diff --git a/.github/workflows/migrations.yml b/.github/workflows/migrations.yml index 0235a0b8342..a4fe361e8ca 100644 --- a/.github/workflows/migrations.yml +++ b/.github/workflows/migrations.yml @@ -25,7 +25,8 @@ jobs: migrate: name: Apply Database Migrations runs-on: ${{ (vars.CI_PROVIDER == '' || vars.CI_PROVIDER == 'blacksmith') && 'blacksmith-4vcpu-ubuntu-2404' || 'ubuntu-latest' }} - timeout-minutes: 45 + # Bulk projection loads and concurrent index builds can outlast ordinary schema changes. + timeout-minutes: 180 steps: - name: Checkout code diff --git a/.github/workflows/test-build.yml b/.github/workflows/test-build.yml index ab28f35e8a5..ce5f92484d3 100644 --- a/.github/workflows/test-build.yml +++ b/.github/workflows/test-build.yml @@ -182,6 +182,12 @@ jobs: lib/memory/message-provenance.postgres.test.ts executor/handlers/agent/memory-harness.postgres.test.ts + - name: Verify Search vector projection upgrade in PostgreSQL + working-directory: packages/db + env: + KNOWLEDGE_ACL_TEST_DATABASE_URL: postgresql://postgres:postgres@127.0.0.1:5432/sim_auth_scim + run: bunx vitest run script-migrations/0016_backfill_search_vectors.postgres.test.ts + - name: Verify Search progress, pagination, and outbox scheduling in PostgreSQL working-directory: apps/sim env: diff --git a/apps/sim/app/api/v1/knowledge/search/route.ts b/apps/sim/app/api/v1/knowledge/search/route.ts index b8209d400cf..ddb66d241cc 100644 --- a/apps/sim/app/api/v1/knowledge/search/route.ts +++ b/apps/sim/app/api/v1/knowledge/search/route.ts @@ -269,6 +269,7 @@ export const POST = withRouteHandler(async (request: NextRequest) => { queryVector: { vector: JSON.stringify(queryEmbeddingResult.embedding), dimensions: queryEmbeddingTarget!.dimensions, + model: queryEmbeddingTarget!.model, }, structuredFilters: hasFilters ? structuredFilters : undefined, }) diff --git a/apps/sim/lib/knowledge/__integration__/filtered-search.integration.ts b/apps/sim/lib/knowledge/__integration__/filtered-search.integration.ts index 6a0b41448de..b1418400258 100644 --- a/apps/sim/lib/knowledge/__integration__/filtered-search.integration.ts +++ b/apps/sim/lib/knowledge/__integration__/filtered-search.integration.ts @@ -139,6 +139,7 @@ describe.each([384, 768, 1024, 1536, 3072] as const)( queryVector: { vector: JSON.stringify([1, ...Array(dimensions - 1).fill(0)]), dimensions, + model: embeddingModel, }, structuredFilters: [ { tagSlot: 'tag1', fieldType: 'text', operator: 'eq', value: 'common' }, diff --git a/apps/sim/lib/knowledge/__integration__/scale.integration.ts b/apps/sim/lib/knowledge/__integration__/scale.integration.ts index 4c1668a71b5..66474521b98 100644 --- a/apps/sim/lib/knowledge/__integration__/scale.integration.ts +++ b/apps/sim/lib/knowledge/__integration__/scale.integration.ts @@ -453,7 +453,7 @@ describe.skipIf(!enabled)('knowledge scale: isolated real PostgreSQL, no provide access: { kind: 'workspace', tokens: WORKSPACE_ACCESS_TOKENS }, searchMode: 'hybrid', query: 'Orion', - queryVector: { vector, dimensions: DIMENSIONS }, + queryVector: { vector, dimensions: DIMENSIONS, model: 'text-embedding-3-small' }, }) ) expect(workspaceResults).toEqual([]) @@ -485,7 +485,11 @@ describe.skipIf(!enabled)('knowledge scale: isolated real PostgreSQL, no provide access, searchMode: mode, query: 'Orion', - queryVector: { vector, dimensions: DIMENSIONS }, + queryVector: { + vector, + dimensions: DIMENSIONS, + model: 'text-embedding-3-small', + }, structuredFilters: filters, }) ) diff --git a/apps/sim/lib/knowledge/__integration__/search-latency.integration.ts b/apps/sim/lib/knowledge/__integration__/search-latency.integration.ts index 65cd54154a3..200952ca7cf 100644 --- a/apps/sim/lib/knowledge/__integration__/search-latency.integration.ts +++ b/apps/sim/lib/knowledge/__integration__/search-latency.integration.ts @@ -36,7 +36,6 @@ import { createKnowledgeAclFixtureIds, seedKnowledgeAclFixture, } from '@/lib/knowledge/__integration__/seed-source-access-fixture' -import { searchScopedKnowledge } from '@/lib/knowledge/application/workspace-search' import { SearchBudget, SearchDeadlineError, @@ -52,6 +51,7 @@ vi.hoisted(() => { if (process.env.KNOWLEDGE_SEARCH_PERFORMANCE_TEST === 'true') { Object.assign(process.env, { OPENAI_API_KEY: 'isolated-embedding-http-fixture', + GEMINI_API_KEY: 'isolated-gemini-http-fixture', CONFLUENCE_CLIENT_ID: 'isolated-confluence-fixture-client', CONFLUENCE_CLIENT_SECRET: 'isolated-confluence-fixture-secret', }) @@ -78,7 +78,8 @@ const fixtureSchema = z.object({ }) const reuseFile = enabled ? process.env.KNOWLEDGE_SEARCH_PERFORMANCE_REUSE_REPORT_FILE : undefined function readFixtureReport(file: string) { - if (statSync(file).size > 8 * 1024 * 1024) throw new Error('Fixture report exceeds 8 MiB') + /** Captured SQL plans include repeated high-dimensional query parameters. */ + if (statSync(file).size > 64 * 1024 * 1024) throw new Error('Fixture report exceeds 64 MiB') return z .object({ fixture: fixtureSchema, unrelatedFixture: fixtureSchema }) .parse(JSON.parse(readFileSync(file, 'utf8'))) @@ -146,14 +147,6 @@ const explainNodeSchema: z.ZodType = z.lazy(() => ) const explainSchema = z.array(z.object({ Plan: explainNodeSchema }).passthrough()).length(1) -function usesVectorIndex(node: ExplainNode): boolean { - return ( - node['Index Name'] === 'embedding_search_binary_hnsw_idx' || - node['Index Name'] === 'embedding_vector_hnsw_idx' || - (node.Plans?.some(usesVectorIndex) ?? false) - ) -} - /** The ANN stage must not fetch full vectors, even for planner-added sort projections. */ function assertCompactCandidates(node: ExplainNode) { expect(node['Relation Name']).not.toBe('embedding') @@ -162,6 +155,14 @@ function assertCompactCandidates(node: ExplainNode) { for (const child of node.Plans ?? []) assertCompactCandidates(child) } +/** Keyword sort memory must scale with identities and scores, not the matched document text. */ +function assertScalarKeywordSorts(node: ExplainNode) { + if (node['Node Type'] === 'Sort') { + expect((node.Output ?? []).join(' ')).not.toContain('content_tsv') + } + for (const child of node.Plans ?? []) assertScalarKeywordSorts(child) +} + function saveReport() { const file = process.env.KNOWLEDGE_SEARCH_PERFORMANCE_REPORT_FILE if (file) writeFileSync(file, JSON.stringify(report, null, 2), { mode: 0o600 }) @@ -172,6 +173,9 @@ const diagnosticSchema = z surface: z.enum(['dashboard', 'copilot']), outcome: z.literal('success'), elapsedMs: z.number(), + vectorBudgetMs: z.number().positive(), + retrievalStatus: z.enum(['complete', 'partial']), + timedOutLegs: z.array(z.enum(['vector', 'keyword', 'tags'])), toolResultBytes: z.number().int().nonnegative().optional(), passageBytes: z.number().int().nonnegative().optional(), maxPassageBytes: z.number().int().nonnegative().optional(), @@ -223,6 +227,45 @@ async function search( ) } +async function searchDashboard(query = 'Orion deployment') { + const authenticate = vi.spyOn(internalSessionAuth, 'authenticate').mockResolvedValue({ + kind: 'session', + userId: ids.aliceId, + sessionId: 'fixture-dashboard', + }) + try { + const response = await searchRoute( + new NextRequest('http://localhost/api/knowledge/search', { + method: 'POST', + headers: { 'content-type': 'application/json' }, + body: JSON.stringify({ + workspaceId: ids.workspaceId, + query, + topK: 15, + }), + }) + ) + expect(response.status).toBe(200) + return { + success: true as const, + data: workspaceKnowledgeSearchDataSchema.parse((await response.json()).data), + } + } finally { + authenticate.mockRestore() + } +} + +/** Allow either index or filtered plans, but require successful retrieval within the real surface budget. */ +function expectCompleteVectorSearch(diagnostics: z.infer) { + const budget = diagnostics.surface === 'dashboard' ? 3000 : 8000 + expect(diagnostics).toMatchObject({ + vectorBudgetMs: budget, + retrievalStatus: 'complete', + timedOutLegs: [], + }) + expect(diagnostics.stages.vector.totalMs).toBeLessThanOrEqual(budget) +} + async function sample(label: string, run: () => ReturnType) { captured.length = 0 diagnosticLog?.mockClear() @@ -253,35 +296,54 @@ async function sample(label: string, run: () => ReturnType) { expect(captured.length).toBeLessThan(300) const searches = captured.filter( (item) => - (item.query.includes('from "embedding"') || item.query.includes('from "embedding_search"')) && - (item.query.includes('order by') || item.query.includes('limit')) + (item.query.includes('from "embedding"') || + item.query.includes('FROM "embedding"') || + item.query.includes('from "embedding_search"') || + item.query.includes('FROM "embedding_search"') || + item.query.includes('from "embedding_keyword_search"') || + item.query.includes('FROM "embedding_keyword_search"')) && + (item.query.includes('order by') || + item.query.includes('limit') || + item.query.includes('WITH visible_search_documents') || + item.query.includes('WITH scored_search_candidates') || + item.query.includes('WITH visible_keyword_documents')) ) const plans = [] for (const query of searches) { const plan = await db.$client.begin(async (tx) => { + await tx.unsafe("SET LOCAL statement_timeout = '45s'") await tx.unsafe("SET LOCAL hnsw.iterative_scan = 'relaxed_order'") await tx.unsafe('SET LOCAL hnsw.max_scan_tuples = 20000') - if (query.query.includes('binary_quantize')) { - await tx.unsafe('SET LOCAL hnsw.max_scan_tuples = 100000') - await tx.unsafe('SET LOCAL hnsw.ef_search = 200') - await tx.unsafe('SET LOCAL hnsw.scan_mem_multiplier = 4') + if ( + query.query.includes('WITH visible_search_documents') || + (query.query.includes('from "embedding_search"') && query.query.includes('order by')) + ) { + await tx.unsafe('SET LOCAL hnsw.max_scan_tuples = 1000') + await tx.unsafe('SET LOCAL hnsw.ef_search = 1000') + await tx.unsafe('SET LOCAL hnsw.scan_mem_multiplier = 2') } return tx.unsafe( `EXPLAIN (ANALYZE, BUFFERS, VERBOSE, FORMAT JSON) ${query.query}`, query.parameters ) }) + const parsedPlan = explainSchema.parse(plan[0]['QUERY PLAN']) + if (query.query.includes('WITH visible_keyword_documents')) { + assertScalarKeywordSorts(parsedPlan[0].Plan) + } plans.push({ kind: query.query.includes('keyword_rank') ? 'keyword' - : query.query.includes('binary_quantize') + : query.query.includes('WITH visible_search_documents') || + (query.query.includes('from "embedding_search"') && query.query.includes('order by')) ? 'vector' - : query.query.includes('order by') + : query.query.includes('order by') || + query.query.includes('WITH scored_search_candidates') ? 'rerank' : 'probe', query: query.query, parameters: query.parameters, - plan: explainSchema.parse(plan[0]['QUERY PLAN']), + plan: parsedPlan, }) } report[label] = { @@ -322,6 +384,29 @@ describe.skipIf(!enabled)('Assistant search latency on a realistic indexed corpu ? new Response(null, { status: 403 }) : Response.json({ type: 'known', accountId: ids.aliceId }) } + if ( + url === + 'https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-001:batchEmbedContents' + ) { + const body = z + .object({ + requests: z + .array( + z.object({ + content: z.object({ parts: z.array(z.object({ text: z.string() })).length(1) }), + }) + ) + .length(1), + }) + .parse(JSON.parse(String(init?.body))) + embeddingCalls++ + const text = body.requests[0].content.parts[0].text + const topic = Number(/^Topic (\d+) deployment$/.exec(text)?.[1] ?? 0) + return Response.json({ + embeddings: [{ values: topicVector(topic) }], + usageMetadata: { promptTokenCount: 4 }, + }) + } if (url !== 'https://api.openai.com/v1/embeddings') throw new Error(`Unexpected outbound request in search fixture: ${new URL(url).origin}`) const body = z @@ -355,7 +440,11 @@ describe.skipIf(!enabled)('Assistant search latency on a realistic indexed corpu } await db .update(knowledgeBase) - .set({ workspaceId: ids.workspaceId, organizationId: null }) + .set({ + workspaceId: ids.workspaceId, + organizationId: null, + embeddingModel: 'gemini-embedding-001', + }) .where(eq(knowledgeBase.id, ids.knowledgeBaseId)) await db .update(knowledgeConnector) @@ -380,6 +469,11 @@ describe.skipIf(!enabled)('Assistant search latency on a realistic indexed corpu } else { await seedKnowledgeAclFixture(ids, { connectorType: 'google_drive' }) await seedKnowledgeAclFixture(unrelated, { connectorType: 'google_drive' }) + /** These arbitrary dense vectors are not trained for prefix shortening. */ + await db + .update(knowledgeBase) + .set({ embeddingModel: 'gemini-embedding-001' }) + .where(inArray(knowledgeBase.id, [ids.knowledgeBaseId, unrelated.knowledgeBaseId])) await db .update(knowledgeBase) .set({ isSearchIndex: true }) @@ -426,6 +520,7 @@ describe.skipIf(!enabled)('Assistant search latency on a realistic indexed corpu await db.execute(sql`ANALYZE document`) await db.execute(sql`ANALYZE embedding`) await db.execute(sql`ANALYZE embedding_search`) + await db.execute(sql`ANALYZE embedding_keyword_search`) report.server = ( await db.execute(sql`SELECT version(), current_setting('work_mem') AS work_mem, (SELECT extversion FROM pg_extension WHERE extname = 'vector') AS pgvector`) @@ -434,7 +529,7 @@ describe.skipIf(!enabled)('Assistant search latency on a realistic indexed corpu if (capture && captured.length < 300) captured.push({ query, parameters: [...parameters] }) } diagnosticLog = vi.spyOn(Logger.prototype, 'info') - }, 60 * 60_000) + }, 120 * 60_000) afterAll(async () => { diagnosticLog?.mockRestore() @@ -577,11 +672,6 @@ describe.skipIf(!enabled)('Assistant search latency on a realistic indexed corpu 'returns incomplete dashboard coverage when %s SQL branches exceed their deadline', async (delayedLegs) => { diagnosticLog?.mockClear() - const authenticate = vi.spyOn(internalSessionAuth, 'authenticate').mockResolvedValue({ - kind: 'session', - userId: ids.aliceId, - sessionId: 'fixture-dashboard', - }) const query = SearchBudget.prototype.query const delayed = vi.spyOn(SearchBudget.prototype, 'query').mockImplementation(function ( this: SearchBudget, @@ -595,18 +685,7 @@ describe.skipIf(!enabled)('Assistant search latency on a realistic indexed corpu }) as Promise }) try { - const response = await searchRoute( - new NextRequest('http://localhost/api/knowledge/search', { - method: 'POST', - headers: { 'content-type': 'application/json' }, - body: JSON.stringify({ - workspaceId: ids.workspaceId, - query: 'Orion deployment', - topK: 15, - }), - }) - ) - expect(response.status).toBe(200) + const { data } = await searchDashboard() const completed = diagnosticLog?.mock.calls.find( ([message]) => message === 'Knowledge search completed' ) @@ -614,7 +693,6 @@ describe.skipIf(!enabled)('Assistant search latency on a realistic indexed corpu expect(diagnostics.vectorBudgetMs).toBe(3000) expect(diagnostics.stages.vector.totalMs).toBeGreaterThan(2500) expect(diagnostics.stages.vector.totalMs).toBeLessThan(4000) - const data = workspaceKnowledgeSearchDataSchema.parse((await response.json()).data) expect(data.retrieval).toEqual({ status: 'partial', timedOutLegs: delayedLegs === 'both' ? ['vector', 'keyword'] : ['vector'], @@ -629,7 +707,6 @@ describe.skipIf(!enabled)('Assistant search latency on a realistic indexed corpu report[`dashboard.deadline.${delayedLegs}`] = { resultCount: data.results.length } } finally { delayed.mockRestore() - authenticate.mockRestore() } }, 30_000 @@ -638,7 +715,8 @@ describe.skipIf(!enabled)('Assistant search latency on a realistic indexed corpu it('records first and repeated application searches with the actual SQL plans', async () => { const before = embeddingCalls for (let iteration = 0; iteration < 2; iteration++) { - const { result, plans } = await sample(`broad.${iteration}`, () => search()) + const { result, plans, diagnostics } = await sample(`broad.${iteration}`, () => search()) + expectCompleteVectorSearch(diagnostics) expect(result.data.results).toHaveLength(15) expect(result.data.results.every((row) => row.knowledgeBaseId === ids.knowledgeBaseId)).toBe( true @@ -646,7 +724,7 @@ describe.skipIf(!enabled)('Assistant search latency on a realistic indexed corpu expect(plans.length).toBeGreaterThanOrEqual(2) const vectorPlans = plans.filter((plan) => plan.kind === 'vector') expect(vectorPlans).toHaveLength(1) - expect(usesVectorIndex(vectorPlans[0].plan[0].Plan)).toBe(true) + expect(vectorPlans[0].plan[0].Plan['Actual Rows']).toBeGreaterThan(0) assertCompactCandidates(vectorPlans[0].plan[0].Plan) expect(plans.some((plan) => plan.kind === 'rerank')).toBe(true) const rerank = plans.find((plan) => plan.kind === 'rerank')! @@ -666,9 +744,10 @@ describe.skipIf(!enabled)('Assistant search latency on a realistic indexed corpu it('preserves exact-neighbor recall across different query vectors', async () => { for (const topic of [3, 11, 23]) { - const { plans } = await sample(`topic.${topic}`, () => + const { plans, diagnostics } = await sample(`topic.