Cleanup
This commit is contained in:
KS Jannette
@@ -4,10 +4,9 @@ import { Readable } from "node:stream";
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import { batch } from "../lib/streams.js";
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const client = new VoyageAIClient({
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apiKey: process.env.VOYAGEAI_API_KEY,
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apiKey: process.env.VOYAGEAI_API_KEY,
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});
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// Well under Voyage's per-request input and token ceilings.
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const EMBED_BATCH_SIZE = 128;
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/**
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@@ -15,28 +14,28 @@ const EMBED_BATCH_SIZE = 128;
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* @returns {Promise<Map<string, number[]>>} noteId → embedding vector
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*/
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export const embedNotes = async (notes) => {
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const embeddingMap = new Map();
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const embeddingMap = new Map();
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if (!notes || notes.length === 0) {
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return embeddingMap;
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}
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await pipeline(
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Readable.from(notes, { objectMode: true }),
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batch(EMBED_BATCH_SIZE),
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async (batches) => {
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for await (const chunk of batches) {
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const response = await client.embed({
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input: chunk.map((n) => n.text),
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model: "voyage-3",
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});
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response.data.forEach((item, i) => {
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embeddingMap.set(chunk[i].id, item.embedding);
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});
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}
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if (!notes || notes.length === 0) {
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return embeddingMap;
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}
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);
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return embeddingMap;
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await pipeline(
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Readable.from(notes, { objectMode: true }),
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batch(EMBED_BATCH_SIZE),
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async (batches) => {
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for await (const chunk of batches) {
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const response = await client.embed({
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input: chunk.map((n) => n.text),
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model: "voyage-3",
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});
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response.data.forEach((item, i) => {
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embeddingMap.set(chunk[i].id, item.embedding);
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});
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}
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}
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);
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return embeddingMap;
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};
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@@ -1,68 +1,67 @@
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/**
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* Structural validation: confirms the LLM output is well-formed
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* before it reaches the frontend.
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* Structural validation for LLM output
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*
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* @param {Array<{label: string, noteIds: string[]}>} clusters
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* @param {string[]} inputNoteIds - the original note IDs that were sent to the LLM
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* @returns {{valid: boolean, reasons: string[]}}
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*/
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export const validateStructure = (clusters, inputNoteIds) => {
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const reasons = [];
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const reasons = [];
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if (!Array.isArray(clusters) || clusters.length === 0) {
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return { valid: false, reasons: ['Response is not a non-empty array'] };
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}
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const assignedIds = [];
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for (const cluster of clusters) {
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if (!cluster.label || typeof cluster.label !== 'string') {
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reasons.push(`Cluster missing a valid label`);
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if (!Array.isArray(clusters) || clusters.length === 0) {
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return { valid: false, reasons: ['Response is not a non-empty array'] };
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}
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if (!Array.isArray(cluster.noteIds) || cluster.noteIds.length === 0) {
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reasons.push(`Cluster "${cluster.label ?? '(unlabeled)'}" has no noteIds`);
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const assignedIds = [];
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for (const cluster of clusters) {
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if (!cluster.label || typeof cluster.label !== 'string') {
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reasons.push(`Cluster missing a valid label`);
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}
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if (!Array.isArray(cluster.noteIds) || cluster.noteIds.length === 0) {
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reasons.push(`Cluster "${cluster.label ?? '(unlabeled)'}" has no noteIds`);
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}
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assignedIds.push(...(cluster.noteIds ?? []));
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}
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assignedIds.push(...(cluster.noteIds ?? []));
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}
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const inputSet = new Set(inputNoteIds);
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const assignedSet = new Set(assignedIds);
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const inputSet = new Set(inputNoteIds);
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const assignedSet = new Set(assignedIds);
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if (assignedIds.length !== assignedSet.size) {
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reasons.push('One or more notes appear in multiple clusters');
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}
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if (assignedIds.length !== assignedSet.size) {
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reasons.push('One or more notes appear in multiple clusters');
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}
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const missing = inputNoteIds.filter((id) => !assignedSet.has(id));
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if (missing.length > 0) {
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reasons.push(`Notes missing from clusters: ${missing.join(', ')}`);
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}
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const missing = inputNoteIds.filter((id) => !assignedSet.has(id));
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if (missing.length > 0) {
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reasons.push(`Notes missing from clusters: ${missing.join(', ')}`);
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}
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const extra = assignedIds.filter((id) => !inputSet.has(id));
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if (extra.length > 0) {
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reasons.push(`Unknown noteIds in clusters: ${[...new Set(extra)].join(', ')}`);
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}
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const extra = assignedIds.filter((id) => !inputSet.has(id));
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if (extra.length > 0) {
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reasons.push(`Unknown noteIds in clusters: ${[...new Set(extra)].join(', ')}`);
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}
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if (clusters.length > inputNoteIds.length) {
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reasons.push(`More clusters (${clusters.length}) than notes (${inputNoteIds.length})`);
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}
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if (clusters.length > inputNoteIds.length) {
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reasons.push(`More clusters (${clusters.length}) than notes (${inputNoteIds.length})`);
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}
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return { valid: reasons.length === 0, reasons };
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return { valid: reasons.length === 0, reasons };
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};
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const cosineSimilarity = (a, b) => {
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let dot = 0;
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let magA = 0;
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let magB = 0;
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for (let i = 0; i < a.length; i++) {
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dot += a[i] * b[i];
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magA += a[i] * a[i];
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magB += b[i] * b[i];
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}
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const denom = Math.sqrt(magA) * Math.sqrt(magB);
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return denom === 0 ? 0 : dot / denom;
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let dot = 0;
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let magA = 0;
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let magB = 0;
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for (let i = 0; i < a.length; i++) {
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dot += a[i] * b[i];
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magA += a[i] * a[i];
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magB += b[i] * b[i];
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}
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const denom = Math.sqrt(magA) * Math.sqrt(magB);
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return denom === 0 ? 0 : dot / denom;
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};
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/**
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* Computes a silhouette-style cohesion score for the clustering.
