14. Using Vector Data

Oracle Database 23ai introduced a new data type VECTOR for artificial intelligence and machine learning search operations. The vector data type is a homogeneous array of 8-bit signed integers, 32-bit floating-point numbers, or 64-bit floating-point numbers. With the vector data type, you can define the number of dimensions for the data and the storage format for each dimension value in the vector.

To create a table with three columns for vector data, for example:

    VCOL32 VECTOR(3, FLOAT32),

In this example, each column can store vector data of three dimensions where each dimension value is of the specified storage format. This example is used in subsequent sections.

You can also create vector columns without specifying the number of dimensions or their storage format. For example:

    dataVec VECTOR

14.1. Inserting Vectors

With node-oracledb, vector data can be inserted using TypedArrays as bind values.

// 32-bit floating-point TypedArray
const float32arr = new Float32Array([1.1, 2.9, 3.14]);

// 64-bit floating-point TypedArray
const float64arr = new Float64Array([7.7, 8.8, 9.9]);

// 8-bit signed integer TypedArray
const int8arr = new Int8Array([126, 125, -23]);

await connection.execute(
    `INSERT INTO vecTable (VCOL32, VCOL64, VCOL8)
     VALUES (:vec32, :vec64, :vec8)`,
    { vec32: float32arr, vec64: float64arr, vec8: int8arr }

To insert TypeArrays in vector columns that are defined without a specific dimension or storage format, you should set the type attribute to oracledb.DB_TYPE_VECTOR as shown below:

// JavaScript array
const arr = [1.1, 2.9, 3.14];

await connection.execute(
    `INSERT INTO vecTable (dataVec) VALUES (:vec)`,
    { vec: { type: oracledb.DB_TYPE_VECTOR, val: arr} }

14.2. Fetching Vectors

With node-oracledb, vector columns are fetched as TypedArrays of signed integer (8-bit), float (32-bit), or double (64-bit) depending on whether the VECTOR column in Oracle Database contains INT8, FLOAT32, or FLOAT64 data. To query a VECTOR column, for example:

const result = await connection.execute(
    `SELECT VCOL32, VCOL64, VCOL8 FROM vecTable`
const vec32 = result.rows[0].VCOL32;
const vec64 = result.rows[0].VCOL64;
const vec8 = result.rows[0].VCOL8;
console.log('Returned Array Type:', vec32.constructor);
console.log('Returned Array:', vec32);
console.log('Returned Array Type:', vec64.constructor);
console.log('Returned Array:', vec64);
console.log('Returned Array Type:', vec8.constructor);
console.log('Returned Array:', vec8);

This prints an output such as:

Returned Array type: [Function: Float32Array]
Returned Array: Float32Array(3) [
Returned Array type: [Function: Float64Array]
Returned Array: Float64Array(3) [
Returned Array type: [Function: Int8Array]
Returned Array: Int8Array(3) [

The minor discrepancies between the input and output values of the Float32 TypedArray are due to the side effects of the floating-point operations in JavaScript.

The vectorDimensions and vectorFormat attributes in the metadata returned by a query contains the number of dimensions of the vector column and the storage format of each dimension value in the vector column respectively. To fetch these attributes, you can use:

const vecDimensions = result.metadata[0].vectorDimensions;
const vecStorageFormat = result.metadata[0].vectorFormat;
console.log('Vector dimensions for the VCOL32 column:', vecDimensions);
console.log('Vector storage format for the VCOL32 column:', vecStorageFormat);

This prints an ouput such as:

Vector dimensions for the VCOL32 column: 3
Vector storage format for the VCOL32 column: 2

This output indicates that the VCOL32 column in vecTable is a 3-dimensional FLOAT32 vector.

Using a fetch type handler, you can convert the vector data that was fetched to a JavaScript array, if required. Consider the following example which converts a TypedArray to a Javascript array.

oracledb.fetchTypeHandler = function(metadata) {
    if (metadata.dbType === oracledb.DB_TYPE_VECTOR) {
        const myConverter = (v) => {
            if (v !== null) {
                return Array.from(v);
            return v;
        return {converter: myConverter};

The fetch type handler is called once for each column in the SELECT query. For each vector column, the converter will be called in Node.js for each of those values. Using it in a query:

const result = await connection.execute(
    `SELECT VCOL32, VCOL64, VCOL8 FROM vecTable`

This prints an output such as:

  VCOL32: [ 1.100000023841858, 2.190000057220459, 3.140000104904175 ],
  VCOL64: [ 7.7, 8.8, 9.9 ],
  VCOL8: [ 126, 125, -23 ]

This shows that the converter function converts the TypedArrays to JavaScript arrays.

See vectortype1.js and vectortype2.js for runnable examples.