${topic}`, () => search(ids.aliceId, `Topic ${topic} deployment`) ) + expectCompleteVectorSearch(diagnostics) const candidates = plans.find((plan) => plan.kind === 'vector')! assertCompactCandidates(candidates.plan[0].Plan) const rerank = plans.find((plan) => plan.kind === 'rerank')! @@ -686,19 +765,10 @@ describe.skipIf(!enabled)('Assistant search latency on a realistic indexed corpu }, 180_000) it('compares the Search tab and Assistant with the same person, query and index', async () => { - const dashboard = await sample('dashboard', async () => { - const result = await searchScopedKnowledge.execute({ - principal: { kind: 'session', userId: ids.aliceId, sessionId: 'fixture-dashboard' }, - input: { - workspaceId: ids.workspaceId, - query: 'Orion deployment', - topK: 15, - surface: 'dashboard', - }, - }) - return resultSchema.parse({ success: true, data: result }) - }) + const dashboard = await sample('dashboard', searchDashboard) const assistant = await sample('assistant.comparison', () => search()) + expectCompleteVectorSearch(dashboard.diagnostics) + expectCompleteVectorSearch(assistant.diagnostics) expect(dashboard.diagnostics.surface).toBe('dashboard') expect(assistant.diagnostics.surface).toBe('copilot') expect(dashboard.diagnostics.stages.result_provenance).toBeUndefined() @@ -711,7 +781,7 @@ describe.skipIf(!enabled)('Assistant search latency on a realistic indexed corpu expect(assistantVector).toHaveLength(1) expect(dashboardVector[0].query).toBe(assistantVector[0].query) expect(dashboardVector[0].parameters).toEqual(assistantVector[0].parameters) - expect(usesVectorIndex(dashboardVector[0].plan[0].Plan)).toBe(true) + expect(dashboardVector[0].plan[0].Plan['Actual Rows']).toBeGreaterThan(0) }, 180_000) it('keeps inaccessible content out of an otherwise identical search', async () => { @@ -719,6 +789,45 @@ describe.skipIf(!enabled)('Assistant search latency on a realistic indexed corpu expect(result.data.results).toEqual([]) }, 180_000) + it('preserves recall when the nearest topic is mostly inaccessible within a broad permission scope', async () => { + const reader = `u:${ids.aliceId}@fixture.test` + /** Four consecutive topics share each document; hide 99% of the query's topic cluster. */ + await db.execute(sql`UPDATE document + SET acl = ARRAY[${`u:${ids.bobId}@fixture.test`}] + WHERE knowledge_base_id = ${ids.knowledgeBaseId} + AND external_id::int % 8 = 0 AND external_id::int % 800 <> 0`) + try { + for (const surface of ['copilot', 'dashboard'] as const) { + const { result, plans, diagnostics } = await sample( + `filtered-neighborhood.${surface}`, + surface === 'copilot' ? () => search() : searchDashboard + ) + expectCompleteVectorSearch(diagnostics) + expect(result.data.results).toHaveLength(15) + const rerank = plans.find((plan) => plan.kind === 'rerank')! + expect(rerank).toBeDefined() + const actual = await db.$client.unsafe(rerank.query, rerank.parameters).values() + const expected = await db.execute<{ id: string }>(sql`SELECT e.id FROM embedding e + INNER JOIN document d ON d.id = e.document_id + WHERE e.knowledge_base_id = ${ids.knowledgeBaseId} AND e.enabled + AND d.acl @> ARRAY[${reader}]::text[] + ORDER BY (e.embedding <=> ${JSON.stringify(queryVector)}::vector) + 0, e.id + LIMIT ${actual.length}`) + expect(expected.length).toBeGreaterThan(0) + const expectedIds = new Set(expected.map(({ id }) => id)) + const recall = actual.filter(([id]) => expectedIds.has(id)).length / expected.length + expect(recall).toBeGreaterThanOrEqual(0.95) + report[`recall.filtered-neighborhood.${surface}`] = { neighbors: expected.length, recall } + saveReport() + } + } finally { + await db + .update(document) + .set({ acl: [reader] }) + .where(eq(document.knowledgeBaseId, ids.knowledgeBaseId)) + } + }, 180_000) + it('ranks a small permission scope by its bounded IDs without a corpus-wide vector probe', async () => { const documentIds = [0, 8, 16].map((index) => `${ids.workspaceId}-doc-${index}`) await db @@ -763,15 +872,36 @@ describe.skipIf(!enabled)('Assistant search latency on a realistic indexed corpu } }, 180_000) + it.each(['copilot', 'dashboard'] as const)( + 'keeps %s searches for common keyword terms complete', + async (surface) => { + const { result, diagnostics } = await sample(`common-keyword.${surface}`, () => + surface === 'dashboard' + ? searchDashboard('Engineering operations') + : search(ids.aliceId, 'Engineering operations') + ) + expectCompleteVectorSearch(diagnostics) + expect(result.data.results).toHaveLength(15) + }, + 180_000 + ) + it('runs two independent Assistant searches concurrently', async () => { + diagnosticLog?.mockClear() const start = performance.now() const results = await Promise.all([search(), search(ids.aliceId, 'Engineering operations')]) + const completed = diagnosticLog!.mock.calls + .filter(([message]) => message === 'Knowledge search completed') + .map(([, metadata]) => diagnosticSchema.parse(metadata)) report.concurrent = { milliseconds: performance.now() - start, resultCounts: results.map((result) => result.data.results.length), + diagnostics: completed, } saveReport() for (const result of results) expect(result.data.results).toHaveLength(15) + expect(completed).toHaveLength(2) + for (const diagnostics of completed) expectCompleteVectorSearch(diagnostics) }, 180_000) it('checks live reader access on every search, including after revocation', async () => { diff --git a/apps/sim/lib/knowledge/access/predicate.postgres.test.ts b/apps/sim/lib/knowledge/access/predicate.postgres.test.ts index 0cab491d968..fbd361b4aa8 100644 --- a/apps/sim/lib/knowledge/access/predicate.postgres.test.ts +++ b/apps/sim/lib/knowledge/access/predicate.postgres.test.ts @@ -55,6 +55,7 @@ describe.runIf(Boolean(databaseUrl))('knowledge ACLs in PostgreSQL', () => { await client.unsafe(approvalMigration.replaceAll('"public".', `"${schemaName}".`)) await client.unsafe(` ALTER TABLE knowledge_connector ADD COLUMN credential_id text, + ADD COLUMN access_rewrite_pending boolean NOT NULL DEFAULT false, ADD COLUMN source_config json NOT NULL DEFAULT '{}', ADD COLUMN credential_group_id text, ADD COLUMN credential_group_option_id text; ALTER TABLE credential ADD COLUMN revoked_at timestamp, ADD COLUMN credential_group_option_id text; diff --git a/apps/sim/lib/knowledge/access/predicate.ts b/apps/sim/lib/knowledge/access/predicate.ts index c2bdf6d6c41..21a653271fb 100644 --- a/apps/sim/lib/knowledge/access/predicate.ts +++ b/apps/sim/lib/knowledge/access/predicate.ts @@ -224,13 +224,11 @@ function storedKnowledgeAccessCondition( (${knowledgeConnector.accessMode} = 'workspace' AND ${document.acl} = ARRAY['ws']::text[]) OR (${document.acl} <> ARRAY['ws']::text[] AND ( (${knowledgeConnector.accessMode} = 'admin' AND ${document.aclVerifiedAt} > ${cutoff}) - OR (${knowledgeConnector.accessMode} = 'members' AND EXISTS ( - SELECT 1 FROM ${knowledgeDocumentObservation} + OR (${knowledgeConnector.accessMode} = 'members' AND (${document.id}, ${document.connectorId}) IN ( + SELECT ${knowledgeDocumentObservation.documentId}, ${knowledgeConnectorMember.connectorId} FROM ${knowledgeDocumentObservation} JOIN ${knowledgeConnectorMember} ON ${knowledgeConnectorMember.id} = ${knowledgeDocumentObservation.memberId} - WHERE ${knowledgeDocumentObservation.documentId} = ${document.id} - AND ${knowledgeConnectorMember.connectorId} = ${document.connectorId} - AND ${knowledgeConnectorMember.status} = 'active' + WHERE ${knowledgeConnectorMember.status} = 'active' AND ${knowledgeConnectorMember.subjectToken} = ANY(${tokens}) AND GREATEST(${knowledgeDocumentObservation.lastSeenAt}, ${knowledgeConnectorMember.memberSyncedThrough}) > ${cutoff} )) diff --git a/apps/sim/lib/knowledge/application/search.ts b/apps/sim/lib/knowledge/application/search.ts index 1f8871529cb..1b7c2adcf40 100644 --- a/apps/sim/lib/knowledge/application/search.ts +++ b/apps/sim/lib/knowledge/application/search.ts @@ -420,6 +420,7 @@ const searchKnowledgeUseCase = defineAuthorizedKnowledgeUseCase({ ? { vector: JSON.stringify(queryEmbedding?.embedding ?? null), dimensions: embeddingTarget!.dimensions, + model: embeddingTarget!.model, } : undefined, structuredFilters: structuredFilters.length > 0 ? structuredFilters : undefined, diff --git a/apps/sim/lib/knowledge/search/diagnostics.ts b/apps/sim/lib/knowledge/search/diagnostics.ts index 8beba6c5025..fe30814a125 100644 --- a/apps/sim/lib/knowledge/search/diagnostics.ts +++ b/apps/sim/lib/knowledge/search/diagnostics.ts @@ -49,6 +49,7 @@ export type SearchStage = | 'vector.ann' | 'vector.rerank' | 'vector.exact' + | 'vector.candidate_search' /** Fixed, content-free fields. Never pass queries, filters, document identities, SQL, or errors. */ export interface SearchDiagnosticMetadata { @@ -69,11 +70,14 @@ export interface SearchDiagnosticMetadata { searchMode?: 'hybrid' | 'vector' boostRecency?: boolean embeddingDimensions?: number - vectorRanking?: 'exact' | 'binary-rerank' - vectorCandidateStorage?: 'stored-binary' + vectorRanking?: 'exact' | 'candidate-rerank' + vectorCandidateStorage?: 'stored-halfvec' + vectorCandidateScan?: 'planned' | 'filtered' vectorBudgetMs?: number vectorCandidateLimit?: number vectorCandidateCount?: number + vectorCandidateDimensions?: number + vectorInitialCandidateCount?: number resultCount?: number /** Tool output before the executor's final egress projection; counts only, never content. */ toolResultBytes?: number diff --git a/apps/sim/lib/knowledge/search/queries.test.ts b/apps/sim/lib/knowledge/search/queries.test.ts index 2a3d95bacc1..0dda4659143 100644 --- a/apps/sim/lib/knowledge/search/queries.test.ts +++ b/apps/sim/lib/knowledge/search/queries.test.ts @@ -67,6 +67,7 @@ describe('retrieval leg budgets', () => { get: async () => access, getForConnectors: async () => access, getForDocuments: async () => access, + liveSourceConnectorCondition: async () => null, }, searchMode: 'hybrid' as const, vectorBudgetMs, @@ -74,7 +75,7 @@ describe('retrieval leg budgets', () => { await retrieveKnowledgeSearch({ ...params, query: 'release', - queryVector: { vector: '[1,0]', dimensions: 1536 }, + queryVector: { vector: '[1,0]', dimensions: 1536, model: 'text-embedding-3-small' }, }) await retrieveKnowledgeSearch({ ...params, @@ -314,7 +315,7 @@ describe('vector scan settings', () => { knowledgeBaseIds: ['kb-small'], topK: 2, access: { kind: 'workspace', tokens: WORKSPACE_ACCESS_TOKENS }, - queryVector: { vector: '[0.1,0.2]', dimensions: 1536 }, + queryVector: { vector: '[0.1,0.2]', dimensions: 1536, model: 'text-embedding-3-small' }, distanceThreshold: 0.8, } @@ -466,7 +467,7 @@ describe('workspace search filters before ranking', () => { knowledgeBaseIds: ['index'], topK: 2, access: { kind: 'workspace', tokens: WORKSPACE_ACCESS_TOKENS }, - queryVector: { vector: '[0.1,0.2]', dimensions: 1536 }, + queryVector: { vector: '[0.1,0.2]', dimensions: 1536, model: 'text-embedding-3-small' }, distanceThreshold: 0.8, filters: { documentIds: ['selected-doc'], @@ -560,19 +561,42 @@ describe('live repository authorization follows ranked candidates', () => { get: async () => identity, getForConnectors, getForDocuments: async () => allowed, + liveSourceConnectorCondition: async () => null, } const params: SearchParams = { knowledgeBaseIds: ['org-index'], topK: 1, access: identity, accessProvider: provider, - queryVector: { vector: '[0.1,0.2]', dimensions: 1536 }, + queryVector: { vector: '[0.1,0.2]', dimensions: 1536, model: 'text-embedding-3-small' }, distanceThreshold: 0.8, structuredFilters: [{ tagSlot: 'tag1', fieldType: 'text', operator: 'eq', value: 'release' }], } + const candidatePages: Array> = [] + const rerankPages: Array>> = [] + const keywordPages: Array>> = [] + function queueRerank(rows: Array>) { + rerankPages.push(rows) + } + function queueCandidates(rows: Array<{ id: string }>, initialCount = rows.length) { + candidatePages.push(rows.map(({ id }) => ({ id, initial_count: initialCount }))) + } + beforeEach(() => { resetDbChainMock() + candidatePages.length = 0 + rerankPages.length = 0 + keywordPages.length = 0 + dbChainMockFns.execute.mockImplementation(async (query) => + render(query).sql.includes('WITH visible_search_documents') + ? (candidatePages.shift() ?? []) + : render(query).sql.includes('WITH scored_search_candidates') + ? (rerankPages.shift() ?? []) + : render(query).sql.includes('WITH visible_keyword_documents') + ? (keywordPages.shift() ?? []) + : [] + ) getForConnectors.mockReset().mockResolvedValue(allowed) }) @@ -583,8 +607,8 @@ describe('live repository authorization follows ranked candidates', () => { schemaMock.embeddingSearch, Array.from({ length: 200 }, (_, index) => candidate(`probe-${index}`, 'allowed-source')) ) - queueTableRows(schemaMock.embeddingSearch, [{ id: 'far' }, { id: 'near' }]) - queueTableRows(schemaMock.embedding, [ + queueCandidates(Array.from({ length: 400 }, (_, index) => ({ id: `candidate-${index}` }))) + queueRerank([ { ...candidate('far', 'allowed-source'), distance: 0.3 }, { ...candidate('near', 'allowed-source'), distance: 0.1 }, ...Array.from({ length: 18 }, (_, index) => candidate(`other-${index}`, 'allowed-source')), @@ -599,21 +623,20 @@ describe('live repository authorization follows ranked candidates', () => { topK: 1, }) expect(rows.map((row) => row.id)).toEqual(['near']) - expect(dbChainMockFns.orderBy.mock.calls[0]).toHaveLength(1) - expect(Object.keys(dbChainMockFns.select.mock.calls[1][0])).toEqual(['id']) - expect(render(dbChainMockFns.orderBy.mock.calls[0][0]).sql).toContain('binary_quantize') - expect(JSON.stringify(dbChainMockFns.orderBy.mock.calls[1][0])).toContain('<=>') - expect(dbChainMockFns.limit.mock.calls[1]).toEqual([4000]) - expect(JSON.stringify(dbChainMockFns.where.mock.calls[1][0])).not.toContain('<=>') - expect(dbChainMockFns.limit.mock.invocationCallOrder[1]).toBeLessThan( - dbChainMockFns.select.mock.invocationCallOrder[2] - ) - const visibilityJoin = render(dbChainMockFns.innerJoin.mock.calls[1][0]) - expect(visibilityJoin.sql).toContain('LATERAL') - expect(visibilityJoin.sql).toContain('OFFSET 0') - expect(JSON.stringify(dbChainMockFns.innerJoin.mock.calls[1][0])).toContain('required_clause') + const candidateQuery = dbChainMockFns.execute.mock.calls.find(([query]) => + render(query).sql.includes('WITH visible_search_documents') + )![0] + expect(render(candidateQuery).sql).toContain('MATERIALIZED') + expect(JSON.stringify(candidateQuery)).toContain('required_clause') + expect(JSON.stringify(candidateQuery)).toContain('subvector') + const rankQuery = dbChainMockFns.execute.mock.calls.find(([query]) => + render(query).sql.includes('WITH scored_search_candidates') + )![0] + expect(render(rankQuery).sql).toContain('MATERIALIZED') expect(getForConnectors).toHaveBeenCalledExactlyOnceWith(['allowed-source'], undefined) - expect(JSON.stringify(dbChainMockFns.where.mock.calls[3][0])).toContain('github_read_grant') + expect(JSON.stringify(dbChainMockFns.where.mock.calls.at(-1)![0])).toContain( + 'github_read_grant' + ) }) it('finishes empty scopes after the bounded probe without scanning HNSW or calling providers', async () => { @@ -650,71 +673,68 @@ describe('live repository authorization follows ranked candidates', () => { expect(getForConnectors).toHaveBeenCalledExactlyOnceWith(['allowed-source'], undefined) }) - it('falls back to exact ranking when the approximate page cannot fill its limit', async () => { + it('scans the filtered projection when ANN cannot fill its limit', async () => { queueTableRows( schemaMock.embeddingSearch, - Array.from({ length: 200 }, (_, index) => candidate(`probe-${index}`, 'allowed-source')) + Array.from({ length: 200 }, (_, index) => ({ id: `probe-${index}` })) ) - queueTableRows(schemaMock.embeddingSearch, [{ id: 'partial' }]) - queueTableRows(schemaMock.embedding, [candidate('partial', 'allowed-source')]) - queueTableRows(schemaMock.embedding, [candidate('selected', 'allowed-source')]) + queueCandidates([{ id: 'selected' }], 1) + queueRerank([candidate('selected', 'allowed-source')]) queueTableRows(schemaMock.embedding, [ { id: 'selected', content: 'Verified fallback', distance: 0.1 }, ]) expect(await handleVectorOnlySearch({ ...params, structuredFilters: undefined })).toEqual([ { id: 'selected', content: 'Verified fallback', distance: 0.1 }, ]) - expect(render(dbChainMockFns.orderBy.mock.calls.at(-1)![0]).sql).toContain('+ 0') + const candidateQuery = dbChainMockFns.execute.mock.calls.find(([query]) => + render(query).sql.includes('WITH visible_search_documents') + )![0] + expect(render(candidateQuery).sql).toContain('UNION ALL') + expect(render(candidateQuery).sql).toContain('+ 0') expect(JSON.stringify(dbChainMockFns.where.mock.calls.at(-1)![0])).toContain( 'github_read_grant' ) }) - it('restarts exact ranking at zero and advances past already considered ANN candidates', async () => { - const probe = Array.from({ length: 200 }, (_, index) => - candidate(`probe-${index}`, 'allowed-source') - ) - const approximate = Array.from({ length: 20 }, (_, index) => - candidate(`approximate-${index}`, 'allowed-source') - ) + it('advances past candidate pages that hydrate no current readable content', async () => { + const probe = Array.from({ length: 200 }, (_, index) => ({ id: `probe-${index}` })) + const identities = Array.from({ length: 400 }, (_, index) => ({ id: `candidate-${index}` })) queueTableRows(schemaMock.embeddingSearch, probe) - queueTableRows( - schemaMock.embeddingSearch, - approximate.map(({ id }) => ({ id })) + queueCandidates(identities) + queueRerank( + Array.from({ length: 20 }, (_, index) => candidate(`candidate-${index}`, 'allowed-source')) ) - queueTableRows(schemaMock.embedding, approximate) queueTableRows(schemaMock.embedding, []) queueTableRows(schemaMock.embeddingSearch, probe) - queueTableRows(schemaMock.embeddingSearch, []) - queueTableRows(schemaMock.embedding, approximate) - queueTableRows(schemaMock.embeddingSearch, probe) - queueTableRows(schemaMock.embedding, [candidate('selected', 'allowed-source')]) + queueCandidates(identities) + queueRerank([candidate('selected', 'allowed-source')]) queueTableRows(schemaMock.embedding, [ - { id: 'selected', content: 'Reachable after the exact restart', distance: 0.1 }, + { id: 'selected', content: 'Current readable content', distance: 0.1 }, ]) - const rows = await handleVectorOnlySearch({ ...params, structuredFilters: undefined }) - expect(rows.map((row) => row.id)).toEqual(['selected']) - expect(dbChainMockFns.offset.mock.calls).toEqual([[0], [0], [20]]) + expect( + dbChainMockFns.execute.mock.calls.filter(([query]) => + render(query).sql.includes('WITH scored_search_candidates') + ) + ).toHaveLength(2) expect(getForConnectors).toHaveBeenCalledTimes(2) - expect(dbChainMockFns.transaction).toHaveBeenCalledTimes(2) }) - it('keeps the nearest exact results when an earlier ANN page hydrated only a farther result', async () => { + it('sorts hydrated candidates across pages by their original-vector distance', async () => { const probe = Array.from({ length: 200 }, (_, index) => candidate(`probe-${index}`, 'allowed-source') ) queueTableRows(schemaMock.embeddingSearch, probe) - queueTableRows(schemaMock.embeddingSearch, [{ id: 'far' }]) - queueTableRows(schemaMock.embedding, [ + queueCandidates(Array.from({ length: 400 }, (_, index) => ({ id: `candidate-${index}` }))) + queueRerank([ { ...candidate('far', 'allowed-source'), distance: 0.7 }, ...Array.from({ length: 19 }, (_, index) => candidate(`hidden-${index}`, 'allowed-source')), ]) queueTableRows(schemaMock.embedding, [{ id: 'far', content: 'Far result', distance: 0.7 }]) queueTableRows(schemaMock.embeddingSearch, probe) - queueTableRows(schemaMock.embeddingSearch, []) - queueTableRows(schemaMock.embedding, [ + queueCandidates(Array.from({ length: 400 }, (_, index) => ({ id: `candidate-${index}` }))) + queueRerank([ candidate('near', 'allowed-source'), candidate('nearer', 'allowed-source'), candidate('far', 'allowed-source'), @@ -731,7 +751,11 @@ describe('live repository authorization follows ranked candidates', () => { }) expect(rows.map((row) => row.id)).toEqual(['nearer', 'near']) - expect(dbChainMockFns.offset.mock.calls).toEqual([[0], [0]]) + expect( + dbChainMockFns.execute.mock.calls.filter(([query]) => + render(query).sql.includes('WITH scored_search_candidates') + ) + ).toHaveLength(2) expect( hasMockCondition( dbChainMockFns.where.mock.calls.at(-1)![0], @@ -748,7 +772,9 @@ describe('live repository authorization follows ranked candidates', () => { it.each(['vector', 'tag-vector', 'tags', 'keyword'] as const)( '%s ranks identifiers before verification and loads content under the full predicate', async (mode) => { - queueTableRows(schemaMock.embedding, [candidate('selected', 'allowed-source')]) + const candidates = [candidate('selected', 'allowed-source')] + if (mode === 'keyword') keywordPages.push(candidates) + else queueTableRows(schemaMock.embedding, candidates) queueTableRows(schemaMock.embedding, [{ id: 'selected', content: 'verified result' }]) const rows = mode === 'vector' @@ -764,22 +790,32 @@ describe('live repository authorization follows ranked candidates', () => { }) expect(rows).toEqual([{ id: 'selected', content: 'verified result' }]) expect(getForConnectors).toHaveBeenCalledWith(['allowed-source'], undefined) - expect(Object.keys(dbChainMockFns.select.mock.calls[0][0]).sort()).toEqual( - [ - 'id', - 'documentId', - 'connectorId', - 'liveAuthorizationSource', - ...