diff --git a/.github/workflows/embeddings.yml b/.github/workflows/embeddings.yml new file mode 100644 index 0000000..64f0ff8 --- /dev/null +++ b/.github/workflows/embeddings.yml @@ -0,0 +1,50 @@ +name: Regenerate Embeddings + +on: + schedule: + - cron: '0 3 * * 0' # weekly Sunday 03:00 UTC + workflow_dispatch: + +jobs: + regenerate: + runs-on: ubuntu-latest + permissions: + contents: write + pull-requests: write + steps: + - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 + + - uses: actions/setup-node@39370e3970a6d050c480ffad4ff0ed4d3fdee5af + with: + node-version: '20' + cache: 'npm' + + - run: npm ci + + - run: npm run build -w @retort-plugins/router + + - name: Regenerate embeddings index + run: node packages/router/scripts/generate-embeddings.mjs + + - name: Commit if changed + run: | + git config user.name "github-actions[bot]" + git config user.email "github-actions[bot]@users.noreply.github.com" + git add packages/router/data/embeddings.json + if git diff --staged --quiet; then + echo "No changes to embeddings index." + exit 0 + fi + git commit -m "chore(router): regenerate embeddings index [skip ci]" + # Try direct push first; if branch protection blocks it, open a PR instead. + if ! git push; then + echo "Direct push rejected (branch protection active) — opening a PR." + BRANCH="chore/embeddings-$(date +%Y%m%d)" + git checkout -b "$BRANCH" + git push origin "$BRANCH" + gh pr create \ + --base main \ + --head "$BRANCH" \ + --title "chore(router): regenerate embeddings index" \ + --body "Automated weekly embeddings regeneration." + fi diff --git a/package-lock.json b/package-lock.json index 3a3354b..b6fe607 100644 --- a/package-lock.json +++ b/package-lock.json @@ -810,6 +810,16 @@ "node": "^12.22.0 || ^14.17.0 || >=16.0.0" } }, + "node_modules/@huggingface/jinja": { + "version": "0.2.2", + "resolved": "https://registry.npmjs.org/@huggingface/jinja/-/jinja-0.2.2.tgz", + "integrity": 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routing for Retort — answers natural-language questions about agent teams", "main": "dist/index.js", "types": "dist/index.d.ts", "scripts": { @@ -16,5 +16,8 @@ "@types/node": "^20.0.0", "typescript": "^5.3.0", "vitest": "^1.0.0" + }, + "optionalDependencies": { + "@xenova/transformers": "^2.17.2" } } diff --git a/packages/router/scripts/generate-embeddings.mjs b/packages/router/scripts/generate-embeddings.mjs new file mode 100644 index 0000000..4479ee6 --- /dev/null +++ b/packages/router/scripts/generate-embeddings.mjs @@ -0,0 +1,147 @@ +#!/usr/bin/env node +/** + * generate-embeddings.mjs + * + * Generates semantic embeddings for the 13 standard Retort agent teams and + * writes the result to packages/router/data/embeddings.json. + * + * Usage: + * node packages/router/scripts/generate-embeddings.mjs + * + * Requires @xenova/transformers to be installed (optional dependency). + * The model is cached automatically in ~/.cache/huggingface. + */ + +import { writeFileSync, mkdirSync } from 'node:fs' +import { dirname, resolve } from 'node:path' +import { fileURLToPath } from 'node:url' + +const __dirname = dirname(fileURLToPath(import.meta.url)) +const OUTPUT_PATH = resolve(__dirname, '../data/embeddings.json') + +/** The 13 standard Retort agent teams. */ +const RETORT_TEAMS = [ + { + id: 'backend', + name: 'Backend', + focus: 'API, services, core logic, database integrations', + scope: ['apps/api/**', 'services/**'], + accepts: ['feature', 'bug', 'performance'], + }, + { + id: 'frontend', + name: 'Frontend', + focus: 'UI, components, PWA, accessibility, React, Next.js', + scope: ['apps/web/**', 'apps/marketing/**'], + accepts: ['feature', 'bug', 'ui'], + }, + { + id: 'data', + name: 