node_modules/drizzle-orm/sql/functions/vector.cjs

Object.defineProperty(exports, Symbol.toStringTag, { value: 'Module' });
const require_sql_sql = require('../sql.cjs');

//#region src/sql/functions/vector.ts
function toSql(value) {
	return JSON.stringify(value);
}
/**
* Used in sorting and in querying, if used in sorting,
* this specifies that the given column or expression should be sorted in an order
* that minimizes the L2 distance to the given value.
* If used in querying, this specifies that it should return the L2 distance
* between the given column or expression and the given value.
*
* ## Examples
*
* ```ts
* // Sort cars by embedding similarity
* // to the given embedding
* db.select().from(cars)
*   .orderBy(l2Distance(cars.embedding, embedding));
* ```
*
* ```ts
* // Select distance of cars and embedding
* // to the given embedding
* db.select({distance: l2Distance(cars.embedding, embedding)}).from(cars)
* ```
*/
function l2Distance(column, value) {
	if (Array.isArray(value)) return require_sql_sql.sql`${column} <-> ${toSql(value)}`;
	return require_sql_sql.sql`${column} <-> ${value}`;
}
/**
* L1 distance is one of the possible distance measures between two probability distribution vectors and it is
* calculated as the sum of the absolute differences.
* The smaller the distance between the observed probability vectors, the higher the accuracy of the synthetic data
*
* ## Examples
*
* ```ts
* // Sort cars by embedding similarity
* // to the given embedding
* db.select().from(cars)
*   .orderBy(l1Distance(cars.embedding, embedding));
* ```
*
* ```ts
* // Select distance of cars and embedding
* // to the given embedding
* db.select({distance: l1Distance(cars.embedding, embedding)}).from(cars)
* ```
*/
function l1Distance(column, value) {
	if (Array.isArray(value)) return require_sql_sql.sql`${column} <+> ${toSql(value)}`;
	return require_sql_sql.sql`${column} <+> ${value}`;
}
/**
* Used in sorting and in querying, if used in sorting,
* this specifies that the given column or expression should be sorted in an order
* that minimizes the inner product distance to the given value.
* If used in querying, this specifies that it should return the inner product distance
* between the given column or expression and the given value.
*
* ## Examples
*
* ```ts
* // Sort cars by embedding similarity
* // to the given embedding
* db.select().from(cars)
*   .orderBy(innerProduct(cars.embedding, embedding));
* ```
*
* ```ts
* // Select distance of cars and embedding
* // to the given embedding
* db.select({ distance: innerProduct(cars.embedding, embedding) }).from(cars)
* ```
*/
function innerProduct(column, value) {
	if (Array.isArray(value)) return require_sql_sql.sql`${column} <#> ${toSql(value)}`;
	return require_sql_sql.sql`${column} <#> ${value}`;
}
/**
* Used in sorting and in querying, if used in sorting,
* this specifies that the given column or expression should be sorted in an order
* that minimizes the cosine distance to the given value.
* If used in querying, this specifies that it should return the cosine distance
* between the given column or expression and the given value.
*
* ## Examples
*
* ```ts
* // Sort cars by embedding similarity
* // to the given embedding
* db.select().from(cars)
*   .orderBy(cosineDistance(cars.embedding, embedding));
* ```
*
* ```ts
* // Select distance of cars and embedding
* // to the given embedding
* db.select({distance: cosineDistance(cars.embedding, embedding)}).from(cars)
* ```
*/
function cosineDistance(column, value) {
	if (Array.isArray(value)) return require_sql_sql.sql`${column} <=> ${toSql(value)}`;
	return require_sql_sql.sql`${column} <=> ${value}`;
}
/**
* Hamming distance between two strings or vectors of equal length is the number of positions at which the
* corresponding symbols are different. In other words, it measures the minimum number of
* substitutions required to change one string into the other, or equivalently,
* the minimum number of errors that could have transformed one string into the other
*
* ## Examples
*
* ```ts
* // Sort cars by embedding similarity
* // to the given embedding
* db.select().from(cars)
*   .orderBy(hammingDistance(cars.embedding, embedding));
* ```
*/
function hammingDistance(column, value) {
	if (Array.isArray(value)) return require_sql_sql.sql`${column} <~> ${toSql(value)}`;
	return require_sql_sql.sql`${column} <~> ${value}`;
}
/**
* ## Examples
*
* ```ts
* // Sort cars by embedding similarity
* // to the given embedding
* db.select().from(cars)
*   .orderBy(jaccardDistance(cars.embedding, embedding));
* ```
*/
function jaccardDistance(column, value) {
	if (Array.isArray(value)) return require_sql_sql.sql`${column} <%> ${toSql(value)}`;
	return require_sql_sql.sql`${column} <%> ${value}`;
}

//#endregion
exports.cosineDistance = cosineDistance;
exports.hammingDistance = hammingDistance;
exports.innerProduct = innerProduct;
exports.jaccardDistance = jaccardDistance;
exports.l1Distance = l1Distance;
exports.l2Distance = l2Distance;
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