(mode === 'keyword' ? ['keywordRank'] : mode === 'tags' ? [] : ['distance']), - ].sort() - ) - expect(dbChainMockFns.select.mock.invocationCallOrder[0]).toBeLessThan( - getForConnectors.mock.invocationCallOrder[0] - ) + if (mode === 'keyword') { + const ranking = render(dbChainMockFns.execute.mock.calls[0][0]).sql + expect(ranking).toContain('scored_keyword_candidates AS MATERIALIZED') + expect(ranking).toContain('ORDER BY keyword_rank DESC, id LIMIT') + expect(ranking).not.toContain('<=>') + expect(ranking).not.toContain('"content"') + } else { + expect(Object.keys(dbChainMockFns.select.mock.calls[0][0]).sort()).toEqual( + [ + 'id', + 'documentId', + 'connectorId', + 'liveAuthorizationSource', + ...(mode === 'tags' ? [] : ['distance']), + ].sort() + ) + } + const rankingOrder = + mode === 'keyword' + ? dbChainMockFns.execute.mock.invocationCallOrder[0] + : dbChainMockFns.select.mock.invocationCallOrder[0] + expect(rankingOrder).toBeLessThan(getForConnectors.mock.invocationCallOrder[0]) expect(getForConnectors.mock.invocationCallOrder[0]).toBeLessThan( - dbChainMockFns.select.mock.invocationCallOrder[1] + dbChainMockFns.select.mock.invocationCallOrder.at(-1)! ) - const fullPredicate = dbChainMockFns.where.mock.calls[1][0] + const fullPredicate = dbChainMockFns.where.mock.calls.at(-1)![0] const serializedPredicate = JSON.stringify(fullPredicate) expect(serializedPredicate).toContain('github_read_grant') expect(serializedPredicate).toContain('allowed-source') @@ -802,9 +838,9 @@ describe('live repository authorization follows ranked candidates', () => { '%s skips discovery for an explicit non-GitHub source and retains full hydration', async (mode) => { getForConnectors.mockResolvedValue(identity) - queueTableRows(schemaMock.embedding, [ - { ...candidate('gmail', 'gmail-source'), installationSource: false }, - ]) + const candidates = [{ ...candidate('gmail', 'gmail-source'), installationSource: false }] + if (mode === 'keyword') keywordPages.push(candidates) + else queueTableRows(schemaMock.embedding, candidates) const hydrated = [{ id: 'gmail', content: 'current permitted content' }] queueTableRows(schemaMock.embedding, hydrated) const searchParams = { ...params, filters: { source: 'gmail' } } @@ -826,10 +862,13 @@ describe('live repository authorization follows ranked candidates', () => { for (const [condition] of dbChainMockFns.where.mock.calls) { expect(JSON.stringify(condition)).toContain('gmail') } - const hydration = JSON.stringify(dbChainMockFns.where.mock.calls[1][0]) + if (mode === 'keyword') { + expect(JSON.stringify(dbChainMockFns.execute.mock.calls[0][0])).toContain('gmail') + } + const hydration = JSON.stringify(dbChainMockFns.where.mock.calls.at(-1)![0]) expect(hydration).toContain('acl') expect(hydration).toContain('knowledgeConnectorMember') - expect(dbChainMockFns.select).toHaveBeenCalledTimes(2) + expect(dbChainMockFns.select).toHaveBeenCalledTimes(mode === 'keyword' ? 1 : 2) } ) @@ -887,6 +926,29 @@ describe('live repository authorization follows ranked candidates', () => { expect(refillPredicate).toContain('revoked-source') }) + it('recomputes keyword candidates after excluding a revoked source and rechecks content access', async () => { + getForConnectors.mockResolvedValueOnce(identity) + keywordPages.push( + [candidate('denied', 'revoked-source')], + [candidate('selected', 'allowed-source')] + ) + queueTableRows(schemaMock.embedding, []) + queueTableRows(schemaMock.embedding, [{ id: 'selected', content: 'verified result' }]) + expect( + await executeKeywordSearch({ ...params, query: 'release', queryVector: params.queryVector! }) + ).toEqual([{ id: 'selected', content: 'verified result' }]) + expect(getForConnectors.mock.calls.map(([ids]) => ids)).toEqual([ + ['revoked-source'], + ['allowed-source'], + ]) + const refill = JSON.stringify(dbChainMockFns.execute.mock.calls[1][0]) + expect(refill).toContain('revoked-source') + expect(refill).toContain('NOT') + expect(JSON.stringify(dbChainMockFns.where.mock.calls.at(-1)![0])).toContain( + 'github_read_grant' + ) + }) + it.each([undefined, 'confluence'])( 'refills a denied Confluence site under its exact reader proof with source filter %s', async (source) => { diff --git a/apps/sim/lib/knowledge/search/queries.ts b/apps/sim/lib/knowledge/search/queries.ts index 39b2f6fcf38..bf541a2fd08 100644 --- a/apps/sim/lib/knowledge/search/queries.ts +++ b/apps/sim/lib/knowledge/search/queries.ts @@ -1,5 +1,11 @@ import { db } from '@sim/db' -import { document, embedding, embeddingSearch, knowledgeConnector } from '@sim/db/schema' +import { + document, + embedding, + embeddingKeywordSearch, + embeddingSearch, + knowledgeConnector, +} from '@sim/db/schema' import { createLogger } from '@sim/logger' import { getErrorMessage, getPostgresErrorCode } from '@sim/utils/errors' import { and, eq, inArray, isNull, type SQL, sql } from 'drizzle-orm' @@ -31,7 +37,11 @@ import { uncompilableTagFilterError, } from '@/lib/knowledge/tags/utils' import type { StructuredFilter } from '@/lib/knowledge/types' -import { embeddingCandidateDistance, embeddingDistance } from '@/lib/knowledge/vector-columns' +import { + embeddingCandidateDimensions, + embeddingCandidateDistance, + embeddingDistance, +} from '@/lib/knowledge/vector-columns' const logger = createLogger('KnowledgeSearchQueries') @@ -39,14 +49,13 @@ const logger = createLogger('KnowledgeSearchQueries') const UNDEFINED_OBJECT_SQLSTATE = '42704' /** Tuples a relaxed-order scan may visit before giving up on filling the limit. */ const HNSW_MAX_SCAN_TUPLES = '20000' -/** Compact graphs can visit a wider frontier without retaining full vectors for each neighbor. */ -const BINARY_HNSW_MAX_SCAN_TUPLES = '100000' -const BINARY_HNSW_EF_SEARCH = '200' -const BINARY_HNSW_SCAN_MEM_MULTIPLIER = '4' -/** Bounded cosine reranking pool, sized for recall under permission filtering and sign quantization. */ -const MIN_VECTOR_RERANK_CANDIDATES = 4000 -const MAX_VECTOR_RERANK_CANDIDATES = 8000 -const VECTOR_RERANK_OVERSAMPLING = 40 +/** Stop a permission-starved graph walk early enough to scan the filtered projection instead. */ +const CANDIDATE_HNSW_MAX_SCAN_TUPLES = '1000' +const CANDIDATE_HNSW_EF_SEARCH = '1000' +const CANDIDATE_HNSW_SCAN_MEM_MULTIPLIER = '2' +const MIN_VECTOR_RERANK_CANDIDATES = 400 +const MAX_VECTOR_RERANK_CANDIDATES = 1600 +const VECTOR_RERANK_OVERSAMPLING = 8 /** How long to stop trying the iterative-scan settings after the server rejected them. */ const HNSW_SETTINGS_UNSUPPORTED_RETRY_MS = 10 * 60 * 1000 @@ -62,9 +71,10 @@ let hnswSettingsUnsupportedUntil = 0 async function withVectorScanSettings( run: (executor: SearchExecutor) => Promise, budget?: SearchBudget, - ranking: 'cosine' | 'binary' = 'cosine' + ranking: 'cosine' | 'candidate' = 'cosine' ): Promise { - const untuned = () => runSearchQuery(budget, 'vector.ann', run) + const stage = ranking === 'candidate' ? 'vector.candidate_search' : 'vector.ann' + const untuned = () => runSearchQuery(budget, stage, run) if (Date.now() < hnswSettingsUnsupportedUntil) return untuned() const acquireStarted = performance.now() let applyingSettings = false @@ -75,8 +85,8 @@ async function withVectorScanSettings( applyingSettings = true await measureSearchStage('vector.settings', () => tx.execute( - ranking === 'binary' - ? sql`SELECT set_config('hnsw.iterative_scan', 'relaxed_order', true), set_config('hnsw.max_scan_tuples', ${BINARY_HNSW_MAX_SCAN_TUPLES}, true), set_config('hnsw.ef_search', ${BINARY_HNSW_EF_SEARCH}, true), set_config('hnsw.scan_mem_multiplier', ${BINARY_HNSW_SCAN_MEM_MULTIPLIER}, true)` + ranking === 'candidate' + ? sql`SELECT set_config('hnsw.iterative_scan', 'relaxed_order', true), set_config('hnsw.max_scan_tuples', ${CANDIDATE_HNSW_MAX_SCAN_TUPLES}, true), set_config('hnsw.ef_search', ${CANDIDATE_HNSW_EF_SEARCH}, true), set_config('hnsw.scan_mem_multiplier', ${CANDIDATE_HNSW_SCAN_MEM_MULTIPLIER}, true)` : sql`SELECT set_config('hnsw.iterative_scan', 'relaxed_order', true), set_config('hnsw.max_scan_tuples', ${HNSW_MAX_SCAN_TUPLES}, true)` ) ) @@ -87,7 +97,7 @@ async function withVectorScanSettings( ) return run(tx) } - return await (budget ? budget.query('vector.ann', tuned) : db.transaction(tuned)) + return await (budget ? budget.query(stage, tuned) : db.transaction(tuned)) } catch (error) { if (!applyingSettings || getPostgresErrorCode(error) !== UNDEFINED_OBJECT_SQLSTATE) throw error hnswSettingsUnsupportedUntil = Date.now() + HNSW_SETTINGS_UNSUPPORTED_RETRY_MS @@ -206,6 +216,7 @@ export interface KnowledgeQueryVector { /** JSON array literal of the embedding, in pgvector's text input format. */ vector: string dimensions: KbEmbeddingDimensions + model: string } export interface SearchParams { @@ -431,10 +442,8 @@ export function getStructuredTagFilters(filters: StructuredFilter[], embeddingTa } /** - * Text-search configuration used to build the query. Must match the config the - * generated `embedding.content_tsv` column was built with - * (`to_tsvector('english', content)`) — a mismatch silently stops Postgres from - * using the `emb_content_fts_idx` GIN index and degrades to a sequential scan. + * Match the normalization used by the stored text vectors so query terms and + * document terms resolve to the same lexemes in both keyword retrieval paths. */ const FTS_CONFIG = 'english' @@ -453,6 +462,16 @@ function getVisibilityConditions( ) { return [ eq(enabledColumn, true), + ...getDocumentVisibilityConditions(access, filters, accessCondition), + ] +} + +function getDocumentVisibilityConditions( + access: KnowledgeAccessScope, + filters?: WorkspaceSearchFilters, + accessCondition: SQL = knowledgeAccessCondition(access) +) { + return [ eq(document.enabled, true), eq(document.processingStatus, 'completed'), eq(document.userExcluded, false), @@ -463,13 +482,12 @@ function getVisibilityConditions( ] } -/** Each ranking strategy owns its cursor; ANN offsets cannot paginate exact ordering. */ interface SearchReadCandidatePage { candidates: SearchReadCandidate[] nextOffset: number } -interface SearchReadCandidate { +type SearchReadCandidate = { id: string documentId: string connectorId: string | null @@ -794,14 +812,10 @@ export async function handleVectorOnlySearch(params: SearchParams): Promise { const conditions = [inArray(embedding.knowledgeBaseId, params.knowledgeBaseIds), ...filters] const queryVector = params.queryVector! - const candidateDistance = embeddingCandidateDistance(queryVector.dimensions, queryVector.vector) + const candidateDistance = embeddingCandidateDistance( + queryVector.dimensions, + queryVector.vector, + queryVector.model + ) const candidateLimit = Math.min( MAX_VECTOR_RERANK_CANDIDATES, Math.max(MIN_VECTOR_RERANK_CANDIDATES, params.topK * VECTOR_RERANK_OVERSAMPLING) ) - let useExactRanking = false const rows = await selectAuthorizedSearchResults({ leg: 'vector', accessProvider, @@ -834,24 +851,21 @@ async function selectLiveVectorResults( ), excludeSearchSources(excludedSources), ] - const candidateVisibility = [ - ...getVisibilityConditions( + const candidateDocumentVisibility = [ + inArray(document.knowledgeBaseId, params.knowledgeBaseIds), + ...getDocumentVisibilityConditions( params.access, params.filters, - knowledgeMetadataCandidateAccessCondition(params.access), - embeddingSearch.enabled + knowledgeMetadataCandidateAccessCondition(params.access) ), excludeSearchSources(excludedSources), ] - const visibleDocument = sql`LATERAL ( - SELECT 1 FROM ${document} - WHERE ${and(eq(document.id, embeddingSearch.documentId), ...candidateVisibility)} - OFFSET 0 - ) AS visible_document` - /** Adding zero prevents an underfilled HNSW scan from being chosen again for fallback. */ + const candidateVisibility = [ + eq(embeddingSearch.enabled, true), + ...candidateDocumentVisibility, + ] + /** Explicitly filtered scopes use exact ordering instead of HNSW traversal. */ const exactPage = async (candidateIds?: string[]) => { - const exactOffset = useExactRanking ? offset : 0 - useExactRanking = true annotateSearchDiagnostics({ vectorRanking: 'exact' }) const candidates = await runSearchQuery(params.budget, 'vector.exact', (executor) => executor @@ -867,9 +881,9 @@ async function selectLiveVectorResults( ) .orderBy(sql`(${distance}) + 0`, embedding.id) .limit(limit) - .offset(exactOffset) + .offset(offset) ) - return { candidates, nextOffset: exactOffset + candidates.length } + return { candidates, nextOffset: offset + candidates.length } } if (params.filters?.documentIds?.length || params.structuredFilters?.length) { return exactPage() @@ -879,56 +893,88 @@ async function selectLiveVectorResults( executor .select({ id: embeddingSearch.id }) .from(embeddingSearch) - .innerJoin(visibleDocument, sql`true`) - .where(inArray(embeddingSearch.knowledgeBaseId, params.knowledgeBaseIds)) + .innerJoin(document, eq(document.id, embeddingSearch.documentId)) + .where( + and( + inArray(embeddingSearch.knowledgeBaseId, params.knowledgeBaseIds), + ...candidateVisibility + ) + ) .limit(LIVE_SEARCH_PAGE_SIZE) ) if (probe.length === 0) return { candidates: [], nextOffset: offset } if (probe.length < LIVE_SEARCH_PAGE_SIZE) { return exactPage(probe.map((candidate) => candidate.id)) } - if (useExactRanking) return exactPage() annotateSearchDiagnostics({ - vectorRanking: 'binary-rerank', - vectorCandidateStorage: 'stored-binary', + vectorRanking: 'candidate-rerank', + vectorCandidateStorage: 'stored-halfvec', vectorCandidateLimit: candidateLimit, + vectorCandidateScan: 'planned', + vectorCandidateDimensions: embeddingCandidateDimensions( + queryVector.dimensions, + queryVector.model + ), }) + const candidateConditions = and( + inArray(embeddingSearch.knowledgeBaseId, params.knowledgeBaseIds), + eq(embeddingSearch.enabled, true), + sql`${embeddingSearch.documentId} IN (SELECT id FROM visible_search_documents)`, + sql`EXISTS (SELECT 1 FROM visible_search_documents)` + ) + /** Materialize only document identities, so neither the hash table nor ACL checks carry vectors. */ const identities = await withVectorScanSettings( (executor) => - executor - .select({ id: embeddingSearch.id }) - .from(embeddingSearch) - .innerJoin(visibleDocument, sql`true`) - .where(inArray(embeddingSearch.knowledgeBaseId, params.knowledgeBaseIds)) - .orderBy(candidateDistance) - .limit(candidateLimit), + executor.execute<{ id: string; initial_count: number }>(sql` + WITH visible_search_documents AS MATERIALIZED ( + SELECT ${document.id} AS id FROM ${document} + WHERE ${and(...candidateDocumentVisibility)} + ), initial_candidates AS MATERIALIZED ( + SELECT ${embeddingSearch.id} AS id FROM ${embeddingSearch} + WHERE ${candidateConditions} + ORDER BY ${candidateDistance} LIMIT ${candidateLimit} + ), candidates AS ( + SELECT id FROM initial_candidates + WHERE (SELECT count(*) FROM initial_candidates) >= ${candidateLimit} + UNION ALL ( + SELECT ${embeddingSearch.id} AS id FROM ${embeddingSearch} + WHERE ${candidateConditions} + AND (SELECT count(*) FROM initial_candidates) < ${candidateLimit} + ORDER BY (${candidateDistance}) + 0, ${embeddingSearch.id} + LIMIT ${candidateLimit} + ) + ) SELECT id, (SELECT count(*)::int FROM initial_candidates) AS initial_count FROM candidates + `), params.budget, - 'binary' + 'candidate' ) + const initialCount = identities[0]?.initial_count ?? 0 annotateSearchDiagnostics({ vectorCandidateCount: identities.length, + vectorInitialCandidateCount: initialCount, + vectorCandidateScan: initialCount < candidateLimit ? 