From 52b72dd5a8b83e62056b9fb97f2ea943e4d285bf Mon Sep 17 00:00:00 2001 From: Robin Braumann <50590409+bjorger@users.noreply.github.com> Date: Thu, 9 Jul 2026 09:14:29 +0200 Subject: [PATCH] feat(core): Add vector store support to the agents SDK (no-changelog) (#33683) Co-authored-by: Cursor --- packages/@n8n/agents/package.json | 53 +- .../fixtures/bulk-vector-fixture.json | 6413 +++++++++++++++++ .../fixtures/generate-bulk-vector-fixture.ts | 146 + .../agent-vector-store-pinecone.test.ts | 57 + .../agent-vector-store-postgres.test.ts | 51 + .../agent-vector-store-qdrant.test.ts | 60 + .../agent-vector-store-supabase.test.ts | 72 + .../integration/pg-vector-store.test.ts | 288 + .../integration/pinecone-vector-store.test.ts | 262 + .../integration/qdrant-vector-store.test.ts | 287 + .../integration/supabase-vector-store.test.ts | 288 + .../vector-store-bulk-pinecone.test.ts | 53 + .../vector-store-bulk-postgres.test.ts | 46 + .../vector-store-bulk-qdrant.test.ts | 56 + .../vector-store-bulk-supabase.test.ts | 61 + .../integration/vector-store-helpers.ts | 341 + .../pinecone-filter-translation.test.ts | 282 + .../src/__tests__/qdrant-point-id.test.ts | 55 + .../supabase-filter-translation.test.ts | 253 + packages/@n8n/agents/src/index.ts | 23 +- .../agents/src/runtime/model/model-factory.ts | 2 +- .../src/sdk/__tests__/vector-store.test.ts | 435 ++ packages/@n8n/agents/src/sdk/agent.ts | 9 + packages/@n8n/agents/src/sdk/tool.ts | 16 + .../agents/src/sdk/vector-store-filter.ts | 105 + packages/@n8n/agents/src/sdk/vector-store.ts | 192 + .../agents/src/storage/base-vector-store.ts | 32 + packages/@n8n/agents/src/types/index.ts | 11 + .../@n8n/agents/src/types/sdk/vector-store.ts | 50 + .../@n8n/agents/src/vector-stores/pinecone.ts | 196 + .../@n8n/agents/src/vector-stores/postgres.ts | 254 + .../@n8n/agents/src/vector-stores/qdrant.ts | 188 + .../@n8n/agents/src/vector-stores/supabase.ts | 256 + packages/@n8n/nodes-langchain/package.json | 4 +- pnpm-lock.yaml | 28 +- pnpm-workspace.yaml | 2 + 36 files changed, 10919 insertions(+), 8 deletions(-) create mode 100644 packages/@n8n/agents/src/__tests__/fixtures/bulk-vector-fixture.json create mode 100644 packages/@n8n/agents/src/__tests__/fixtures/generate-bulk-vector-fixture.ts create mode 100644 packages/@n8n/agents/src/__tests__/integration/agent-vector-store-pinecone.test.ts create mode 100644 packages/@n8n/agents/src/__tests__/integration/agent-vector-store-postgres.test.ts create mode 100644 packages/@n8n/agents/src/__tests__/integration/agent-vector-store-qdrant.test.ts create mode 100644 packages/@n8n/agents/src/__tests__/integration/agent-vector-store-supabase.test.ts create mode 100644 packages/@n8n/agents/src/__tests__/integration/pg-vector-store.test.ts create mode 100644 packages/@n8n/agents/src/__tests__/integration/pinecone-vector-store.test.ts create mode 100644 packages/@n8n/agents/src/__tests__/integration/qdrant-vector-store.test.ts create mode 100644 packages/@n8n/agents/src/__tests__/integration/supabase-vector-store.test.ts create mode 100644 packages/@n8n/agents/src/__tests__/integration/vector-store-bulk-pinecone.test.ts create mode 100644 packages/@n8n/agents/src/__tests__/integration/vector-store-bulk-postgres.test.ts create mode 100644 packages/@n8n/agents/src/__tests__/integration/vector-store-bulk-qdrant.test.ts create mode 100644 packages/@n8n/agents/src/__tests__/integration/vector-store-bulk-supabase.test.ts create mode 100644 packages/@n8n/agents/src/__tests__/integration/vector-store-helpers.ts create mode 100644 packages/@n8n/agents/src/__tests__/pinecone-filter-translation.test.ts create mode 100644 packages/@n8n/agents/src/__tests__/qdrant-point-id.test.ts create mode 100644 packages/@n8n/agents/src/__tests__/supabase-filter-translation.test.ts create mode 100644 packages/@n8n/agents/src/sdk/__tests__/vector-store.test.ts create mode 100644 packages/@n8n/agents/src/sdk/vector-store-filter.ts create mode 100644 packages/@n8n/agents/src/sdk/vector-store.ts create mode 100644 packages/@n8n/agents/src/storage/base-vector-store.ts create mode 100644 packages/@n8n/agents/src/types/sdk/vector-store.ts create mode 100644 packages/@n8n/agents/src/vector-stores/pinecone.ts create mode 100644 packages/@n8n/agents/src/vector-stores/postgres.ts create mode 100644 packages/@n8n/agents/src/vector-stores/qdrant.ts create mode 100644 packages/@n8n/agents/src/vector-stores/supabase.ts diff --git a/packages/@n8n/agents/package.json b/packages/@n8n/agents/package.json index b0f920f7435..b5176d96c92 100644 --- a/packages/@n8n/agents/package.json +++ b/packages/@n8n/agents/package.json @@ -15,6 +15,18 @@ ], "sandbox": [ "dist/workspace/sandbox/index.d.ts" + ], + "vector-stores/postgres": [ + "dist/vector-stores/postgres.d.ts" + ], + "vector-stores/pinecone": [ + "dist/vector-stores/pinecone.d.ts" + ], + "vector-stores/qdrant": [ + "dist/vector-stores/qdrant.d.ts" + ], + "vector-stores/supabase": [ + "dist/vector-stores/supabase.d.ts" ] } }, @@ -35,6 +47,22 @@ "./sandbox": { "types": "./dist/workspace/sandbox/index.d.ts", "default": "./dist/workspace/sandbox/index.js" + }, + "./vector-stores/postgres": { + "types": "./dist/vector-stores/postgres.d.ts", + "default": "./dist/vector-stores/postgres.js" + }, + "./vector-stores/pinecone": { + "types": "./dist/vector-stores/pinecone.d.ts", + "default": "./dist/vector-stores/pinecone.js" + }, + "./vector-stores/qdrant": { + "types": "./dist/vector-stores/qdrant.d.ts", + "default": "./dist/vector-stores/qdrant.js" + }, + "./vector-stores/supabase": { + "types": "./dist/vector-stores/supabase.d.ts", + "default": "./dist/vector-stores/supabase.js" } }, "files": [ @@ -56,7 +84,8 @@ "test:dev": "vitest --silent=false", "test:integration": "vitest run --config vitest.integration.config.mjs", "test:integration:replay": "cross-env CI=1 vitest run --config vitest.integration.config.mjs", - "test:integration:record": "cross-env VCR_MODE=record vitest run --config vitest.integration.config.mjs" + "test:integration:record": "cross-env VCR_MODE=record vitest run --config vitest.integration.config.mjs", + "fixtures:generate": "tsx src/__tests__/fixtures/generate-bulk-vector-fixture.ts" }, "dependencies": { "@ai-sdk/amazon-bedrock": "catalog:", @@ -89,6 +118,10 @@ "zod-to-json-schema": "catalog:" }, "peerDependencies": { + "@pinecone-database/pinecone": "catalog:", + "@qdrant/js-client-rest": "catalog:", + "@supabase/supabase-js": "catalog:", + "pg": "catalog:", "zod": "catalog:" }, "peerDependenciesMeta": { @@ -103,16 +136,34 @@ }, "@opentelemetry/exporter-trace-otlp-http": { "optional": true + }, + "pg": { + "optional": true + }, + "@pinecone-database/pinecone": { + "optional": true + }, + "@qdrant/js-client-rest": { + "optional": true + }, + "@supabase/supabase-js": { + "optional": true } }, "devDependencies": { "@n8n/typescript-config": "workspace:*", "@n8n/vitest-config": "workspace:*", + "@pinecone-database/pinecone": "catalog:", + "@qdrant/js-client-rest": "catalog:", + "@supabase/supabase-js": "catalog:", "@types/json-schema": "catalog:", + "@types/pg": "^8.15.6", "@vitest/coverage-v8": "catalog:", "dotenv": "catalog:", "cross-env": "catalog:", "nock": "catalog:", + "pg": "catalog:", + "tsx": "catalog:", "vite": "catalog:", "vitest": "catalog:", "vitest-mock-extended": "catalog:", diff --git a/packages/@n8n/agents/src/__tests__/fixtures/bulk-vector-fixture.json b/packages/@n8n/agents/src/__tests__/fixtures/bulk-vector-fixture.json new file mode 100644 index 00000000000..85aae8c55c8 --- /dev/null +++ b/packages/@n8n/agents/src/__tests__/fixtures/bulk-vector-fixture.json @@ -0,0 +1,6413 @@ +{ + "dimensions": 1536, + "documents": [ + { + "id": "09fdf437-24d3-428f-9b0a-f7891fd5d52b", + "content": "A parabolic (or paraboloid or paraboloidal) reflector (or dish or mirror) is a reflective surface used to collect or project energy such as light, sound, or radio waves. 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He had been a member of the federal cabinet, in various positions, since 2006. Previously he was a provincial cabinet minister in Ontario during the governments of Premiers Mike Harris and Ernie Eves. Baird resigned from cabinet on February 3, 2015, and as a Member of Parliament on March 16, 2015.", + "metadata": { "title": "John Baird (Canadian politician)", "category": "science" }, + "vector": [ + -0.054599, -0.032071, 0.016681, 0.025558, -0.041465, -0.025451, -0.014144, 0.056017, + 0.000883, 0.038434, 0.059801, -0.025537, -0.015842, -0.008899, -0.000529, 0.052578, + 0.034973, 0.0506, -0.027514, 0.057995, 0.008523, 0.016971, -0.032695, 0.044625, -0.006647, + 0.013488, 0.017917, -0.03794, -0.015197, -0.014681, 0.011145, -0.004235, -0.012349, + 0.048064, 0.003721, -0.044754, -0.005841, -0.043851, 0.044668, -0.040541, -0.013564, + 0.00984, 0.016777, 0.005772, 0.021324, -0.041959, -0.044625, 0.017046, 0.009173, 0.016756, + -0.048967, 0.006137, -0.017841, 0.042927, 0.007126, 0.011403, -0.008781, -0.042604, + 0.022721, -0.023753, 0.039036, 0.014982, -0.061219, 0.049311, -0.030481, -0.019131, + 0.001509, 0.017863, 0.027149, 0.017368, -0.010882, 0.038391, 0.001569, 0.010613, 0.034844, + 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Contributors to the book included Henry Beard, Valerie Curtin, Amy Ephron, Jeff Greenfield, Abbie Hoffman, Sean Kelly, B.", + "metadata": { + "title": "The 80s: A Look Back at the Tumultuous Decade 1980-1989", + "category": "geography" + }, + "vector": [ + -0.02308, 0.04082, 0.000029, 0.035504, -0.022446, -0.034187, -0.007004, 0.047062, -0.030212, + 0.025531, 0.044624, -0.038601, -0.017057, 0.016569, -0.015106, 0.057255, -0.040698, + -0.033065, 0.008443, 0.021873, 0.01546, 0.039454, -0.012473, 0.019081, -0.005788, 0.070471, + 0.028774, 0.034358, 0.029456, -0.001699, -0.0094, -0.021288, 0.011503, -0.017886, 0.011881, + 0.026628, -0.003889, -0.02675, -0.004593, 0.030115, -0.023836, -0.007151, 0.058767, 0.03031, + -0.006937, 0.031944, 0.01596, -0.001132, 0.028384, 0.045502, -0.046477, 0.017337, 0.009327, + -0.001143, -0.026116, 0.018886, -0.036845, 0.001256, 0.011741, 0.006645, 0.005938, + -0.005401, 0.007669, -0.036601, -0.041234, 0.004993, -0.030749, 0.004249, 0.00901, + -0.003713, 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His best-known films were made in the 1950s and 60s when he was known as the \"Prince of Korean Cinema\". He received the Gold Crown Cultural Medal, the country's top honor for an artist.", + "metadata": { "title": "Shin Sang-ok", "category": "history" }, + "vector": [ + -0.010079, -0.05257, -0.028327, -0.000022, -0.01088, -0.017237, 0.009811, 0.00639, + -0.046118, -0.073185, 0.029186, -0.029912, 0.041384, -0.016407, 0.011644, 0.00985, -0.03308, + 0.013352, 0.002539, -0.018564, 0.039628, -0.020806, 0.043865, 0.056578, -0.008208, + -0.011806, -0.037127, 0.04837, 0.001271, -0.046156, -0.040544, -0.019098, 0.007487, + -0.062076, 0.006285, -0.038998, -0.000723, 0.01487, -0.017476, -0.044247, 0.001755, + -0.018182, 0.015567, 0.047759, 0.048676, -0.009415, 0.009172, -0.020024, 0.013419, 0.028423, + 0.018373, 0.028652, 0.021646, 0.057915, 0.011797, 0.014717, 0.018315, -0.048332, -0.005583, + -0.041117, 0.047645, -0.004417, 0.001785, 0.008652, -0.004333, -0.030503, -0.068604, + 0.001728, 0.030236, 0.01214, -0.003541, -0.047072, -0.016578, 0.001364, -0.045774, + -0.031439, 0.023536, 0.001256, 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A celebrated interpreter of classical and romantic repertoire, particularly noted for his performances and recordings of Mozart, he was also a tremendous interpreter of Beethoven, Schumann, Brahms and Bartók.In his heyday he was regarded as an amazing artist, possessed of a beautiful, natural and flawless technique that gave his concerts a unique quality.", + "metadata": { "title": "Géza Anda", "category": "science" }, + "vector": [ + -0.04579, -0.005595, -0.022048, -0.000972, -0.040727, -0.016376, -0.013028, -0.003433, + -0.022709, -0.05794, -0.000398, 0.01371, -0.004053, 0.005145, 0.025044, 0.055254, -0.012894, + -0.020488, 0.037525, 0.017646, -0.04579, -0.027296, -0.019403, 0.035128, 0.012119, -0.03895, + -0.02587, 0.032276, 0.073727, -0.034115, -0.007155, 0.016768, 0.0035, -0.015859, 0.011592, + -0.049633, -0.026387, -0.01045, -0.043558, -0.024775, -0.058105, 0.008379, 0.042442, + 0.054799, 0.059634, -0.006685, 0.003949, 0.00762, -0.005336, 0.003211, 0.013927, -0.023598, + 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The plot revolves around Springfield's purchase of a monorail from a conman, and Marge's dislike of the purchase. It was written by Conan O'Brien and directed by Rich Moore. Guest stars include Leonard Nimoy as himself and Phil Hartman as Lyle Lanley.", + "metadata": { "title": "Marge vs. the Monorail", "category": "geography" }, + "vector": [ + 0.014359, 0.072168, -0.048636, -0.038621, -0.012496, 0.004342, -0.008005, 0.000273, + -0.021433, -0.017177, 0.024767, -0.013686, -0.044145, 0.042843, 0.005816, -0.024992, + 0.003141, -0.02717, -0.045852, 0.006955, -0.009824, 0.058695, 0.003556, -0.028809, 0.007253, + 0.07612, -0.011182, 0.00695, 0.010683, -0.024453, 0.019266, -0.011654, -0.018671, 0.001299, + 0.008965, -0.005978, -0.037162, -0.007634, -0.00796, -0.01821, -0.061435, -0.000286, + 0.010649, 0.004224, 0.035859, -0.027798, 0.007634, 0.039744, -0.001785, 0.036308, -0.047334, + 0.00595, -0.004839, -0.007208, -0.040305, 0.009442, 0.031054, 0.005552, -0.015819, -0.03541, + 0.015718, -0.042259, -0.033524, -0.011755, -0.048771, 0.047603, -0.037566, -0.000194, + 0.023914, -0.002177, 0.004025, 0.006663, 0.042124, 0.015976, -0.012574, 0.001078, 0.003725, + 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The incumbent Quebec Liberal Party, led by Robert Bourassa, won re-election, defeating the Parti Québécois, led by Jacques Parizeau.This election was notable for the arrival of the Equality Party, which advocated English-speaking minority rights. It won four seats, but never had any success in any subsequent election.", + "metadata": { "title": "Quebec general election, 1989", "category": "history" }, + "vector": [ + 0.005085, 0.00939, 0.031904, 0.012291, -0.009121, 0.004426, -0.02684, 0.036272, -0.061572, + -0.003587, 0.060179, -0.037517, -0.014591, 0.019275, 0.003753, 0.01477, 0.047814, 0.00958, + 0.006557, 0.0209, -0.041421, -0.017524, -0.055115, -0.035934, 0.021449, 0.027051, 0.006858, + 0.018685, 0.031588, -0.004766, 0.007227, -0.032938, 0.015361, 0.025447, -0.01169, -0.024118, + -0.026481, -0.06001, 0.007633, -0.022725, -0.010392, -0.018748, 0.021713, 0.008973, + -0.006525, 0.003893, -0.002711, -0.037285, -0.047603, 0.037622, -0.051275, 0.008509, + -0.024224, 0.046126, -0.00228, -0.008709, -0.040935, 0.019413, 0.015256, -0.008783, + 0.029203, 0.02243, 0.022958, 0.07035, 0.033318, 0.006937, 0.028992, -0.007042, 0.035133, + 0.027768, -0.051528, 0.026819, 0.017514, -0.062542, 0.002262, 0.003927, 0.022092, 0.031672, + 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Todd Secondary School is a public high school in Prince George, British Columbia and is part of School District 57 Prince George.", + "metadata": { "title": "D. P. 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In 1970 he became one of the first prominent South African cricketers to speak out in favour of mixed-race sport in South Africa. Shortly afterwards he founded the Cricket Club of South Africa, one of the country's first multi-racial teams.", + "metadata": { "title": "Dennis Gamsy", "category": "geography" }, + "vector": [ + 0.003902, 0.056018, 0.057869, 0.026835, -0.03399, 0.024014, 0.015787, -0.007059, -0.000375, + -0.022242, 0.042363, -0.076601, -0.011951, 0.054799, -0.007177, 0.016487, -0.008452, + 0.021294, 0.002287, 0.041731, 0.041483, 0.058771, -0.000573, -0.013339, -0.015347, 0.008402, + 0.027558, -0.009479, -0.039655, -0.033764, 0.021938, 0.008351, -0.01792, -0.00501, 0.043108, + -0.009767, 0.004243, 0.020301, -0.00547, -0.025436, -0.004799, -0.067619, 0.02767, 0.017119, + 0.012583, -0.017875, -0.02591, -0.053264, 0.008746, 0.023924, 0.000035, -0.028663, 0.000784, + 0.081792, 0.02907, 0.001281, -0.029273, -0.037804, -0.000581, 0.002556, -0.046539, 0.007657, + -0.008938, 0.023946, -0.022355, -0.062518, -0.015551, 0.042973, -0.008988, 0.041393, + 0.014986, -0.024443, 0.01186, -0.017029, -0.088067, 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It was originally formed as part of the Royal Navy's Fleet Air Arm for service during World War II and took part in combat operations in Norway, North Africa, Sicily and off the coast of France. Following the conclusion of hostilities, the squadron was disbanded in 1945. In 1950, it was re-raised as part of the Royal Australian Navy and subsequently took part in the Korean War.", + "metadata": { "title": "817 Squadron RAN", "category": "science" }, + "vector": [ + -0.067463, 0.042009, 0.073793, -0.007774, 0.008416, 0.013546, -0.036055, -0.000253, + -0.009495, 0.029703, 0.023107, -0.01328, 0.017275, -0.017264, 0.011886, 0.01037, 0.015195, + 0.002353, -0.048472, 0.009661, -0.027977, 0.002883, -0.037583, -0.043005, -0.009955, + 0.034108, -0.025254, -0.013014, 0.03289, -0.038645, 0.012826, -0.025542, -0.023152, + 0.071845, -0.008544, -0.028995, 0.070252, -0.009816, -0.050863, 0.013845, -0.021027, + -0.027689, 0.020728, -0.009213, -0.017508, -0.017364, -0.032691, -0.038313, 0.019909, + 0.017906, 0.014641, -0.017762, -0.036719, 0.052899, -0.020363, 0.024126, 0.019334, -0.00799, + 0.012273, -0.037162, 0.002626, 0.022775, -0.001403, -0.003835, -0.032558, -0.009833, + -0.009783, 0.04212, -0.020562, -0.016866, 0.015261, -0.028397, 0.004966, -0.048251, + 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It is owned by the Romualdez family. Romualdezes also owns Journal Publications, Inc., the proprietor of the People's Journal, a tabloid newspaper.Initially established as the Manila Standard, it merged with another newspaper of record, Today, on March 6, 2005 and became Manila Standard Today or MST and recently rebranded as The Standard.", + "metadata": { "title": "The Standard (Philippines)", "category": "geography" }, + "vector": [ + 0.034546, 0.01823, -0.008547, 0.029363, -0.02349, 0.031303, -0.025456, 0.018575, -0.007558, + -0.053874, -0.033525, 0.002121, -0.068785, 0.01809, 0.048487, 0.014145, 0.025188, 0.007028, + -0.014094, 0.011222, 0.037355, 0.012709, -0.040929, 0.01952, 0.00976, 0.009594, -0.04601, + 0.017605, 0.004717, 0.009109, -0.029414, -0.008611, 0.019379, 0.007685, -0.027805, 0.008994, + -0.018205, -0.014375, 0.005719, 0.042691, -0.041082, -0.007379, -0.045091, 0.024894, + 0.009517, 0.015741, 0.031073, 0.010481, 0.041874, 0.015932, -0.034393, -0.022699, 0.04024, + 0.02552, 0.005553, 0.010985, 0.032963, 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The Clovis culture appears around 11,500–11,000 uncal RCYBP (uncalibrated radiocarbon years before present), at the end of the last glacial period, and is characterized by the manufacture of \"Clovis points\" and distinctive bone and ivory tools.", + "metadata": { "title": "Clovis culture", "category": "science" }, + "vector": [ + 0.034669, 0.060753, -0.010373, 0.007797, -0.044741, -0.011765, 0.010425, 0.000017, -0.03511, + -0.017636, 0.041724, -0.023902, -0.014086, -0.005311, 0.058571, 0.020862, 0.001842, + 0.007275, -0.020862, 0.01238, 0.011963, -0.005569, -0.03098, -0.002347, 0.001697, 0.023705, + -0.021187, 0.037454, -0.004537, -0.00268, -0.041097, -0.031072, -0.00065, 0.01542, + -0.035806, -0.00019, 0.011348, -0.002596, -0.005804, 0.014295, -0.034205, -0.01694, + 0.042188, 0.01303, 0.018437, -0.007582, -0.003049, 0.053234, -0.023589, 0.054858, -0.014596, + -0.031861, -0.05523, 0.024644, -0.013947, -0.03221, 0.028682, 0.001505, -0.015235, 0.011348, + 0.023519, -0.005613, 0.029425, 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children of families who migrate to find work in the agricultural and fishing industries. 