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* Computes silhouette-style cohesion score for the clustering.
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*
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* For each note, measures how much more similar it is to its own cluster
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* versus the nearest neighboring cluster. Returns a score in [-1, 1]
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@@ -73,57 +72,57 @@ const cosineSimilarity = (a, b) => {
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* @returns {number} average silhouette score
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*/
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export const computeCohesionScore = (clusters, embeddingMap) => {
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if (clusters.length <= 1) return 1.0;
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if (clusters.length <= 1) return 1.0;
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const scores = [];
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const scores = [];
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for (let ci = 0; ci < clusters.length; ci++) {
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const clusterIds = clusters[ci].noteIds;
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if (clusterIds.length <= 1) {
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scores.push(0);
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continue;
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for (let ci = 0; ci < clusters.length; ci++) {
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const clusterIds = clusters[ci].noteIds;
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if (clusterIds.length <= 1) {
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scores.push(0);
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continue;
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}
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for (const noteId of clusterIds) {
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const vec = embeddingMap.get(noteId);
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if (!vec) continue;
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// a(i): avg distance to other notes in same cluster
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let intraSum = 0;
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let intraCount = 0;
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for (const otherId of clusterIds) {
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if (otherId === noteId) continue;
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const otherVec = embeddingMap.get(otherId);
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if (!otherVec) continue;
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intraSum += 1 - cosineSimilarity(vec, otherVec);
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intraCount++;
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}
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const a = intraCount > 0 ? intraSum / intraCount : 0;
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// b(i): min avg distance to notes in any other cluster
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let b = Infinity;
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for (let oi = 0; oi < clusters.length; oi++) {
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if (oi === ci) continue;
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const otherClusterIds = clusters[oi].noteIds;
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let interSum = 0;
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let interCount = 0;
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for (const otherId of otherClusterIds) {
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const otherVec = embeddingMap.get(otherId);
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if (!otherVec) continue;
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interSum += 1 - cosineSimilarity(vec, otherVec);
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interCount++;
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}
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if (interCount > 0) {
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b = Math.min(b, interSum / interCount);
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}
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}
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if (b === Infinity) b = 0;
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const max = Math.max(a, b);
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scores.push(max === 0 ? 0 : (b - a) / max);
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}
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}
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for (const noteId of clusterIds) {
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const vec = embeddingMap.get(noteId);
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if (!vec) continue;
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// a(i): avg distance to other notes in same cluster
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let intraSum = 0;
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let intraCount = 0;
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for (const otherId of clusterIds) {
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if (otherId === noteId) continue;
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const otherVec = embeddingMap.get(otherId);
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if (!otherVec) continue;
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intraSum += 1 - cosineSimilarity(vec, otherVec);
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intraCount++;
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}
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const a = intraCount > 0 ? intraSum / intraCount : 0;
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// b(i): min avg distance to notes in any other cluster
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let b = Infinity;
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for (let oi = 0; oi < clusters.length; oi++) {
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if (oi === ci) continue;
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const otherClusterIds = clusters[oi].noteIds;
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let interSum = 0;
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let interCount = 0;
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for (const otherId of otherClusterIds) {
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const otherVec = embeddingMap.get(otherId);
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if (!otherVec) continue;
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interSum += 1 - cosineSimilarity(vec, otherVec);
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interCount++;
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}
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if (interCount > 0) {
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b = Math.min(b, interSum / interCount);
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}
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}
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if (b === Infinity) b = 0;
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const max = Math.max(a, b);
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scores.push(max === 0 ? 0 : (b - a) / max);
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}
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}
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if (scores.length === 0) return 0;
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return scores.reduce((sum, s) => sum + s, 0) / scores.length;
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if (scores.length === 0) return 0;
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return scores.reduce((sum, s) => sum + s, 0) / scores.length;
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};
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