'filtered' : 'planned', }) - if (!identities.length) return exactPage() + if (!identities.length) return { candidates: [], nextOffset: offset } + /** Score each bounded candidate once; sorting the materialized scalar cannot invoke HNSW again. */ const page = await runSearchQuery(params.budget, 'vector.rerank', (executor) => - executor - .select({ ...SEARCH_READ_CANDIDATE_FIELDS, distance: distance.as('distance') }) - .from(embedding) - .innerJoin(document, eq(document.id, embedding.documentId)) - .where( - and( + executor.execute(sql` + WITH scored_search_candidates AS MATERIALIZED ( + SELECT ${embedding.id} AS id, ${document.id} AS "documentId", + ${document.connectorId} AS "connectorId", + ${SEARCH_READ_CANDIDATE_FIELDS.liveAuthorizationSource} AS "liveAuthorizationSource", + ${distance} AS distance + FROM ${embedding} INNER JOIN ${document} ON ${document.id} = ${embedding.documentId} + WHERE ${and( inArray( embedding.id, identities.map(({ id }) => id) ), ...conditions, ...visibility - ) - ) - .orderBy(sql`(${distance}) + 0`, embedding.id) - .limit(limit) - .offset(offset) + )} + ) SELECT * FROM scored_search_candidates ORDER BY distance, id LIMIT ${limit} OFFSET ${offset} + `) ) - if (page.length < limit) return exactPage() return { candidates: page, nextOffset: offset + page.length, @@ -1032,6 +1078,8 @@ export async function executeKeywordSearch(params: KeywordSearchParams): Promise sql`${embedding.contentTsv} @@ ${tsQuery}`, ...tagFilterConditions, ] + const candidateRank = sql`ts_rank_cd(${embeddingKeywordSearch.contentTsv}, ${tsQuery})` + /** Keep readable identities and rank scalars separate so sorts never carry full text-search vectors. */ return selectAuthorizedSearchResults({ leg: 'keyword', accessProvider: params.accessProvider, @@ -1041,24 +1089,47 @@ export async function executeKeywordSearch(params: KeywordSearchParams): Promise topK, selectPage: async (limit, offset, excludedSources) => { const candidates = await runSearchQuery(params.budget, 'keyword.sql', (executor) => - executor - .select({ ...SEARCH_READ_CANDIDATE_FIELDS, keywordRank: rankExpr.as('keyword_rank') }) - .from(embedding) - .innerJoin(document, eq(embedding.documentId, document.id)) - .where( - and( - ...conditions, - ...getVisibilityConditions( + executor.execute(sql` + WITH visible_keyword_documents AS MATERIALIZED ( + SELECT ${document.id} AS id FROM ${document} + WHERE ${and( + inArray(document.knowledgeBaseId, knowledgeBaseIds), + ...getDocumentVisibilityConditions( access, params.filters, knowledgeMetadataCandidateAccessCondition(access) ), excludeSearchSources(excludedSources) - ) + )} + ), scored_keyword_candidates AS MATERIALIZED ( + SELECT ${embeddingKeywordSearch.id} AS id, + ${embeddingKeywordSearch.documentId} AS document_id, + ${candidateRank} AS keyword_rank + FROM ${embeddingKeywordSearch} + WHERE ${and( + inArray(embeddingKeywordSearch.knowledgeBaseId, knowledgeBaseIds), + eq(embeddingKeywordSearch.enabled, true), + sql`${embeddingKeywordSearch.contentTsv} @@ ${tsQuery}`, + sql`${embeddingKeywordSearch.documentId} IN (SELECT id FROM visible_keyword_documents)`, + sql`EXISTS (SELECT 1 FROM visible_keyword_documents)`, + tagFilterConditions.length + ? sql`EXISTS ( + SELECT 1 FROM ${embedding} WHERE ${embedding.id} = ${embeddingKeywordSearch.id} + AND ${and(...tagFilterConditions)} + )` + : undefined + )} + ), ranked_keyword_candidates AS MATERIALIZED ( + SELECT * FROM scored_keyword_candidates + ORDER BY keyword_rank DESC, id LIMIT ${limit} OFFSET ${offset} ) - .orderBy(sql`${rankExpr} DESC`, embedding.id) - .limit(limit) - .offset(offset) + SELECT ranked_keyword_candidates.id, ${document.id} AS "documentId", + ${document.connectorId} AS "connectorId", + ${SEARCH_READ_CANDIDATE_FIELDS.liveAuthorizationSource} AS "liveAuthorizationSource" + FROM ranked_keyword_candidates INNER JOIN ${document} + ON ${document.id} = ranked_keyword_candidates.document_id + ORDER BY ranked_keyword_candidates.keyword_rank DESC, ranked_keyword_candidates.id + `) ) return { candidates, nextOffset: offset + candidates.length } }, diff --git a/apps/sim/lib/knowledge/vector-columns.test.ts b/apps/sim/lib/knowledge/vector-columns.test.ts index eb3ef9e9cc0..b3d2d6df775 100644 --- a/apps/sim/lib/knowledge/vector-columns.test.ts +++ b/apps/sim/lib/knowledge/vector-columns.test.ts @@ -22,14 +22,22 @@ describe('embeddingVectorColumn', () => { }) describe('embeddingCandidateDistance', () => { - it('orders stored compact vectors without recomputing full-vector quantization', () => { + it('preserves dimensions for models without prefix-shortening support', () => { for (const width of KB_EMBEDDING_STORAGE_DIMENSIONS) { - const rendered = embeddingCandidateDistance(width, '[1,2]').toSQL() - expect(rendered.sql).toContain('? <~> binary_quantize(?::vector)::bit(?)') - expect(rendered.params[0]).toBe(`embeddingSearch.binary${width === 1536 ? '' : width}`) - expect(rendered.params[1]).toBe('[1,2]') - expect(JSON.stringify(rendered.params[2])).toContain(String(width)) - expect(rendered.sql).not.toContain('<=>') + const rendered = embeddingCandidateDistance(width, '[1,2]', 'text-embedding-ada-002').toSQL() + expect(rendered.sql).toContain('<=>') + expect(rendered.sql).not.toContain('subvector') + expect(rendered.params[0]).toBe(`embeddingSearch.vector${width === 1536 ? '' : width}`) + } + }) + it('shortens compatible models only when the original is wider than 512', () => { + for (const model of ['text-embedding-3-small', 'text-embedding-3-large']) { + const wide = embeddingCandidateDistance(1536, '[1,2]', model).toSQL() + expect(JSON.stringify(wide)).toContain('subvector') + expect(wide.params[0]).toBe('embeddingSearch.vector512') + const narrow = embeddingCandidateDistance(384, '[1,2]', model).toSQL() + expect(narrow.sql).not.toContain('subvector') + expect(narrow.params[0]).toBe('embeddingSearch.vector384') } }) }) diff --git a/apps/sim/lib/knowledge/vector-columns.ts b/apps/sim/lib/knowledge/vector-columns.ts index 037cb00a4f0..f1d4f12d636 100644 --- a/apps/sim/lib/knowledge/vector-columns.ts +++ b/apps/sim/lib/knowledge/vector-columns.ts @@ -26,11 +26,12 @@ const VECTOR_FIELD_BY_WIDTH = { const VECTOR_FIELDS = Object.values(VECTOR_FIELD_BY_WIDTH) as readonly VectorField[] const CANDIDATE_COLUMN_BY_WIDTH = { - 384: embeddingSearch.binary384, - 768: embeddingSearch.binary768, - 1024: embeddingSearch.binary1024, - 1536: embeddingSearch.binary, - 3072: embeddingSearch.binary3072, + 384: embeddingSearch.vector384, + 512: embeddingSearch.vector512, + 768: embeddingSearch.vector768, + 1024: embeddingSearch.vector1024, + 1536: embeddingSearch.vector, + 3072: embeddingSearch.vector3072, } as const export function embeddingVectorColumn(dimensions: KbEmbeddingDimensions) { @@ -78,11 +79,28 @@ export function embeddingDistance( return sql`${embeddingVectorColumn(dimensions)} <=> ${queryVector}::vector` } -/** Compact HNSW candidate ordering; final scores always use the stored vector's cosine distance. */ +/** Only models trained for prefix shortening can use a smaller candidate dimension. */ +export function embeddingCandidateDimensions(dimensions: KbEmbeddingDimensions, model: string) { + if ( + dimensions > 512 && + (model === 'text-embedding-3-small' || model === 'text-embedding-3-large') + ) { + return 512 + } + return dimensions +} + +/** Candidate scores never escape retrieval; final scores use the original vector's cosine distance. */ export function embeddingCandidateDistance( dimensions: KbEmbeddingDimensions, - queryVector: string + queryVector: string, + model: string ): SQL { - const width = sql.raw(String(dimensions)) - return sql`${CANDIDATE_COLUMN_BY_WIDTH[dimensions]} <~> binary_quantize(${queryVector}::vector)::bit(${width})` + const candidateDimensions = embeddingCandidateDimensions(dimensions, model) + const width = sql.raw(String(candidateDimensions)) + const query = + candidateDimensions === dimensions + ? sql`${queryVector}::halfvec(${width})` + : sql`subvector(${queryVector}::vector, 1, ${width})::halfvec(${width})` + return sql`${CANDIDATE_COLUMN_BY_WIDTH[candidateDimensions]} <=> ${query}` } diff --git a/packages/db/migrations/0346_half_precision_search_candidates.sql b/packages/db/migrations/0346_half_precision_search_candidates.sql new file mode 100644 index 00000000000..99e261372f8 --- /dev/null +++ b/packages/db/migrations/0346_half_precision_search_candidates.sql @@ -0,0 +1,16 @@ +/** New secondary indexes are built after the bulk load in script migration 0016. */ +CREATE TABLE IF NOT EXISTS "embedding_keyword_search" ( + "id" text PRIMARY KEY NOT NULL, + "knowledge_base_id" text NOT NULL, + "document_id" text NOT NULL, + "enabled" boolean NOT NULL, + "content_tsv" "tsvector" NOT NULL, + CONSTRAINT "embedding_keyword_search_id_embedding_id_fk" FOREIGN KEY ("id") REFERENCES "public"."embedding"("id") ON DELETE cascade ON UPDATE no action +); +--> statement-breakpoint +ALTER TABLE "embedding_search" ADD COLUMN IF NOT EXISTS "vector" halfvec(1536);--> statement-breakpoint +ALTER TABLE "embedding_search" ADD COLUMN IF NOT EXISTS "vector_384" halfvec(384);--> statement-breakpoint +ALTER TABLE "embedding_search" ADD COLUMN IF NOT EXISTS "vector_512" halfvec(512);--> statement-breakpoint +ALTER TABLE "embedding_search" ADD COLUMN IF NOT EXISTS "vector_768" halfvec(768);--> statement-breakpoint +ALTER TABLE "embedding_search" ADD COLUMN IF NOT EXISTS "vector_1024" halfvec(1024);--> statement-breakpoint +ALTER TABLE "embedding_search" ADD COLUMN IF NOT EXISTS "vector_3072" halfvec(3072); \ No newline at end of file diff --git a/packages/db/migrations/meta/0346_snapshot.json b/packages/db/migrations/meta/0346_snapshot.json new file mode 100644 index 00000000000..a1f7d707da3 --- /dev/null +++ b/packages/db/migrations/meta/0346_snapshot.json @@ -0,0 +1,26862 @@ +{ + "id": "44929069-8639-4927-a122-96c84680d631", + "prevId": "3f607ef9-8cab-431c-b9be-e518b7fe0486", + "version": "7", + "dialect": "postgresql", + "tables": { + "public.academy_certificate": { + "name": "academy_certificate", + "schema": "", + "columns": { + "id": { + "name": "id", + "type": "text", + "primaryKey": true, + "notNull": true + }, + "user_id": { + "name": "user_id", + "type": "text", + "primaryKey": false, + "notNull": true + }, + "course_id": { + "name": "course_id", + "type": "text", + "primaryKey": false, + "notNull": true + }, + "status": { + "name": "status", + "type": "academy_cert_status", + "typeSchema": "public", + "primaryKey": false, + "notNull": true, + "default": "'active'" + }, + "issued_at": { + "name": "issued_at", + "type": "timestamp", + "primaryKey": false, + "notNull": true, + "default": "now()" + }, + "expires_at": { + "name": "expires_at", + "type": "timestamp", + "primaryKey": false, + "notNull": false + }, + "certificate_number": { + "name": "certificate_number", + "type": "text", + "primaryKey": false, + "notNull": true + }, + "metadata": { + "name": "metadata", + "type": "jsonb", + "primaryKey": false, + "notNull": false + }, + "created_at": { + "name": "created_at", + "type": "timestamp", + "primaryKey": false, + "notNull": true, + "default": "now()" + } + }, + "indexes": { + "academy_certificate_user_id_idx": { + "name": "academy_certificate_user_id_idx", + "columns": [ + { + "expression": "user_id", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + }, + "academy_certificate_course_id_idx": { + "name": "academy_certificate_course_id_idx", + "columns": [ + { + "expression": "course_id", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + }, + "academy_certificate_user_course_unique": { + "name": "academy_certificate_user_course_unique", + "columns": [ + { + "expression": "user_id", + "isExpression": false, + "asc": true, + "nulls": "last" + }, + { + "expression": "course_id", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": true, + "concurrently": false, + "method": "btree", + "with": {} + }, + "academy_certificate_status_idx": { + "name": "academy_certificate_status_idx", + "columns": [ + { + "expression": "status", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + } + }, + "foreignKeys": { + "academy_certificate_user_id_user_id_fk": { + "name": "academy_certificate_user_id_user_id_fk", + "tableFrom": "academy_certificate", + "tableTo": "user", + "columnsFrom": ["user_id"], + "columnsTo": ["id"], + "onDelete": "cascade", + "onUpdate": "no action" + } + }, + "compositePrimaryKeys": {}, + "uniqueConstraints": { + "academy_certificate_certificate_number_unique": { + "name": "academy_certificate_certificate_number_unique", + "nullsNotDistinct": false, + "columns": ["certificate_number"] + } + }, + "policies": {}, + "checkConstraints": {}, + "isRLSEnabled": false + }, + "public.account": { + "name": "account", + "schema": "", + "columns": { + "id": { + "name": "id", + "type": "text", + "primaryKey": true, + "notNull": true + }, + "account_id": { + "name": "account_id", + "type": "text", + "primaryKey": false, + "notNull": true + }, + "provider_id": { + "name": "provider_id", + "type": "text", + "primaryKey": false, + "notNull": true + }, + "user_id": { + "name": "user_id", + "type": "text", + "primaryKey": false, + "notNull": true + }, + "access_token": { + "name": "access_token", + "type": "text", + "primaryKey": false, + "notNull": false + }, + "refresh_token": { + "name": "refresh_token", + "type": "text", + "primaryKey": false, + "notNull": false + }, + "id_token": { + "name": "id_token", + "type": "text", + "primaryKey": false, + "notNull": false + }, + "access_token_expires_at": { + "name": "access_token_expires_at", + "type": "timestamp", + "primaryKey": false, + "notNull": false + }, + "refresh_token_expires_at": { + "name": "refresh_token_expires_at", + "type": "timestamp", + "primaryKey": false, + "notNull": false + }, + "scope": { + "name": "scope", + "type": "text", + "primaryKey": false, + "notNull": false + }, + "password": { + "name": "password", + "type": "text", + "primaryKey": false, + "notNull": false + }, + 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"credential_provider_id_idx", + "columns": [ + { + "expression": "provider_id", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + }, + "credential_account_id_idx": { + "name": "credential_account_id_idx", + "columns": [ + { + "expression": "account_id", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + }, + "credential_env_owner_user_id_idx": { + "name": "credential_env_owner_user_id_idx", + "columns": [ + { + "expression": "env_owner_user_id", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + }, + "credential_group_enrollment_idx": { + "name": "credential_group_enrollment_idx", + "columns": [ + { + "expression": "credential_group_enrollment_id", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + }, + "credential_mcp_server_idx": { + "name": "credential_mcp_server_idx", + "columns": [ + { + "expression": "mcp_server_id", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + }, + "credential_group_option_unique": { + "name": "credential_group_option_unique", + "columns": [ + { + "expression": "credential_group_enrollment_id", + "isExpression": false, + "asc": true, + "nulls": "last" + }, + { + "expression": "credential_group_option_id", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": true, + "where": "\"credential\".\"type\" = 'managed_oauth'", + "concurrently": false, + "method": "btree", + "with": {} + }, + "credential_managed_mcp_enrollment_server_unique": { + "name": "credential_managed_mcp_enrollment_server_unique", + "columns": [ + { + "expression": "credential_group_enrollment_id", + "isExpression": false, + "asc": true, + "nulls": "last" + }, + { + "expression": "mcp_server_id", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": true, + "where": "\"credential\".