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The conference is held on the first Friday in August (hence its name), usually in the Tel Aviv area. It lasts one day and includes technical talks, projects' status updates, social meetings and followed by a keysigning party.During the conference, the winner of Hamakor Prize for free software-related achievements is announced.", + "metadata": { "title": "August Penguin", "category": "history" }, + "vector": [ + -0.042625, -0.013102, 0.032951, 0.014304, -0.019292, 0.002482, -0.015019, 0.038585, + -0.021161, -0.016901, 0.022219, -0.025228, 0.000933, -0.026493, -0.008698, 0.049028, + -0.081841, -0.037156, -0.035479, 0.021821, -0.024473, -0.021683, 0.016489, 0.018427, + -0.004978, 0.012037, -0.063264, 0.051529, 0.072333, -0.036688, 0.033638, -0.046939, + 0.003995, -0.003779, -0.004349, 0.003451, 0.002175, -0.015624, -0.012669, 0.015472, + -0.025407, -0.035644, -0.004033, 0.027688, 0.020227, -0.001262, 0.030505, -0.043559, + -0.006475, 0.027372, 0.029241, 0.030642, 0.009612, 0.013796, 0.027317, -0.034545, 0.008732, + 0.038008, 0.01954, -0.018784, 0.040041, -0.041992, 0.049, -0.003909, 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As described by the biographer of the FEA's chief, Leo Crowley, the agency was designed and run by \"The Nation's #1 Pinch-hitter\".S. 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He was the son of Wenceslaus II, King of Bohemia, who was later also crowned king of Poland, and Judith of Habsburg. Still a child, Wenceslaus was betrothed to Elizabeth, the sole daughter of Andrew III of Hungary.", + "metadata": { "title": "Wenceslaus III of Bohemia", "category": "science" }, + "vector": [ + -0.037575, 0.026613, 0.016112, -0.001886, -0.081563, 0.027334, 0.026573, -0.016393, + -0.011783, -0.044409, 0.02501, 0.038497, -0.033407, 0.017565, 0.023226, 0.052264, 0.005987, + 0.036072, -0.006388, 0.028236, -0.053226, 0.010741, 0.022425, -0.005666, -0.034829, + -0.019799, 0.026413, -0.017435, 0.023647, -0.042845, 0.022585, 0.00519, -0.030361, + -0.004381, -0.036874, -0.046172, -0.038837, 0.016904, -0.017445, 0.015671, -0.030761, + -0.064649, -0.014008, 0.023387, 0.009003, -0.041643, -0.006403, 0.026132, -0.033567, + 0.026493, 0.02531, 0.016523, -0.012705, 0.076272, 0.011804, -0.03491, 0.051222, -0.002988, + -0.017505, -0.011663, -0.043407, -0.002821, 0.010681, -0.01524, 0.017194, 0.018667, + -0.037896, -0.014158, 0.027715, -0.036813, -0.001609, 0.033928, -0.041883, -0.018276, + -0.03012, 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From 1974 to 1984, she starred as Laura Ingalls Wilder on the NBC series Little House on the Prairie.", + "metadata": { "title": "Melissa Gilbert", "category": "geography" }, + "vector": [ + 0.03502, 0.001011, 0.025693, 0.021746, -0.005324, -0.014408, 0.027746, 0.037523, -0.076842, + 0.025864, 0.003971, -0.034271, 0.00546, -0.024366, 0.018879, 0.034956, -0.030527, -0.028003, + 0.01152, 0.022997, 0.024109, 0.028773, -0.017681, -0.035918, 0.045695, -0.01629, -0.00415, + -0.014943, 0.015617, 0.020366, -0.010386, 0.016622, -0.062595, -0.06375, 0.036517, 0.002245, + -0.026955, -0.017029, 0.011499, 0.012322, -0.047149, 0.006311, 0.02058, 0.070852, 0.01843, + -0.005733, -0.003407, 0.035383, -0.009311, -0.000213, -0.011595, -0.017692, -0.01736, + 0.050016, -0.014322, -0.016868, 0.028345, 0.005124, 0.020943, 0.019489, 0.037672, -0.017788, + -0.008028, 0.000125, -0.018002, 0.025671, -0.04071, -0.002353, -0.003663, 0.025158, + -0.032089, -0.010734, -0.041031, -0.035298, -0.020986, 0.017628, 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Javits Federal Office Building at 26 Federal Plaza on Foley Square in the Civic Center district of Manhattan, New York City houses many Federal government agencies, and, at over 41 stories, is the tallest federal building in the United States. It was built in 1963-69 and was designed by Alfred Easton Poor and Kahn & Jacobs, with Eggers & Higgins as associate architects.", + "metadata": { "title": "Jacob K. 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It was created on 25 January 1938 for Sir William Birdwood, 1st Baronet. He is chiefly remembered as the commander of the Australian and New Zealand Army Corps (ANZAC) during the Gallipoli Campaign of 1915. Birdwood had already been created a Baronet, of Anzac and Totnes, on 6 October 1919.", + "metadata": { "title": "Baron Birdwood", "category": "history" }, + "vector": [ + 0.012651, 0.031505, 0.036885, 0.020599, -0.059096, -0.007965, 0.043622, -0.016597, + -0.023901, -0.004978, 0.004274, 0.013095, 0.014819, -0.044311, 0.034328, 0.044289, 0.034929, + 0.034684, -0.00816, 0.011873, 0.00816, 0.023612, -0.002974, -0.010394, -0.033728, 0.033106, + -0.029192, -0.024168, 0.008538, -0.054338, 0.037864, -0.012162, -0.027347, 0.0226, + -0.027947, -0.027992, 0.020588, -0.090312, 0.010294, -0.016364, -0.011406, -0.023056, + 0.002976, 0.041087, -0.023901, -0.033417, 0.010311, -0.012506, -0.026902, 0.005433, + -0.020066, 0.03255, 0.007604, 0.036907, -0.024279, 0.028059, -0.041065, -0.010422, + -0.001594, 0.009427, 0.005508, 0.039042, -0.003368, -0.027169, -0.014429, -0.048647, + -0.034773, 0.039331, 0.006509, -0.008415, 0.015052, 0.055272, -0.039353, 0.011183, + -0.015152, 0.008738, 0.020199, 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He was born in Tyler, Texas, where he was raised by his single mother and five older siblings. Among his siblings was older brother Gilbert Johnson, who played quarterback at Southern Methodist University with the iconic running back Doak Walker.", + "metadata": { "title": "Bill Johnson (center)", "category": "science" }, + "vector": [ + 0.014291, 0.021018, -0.005631, 0.052079, -0.016177, -0.044112, 0.052672, 0.020456, + -0.050087, 0.041358, 0.027099, -0.00919, 0.005435, 0.006123, -0.011505, 0.060172, 0.022988, + 0.00652, -0.045722, 0.039472, 0.020298, -0.001496, -0.014916, -0.039472, 0.049324, 0.019503, + 0.00777, -0.005954, 0.004645, -0.048434, 0.045977, 0.001143, -0.016579, -0.027205, 0.01463, + -0.056274, 0.058943, -0.000193, 0.021781, 0.041591, -0.039154, 0.012903, 0.017405, 0.046443, + -0.011674, -0.0022, 0.010996, -0.029853, -0.018656, 0.037841, 0.058859, -0.057715, 0.01784, + 0.043434, 0.026145, 0.000396, 0.011113, -0.023454, 0.030277, 0.026972, -0.044917, 0.038858, + -0.066401, -0.020022, -0.00625, -0.039303, -0.033963, -0.011123, 0.008528, -0.037862, + -0.003099, 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The main purpose is the dissemination of EUMETSAT's own data, but various data from other providers are broadcast as well.EUMETCast is a contribution to GEONETCast and IGDDS (WMO's Integrated Global Data Dissemination Service) and provides data for GEOSS and GMES.", + "metadata": { "title": "Eumetcast", "category": "geography" }, + "vector": [ + 0.023097, 0.021935, 0.024471, -0.015958, -0.023884, 0.020972, 0.02136, 0.003235, 0.008736, + -0.016369, 0.003329, -0.019751, -0.017813, -0.020044, -0.01021, 0.016181, -0.020702, + 0.010603, -0.029708, 0.024964, 0.002526, -0.022452, 0.017109, 0.000873, -0.075762, + -0.032926, -0.037036, 0.068858, 0.019692, 0.015852, 0.067871, -0.012835, 0.005126, + -0.023203, 0.029638, 0.002031, -0.026608, 0.004377, -0.053358, -0.02278, 0.009711, -0.02042, + -0.019175, -0.015148, -0.062799, -0.060779, -0.043541, -0.032597, 0.081728, 0.004773, + 0.047087, 0.007509, -0.003731, -0.011226, 0.02426, -0.030648, -0.012811, 0.036261, + -0.002401, -0.077125, -0.008883, 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He interpreted some of the toys' box art and created the models that would become the visual guidelines both for the comics book and the animated cartoon appearances of those characters.", + "metadata": { "title": "Floro Dery", "category": "science" }, + "vector": [ + 0.01834, 0.003151, -0.060469, 0.004653, -0.002803, -0.022214, 0.021444, 0.036703, -0.046039, + 0.03784, -0.011776, -0.028765, 0.016101, -0.069852, -0.015887, 0.03149, -0.007322, + -0.051086, 0.012795, 0.009081, 0.055351, -0.013352, -0.030448, 0.01462, -0.009045, 0.037959, + 0.008121, 0.035021, 0.034926, -0.014549, -0.028363, -0.005856, 0.019442, -0.043527, + 0.023588, -0.008003, -0.005423, 0.00664, -0.004706, 0.017096, -0.001952, -0.019655, + -0.017439, 0.084448, 0.004004, 0.002267, 0.019193, -0.020839, 0.018209, 0.026396, -0.008566, + 0.041726, 0.021041, 0.036466, -0.016077, 0.06663, -0.052887, 0.023742, -0.017167, -0.049617, + -0.027604, 0.006202, 0.011859, 0.016195, -0.073122, 0.029713, -0.069947, 0.007138, + 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b/packages/@n8n/agents/src/__tests__/fixtures/generate-bulk-vector-fixture.ts new file mode 100644 index 00000000000..8442bd8930d --- /dev/null +++ b/packages/@n8n/agents/src/__tests__/fixtures/generate-bulk-vector-fixture.ts @@ -0,0 +1,146 @@ +/** + * One-time (re-)generation script for bulk-vector-fixture.json — a committed + * corpus of real documents with precomputed embeddings used by the + * vector-store-bulk-*.test.ts and agent-vector-store-*.test.ts suites to + * exercise all three vector store backends against a real corpus without + * making OpenAI calls to embed documents at test time. + * + * Pulls 30 rows from the Hugging Face dataset + * Qdrant/dbpedia-entities-openai3-text-embedding-3-small-1536-100K (already + * embedded with text-embedding-3-small at its native 1536 dims — matches + * live query embeddings from the same model with no truncation needed), and + * assigns synthetic round-robin categories for filter tests. Embeds a + * handful of natural-language queries via the AI SDK and computes each + * query's local top match so the test suite has ground truth that doesn't + * depend on any backend's exact search semantics. + * + * Run with: pnpm fixtures:generate + */ +import { randomUUID } from 'node:crypto'; +import { rm, writeFile } from 'node:fs/promises'; +import path from 'node:path'; + +import { config as loadEnv } from 'dotenv'; +import { embedMany } from 'ai'; + +import { + cosineSimilarity, + type BulkFixture, + type FixtureDocument, + type FixtureQuery, +} from '../integration/vector-store-helpers'; +import { createEmbeddingModel } from '../../runtime/model/model-factory'; + +loadEnv({ path: path.resolve(__dirname, '../../../.env') }); + +const DOC_COUNT = 30; +const DIMENSIONS = 1536; +const CATEGORIES = ['history', 'science', 'geography'] as const; +const HF_DATASET = 'Qdrant/dbpedia-entities-openai3-text-embedding-3-small-1536-100K'; +const EMBEDDING_FIELD = 'text-embedding-3-small-1536-embedding'; +const OUT_PATH = path.join(__dirname, 'bulk-vector-fixture.json'); + +// Picked from the corpus after inspecting the printed titles below — distinctive +// enough that the top match isn't ambiguous with the #2 result. +const QUERIES = [ + 'What lake is located in Jefferson County, Florida?', + 'What is the tallest federal building in Manhattan, and how many stories does it have?', + 'Which cricketer played as a wicketkeeper for South Africa against Australia in 1970?', + 'Who commanded the ANZAC forces during the Gallipoli campaign?', +]; + +interface HfRow { + title: string; + text: string; + [EMBEDDING_FIELD]: number[]; +} + +function roundVector(vector: number[]): number[] { + return vector.map((value) => Math.round(value * 1e6) / 1e6); +} + +async function fetchWithRetry(url: string, retries = 5): Promise { + for (let attempt = 1; attempt <= retries; attempt++) { + const res = await fetch(url); + if (res.ok) return res; + if (attempt === retries) { + throw new Error( + `Request failed after ${retries} attempts: ${res.status} ${await res.text()}`, + ); + } + console.warn(` Request failed (${res.status}), retrying (${attempt}/${retries})...`); + await new Promise((resolve) => setTimeout(resolve, attempt * 2000)); + } + throw new Error('unreachable'); +} + +async function fetchHfRows(): Promise { + const url = + `https://datasets-server.huggingface.co/rows?dataset=${encodeURIComponent(HF_DATASET)}` + + `&config=default&split=train&offset=0&length=${DOC_COUNT}`; + const res = await fetchWithRetry(url); + const body = (await res.json()) as { rows: Array<{ row: HfRow }> }; + return body.rows.map((r) => r.row); +} + +async function embedQueries(queries: string[]): Promise { + if (!process.env.OPENAI_API_KEY) { + throw new Error('OPENAI_API_KEY is required to embed the fixture queries.'); + } + const { embeddings } = await embedMany({ + model: createEmbeddingModel('openai/text-embedding-3-small'), + values: queries, + }); + return embeddings; +} + +async function main(): Promise { + await rm(OUT_PATH, { force: true }); + + console.log(`Fetching ${DOC_COUNT} rows from ${HF_DATASET}...`); + const rows = await fetchHfRows(); + console.log('Titles fetched (for picking distinctive queries):'); + for (const row of rows) console.log(` - ${row.title}`); + + const documents: FixtureDocument[] = rows.map((row, index) => ({ + id: randomUUID(), + content: row.text, + metadata: { title: row.title, category: CATEGORIES[index % CATEGORIES.length] }, + vector: roundVector(row[EMBEDDING_FIELD]), + })); + + console.log(`\nEmbedding ${QUERIES.length} queries...`); + const queryEmbeddings = await embedQueries(QUERIES); + + const queries: FixtureQuery[] = QUERIES.map((text, i) => { + const vector = roundVector(queryEmbeddings[i]); + const ranked = documents + .map((doc) => ({ + id: doc.id, + title: doc.metadata.title, + score: cosineSimilarity(vector, doc.vector), + })) + .sort((a, b) => b.score - a.score) + .slice(0, 3); + + console.log(`\nQuery: "${text}"`); + for (const r of ranked) console.log(` ${r.score.toFixed(4)} ${r.title}`); + const gap = ranked[0].score - ranked[1].score; + if (gap < 0.02) { + console.warn( + ` WARNING: top-1/top-2 score gap is only ${gap.toFixed(4)} — consider picking a more distinctive query.`, + ); + } + + return { text, vector, expectedTopId: ranked[0].id }; + }); + + const fixture: BulkFixture = { dimensions: DIMENSIONS, documents, queries }; + await writeFile(OUT_PATH, JSON.stringify(fixture)); + console.log(`\nWrote ${documents.length} documents and ${queries.length} queries to ${OUT_PATH}`); +} + +main().catch((error: unknown) => { + console.error(error); + process.exitCode = 1; +}); diff --git a/packages/@n8n/agents/src/__tests__/integration/agent-vector-store-pinecone.test.ts b/packages/@n8n/agents/src/__tests__/integration/agent-vector-store-pinecone.test.ts new file mode 100644 index 00000000000..bf56d620f57 --- /dev/null +++ b/packages/@n8n/agents/src/__tests__/integration/agent-vector-store-pinecone.test.ts @@ -0,0 +1,57 @@ +/** + * End-to-end: a real model searches a real Pinecone-backed VectorStore + * populated with the fixture corpus (see fixtures/generate-bulk-vector-fixture.ts) + * and answers from the results. Gated on PINECONE_TEST_API_KEY plus both + * provider keys; self-skips in CI replay since Pinecone can't be replayed + * from cassettes. + */ +import type { Pinecone } from '@pinecone-database/pinecone'; +import { afterAll, beforeAll, describe } from 'vitest'; + +import { + createPineconeIndex, + loadBulkFixture, + registerAgentVectorStoreTests, + upsertFixtureDocuments, + waitForPineconeRecordCount, +} from './vector-store-helpers'; +import { VectorStore } from '../../index'; +import { PineconeVectorStore } from '../../vector-stores/pinecone'; + +const PINECONE_API_KEY = process.env.PINECONE_TEST_API_KEY; +const hasKeys = Boolean(process.env.ANTHROPIC_API_KEY) && Boolean(process.env.OPENAI_API_KEY); + +const HOOK_TIMEOUT_MS = 240_000; + +const fixture = loadBulkFixture(); + +describe.skipIf(!PINECONE_API_KEY || !hasKeys)('Agent + PineconeVectorStore end-to-end', () => { + const indexName = `vs-e2e-${Date.now()}-${Math.floor(Math.random() * 1e6)}`; + let adminClient: Pinecone; + let store: PineconeVectorStore; + let knowledge: VectorStore; + + beforeAll(async () => { + const { Pinecone: PineconeCtor } = await import('@pinecone-database/pinecone'); + adminClient = new PineconeCtor({ apiKey: PINECONE_API_KEY! }); + await createPineconeIndex(adminClient, indexName, fixture.dimensions); + + store = new PineconeVectorStore('knowledge-base', { + apiKey: PINECONE_API_KEY!, + indexName, + }); + await upsertFixtureDocuments(store, fixture); + await waitForPineconeRecordCount(adminClient.index(indexName), fixture.documents.length); + knowledge = new VectorStore('knowledge-base') + .store(store) + .embeddingModel('openai/text-embedding-3-small') + .description('Search the knowledge base of encyclopedia articles'); + }, HOOK_TIMEOUT_MS); + + afterAll(async () => { + await adminClient.deleteIndex(indexName); + store.close(); + }); + + registerAgentVectorStoreTests(() => knowledge); +}); diff --git a/packages/@n8n/agents/src/__tests__/integration/agent-vector-store-postgres.test.ts b/packages/@n8n/agents/src/__tests__/integration/agent-vector-store-postgres.test.ts new file mode 100644 index 00000000000..fa52cf7c5db --- /dev/null +++ b/packages/@n8n/agents/src/__tests__/integration/agent-vector-store-postgres.test.ts @@ -0,0 +1,51 @@ +/** + * End-to-end: a real model searches a real Postgres-backed VectorStore + * populated with the fixture corpus (see fixtures/generate-bulk-vector-fixture.ts) + * and answers from the results. Gated on PG_VECTOR_TEST_URL plus both + * provider keys; self-skips in CI replay since Postgres can't be replayed + * from cassettes. + */ +import type { Pool } from 'pg'; +import { afterAll, beforeAll, describe } from 'vitest'; + +import { + createPgVectorTable, + loadBulkFixture, + registerAgentVectorStoreTests, + upsertFixtureDocuments, +} from './vector-store-helpers'; +import { VectorStore } from '../../index'; +import { PgVectorStore } from '../../vector-stores/postgres'; + +const PG_URL = process.env.PG_VECTOR_TEST_URL; +const hasKeys = Boolean(process.env.ANTHROPIC_API_KEY) && Boolean(process.env.OPENAI_API_KEY); + +const fixture = loadBulkFixture(); + +describe.skipIf(!PG_URL || !hasKeys)('Agent + PgVectorStore end-to-end', () => { + const tableName = `vs_e2e_${Date.now()}_${Math.floor(Math.random() * 1e6)}`; + let adminPool: Pool; + let store: PgVectorStore; + let knowledge: VectorStore; + + beforeAll(async () => { + const { Pool: PoolCtor } = await import('pg'); + adminPool = new PoolCtor({ connectionString: PG_URL }); + await createPgVectorTable(adminPool, tableName, { dimensions: fixture.dimensions }); + + store = new PgVectorStore('knowledge-base', { connectionString: PG_URL!, tableName }); + await upsertFixtureDocuments(store, fixture); + knowledge = new VectorStore('knowledge-base') + .store(store) + .embeddingModel('openai/text-embedding-3-small') + .description('Search the knowledge base of encyclopedia articles'); + }); + + afterAll(async () => { + await adminPool.query(`DROP TABLE IF EXISTS "${tableName}";`); + await store.close(); + await adminPool.end(); + }); + + registerAgentVectorStoreTests(() => knowledge); +}); diff --git a/packages/@n8n/agents/src/__tests__/integration/agent-vector-store-qdrant.test.ts b/packages/@n8n/agents/src/__tests__/integration/agent-vector-store-qdrant.test.ts new file mode 100644 index 00000000000..73c00760e24 --- /dev/null +++ b/packages/@n8n/agents/src/__tests__/integration/agent-vector-store-qdrant.test.ts @@ -0,0 +1,60 @@ +/** + * End-to-end: a real model searches a real Qdrant-backed VectorStore + * populated with the fixture corpus (see fixtures/generate-bulk-vector-fixture.ts) + * and answers from the results. Gated on QDRANT_TEST_URL plus both provider + * keys; self-skips in CI replay since Qdrant can't be replayed from cassettes. + */ +import type { QdrantClient } from '@qdrant/js-client-rest'; +import { afterAll, beforeAll, describe } from 'vitest'; + +import { + loadBulkFixture, + registerAgentVectorStoreTests, + upsertFixtureDocuments, +} from './vector-store-helpers'; +import { VectorStore } from '../../index'; +import { QdrantVectorStore } from '../../vector-stores/qdrant'; + +const QDRANT_URL = process.env.QDRANT_TEST_URL; +const QDRANT_API_KEY = process.env.QDRANT_TEST_API_KEY; +const hasKeys = Boolean(process.env.ANTHROPIC_API_KEY) && Boolean(process.env.OPENAI_API_KEY); + +const fixture = loadBulkFixture(); + +describe.skipIf(!QDRANT_URL || !hasKeys)('Agent + QdrantVectorStore end-to-end', () => { + const collectionName = `vs_e2e_${Date.now()}_${Math.floor(Math.random() * 1e6)}`; + let adminClient: QdrantClient; + let store: QdrantVectorStore; + let knowledge: VectorStore; + + beforeAll(async () => { + const { QdrantClient: QdrantClientCtor } = await import('@qdrant/js-client-rest'); + adminClient = new QdrantClientCtor({ url: QDRANT_URL, apiKey: QDRANT_API_KEY }); + await adminClient.createCollection(collectionName, { + vectors: { size: fixture.dimensions, distance: 'Cosine' }, + }); + // Qdrant Cloud requires a payload index to filter on a field at all. + await adminClient.createPayloadIndex(collectionName, { + field_name: 'metadata.category', + field_schema: 'keyword', + }); + + store = new QdrantVectorStore('knowledge-base', { + url: QDRANT_URL!, + apiKey: QDRANT_API_KEY, + collectionName, + }); + await upsertFixtureDocuments(store, fixture); + knowledge = new VectorStore('knowledge-base') + .store(store) + .embeddingModel('openai/text-embedding-3-small') + .description('Search the knowledge base of encyclopedia articles'); + }); + + afterAll(async () => { + await adminClient.deleteCollection(collectionName); + store.close(); + }); + + registerAgentVectorStoreTests(() => knowledge); +}); diff --git a/packages/@n8n/agents/src/__tests__/integration/agent-vector-store-supabase.test.ts b/packages/@n8n/agents/src/__tests__/integration/agent-vector-store-supabase.test.ts new file mode 100644 index 00000000000..96b2b838f7d --- /dev/null +++ b/packages/@n8n/agents/src/__tests__/integration/agent-vector-store-supabase.test.ts @@ -0,0 +1,72 @@ +/** + * End-to-end: a real model searches a real Supabase-backed VectorStore + * populated with the fixture corpus (see fixtures/generate-bulk-vector-fixture.ts) + * and answers from the results. Gated on SUPABASE_TEST_* plus both provider + * keys; self-skips in CI replay since Supabase can't be replayed from cassettes. + */ +import type { Pool } from 'pg'; +import { afterAll, beforeAll, describe } from 'vitest'; + +import { + createSupabaseVectorTableAndFunction, + dropSupabaseVectorTableAndFunction, + loadBulkFixture, + registerAgentVectorStoreTests, + upsertFixtureDocuments, + waitUntilQueryable, +} from './vector-store-helpers'; +import { VectorStore } from '../../index'; +import { SupabaseVectorStore } from '../../vector-stores/supabase'; + +const SUPABASE_URL = process.env.SUPABASE_TEST_URL; +const SUPABASE_API_KEY = process.env.SUPABASE_TEST_API_KEY; +const SUPABASE_DB_URL = process.env.SUPABASE_TEST_DB_URL; +const hasKeys = Boolean(process.env.ANTHROPIC_API_KEY) && Boolean(process.env.OPENAI_API_KEY); + +const HOOK_TIMEOUT_MS = 45_000; + +const fixture = loadBulkFixture(); + +describe.skipIf(!SUPABASE_URL || !SUPABASE_API_KEY || !SUPABASE_DB_URL || !hasKeys)( + 'Agent + SupabaseVectorStore end-to-end', + () => { + const suffix = `${Date.now()}_${Math.floor(Math.random() * 1e6)}`; + const tableName = `vs_e2e_${suffix}`; + const queryName = `match_vs_e2e_${suffix}`; + let adminPool: Pool; + let store: SupabaseVectorStore; + let knowledge: VectorStore; + + beforeAll(async () => { + const { Pool: PoolCtor } = await import('pg'); + adminPool = new PoolCtor({ connectionString: SUPABASE_DB_URL }); + await createSupabaseVectorTableAndFunction( + adminPool, + tableName, + queryName, + fixture.dimensions, + ); + + store = new SupabaseVectorStore('knowledge-base', { + url: SUPABASE_URL!, + apiKey: SUPABASE_API_KEY!, + tableName, + queryName, + }); + await waitUntilQueryable(store, fixture.dimensions); + await upsertFixtureDocuments(store, fixture); + knowledge = new VectorStore('knowledge-base') + .store(store) + .embeddingModel('openai/text-embedding-3-small') + .description('Search the knowledge base of encyclopedia articles'); + }, HOOK_TIMEOUT_MS); + + afterAll(async () => { + await dropSupabaseVectorTableAndFunction(adminPool, tableName, queryName); + store.close(); + await adminPool.end(); + }); + + registerAgentVectorStoreTests(() => knowledge); + }, +); diff --git a/packages/@n8n/agents/src/__tests__/integration/pg-vector-store.test.ts b/packages/@n8n/agents/src/__tests__/integration/pg-vector-store.test.ts new file mode 100644 index 00000000000..e177f25a3be --- /dev/null +++ b/packages/@n8n/agents/src/__tests__/integration/pg-vector-store.test.ts @@ -0,0 +1,288 @@ +/** + * Integration tests against a real Postgres + pgvector instance. Gated on + * PG_VECTOR_TEST_URL (not the usual API-key/cassette convention) since + * Postgres is raw TCP, not HTTP — these self-skip when the var is unset. + */ +import { MockEmbeddingModelV3 } from 'ai/test'; +import type { Pool } from 'pg'; +import { afterAll, beforeAll, describe, expect, it } from 'vitest'; + +import { createPgVectorTable } from './vector-store-helpers'; +import { VectorStore } from '../../sdk/vector-store'; +import { PgVectorStore } from '../../vector-stores/postgres'; + +const PG_URL = process.env.PG_VECTOR_TEST_URL; + +describe.skipIf(!PG_URL)('PgVectorStore', () => { + const tableName = `vs_test_${Date.now()}_${Math.floor(Math.random() * 1e6)}`; + let store: PgVectorStore; + let adminPool: Pool; + + beforeAll(async () => { + const { Pool: PoolCtor } = await import('pg'); + adminPool = new PoolCtor({ connectionString: PG_URL }); + await createPgVectorTable(adminPool, tableName, { dimensions: 3 }); + store = new PgVectorStore('pg-integration', { connectionString: PG_URL!, tableName }); + }); + + afterAll(async () => { + await adminPool.query(`DROP TABLE IF EXISTS "${tableName}";`); + await store.close(); + await adminPool.end(); + }); + + it('upserts vectors with metadata and queries by similarity', async () => { + await store.upsert([ + { id: 'exact', vector: [1, 0, 0], content: 'exact match', metadata: { topic: 'a' } }, + { id: 'near', vector: [0.9, 0.1, 0], content: 'near match', metadata: {} }, + { id: 'far', vector: [0, 1, 0], content: 'far match', metadata: {} }, + ]); + + const results = await store.query([1, 0, 0], { topK: 2 }); + + expect(results).toHaveLength(2); + expect(results[0].id).toBe('exact'); + expect(results[0].score).toBeCloseTo(1, 6); + expect(results[0].metadata).toEqual({ topic: 'a' }); + expect(results[1].id).toBe('near'); + }); + + it('re-upserting the same id updates its content', async () => { + await store.upsert([ + { id: 'exact', vector: [1, 0, 0], content: 'updated content', metadata: {} }, + ]); + + const results = await store.query([1, 0, 0], { topK: 1 }); + + expect(results[0].id).toBe('exact'); + expect(results[0].content).toBe('updated content'); + }); + + it('deletes by ids', async () => { + await store.delete({ ids: ['far'] }); + + const results = await store.query([0, 1, 0], { topK: 5 }); + + expect(results.find((r) => r.id === 'far')).toBeUndefined(); + }); + + it('round-trips through the VectorStore orchestrator with a mock embedding model', async () => { + const roundTripTable = `${tableName}_roundtrip`; + await createPgVectorTable(adminPool, roundTripTable, { dimensions: 3 }); + const roundTripStore = new PgVectorStore('pg-integration-roundtrip', { + connectionString: PG_URL!, + tableName: roundTripTable, + }); + const embeddingModel = new MockEmbeddingModelV3({ + doEmbed: async ({ values }: { values: string[] }) => ({ + embeddings: values.map(() => [1, 0, 0]), + warnings: [], + }), + }); + + try { + const knowledge = new VectorStore('roundtrip') + .store(roundTripStore) + .embeddingModel(embeddingModel); + await knowledge.addDocuments([{ content: 'refunds take 5 days' }]); + + const results = await knowledge.search('how long do refunds take'); + + expect(results[0].content).toBe('refunds take 5 days'); + } finally { + await adminPool.query(`DROP TABLE IF EXISTS "${roundTripTable}";`); + await roundTripStore.close(); + } + }); +}); + +describe.skipIf(!PG_URL)('PgVectorStore — metadata filtering', () => { + const filterTableName = `vs_filter_test_${Date.now()}_${Math.floor(Math.random() * 1e6)}`; + let filterStore: PgVectorStore; + let adminPool: Pool; + + beforeAll(async () => { + const { Pool: PoolCtor } = await import('pg'); + adminPool = new PoolCtor({ connectionString: PG_URL }); + await createPgVectorTable(adminPool, filterTableName, { dimensions: 3, ginIndex: true }); + filterStore = new PgVectorStore('pg-filter-integration', { + connectionString: PG_URL!, + tableName: filterTableName, + }); + await filterStore.upsert([ + { + id: 'a', + content: 'Row A content', + vector: [1, 0, 0], + metadata: { topic: 'billing', count: 5, active: true }, + }, + { + id: 'b', + content: 'Row B content', + vector: [0.9, 0.1, 0], + metadata: { topic: 'ops', count: 10, active: false }, + }, + // Missing all keys — required to prove ne/nin match rows that never had the key. + { id: 'c', content: 'Row C content', vector: [0, 1, 0], metadata: {} }, + // Stores count as a JSON string, not a number — proves eq is type-correct. + { id: 'd', content: 'Row D content', vector: [0, 0.5, 0.5], metadata: { count: '5' } }, + ]); + }); + + afterAll(async () => { + await adminPool.query(`DROP TABLE IF EXISTS "${filterTableName}";`); + await filterStore.close(); + await adminPool.end(); + }); + + async function idsFor(filter: Parameters[1]['filter']) { + const results = await filterStore.query([1, 0, 0], { topK: 10, filter }); + return results.map((r) => r.id).sort(); + } + + it('eq matches on string, number, and boolean, and is type-correct', async () => { + expect( + await idsFor({ conditions: [{ key: 'topic', operator: 'eq', value: 'billing' }] }), + ).toEqual(['a']); + expect(await idsFor({ conditions: [{ key: 'count', operator: 'eq', value: 5 }] })).toEqual([ + 'a', + ]); + expect(await idsFor({ conditions: [{ key: 'active', operator: 'eq', value: true }] })).toEqual([ + 'a', + ]); + expect( + await idsFor({ conditions: [{ key: 'topic', operator: 'eq', value: 'nonexistent' }] }), + ).toEqual([]); + }); + + it('combines multiple conditions with AND', async () => { + expect( + await idsFor({ + conditions: [ + { key: 'topic', operator: 'eq', value: 'billing' }, + { key: 'count', operator: 'eq', value: 5 }, + ], + combineWith: 'and', + }), + ).toEqual(['a']); + }); + + it('ne excludes only the matching row, including rows missing the key', async () => { + const ids = await idsFor({ conditions: [{ key: 'topic', operator: 'ne', value: 'billing' }] }); + expect(ids).not.toContain('a'); + expect(ids).toContain('b'); + expect(ids).toContain('c'); + }); + + it('in matches only listed values', async () => { + expect( + await idsFor({ conditions: [{ key: 'topic', operator: 'in', value: ['billing', 'ops'] }] }), + ).toEqual(['a', 'b']); + }); + + it('in is type-correct: a numeric candidate does not match a string-valued row', async () => { + expect(await idsFor({ conditions: [{ key: 'count', operator: 'in', value: [5] }] })).toEqual([ + 'a', + ]); + expect(await idsFor({ conditions: [{ key: 'count', operator: 'in', value: ['5'] }] })).toEqual([ + 'd', + ]); + }); + + it('nin excludes listed values but matches rows missing the key (the reference NIN bug case)', async () => { + const ids = await idsFor({ + conditions: [{ key: 'topic', operator: 'nin', value: ['billing'] }], + }); + expect(ids).not.toContain('a'); + expect(ids).toContain('b'); + expect(ids).toContain('c'); // missing key — must match, not be excluded like `!= ANY` incorrectly does + }); + + it('supports an OR-combined group', async () => { + expect( + await idsFor({ + conditions: [ + { key: 'topic', operator: 'eq', value: 'billing' }, + { key: 'topic', operator: 'eq', value: 'ops' }, + ], + combineWith: 'or', + }), + ).toEqual(['a', 'b']); + }); +}); + +describe.skipIf(!PG_URL)('PgVectorStore — filtered HNSW under-return (iterative scan)', () => { + const iterTableName = `vs_iter_test_${Date.now()}_${Math.floor(Math.random() * 1e6)}`; + let iterStore: PgVectorStore; + let adminPool: Pool; + let targetIds: string[]; + const queryVector = [1, 0, 0]; + + function mulberry32(seed: number) { + return function random() { + let t = (seed += 0x6d2b79f5); + t = Math.imul(t ^ (t >>> 15), t | 1); + t ^= t + Math.imul(t ^ (t >>> 7), t | 61); + return ((t ^ (t >>> 14)) >>> 0) / 4294967296; + }; + } + + beforeAll(async () => { + const { Pool: PoolCtor } = await import('pg'); + adminPool = new PoolCtor({ connectionString: PG_URL }); + await createPgVectorTable(adminPool, iterTableName, { dimensions: 3, hnswIndex: true }); + iterStore = new PgVectorStore('pg-iterative-scan', { + connectionString: PG_URL!, + tableName: iterTableName, + }); + + const random = mulberry32(42); + const records: Array<{ + id: string; + content: string; + vector: number[]; + metadata: Record; + }> = []; + + // Noise: tightly clustered around the query vector, satisfying HNSW's default candidate search alone. + for (let i = 0; i < 1995; i++) { + records.push({ + id: `noise-${i}`, + content: `noise ${i}`, + vector: [1 + (random() - 0.5) * 0.01, (random() - 0.5) * 0.01, (random() - 0.5) * 0.01], + metadata: { group: 'noise' }, + }); + } + + // Target: far from the query, the only rows matching the filter below. + targetIds = []; + for (let i = 0; i < 5; i++) { + const id = `target-${i}`; + targetIds.push(id); + records.push({ + id, + content: `target ${i}`, + vector: [(random() - 0.5) * 0.01, (random() - 0.5) * 0.01, 1 + (random() - 0.5) * 0.01], + metadata: { group: 'target' }, + }); + } + targetIds.sort(); + + await iterStore.upsert(records); + }); + + afterAll(async () => { + await adminPool.query(`DROP TABLE IF EXISTS "${iterTableName}";`); + await iterStore.close(); + await adminPool.end(); + }); + + it('returns all matching rows through the adapter despite the HNSW under-return bug', async () => { + const results = await iterStore.query(queryVector, { + topK: 5, + filter: { conditions: [{ key: 'group', operator: 'eq', value: 'target' }] }, + }); + + expect(results.map((r) => r.id).sort()).toEqual(targetIds); + }); +}); diff --git a/packages/@n8n/agents/src/__tests__/integration/pinecone-vector-store.test.ts b/packages/@n8n/agents/src/__tests__/integration/pinecone-vector-store.test.ts new file mode 100644 index 00000000000..7476517c10f --- /dev/null +++ b/packages/@n8n/agents/src/__tests__/integration/pinecone-vector-store.test.ts @@ -0,0 +1,262 @@ +/** + * Integration tests against a real Pinecone project. Gated on + * PINECONE_TEST_API_KEY (not the usual API-key/cassette convention) since + * Pinecone is a separate service, not LLM HTTP — these self-skip when it's + * unset. Pinecone writes and deletes are eventually consistent, so every + * assertion that depends on a preceding mutation is wrapped in `eventually`, + * which retries the assertion until it passes or a timeout elapses. + */ +import type { Pinecone } from '@pinecone-database/pinecone'; +import { MockEmbeddingModelV3 } from 'ai/test'; +import { afterAll, beforeAll, describe, expect, it } from 'vitest'; + +import { createPineconeIndex } from './vector-store-helpers'; +import { VectorStore } from '../../sdk/vector-store'; +import { PineconeVectorStore } from '../../vector-stores/pinecone'; + +const PINECONE_API_KEY = process.env.PINECONE_TEST_API_KEY; + +const HOOK_TIMEOUT_MS = 180_000; + +async function eventually(fn: () => void | Promise, timeoutMs = 15_000): Promise { + const deadline = Date.now() + timeoutMs; + for (;;) { + try { + await fn(); + return; + } catch (error) { + if (Date.now() > deadline) throw error; + await new Promise((resolve) => setTimeout(resolve, 500)); + } + } +} + +describe.skipIf(!PINECONE_API_KEY)('PineconeVectorStore', () => { + const indexName = `vs-test-${Date.now()}-${Math.floor(Math.random() * 1e6)}`; + let store: PineconeVectorStore; + let adminClient: Pinecone; + + beforeAll(async () => { + const { Pinecone: PineconeCtor } = await import('@pinecone-database/pinecone'); + adminClient = new PineconeCtor({ apiKey: PINECONE_API_KEY! }); + await createPineconeIndex(adminClient, indexName, 3); + store = new PineconeVectorStore('pinecone-integration', { + apiKey: PINECONE_API_KEY!, + indexName, + }); + }, HOOK_TIMEOUT_MS); + + afterAll(async () => { + await adminClient.deleteIndex(indexName); + store.close(); + }); + + it('upserts vectors with metadata and queries by similarity', async () => { + await store.upsert([ + { id: 'exact', vector: [1, 0, 0], content: 'exact match', metadata: { topic: 'a' } }, + { id: 'near', vector: [0.9, 0.1, 0], content: 'near match', metadata: {} }, + { id: 'far', vector: [0, 1, 0], content: 'far match', metadata: {} }, + ]); + + await eventually(async () => { + const results = await store.query([1, 0, 0], { topK: 2 }); + expect(results).toHaveLength(2); + expect(results[0].id).toBe('exact'); + expect(results[0].score).toBeCloseTo(1, 3); + expect(results[0].metadata).toEqual({ topic: 'a' }); + expect(results[1].id).toBe('near'); + }); + }); + + it('re-upserting the same id updates its content', async () => { + await store.upsert([ + { id: 'exact', vector: [1, 0, 0], content: 'updated content', metadata: {} }, + ]); + + await eventually(async () => { + const results = await store.query([1, 0, 0], { topK: 1 }); + expect(results[0].id).toBe('exact'); + expect(results[0].content).toBe('updated content'); + }); + }); + + it('deletes by ids', async () => { + await store.delete({ ids: ['far'] }); + + await eventually(async () => { + const results = await store.query([0, 1, 0], { topK: 5 }); + expect(results.find((r) => r.id === 'far')).toBeUndefined(); + }); + }); + + it('rejects upserting metadata that uses the reserved _content key', async () => { + await expect( + store.upsert([{ id: 'bad', vector: [1, 0, 0], content: 'x', metadata: { _content: 'y' } }]), + ).rejects.toThrow(/reserved for the document content/); + }); + + it('rejects upserting a nested-object metadata value', async () => { + await expect( + store.upsert([ + { id: 'bad', vector: [1, 0, 0], content: 'x', metadata: { nested: { a: 1 } } }, + ]), + ).rejects.toThrow(/unsupported/); + }); + + it('round-trips through the VectorStore orchestrator with a mock embedding model', async () => { + const roundTripStore = new PineconeVectorStore('pinecone-integration-roundtrip', { + apiKey: PINECONE_API_KEY!, + indexName, + namespace: 'roundtrip', + }); + const embeddingModel = new MockEmbeddingModelV3({ + doEmbed: async ({ values }: { values: string[] }) => ({ + embeddings: values.map(() => [1, 0, 0]), + warnings: [], + }), + }); + + try { + const knowledge = new VectorStore('roundtrip') + .store(roundTripStore) + .embeddingModel(embeddingModel); + await knowledge.addDocuments([{ content: 'refunds take 5 days' }]); + + await eventually(async () => { + const results = await knowledge.search('how long do refunds take'); + expect(results[0].content).toBe('refunds take 5 days'); + }); + } finally { + roundTripStore.close(); + } + }); +}); + +describe.skipIf(!PINECONE_API_KEY)('PineconeVectorStore — metadata filtering', () => { + const indexName = `vs-test-filter-${Date.now()}-${Math.floor(Math.random() * 1e6)}`; + let filterStore: PineconeVectorStore; + let adminClient: Pinecone; + + beforeAll(async () => { + const { Pinecone: PineconeCtor } = await import('@pinecone-database/pinecone'); + adminClient = new PineconeCtor({ apiKey: PINECONE_API_KEY! }); + await createPineconeIndex(adminClient, indexName, 3); + filterStore = new PineconeVectorStore('pinecone-filter-integration', { + apiKey: PINECONE_API_KEY!, + indexName, + }); + await filterStore.upsert([ + { + id: 'a', + content: 'Row A content', + vector: [1, 0, 0], + metadata: { topic: 'billing', count: 5, active: true }, + }, + { + id: 'b', + content: 'Row B content', + vector: [0.9, 0.1, 0], + metadata: { topic: 'ops', count: 10, active: false }, + }, + // Missing all keys — required to prove ne/nin match rows that never had the key. + { id: 'c', content: 'Row C content', vector: [0, 1, 0], metadata: {} }, + // Stores count as a JSON string, not a number — proves a numeric filter + // doesn't match it, and (unlike Qdrant, which restricts a field to one + // index type) that a string filter on the same field matches it back. + { id: 'd', content: 'Row D content', vector: [0, 0.5, 0.5], metadata: { count: '5' } }, + ]); + // Pinecone writes are eventually consistent — wait for the full corpus to be queryable before any test runs. + await eventually(async () => { + const results = await filterStore.query([1, 0, 0], { topK: 10 }); + expect(results.map((r) => r.id).sort()).toEqual(['a', 'b', 'c', 'd']); + }, 30_000); + }, HOOK_TIMEOUT_MS); + + afterAll(async () => { + await adminClient.deleteIndex(indexName); + filterStore.close(); + }); + + async function idsFor(filter: Parameters[1]['filter']) { + const results = await filterStore.query([1, 0, 0], { topK: 10, filter }); + return results.map((r) => r.id).sort(); + } + + it('eq matches on string, number, and boolean, and is type-correct', async () => { + expect( + await idsFor({ conditions: [{ key: 'topic', operator: 'eq', value: 'billing' }] }), + ).toEqual(['a']); + expect(await idsFor({ conditions: [{ key: 'count', operator: 'eq', value: 5 }] })).toEqual([ + 'a', + ]); + expect(await idsFor({ conditions: [{ key: 'active', operator: 'eq', value: true }] })).toEqual([ + 'a', + ]); + expect( + await idsFor({ conditions: [{ key: 'topic', operator: 'eq', value: 'nonexistent' }] }), + ).toEqual([]); + }); + + it('eq is type-correct in both directions between numeric and string-typed values', async () => { + expect(await idsFor({ conditions: [{ key: 'count', operator: 'eq', value: 5 }] })).toEqual([ + 'a', + ]); + expect(await idsFor({ conditions: [{ key: 'count', operator: 'eq', value: '5' }] })).toEqual([ + 'd', + ]); + }); + + it('combines multiple conditions with AND', async () => { + expect( + await idsFor({ + conditions: [ + { key: 'topic', operator: 'eq', value: 'billing' }, + { key: 'count', operator: 'eq', value: 5 }, + ], + combineWith: 'and', + }), + ).toEqual(['a']); + }); + + it('ne excludes only the matching row, including rows missing the key', async () => { + const ids = await idsFor({ + conditions: [{ key: 'topic', operator: 'ne', value: 'billing' }], + }); + expect(ids).not.toContain('a'); + expect(ids).toContain('b'); + expect(ids).toContain('c'); + }); + + it('in matches only listed values', async () => { + expect( + await idsFor({ conditions: [{ key: 'topic', operator: 'in', value: ['billing', 'ops'] }] }), + ).toEqual(['a', 'b']); + }); + + it('in is type-correct: a numeric candidate does not match a string-valued row', async () => { + expect(await idsFor({ conditions: [{ key: 'count', operator: 'in', value: [5] }] })).toEqual([ + 'a', + ]); + }); + + it('nin excludes listed values but matches rows missing the key (the reference NIN bug case)', async () => { + const ids = await idsFor({ + conditions: [{ key: 'topic', operator: 'nin', value: ['billing'] }], + }); + expect(ids).not.toContain('a'); + expect(ids).toContain('b'); + expect(ids).toContain('c'); // missing key — must match, not be excluded like `!= ANY` incorrectly does + }); + + it('supports an OR-combined group', async () => { + expect( + await idsFor({ + conditions: [ + { key: 'topic', operator: 'eq', value: 'billing' }, + { key: 'topic', operator: 'eq', value: 'ops' }, + ], + combineWith: 'or', + }), + ).toEqual(['a', 'b']); + }); +}); diff --git a/packages/@n8n/agents/src/__tests__/integration/qdrant-vector-store.test.ts b/packages/@n8n/agents/src/__tests__/integration/qdrant-vector-store.test.ts new file mode 100644 index 00000000000..7426652a463 --- /dev/null +++ b/packages/@n8n/agents/src/__tests__/integration/qdrant-vector-store.test.ts @@ -0,0 +1,287 @@ +/** + * Integration tests against a real Qdrant instance — local or cloud. Gated on + * QDRANT_TEST_URL (not the usual API-key/cassette convention) since Qdrant is + * a separate service, not LLM HTTP — these self-skip when it's unset. + * QDRANT_TEST_API_KEY is optional, for cloud instances that require auth. + */ +import type { QdrantClient } from '@qdrant/js-client-rest'; +import { MockEmbeddingModelV3 } from 'ai/test'; +import { afterAll, beforeAll, describe, expect, it } from 'vitest'; + +import { VectorStore } from '../../sdk/vector-store'; +import { QdrantVectorStore } from '../../vector-stores/qdrant'; + +const QDRANT_URL = process.env.QDRANT_TEST_URL; +const QDRANT_API_KEY = process.env.QDRANT_TEST_API_KEY; + +// Qdrant only accepts UUID or unsigned-integer point ids. +const ID_EXACT = '00000000-0000-4000-8000-000000000001'; +const ID_NEAR = '00000000-0000-4000-8000-000000000002'; +const ID_FAR = '00000000-0000-4000-8000-000000000003'; + +const ID_A = '00000000-0000-4000-8000-00000000000a'; +const ID_B = '00000000-0000-4000-8000-00000000000b'; +const ID_C = '00000000-0000-4000-8000-00000000000c'; +const ID_D = '00000000-0000-4000-8000-00000000000d'; + +/** Stands in for the BYO user's own setup, since QdrantVectorStore itself never creates collections. */ +async function createVectorCollection( + client: QdrantClient, + collectionName: string, + opts: { dimensions: number }, +): Promise { + await client.createCollection(collectionName, { + vectors: { size: opts.dimensions, distance: 'Cosine' }, + }); +} + +describe.skipIf(!QDRANT_URL)('QdrantVectorStore', () => { + const collectionName = `vs_test_${Date.now()}_${Math.floor(Math.random() * 1e6)}`; + let store: QdrantVectorStore; + let adminClient: QdrantClient; + + beforeAll(async () => { + const { QdrantClient: QdrantClientCtor } = await import('@qdrant/js-client-rest'); + adminClient = new QdrantClientCtor({ url: QDRANT_URL, apiKey: QDRANT_API_KEY }); + await createVectorCollection(adminClient, collectionName, { dimensions: 3 }); + store = new QdrantVectorStore('qdrant-integration', { + url: QDRANT_URL!, + apiKey: QDRANT_API_KEY, + collectionName, + }); + }); + + afterAll(async () => { + await adminClient.deleteCollection(collectionName); + store.close(); + }); + + it('upserts vectors with metadata and queries by similarity', async () => { + await store.upsert([ + { id: ID_EXACT, vector: [1, 0, 0], content: 'exact match', metadata: { topic: 'a' } }, + { id: ID_NEAR, vector: [0.9, 0.1, 0], content: 'near match', metadata: {} }, + { id: ID_FAR, vector: [0, 1, 0], content: 'far match', metadata: {} }, + ]); + + const results = await store.query([1, 0, 0], { topK: 2 }); + + expect(results).toHaveLength(2); + expect(results[0].id).toBe(ID_EXACT); + expect(results[0].score).toBeCloseTo(1, 6); + expect(results[0].metadata).toEqual({ topic: 'a' }); + expect(results[1].id).toBe(ID_NEAR); + }); + + it('re-upserting the same id updates its content', async () => { + await store.upsert([ + { id: ID_EXACT, vector: [1, 0, 0], content: 'updated content', metadata: {} }, + ]); + + const results = await store.query([1, 0, 0], { topK: 1 }); + + expect(results[0].id).toBe(ID_EXACT); + expect(results[0].content).toBe('updated content'); + }); + + it('deletes by ids', async () => { + await store.delete({ ids: [ID_FAR] }); + + const results = await store.query([0, 1, 0], { topK: 5 }); + + expect(results.find((r) => r.id === ID_FAR)).toBeUndefined(); + }); + + it('rejects a non-UUID, non-integer id', async () => { + await expect( + store.upsert([{ id: 'exact', vector: [1, 0, 0], content: 'bad id', metadata: {} }]), + ).rejects.toThrow(/Qdrant requires ids to be a UUID or a canonical unsigned integer/); + }); + + it('rejects a non-canonical numeric id', async () => { + await expect( + store.upsert([{ id: '007', vector: [1, 0, 0], content: 'bad id', metadata: {} }]), + ).rejects.toThrow(/canonical unsigned integer/); + }); + + it('round-trips through the VectorStore orchestrator with a mock embedding model', async () => { + const roundTripCollection = `${collectionName}_roundtrip`; + await createVectorCollection(adminClient, roundTripCollection, { dimensions: 3 }); + const roundTripStore = new QdrantVectorStore('qdrant-integration-roundtrip', { + url: QDRANT_URL!, + apiKey: QDRANT_API_KEY, + collectionName: roundTripCollection, + }); + const embeddingModel = new MockEmbeddingModelV3({ + doEmbed: async ({ values }: { values: string[] }) => ({ + embeddings: values.map(() => [1, 0, 0]), + warnings: [], + }), + }); + + try { + const knowledge = new VectorStore('roundtrip') + .store(roundTripStore) + .embeddingModel(embeddingModel); + await knowledge.addDocuments([{ content: 'refunds take 5 days' }]); + + const results = await knowledge.search('how long do refunds take'); + + expect(results[0].content).toBe('refunds take 5 days'); + } finally { + await adminClient.deleteCollection(roundTripCollection); + roundTripStore.close(); + } + }); +}); + +describe.skipIf(!QDRANT_URL)('QdrantVectorStore — metadata filtering', () => { + const filterCollectionName = `vs_test_filter_${Date.now()}_${Math.floor(Math.random() * 1e6)}`; + let filterStore: QdrantVectorStore; + let adminClient: QdrantClient; + + beforeAll(async () => { + const { QdrantClient: QdrantClientCtor } = await import('@qdrant/js-client-rest'); + adminClient = new QdrantClientCtor({ url: QDRANT_URL, apiKey: QDRANT_API_KEY }); + await createVectorCollection(adminClient, filterCollectionName, { dimensions: 3 }); + // Qdrant Cloud requires a payload index to filter on a field at all, and + // allows only one index type per field (a second `createPayloadIndex` + // call on the same field replaces rather than adds) — see the `count` + // fixture note below for how that constrains what this suite can assert. + await adminClient.createPayloadIndex(filterCollectionName, { + field_name: 'metadata.topic', + field_schema: 'keyword', + }); + await adminClient.createPayloadIndex(filterCollectionName, { + field_name: 'metadata.count', + field_schema: 'integer', + }); + await adminClient.createPayloadIndex(filterCollectionName, { + field_name: 'metadata.active', + field_schema: 'bool', + }); + filterStore = new QdrantVectorStore('qdrant-filter-integration', { + url: QDRANT_URL!, + apiKey: QDRANT_API_KEY, + collectionName: filterCollectionName, + }); + await filterStore.upsert([ + { + id: ID_A, + content: 'Row A content', + vector: [1, 0, 0], + metadata: { topic: 'billing', count: 5, active: true }, + }, + { + id: ID_B, + content: 'Row B content', + vector: [0.9, 0.1, 0], + metadata: { topic: 'ops', count: 10, active: false }, + }, + // Missing all keys — required to prove ne/nin match rows that never had the key. + { id: ID_C, content: 'Row C content', vector: [0, 1, 0], metadata: {} }, + // Stores count as a JSON string, not a number — proves a numeric filter + // doesn't match it. (The reverse — a string filter matching this row — + // isn't tested here: Qdrant's `metadata.count` field is indexed as + // `integer` above, and a field can only carry one index type at a time, + // so a `keyword` filter query on the same field isn't servable.) + { id: ID_D, content: 'Row D content', vector: [0, 0.5, 0.5], metadata: { count: '5' } }, + ]); + }); + + afterAll(async () => { + await adminClient.deleteCollection(filterCollectionName); + filterStore.close(); + }); + + async function idsFor(filter: Parameters[1]['filter']) { + const results = await filterStore.query([1, 0, 0], { topK: 10, filter }); + return results.map((r) => r.id).sort(); + } + + it('eq matches on string, number, and boolean, and is type-correct', async () => { + expect( + await idsFor({ conditions: [{ key: 'topic', operator: 'eq', value: 'billing' }] }), + ).toEqual([ID_A]); + expect(await idsFor({ conditions: [{ key: 'count', operator: 'eq', value: 5 }] })).toEqual([ + ID_A, + ]); + expect(await idsFor({ conditions: [{ key: 'active', operator: 'eq', value: true }] })).toEqual([ + ID_A, + ]); + expect( + await idsFor({ conditions: [{ key: 'topic', operator: 'eq', value: 'nonexistent' }] }), + ).toEqual([]); + }); + + it('combines multiple conditions with AND', async () => { + expect( + await idsFor({ + conditions: [ + { key: 'topic', operator: 'eq', value: 'billing' }, + { key: 'count', operator: 'eq', value: 5 }, + ], + combineWith: 'and', + }), + ).toEqual([ID_A]); + }); + + it('ne excludes only the matching row, including rows missing the key', async () => { + const ids = await idsFor({ + conditions: [{ key: 'topic', operator: 'ne', value: 'billing' }], + }); + expect(ids).not.toContain(ID_A); + expect(ids).toContain(ID_B); + expect(ids).toContain(ID_C); + }); + + it('in matches only listed values', async () => { + expect( + await idsFor({ conditions: [{ key: 'topic', operator: 'in', value: ['billing', 'ops'] }] }), + ).toEqual([ID_A, ID_B].sort()); + }); + + it('in is type-correct: a numeric candidate does not match a string-valued row', async () => { + expect(await idsFor({ conditions: [{ key: 'count', operator: 'in', value: [5] }] })).toEqual([ + ID_A, + ]); + }); + + it('nin excludes listed values but matches rows missing the key (the reference NIN bug case)', async () => { + const ids = await idsFor({ + conditions: [{ key: 'topic', operator: 'nin', value: ['billing'] }], + }); + expect(ids).not.toContain(ID_A); + expect(ids).toContain(ID_B); + expect(ids).toContain(ID_C); // missing key — must match, not be excluded like `!