'Data', + focus: 'Database models, migrations, query optimisation, Prisma', + scope: ['db/**', 'migrations/**', 'prisma/**'], + accepts: ['migration', 'feature', 'bug'], + }, + { + id: 'infra', + name: 'Infrastructure', + focus: 'IaC, cloud resources, Terraform, Bicep, Azure', + scope: ['infra/**', 'terraform/**', 'bicep/**'], + accepts: ['chore', 'feature', 'ci'], + }, + { + id: 'devops', + name: 'DevOps', + focus: 'CI/CD, pipelines, containers, Docker, deployment automation', + scope: ['.github/workflows/**', 'docker/**'], + accepts: ['chore', 'ci', 'performance'], + }, + { + id: 'testing', + name: 'Testing', + focus: 'Unit tests, integration tests, E2E tests, coverage, Vitest, Playwright', + scope: ['**/*.test.*', 'tests/**', 'e2e/**'], + accepts: ['test', 'bug', 'quality'], + }, + { + id: 'security', + name: 'Security', + focus: 'Auth, compliance, audit, secrets management, RBAC', + scope: ['auth/**', 'security/**'], + accepts: ['security', 'bug', 'compliance'], + }, + { + id: 'docs', + name: 'Documentation', + focus: 'Technical documentation, ADRs, guides, changelogs, API docs', + scope: ['docs/**', 'README.md', 'CHANGELOG.md'], + accepts: ['docs', 'chore'], + }, + { + id: 'product', + name: 'Product', + focus: 'Product requirements, features, roadmap, PRDs, user stories', + scope: ['docs/product/**', 'docs/prd/**'], + accepts: ['feature', 'epic', 'product'], + }, + { + id: 'quality', + name: 'Quality', + focus: 'Code review, refactoring, bug triage, technical debt', + scope: ['**/*'], + accepts: ['bug', 'refactor', 'review'], + }, + { + id: 'forge', + name: 'TeamForge', + focus: 'Agent team spec creation, validation, Retort sync, AgentKit configuration', + scope: ['.agentkit/spec/**'], + accepts: ['chore', 'feat', 'config'], + }, + { + id: 'strategic-ops', + name: 'Strategic Ops', + focus: 'Cross-project coordination, planning, architectural strategy, roadmap alignment', + scope: ['docs/planning/**', 'docs/architecture/**'], + accepts: ['planning', 'strategy', 'coordination'], + }, + { + id: 'cost-ops', + name: 'Cost Ops', + focus: 'AI cost reduction, LLM spend tracking, vendor optimisation, token budgets', + scope: ['docs/cost-ops/**', 'config/models/**'], + accepts: ['cost', 'optimisation', 'vendor'], + }, +] + +async function main() { + console.log('Loading @xenova/transformers pipeline…') + + // Dynamic import so we get a clear error if the optional dep is missing. + let buildSemanticIndex + try { + const mod = await import('../dist/index.js') + buildSemanticIndex = mod.buildSemanticIndex + } catch (err) { + console.error( + 'ERROR: Could not import dist/index.js. Run `npm run build -w @retort-plugins/router` first.', + ) + console.error('Underlying error:', err) + process.exit(1) + } + + console.log(`Generating embeddings for ${RETORT_TEAMS.length} teams…`) + const index = await buildSemanticIndex(RETORT_TEAMS) + + mkdirSync(dirname(OUTPUT_PATH), { recursive: true }) + writeFileSync(OUTPUT_PATH, JSON.stringify(index, null, 2), 'utf-8') + + console.log(`Embeddings written to ${OUTPUT_PATH}`) + console.log(`Indexed at: ${index.indexedAt}`) + console.log(`Teams indexed: ${index.entries.length}`) +} + +main().catch((err) => { + console.error('Fatal error:', err) + process.exit(1) +}) diff --git a/packages/router/src/embed.ts b/packages/router/src/embed.ts new file mode 100644 index 0000000..62b9adf --- /dev/null +++ b/packages/router/src/embed.ts @@ -0,0 +1,64 @@ +/** + * Embedding helper — wraps @xenova/transformers for Phase 2 semantic routing. + * + * The pipeline is a singleton: it is loaded once and reused across calls so + * the model weights are not re-downloaded on every invocation. + */ + +// Dynamic