\"type\" = 'managed_mcp'", + "concurrently": false, + "method": "btree", + "with": {} + }, + "credential_workspace_account_unique": { + "name": "credential_workspace_account_unique", + "columns": [ + { + "expression": "workspace_id", + "isExpression": false, + "asc": true, + "nulls": "last" + }, + { + "expression": "account_id", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": true, + "where": "account_id IS NOT NULL", + "concurrently": false, + "method": "btree", + "with": {} + }, + "credential_workspace_env_unique": { + "name": "credential_workspace_env_unique", + "columns": [ + { + "expression": "workspace_id", + "isExpression": false, + "asc": true, + "nulls": "last" + }, + { + "expression": "type", + "isExpression": false, + "asc": true, + "nulls": "last" + }, + { + "expression": "env_key", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": true, + "where": "type = 'env_workspace'", + "concurrently": false, + "method": "btree", + "with": {} + }, + "credential_workspace_personal_env_unique": { + "name": "credential_workspace_personal_env_unique", + "columns": [ + { + "expression": "workspace_id", + "isExpression": false, + "asc": true, + "nulls": "last" + }, + { + "expression": "type", + "isExpression": false, + "asc": true, + "nulls": "last" + }, + { + "expression": "env_key", + "isExpression": false, + "asc": true, + "nulls": "last" + }, + { + "expression": "env_owner_user_id", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": true, + "where": "type = 'env_personal'", + "concurrently": false, + "method": "btree", + "with": {} + }, + "credential_personal_token_identity_unique": { + "name": "credential_personal_token_identity_unique", + "columns": [ + { + "expression": "workspace_id", + "isExpression": false, + "asc": true, + "nulls": "last" + }, + { + "expression": "created_by", + "isExpression": false, + "asc": true, + "nulls": "last" + }, + { + "expression": "provider_id", + "isExpression": false, + "asc": true, + "nulls": "last" + }, + { + "expression": "provider_tenant_id", + "isExpression": false, + "asc": true, + "nulls": "last" + }, + { + "expression": "provider_subject_id", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": true, + "where": "type = 'personal_token'", + "concurrently": false, + "method": "btree", + "with": {} + } + }, + "foreignKeys": { + "credential_workspace_id_workspace_id_fk": { + "name": "credential_workspace_id_workspace_id_fk", + "tableFrom": "credential", + "tableTo": "workspace", + "columnsFrom": ["workspace_id"], + "columnsTo": ["id"], + "onDelete": "cascade", + "onUpdate": "no action" + }, + "credential_organization_id_organization_id_fk": { + "name": "credential_organization_id_organization_id_fk", + "tableFrom": "credential", + "tableTo": "organization", + "columnsFrom": ["organization_id"], + "columnsTo": ["id"], + "onDelete": "cascade", + "onUpdate": "no action" + }, + "credential_slack_app_id_slack_app_id_fk": { + "name": "credential_slack_app_id_slack_app_id_fk", + "tableFrom": "credential", + "tableTo": "slack_app", + "columnsFrom": ["slack_app_id"], + "columnsTo": ["id"], + "onDelete": "no action", + "onUpdate": "no action" + }, + "credential_account_id_account_id_fk": { + "name": "credential_account_id_account_id_fk", + "tableFrom": "credential", + "tableTo": "account", + "columnsFrom": ["account_id"], + "columnsTo": ["id"], + "onDelete": "cascade", + "onUpdate": "no action" + }, + "credential_env_owner_user_id_user_id_fk": { + "name": "credential_env_owner_user_id_user_id_fk", + "tableFrom": "credential", + "tableTo": "user", + "columnsFrom": ["env_owner_user_id"], + "columnsTo": ["id"], + "onDelete": "cascade", + "onUpdate": "no action" + }, + "credential_credential_group_enrollment_id_credential_group_enrollment_id_fk": { + "name": "credential_credential_group_enrollment_id_credential_group_enrollment_id_fk", + "tableFrom": "credential", + "tableTo": "credential_group_enrollment", + "columnsFrom": ["credential_group_enrollment_id"], + "columnsTo": ["id"], + "onDelete": "cascade", + "onUpdate": "no action" + }, + "credential_mcp_server_id_mcp_servers_id_fk": { + "name": "credential_mcp_server_id_mcp_servers_id_fk", + "tableFrom": "credential", + "tableTo": "mcp_servers", + "columnsFrom": ["mcp_server_id"], + "columnsTo": ["id"], + "onDelete": "cascade", + "onUpdate": "no action" + }, + "credential_created_by_user_id_fk": { + "name": "credential_created_by_user_id_fk", + "tableFrom": "credential", + "tableTo": "user", + "columnsFrom": ["created_by"], + "columnsTo": ["id"], + "onDelete": "cascade", + "onUpdate": "no action" + } + }, + "compositePrimaryKeys": {}, + "uniqueConstraints": {}, + "policies": {}, + "checkConstraints": { + "credential_owner_check": { + "name": "credential_owner_check", + "value": "num_nonnulls(\"credential\".\"workspace_id\", \"credential\".\"organization_id\") = 1" + }, + "credential_organization_type_check": { + "name": "credential_organization_type_check", + "value": "\"credential\".\"organization_id\" IS NULL OR \"credential\".\"type\" IN ('oauth', 'managed_oauth', 'managed_mcp', 'service_account', 'personal_token')" + }, + "credential_personal_token_source_check": { + "name": "credential_personal_token_source_check", + "value": "(type::text <> 'personal_token') OR (\n created_by IS NOT NULL\n AND provider_id IS NOT NULL\n AND provider_id = 'gitlab'\n AND provider_subject_id IS NOT NULL\n AND provider_tenant_id IS NOT NULL\n AND encrypted_personal_token IS NOT NULL\n AND granted_scopes IS NOT NULL\n AND cardinality(granted_scopes) > 0\n AND account_id IS NULL\n AND env_key IS NULL\n AND env_owner_user_id IS NULL\n AND authorization_app_id IS NULL\n AND encrypted_oauth_token_set IS NULL\n AND encrypted_service_account_key IS NULL\n AND unredacted = false\n )" + }, + "credential_oauth_source_check": { + "name": "credential_oauth_source_check", + "value": "(type <> 'oauth') OR (account_id IS NOT NULL AND provider_id IS NOT NULL)" + }, + "credential_managed_oauth_source_check": { + "name": "credential_managed_oauth_source_check", + "value": "(type::text <> 'managed_oauth') OR (\n account_id IS NULL\n AND provider_id IS NOT NULL\n AND authorization_app_id IS NOT NULL\n AND provider_subject_id IS NOT NULL\n AND managed_oauth_status IS NOT NULL\n AND granted_scopes IS NOT NULL\n AND encrypted_oauth_token_set IS NOT NULL\n AND granted_at IS NOT NULL\n )" + }, + "credential_managed_oauth_group_binding_check": { + "name": "credential_managed_oauth_group_binding_check", + "value": "(type::text <> 'managed_oauth') OR (\n credential_group_enrollment_id IS NOT NULL\n AND credential_group_option_id IS NOT NULL\n AND managed_oauth_scope_version IS NOT NULL\n AND managed_oauth_scope_version > 0\n )" + }, + "credential_managed_mcp_source_check": { + "name": "credential_managed_mcp_source_check", + "value": "(type::text <> 'managed_mcp') OR (\n id LIKE 'mcp-cg-%'\n AND account_id IS NULL\n AND provider_id IS NULL\n AND authorization_app_id IS NULL\n AND credential_group_enrollment_id IS NOT NULL\n AND credential_group_option_id IS NULL\n AND mcp_server_id IS NOT NULL\n AND managed_oauth_status IS NOT NULL\n AND (managed_oauth_status <> 'active' OR (\n encrypted_oauth_token_set IS NOT NULL\n AND mcp_tools IS NOT NULL\n ))\n AND granted_at IS NOT NULL\n AND managed_oauth_scope_version IS NULL\n AND provider_subject_id IS NULL\n AND provider_tenant_id IS NULL\n AND granted_scopes IS NULL\n AND provider_metadata IS NULL\n AND created_by IS NULL\n AND env_key IS NULL\n AND env_owner_user_id IS NULL\n AND encrypted_service_account_key IS NULL\n AND unredacted = false\n )" + }, + "credential_creator_source_check": { + "name": "credential_creator_source_check", + "value": "(type::text = 'managed_mcp') OR created_by IS NOT NULL" + }, + "credential_workspace_env_source_check": { + "name": "credential_workspace_env_source_check", + "value": "(type <> 'env_workspace') OR (env_key IS NOT NULL AND env_owner_user_id IS NULL)" + }, + "credential_personal_env_source_check": { + "name": "credential_personal_env_source_check", + "value": "(type <> 'env_personal') OR (env_key IS NOT NULL AND env_owner_user_id IS NOT NULL)" + } + }, + "isRLSEnabled": false + }, + "public.credential_group": { + "name": "credential_group", + "schema": "", + "columns": { + "id": { + "name": "id", + "type": "text", + "primaryKey": true, + "notNull": true + }, + "workspace_id": { + "name": "workspace_id", + "type": "text", + "primaryKey": false, + "notNull": false + }, + "organization_id": { + "name": "organization_id", + "type": "text", + "primaryKey": false, + "notNull": false + }, + "public_id": { + "name": "public_id", + "type": "text", + "primaryKey": false, + "notNull": true + }, + "name": { + "name": "name", + "type": "text", + "primaryKey": false, + "notNull": true + }, + "description": { + "name": "description", + "type": "text", + "primaryKey": false, + "notNull": false + }, + "options": { + "name": "options", + "type": "jsonb", + "primaryKey": false, + "notNull": true + }, + "encrypted_provider_configuration": { + "name": "encrypted_provider_configuration", + "type": "text", + "primaryKey": false, + "notNull": false + }, + "status": { + "name": "status", + "type": "credential_group_status", + "typeSchema": "public", + "primaryKey": false, + "notNull": true, + "default": "'active'" + }, + "created_by": { + "name": "created_by", + "type": "text", + "primaryKey": false, + "notNull": false + }, + "created_at": { + "name": "created_at", + "type": "timestamp", + "primaryKey": false, + "notNull": true, + "default": "now()" + }, + "updated_at": { + "name": "updated_at", + "type": "timestamp", + "primaryKey": false, + "notNull": true, + "default": "now()" + } + }, + "indexes": { + "credential_group_organization_id_idx": { + "name": "credential_group_organization_id_idx", + "columns": [ + { + "expression": "organization_id", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + }, + "credential_group_organization_unique": { + "name": "credential_group_organization_unique", + "columns": [ + { + "expression": "organization_id", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": true, + "concurrently": false, + "method": "btree", + "with": {} + }, + "credential_group_public_id_unique": { + "name": "credential_group_public_id_unique", + "columns": [ + { + "expression": "public_id", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": true, + "concurrently": false, + "method": "btree", + "with": {} + }, + "credential_group_workspace_unique": { + "name": "credential_group_workspace_unique", + "columns": [ + { + "expression": "workspace_id", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": true, + "concurrently": false, + "method": "btree", + "with": {} + } + }, + "foreignKeys": { + "credential_group_workspace_id_workspace_id_fk": { + "name": "credential_group_workspace_id_workspace_id_fk", + "tableFrom": "credential_group", + "tableTo": "workspace", + "columnsFrom": ["workspace_id"], + "columnsTo": ["id"], + "onDelete": "cascade", + "onUpdate": "no action" + }, + "credential_group_organization_id_organization_id_fk": { + "name": "credential_group_organization_id_organization_id_fk", + "tableFrom": "credential_group", + "tableTo": "organization", + "columnsFrom": ["organization_id"], + "columnsTo": ["id"], + "onDelete": "cascade", + "onUpdate": "no action" + }, + "credential_group_created_by_user_id_fk": { + "name": "credential_group_created_by_user_id_fk", + "tableFrom": "credential_group", + "tableTo": "user", + "columnsFrom": ["created_by"], + "columnsTo": ["id"], + "onDelete": "set null", + "onUpdate": "no action" + } + }, + "compositePrimaryKeys": {}, + "uniqueConstraints": {}, + "policies": {}, + "checkConstraints": { + "credential_group_owner_check": { + "name": "credential_group_owner_check", + "value": "num_nonnulls(\"credential_group\".\"workspace_id\", \"credential_group\".\"organization_id\") = 1" + } + }, + "isRLSEnabled": false + }, + "public.credential_group_enrollment": { + "name": "credential_group_enrollment", + "schema": "", + "columns": { + "id": { + "name": "id", + "type": "text", + "primaryKey": true, + "notNull": true + }, + "credential_group_id": { + "name": "credential_group_id", + "type": "text", + "primaryKey": false, + "notNull": true + }, + "email": { + "name": "email", + "type": "text", + "primaryKey": false, + "notNull": true + }, + "user_id": { + "name": "user_id", + "type": "text", + "primaryKey": false, + "notNull": false + }, + "status": { + "name": "status", + "type": "credential_group_enrollment_status", + "typeSchema": "public", + "primaryKey": false, + "notNull": true, + "default": "'invited'" + }, + "invitation_token_hash": { + "name": "invitation_token_hash", + "type": "text", + "primaryKey": false, + "notNull": true + }, + "invitation_expires_at": { + "name": "invitation_expires_at", + "type": "timestamp", + "primaryKey": false, + "notNull": true + }, + "invited_at": { + "name": "invited_at", + "type": "timestamp", + "primaryKey": false, + "notNull": true + }, + "sent_at": { + "name": "sent_at", + "type": "timestamp", + "primaryKey": false, + "notNull": false + }, + "completed_at": { + "name": "completed_at", + "type": "timestamp", + "primaryKey": false, + "notNull": false + }, + "revoked_at": { + "name": "revoked_at", + "type": "timestamp", + "primaryKey": false, + "notNull": false + }, + "last_delivery_error": { + "name": "last_delivery_error", + "type": "text", + "primaryKey": false, + "notNull": false + }, + "created_by": { + "name": "created_by", + "type": "text", + "primaryKey": false, + "notNull": false + }, + "created_at": { + "name": "created_at", + "type": "timestamp", + "primaryKey": false, + "notNull": true, + "default": "now()" + }, + "updated_at": { + "name": "updated_at", + "type": "timestamp", + "primaryKey": false, + "notNull": true, + "default": "now()" + } + }, + "indexes": { + "credential_group_enrollment_group_user_unique": { + "name": "credential_group_enrollment_group_user_unique", + "columns": [ + { + "expression": "credential_group_id", + "isExpression": false, + "asc": true, + "nulls": "last" + }, + { + "expression": "user_id", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": true, + "where": "\"credential_group_enrollment\".\"user_id\" IS NOT NULL", + "concurrently": false, + "method": "btree", + "with": {} + }, + "credential_group_enrollment_user_id_idx": { + "name": "credential_group_enrollment_user_id_idx", + "columns": [ + { + "expression": "user_id", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + }, + "credential_group_enrollment_group_email_unique": { + "name": "credential_group_enrollment_group_email_unique", + "columns": [ + { + "expression": "credential_group_id", + "isExpression": false, + "asc": true, + "nulls": "last" + }, + { + "expression": "email", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": true, + "concurrently": false, + "method": "btree", + "with": {} + }, + "credential_group_enrollment_invitation_token_hash_unique": { + "name": "credential_group_enrollment_invitation_token_hash_unique", + "columns": [ + { + "expression": "invitation_token_hash", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": true, + "concurrently": false, + "method": "btree", + "with": {} + }, + "credential_group_enrollment_group_status_idx": { + "name": "credential_group_enrollment_group_status_idx", + "columns": [ + { + "expression": "credential_group_id", + "isExpression": false, + "asc": true, + "nulls": "last" + }, + { + "expression": "status", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + }, + "credential_group_enrollment_group_invited_at_id_idx": { + "name": "credential_group_enrollment_group_invited_at_id_idx", + "columns": [ + { + "expression": "credential_group_id", + "isExpression": false, + "asc": true, + "nulls": "last" + }, + { + "expression": "invited_at", + "isExpression": false, + "asc": true, + "nulls": "last" + }, + { + "expression": "id", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + } + }, + 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"workspace_fork_resource_map_child_type_parent_unique", + "columns": [ + { + "expression": "child_workspace_id", + "isExpression": false, + "asc": true, + "nulls": "last" + }, + { + "expression": "resource_type", + "isExpression": false, + "asc": true, + "nulls": "last" + }, + { + "expression": "parent_resource_id", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": true, + "concurrently": false, + "method": "btree", + "with": {} + } + }, + "foreignKeys": { + "workspace_fork_resource_map_child_workspace_id_workspace_id_fk": { + "name": "workspace_fork_resource_map_child_workspace_id_workspace_id_fk", + "tableFrom": "workspace_fork_resource_map", + "tableTo": "workspace", + "columnsFrom": ["child_workspace_id"], + "columnsTo": ["id"], + "onDelete": "cascade", + "onUpdate": "no action" + }, + "workspace_fork_resource_map_created_by_user_id_fk": { + "name": "workspace_fork_resource_map_created_by_user_id_fk", + "tableFrom": "workspace_fork_resource_map", + "tableTo": "user", + "columnsFrom": ["created_by"], + "columnsTo": ["id"], + "onDelete": "set null", + "onUpdate": "no action" + } + }, + "compositePrimaryKeys": {}, + "uniqueConstraints": {}, + "policies": {}, + "checkConstraints": {}, + "isRLSEnabled": false + }, + "public.workspace_operation_receipt": { + "name": "workspace_operation_receipt", + "schema": "", + "columns": { + "id": { + "name": "id", + "type": "text", + "primaryKey": true, + "notNull": true + }, + "workspace_id": { + "name": "workspace_id", + "type": "text", + "primaryKey": false, + "notNull": true + }, + "request_id": { + "name": "request_id", + "type": "text", + "primaryKey": false, + "notNull": true + }, + "request_hash": { + "name": "request_hash", + "type": "text", + "primaryKey": false, + "notNull": true + }, + "kind": { + "name": "kind", + "type": "text", + "primaryKey": false, + "notNull": true + }, + "report": { + "name": "report", + "type": "jsonb", + "primaryKey": false, + "notNull": true + }, + "created_at": { + "name": "created_at", + "type": "timestamp", + "primaryKey": false, + "notNull": true, + "default": "now()" + }, + "updated_at": { + "name": "updated_at", + "type": "timestamp", + "primaryKey": false, + "notNull": true, + "default": "now()" + } + }, + "indexes": { + "workspace_operation_receipt_request_unique": { + "name": "workspace_operation_receipt_request_unique", + "columns": [ + { + "expression": "workspace_id", + "isExpression": false, + "asc": true, + "nulls": "last" + }, + { + "expression": "request_id", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": true, + "concurrently": false, + "method": "btree", + "with": {} + }, + "workspace_operation_receipt_workspace_created_idx": { + "name": "workspace_operation_receipt_workspace_created_idx", + "columns": [ + { + "expression": "workspace_id", + "isExpression": false, + "asc": true, + "nulls": "last" + }, + { + "expression": "created_at", + "isExpression": false, + "asc": true, + "nulls": "last" + }, + { + "expression": "id", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + } + }, + "foreignKeys": { + "workspace_operation_receipt_workspace_id_workspace_id_fk": { + "name": "workspace_operation_receipt_workspace_id_workspace_id_fk", + "tableFrom": "workspace_operation_receipt", + "tableTo": "workspace", + "columnsFrom": ["workspace_id"], + "columnsTo": ["id"], + "onDelete": "cascade", + "onUpdate": "no action" + } + }, + "compositePrimaryKeys": {}, + "uniqueConstraints": {}, + "policies": {}, + "checkConstraints": {}, + "isRLSEnabled": false + }, + "public.workspace_sandbox": { + "name": "workspace_sandbox", + "schema": "", + "columns": { + "id": { + "name": "id", + "type": "text", + "primaryKey": true, + "notNull": true + }, + "workspace_id": { + "name": "workspace_id", + "type": "text", + "primaryKey": false, + "notNull": true + }, + "name": { + "name": "name", + "type": "text", + "primaryKey": false, + "notNull": true + }, + "language": { + "name": "language", + "type": "sandbox_language", + "typeSchema": "public", + "primaryKey": false, + "notNull": true + }, + "dependencies": { + "name": "dependencies", + "type": "jsonb", + "primaryKey": false, + "notNull": true, + "default": "'[]'::jsonb" + }, + "cli_tools": { + "name": "cli_tools", + "type": "jsonb", + "primaryKey": false, + "notNull": true, + "default": "'[]'::jsonb" + }, + "system_packages": { + "name": "system_packages", + "type": "jsonb", + "primaryKey": false, + "notNull": true, + "default": "'[]'::jsonb" + }, + "spec_hash": { + "name": "spec_hash", + "type": "text", + "primaryKey": false, + "notNull": true + }, + "created_by": { + "name": "created_by", + "type": "text", + "primaryKey": false, + "notNull": false + }, + "created_at": { + "name": "created_at", + "type": "timestamp", + "primaryKey": false, + "notNull": true, + "default": "now()" + }, + "updated_at": { + "name": "updated_at", + "type": "timestamp", + "primaryKey": false, + "notNull": true, + "default": "now()" + } + }, + "indexes": { + "workspace_sandbox_workspace_name_unique": { + "name": "workspace_sandbox_workspace_name_unique", + "columns": [ + { + "expression": "workspace_id", + "isExpression": false, + "asc": true, + "nulls": "last" + }, + { + "expression": "name", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": true, + "concurrently": false, + "method": "btree", + "with": {} + }, + "workspace_sandbox_workspace_idx": { + "name": "workspace_sandbox_workspace_idx", + "columns": [ + { + "expression": "workspace_id", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + }, + "workspace_sandbox_spec_hash_idx": { + "name": "workspace_sandbox_spec_hash_idx", + "columns": [ + { + "expression": "spec_hash", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + } + }, + "foreignKeys": { + "workspace_sandbox_workspace_id_workspace_id_fk": { + "name": "workspace_sandbox_workspace_id_workspace_id_fk", + "tableFrom": "workspace_sandbox", + "tableTo": "workspace", + "columnsFrom": ["workspace_id"], + "columnsTo": ["id"], + "onDelete": "cascade", + "onUpdate": "no action" + }, + "workspace_sandbox_created_by_user_id_fk": { + "name": "workspace_sandbox_created_by_user_id_fk", + "tableFrom": "workspace_sandbox", + "tableTo": "user", + "columnsFrom": ["created_by"], + "columnsTo": ["id"], + "onDelete": "set null", + "onUpdate": "no action" + } + }, + "compositePrimaryKeys": {}, + "uniqueConstraints": {}, + "policies": {}, + "checkConstraints": {}, + "isRLSEnabled": false + } + }, + "enums": { + "public.academy_cert_status": { + "name": "academy_cert_status", + "schema": "public", + "values": ["active", "revoked", "expired"] + }, + "public.background_work_kind": { + "name": "background_work_kind", + "schema": "public", + "values": ["deployment_side_effects", "fork_content_copy", "fork_sync", "fork_rollback"] + }, + "public.background_work_status_value": { + "name": "background_work_status_value", + "schema": "public", + "values": ["pending", "processing", "completed", "completed_with_warnings", "failed"] + }, + "public.billing_blocked_reason": { + "name": "billing_blocked_reason", + "schema": "public", + "values": ["payment_failed", "dispute"] + }, + "public.billing_entity_type": { + "name": "billing_entity_type", + "schema": "public", + "values": ["user", "organization"] + }, + "public.chat_type": { + "name": "chat_type", + "schema": "public", + "values": ["mothership", "copilot"] + }, + "public.copilot_async_tool_status": { + "name": "copilot_async_tool_status", + "schema": "public", + "values": ["pending", "running", "completed", "failed", "cancelled", "delivered"] + }, + "public.copilot_run_status": { + "name": "copilot_run_status", + "schema": "public", + "values": ["active", "paused_waiting_for_tool", "resuming", "complete", "error", "cancelled"] + }, + "public.copilot_tool_permission_decision": { + "name": "copilot_tool_permission_decision", + "schema": "public", + "values": ["allow", "allow_chat", "always_allow", "skip"] + }, + "public.credential_group_enrollment_status": { + "name": "credential_group_enrollment_status", + "schema": "public", + "values": ["invited", "delivery_failed", "in_progress", "completed", "revoked"] + }, + "public.credential_group_status": { + "name": "credential_group_status", + "schema": "public", + "values": ["active", "disabled"] + }, + "public.credential_member_role": { + "name": "credential_member_role", + "schema": "public", + "values": ["admin", "member"] + }, + "public.credential_member_status": { + "name": "credential_member_status", + "schema": "public", + "values": ["active", "pending", "revoked"] + }, + "public.credential_type": { + "name": "credential_type", + "schema": "public", + "values": [ + "oauth", + "managed_oauth", + "managed_mcp", + "env_workspace", + "env_personal", + "service_account", + "personal_token" + ] + }, + "public.data_drain_cadence": { + "name": "data_drain_cadence", + "schema": "public", + "values": ["hourly", "daily"] + }, + "public.data_drain_destination": { + "name": "data_drain_destination", + "schema": "public", + "values": ["s3", "gcs", "azure_blob", "datadog", "bigquery", "snowflake", "webhook"] + }, + "public.data_drain_run_status": { + "name": "data_drain_run_status", + "schema": "public", + "values": ["running", "success", "failed"] + }, + "public.data_drain_run_trigger": { + "name": "data_drain_run_trigger", + "schema": "public", + "values": ["cron", "manual"] + }, + "public.data_drain_source": { + "name": "data_drain_source", + "schema": "public", + "values": ["workflow_logs", "job_logs", "audit_logs", "copilot_chats", "copilot_runs"] + }, + "public.execution_large_value_reference_source": { + "name": "execution_large_value_reference_source", + "schema": "public", + "values": ["execution_log", "paused_snapshot"] + }, + "public.folder_resource_type": { + "name": "folder_resource_type", + "schema": "public", + "values": ["workflow", "file", "knowledge_base", "table"] + }, + "public.invitation_kind": { + "name": "invitation_kind", + "schema": "public", + "values": ["organization", "workspace"] + }, + "public.invitation_membership_intent": { + "name": "invitation_membership_intent", + "schema": "public", + "values": ["internal", "external"] + }, + "public.invitation_status": { + "name": "invitation_status", + "schema": "public", + "values": ["pending", "accepted", "rejected", "cancelled", "expired"] + }, + "public.managed_oauth_credential_status": { + "name": "managed_oauth_credential_status", + "schema": "public", + "values": ["active", "needs_reauth", "revoked"] + }, + "public.permission_type": { + "name": "permission_type", + "schema": "public", + "values": ["admin", "write", "read"] + }, + "public.sandbox_image_status": { + "name": "sandbox_image_status", + "schema": "public", + "values": ["pending", "building", "ready", "failed"] + }, + "public.sandbox_language": { + "name": "sandbox_language", + "schema": "public", + "values": ["javascript", "python"] + }, + "public.secret_usage_scope": { + "name": "secret_usage_scope", + "schema": "public", + "values": ["workspace", "personal"] + }, + "public.secret_usage_source": { + "name": "secret_usage_source", + "schema": "public", + "values": ["workflow", "copilot", "mcp"] + }, + "public.upload_session_method": { + "name": "upload_session_method", + "schema": "public", + "values": ["put", "multipart"] + }, + "public.upload_session_provider": { + "name": "upload_session_provider", + "schema": "public", + "values": ["local", "s3", "blob", "gcs"] + }, + "public.upload_session_purpose": { + "name": "upload_session_purpose", + "schema": "public", + "values": [ + "workspace_file", + "table_import", + "knowledge_document", + "profile_picture", + "workspace_logo", + "organization_logo", + "mothership_attachment", + "execution_attachment" + ] + }, + "public.upload_session_status": { + "name": "upload_session_status", + "schema": "public", + "values": [ + "uploading", + "completing", + "finalizing", + "completed", + "aborting", + "aborted", + "failed", + "expired" + ] + }, + "public.usage_log_category": { + "name": "usage_log_category", + "schema": "public", + "values": ["model", "fixed", "tool", "model_unbilled"] + }, + "public.usage_log_source": { + "name": "usage_log_source", + "schema": "public", + "values": [ + "workflow", + "wand", + "copilot", + "workspace-chat", + "mcp_copilot", + "mothership_block", + "knowledge-base", + "voice-input", + "enrichment", + "voice-output", + "api-tool" + ] + }, + "public.workspace_file_search_index_status": { + "name": "workspace_file_search_index_status", + "schema": "public", + "values": ["pending", "ready", "skipped", "failed"] + }, + "public.workspace_fork_promote_direction": { + "name": "workspace_fork_promote_direction", + "schema": "public", + "values": ["push", "pull"] + }, + "public.workspace_fork_resource_type": { + "name": "workspace_fork_resource_type", + "schema": "public", + "values": [ + "workflow", + "oauth_credential", + "service_account_credential", + "env_var", + "table", + "knowledge_base", + "knowledge_document", + "file", + "file_folder", + "mcp_server", + "workflow_mcp_server", + "custom_block", + "custom_tool", + "skill", + "sandbox" + ] + }, + "public.workspace_mode": { + "name": "workspace_mode", + "schema": "public", + "values": ["personal", "organization", "grandfathered_shared"] + } + }, + "schemas": {}, + "sequences": {}, + "roles": {}, + "policies": {}, + "views": {}, + "_meta": { + "columns": {}, + "schemas": {}, + "tables": {} + } +} diff --git a/packages/db/migrations/meta/_journal.json b/packages/db/migrations/meta/_journal.json index 9a13f04f6fe..5653a615791 100644 --- a/packages/db/migrations/meta/_journal.json +++ b/packages/db/migrations/meta/_journal.json @@ -2416,6 +2416,13 @@ "when": 1789427851762, "tag": "0345_document_connector_processing_status", "breakpoints": true + }, + { + "idx": 346, + "version": "7", + "when": 1789430644029, + "tag": "0346_half_precision_search_candidates", + "breakpoints": true } ] } diff --git a/packages/db/package.json b/packages/db/package.json index a96faf14e4e..390601a4e9a 100644 --- a/packages/db/package.json +++ b/packages/db/package.json @@ -28,7 +28,7 @@ }, "scripts": { "db:generate": "bunx drizzle-kit generate --config=./drizzle.config.ts", - "db:push": "bunx drizzle-kit push --config=./drizzle.config.ts && bun --env-file=.env run ./scripts/reconcile-credential-group-resource-policies.ts && bun --env-file=.env run ./scripts/reconcile-oauth-provider.ts && bun --env-file=.env run ./script-migrations/0015_backfill_embedding_search.ts", + "db:push": "bunx drizzle-kit push --config=./drizzle.config.ts && bun --env-file=.env run ./scripts/reconcile-credential-group-resource-policies.ts && bun --env-file=.env run ./scripts/reconcile-oauth-provider.ts && bun --env-file=.env run ./script-migrations/0016_backfill_search_vectors.ts", "db:migrate": "bun --env-file=.env run ./scripts/migrate.ts", "db:apply-dev-workspace-file-size-cutover": "bun --env-file=.env run ./scripts/apply-dev-workspace-file-size-cutover.ts", "db:reconcile-fork-kb-file-ownership": "bun --env-file=.env run ./scripts/reconcile-fork-kb-file-ownership.ts", diff --git a/packages/db/schema.ts b/packages/db/schema.ts index 9a2155b0982..eb434fd29b9 100644 --- a/packages/db/schema.ts +++ b/packages/db/schema.ts @@ -11,6 +11,7 @@ import { decimal, doublePrecision, foreignKey, + halfvec, index, integer, json, @@ -3336,9 +3337,28 @@ export const embedding = pgTable( }) ) +/** Keyword ranking reads text-search vectors independently of chunk content and semantic vectors. */ +export const embeddingKeywordSearch = pgTable( + 'embedding_keyword_search', + { + id: text('id') + .primaryKey() + .references(() => embedding.id, { onDelete: 'cascade' }), + knowledgeBaseId: text('knowledge_base_id').notNull(), + documentId: text('document_id').notNull(), + enabled: boolean('enabled').notNull(), + contentTsv: tsvector('content_tsv').notNull(), + }, + (table) => ({ + knowledgeBaseIdx: index('embedding_keyword_search_kb_idx').on(table.knowledgeBaseId), + documentIdx: index('embedding_keyword_search_document_idx').on(table.documentId), + contentIdx: index('embedding_keyword_search_content_idx').using('gin', table.contentTsv), + }) +) + /** - * Transactionally maintained candidate projection. Keeping identities and stored bits apart - * from content and full vectors prevents ANN traversal from fetching or requantizing TOAST values. + * Transactionally maintained candidate projection. Keeping identities and half-precision vectors apart + * from content prevents candidate scans from fetching full-precision TOAST values. * The embedding write trigger owns this projection; application writers only change embedding. */ export const embeddingSearch = pgTable( @@ -3350,11 +3370,18 @@ export const embeddingSearch = pgTable( knowledgeBaseId: text('knowledge_base_id').notNull(), documentId: text('document_id').notNull(), enabled: boolean('enabled').notNull(), + /** contract-pending(after half-precision search is fully deployed): drop binary columns and indexes — the previous app is their final reader. */ binary: bit('binary', { dimensions: 1536 }), binary384: bit('binary_384', { dimensions: 384 }), binary768: bit('binary_768', { dimensions: 768 }), binary1024: bit('binary_1024', { dimensions: 1024 }), binary3072: bit('binary_3072', { dimensions: 3072 }), + vector: halfvec('vector', { dimensions: 1536 }), + vector384: halfvec('vector_384', { dimensions: 384 }), + vector512: halfvec('vector_512', { dimensions: 512 }), + vector768: halfvec('vector_768', { dimensions: 768 }), + vector1024: halfvec('vector_1024', { dimensions: 1024 }), + vector3072: halfvec('vector_3072', { dimensions: 3072 }), }, (table) => ({ knowledgeBaseIdx: index('embedding_search_kb_idx').on(table.knowledgeBaseId), @@ -3373,6 +3400,24 @@ export const embeddingSearch = pgTable( binary3072Idx: index('embedding_search_3072_binary_hnsw_idx') .using('hnsw', table.binary3072.op('bit_hamming_ops')) .with({ m: 16, ef_construction: 64 }), + vectorIdx: index('embedding_search_cosine_hnsw_idx') + .using('hnsw', table.vector.op('halfvec_cosine_ops')) + .with({ m: 16, ef_construction: 64 }), + vector512Idx: index('embedding_search_512_cosine_hnsw_idx') + .using('hnsw', table.vector512.op('halfvec_cosine_ops')) + .with({ m: 16, ef_construction: 64 }), + vector384Idx: index('embedding_search_384_cosine_hnsw_idx') + .using('hnsw', table.vector384.op('halfvec_cosine_ops')) + .with({ m: 16, ef_construction: 64 }), + vector768Idx: index('embedding_search_768_cosine_hnsw_idx') + .using('hnsw', table.vector768.op('halfvec_cosine_ops')) + .with({ m: 16, ef_construction: 64 }), + vector1024Idx: index('embedding_search_1024_cosine_hnsw_idx') + .using('hnsw', table.vector1024.op('halfvec_cosine_ops')) + .with({ m: 16, ef_construction: 64 }), + vector3072Idx: index('embedding_search_3072_cosine_hnsw_idx') + .using('hnsw', table.vector3072.op('halfvec_cosine_ops')) + .with({ m: 16, ef_construction: 64 }), widthCheck: check( 'embedding_search_width_check', sql`num_nonnulls("binary", "binary_384", "binary_768", "binary_1024", "binary_3072") = 1` diff --git a/packages/db/script-migrations-paused-billing-attribution.test.ts b/packages/db/script-migrations-paused-billing-attribution.test.ts index 2e2cea67aff..38016621e23 100644 --- a/packages/db/script-migrations-paused-billing-attribution.test.ts +++ b/packages/db/script-migrations-paused-billing-attribution.test.ts @@ -451,7 +451,7 @@ describe('script migration registry', () => { '0012_reconcile_oauth_provider_lifecycle', '0013_backfill_legacy_knowledge_base_workspaces', '0014_require_knowledge_base_owner', - '0015_backfill_embedding_search', + '0016_backfill_search_vectors', ]) }) }) diff --git a/packages/db/script-migrations/0015_backfill_embedding_search.ts b/packages/db/script-migrations/0015_backfill_embedding_search.ts index 96a4b024ba0..b8540a6c688 100644 --- a/packages/db/script-migrations/0015_backfill_embedding_search.ts +++ b/packages/db/script-migrations/0015_backfill_embedding_search.ts @@ -9,6 +9,7 @@ const BATCH_SIZE = 500 export async function backfillEmbeddingSearch(sql: Sql): Promise { await sql.begin(async (tx) => { await tx.unsafe("SET LOCAL lock_timeout = '5s'") + await tx.unsafe('LOCK TABLE embedding IN SHARE ROW EXCLUSIVE MODE') await tx.unsafe(`CREATE OR REPLACE FUNCTION sync_embedding_search() RETURNS trigger LANGUAGE plpgsql AS $$ BEGIN @@ -34,20 +35,24 @@ export async function backfillEmbeddingSearch(sql: Sql): Promise { let afterId = '' let count = 0 + let scanned = 0 + const startedAt = Date.now() for (;;) { - const rows = await sql.begin(async (tx) => { + const [page] = await sql.begin(async (tx) => { await tx.unsafe("SET LOCAL lock_timeout = '5s'") await tx.unsafe("SET LOCAL statement_timeout = '60s'") /** Key-share locks keep selected parents alive; a concurrent trigger always wins a conflict. */ - return tx>` - WITH batch AS MATERIALIZED ( + return tx>` + WITH source_page AS MATERIALIZED ( + SELECT id FROM embedding WHERE id > ${afterId} ORDER BY id LIMIT ${BATCH_SIZE} + ), missing AS MATERIALIZED ( + SELECT p.id FROM source_page p + WHERE NOT EXISTS (SELECT 1 FROM embedding_search s WHERE s.id = p.id) + ), batch AS MATERIALIZED ( SELECT e.id, e.knowledge_base_id, e.document_id, e.enabled, e.embedding, e.embedding_384, e.embedding_768, e.embedding_1024, e.embedding_3072 - FROM embedding e - WHERE e.id > ${afterId} - AND NOT EXISTS (SELECT 1 FROM embedding_search s WHERE s.id = e.id) - ORDER BY e.id LIMIT ${BATCH_SIZE} - FOR KEY SHARE OF e + FROM missing m INNER JOIN embedding e ON e.id = m.id + ORDER BY e.id FOR KEY SHARE OF e ), inserted AS ( INSERT INTO embedding_search (id, knowledge_base_id, document_id, enabled, "binary", binary_384, binary_768, binary_1024, binary_3072) @@ -56,14 +61,23 @@ export async function backfillEmbeddingSearch(sql: Sql): Promise { binary_quantize(embedding_768)::bit(768), binary_quantize(embedding_1024)::bit(1024), binary_quantize(embedding_3072)::bit(3072) FROM batch - ON CONFLICT (id) DO NOTHING + ON CONFLICT (id) DO NOTHING RETURNING id ) - SELECT id FROM batch ORDER BY id + SELECT max(id) AS after_id, count(*)::int AS scanned, + (SELECT count(*)::int FROM inserted) AS inserted FROM source_page ` }) - if (!rows.length) break - afterId = rows[rows.length - 1].id - count += rows.length + if (!page.after_id) break + afterId = page.after_id + count += page.inserted + scanned += page.scanned + if (scanned % (BATCH_SIZE * 100) === 0) { + logger.info('Embedding candidate backfill progress', { + scanned, + inserted: count, + elapsedMs: Date.now() - startedAt, + }) + } } /** New projections need usable cardinality estimates before the first app image reads them. */ await sql.unsafe('ANALYZE embedding_search') @@ -82,7 +96,7 @@ export const backfillEmbeddingSearchMigration: ScriptMigration = { if (import.meta.main) { const url = process.env.MIGRATION_DATABASE_URL ?? process.env.DATABASE_URL if (!url) throw new Error('DATABASE_URL is required to initialize embedding search') - const sql = postgres(url, { max: 1, onnotice: () => undefined }) + const sql = postgres(url, { max: 1, max_lifetime: null, onnotice: () => undefined }) try { await backfillEmbeddingSearchMigration.up(sql) } finally { diff --git a/packages/db/script-migrations/0016_backfill_search_vectors.postgres.test.ts b/packages/db/script-migrations/0016_backfill_search_vectors.postgres.test.ts new file mode 100644 index 00000000000..de22b5c5c29 --- /dev/null +++ b/packages/db/script-migrations/0016_backfill_search_vectors.postgres.test.ts @@ -0,0 +1,366 @@ +import { readFileSync } from 'node:fs' +import { backfillEmbeddingSearch } from '@sim/db/script-migrations/0015_backfill_embedding_search' +import { + backfillSearchKeywords, + backfillSearchVectors, + buildSearchIndexes, +} from '@sim/db/script-migrations/0016_backfill_search_vectors' +import { runScriptMigrations, scriptMigrations } from '@sim/db/script-migrations/index' +import { generateId } from '@sim/utils/id' +import postgres, { type Sql } from 'postgres' +import { afterAll, beforeAll, describe, expect, it, vi } from 'vitest' + +const databaseUrl = process.env.KNOWLEDGE_ACL_TEST_DATABASE_URL +const schemaName = `search_projection_${generateId().replaceAll('-', '')}` +const fields = ['vector', 'vector_384', 'vector_512', 'vector_768', 'vector_1024', 'vector_3072'] + +describe.runIf(Boolean(databaseUrl))('search projection upgrade in PostgreSQL', () => { + let admin: Sql + let sql: Sql + + beforeAll(async () => { + const url = new URL(databaseUrl!) + if ( + !