Merge PR #6280: perf(mnemopi): run recall vector kernels natively (@wolfiesch)

packages/natives/CHANGELOG.md

@@ -8,6 +8,9 @@
 ### Added
 
 - Added jsdiff-compatible native diff exports: `diffLines`, `diffWords`, `diffLineRuns`, and `structuredPatchHunks` ([#6279](https://github.com/can1357/oh-my-pi/pull/6279) by [@wolfiesch](https://github.com/wolfiesch)).
+### Added
+
+- Added batch vector kernels for mnemopi recall paths: `cosineSimilarityPairs`, `vectorIndexTopK`, and `mmrRerankIndices` ([#6280](https://github.com/can1357/oh-my-pi/pull/6280) by [@wolfiesch](https://github.com/wolfiesch)).
 
 ## [17.0.5] - 2026-07-18
 

packages/natives/native/index.d.ts

@@ -470,6 +470,17 @@ export interface ContextLine {
  */
 export declare function copyToClipboard(text: string): void
 
+/**
+ * All pairs `(i, j)` with `i < j` whose cosine similarity meets `threshold`.
+ *
+ * `vectors` is `count` vectors flattened row-major at `dim` `f64` elements
+ * per row (zero-padded, which matches the TS `?? 0` missing-element
+ * semantics), so the similarity is bit-identical to the TS pairwise loop in
+ * `clusterBySimilarity`. Returns pairs flattened as `[i0, j0, i1, j1, ...]`
+ * in the same `(i, j)` visit order as the TS nested loop.
+ */
+export declare function cosineSimilarityPairs(vectors: Float64Array, count: number, dim: number, threshold: number): Uint32Array
+
 /**
  * Count tokens in `input`.
  *
@@ -1250,6 +1261,28 @@ export interface MinimizerResult {
   outputBytes: number
 }
 
+/**
+ * MMR selection over pre-sorted candidates using Jaccard word similarity.
+ *
+ * `contents[i]` and `scores[i]` describe candidate `i`, already sorted by
+ * relevance exactly as the TS `mmrRerank` sorts them (the JS stable sort
+ * stays on the TS side so its tie and NaN semantics are preserved).
+ * Replicates the TS selection loop exactly: candidate `0` is always taken
+ * first; each round picks the remaining candidate maximizing
+ * `lambda * score - (1 - lambda) * maxSimilarity(selected)` with strict
+ * `>` comparisons, so ties keep the earliest remaining candidate — and a
+ * round where every score is `NaN` picks the first remaining candidate,
+ * matching the TS `bestIdx = 0` initialisation. Returns the selected
+ * indices into the input order.
+ *
+ * Word tokenization matches `text.toLowerCase().split(/\s+/)` (ECMA `\s`,
+ * Unicode default full case conversion). Known divergence: unpaired
+ * surrogates arrive here as U+FFFD, while JS keeps the lone surrogate; both
+ * tokenize to a single non-whitespace word so Jaccard counts still agree
+ * unless a text mixes U+FFFD words with lone-surrogate words.
+ */
+export declare function mmrRerankIndices(contents: Array<string>, scores: Float64Array, lambdaParam: number, topK: number): Uint32Array
+
 /** Parsed Kitty keyboard protocol sequence result for a Kitty input sequence. */
 export interface ParsedKittyResult {
   /** Primary codepoint associated with the key. */
@@ -1668,6 +1701,31 @@ export declare function supportsLanguage(lang: string): boolean
  */
 export declare function truncateToWidth(text: string, maxWidth: number, ellipsisKind: Ellipsis | undefined | null, pad: boolean | undefined | null, tabWidth: number): string
 
+/**
+ * Score every row of a normalized `f32` matrix against `query` and return
+ * the top `limit` rows.
+ *
+ * Mirrors the TS `searchExactVectorIndex` loop bit-exactly: the query is
+ * normalized by the L2 norm of its *full* length, each row score sums
+ * `matrix[row][col] * (query[col] / norm)` over
+ * `min(query.len, dimensions)` columns in column order. Ranking matches the
+ * TS stable sort: score descending, lower row index first on exact ties
+ * (`-0.0` and `+0.0` compare equal). Callers are expected to enforce the TS
+ * guards first (finite query with a positive norm, non-empty matrix).
+ */
+export declare function vectorIndexTopK(matrix: Float32Array, dimensions: number, query: Float64Array, limit: number): VectorTopK
+
+/**
+ * Top-k rows of a normalized vector matrix ranked by dot product with a
+ * normalized query.
+ */
+export interface VectorTopK {
+  /** Row indices of the selected hits, best score first. */
+  indices: Uint32Array
+  /** Scores aligned with `indices`. */
+  scores: Float64Array
+}
+
 /**
  * Calculate visible width of text, excluding ANSI escape sequences.
  *

packages/natives/native/index.js

@@ -30,6 +30,7 @@ export const astGrep = nativeBindings.astGrep;
 export const astMatch = nativeBindings.astMatch;
 export const blockRangeAt = nativeBindings.blockRangeAt;
 export const copyToClipboard = nativeBindings.copyToClipboard;
+export const cosineSimilarityPairs = nativeBindings.cosineSimilarityPairs;
 export const countTokens = nativeBindings.countTokens;
 export const detectMacOSAppearance = nativeBindings.detectMacOSAppearance;
 export const diffLineRuns = nativeBindings.diffLineRuns;
@@ -59,6 +60,7 @@ export const listWorkspace = nativeBindings.listWorkspace;
 export const matchesKey = nativeBindings.matchesKey;
 export const matchesKittySequence = nativeBindings.matchesKittySequence;
 export const matchesLegacySequence = nativeBindings.matchesLegacySequence;
+export const mmrRerankIndices = nativeBindings.mmrRerankIndices;
 export const parseKey = nativeBindings.parseKey;
 export const parseKittySequence = nativeBindings.parseKittySequence;
 export const readImageFromClipboard = nativeBindings.readImageFromClipboard;
@@ -71,6 +73,7 @@ export const structuredPatchHunks = nativeBindings.structuredPatchHunks;
 export const summarizeCode = nativeBindings.summarizeCode;
 export const supportsLanguage = nativeBindings.supportsLanguage;
 export const truncateToWidth = nativeBindings.truncateToWidth;
+export const vectorIndexTopK = nativeBindings.vectorIndexTopK;
 export const visibleWidth = nativeBindings.visibleWidth;
 export const wrapTextWithAnsi = nativeBindings.wrapTextWithAnsi;