import keeps @xenova/transformers as an optional runtime dep; the +// package is listed as an optionalDependency in package.json so consumers +// that only use Phase-1 keyword routing don't have to install it. +type PipelineType = ( + texts: string[], + options?: { pooling?: string; normalize?: boolean }, +) => Promise<{ data: Float32Array | number[] }> + +type FeatureExtractionPipeline = (texts: string[], opts?: object) => Promise<{ data: Float32Array | number[] }> + +let _pipeline: FeatureExtractionPipeline | undefined + +/** + * Returns (and caches) the feature-extraction pipeline. + * Throws if @xenova/transformers is not installed. + */ +async function getPipeline(): Promise { + if (_pipeline) return _pipeline + + // eslint-disable-next-line @typescript-eslint/no-explicit-any + const { pipeline } = (await import('@xenova/transformers')) as { pipeline: (task: string, model: string) => Promise } + _pipeline = (await pipeline('feature-extraction', 'Xenova/all-MiniLM-L6-v2')) as unknown as FeatureExtractionPipeline + return _pipeline +} + +/** + * Embed a batch of texts and return normalised float vectors. + * + * @param texts - Array of strings to embed (must be non-empty). + * @returns - Array of normalised float arrays, one per input text. + */ +// all-MiniLM-L6-v2 produces 384-dimensional embeddings. +const EMBEDDING_DIM = 384 + +export async function embed(texts: string[]): Promise { + if (texts.length === 0) return [] + + const extractor = await getPipeline() + + // Process the whole batch in a single pipeline call instead of N serial calls. + // output.data is a flat Float32Array of shape [batch_size × EMBEDDING_DIM]. + const output = await extractor(texts, { pooling: 'mean', normalize: true }) + const results: number[][] = [] + for (let i = 0; i < texts.length; i++) { + const start = i * EMBEDDING_DIM + results.push(Array.from(output.data.slice(start, start + EMBEDDING_DIM))) + } + return results +} + +/** + * Resets the cached pipeline singleton. Intended for use in tests only. + * @internal + */ +export function _resetPipelineCache(): void { + _pipeline = undefined +} diff --git a/packages/router/src/index-builder.ts b/packages/router/src/index-builder.ts index 6ca6e2f..0a8e798 100644 --- a/packages/router/src/index-builder.ts +++ b/packages/router/src/index-builder.ts @@ -34,6 +34,42 @@ export function buildIndex(teams: TeamLike[]): RouterIndex { return { entries, indexedAt: new Date().toISOString() } } +/** + * Builds a semantic index from an array of team objects by generating + * embeddings for each entry. + * + * Phase 2: uses ML embeddings via @xenova/transformers. + * Requires the optional @xenova/transformers dependency to be installed. + * + * @param teams - Team definitions to index. + * @param embedFn - Embedding function; defaults to the built-in `embed()`. + * @returns RouterIndex with `embedding` populated on each entry. + */ +export async function buildSemanticIndex( + teams: TeamLike[], + embedFn?: (texts: string[]) => Promise, +): Promise { + const { embed } = await import('./embed.js') + const doEmbed = embedFn ?? embed + + // Build plain entries first (reuse Phase-1 logic). + const base = buildIndex(teams) + + // Compute text representations for embedding. + const texts = teams.map((team) => + [team.name, team.focus, ...team.scope, ...team.accepts].join(' '), + ) + + const embeddings = await doEmbed(texts) + + const entries: RouterEntry[] = base.entries.map((entry, i) => ({ + ...entry, + embedding: embeddings[i], + })) + + return { entries, indexedAt: base.indexedAt } +} + /** Lowercase, strip punctuation, split on whitespace, deduplicate. */ export function tokenise(text: string): string[] { const tokens = text diff --git a/packages/router/src/index.ts b/packages/router/src/index.ts index 361bc42..835868d 100644 --- a/packages/router/src/index.ts +++ b/packages/router/src/index.ts @@ -1,5 +1,6 @@ -export { buildIndex, tokenise } from './index-builder.js' +export { buildIndex, buildSemanticIndex, tokenise } from './index-builder.js' export type { TeamLike } from './index-builder.js' -export { search } from './search.js' -export { route } from './router.js' +export { search, semanticSearch, cosineSimilarity } from './search.js' +export { route, routeSemantic } from './router.js' +export { embed } from './embed.js' export type { RouteResult, RouterEntry, RouterIndex, AskResponse } from './types.js' diff --git a/packages/router/src/router.test.ts b/packages/router/src/router.test.ts index febb639..972955e 100644 --- a/packages/router/src/router.test.ts +++ b/packages/router/src/router.test.ts @@ -1,8 +1,8 @@ -import { describe, it, expect } from 'vitest' -import { buildIndex } from './index-builder.js' +import { describe, it, expect, vi, beforeEach } from 'vitest' +import { buildIndex, buildSemanticIndex } from './index-builder.js' import type { TeamLike } from './index-builder.js' -import { route } from './router.js' -import { search } from './search.js' +import { route, routeSemantic } from './router.js' +import { search, cosineSimilarity, semanticSearch } from './search.js' const TEAMS: TeamLike[] = [ { @@ -42,6 +42,10 @@ const TEAMS: TeamLike[] = [ }, ] +// --------------------------------------------------------------------------- +// Phase 1 tests (must all continue to pass) +// --------------------------------------------------------------------------- + describe('buildIndex', () => { it('creates one entry per team', () => { const index = buildIndex(TEAMS) @@ -132,3 +136,158 @@ describe('search', () => { expect(results).toHaveLength(0) }) }) + +// --------------------------------------------------------------------------- +// Phase 2 tests +// --------------------------------------------------------------------------- + +describe('cosineSimilarity', () => { + it('returns 1 for identical vectors', () => { + const v = [1, 0, 0] + expect(cosineSimilarity(v, v)).toBeCloseTo(1, 5) + }) + + it('returns 0 for orthogonal vectors', () => { + expect(cosineSimilarity([1, 0, 0], [0, 1, 0])).toBeCloseTo(0, 5) + }) + + it('returns a positive value for similar (non-orthogonal) vectors', () => { + const result = cosineSimilarity([1, 1, 0], [1, 0, 0]) + expect(result).toBeGreaterThan(0) + expect(result).toBeLessThan(1) + }) + + it('returns 0 for zero-magnitude vectors (no division by zero)', () => { + expect(cosineSimilarity([0, 0, 0], [1, 2, 3])).toBe(0) + expect(cosineSimilarity([0, 0, 0], [0, 0, 0])).toBe(0) + }) +}) + +describe('semanticSearch', () => { + // Controlled mock embeddings: 3-dimensional unit vectors + // TEAMS order: [frontend, backend, security, devops, product] + // frontend → [0, 1, 0] + // backend → [1, 0, 0] ← query [1,0,0] should match this + // security → [0, 0, 1] + // devops → [0.7071, 0.7071, 0] + // product → [0, 0.7071, 0.7071] + + const mockEntries = TEAMS.map((team, i) => { + const vectors: number[][] = [ + [0, 1, 0], + [1, 0, 0], + [0, 0, 1], + [0.7071, 0.7071, 0], + [0, 0.7071, 0.7071], + ] + return { + type: 'team' as const, + id: team.id, + name: team.name, + keywords: [], + explanation: `The ${team.name} team.