['localhost', '127.0.0.1'].includes(url.hostname) || + (!url.pathname.startsWith('/sim_acl_test') && url.pathname !== '/sim_auth_scim') + ) { + throw new Error('Projection tests require a disposable local integration database') + } + admin = postgres(url.toString(), { max: 1, onnotice: () => undefined }) + await admin.unsafe(`CREATE SCHEMA "${schemaName}"`) + for (const table of ['knowledge_base', 'embedding', 'embedding_search']) { + await admin.unsafe( + `CREATE TABLE "${schemaName}"."${table}" (LIKE public."${table}" INCLUDING DEFAULTS INCLUDING CONSTRAINTS INCLUDING INDEXES INCLUDING GENERATED)` + ) + } + sql = postgres(url.toString(), { + max: 1, + connection: { search_path: `${schemaName},public` }, + onnotice: () => undefined, + }) + await sql.unsafe( + 'ALTER TABLE embedding_search ADD FOREIGN KEY (id) REFERENCES embedding(id) ON DELETE CASCADE' + ) + await sql.unsafe( + `ALTER TABLE embedding_search ${fields.map((field) => `DROP COLUMN ${field}`).join(', ')}` + ) + await sql`INSERT INTO knowledge_base (id, user_id, name, workspace_id, embedding_model) VALUES + ('prefix', 'reader', 'Prefix fixture', 'workspace', 'text-embedding-3-small'), + ('full', 'reader', 'Full fixture', 'workspace', 'gemini-embedding-001')` + await backfillEmbeddingSearch(sql) + await sql.unsafe(`INSERT INTO embedding + (id, knowledge_base_id, document_id, chunk_index, chunk_hash, content, content_length, token_count, start_offset, end_offset, embedding) + SELECT 'chunk-' || n, CASE WHEN n % 2 = 0 THEN 'prefix' ELSE 'full' END, 'document-' || n, + 0, 'hash-' || n, 'Synthetic fixture', 17, 4, 0, 17, + array_fill(0.01::real, ARRAY[1536])::vector(1536) + FROM generate_series(1, 501) n`) + }, 60_000) + + afterAll(async () => { + await sql?.end() + if (admin) { + await admin.unsafe(`DROP SCHEMA IF EXISTS "${schemaName}" CASCADE`) + await admin.end() + } + }) + + it('replays the additive schema before backfilling and building new indexes', async () => { + const statements = readFileSync( + new URL('../migrations/0346_half_precision_search_candidates.sql', import.meta.url), + 'utf8' + ) + .split('--> statement-breakpoint') + .map((statement) => statement.trim().replaceAll('"public".', `"${schemaName}".`)) + .filter(Boolean) + for (let replay = 0; replay < 2; replay++) { + await sql.begin(async (tx) => { + for (const statement of statements) await tx.unsafe(statement) + }) + } + expect( + await sql`SELECT 1 FROM pg_index JOIN pg_class ON pg_class.oid = pg_index.indexrelid + WHERE indrelid = 'embedding_search'::regclass AND relname LIKE '%cosine_hnsw_idx'` + ).toHaveLength(0) + expect((await sql`SELECT count(*)::int AS count FROM embedding_search`)[0].count).toBe(501) + }, 60_000) + + it('serializes behind existing writers before upgrading the projection in batches', async () => { + const writer = postgres(databaseUrl!, { + max: 1, + connection: { search_path: `${schemaName},public` }, + onnotice: () => undefined, + }) + const [{ pid }] = await sql`SELECT pg_backend_pid() AS pid` + await writer`BEGIN` + await writer`LOCK TABLE embedding IN ROW EXCLUSIVE MODE` + const upgrade = Promise.allSettled([backfillSearchVectors(sql)]) + try { + await vi.waitFor(async () => { + const [{ waiting }] = await admin`SELECT EXISTS ( + SELECT 1 FROM pg_locks WHERE pid = ${pid} + AND relation = ${`${schemaName}.embedding`}::regclass AND NOT granted + ) AS waiting` + expect(waiting).toBe(true) + }) + await writer`UPDATE embedding SET enabled = false WHERE id = 'chunk-1'` + await writer`COMMIT` + const [result] = await upgrade + if (result.status === 'rejected') throw result.reason + expect(result.value).toBe(501) + } finally { + await writer`ROLLBACK` + await upgrade + await writer.end() + } + expect((await sql`SELECT enabled FROM embedding_search WHERE id = 'chunk-1'`)[0].enabled).toBe( + false + ) + const rows = await sql.unsafe(`SELECT knowledge_base_id, + vector_dims(coalesce(${fields.join(', ')})) AS dimensions, + num_nonnulls(${fields.join(', ')}) AS populated, + "binary" IS NOT NULL AS legacy FROM embedding_search`) + expect(rows).toHaveLength(501) + for (const row of rows) { + expect(row.dimensions).toBe(row.knowledge_base_id === 'prefix' ? 512 : 1536) + expect(row.populated).toBe(1) + expect(row.legacy).toBe(true) + } + expect(await backfillSearchVectors(sql)).toBe(0) + }, 60_000) + + it('backfills identical keyword vectors and keeps content edits, scope changes, and deletes current', async () => { + expect(await backfillSearchKeywords(sql)).toBe(501) + expect(await backfillSearchKeywords(sql)).toBe(0) + const [initial] = await sql`SELECT count(*)::int AS count FROM embedding e + JOIN embedding_keyword_search s ON s.id = e.id + WHERE e.content_tsv IS DISTINCT FROM s.content_tsv + OR e.enabled IS DISTINCT FROM s.enabled + OR e.knowledge_base_id IS DISTINCT FROM s.knowledge_base_id + OR e.document_id IS DISTINCT FROM s.document_id` + expect(initial.count).toBe(0) + await sql`UPDATE embedding SET content = 'Revised deployment instructions', + knowledge_base_id = 'full', document_id = 'revised-document', enabled = false + WHERE id = 'chunk-2'` + expect( + await sql`SELECT knowledge_base_id, document_id, enabled, + content_tsv = to_tsvector('english', 'Revised deployment instructions') AS current + FROM embedding_keyword_search WHERE id = 'chunk-2'` + ).toEqual([ + { knowledge_base_id: 'full', document_id: 'revised-document', enabled: false, current: true }, + ]) + await sql`DELETE FROM embedding WHERE id = 'chunk-2'` + expect(await sql`SELECT id FROM embedding_keyword_search WHERE id = 'chunk-2'`).toHaveLength(0) + expect(await backfillSearchKeywords(sql)).toBe(0) + }) + + it('repairs an interrupted index build and preserves valid indexes on replay', async () => { + await expect( + sql.unsafe( + 'CREATE UNIQUE INDEX CONCURRENTLY embedding_search_cosine_hnsw_idx ON embedding_search (knowledge_base_id)' + ) + ).rejects.toMatchObject({ code: '23505' }) + expect( + ( + await sql`SELECT indisvalid FROM pg_index WHERE indexrelid = 'embedding_search_cosine_hnsw_idx'::regclass` + )[0].indisvalid + ).toBe(false) + await buildSearchIndexes(sql) + const indexes = await sql`SELECT indexrelid, indisvalid FROM pg_index + INNER JOIN pg_class ON pg_class.oid = pg_index.indexrelid + WHERE indrelid IN ('embedding_search'::regclass, 'embedding_keyword_search'::regclass) + AND (relname LIKE '%cosine_hnsw_idx' OR relname IN + ('embedding_keyword_search_kb_idx', 'embedding_keyword_search_document_idx', 'embedding_keyword_search_content_idx')) + ORDER BY indexrelid` + expect(indexes).toHaveLength(9) + expect(indexes.every((index) => index.indisvalid)).toBe(true) + await buildSearchIndexes(sql) + const replay = await sql`SELECT indexrelid, indisvalid FROM pg_index + INNER JOIN pg_class ON pg_class.oid = pg_index.indexrelid + WHERE indrelid IN ('embedding_search'::regclass, 'embedding_keyword_search'::regclass) + AND (relname LIKE '%cosine_hnsw_idx' OR relname IN + ('embedding_keyword_search_kb_idx', 'embedding_keyword_search_document_idx', 'embedding_keyword_search_content_idx')) + ORDER BY indexrelid` + expect(replay).toEqual(indexes) + }, 60_000) + + it('keeps inserts, state changes, width changes, and deletes synchronous after the upgrade', async () => { + await sql.unsafe(`INSERT INTO embedding + (id, knowledge_base_id, document_id, chunk_index, chunk_hash, content, content_length, token_count, start_offset, end_offset, embedding_384) + VALUES ('inserted', 'prefix', 'inserted-document', 0, 'inserted-hash', 'Synthetic fixture', 17, 4, 0, 17, + array_fill(0.01::real, ARRAY[384])::vector(384))`) + const [inserted] = await sql.unsafe(`SELECT vector_dims(vector_384) AS dimensions, + num_nonnulls(${fields.join(', ')}) AS populated, binary_384 IS NOT NULL AS legacy + FROM embedding_search WHERE id = 'inserted'`) + expect(inserted).toEqual({ dimensions: 384, populated: 1, legacy: true }) + await sql`UPDATE embedding SET enabled = false WHERE id = 'chunk-1'` + expect((await sql`SELECT enabled FROM embedding_search WHERE id = 'chunk-1'`)[0].enabled).toBe( + false + ) + await sql.unsafe(`UPDATE embedding SET embedding = NULL, + embedding_3072 = array_fill(0.02::real, ARRAY[3072])::vector(3072) WHERE id = 'chunk-1'`) + const [row] = await sql.unsafe(`SELECT vector_dims(vector_3072) AS dimensions, + num_nonnulls(${fields.join(', ')}) AS populated, binary_3072 IS NOT NULL AS legacy + FROM embedding_search WHERE id = 'chunk-1'`) + expect(row).toEqual({ dimensions: 3072, populated: 1, legacy: true }) + expect(await backfillSearchVectors(sql)).toBe(0) + await sql`DELETE FROM embedding WHERE id = 'chunk-1'` + expect(await sql`SELECT id FROM embedding_search WHERE id = 'chunk-1'`).toHaveLength(0) + }) + + it.each([128, 512])( + 'stores a %i-token keyword vector without widening semantic candidates', + async (tokens) => { + const id = generateId() + await sql.unsafe( + `INSERT INTO embedding + (id, knowledge_base_id, document_id, chunk_index, chunk_hash, content, content_length, + token_count, start_offset, end_offset, embedding) + SELECT $1, 'full', $1, 0, $1, string_agg(md5(n::text), ' '), $2 * 33, + $2, 0, $2 * 33, array_fill(0.01::real, ARRAY[1536])::vector(1536) + FROM generate_series(1, $2::int) n`, + [id, tokens] + ) + const [row] = await sql`SELECT s.content_tsv = e.content_tsv AS identical, + pg_column_toast_chunk_id(s.content_tsv) IS NULL AS inline, + pg_column_size(s.content_tsv) AS bytes + FROM embedding_keyword_search s JOIN embedding e ON e.id = s.id WHERE s.id = ${id}` + expect(row.identical).toBe(true) + expect(row.bytes).toBeGreaterThan(2048) + expect(row.inline).toBe(tokens === 128) + expect( + (await sql`SELECT count(*)::int AS count FROM embedding_search WHERE id = ${id}`)[0].count + ).toBe(1) + } + ) + + it.each([1536, 3072])( + 'keeps lookup identities inline beside %i-dimensional vectors', + async (width) => { + const knowledgeBaseId = generateId() + const documentId = generateId() + const chunkId = generateId() + await sql`INSERT INTO knowledge_base (id, user_id, name, workspace_id, embedding_model) + VALUES (${knowledgeBaseId}, 'reader', ${`Wide fixture ${width}`}, 'workspace', 'gemini-embedding-001')` + const vectorColumn = width === 1536 ? 'embedding' : 'embedding_3072' + await sql.unsafe( + `INSERT INTO embedding + (id, knowledge_base_id, document_id, chunk_index, chunk_hash, content, content_length, token_count, start_offset, end_offset, ${vectorColumn}) + VALUES ($1, $2, $3, 0, 'wide-hash', 'Synthetic fixture', 17, 4, 0, 17, + array_fill(0.01::real, ARRAY[${width}])::vector(${width}))`, + [chunkId, knowledgeBaseId, documentId] + ) + const [row] = await sql`SELECT pg_column_toast_chunk_id(id) AS chunk, + pg_column_toast_chunk_id(knowledge_base_id) AS knowledge_base, + pg_column_toast_chunk_id(document_id) AS document + FROM embedding_search WHERE id = ${chunkId}` + expect(row).toEqual({ chunk: null, knowledge_base: null, document: null }) + } + ) + + it.each([ + { name: 'binary', table: 'embedding_search', backfill: backfillEmbeddingSearch }, + { name: 'vector', table: 'embedding_search', backfill: backfillSearchVectors }, + { name: 'keyword', table: 'embedding_keyword_search', backfill: backfillSearchKeywords }, + ])( + 'resumes $name after cancellation and traverses fully populated source pages', + async ({ name, table, backfill }) => { + await backfillSearchVectors(sql) + await sql.unsafe(`INSERT INTO embedding + (id, knowledge_base_id, document_id, chunk_index, chunk_hash, content, content_length, + token_count, start_offset, end_offset, embedding) + SELECT 'recovery-' || lpad(n::text, 4, '0'), 'prefix', 'recovery-document', n, + 'recovery-hash-' || n, 'Synthetic recovery fixture', 26, 4, 0, 26, + array_fill(0.01::real, ARRAY[1536])::vector(1536) + FROM generate_series(1, 1001) n`) + if (name === 'vector') { + await sql.unsafe(`UPDATE embedding_search SET ${fields.map((field) => `${field} = NULL`).join(', ')} + WHERE id LIKE 'recovery-%'`) + } else { + await sql.unsafe(`DELETE FROM ${table} WHERE id LIKE 'recovery-%'`) + } + await sql.unsafe(`CREATE FUNCTION cancel_projection_batch() RETURNS trigger LANGUAGE plpgsql AS $$ + BEGIN + IF NEW.id = 'recovery-0750' THEN + RAISE EXCEPTION 'Synthetic statement cancellation' USING ERRCODE = '57014'; + END IF; + RETURN NEW; + END; + $$`) + await sql.unsafe(`CREATE TRIGGER cancel_projection_batch BEFORE INSERT OR UPDATE ON ${table} + FOR EACH ROW EXECUTE FUNCTION cancel_projection_batch()`) + try { + await expect(backfill(sql)).rejects.toMatchObject({ code: '57014' }) + } finally { + await sql.unsafe(`DROP TRIGGER cancel_projection_batch ON ${table}`) + await sql.unsafe('DROP FUNCTION cancel_projection_batch()') + } + const populated = name === 'vector' ? `AND coalesce(${fields.join(', ')}) IS NOT NULL` : '' + const [{ committed }] = await sql.unsafe>(` + SELECT count(*)::int AS committed FROM ${table} WHERE id LIKE 'recovery-%' ${populated}`) + expect(committed).toBeGreaterThan(0) + expect(committed).toBeLessThan(750) + expect(await backfill(sql)).toBe(1001 - committed) + expect(await backfill(sql)).toBe(0) + if (name === 'vector') { + await sql.unsafe(`UPDATE embedding_search SET ${fields.map((field) => `${field} = NULL`).join(', ')} + WHERE id = 'recovery-1001'`) + } else { + await sql.unsafe(`DELETE FROM ${table} WHERE id = 'recovery-1001'`) + } + expect(await backfill(sql)).toBe(1) + await sql`DELETE FROM embedding WHERE id LIKE 'recovery-%'` + }, + 60_000 + ) + + it('runs 0016 directly after a partially committed, unjournaled 0015', async () => { + await sql`CREATE TABLE script_migrations (name text PRIMARY KEY, applied_at timestamptz NOT NULL DEFAULT now())` + for (const migration of scriptMigrations) { + if (migration.name !== '0016_backfill_search_vectors') { + await sql`INSERT INTO script_migrations (name) VALUES (${migration.name})` + } + } + await backfillEmbeddingSearch(sql) + await sql.unsafe(`INSERT INTO embedding + (id, knowledge_base_id, document_id, chunk_index, chunk_hash, content, content_length, + token_count, start_offset, end_offset, embedding) + SELECT 'upgrade-' || lpad(n::text, 4, '0'), 'prefix', 'upgrade-document', n, + 'upgrade-hash-' || n, 'Synthetic upgrade fixture', 25, 4, 0, 25, + array_fill(0.01::real, ARRAY[1536])::vector(1536) + FROM generate_series(1, 1001) n`) + await sql`DELETE FROM embedding_search WHERE id > 'upgrade-0501' AND id LIKE 'upgrade-%'` + await sql`DELETE FROM embedding_keyword_search WHERE id LIKE 'upgrade-%'` + await sql.unsafe(`CREATE FUNCTION cancel_projection_upgrade() RETURNS trigger LANGUAGE plpgsql AS $$ + BEGIN + IF NEW.id = 'upgrade-0750' THEN + RAISE EXCEPTION 'Synthetic upgrade cancellation' USING ERRCODE = '57014'; + END IF; + RETURN NEW; + END; + $$`) + await sql.unsafe(`CREATE TRIGGER cancel_projection_upgrade BEFORE INSERT OR UPDATE ON embedding_search + FOR EACH ROW EXECUTE FUNCTION cancel_projection_upgrade()`) + try { + await expect(runScriptMigrations(sql)).rejects.toMatchObject({ code: '57014' }) + expect(await sql`SELECT name FROM script_migrations WHERE name >= '0015'`).toHaveLength(0) + } finally { + await sql.unsafe('DROP TRIGGER cancel_projection_upgrade ON embedding_search') + await sql.unsafe('DROP FUNCTION cancel_projection_upgrade()') + } + await runScriptMigrations(sql) + expect( + await sql`SELECT name FROM script_migrations WHERE name >= '0015' ORDER BY name` + ).toEqual([ + { name: '0015_backfill_embedding_search' }, + { name: '0016_backfill_search_vectors' }, + ]) + const [{ complete }] = await sql`SELECT count(*)::int AS complete FROM embedding e + JOIN embedding_search s ON s.id = e.id JOIN embedding_keyword_search k ON k.id = e.id + WHERE e.id LIKE 'upgrade-%' AND s."binary" = binary_quantize(e.embedding)::bit(1536) + AND s.vector_512 = subvector(e.embedding, 1, 512)::halfvec(512) + AND k.content_tsv = e.content_tsv` + expect(complete).toBe(1001) + await runScriptMigrations(sql) + await sql`DELETE FROM embedding WHERE id LIKE 'upgrade-%'` + }, 60_000) +}) diff --git a/packages/db/script-migrations/0016_backfill_search_vectors.ts b/packages/db/script-migrations/0016_backfill_search_vectors.ts new file mode 100644 index 00000000000..3b589cfbd82 --- /dev/null +++ b/packages/db/script-migrations/0016_backfill_search_vectors.ts @@ -0,0 +1,252 @@ +import type { ScriptMigration } from '@sim/db/script-migrations/types' +import { createLogger } from '@sim/logger' +import postgres, { type Sql } from 'postgres' + +const logger = createLogger('SearchVectorProjection') +const BATCH_SIZE = 500 +const WIDTHS = [1536, 384, 512, 768, 1024, 3072] as const +const SOURCE_WIDTHS = [1536, 384, 768, 1024, 3072] as const +const field = (name: string, width: number) => (width === 1536 ? name : `${name}_${width}`) +const columns = WIDTHS.map((width) => field('vector', width)) +const missing = columns.map((column) => `s.