Add or Store Embeddings

40 Add or Store Embeddings

This section describes how to store embeddings in vector columns using either client-provided vectors (BYOV) or service-generated embeddings (Auto Trigger / Auto Scheduler).

Parent topic: Vectors

40.1 Storing Embeddings Using BYOV

In BYOV mode, the client generates embeddings and passes the embedding data to SDK APIs.

import { doc, setDoc, denseVector, sparseVector, deleteVector } from "fusabase/oracledb";

// Base reference
const reviewsRef = doc(db, "users", "Scott", "reviews", "cookies-id-1");

// Add a review for the recipe Cookies.
await setDoc(reviewsRef, {
  recipe_id: "Cookies",
  comment: "The Cookies recipe was easy to follow, and was super delicious. Easy to bake and produces a great munching sound.",
  review_emb: denseVector([0.12, -0.91, 0.44]),
  recipe_emb: sparseVector(1000, [3, 40, 777], [0.5, 0.8, 0.33]),
});

import oracledb from "fusabase-ns/oracledb";

db.collection("users").doc("Scott")
  .collection("reviews").doc("cookies-id-1")
  .set({
    "recipe_id": "Cookies",
    "comment": "The Cookies recipe was easy to follow, and was super delicious. Easy to bake and produces a great munching sound.",
    "review_emb": oracledb.FieldValue.denseVector([0.12, -0.91, 0.44]),
    "recipe_emb": oracledb.FieldValue.sparseVector(1000, [3, 40, 777], [0.5, 0.8, 0.33]),
  });

import com.oracle.mobile.fusabase.oracledb.FieldValue;
import com.oracle.mobile.fusabase.oracledb.FusabaseOracledb;
import com.oracle.mobile.fusabase.oracledb.SetOptions;
import com.oracle.mobile.fusabase.oracledb.VectorValue;
import java.util.Arrays;
import java.util.HashMap;
import java.util.Map;

FusabaseOracledb db = FusabaseOracledb.getInstance();

// Add a review for the recipe Cookies.
Map<String, Object> review = new HashMap<>();
review.put("recipe_id", "Cookies");
review.put("comment", "The Cookies recipe was easy to follow, and was super delicious. Easy to bake and produces a great munching sound.");
review.put("review_emb", VectorValue.denseEmbedding(
    Arrays.asList(0.12, -0.44, 0.91)
));
review.put("recipe_emb", VectorValue.sparseEmbedding(
    1024,
    Arrays.asList(12, 57, 301),
    Arrays.asList(0.8, 0.24, 0.93)
));

db.collection("users")
    .document("Scott")
    .collection("reviews")
    .document("cookies-id-1")
    .set(review, SetOptions.merge());

 // Base reference
final DocumentReference reviewRef = db
    .collection('users')
    .doc('Scott')
    .collection('reviews')
    .doc('cookies-id-1');

// Add a review for the recipe Cookies.
await reviewRef.set({
  'recipe_id': 'Cookies',
  'comment': 'The Cookies recipe was easy to follow, and was super delicious. Easy to bake and produces a great munching sound.',
  'review_emb': FieldValue.denseVector([0.12, -0.44, 0.91]),
  'recipe_emb': FieldValue.sparseVector(1000, [3, 40, 777], [0.5, 0.8, 0.33]),
});

import FusabaseOracledb

let db = FusabaseOracledb.oracledb()

let reviewRef = db
    .collection("users")
    .document("Scott")
    .collection("reviews")
    .document("cookies-id-1")

// Add a review for the recipe Cookies.
reviewRef.setData([
    "recipe_id": "Cookies",
    "comment": "The Cookies recipe was easy to follow, and was super delicious. Easy to bake and produces a great munching sound.",
    "review_emb": try denseVector([0.12, -0.91, 0.44]),
    "recipe_emb": try sparseVector(
                      dimension: 1000,
                      indices: [2, 7, 900],
                      values: [0.9, 0.3, 0.5]
                  ),
])

Parent topic: Add or Store Embeddings

40.2 Storing Embeddings Using Auto Trigger / Auto Scheduler

When the collection is set up with AUTO_TRIGGER or AUTO_SCHEDULER, you don't pass the embedding at all. Just insert your document like normal. The database fills in the vector column itself, using whichever source you configured.

This means your application code doesn't change between BYOV and auto-generation collections, except for the absence of the vector field. Same addDoc and setDoc calls.