= ANY` incorrectly does + }); + + it('supports an OR-combined group', async () => { + expect( + await idsFor({ + conditions: [ + { key: 'topic', operator: 'eq', value: 'billing' }, + { key: 'topic', operator: 'eq', value: 'ops' }, + ], + combineWith: 'or', + }), + ).toEqual([ID_A, ID_B].sort()); + }); + + it('rejects a float value for eq', async () => { + await expect( + filterStore.query([1, 0, 0], { + topK: 10, + filter: { conditions: [{ key: 'count', operator: 'eq', value: 5.5 }] }, + }), + ).rejects.toThrow(/does not support float values/); + }); + + it('rejects a mixed-type array for in', async () => { + await expect( + filterStore.query([1, 0, 0], { + topK: 10, + filter: { conditions: [{ key: 'topic', operator: 'in', value: ['billing', 5] }] }, + }), + ).rejects.toThrow(/mixed-type or float values/); + }); +}); diff --git a/packages/@n8n/agents/src/__tests__/integration/supabase-vector-store.test.ts b/packages/@n8n/agents/src/__tests__/integration/supabase-vector-store.test.ts new file mode 100644 index 00000000000..f7204939631 --- /dev/null +++ b/packages/@n8n/agents/src/__tests__/integration/supabase-vector-store.test.ts @@ -0,0 +1,288 @@ +/** + * Integration tests against a real Supabase project. Gated on + * SUPABASE_TEST_URL / SUPABASE_TEST_API_KEY / SUPABASE_TEST_DB_URL (not the + * usual API-key/cassette convention) since Supabase is a separate service, + * not LLM HTTP — these self-skip when any are unset. SUPABASE_TEST_DB_URL is + * the project's direct Postgres connection string (Dashboard > Connect > + * Session pooler), used only to provision the table/function via `pg` since + * supabase-js (PostgREST) cannot run DDL. + */ +import { MockEmbeddingModelV3 } from 'ai/test'; +import type { Pool } from 'pg'; +import { afterAll, beforeAll, describe, expect, it } from 'vitest'; + +import { + createSupabaseVectorTableAndFunction, + dropSupabaseVectorTableAndFunction, + waitUntilQueryable, +} from './vector-store-helpers'; +import { VectorStore } from '../../sdk/vector-store'; +import { SupabaseVectorStore } from '../../vector-stores/supabase'; + +const SUPABASE_URL = process.env.SUPABASE_TEST_URL; +const SUPABASE_API_KEY = process.env.SUPABASE_TEST_API_KEY; +const SUPABASE_DB_URL = process.env.SUPABASE_TEST_DB_URL; + +const HOOK_TIMEOUT_MS = 45_000; + +describe.skipIf(!SUPABASE_URL || !SUPABASE_API_KEY || !SUPABASE_DB_URL)( + 'SupabaseVectorStore', + () => { + const suffix = `${Date.now()}_${Math.floor(Math.random() * 1e6)}`; + const tableName = `vs_test_${suffix}`; + const queryName = `match_vs_test_${suffix}`; + let store: SupabaseVectorStore; + let adminPool: Pool; + + beforeAll(async () => { + const { Pool: PoolCtor } = await import('pg'); + adminPool = new PoolCtor({ connectionString: SUPABASE_DB_URL }); + await createSupabaseVectorTableAndFunction(adminPool, tableName, queryName, 3); + store = new SupabaseVectorStore('supabase-integration', { + url: SUPABASE_URL!, + apiKey: SUPABASE_API_KEY!, + tableName, + queryName, + }); + await waitUntilQueryable(store, 3); + }, HOOK_TIMEOUT_MS); + + afterAll(async () => { + await dropSupabaseVectorTableAndFunction(adminPool, tableName, queryName); + store.close(); + await adminPool.end(); + }); + + it('upserts vectors with metadata and queries by similarity', async () => { + await store.upsert([ + { id: 'exact', vector: [1, 0, 0], content: 'exact match', metadata: { topic: 'a' } }, + { id: 'near', vector: [0.9, 0.1, 0], content: 'near match', metadata: {} }, + { id: 'far', vector: [0, 1, 0], content: 'far match', metadata: {} }, + ]); + + const results = await store.query([1, 0, 0], { topK: 2 }); + + expect(results).toHaveLength(2); + expect(results[0].id).toBe('exact'); + expect(results[0].score).toBeCloseTo(1, 6); + expect(results[0].metadata).toEqual({ topic: 'a' }); + expect(results[1].id).toBe('near'); + }); + + it('re-upserting the same id updates its content', async () => { + await store.upsert([ + { id: 'exact', vector: [1, 0, 0], content: 'updated content', metadata: {} }, + ]); + + const results = await store.query([1, 0, 0], { topK: 1 }); + + expect(results[0].id).toBe('exact'); + expect(results[0].content).toBe('updated content'); + }); + + it('deletes by ids', async () => { + await store.delete({ ids: ['far'] }); + + const results = await store.query([0, 1, 0], { topK: 5 }); + + expect(results.find((r) => r.id === 'far')).toBeUndefined(); + }); + + it( + 'round-trips through the VectorStore orchestrator with a mock embedding model', + async () => { + const roundTripTable = `${tableName}_roundtrip`; + const roundTripQueryName = `${queryName}_roundtrip`; + await createSupabaseVectorTableAndFunction( + adminPool, + roundTripTable, + roundTripQueryName, + 3, + ); + const roundTripStore = new SupabaseVectorStore('supabase-integration-roundtrip', { + url: SUPABASE_URL!, + apiKey: SUPABASE_API_KEY!, + tableName: roundTripTable, + queryName: roundTripQueryName, + }); + await waitUntilQueryable(roundTripStore, 3); + const embeddingModel = new MockEmbeddingModelV3({ + doEmbed: async ({ values }: { values: string[] }) => ({ + embeddings: values.map(() => [1, 0, 0]), + warnings: [], + }), + }); + + try { + const knowledge = new VectorStore('roundtrip') + .store(roundTripStore) + .embeddingModel(embeddingModel); + await knowledge.addDocuments([{ content: 'refunds take 5 days' }]); + + const results = await knowledge.search('how long do refunds take'); + + expect(results[0].content).toBe('refunds take 5 days'); + } finally { + await dropSupabaseVectorTableAndFunction(adminPool, roundTripTable, roundTripQueryName); + roundTripStore.close(); + } + }, + HOOK_TIMEOUT_MS, + ); + }, +); + +describe.skipIf(!SUPABASE_URL || !SUPABASE_API_KEY || !SUPABASE_DB_URL)( + 'SupabaseVectorStore — metadata filtering', + () => { + const suffix = `${Date.now()}_${Math.floor(Math.random() * 1e6)}`; + const filterTableName = `vs_filter_test_${suffix}`; + const filterQueryName = `match_vs_filter_test_${suffix}`; + let filterStore: SupabaseVectorStore; + let adminPool: Pool; + + beforeAll(async () => { + const { Pool: PoolCtor } = await import('pg'); + adminPool = new PoolCtor({ connectionString: SUPABASE_DB_URL }); + await createSupabaseVectorTableAndFunction(adminPool, filterTableName, filterQueryName, 3); + filterStore = new SupabaseVectorStore('supabase-filter-integration', { + url: SUPABASE_URL!, + apiKey: SUPABASE_API_KEY!, + tableName: filterTableName, + queryName: filterQueryName, + }); + await waitUntilQueryable(filterStore, 3); + await filterStore.upsert([ + { + id: 'a', + content: 'Row A content', + vector: [1, 0, 0], + metadata: { topic: 'billing', count: 5, active: true }, + }, + { + id: 'b', + content: 'Row B content', + vector: [0.9, 0.1, 0], + metadata: { topic: 'ops', count: 10, active: false }, + }, + // Missing all keys — required to prove ne/nin match rows that never had the key. + { id: 'c', content: 'Row C content', vector: [0, 1, 0], metadata: {} }, + // Stores count as a JSON string, not a number — proves eq is type-correct. + { id: 'd', content: 'Row D content', vector: [0, 0.5, 0.5], metadata: { count: '5' } }, + ]); + }, HOOK_TIMEOUT_MS); + + afterAll(async () => { + await dropSupabaseVectorTableAndFunction(adminPool, filterTableName, filterQueryName); + filterStore.close(); + await adminPool.end(); + }); + + async function idsFor(filter: Parameters[1]['filter']) { + const results = await filterStore.query([1, 0, 0], { topK: 10, filter }); + return results.map((r) => r.id).sort(); + } + + it('eq matches on string, number, and boolean, and is type-correct', async () => { + expect( + await idsFor({ conditions: [{ key: 'topic', operator: 'eq', value: 'billing' }] }), + ).toEqual(['a']); + expect(await idsFor({ conditions: [{ key: 'count', operator: 'eq', value: 5 }] })).toEqual([ + 'a', + ]); + expect( + await idsFor({ conditions: [{ key: 'active', operator: 'eq', value: true }] }), + ).toEqual(['a']); + expect( + await idsFor({ conditions: [{ key: 'topic', operator: 'eq', value: 'nonexistent' }] }), + ).toEqual([]); + }); + + it('combines multiple conditions with AND', async () => { + expect( + await idsFor({ + conditions: [ + { key: 'topic', operator: 'eq', value: 'billing' }, + { key: 'count', operator: 'eq', value: 5 }, + ], + combineWith: 'and', + }), + ).toEqual(['a']); + }); + + it('combines in with another condition under AND', async () => { + expect( + await idsFor({ + conditions: [ + { key: 'topic', operator: 'in', value: ['billing', 'ops'] }, + { key: 'active', operator: 'eq', value: true }, + ], + combineWith: 'and', + }), + ).toEqual(['a']); + }); + + it('ne excludes only the matching row, including rows missing the key', async () => { + const ids = await idsFor({ + conditions: [{ key: 'topic', operator: 'ne', value: 'billing' }], + }); + expect(ids).not.toContain('a'); + expect(ids).toContain('b'); + expect(ids).toContain('c'); + }); + + it('in matches only listed values', async () => { + expect( + await idsFor({ conditions: [{ key: 'topic', operator: 'in', value: ['billing', 'ops'] }] }), + ).toEqual(['a', 'b']); + }); + + it('in is type-correct: a numeric candidate does not match a string-valued row', async () => { + expect(await idsFor({ conditions: [{ key: 'count', operator: 'in', value: [5] }] })).toEqual([ + 'a', + ]); + expect( + await idsFor({ conditions: [{ key: 'count', operator: 'in', value: ['5'] }] }), + ).toEqual(['d']); + }); + + it('nin excludes listed values but matches rows missing the key (the reference NIN bug case)', async () => { + const ids = await idsFor({ + conditions: [{ key: 'topic', operator: 'nin', value: ['billing'] }], + }); + expect(ids).not.toContain('a'); + expect(ids).toContain('b'); + expect(ids).toContain('c'); // missing key — must match, not be excluded like `!= ANY` incorrectly does + }); + + it('supports an OR-combined group', async () => { + expect( + await idsFor({ + conditions: [ + { key: 'topic', operator: 'eq', value: 'billing' }, + { key: 'topic', operator: 'eq', value: 'ops' }, + ], + combineWith: 'or', + }), + ).toEqual(['a', 'b']); + }); + + it('rejects an empty array for in', async () => { + await expect( + filterStore.query([1, 0, 0], { + topK: 10, + filter: { conditions: [{ key: 'topic', operator: 'in', value: [] }] }, + }), + ).rejects.toThrow(/requires a non-empty array value/); + }); + + it('rejects an empty array for nin', async () => { + await expect( + filterStore.query([1, 0, 0], { + topK: 10, + filter: { conditions: [{ key: 'topic', operator: 'nin', value: [] }] }, + }), + ).rejects.toThrow(/requires a non-empty array value/); + }); + }, +); diff --git a/packages/@n8n/agents/src/__tests__/integration/vector-store-bulk-pinecone.test.ts b/packages/@n8n/agents/src/__tests__/integration/vector-store-bulk-pinecone.test.ts new file mode 100644 index 00000000000..b6d1b33eedb --- /dev/null +++ b/packages/@n8n/agents/src/__tests__/integration/vector-store-bulk-pinecone.test.ts @@ -0,0 +1,53 @@ +/** + * Bulk retrieval-quality suite run against PineconeVectorStore using a + * committed 30-document fixture with precomputed embeddings (see + * fixtures/generate-bulk-vector-fixture.ts). No OpenAI calls at test time — + * both document and query vectors are precomputed, so "ground truth" is + * computed locally via cosine similarity and compared against what the + * backend actually returns. + */ +import type { Pinecone } from '@pinecone-database/pinecone'; +import { afterAll, beforeAll, describe } from 'vitest'; + +import { + createPineconeIndex, + loadBulkFixture, + registerBulkVectorStoreTests, + upsertFixtureDocuments, + waitForPineconeRecordCount, +} from './vector-store-helpers'; +import { PineconeVectorStore } from '../../vector-stores/pinecone'; + +const PINECONE_API_KEY = process.env.PINECONE_TEST_API_KEY; + +const HOOK_TIMEOUT_MS = 240_000; + +const fixture = loadBulkFixture(); + +describe.skipIf(!PINECONE_API_KEY)('PineconeVectorStore — bulk fixture corpus', () => { + const indexName = `vs-bulk-${Date.now()}-${Math.floor(Math.random() * 1e6)}`; + let adminClient: Pinecone; + let store: PineconeVectorStore; + + beforeAll(async () => { + const { Pinecone: PineconeCtor } = await import('@pinecone-database/pinecone'); + adminClient = new PineconeCtor({ apiKey: PINECONE_API_KEY! }); + await createPineconeIndex(adminClient, indexName, fixture.dimensions); + store = new PineconeVectorStore('bulk-pinecone', { + apiKey: PINECONE_API_KEY!, + indexName, + }); + await upsertFixtureDocuments(store, fixture); + await waitForPineconeRecordCount(adminClient.index(indexName), fixture.documents.length); + }, HOOK_TIMEOUT_MS); + + afterAll(async () => { + await adminClient.deleteIndex(indexName); + store.close(); + }); + + registerBulkVectorStoreTests(fixture, () => store, { + afterDelete: async (remainingCount) => + await waitForPineconeRecordCount(adminClient.index(indexName), remainingCount), + }); +}); diff --git a/packages/@n8n/agents/src/__tests__/integration/vector-store-bulk-postgres.test.ts b/packages/@n8n/agents/src/__tests__/integration/vector-store-bulk-postgres.test.ts new file mode 100644 index 00000000000..dcaf1134d1d --- /dev/null +++ b/packages/@n8n/agents/src/__tests__/integration/vector-store-bulk-postgres.test.ts @@ -0,0 +1,46 @@ +/** + * Bulk retrieval-quality suite run against PgVectorStore using a committed + * 30-document fixture with precomputed embeddings (see + * fixtures/generate-bulk-vector-fixture.ts). No OpenAI calls at test time — + * both document and query vectors are precomputed, so "ground truth" is + * computed locally via cosine similarity and compared against what the + * backend actually returns. + */ +import type { Pool } from 'pg'; +import { afterAll, beforeAll, describe } from 'vitest'; + +import { + createPgVectorTable, + loadBulkFixture, + registerBulkVectorStoreTests, + upsertFixtureDocuments, +} from './vector-store-helpers'; +import { PgVectorStore } from '../../vector-stores/postgres'; + +const PG_URL = process.env.PG_VECTOR_TEST_URL; + +const HOOK_TIMEOUT_MS = 60_000; + +const fixture = loadBulkFixture(); + +describe.skipIf(!PG_URL)('PgVectorStore — bulk fixture corpus', () => { + const tableName = `vs_bulk_${Date.now()}_${Math.floor(Math.random() * 1e6)}`; + let pool: Pool; + let store: PgVectorStore; + + beforeAll(async () => { + const { Pool: PoolCtor } = await import('pg'); + pool = new PoolCtor({ connectionString: PG_URL }); + await createPgVectorTable(pool, tableName, { dimensions: fixture.dimensions }); + store = new PgVectorStore('bulk-pg', { connectionString: PG_URL!, tableName }); + await upsertFixtureDocuments(store, fixture); + }, HOOK_TIMEOUT_MS); + + afterAll(async () => { + await pool.query(`DROP TABLE IF EXISTS "${tableName}";`); + await store.close(); + await pool.end(); + }); + + registerBulkVectorStoreTests(fixture, () => store); +}); diff --git a/packages/@n8n/agents/src/__tests__/integration/vector-store-bulk-qdrant.test.ts b/packages/@n8n/agents/src/__tests__/integration/vector-store-bulk-qdrant.test.ts new file mode 100644 index 00000000000..824218417f4 --- /dev/null +++ b/packages/@n8n/agents/src/__tests__/integration/vector-store-bulk-qdrant.test.ts @@ -0,0 +1,56 @@ +/** + * Bulk retrieval-quality suite run against QdrantVectorStore using a + * committed 30-document fixture with precomputed embeddings (see + * fixtures/generate-bulk-vector-fixture.ts). No OpenAI calls at test time — + * both document and query vectors are precomputed, so "ground truth" is + * computed locally via cosine similarity and compared against what the + * backend actually returns. + */ +import type { QdrantClient } from '@qdrant/js-client-rest'; +import { afterAll, beforeAll, describe } from 'vitest'; + +import { + loadBulkFixture, + registerBulkVectorStoreTests, + upsertFixtureDocuments, +} from './vector-store-helpers'; +import { QdrantVectorStore } from '../../vector-stores/qdrant'; + +const QDRANT_URL = process.env.QDRANT_TEST_URL; +const QDRANT_API_KEY = process.env.QDRANT_TEST_API_KEY; + +const HOOK_TIMEOUT_MS = 60_000; + +const fixture = loadBulkFixture(); + +describe.skipIf(!QDRANT_URL)('QdrantVectorStore — bulk fixture corpus', () => { + const collectionName = `vs_bulk_${Date.now()}_${Math.floor(Math.random() * 1e6)}`; + let adminClient: QdrantClient; + let store: QdrantVectorStore; + + beforeAll(async () => { + const { QdrantClient: QdrantClientCtor } = await import('@qdrant/js-client-rest'); + adminClient = new QdrantClientCtor({ url: QDRANT_URL, apiKey: QDRANT_API_KEY }); + await adminClient.createCollection(collectionName, { + vectors: { size: fixture.dimensions, distance: 'Cosine' }, + }); + // Qdrant Cloud requires a payload index to filter on a field at all. + await adminClient.createPayloadIndex(collectionName, { + field_name: 'metadata.category', + field_schema: 'keyword', + }); + store = new QdrantVectorStore('bulk-qdrant', { + url: QDRANT_URL!, + apiKey: QDRANT_API_KEY, + collectionName, + }); + await upsertFixtureDocuments(store, fixture); + }, HOOK_TIMEOUT_MS); + + afterAll(async () => { + await adminClient.deleteCollection(collectionName); + store.close(); + }); + + registerBulkVectorStoreTests(fixture, () => store); +}); diff --git a/packages/@n8n/agents/src/__tests__/integration/vector-store-bulk-supabase.test.ts b/packages/@n8n/agents/src/__tests__/integration/vector-store-bulk-supabase.test.ts new file mode 100644 index 00000000000..1963e7e65b9 --- /dev/null +++ b/packages/@n8n/agents/src/__tests__/integration/vector-store-bulk-supabase.test.ts @@ -0,0 +1,61 @@ +/** + * Bulk retrieval-quality suite run against SupabaseVectorStore using a + * committed 30-document fixture with precomputed embeddings (see + * fixtures/generate-bulk-vector-fixture.ts). No OpenAI calls at test time — + * both document and query vectors are precomputed, so "ground truth" is + * computed locally via cosine similarity and compared against what the + * backend actually returns. + */ +import type { Pool } from 'pg'; +import { afterAll, beforeAll, describe } from 'vitest'; + +import { + createSupabaseVectorTableAndFunction, + dropSupabaseVectorTableAndFunction, + loadBulkFixture, + registerBulkVectorStoreTests, + upsertFixtureDocuments, + waitUntilQueryable, +} from './vector-store-helpers'; +import { SupabaseVectorStore } from '../../vector-stores/supabase'; + +const SUPABASE_URL = process.env.SUPABASE_TEST_URL; +const SUPABASE_API_KEY = process.env.SUPABASE_TEST_API_KEY; +const SUPABASE_DB_URL = process.env.SUPABASE_TEST_DB_URL; + +const HOOK_TIMEOUT_MS = 60_000; + +const fixture = loadBulkFixture(); + +describe.skipIf(!SUPABASE_URL || !SUPABASE_API_KEY || !SUPABASE_DB_URL)( + 'SupabaseVectorStore — bulk fixture corpus', + () => { + const suffix = `${Date.now()}_${Math.floor(Math.random() * 1e6)}`; + const tableName = `vs_bulk_${suffix}`; + const queryName = `match_vs_bulk_${suffix}`; + let pool: Pool; + let store: SupabaseVectorStore; + + beforeAll(async () => { + const { Pool: PoolCtor } = await import('pg'); + pool = new PoolCtor({ connectionString: SUPABASE_DB_URL }); + await createSupabaseVectorTableAndFunction(pool, tableName, queryName, fixture.dimensions); + store = new SupabaseVectorStore('bulk-supabase', { + url: SUPABASE_URL!, + apiKey: SUPABASE_API_KEY!, + tableName, + queryName, + }); + await waitUntilQueryable(store, fixture.dimensions); + await upsertFixtureDocuments(store, fixture); + }, HOOK_TIMEOUT_MS); + + afterAll(async () => { + await dropSupabaseVectorTableAndFunction(pool, tableName, queryName); + store.close(); + await pool.end(); + }); + + registerBulkVectorStoreTests(fixture, () => store); + }, +); diff --git a/packages/@n8n/agents/src/__tests__/integration/vector-store-helpers.ts b/packages/@n8n/agents/src/__tests__/integration/vector-store-helpers.ts new file mode 100644 index 00000000000..1a8461c4254 --- /dev/null +++ b/packages/@n8n/agents/src/__tests__/integration/vector-store-helpers.ts @@ -0,0 +1,341 @@ +/** + * Shared helpers for the vector-store integration suites (Postgres, Qdrant, + * Supabase, Pinecone) — fixture loading, locally computed ground truth, + * Postgres/Supabase DDL setup, Pinecone index provisioning, and shared + * test-body registration, deduplicated from the per-backend test files. + */ +import type { Index, Pinecone } from '@pinecone-database/pinecone'; +import { readFileSync } from 'node:fs'; +import path from 'node:path'; +import type { Pool } from 'pg'; +import { expect, it } from 'vitest'; + +import { findLastTextContent } from './helpers'; +import { Agent } from '../../index'; +import type { VectorStore } from '../../index'; +import type { BaseVectorStore } from '../../storage/base-vector-store'; +import type { SupabaseVectorStore } from '../../vector-stores/supabase'; + +const SCHEMA_WAIT_TIMEOUT_MS = 15_000; + +export interface FixtureDocument { + id: string; + content: string; + metadata: { title: string; category: string }; + vector: number[]; +} + +export interface FixtureQuery { + text: string; + vector: number[]; + expectedTopId: string; +} + +export interface BulkFixture { + dimensions: number; + documents: FixtureDocument[]; + queries: FixtureQuery[]; +} + +// vitest injects __dirname for TypeScript test files in the node environment. +export function loadBulkFixture(): BulkFixture { + return JSON.parse( + readFileSync(path.resolve(__dirname, '../fixtures/bulk-vector-fixture.json'), 'utf-8'), + ) as BulkFixture; +} + +export function cosineSimilarity(a: number[], b: number[]): number { + let dot = 0; + let magA = 0; + let magB = 0; + for (let i = 0; i < a.length; i++) { + dot += a[i] * b[i]; + magA += a[i] * a[i]; + magB += b[i] * b[i]; + } + return dot / (Math.sqrt(magA) * Math.sqrt(magB)); +} + +/** Locally computed ground truth — independent of any backend's search semantics. */ +export function localTopIds( + docs: FixtureDocument[], + queryVector: number[], + topK: number, +): string[] { + return [...docs] + .map((doc) => ({ id: doc.id, score: cosineSimilarity(queryVector, doc.vector) })) + .sort((a, b) => b.score - a.score) + .slice(0, topK) + .map((doc) => doc.id); +} + +/** Stands in for the BYO user's own setup; dimensions match the embedding model in use. */ +export async function createPgVectorTable( + pool: Pool, + tableName: string, + opts: { dimensions: number; hnswIndex?: boolean; ginIndex?: boolean }, +): Promise { + await pool.query('CREATE EXTENSION IF NOT EXISTS vector;'); + await pool.query( + `CREATE TABLE IF NOT EXISTS "${tableName}" ( + id TEXT PRIMARY KEY, + content TEXT NOT NULL, + metadata JSONB NOT NULL DEFAULT '{}', + embedding vector(${opts.dimensions}) NOT NULL + );`, + ); + if (opts.hnswIndex) { + await pool.query( + `CREATE INDEX IF NOT EXISTS "${tableName}_embedding_idx" ON "${tableName}" USING hnsw (embedding vector_cosine_ops);`, + ); + } + if (opts.ginIndex) { + await pool.query( + `CREATE INDEX IF NOT EXISTS "${tableName}_metadata_idx" ON "${tableName}" USING gin (metadata jsonb_path_ops);`, + ); + } +} + +/** Stands in for the BYO user's own setup, since SupabaseVectorStore itself never runs DDL. */ +export async function createSupabaseVectorTableAndFunction( + pool: Pool, + tableName: string, + queryName: string, + dimensions: number, +): Promise { + await createPgVectorTable(pool, tableName, { dimensions }); + await pool.query( + `CREATE OR REPLACE FUNCTION "${queryName}"(query_embedding vector(${dimensions})) + RETURNS TABLE (id text, content text, metadata jsonb, similarity float) + LANGUAGE sql STABLE AS $$ + SELECT id, content, metadata, 1 - (embedding <=> query_embedding) AS similarity + FROM "${tableName}" + ORDER BY embedding <=> query_embedding; + $$;`, + ); + // PostgREST caches the schema; new tables/functions aren't servable until it reloads. + await pool.query("NOTIFY pgrst, 'reload schema';"); +} + +export async function dropSupabaseVectorTableAndFunction( + pool: Pool, + tableName: string, + queryName: string, +): Promise { + await pool.query(`DROP FUNCTION IF EXISTS "${queryName}"(vector);`); + await pool.query(`DROP TABLE IF EXISTS "${tableName}";`); +} + +/** PostgREST's schema cache reloads asynchronously after DDL — poll until the new function is servable. */ +export async function waitUntilQueryable( + store: SupabaseVectorStore, + dimensions: number, +): Promise { + const zeroVector = new Array(dimensions).fill(0); + const deadline = Date.now() + SCHEMA_WAIT_TIMEOUT_MS; + for (;;) { + try { + await store.query(zeroVector, { topK: 1 }); + return; + } catch (error) { + if (Date.now() > deadline) throw error; + await new Promise((resolve) => setTimeout(resolve, 500)); + } + } +} + +/** Stands in for the BYO user's own setup, since PineconeVectorStore itself never creates indexes. */ +export async function createPineconeIndex( + pc: Pinecone, + name: string, + dimensions: number, +): Promise { + await pc.createIndex({ + name, + dimension: dimensions, + metric: 'cosine', + spec: { serverless: { cloud: 'aws', region: 'us-east-1' } }, + waitUntilReady: true, + }); +} + +/** Pinecone writes are eventually consistent — poll index stats until the expected record count is visible. */ +export async function waitForPineconeRecordCount(index: Index, expected: number): Promise { + const deadline = Date.now() + SCHEMA_WAIT_TIMEOUT_MS * 4; + for (;;) { + const stats = await index.describeIndexStats(); + if (stats.totalRecordCount === expected) return; + if (Date.now() > deadline) { + throw new Error( + `Timed out waiting for Pinecone record count to reach ${expected} (last seen: ${stats.totalRecordCount ?? 0}).`, + ); + } + await new Promise((resolve) => setTimeout(resolve, 1000)); + } +} + +export async function upsertFixtureDocuments( + store: BaseVectorStore, + fixture: BulkFixture, +): Promise { + await store.upsert( + fixture.documents.map((doc) => ({ + id: doc.id, + vector: doc.vector, + content: doc.content, + metadata: doc.metadata, + })), + ); +} + +/** + * Registers the bulk retrieval-quality `it` blocks shared by all four + * backends' `vector-store-bulk-*.test.ts` suites. Must be called inside the + * suite's `describe` body, after `getStore` is guaranteed to resolve (i.e. + * after `beforeAll` populates the store). Order matters: the delete test + * mutates the corpus and must run last. + */ +export function registerBulkVectorStoreTests( + fixture: BulkFixture, + getStore: () => BaseVectorStore, + opts?: { afterDelete?: (remainingCount: number) => Promise }, +): void { + it('returns the expected best match for each known-answer query', async () => { + const store = getStore(); + for (const query of fixture.queries) { + const results = await store.query(query.vector, { topK: 5 }); + + expect(results).toHaveLength(5); + expect(results[0].id).toBe(query.expectedTopId); + expect(results[0].content.length).toBeGreaterThan(0); + expect(typeof results[0].metadata.title).toBe('string'); + for (let i = 1; i < results.length; i++) { + expect(results[i].score).toBeLessThanOrEqual(results[i - 1].score); + } + } + }); + + it('filters by category over the large corpus and matches locally computed ground truth', async () => { + const store = getStore(); + const scienceDocs = fixture.documents.filter((doc) => doc.metadata.category === 'science'); + const expectedIds = localTopIds(scienceDocs, fixture.queries[0].vector, 10); + + const results = await store.query(fixture.queries[0].vector, { + topK: 10, + filter: { conditions: [{ key: 'category', operator: 'eq', value: 'science' }] }, + }); + + expect(results.map((r) => r.id)).toEqual(expectedIds); + for (const result of results) expect(result.metadata.category).toBe('science'); + }); + + it('nin filter excludes categories across the corpus', async () => { + const store = getStore(); + const geographyDocs = fixture.documents.filter((doc) => doc.metadata.category === 'geography'); + const expectedIds = localTopIds(geographyDocs, fixture.queries[0].vector, 10); + + const results = await store.query(fixture.queries[0].vector, { + topK: 10, + filter: { conditions: [{ key: 'category', operator: 'nin', value: ['history', 'science'] }] }, + }); + + expect(results.map((r) => r.id)).toEqual(expectedIds); + for (const result of results) expect(result.metadata.category).toBe('geography'); + }); + + it('deletes a batch and stops returning the deleted documents', async () => { + const store = getStore(); + const idsToDelete = localTopIds(fixture.documents, fixture.queries[1].vector, 20); + expect(idsToDelete).toContain(fixture.queries[1].expectedTopId); + + await store.delete({ ids: idsToDelete }); + await opts?.afterDelete?.(fixture.documents.length - idsToDelete.length); + + const remaining = fixture.documents.filter((doc) => !idsToDelete.includes(doc.id)); + const expectedTopIds = localTopIds(remaining, fixture.queries[1].vector, 5); + + const results = await store.query(fixture.queries[1].vector, { topK: 5 }); + const resultIds = results.map((r) => r.id); + + expect(resultIds).toEqual(expectedTopIds); + for (const deletedId of idsToDelete) expect(resultIds).not.toContain(deletedId); + }); +} + +/** + * Registers the agent end-to-end `it` blocks shared by all four backends' + * `agent-vector-store-*.test.ts` suites. Must be called inside the suite's + * `describe` body, after `getKnowledge` is guaranteed to resolve. + */ +export function registerAgentVectorStoreTests(getKnowledge: () => VectorStore): void { + it('answers a question using knowledge retrieved from the backend', async () => { + const agent = new Agent('kb-assistant') + .model('anthropic/claude-haiku-4-5') + .instructions( + 'You answer questions from the knowledge base. Always search it before answering. Be concise.', + ) + .vectorStore(getKnowledge()); + + const result = await agent.generate( + 'What is the tallest federal building in Manhattan, and how many stories does it have?', + ); + + const searchCalls = (result.toolCalls ?? []).filter( + (tc) => tc.tool === 'search_knowledge_base', + ); + expect(searchCalls.length).toBeGreaterThanOrEqual(1); + + const searchOutput = searchCalls[0].output as { + results: Array<{ content: string; score: number; metadata: { title?: string } }>; + }; + expect(searchOutput.results.length).toBeGreaterThanOrEqual(1); + expect(searchOutput.results[0].metadata.title).toBe('Jacob K. Javits Federal Building'); + + const answer = findLastTextContent(result.messages); + expect(answer).toMatch(/41/); + }); + + it('narrows results with a model-controlled metadata filter', async () => { + const agent = new Agent('kb-assistant-filtered') + .model('anthropic/claude-haiku-4-5') + .instructions( + 'You answer questions from the knowledge base. Always search it before answering. ' + + 'Always pass a filter on the category key when the user names a specific category.', + ) + .vectorStore(getKnowledge(), { + filterableKeys: { + category: "Document category, exactly one of: 'history', 'science', 'geography'", + }, + }); + + const result = await agent.generate( + 'Search only the geography category: what places are described?', + ); + + const searchCalls = (result.toolCalls ?? []).filter( + (tc) => tc.tool === 'search_knowledge_base', + ); + expect(searchCalls.length).toBeGreaterThanOrEqual(1); + + const input = searchCalls[0].input as { + filter?: Array<{ key: string; operator: string; value: unknown }>; + }; + const categoryCondition = input.filter?.find((c) => c.key === 'category'); + expect(categoryCondition).toBeDefined(); + if (categoryCondition?.operator === 'in') { + expect(categoryCondition.value).toContain('geography'); + } else { + expect(categoryCondition?.value).toBe('geography'); + } + + const searchOutput = searchCalls[0].output as { + results: Array<{ metadata: { category?: string } }>; + }; + for (const searchResult of searchOutput.results) { + expect(searchResult.metadata.category).toBe('geography'); + } + + const answer = findLastTextContent(result.messages); + expect(answer).toBeTruthy(); + }); +} diff --git a/packages/@n8n/agents/src/__tests__/pinecone-filter-translation.test.ts b/packages/@n8n/agents/src/__tests__/pinecone-filter-translation.test.ts new file mode 100644 index 00000000000..859af211803 --- /dev/null +++ b/packages/@n8n/agents/src/__tests__/pinecone-filter-translation.test.ts @@ -0,0 +1,282 @@ +/** + * Unit coverage for the metadata-filter translation and metadata-shape + * validation in `PineconeVectorStore` — logic that never runs in CI because + * the integration suites self-skip without a real Pinecone project. + * + * Exercises `query()`/`upsert()` through the public API with a stubbed + * `Index` (assigned directly to the private `index` field) so no network + * calls or `@pinecone-database/pinecone` import happen. + */ +import { describe, expect, it, vi } from 'vitest'; + +import { PineconeVectorStore } from '../vector-stores/pinecone'; + +function createStub(queryResult: { matches: unknown[] }) { + const query = vi.fn().mockResolvedValue(queryResult); + const upsert = vi.fn().mockResolvedValue(undefined); + return { query, upsert }; +} + +function createStore(): { store: PineconeVectorStore; stub: ReturnType } { + const store = new PineconeVectorStore('test-store', { + apiKey: 'test-key', + indexName: 'docs', + }); + const stub = createStub({ matches: [] }); + (store as unknown as { index: unknown }).index = stub; + return { store, stub }; +} + +describe('PineconeVectorStore filter translation', () => { + it('queries with topK and includeMetadata, with no filter key when absent', async () => { + const { store, stub } = createStore(); + + await store.query([1, 2, 3], { topK: 4 }); + + expect(stub.query).toHaveBeenCalledWith({ + vector: [1, 2, 3], + topK: 4, + includeMetadata: true, + }); + }); + + it('omits the filter key when the filter has no conditions', async () => { + const { store, stub } = createStore(); + + await store.query([0], { topK: 1, filter: { conditions: [] } }); + + expect(stub.query).toHaveBeenCalledWith({ vector: [0], topK: 1, includeMetadata: true }); + }); + + it('translates eq to an $eq term wrapped in $and', async () => { + const { store, stub } = createStore(); + + await store.query([0], { + topK: 1, + filter: { conditions: [{ key: 'topic', operator: 'eq', value: 'billing' }] }, + }); + + expect(stub.query).toHaveBeenCalledWith( + expect.objectContaining({ filter: { $and: [{ topic: { $eq: 'billing' } }] } }), + ); + }); + + it('translates ne to an $or with an $exists:false arm', async () => { + const { store, stub } = createStore(); + + await store.query([0], { + topK: 1, + filter: { conditions: [{ key: 'topic', operator: 'ne', value: 'billing' }] }, + }); + + expect(stub.query).toHaveBeenCalledWith( + expect.objectContaining({ + filter: { + $and: [{ $or: [{ topic: { $ne: 'billing' } }, { topic: { $exists: false } }] }], + }, + }), + ); + }); + + it('translates in to an $in term', async () => { + const { store, stub } = createStore(); + + await store.query([0], { + topK: 1, + filter: { conditions: [{ key: 'topic', operator: 'in', value: ['billing', 'ops'] }] }, + }); + + expect(stub.query).toHaveBeenCalledWith( + expect.objectContaining({ filter: { $and: [{ topic: { $in: ['billing', 'ops'] } }] } }), + ); + }); + + it('translates nin to an $or with an $exists:false arm', async () => { + const { store, stub } = createStore(); + + await store.query([0], { + topK: 1, + filter: { conditions: [{ key: 'topic', operator: 'nin', value: ['billing', 'ops'] }] }, + }); + + expect(stub.query).toHaveBeenCalledWith( + expect.objectContaining({ + filter: { + $and: [ + { + $or: [{ topic: { $nin: ['billing', 'ops'] } }, { topic: { $exists: false } }], + }, + ], + }, + }), + ); + }); + + it('combines multiple conditions under $and, in condition order', async () => { + const { store, stub } = createStore(); + + await store.query([0], { + topK: 1, + filter: { + conditions: [ + { key: 'topic', operator: 'eq', value: 'billing' }, + { key: 'active', operator: 'eq', value: true }, + ], + combineWith: 'and', + }, + }); + + expect(stub.query).toHaveBeenCalledWith( + expect.objectContaining({ + filter: { $and: [{ topic: { $eq: 'billing' } }, { active: { $eq: true } }] }, + }), + ); + }); + + it('combines multiple conditions under $or', async () => { + const { store, stub } = createStore(); + + await store.query([0], { + topK: 1, + filter: { + conditions: [ + { key: 'topic', operator: 'eq', value: 'billing' }, + { key: 'topic', operator: 'eq', value: 'ops' }, + ], + combineWith: 'or', + }, + }); + + expect(stub.query).toHaveBeenCalledWith( + expect.objectContaining({ + filter: { $or: [{ topic: { $eq: 'billing' } }, { topic: { $eq: 'ops' } }] }, + }), + ); + }); + + it('rejects an empty array for in without calling query', async () => { + const { store, stub } = createStore(); + + await expect( + store.query([0], { + topK: 1, + filter: { conditions: [{ key: 'topic', operator: 'in', value: [] }] }, + }), + ).rejects.toThrow(/requires a non-empty array value/); + expect(stub.query).not.toHaveBeenCalled(); + }); + + it('rejects an empty array for nin without calling query', async () => { + const { store, stub } = createStore(); + + await expect( + store.query([0], { + topK: 1, + filter: { conditions: [{ key: 'topic', operator: 'nin', value: [] }] }, + }), + ).rejects.toThrow(/requires a non-empty array value/); + expect(stub.query).not.toHaveBeenCalled(); + }); + + it('maps matches to query results, stripping _content into content', async () => { + const { store, stub } = createStore(); + stub.query.mockResolvedValue({ + matches: [{ id: 7, score: 0.5, metadata: { _content: 'hello', topic: 'billing' } }], + }); + + const results = await store.query([0], { topK: 1 }); + + expect(results).toEqual([ + { id: '7', content: 'hello', metadata: { topic: 'billing' }, score: 0.5 }, + ]); + }); + + it('defaults content to an empty string and score to 0 when absent', async () => { + const { store, stub } = createStore(); + stub.query.mockResolvedValue({ matches: [{ id: 'a', metadata: {} }] }); + + const results = await store.query([0], { topK: 1 }); + + expect(results).toEqual([{ id: 'a', content: '', metadata: {}, score: 0 }]); + }); + + it('upserts records with content stored under the reserved _content key', async () => { + const { store, stub } = createStore(); + + await store.upsert([ + { id: '1', vector: [1, 0], content: 'hello', metadata: { topic: 'billing' } }, + ]); + + expect(stub.upsert).toHaveBeenCalledWith([ + { id: '1', values: [1, 0], metadata: { _content: 'hello', topic: 'billing' } }, + ]); + }); + + it('rejects upserting metadata that uses the reserved _content key', async () => { + const { store } = createStore(); + + await expect( + store.upsert([{ id: '1', vector: [0], content: 'hi', metadata: { _content: 'x' } }]), + ).rejects.toThrow(/reserved for the document content/); + }); + + it('rejects upserting a nested-object metadata value', async () => { + const { store } = createStore(); + + await expect( + store.upsert([{ id: '1', vector: [0], content: 'hi', metadata: { nested: { a: 1 } } }]), + ).rejects.toThrow(/unsupported/); + }); + + it('rejects upserting a mixed-type array metadata value', async () => { + const { store } = createStore(); + + await expect( + store.upsert([{ id: '1', vector: [0], content: 'hi', metadata: { tags: ['a', 1] } }]), + ).rejects.toThrow(/unsupported/); + }); + + it('accepts a string-array metadata value', async () => { + const { store, stub } = createStore(); + + await store.upsert([{ id: '1', vector: [0], content: 'hi', metadata: { tags: ['a', 'b'] } }]); + + expect(stub.upsert).toHaveBeenCalledWith([ + { id: '1', values: [0], metadata: { _content: 'hi', tags: ['a', 'b'] } }, + ]); + }); + + it('splits large upserts into sequential batches of 200', async () => { + const { store, stub } = createStore(); + const