: {
    collection: "your-collection";
    databaseId: "your-db";

When using Firestore-based trace storage you will want to enable TTL for the trace documents:

Cloud Functions

The plugin provides the onFlow() constructor, which creates a flow backed by a Cloud Functions for Firebase HTTPS-triggered function. These functions conform to Firebase's callable function interface and you can use the Cloud Functions client SDKs to call them.

import {firebase} from "@genkit-ai/firebase";
import {onFlow, noAuth} from "@genkit-ai/firebase/functions";

  plugins: [firebase()],

export const exampleFlow = onFlow(
    name: "exampleFlow",
    authPolicy: noAuth(), // WARNING: noAuth() creates an open endpoint!
  async (prompt) => {
    // Flow logic goes here.

    return response;

Deploy your flow using the Firebase CLI:

firebase deploy --only functions

The onFlow() function has some options not present in defineFlow():

  • httpsOptions: an HttpsOptions object used to configure your Cloud Function: js export const exampleFlow = onFlow( { name: "exampleFlow", httpsOptions: { cors: true, }, // ... }, async (prompt) => { // ... } );

  • enforceAppCheck: when true, reject requests with missing or invalid App Check tokens.

  • consumeAppCheckToken: when true, invalidate the App Check token after verifying it.

    See Replay protection.

Firebase Auth

This plugin provides a helper function to create authorization policies around Firebase Auth:

import {firebaseAuth} from "@genkit-ai/firebase/auth";

export const exampleFlow = onFlow(
    name: "exampleFlow",
    authPolicy: firebaseAuth((user) => {
      if (!user.email_verified) throw new Error("Requires verification!");
  async (prompt) => {
    // ...

To define an auth policy, provide firebaseAuth() with a callback function that takes a DecodedIdToken as its only parameter. In this function, examine the user token and throw an error if the user fails to meet any of the criteria you want to require.

See Authorization and integrity for a more thorough discussion of this topic.