ahmido
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## Summary - introduce surface-aware compressed governance outcomes and reuse the shared truth/explanation seams for operator-first summaries - apply the compressed outcome hierarchy across baseline, evidence, review, review-pack, canonical review/evidence, and artifact-oriented operation-run surfaces - expand spec 214 fixtures and Pest coverage, and fix tenant-panel route assertions by generating explicit tenant-panel URLs in the affected Filament tests ## Validation - `cd apps/platform && ./vendor/bin/sail bin pint --dirty --format agent` - focused governance compression suite from `specs/214-governance-outcome-compression/quickstart.md` passed (`68` tests, `445` assertions) - `cd apps/platform && ./vendor/bin/sail artisan test --compact tests/Feature/Filament/InventoryItemResourceTest.php tests/Feature/Filament/BackupSetUiEnforcementTest.php tests/Feature/Filament/RestoreRunUiEnforcementTest.php` passed (`18` tests, `81` assertions) Co-authored-by: Ahmed Darrazi <ahmed.darrazi@live.de> Reviewed-on: #253
121 lines
4.4 KiB
Plaintext
121 lines
4.4 KiB
Plaintext
import type { AnyColumn } from "../../column.cjs";
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import type { TypedQueryBuilder } from "../../query-builders/query-builder.cjs";
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import { type SQL, type SQLWrapper } from "../sql.cjs";
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/**
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* Used in sorting and in querying, if used in sorting,
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* this specifies that the given column or expression should be sorted in an order
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* that minimizes the L2 distance to the given value.
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* If used in querying, this specifies that it should return the L2 distance
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* between the given column or expression and the given value.
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*
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* ## Examples
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*
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* ```ts
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* // Sort cars by embedding similarity
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* // to the given embedding
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* db.select().from(cars)
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* .orderBy(l2Distance(cars.embedding, embedding));
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* ```
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*
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* ```ts
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* // Select distance of cars and embedding
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* // to the given embedding
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* db.select({distance: l2Distance(cars.embedding, embedding)}).from(cars)
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* ```
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*/
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export declare function l2Distance(column: SQLWrapper | AnyColumn, value: number[] | string[] | TypedQueryBuilder<any> | string): SQL;
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/**
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* L1 distance is one of the possible distance measures between two probability distribution vectors and it is
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* calculated as the sum of the absolute differences.
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* The smaller the distance between the observed probability vectors, the higher the accuracy of the synthetic data
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*
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* ## Examples
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*
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* ```ts
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* // Sort cars by embedding similarity
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* // to the given embedding
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* db.select().from(cars)
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* .orderBy(l1Distance(cars.embedding, embedding));
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* ```
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*
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* ```ts
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* // Select distance of cars and embedding
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* // to the given embedding
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* db.select({distance: l1Distance(cars.embedding, embedding)}).from(cars)
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* ```
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*/
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export declare function l1Distance(column: SQLWrapper | AnyColumn, value: number[] | string[] | TypedQueryBuilder<any> | string): SQL;
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/**
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* Used in sorting and in querying, if used in sorting,
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* this specifies that the given column or expression should be sorted in an order
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* that minimizes the inner product distance to the given value.
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* If used in querying, this specifies that it should return the inner product distance
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* between the given column or expression and the given value.
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*
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* ## Examples
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*
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* ```ts
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* // Sort cars by embedding similarity
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* // to the given embedding
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* db.select().from(cars)
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* .orderBy(innerProduct(cars.embedding, embedding));
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* ```
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*
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* ```ts
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* // Select distance of cars and embedding
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* // to the given embedding
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* db.select({ distance: innerProduct(cars.embedding, embedding) }).from(cars)
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* ```
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*/
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export declare function innerProduct(column: SQLWrapper | AnyColumn, value: number[] | string[] | TypedQueryBuilder<any> | string): SQL;
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/**
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* Used in sorting and in querying, if used in sorting,
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* this specifies that the given column or expression should be sorted in an order
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* that minimizes the cosine distance to the given value.
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* If used in querying, this specifies that it should return the cosine distance
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* between the given column or expression and the given value.
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*
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* ## Examples
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*
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* ```ts
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* // Sort cars by embedding similarity
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* // to the given embedding
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* db.select().from(cars)
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* .orderBy(cosineDistance(cars.embedding, embedding));
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* ```
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*
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* ```ts
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* // Select distance of cars and embedding
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* // to the given embedding
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* db.select({distance: cosineDistance(cars.embedding, embedding)}).from(cars)
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* ```
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*/
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export declare function cosineDistance(column: SQLWrapper | AnyColumn, value: number[] | string[] | TypedQueryBuilder<any> | string): SQL;
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/**
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* Hamming distance between two strings or vectors of equal length is the number of positions at which the
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* corresponding symbols are different. In other words, it measures the minimum number of
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* substitutions required to change one string into the other, or equivalently,
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* the minimum number of errors that could have transformed one string into the other
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*
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* ## Examples
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*
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* ```ts
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* // Sort cars by embedding similarity
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* // to the given embedding
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* db.select().from(cars)
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* .orderBy(hammingDistance(cars.embedding, embedding));
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* ```
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*/
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export declare function hammingDistance(column: SQLWrapper | AnyColumn, value: number[] | string[] | TypedQueryBuilder<any> | string): SQL;
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/**
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* ## Examples
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*
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* ```ts
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* // Sort cars by embedding similarity
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* // to the given embedding
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* db.select().from(cars)
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* .orderBy(jaccardDistance(cars.embedding, embedding));
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* ```
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*/
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export declare function jaccardDistance(column: SQLWrapper | AnyColumn, value: number[] | string[] | TypedQueryBuilder<any> | string): SQL;
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