records = Array.from({ length: 401 }, (_, i) => ({ + id: String(i), + vector: [0], + content: 'c', + metadata: {}, + })); + + await store.upsert(records); + + expect(stub.upsert).toHaveBeenCalledTimes(3); + const calls = stub.upsert.mock.calls as Array<[Array<{ id: string }>]>; + expect(calls[0][0]).toHaveLength(200); + expect(calls[1][0]).toHaveLength(200); + expect(calls[2][0]).toHaveLength(1); + expect(calls[0][0][0].id).toBe('0'); + expect(calls[1][0][0].id).toBe('200'); + expect(calls[2][0][0].id).toBe('400'); + }); + + it('rejects invalid metadata without sending any batch', async () => { + const { store, stub } = createStore(); + const records = Array.from({ length: 201 }, (_, i) => ({ + id: String(i), + vector: [0], + content: 'c', + metadata: i === 200 ? { nested: { a: 1 } } : {}, + })); + + await expect(store.upsert(records)).rejects.toThrow(/unsupported/); + expect(stub.upsert).not.toHaveBeenCalled(); + }); +}); diff --git a/packages/@n8n/agents/src/__tests__/qdrant-point-id.test.ts b/packages/@n8n/agents/src/__tests__/qdrant-point-id.test.ts new file mode 100644 index 00000000000..970b6117246 --- /dev/null +++ b/packages/@n8n/agents/src/__tests__/qdrant-point-id.test.ts @@ -0,0 +1,55 @@ +/** + * Unit coverage for the point-id validation in `QdrantVectorStore` — pure + * string-matching logic that never runs in CI because the integration suite + * self-skips without a real Qdrant instance (gated on `QDRANT_TEST_URL`). + * + * Exercises `upsert()`/`delete()` through the public API with a stubbed + * client (assigned directly to the private `client` field) so no network + * calls or `@qdrant/js-client-rest` connection happen. + */ +import { describe, expect, it, vi } from 'vitest'; + +import { QdrantVectorStore } from '../vector-stores/qdrant'; + +function createStore(): { store: QdrantVectorStore; upsert: ReturnType } { + const store = new QdrantVectorStore('test-store', { + url: 'http://localhost:6333', + collectionName: 'docs', + }); + const upsert = vi.fn().mockResolvedValue(undefined); + (store as unknown as { client: unknown }).client = { upsert }; + return { store, upsert }; +} + +describe('QdrantVectorStore point id validation', () => { + it.each([ + ['hyphenated', '550e8400-e29b-41d4-a716-446655440000'], + ['simple (no hyphens)', '936da01f9abd4d9d80c702af85c822a8'], + ['urn-prefixed', 'urn:uuid:550e8400-e29b-41d4-a716-446655440000'], + ['braced', '{550e8400-e29b-41d4-a716-446655440000}'], + ['uppercase hyphenated', '550E8400-E29B-41D4-A716-446655440000'], + ['canonical unsigned integer', '42'], + ])('accepts a %s id', async (_label, id) => { + const { store, upsert } = createStore(); + + await store.upsert([{ id, vector: [1, 0, 0], content: 'c', metadata: {} }]); + + expect(upsert).toHaveBeenCalledWith('docs', { + wait: true, + points: [ + { id: id === '42' ? 42 : id, vector: [1, 0, 0], payload: { content: 'c', metadata: {} } }, + ], + }); + }); + + it.each([ + ['non-canonical numeric id', '007'], + ['arbitrary non-UUID string', 'not-a-uuid'], + ])('rejects a %s', async (_label, id) => { + const { store } = createStore(); + + await expect( + store.upsert([{ id, vector: [1, 0, 0], content: 'c', metadata: {} }]), + ).rejects.toThrow(/Qdrant requires ids to be a UUID or a canonical unsigned integer/); + }); +}); diff --git a/packages/@n8n/agents/src/__tests__/supabase-filter-translation.test.ts b/packages/@n8n/agents/src/__tests__/supabase-filter-translation.test.ts new file mode 100644 index 00000000000..eda060deb9a --- /dev/null +++ b/packages/@n8n/agents/src/__tests__/supabase-filter-translation.test.ts @@ -0,0 +1,253 @@ +/** + * Unit coverage for the PostgREST filter-string translation in + * `SupabaseVectorStore` — string-building code (quoting, nested + * `or(...)`/`and(...)` groups) that never runs in CI because the + * integration suites self-skip without a real Supabase project. + * + * Exercises `query()` through the public API with a stubbed PostgREST + * filter chain (assigned directly to the private `client` field) so no + * network calls or `@supabase/supabase-js` import happen. + */ +import { describe, expect, it, vi } from 'vitest'; + +import { SupabaseVectorStore } from '../vector-stores/supabase'; + +type ChainCall = { method: 'filter' | 'not' | 'or'; args: unknown[] }; + +function createStub(result: { data: unknown[] | null; error: { message: string } | null }) { + const calls: ChainCall[] = []; + let limitArg: number | undefined; + const chain = { + filter: (...args: unknown[]) => { + calls.push({ method: 'filter', args }); + return chain; + }, + not: (...args: unknown[]) => { + calls.push({ method: 'not', args }); + return chain; + }, + or: (...args: unknown[]) => { + calls.push({ method: 'or', args }); + return chain; + }, + // eslint-disable-next-line @typescript-eslint/promise-function-async -- no await needed; matches `require-await` + limit: (n: number) => { + limitArg = n; + return Promise.resolve(result); + }, + }; + const rpc = vi.fn(() => chain); + return { client: { rpc }, calls, rpc, getLimitArg: () => limitArg }; +} + +function createStore(queryName?: string): { + store: SupabaseVectorStore; + stub: ReturnType; +} { + const store = new SupabaseVectorStore('test-store', { + url: 'http://localhost', + apiKey: 'test-key', + tableName: 'docs', + ...(queryName ? { queryName } : {}), + }); + const stub = createStub({ data: [], error: null }); + (store as unknown as { client: unknown }).client = stub.client; + return { store, stub }; +} + +const containment = (key: string, value: unknown): string => JSON.stringify({ [key]: value }); +const quoteOr = (value: string): string => + `"${value.replaceAll('\\', '\\\\').replaceAll('"', '\\"')}"`; + +describe('SupabaseVectorStore filter translation', () => { + it('invokes rpc with the vector and applies limit, with no filter calls', async () => { + const { store, stub } = createStore(); + + await store.query([1, 2, 3], { topK: 4 }); + + expect(stub.rpc).toHaveBeenCalledTimes(1); + expect(stub.rpc).toHaveBeenCalledWith('match_documents', { query_embedding: [1, 2, 3] }); + expect(stub.calls).toEqual([]); + expect(stub.getLimitArg()).toBe(4); + }); + + it('uses a custom queryName when provided', async () => { + const { store, stub } = createStore('custom_fn'); + + await store.query([1, 0, 0], { topK: 1 }); + + expect(stub.rpc).toHaveBeenCalledWith('custom_fn', { query_embedding: [1, 0, 0] }); + }); + + it('translates eq to a single filter call', async () => { + const { store, stub } = createStore(); + + await store.query([0], { + topK: 1, + filter: { conditions: [{ key: 'topic', operator: 'eq', value: 'billing' }] }, + }); + + expect(stub.calls).toEqual([ + { method: 'filter', args: ['metadata', 'cs', containment('topic', 'billing')] }, + ]); + }); + + it('translates ne to a single not call', async () => { + const { store, stub } = createStore(); + + await store.query([0], { + topK: 1, + filter: { conditions: [{ key: 'topic', operator: 'ne', value: 'billing' }] }, + }); + + expect(stub.calls).toEqual([ + { method: 'not', args: ['metadata', 'cs', containment('topic', 'billing')] }, + ]); + }); + + it('translates in (AND path) to a single or call', async () => { + const { store, stub } = createStore(); + + await store.query([0], { + topK: 1, + filter: { conditions: [{ key: 'topic', operator: 'in', value: ['billing', 'ops'] }] }, + }); + + const expected = [ + `metadata.cs.${quoteOr(containment('topic', 'billing'))}`, + `metadata.cs.${quoteOr(containment('topic', 'ops'))}`, + ].join(','); + expect(stub.calls).toEqual([{ method: 'or', args: [expected] }]); + }); + + it('translates nin (AND path) to one not call per value', async () => { + const { store, stub } = createStore(); + + await store.query([0], { + topK: 1, + filter: { conditions: [{ key: 'topic', operator: 'nin', value: ['billing', 'ops'] }] }, + }); + + expect(stub.calls).toEqual([ + { method: 'not', args: ['metadata', 'cs', containment('topic', 'billing')] }, + { method: 'not', args: ['metadata', 'cs', containment('topic', 'ops')] }, + ]); + }); + + it('combines in with another condition under AND, in condition order', async () => { + const { store, stub } = createStore(); + + await store.query([0], { + topK: 1, + filter: { + conditions: [ + { key: 'topic', operator: 'in', value: ['billing', 'ops'] }, + { key: 'active', operator: 'eq', value: true }, + ], + combineWith: 'and', + }, + }); + + const orExpected = [ + `metadata.cs.${quoteOr(containment('topic', 'billing'))}`, + `metadata.cs.${quoteOr(containment('topic', 'ops'))}`, + ].join(','); + expect(stub.calls).toEqual([ + { method: 'or', args: [orExpected] }, + { method: 'filter', args: ['metadata', 'cs', containment('active', true)] }, + ]); + }); + + it('builds a single or call for a combineWith "or" group with all operators', async () => { + const { store, stub } = createStore(); + + await store.query([0], { + topK: 1, + filter: { + conditions: [ + { key: 'topic', operator: 'eq', value: 'billing' }, + { key: 'count', operator: 'ne', value: 5 }, + { key: 'category', operator: 'in', value: ['a', 'b'] }, + { key: 'category', operator: 'nin', value: ['c'] }, + ], + combineWith: 'or', + }, + }); + + const expected = [ + `metadata.cs.${quoteOr(containment('topic', 'billing'))}`, + `metadata.not.cs.${quoteOr(containment('count', 5))}`, + `or(metadata.cs.${quoteOr(containment('category', 'a'))},metadata.cs.${quoteOr(containment('category', 'b'))})`, + `and(metadata.not.cs.${quoteOr(containment('category', 'c'))})`, + ].join(','); + expect(stub.calls).toEqual([{ method: 'or', args: [expected] }]); + }); + + it('escapes quotes and backslashes in OR-combined logic terms', async () => { + const { store, stub } = createStore(); + + await store.query([0], { + topK: 1, + filter: { + conditions: [{ key: 'k', operator: 'eq', value: 'a"b\\c' }], + combineWith: 'or', + }, + }); + + const expected = `metadata.cs.${quoteOr(containment('k', 'a"b\\c'))}`; + expect(stub.calls).toEqual([{ method: 'or', args: [expected] }]); + }); + + it('rejects an empty array for in without calling the filter chain', async () => { + const { store, stub } = createStore(); + + await expect( + store.query([0], { + topK: 1, + filter: { conditions: [{ key: 'topic', operator: 'in', value: [] }] }, + }), + ).rejects.toThrow(/requires a non-empty array value/); + expect(stub.calls).toEqual([]); + }); + + it('rejects an empty array for nin without calling the filter chain', async () => { + const { store, stub } = createStore(); + + await expect( + store.query([0], { + topK: 1, + filter: { conditions: [{ key: 'topic', operator: 'nin', value: [] }] }, + }), + ).rejects.toThrow(/requires a non-empty array value/); + expect(stub.calls).toEqual([]); + }); + + it('wraps a PostgREST error with a descriptive message', async () => { + const store = new SupabaseVectorStore('test-store', { + url: 'http://localhost', + apiKey: 'test-key', + tableName: 'docs', + }); + const stub = createStub({ data: null, error: { message: 'boom' } }); + (store as unknown as { client: unknown }).client = stub.client; + + await expect(store.query([0], { topK: 1 })).rejects.toThrow('Supabase query failed: boom'); + }); + + it('maps rows to query results, coercing id to a string and defaulting metadata', async () => { + const store = new SupabaseVectorStore('test-store', { + url: 'http://localhost', + apiKey: 'test-key', + tableName: 'docs', + }); + const stub = createStub({ + data: [{ id: 7, content: 'c', metadata: null, similarity: 0.5 }], + error: null, + }); + (store as unknown as { client: unknown }).client = stub.client; + + const results = await store.query([0], { topK: 1 }); + + expect(results).toEqual([{ id: '7', content: 'c', metadata: {}, score: 0.5 }]); + }); +}); diff --git a/packages/@n8n/agents/src/index.ts b/packages/@n8n/agents/src/index.ts index 61bc3761c18..afb498439ac 100644 --- a/packages/@n8n/agents/src/index.ts +++ b/packages/@n8n/agents/src/index.ts @@ -105,8 +105,16 @@ export { export { createCancellation, isCancellation, CANCELLATION_TYPE } from './sdk/cancellation'; export type { Cancellation } from './sdk/cancellation'; -export { Tool, wrapToolForApproval } from './sdk/tool'; +export { Tool, wrapToolForApproval, sanitizeToolName } from './sdk/tool'; export { Memory } from './sdk/memory'; +export { VectorStore } from './sdk/vector-store'; +export { + FILTER_OPERATORS, + normalizeFilterInput, + assertValidFilter, + buildFilterInputSchema, +} from './sdk/vector-store-filter'; +export type { VectorFilterInput } from './sdk/vector-store-filter'; export { Guardrail } from './sdk/guardrail'; export { redactText, @@ -217,10 +225,21 @@ export type { ModelLimits, } from './sdk/catalog'; export { BaseMemory } from './storage/base-memory'; +export { BaseVectorStore } from './storage/base-vector-store'; export type { ToolDescriptor } from './types/sdk/tool-descriptor'; +export type { + BuiltVectorStoreBackend, + VectorDocument, + VectorRecord, + VectorQueryResult, + FilterOperator, + FilterValue, + FilterCondition, + VectorFilter, +} from './types'; export { createModel } from './runtime/model/model-factory'; -export type { FetchFn } from './runtime/model/model-factory'; +export type { FetchFn, EmbeddingProviderOptions } from './runtime/model/model-factory'; export { DEFAULT_SUB_AGENT_MAX_CHILDREN, ROOT_SUB_AGENT_TASK_PATH, diff --git a/packages/@n8n/agents/src/runtime/model/model-factory.ts b/packages/@n8n/agents/src/runtime/model/model-factory.ts index dd135b25e14..ce1432b8ac3 100644 --- a/packages/@n8n/agents/src/runtime/model/model-factory.ts +++ b/packages/@n8n/agents/src/runtime/model/model-factory.ts @@ -15,7 +15,7 @@ import type { ModelConfig } from '../../types/sdk/agent'; * model calls route through the configured HTTP(S)_PROXY. */ export type FetchFn = typeof globalThis.fetch; -type EmbeddingProviderOptions = { +export type EmbeddingProviderOptions = { apiKey?: string; baseURL?: string; fetch?: FetchFn; diff --git a/packages/@n8n/agents/src/sdk/__tests__/vector-store.test.ts b/packages/@n8n/agents/src/sdk/__tests__/vector-store.test.ts new file mode 100644 index 00000000000..e5ca086e917 --- /dev/null +++ b/packages/@n8n/agents/src/sdk/__tests__/vector-store.test.ts @@ -0,0 +1,435 @@ +import { MockEmbeddingModelV3 } from 'ai/test'; + +import type { BuiltVectorStoreBackend, VectorFilter, VectorQueryResult } from '../../types'; +import { isZodSchema } from '../../utils/zod'; +import { Agent } from '../agent'; +import { sanitizeToolName } from '../tool'; +import { VectorStore } from '../vector-store'; +import { assertValidFilter, normalizeFilterInput } from '../vector-store-filter'; + +function makeEmbeddingModel(vector: number[] = [1, 0, 0]) { + return new MockEmbeddingModelV3({ + doEmbed: async ({ values }) => + await Promise.resolve({ + embeddings: values.map(() => vector), + warnings: [], + }), + }); +} + +function makeBackend(overrides: Partial = {}): BuiltVectorStoreBackend { + return { + upsert: vi.fn().mockResolvedValue(undefined), + query: vi.fn().mockResolvedValue([]), + delete: vi.fn().mockResolvedValue(undefined), + ...overrides, + }; +} + +describe('normalizeFilterInput', () => { + it('wraps plain object shorthand into an eq-conditions AND group', () => { + expect(normalizeFilterInput({ plan: 'cloud' })).toEqual({ + conditions: [{ key: 'plan', operator: 'eq', value: 'cloud' }], + combineWith: 'and', + }); + }); + + it('passes a VectorFilter through unchanged', () => { + const filter: VectorFilter = { + conditions: [{ key: 'plan', operator: 'eq', value: 'cloud' }], + combineWith: 'or', + }; + expect(normalizeFilterInput(filter)).toBe(filter); + }); +}); + +describe('assertValidFilter', () => { + it('throws when in has a non-array or empty-array value', () => { + expect(() => + assertValidFilter({ conditions: [{ key: 'plan', operator: 'in', value: 'cloud' }] }), + ).toThrow(/"in" on key "plan" requires a non-empty array value/); + expect(() => + assertValidFilter({ conditions: [{ key: 'plan', operator: 'in', value: [] }] }), + ).toThrow(/"in" on key "plan" requires a non-empty array value/); + }); + + it('throws when eq has an array value', () => { + expect(() => + assertValidFilter({ conditions: [{ key: 'plan', operator: 'eq', value: ['cloud'] }] }), + ).toThrow(/"eq" on key "plan" requires a string, number, or boolean value/); + }); + + it('throws when in/nin has a non-string/number array element', () => { + expect(() => + assertValidFilter({ + conditions: [{ key: 'plan', operator: 'in', value: [true as never] }], + }), + ).toThrow(/"in" on key "plan" requires array elements to be strings or numbers/); + }); + + it('throws on an unknown (removed) operator', () => { + expect(() => + assertValidFilter({ + conditions: [{ key: 'plan', operator: 'text_match' as never, value: 'cloud' }], + }), + ).toThrow(/Invalid filter operator "text_match"/); + }); +}); + +describe('VectorStore — configuration validation', () => { + it('throws when .store() is not set', async () => { + const vectorStore = new VectorStore('kb').embeddingModel(makeEmbeddingModel()); + await expect(vectorStore.search('hello')).rejects.toThrow(/requires a backend/); + }); + + it('throws when .embeddingModel() is not set', async () => { + const vectorStore = new VectorStore('kb').store(makeBackend()); + await expect(vectorStore.search('hello')).rejects.toThrow(/requires an embedding model/); + }); +}); + +describe('VectorStore — search()', () => { + it('embeds the query and calls backend.query with resolved topK', async () => { + const backend = makeBackend(); + const vectorStore = new VectorStore('kb') + .store(backend) + .embeddingModel(makeEmbeddingModel([1, 2, 3])); + + await vectorStore.search('hello'); + expect(backend.query).toHaveBeenCalledWith([1, 2, 3], { topK: 4 }); + + await vectorStore.search('hello', { topK: 10 }); + expect(backend.query).toHaveBeenLastCalledWith([1, 2, 3], { topK: 10 }); + }); + + it('returns the results from the backend', async () => { + const results: VectorQueryResult[] = [{ id: '1', content: 'a', metadata: {}, score: 0.9 }]; + const backend = makeBackend({ query: vi.fn().mockResolvedValue(results) }); + const vectorStore = new VectorStore('kb').store(backend).embeddingModel(makeEmbeddingModel()); + + await expect(vectorStore.search('hello')).resolves.toEqual(results); + }); + + it('normalizes an object-shorthand per-call filter before reaching the backend', async () => { + const backend = makeBackend(); + const vectorStore = new VectorStore('kb').store(backend).embeddingModel(makeEmbeddingModel()); + + await vectorStore.search('hello', { filter: { plan: 'cloud' } }); + + expect(backend.query).toHaveBeenCalledWith([1, 0, 0], { + topK: 4, + filter: { conditions: [{ key: 'plan', operator: 'eq', value: 'cloud' }], combineWith: 'and' }, + }); + }); + + it('omits filter when an empty object