${column} IS NULL`).join(' AND ') + +/** Shortening is valid only for the two OpenAI models trained for prefix retrieval. */ +function projections(prefix: string, shortened: string): string { + return WIDTHS.map((width) => + width === 512 + ? `CASE WHEN ${shortened} THEN subvector(coalesce(${SOURCE_WIDTHS.map((size) => `${prefix}.${field('embedding', size)}`).join(', ')}), 1, 512)::halfvec(512) END` + : `CASE WHEN NOT (${shortened}) THEN ${prefix}.${field('embedding', width)}::halfvec(${width}) END` + ).join(', ') +} + +/** Replaces the projection atomically, then fills only missing rows in independently committed pages. */ +export async function backfillSearchVectors(sql: Sql): Promise { + await sql.begin(async (tx) => { + await tx.unsafe("SET LOCAL lock_timeout = '5s'") + /** Acquire source and projection locks in the same order as embedding writers. */ + await tx.unsafe('LOCK TABLE embedding IN SHARE ROW EXCLUSIVE MODE') + /** Wide inline vectors must not push lookup identities into overflow storage. */ + await tx.unsafe( + `ALTER TABLE embedding_search ${['id', 'knowledge_base_id', 'document_id', ...columns].map((column) => `ALTER COLUMN ${column} SET STORAGE PLAIN`).join(', ')}` + ) + await tx.unsafe(`CREATE OR REPLACE FUNCTION sync_embedding_search() + RETURNS trigger LANGUAGE plpgsql AS $$ + DECLARE shortened boolean; + BEGIN + SELECT embedding_model IN ('text-embedding-3-small', 'text-embedding-3-large') + AND NEW.embedding_384 IS NULL INTO STRICT shortened + FROM knowledge_base WHERE id = NEW.knowledge_base_id; + INSERT INTO embedding_search + (id, knowledge_base_id, document_id, enabled, + "binary", binary_384, binary_768, binary_1024, binary_3072, ${columns.join(', ')}) + VALUES (NEW.id, NEW.knowledge_base_id, NEW.document_id, NEW.enabled, + binary_quantize(NEW.embedding)::bit(1536), binary_quantize(NEW.embedding_384)::bit(384), + binary_quantize(NEW.embedding_768)::bit(768), binary_quantize(NEW.embedding_1024)::bit(1024), + binary_quantize(NEW.embedding_3072)::bit(3072), ${projections('NEW', 'shortened')}) + ON CONFLICT (id) DO UPDATE SET + knowledge_base_id = EXCLUDED.knowledge_base_id, document_id = EXCLUDED.document_id, + enabled = EXCLUDED.enabled, "binary" = EXCLUDED."binary", binary_384 = EXCLUDED.binary_384, + binary_768 = EXCLUDED.binary_768, binary_1024 = EXCLUDED.binary_1024, + binary_3072 = EXCLUDED.binary_3072, + ${columns.map((column) => `${column} = EXCLUDED.${column}`).join(', ')}; + RETURN NEW; + END; + $$`) + await tx.unsafe(`CREATE OR REPLACE TRIGGER embedding_search_sync + AFTER INSERT OR UPDATE OF knowledge_base_id, document_id, enabled, + embedding, embedding_384, embedding_768, embedding_1024, embedding_3072 ON embedding + FOR EACH ROW EXECUTE FUNCTION sync_embedding_search()`) + }) + + let afterId = '' + let count = 0 + let scanned = 0 + const startedAt = Date.now() + for (;;) { + const [page] = await sql.begin(async (tx) => { + await tx.unsafe("SET LOCAL lock_timeout = '5s'") + await tx.unsafe("SET LOCAL statement_timeout = '60s'") + return tx.unsafe>( + ` + WITH source_page AS MATERIALIZED ( + SELECT id FROM embedding WHERE id > $1 ORDER BY id LIMIT ${BATCH_SIZE} + ), missing AS MATERIALIZED ( + SELECT p.id FROM source_page p WHERE NOT EXISTS ( + SELECT 1 FROM embedding_search s WHERE s.id = p.id AND NOT (${missing}) + ) + ), batch AS MATERIALIZED ( + SELECT e.id, e.knowledge_base_id, e.document_id, e.enabled, + e.embedding, e.embedding_384, e.embedding_768, e.embedding_1024, e.embedding_3072, + k.embedding_model IN ('text-embedding-3-small', 'text-embedding-3-large') + AND e.embedding_384 IS NULL AS shortened + FROM missing m INNER JOIN embedding e ON e.id = m.id + INNER JOIN knowledge_base k ON k.id = e.knowledge_base_id + ORDER BY e.id FOR KEY SHARE OF e + ), inserted AS ( + INSERT INTO embedding_search AS s + (id, knowledge_base_id, document_id, enabled, + "binary", binary_384, binary_768, binary_1024, binary_3072, ${columns.join(', ')}) + SELECT id, knowledge_base_id, document_id, enabled, + binary_quantize(embedding)::bit(1536), binary_quantize(embedding_384)::bit(384), + binary_quantize(embedding_768)::bit(768), binary_quantize(embedding_1024)::bit(1024), + binary_quantize(embedding_3072)::bit(3072), ${projections('batch', 'shortened')} + FROM batch ON CONFLICT (id) DO UPDATE SET + ${columns.map((column) => `${column} = EXCLUDED.${column}`).join(', ')} + WHERE ${missing} RETURNING id + ) SELECT max(id) AS after_id, count(*)::int AS scanned, + (SELECT count(*)::int FROM inserted) AS inserted FROM source_page`, + [afterId] + ) + }) + if (!page.after_id) break + afterId = page.after_id + count += page.inserted + scanned += page.scanned + if (scanned % (BATCH_SIZE * 100) === 0) { + logger.info('Vector search backfill progress', { + scanned, + inserted: count, + elapsedMs: Date.now() - startedAt, + }) + } + } + await sql.unsafe('VACUUM (ANALYZE) embedding_search') + return count +} + +/** Keeps keyword scoring independent of the full-vector and chunk-content storage working sets. */ +export async function backfillSearchKeywords(sql: Sql): Promise { + await sql.begin(async (tx) => { + await tx.unsafe("SET LOCAL lock_timeout = '5s'") + await tx.unsafe('LOCK TABLE embedding IN SHARE ROW EXCLUSIVE MODE') + await tx.unsafe(`ALTER TABLE embedding_keyword_search + ALTER COLUMN id SET STORAGE PLAIN, + ALTER COLUMN knowledge_base_id SET STORAGE PLAIN, + ALTER COLUMN document_id SET STORAGE PLAIN, + ALTER COLUMN content_tsv SET STORAGE MAIN`) + await tx.unsafe(`CREATE OR REPLACE FUNCTION sync_embedding_keyword_search() + RETURNS trigger LANGUAGE plpgsql AS $$ + BEGIN + INSERT INTO embedding_keyword_search (id, knowledge_base_id, document_id, enabled, content_tsv) + VALUES (NEW.id, NEW.knowledge_base_id, NEW.document_id, NEW.enabled, NEW.content_tsv) + ON CONFLICT (id) DO UPDATE SET + knowledge_base_id = EXCLUDED.knowledge_base_id, document_id = EXCLUDED.document_id, + enabled = EXCLUDED.enabled, content_tsv = EXCLUDED.content_tsv; + RETURN NEW; + END; + $$`) + await tx.unsafe(`CREATE OR REPLACE TRIGGER embedding_keyword_search_sync + AFTER INSERT OR UPDATE OF knowledge_base_id, document_id, enabled, content ON embedding + FOR EACH ROW EXECUTE FUNCTION sync_embedding_keyword_search()`) + }) + let afterId = '' + let count = 0 + let scanned = 0 + const startedAt = Date.now() + for (;;) { + const [page] = await sql.begin(async (tx) => { + await tx.unsafe("SET LOCAL lock_timeout = '5s'") + await tx.unsafe("SET LOCAL statement_timeout = '60s'") + return tx.unsafe>( + ` + WITH source_page AS MATERIALIZED ( + SELECT id FROM embedding WHERE id > $1 ORDER BY id LIMIT ${BATCH_SIZE} + ), missing AS MATERIALIZED ( + SELECT p.id FROM source_page p WHERE NOT EXISTS ( + SELECT 1 FROM embedding_keyword_search s WHERE s.id = p.id + ) + ), batch AS MATERIALIZED ( + SELECT e.id, e.knowledge_base_id, e.document_id, e.enabled, e.content_tsv + FROM missing m INNER JOIN embedding e ON e.id = m.id + ORDER BY e.id FOR KEY SHARE OF e + ), inserted AS ( + INSERT INTO embedding_keyword_search (id, knowledge_base_id, document_id, enabled, content_tsv) + SELECT id, knowledge_base_id, document_id, enabled, content_tsv FROM batch + ON CONFLICT (id) DO NOTHING RETURNING id + ) SELECT max(id) AS after_id, count(*)::int AS scanned, + (SELECT count(*)::int FROM inserted) AS inserted FROM source_page`, + [afterId] + ) + }) + if (!page.after_id) break + afterId = page.after_id + count += page.inserted + scanned += page.scanned + if (scanned % (BATCH_SIZE * 100) === 0) { + logger.info('Keyword search backfill progress', { + scanned, + inserted: count, + elapsedMs: Date.now() - startedAt, + }) + } + } + await sql.unsafe('VACUUM (ANALYZE) embedding_keyword_search') + return count +} + +/** Builds new indexes after bulk loading; interrupted builds are repaired without rebuilding valid ones. */ +export async function buildSearchIndexes(sql: Sql): Promise { + const indexes = [ + ...WIDTHS.map((width) => ({ + name: `embedding_search${width === 1536 ? '' : `_${width}`}_cosine_hnsw_idx`, + table: 'embedding_search', + definition: `ON embedding_search USING hnsw (${field('vector', width)} halfvec_cosine_ops) WITH (m=16, ef_construction=64)`, + })), + { + name: 'embedding_keyword_search_kb_idx', + table: 'embedding_keyword_search', + definition: 'ON embedding_keyword_search (knowledge_base_id)', + }, + { + name: 'embedding_keyword_search_document_idx', + table: 'embedding_keyword_search', + definition: 'ON embedding_keyword_search (document_id)', + }, + { + name: 'embedding_keyword_search_content_idx', + table: 'embedding_keyword_search', + definition: 'ON embedding_keyword_search USING gin (content_tsv)', + }, + ] + const [{ timeout }] = await sql`SELECT current_setting('lock_timeout') AS timeout` + await sql.unsafe('SET lock_timeout = 0') + try { + for (const { name, table, definition } of indexes) { + const [existing] = + await sql`SELECT i.indisvalid, format('%I.%I', n.nspname, c.relname) AS qualified_name + FROM pg_index i JOIN pg_class c ON c.oid = i.indexrelid + JOIN pg_namespace n ON n.oid = c.relnamespace + WHERE i.indrelid = to_regclass(${table}) AND c.relname = ${name}` + if (existing?.indisvalid) continue + if (existing) await sql.unsafe(`DROP INDEX CONCURRENTLY ${existing.qualified_name}`) + const startedAt = Date.now() + logger.info('Building search index', { index: name }) + await sql.unsafe(`CREATE INDEX CONCURRENTLY ${name} ${definition}`) + logger.info('Search index built', { index: name, elapsedMs: Date.now() - startedAt }) + } + } finally { + await sql`SELECT set_config('lock_timeout', ${timeout}, false)` + } +} + +export const backfillSearchVectorsMigration: ScriptMigration = { + name: '0016_backfill_search_vectors', + supersedes: ['0015_backfill_embedding_search'], + async up(sql) { + const rows = await backfillSearchVectors(sql) + const keywordRows = await backfillSearchKeywords(sql) + await buildSearchIndexes(sql) + logger.info('Search projections initialized', { rows, keywordRows }) + }, +} + +if (import.meta.main) { + const url = process.env.MIGRATION_DATABASE_URL ?? process.env.DATABASE_URL + if (!url) throw new Error('DATABASE_URL is required to initialize search vectors') + const sql = postgres(url, { max: 1, max_lifetime: null, onnotice: () => undefined }) + try { + await backfillSearchVectorsMigration.up(sql) + } finally { + await sql.end() + } +} diff --git a/packages/db/script-migrations/index.ts b/packages/db/script-migrations/index.ts index ce468612393..6a5d7e9db33 100644 --- a/packages/db/script-migrations/index.ts +++ b/packages/db/script-migrations/index.ts @@ -1,7 +1,7 @@ import { reconcileOAuthProviderLifecycleMigration } from '@sim/db/script-migrations/0012_reconcile_oauth_provider_lifecycle' import { backfillLegacyKnowledgeBaseWorkspacesMigration } from '@sim/db/script-migrations/0013_backfill_legacy_knowledge_base_workspaces' import { requireKnowledgeBaseOwnerMigration } from '@sim/db/script-migrations/0014_require_knowledge_base_owner' -import { backfillEmbeddingSearchMigration } from '@sim/db/script-migrations/0015_backfill_embedding_search' +import { backfillSearchVectorsMigration } from '@sim/db/script-migrations/0016_backfill_search_vectors' import type { Sql } from 'postgres' import { backfillTableOrderKeys } from './0001_backfill_table_order_keys' import { backfillPausedBillingAttribution } from './0002_backfill_paused_billing_attribution' @@ -20,7 +20,7 @@ export type { ScriptMigration } from './types' /** * Ordered, append-only registry of script migrations. An entry may be deleted - * once a later SQL migration supersedes it (accepting that deployments which + * once a later migration supersedes it (accepting that deployments which * never ran it skip the backfill) — never renamed or reordered. */ export const scriptMigrations: readonly ScriptMigration[] = [ @@ -38,7 +38,8 @@ export const scriptMigrations: readonly ScriptMigration[] = [ reconcileOAuthProviderLifecycleMigration, backfillLegacyKnowledgeBaseWorkspacesMigration, requireKnowledgeBaseOwnerMigration, - backfillEmbeddingSearchMigration, + /** 0016 completes partially applied 0015 binary projections together with the new search vectors. */ + backfillSearchVectorsMigration, ] /** @@ -98,10 +99,11 @@ export async function runScriptMigrations(sql: Sql): Promise { console.log(`Applying script migration ${migration.name}...`) const startedAt = Date.now() await migration.up(sql) - await sql` - INSERT INTO script_migrations (name) VALUES (${migration.name}) - ON CONFLICT (name) DO NOTHING - ` + await sql.begin(async (tx) => { + for (const name of [migration.name, ...(migration.supersedes ?? [])]) { + await tx`INSERT INTO script_migrations (name) VALUES (${name}) ON CONFLICT (name) DO NOTHING` + } + }) console.log(`Script migration ${migration.name} applied in ${Date.now() - startedAt}ms.`) } } diff --git a/packages/db/script-migrations/types.ts b/packages/db/script-migrations/types.ts index 677b7d9c30e..d7008144ce0 100644 --- a/packages/db/script-migrations/types.ts +++ b/packages/db/script-migrations/types.ts @@ -24,6 +24,8 @@ import type { Sql } from 'postgres' export interface ScriptMigration { /** Unique stable identifier recorded in `script_migrations`; never rename after release. */ name: string + /** Earlier, unregistered migrations whose work this migration fully completes. Recorded only on success. */ + supersedes?: readonly string[] /** Env vars the migration needs; the runner throws before `up` if any is unset. */ requiredEnv?: readonly string[] /** diff --git a/packages/testing/src/mocks/schema.mock.ts b/packages/testing/src/mocks/schema.mock.ts index aef3d8c8ee0..6d1445be5b4 100644 --- a/packages/testing/src/mocks/schema.mock.ts +++ b/packages/testing/src/mocks/schema.mock.ts @@ -904,6 +904,13 @@ export const schemaMock = { createdAt: 'knowledgeBaseTagDefinitions.createdAt', updatedAt: 'knowledgeBaseTagDefinitions.updatedAt', }, + embeddingKeywordSearch: { + id: 'embeddingKeywordSearch.id', + knowledgeBaseId: 'embeddingKeywordSearch.knowledgeBaseId', + documentId: 'embeddingKeywordSearch.documentId', + enabled: 'embeddingKeywordSearch.enabled', + contentTsv: 'embeddingKeywordSearch.contentTsv', + }, embeddingSearch: { id: 'embeddingSearch.id', knowledgeBaseId: 'embeddingSearch.knowledgeBaseId', @@ -914,6 +921,12 @@ export const schemaMock = { binary768: 'embeddingSearch.binary768', binary1024: 'embeddingSearch.binary1024', binary3072: 'embeddingSearch.binary3072', + vector: 'embeddingSearch.vector', + vector384: 'embeddingSearch.vector384', + vector512: 'embeddingSearch.vector512', + vector768: 'embeddingSearch.vector768', + vector1024: 'embeddingSearch.vector1024', + vector3072: 'embeddingSearch.vector3072', }, embedding: { id: 'embedding.id', diff --git a/scripts/test-knowledge-acls.ts b/scripts/test-knowledge-acls.ts index 5600ac7ecb8..b8c00c6fad6 100644 --- a/scripts/test-knowledge-acls.ts +++ b/scripts/test-knowledge-acls.ts @@ -18,6 +18,7 @@ const container = `sim-acl-test-${generateId()}` const redisContainer = `${container}-redis` const database = 'sim_acl_test_application' const scale = process.env.KNOWLEDGE_SCALE_TEST === 'true' +const searchPerformance = process.env.KNOWLEDGE_SEARCH_PERFORMANCE_TEST === 'true' const testFilters = process.argv.slice(2) if (testFilters.some((filter) => filter.startsWith('-')) || (scale && testFilters.length)) { throw new Error('Pass only filename filters, and do not combine them with the scale suite') @@ -54,14 +55,21 @@ try { 'run', '--rm', '--detach', - ...(scale ? ['--shm-size', '3g'] : []), + ...(scale + ? ['--shm-size', '3g'] + : searchPerformance + ? ['--cpus', '2', '--memory', '1g', '--shm-size', '512m'] + : []), '--name', container, '--env', 'POSTGRES_HOST_AUTH_METHOD=trust', '--publish', '127.0.0.1::5432', - 'pgvector/pgvector:pg17', + process.env.KNOWLEDGE_TEST_POSTGRES_IMAGE ?? 'pgvector/pgvector:pg17', + ...(searchPerformance + ? ['-c', 'shared_buffers=256MB', '-c', 'work_mem=8MB', '-c', 'maintenance_work_mem=256MB'] + : []), ], { capture: true } ) @@ -154,7 +162,7 @@ try { cwd: path.join(root, 'packages/db'), env: environment, }) - run('bun', ['./script-migrations/0015_backfill_embedding_search.ts'], { + run('bun', ['./script-migrations/0016_backfill_search_vectors.ts'], { cwd: path.join(root, 'packages/db'), env: environment, }) @@ -172,7 +180,7 @@ try { env: environment, } ) - if (!scale && testFilters.length === 0) + if (!scale && testFilters.length === 0) { run( 'bunx', [ @@ -187,6 +195,15 @@ try { env: environment, } ) + run( + 'bunx', + ['vitest', 'run', 'script-migrations/0016_backfill_search_vectors.postgres.test.ts'], + { + cwd: path.join(root, 'packages/db'), + env: environment, + } + ) + } logger.info( scale ? 'Opt-in knowledge scale measurements passed'