`, + embedding: vectors[i], + } + }) + + const mockEmbedFn = vi.fn(async (texts: string[]) => { + // Query "api backend" → [1, 0, 0] (points toward backend) + return texts.map(() => [1, 0, 0]) + }) + + it('returns results sorted by descending cosine similarity', async () => { + const results = await semanticSearch('api backend', mockEntries, mockEmbedFn) + for (let i = 1; i < results.length; i++) { + expect(results[i - 1].confidence).toBeGreaterThanOrEqual(results[i].confidence) + } + }) + + it('returns the backend team first when query embedding matches backend vector', async () => { + const results = await semanticSearch('api backend', mockEntries, mockEmbedFn) + expect(results[0].id).toBe('backend') + }) + + it('calls embedFn with the query text', async () => { + const fn = vi.fn(async (texts: string[]) => texts.map(() => [1, 0, 0])) + await semanticSearch('hello', mockEntries, fn) + expect(fn).toHaveBeenCalledWith(['hello']) + }) + + it('falls back to keyword search when no entries have embeddings', async () => { + const noEmbeddingEntries = TEAMS.map((team) => ({ + type: 'team' as const, + id: team.id, + name: team.name, + keywords: ['api', 'backend', 'services'], + explanation: `The ${team.name} team.`, + })) + const fn = vi.fn(async (texts: string[]) => texts.map(() => [1, 0, 0])) + const results = await semanticSearch('api backend', noEmbeddingEntries, fn) + // fn should NOT be called because we fell back to keyword search + expect(fn).not.toHaveBeenCalled() + // keyword search should still return results + expect(results.length).toBeGreaterThan(0) + }) +}) + +describe('buildSemanticIndex', () => { + it('populates embedding on each entry', async () => { + const mockEmbed = vi.fn(async (texts: string[]) => + texts.map(() => [0.1, 0.2, 0.3]), + ) + const index = await buildSemanticIndex(TEAMS, mockEmbed) + for (const entry of index.entries) { + expect(entry.embedding).toBeDefined() + expect(Array.isArray(entry.embedding)).toBe(true) + } + }) + + it('produces one entry per team', async () => { + const mockEmbed = vi.fn(async (texts: string[]) => + texts.map(() => [0.1, 0.2, 0.3]), + ) + const index = await buildSemanticIndex(TEAMS, mockEmbed) + expect(index.entries).toHaveLength(TEAMS.length) + }) +}) + +describe('routeSemantic', () => { + const mockEmbedFn = vi.fn(async (texts: string[]) => + // backend-pointing vector + texts.map(() => [1, 0, 0]), + ) + + beforeEach(() => { mockEmbedFn.mockClear() }) + + it('returns results for a valid in-scope query', async () => { + const mockEmbed = vi.fn(async (texts: string[]) => + texts.map(() => [0.1, 0.2, 0.3]), + ) + const index = await buildSemanticIndex(TEAMS, mockEmbed) + const response = await routeSemantic('which team handles API work', index, mockEmbedFn) + expect(response.results.length).toBeGreaterThan(0) + expect(response.query).toBe('which team handles API work') + expect(response.scopeViolation).toBeUndefined() + }) + + it('returns scopeViolation and empty results for an off-topic query', async () => { + const mockEmbed = vi.fn(async (texts: string[]) => + texts.map(() => [0.1, 0.2, 0.3]), + ) + const index = await buildSemanticIndex(TEAMS, mockEmbed) + const response = await routeSemantic('write a react component', index, mockEmbedFn) + expect(response.scopeViolation).toBe(true) + expect(response.results).toHaveLength(0) + // embedFn should not be called for off-topic queries + expect(mockEmbedFn).not.toHaveBeenCalled() + }) + + it('includes indexedAt from the index', async () => { + const mockEmbed = vi.fn(async (texts: string[]) => + texts.map(() => [0.1, 0.2, 0.3]), + ) + const index = await buildSemanticIndex(TEAMS, mockEmbed) + const response = await routeSemantic('devops pipelines', index, mockEmbedFn) + expect(response.indexedAt).toBe(index.indexedAt) + }) +}) diff --git a/packages/router/src/router.ts b/packages/router/src/router.ts index a9c0e59..648ac71 100644 --- a/packages/router/src/router.ts +++ b/packages/router/src/router.ts @@ -1,4 +1,4 @@ -import { search } from './search.js' +import { search, semanticSearch } from './search.js' import type { AskResponse, RouterIndex, RouteResult } from './types.js' /** @@ -41,3 +41,36 @@ export function route(query: string, index: RouterIndex): AskResponse { indexedAt: index.indexedAt, } } + +/** + * Routes a natural-language query using semantic (embedding-based) similarity. + * + * Off-topic queries still return `scopeViolation: true` without running the + * (potentially expensive) embedding step. + * + * @param query - Natural-language query. + * @param index - RouterIndex with `embedding` fields populated. + * @param embedFn - Function that embeds an array of texts into vectors. + */ +export async function routeSemantic( + query: string, + index: RouterIndex, + embedFn: (texts: string[]) => Promise, +): Promise { + if (isOffTopic(query)) { + return { + results: [], + query, + indexedAt: index.indexedAt, + scopeViolation: true, + } + } + + const results = await semanticSearch(query, index.entries, embedFn) + + return { + results, + query, + indexedAt: index.indexedAt, + } +} diff --git a/packages/router/src/search.ts b/packages/router/src/search.ts index 56e3b6e..2f57448 100644 --- a/packages/router/src/search.ts +++ b/packages/router/src/search.ts @@ -37,3 +37,66 @@ export function search(query: string, entries: RouterEntry[]): RouteResult[] { suggestedCommand: entry.suggestedCommand, })) } + +/** + * Computes the cosine similarity between two equal-length vectors. + * + * Returns a value in [−1, 1]. Returns 0 for zero-magnitude vectors to avoid + * division by zero. + */ +export function cosineSimilarity(a: number[], b: number[]): number { + let dot = 0 + let normA = 0 + let normB = 0 + + for (let i = 0; i < a.length; i++) { + dot += a[i] * b[i] + normA += a[i] * a[i] + normB += b[i] * b[i] + } + + const denom = Math.sqrt(normA) * Math.sqrt(normB) + return denom === 0 ? 0 : dot / denom +} + +/** + * Semantic search using pre-computed embeddings. + * + * If entries have no embeddings (Phase-1 index), falls back to keyword + * `search()` so callers never need to branch on index version. + * + * @param query - Natural-language query string. + * @param entries - RouterEntry array (may or may not have `embedding`). + * @param embedFn - Function that embeds an array of texts. + * @returns Top-K results sorted by descending cosine similarity. + */ +export async function semanticSearch( + query: string, + entries: RouterEntry[], + embedFn: (texts: string[]) => Promise, +): Promise { + const entriesWithEmbeddings = entries.filter((e) => e.embedding !== undefined) + + // Fall back to keyword search when no embeddings are available. + if (entriesWithEmbeddings.length === 0) { + return search(query, entries) + } + + const [queryEmbedding] = await embedFn([query]) + + return entriesWithEmbeddings + .map((entry) => ({ + entry, + confidence: cosineSimilarity(queryEmbedding, entry.embedding as number[]), + })) + .sort((a, b) => b.confidence - a.confidence) + .slice(0, TOP_K) + .map(({ entry, confidence }) => ({ + type: entry.type, + id: entry.id, + name: entry.name, + confidence: Math.round(confidence * 100) / 100, + explanation: entry.explanation, + suggestedCommand: entry.suggestedCommand, + })) +} diff --git a/packages/router/src/types.ts b/packages/router/src/types.ts index bfb4202..fa41f04 100644 --- a/packages/router/src/types.ts +++ b/packages/router/src/types.ts @@ -17,6 +17,8 @@ export interface RouterEntry { keywords: string[] explanation: string suggestedCommand?: string + /** Normalised embedding vector (Phase 2 — optional) */ + embedding?: number[] } export interface RouterIndex {