Advanced Features Reference

Contents

• Vector Similarity Search
• Full-Text Search
• Cloud Functions Integration
• Data Seeding & Bulk Operations

---

Vector Similarity Search

Semantic search using Vertex AI embeddings and PostgreSQL's pgvector.

Schema Setup

type Movie @table {
  id: UUID! @default(expr: "uuidV4()")
  title: String!
  description: String
  # Vector field for embeddings - size must match model output (768 for gecko)
  descriptionEmbedding: Vector! @col(size: 768)
}

Generate Embeddings in Mutations

Use _embed server value to auto-generate embeddings via Vertex AI:

mutation CreateMovieWithEmbedding($title: String!, $description: String!) 
  @auth(level: USER) {
  movie_insert(data: {
    title: $title,
    description: $description,
    descriptionEmbedding_embed: {
      model: "textembedding-gecko@003",
      text: $description
    }
  })
}

Similarity Search Query

Data Connect generates _similarity fields for Vector columns:

query SearchMovies($query: String!) @auth(level: PUBLIC) {
  movies_descriptionEmbedding_similarity(
    compare_embed: { model: "textembedding-gecko@003", text: $query },
    method: L2,         # L2, COSINE, or INNER_PRODUCT
    within: 2.0,        # Max distance threshold
    limit: 5
  ) {
    id
    title
    description
    _metadata { distance }  # See how close each result is
  }
}

Similarity Parameters

| Parameter | Description | |-----------|-------------| | compare | Raw Vector to compare against | | compare_embed | Generate embedding from text via Vertex AI | | method | Distance function: L2, COSINE, INNER_PRODUCT | | within | Max distance (results further are excluded) | | where | Additional filters | | limit | Max results to return |

Custom Embeddings

Pass pre-computed vectors directly:

mutation StoreCustomEmbedding($id: UUID!, $embedding: Vector!) @auth(level: USER) {
  movie_update(id: $id, data: { descriptionEmbedding: $embedding })
}

query SearchWithCustomVector($vector: Vector!) @auth(level: PUBLIC) {
  movies_descriptionEmbedding_similarity(
    compare: $vector,
    method: COSINE,
    limit: 10
  ) { id title }
}

---

Full-Text Search

Fast keyword/phrase search using PostgreSQL's full-text capabilities.

Enable with @searchable

type Movie @table {
  title: String! @searchable
  description: String @searchable(language: "english")
  genre: String @searchable
}

Search Query

Data Connect generates _search fields:

query SearchMovies($query: String!) @auth(level: PUBLIC) {
  movies_search(
    query: $query,
    queryFormat: QUERY,  # QUERY, PLAIN, PHRASE, or ADVANCED
    limit: 20
  ) {
    id title description
    _metadata { relevance }  # Relevance score
  }
}

Query Formats

| Format | Description | |--------|-------------| | QUERY | Web-style (default): quotes, AND, OR supported | | PLAIN | Match all words, any order | | PHRASE | Match exact phrase | | ADVANCED | Full tsquery syntax |

Tuning Results

query SearchWithThreshold($query: String!) @auth(level: PUBLIC) {
  movies_search(
    query: $query,
    relevanceThreshold: 0.05,  # Min relevance score
    where: { genre: { eq: "Action" }},
    orderBy: [{ releaseYear: DESC }]
  ) { id title }
}

Supported Languages

english (default), french, german, spanish, italian, portuguese, dutch, danish, finnish, norwegian, swedish, russian, arabic, hindi, simple

---

Cloud Functions Integration

Trigger Cloud Functions when mutations execute.

Basic Trigger (Node.js)

import { onMutationExecuted } from "firebase-functions/dataconnect";
import { logger } from "firebase-functions";

export const onUserCreate = onMutationExecuted(
  {
    service: "myService",
    connector: "default",
    operation: "CreateUser",
    region: "us-central1"  // Must match Data Connect location
  },
  (event) => {
    const variables = event.data.payload.variables;
    const returnedData = event.data.payload.data;
    
    logger.info("User created:", returnedData);
    // Send welcome email, sync to analytics, etc.
  }
);

Basic Trigger (Python)

from firebase_functions import dataconnect_fn, logger

@dataconnect_fn.on_mutation_executed(
  service="myService",
  connector="default",
  operation="CreateUser"
)
def on_user_create(event: dataconnect_fn.Event):
  variables = event.data.payload.variables
  returned_data = event.data.payload.data
  logger.info("User created:", returned_data)

Event Data

// event.authType: "app_user" | "unauthenticated" | "admin"
// event.authId: Firebase Auth UID (for app_user)
// event.data.payload.variables: mutation input variables
// event.data.payload.data: mutation response data
// event.data.payload.errors: any errors that occurred

Filtering with Wildcards

// Trigger on all User* mutations
export const onUserMutation = onMutationExecuted(
  { operation: "User*" },
  (event) => { /* ... */ }
);

// Capture operation name
export const onAnyMutation = onMutationExecuted(
  { service: "myService", operation: "{operationName}" },
  (event) => {
    console.log("Operation:", event.params.operationName);
  }
);

Use Cases

• Data sync: Replicate to Firestore, BigQuery, external APIs
• Notifications: Send emails, push notifications on events
• Async workflows: Image processing, data aggregation
• Audit logging: Track all data changes

⚠️ Avoid infinite loops: Don't trigger mutations that would fire the same trigger. Use filters to exclude self-triggered events.

---

Data Seeding & Bulk Operations

Local Prototyping with _insertMany

mutation SeedMovies @transaction {
  movie_insertMany(data: [
    { id: "uuid-1", title: "Movie 1", genre: "Action" },
    { id: "uuid-2", title: "Movie 2", genre: "Drama" },
    { id: "uuid-3", title: "Movie 3", genre: "Comedy" }
  ])
}

Reset Data with _upsertMany

mutation ResetData {
  movie_upsertMany(data: [
    { id: "uuid-1", title: "Movie 1", genre: "Action" },
    { id: "uuid-2", title: "Movie 2", genre: "Drama" }
  ])
}

Clear All Data

mutation ClearMovies {
  movie_deleteMany(all: true)
}

Production: Admin SDK Bulk Operations

import { initializeApp } from 'firebase-admin/app';
import { getDataConnect } from 'firebase-admin/data-connect';

const app = initializeApp();
const dc = getDataConnect({ location: "us-central1", serviceId: "my-service" });

const movies = [
  { id: "uuid-1", title: "Movie 1", genre: "Action" },
  { id: "uuid-2", title: "Movie 2", genre: "Drama" }
];

// Bulk insert
await dc.insertMany("movie", movies);

// Bulk upsert
await dc.upsertMany("movie", movies);

// Single operations
await dc.insert("movie", movies[0]);
await dc.upsert("movie", movies[0]);

Emulator Data Persistence

# Export emulator data
npx -y firebase-tools@latest emulators:export ./seed-data

# Start with saved data
npx -y firebase-tools@latest emulators:start --only dataconnect --import=./seed-data