shorthand is passed', async () => { + const backend = makeBackend(); + const vectorStore = new VectorStore('kb').store(backend).embeddingModel(makeEmbeddingModel()); + + await vectorStore.search('hello', { filter: {} }); + + expect(backend.query).toHaveBeenCalledWith([1, 0, 0], { topK: 4 }); + }); + + it('rejects a non-integer or non-positive topK on the builder', () => { + const vectorStore = new VectorStore('kb'); + + expect(() => vectorStore.topK(0)).toThrow(/topK must be an integer >= 1/); + expect(() => vectorStore.topK(-5)).toThrow(/topK must be an integer >= 1/); + expect(() => vectorStore.topK(2.5)).toThrow(/topK must be an integer >= 1/); + expect(() => vectorStore.topK(NaN)).toThrow(/topK must be an integer >= 1/); + }); + + it('rejects an invalid per-call topK without touching the backend', async () => { + const backend = makeBackend(); + const vectorStore = new VectorStore('kb').store(backend).embeddingModel(makeEmbeddingModel()); + + await expect(vectorStore.search('hello', { topK: 0 })).rejects.toThrow( + /topK must be an integer >= 1/, + ); + expect(backend.query).not.toHaveBeenCalled(); + }); +}); + +describe('VectorStore — addDocuments()', () => { + it('returns [] and never touches the embedder or backend for an empty array', async () => { + const backend = makeBackend(); + const embeddingModel = makeEmbeddingModel(); + const vectorStore = new VectorStore('kb').store(backend).embeddingModel(embeddingModel); + + const ids = await vectorStore.addDocuments([]); + + expect(ids).toEqual([]); + expect(backend.upsert).not.toHaveBeenCalled(); + }); + + it('generates an id when none is provided and preserves a given id', async () => { + const backend = makeBackend(); + const vectorStore = new VectorStore('kb') + .store(backend) + .embeddingModel(makeEmbeddingModel([1, 0, 0])); + + const ids = await vectorStore.addDocuments([ + { content: 'no id given' }, + { id: 'explicit-id', content: 'has an id' }, + ]); + + expect(ids[1]).toBe('explicit-id'); + expect(ids[0]).toEqual(expect.any(String)); + expect(ids[0]).not.toBe('explicit-id'); + }); + + it('upserts records with embeddings, provided metadata, and a default of {}', async () => { + const backend = makeBackend(); + const vectorStore = new VectorStore('kb') + .store(backend) + .embeddingModel(makeEmbeddingModel([1, 0, 0])); + + const ids = await vectorStore.addDocuments([ + { content: 'hello', metadata: { topic: 'a' } }, + { content: 'world' }, + ]); + + expect(backend.upsert).toHaveBeenCalledWith([ + { id: ids[0], vector: [1, 0, 0], content: 'hello', metadata: { topic: 'a' } }, + { id: ids[1], vector: [1, 0, 0], content: 'world', metadata: {} }, + ]); + }); + + it('rejects an empty-content document, naming its index', async () => { + const backend = makeBackend(); + const vectorStore = new VectorStore('kb').store(backend).embeddingModel(makeEmbeddingModel()); + + await expect(vectorStore.addDocuments([{ content: '' }])).rejects.toThrow( + /Document at index 0 has empty content/, + ); + }); + + it('rejects a whitespace-only content document without touching the backend', async () => { + const backend = makeBackend(); + const vectorStore = new VectorStore('kb').store(backend).embeddingModel(makeEmbeddingModel()); + + await expect(vectorStore.addDocuments([{ content: 'ok' }, { content: ' ' }])).rejects.toThrow( + /Document at index 1 has empty content/, + ); + expect(backend.upsert).not.toHaveBeenCalled(); + }); +}); + +describe('VectorStore — deleteDocuments()', () => { + it('returns early and never touches the backend for an empty array', async () => { + const backend = makeBackend(); + const vectorStore = new VectorStore('kb').store(backend).embeddingModel(makeEmbeddingModel()); + + await vectorStore.deleteDocuments([]); + + expect(backend.delete).not.toHaveBeenCalled(); + }); + + it('calls backend.delete with the given ids', async () => { + const backend = makeBackend(); + const vectorStore = new VectorStore('kb').store(backend).embeddingModel(makeEmbeddingModel()); + + await vectorStore.deleteDocuments(['a', 'b']); + + expect(backend.delete).toHaveBeenCalledWith({ ids: ['a', 'b'] }); + }); +}); + +describe('VectorStore — asTool()', () => { + it('defaults the tool name to search_', () => { + const vectorStore = new VectorStore('product-docs').description('Search the docs'); + const tool = vectorStore.asTool().build(); + + expect(tool.name).toBe('search_product-docs'); + }); + + it('throws when no description is set anywhere', () => { + const vectorStore = new VectorStore('product-docs'); + + expect(() => vectorStore.asTool()).toThrow(/requires a description/); + }); + + it('uses the per-call description over the builder default', () => { + const vectorStore = new VectorStore('product-docs').description('default description'); + const tool = vectorStore.asTool({ description: 'override description' }).build(); + + expect(tool.description).toBe('override description'); + }); + + it('Agent.vectorStore() registers a search_ tool', () => { + const vectorStore = new VectorStore('product-docs').description('Search the docs'); + const agent = new Agent('assistant').vectorStore(vectorStore); + + expect(agent.snapshot.tools).toEqual([ + { name: 'search_product-docs', description: 'Search the docs' }, + ]); + }); + + it('handler returns { results } from search()', async () => { + const results: VectorQueryResult[] = [{ id: '1', content: 'a', metadata: {}, score: 0.9 }]; + const backend = makeBackend({ query: vi.fn().mockResolvedValue(results) }); + const vectorStore = new VectorStore('product-docs') + .store(backend) + .embeddingModel(makeEmbeddingModel()) + .description('Search the docs'); + const tool = vectorStore.asTool().build(); + + const output = await tool.handler!({ query: 'hello' }, {}); + + expect(output).toEqual({ results }); + }); + + it('without filterableKeys, the schema has no filter field', () => { + const vectorStore = new VectorStore('product-docs').description('Search the docs'); + const tool = vectorStore.asTool().build(); + + expect(isZodSchema(tool.inputSchema)).toBe(true); + if (!isZodSchema(tool.inputSchema)) return; + const parsed = tool.inputSchema.safeParse({ + query: 'hello', + filter: [{ key: 'x', operator: 'eq', value: 'y' }], + }); + expect(parsed.success).toBe(true); + expect(parsed.data).toEqual({ query: 'hello' }); + }); + + describe('with filterableKeys', () => { + it('accepts a valid filter array', () => { + const vectorStore = new VectorStore('product-docs').description('Search the docs'); + const tool = vectorStore.asTool({ filterableKeys: { plan: 'cloud or self-hosted' } }).build(); + + expect(isZodSchema(tool.inputSchema)).toBe(true); + if (!isZodSchema(tool.inputSchema)) return; + const parsed = tool.inputSchema.safeParse({ + query: 'hello', + filter: [{ key: 'plan', operator: 'eq', value: 'cloud' }], + }); + expect(parsed.success).toBe(true); + }); + + it('rejects a filter key outside filterableKeys', () => { + const vectorStore = new VectorStore('product-docs').description('Search the docs'); + const tool = vectorStore.asTool({ filterableKeys: { plan: 'cloud or self-hosted' } }).build(); + + expect(isZodSchema(tool.inputSchema)).toBe(true); + if (!isZodSchema(tool.inputSchema)) return; + const parsed = tool.inputSchema.safeParse({ + query: 'hello', + filter: [{ key: 'unlisted', operator: 'eq', value: 'x' }], + }); + expect(parsed.success).toBe(false); + }); + + it('rejects an unknown (removed) operator', () => { + const vectorStore = new VectorStore('product-docs').description('Search the docs'); + const tool = vectorStore.asTool({ filterableKeys: { plan: 'cloud or self-hosted' } }).build(); + + expect(isZodSchema(tool.inputSchema)).toBe(true); + if (!isZodSchema(tool.inputSchema)) return; + const parsed = tool.inputSchema.safeParse({ + query: 'hello', + filter: [{ key: 'plan', operator: 'text_match', value: 'x' }], + }); + expect(parsed.success).toBe(false); + }); + + it('rejects an array value for a scalar operator (eq/ne)', () => { + const vectorStore = new VectorStore('product-docs').description('Search the docs'); + const tool = vectorStore.asTool({ filterableKeys: { plan: 'cloud or self-hosted' } }).build(); + + expect(isZodSchema(tool.inputSchema)).toBe(true); + if (!isZodSchema(tool.inputSchema)) return; + const parsed = tool.inputSchema.safeParse({ + query: 'hello', + filter: [{ key: 'plan', operator: 'eq', value: ['cloud'] }], + }); + expect(parsed.success).toBe(false); + }); + + it('rejects a scalar value for an array operator (in/nin)', () => { + const vectorStore = new VectorStore('product-docs').description('Search the docs'); + const tool = vectorStore.asTool({ filterableKeys: { plan: 'cloud or self-hosted' } }).build(); + + expect(isZodSchema(tool.inputSchema)).toBe(true); + if (!isZodSchema(tool.inputSchema)) return; + const parsed = tool.inputSchema.safeParse({ + query: 'hello', + filter: [{ key: 'plan', operator: 'in', value: 'cloud' }], + }); + expect(parsed.success).toBe(false); + }); + + it('rejects a condition missing a value', () => { + const vectorStore = new VectorStore('product-docs').description('Search the docs'); + const tool = vectorStore.asTool({ filterableKeys: { plan: 'cloud or self-hosted' } }).build(); + + expect(isZodSchema(tool.inputSchema)).toBe(true); + if (!isZodSchema(tool.inputSchema)) return; + const parsed = tool.inputSchema.safeParse({ + query: 'hello', + filter: [{ key: 'plan', operator: 'eq' }], + }); + expect(parsed.success).toBe(false); + }); + + it('passes the model filter to backend.query as the single filter group', async () => { + const backend = makeBackend(); + const vectorStore = new VectorStore('product-docs') + .store(backend) + .embeddingModel(makeEmbeddingModel()) + .description('Search the docs'); + const tool = vectorStore.asTool({ filterableKeys: { plan: 'cloud or self-hosted' } }).build(); + + await tool.handler!( + { query: 'hello', filter: [{ key: 'plan', operator: 'eq', value: 'cloud' }] }, + {}, + ); + + expect(backend.query).toHaveBeenCalledWith([1, 0, 0], { + topK: 4, + filter: { + conditions: [{ key: 'plan', operator: 'eq', value: 'cloud' }], + combineWith: 'and', + }, + }); + }); + + it('omits the filter key when the model calls without a filter', async () => { + const backend = makeBackend(); + const vectorStore = new VectorStore('product-docs') + .store(backend) + .embeddingModel(makeEmbeddingModel()) + .description('Search the docs'); + const tool = vectorStore.asTool({ filterableKeys: { plan: 'cloud or self-hosted' } }).build(); + + await tool.handler!({ query: 'hello' }, {}); + + expect(backend.query).toHaveBeenCalledWith([1, 0, 0], { topK: 4 }); + }); + }); +}); + +describe('sanitizeToolName', () => { + it('preserves hyphens', () => { + expect(sanitizeToolName('product-docs')).toBe('product-docs'); + }); + + it('collapses runs of invalid characters into a single underscore', () => { + expect(sanitizeToolName('My Docs!!')).toBe('My_Docs_'); + }); + + it('truncates to 64 chars with no trailing underscore or hyphen', () => { + const sanitized = sanitizeToolName('x'.repeat(80)); + + expect(sanitized.length).toBeLessThanOrEqual(64); + expect(sanitized).not.toMatch(/[_-]$/); + }); + + it('caps the composed default tool name in asTool() at 64 chars', () => { + const tool = new VectorStore('x'.repeat(80)).description('d').asTool().build(); + + expect(tool.name.length).toBeLessThanOrEqual(64); + expect(tool.name.startsWith('search_')).toBe(true); + }); +}); diff --git a/packages/@n8n/agents/src/sdk/agent.ts b/packages/@n8n/agents/src/sdk/agent.ts index 16af7570cdc..db3343a5497 100644 --- a/packages/@n8n/agents/src/sdk/agent.ts +++ b/packages/@n8n/agents/src/sdk/agent.ts @@ -8,6 +8,7 @@ import type { McpClient } from './mcp-client'; import { Memory, normalizeMemoryConfig, resolveMemoryConfigDefaults } from './memory'; import { Telemetry } from './telemetry'; import { wrapToolForApproval } from './tool'; +import type { VectorStore } from './vector-store'; import { AgentRuntime, type AgentRuntimeConfig } from '../runtime/loop/agent-runtime'; import { RECALL_MEMORY_TOOL_NAME } from '../runtime/memory/episodic-memory'; import type { ScopedMemoryTaskEvent } from '../runtime/memory/scoped-memory-task-runner'; @@ -259,6 +260,14 @@ export class Agent implements BuiltAgent, AgentBuilder { return this; } + /** Attach a vector store as a search tool. Accepts a VectorStore builder. */ + vectorStore( + store: VectorStore, + options?: { name?: string; description?: string; filterableKeys?: Record }, + ): this { + return this.tool(store.asTool(options)); + } + /** Add tools that are searchable through `search_tools` and activated on demand with `load_tool`. */ deferredTool(t: ToolParameter | ToolParameter[], options?: DeferredToolOptions): this { const tools = Array.isArray(t) ? t : [t]; diff --git a/packages/@n8n/agents/src/sdk/tool.ts b/packages/@n8n/agents/src/sdk/tool.ts index c7e5b23f2bc..699a49bfa99 100644 --- a/packages/@n8n/agents/src/sdk/tool.ts +++ b/packages/@n8n/agents/src/sdk/tool.ts @@ -378,3 +378,19 @@ export class Tool< }; } } + +const MAX_TOOL_NAME_LENGTH = 64; + +/** + * Coerce an arbitrary string into a provider-safe tool name + * (`[a-zA-Z0-9_-]`, max 64 chars — OpenAI's limit). Mirrors + * `nodeNameToToolName` in `n8n-workflow`; keep the two in sync — the SDK + * deliberately has no dependency on that package. + */ +export function sanitizeToolName(name: string): string { + let toolName = name.replace(/[^a-zA-Z0-9_-]+/g, '_'); + if (toolName.length > MAX_TOOL_NAME_LENGTH) { + toolName = toolName.slice(0, MAX_TOOL_NAME_LENGTH).replace(/[_-]+$/, ''); + } + return toolName; +} diff --git a/packages/@n8n/agents/src/sdk/vector-store-filter.ts b/packages/@n8n/agents/src/sdk/vector-store-filter.ts new file mode 100644 index 00000000000..fc0d2b0dcbe --- /dev/null +++ b/packages/@n8n/agents/src/sdk/vector-store-filter.ts @@ -0,0 +1,105 @@ +import { z } from 'zod'; + +import type { FilterCondition, FilterOperator, VectorFilter } from '../types/sdk/vector-store'; + +const SCALAR_OPERATORS = ['eq', 'ne'] as const satisfies readonly FilterOperator[]; +const ARRAY_OPERATORS = ['in', 'nin'] as const satisfies readonly FilterOperator[]; + +export const FILTER_OPERATORS = [ + ...SCALAR_OPERATORS, + ...ARRAY_OPERATORS, +] as const satisfies readonly FilterOperator[]; + +export type VectorFilterInput = VectorFilter | Record; + +function isVectorFilter(input: VectorFilterInput): input is VectorFilter { + return Array.isArray(input.conditions); +} + +/** Normalizes the `.search({ filter })` shorthand into a canonical `VectorFilter`. */ +export function normalizeFilterInput(input: VectorFilterInput): VectorFilter { + if (isVectorFilter(input)) { + return input; + } + const conditions: FilterCondition[] = Object.entries(input).map(([key, value]) => ({ + key, + operator: 'eq', + value, + })); + return { conditions, combineWith: 'and' }; +} + +/** Validates operator/value pairing per condition; throws rather than silently ignoring a bad filter. */ +export function assertValidFilter(filter: VectorFilter): void { + for (const condition of filter.conditions) { + assertValidCondition(condition); + } +} + +function assertValidCondition(condition: FilterCondition): void { + const { key, operator, value } = condition; + + if (!(FILTER_OPERATORS as readonly string[]).includes(operator)) { + throw new Error( + `Invalid filter operator "${operator}" for key "${key}". Supported operators: ${FILTER_OPERATORS.join(', ')}`, + ); + } + + if (operator === 'in' || operator === 'nin') { + if (!Array.isArray(value) || value.length === 0) { + throw new Error( + `Filter operator "${operator}" on key "${key}" requires a non-empty array value.`, + ); + } + if (value.some((v) => typeof v !== 'string' && typeof v !== 'number')) { + throw new Error( + `Filter operator "${operator}" on key "${key}" requires array elements to be strings or numbers.`, + ); + } + return; + } + + // eq, ne + if (typeof value !== 'string' && typeof value !== 'number' && typeof value !== 'boolean') { + throw new Error( + `Filter operator "${operator}" on key "${key}" requires a string, number, or boolean value.`, + ); + } +} + +function isNonEmptyArray(arr: string[]): arr is [string, ...string[]] { + return arr.length > 0; +} + +/** Builds the zod schema for the model-facing `filter` tool input, scoped to `keys` (key -> description). */ +export function buildFilterInputSchema(keys: Record) { + const keyNames = Object.keys(keys); + if (!isNonEmptyArray(keyNames)) { + throw new Error('filterableKeys must contain at least one key'); + } + + const keyDescriptions = keyNames.map((key) => `- ${key}: ${keys[key]}`).join('\n'); + + // Discriminated by `operator` so the model-facing schema rejects the same + // operator/value mismatches the runtime validator does (e.g. an array for + // "eq", or a scalar for "in"), instead of failing later during search. + return z + .array( + z.discriminatedUnion('operator', [ + z.object({ + key: z.enum(keyNames), + operator: z.enum(SCALAR_OPERATORS), + value: z.union([z.string(), z.number(), z.boolean()]), + }), + z.object({ + key: z.enum(keyNames), + operator: z.enum(ARRAY_OPERATORS), + value: z.array(z.union([z.string(), z.number()])).min(1), + }), + ]), + ) + .optional() + .describe( + `Optional metadata filter conditions (combined with AND). Filterable keys:\n${keyDescriptions}`, + ); +} diff --git a/packages/@n8n/agents/src/sdk/vector-store.ts b/packages/@n8n/agents/src/sdk/vector-store.ts new file mode 100644 index 00000000000..edb6031df15 --- /dev/null +++ b/packages/@n8n/agents/src/sdk/vector-store.ts @@ -0,0 +1,192 @@ +import type { EmbeddingModel } from 'ai'; +import { z } from 'zod'; + +import { sanitizeToolName, Tool } from './tool'; +import { + assertValidFilter, + buildFilterInputSchema, + normalizeFilterInput, + type VectorFilterInput, +} from './vector-store-filter'; +import { + createEmbeddingModel, + type EmbeddingProviderOptions, +} from '../runtime/model/model-factory'; +import type { BuiltVectorStoreBackend, VectorDocument, VectorQueryResult } from '../types'; +import type { VectorFilter } from '../types/sdk/vector-store'; + +const DEFAULT_TOP_K = 4; + +/** + * Pairs a vector store backend with an embedding model and owns the + * embed-then-search / embed-then-upsert orchestration. Backends operate + * purely on vectors — this is the only place text gets embedded. + */ +export class VectorStore { + private backend?: BuiltVectorStoreBackend; + + private embeddingModelValue?: EmbeddingModel; + + private topKValue: number = DEFAULT_TOP_K; + + private descriptionValue?: string; + + constructor(private readonly name: string) {} + + /** Set the vector store backend (e.g. `new PgVectorStore(...)`). Required before building. */ + store(backend: BuiltVectorStoreBackend): this { + this.backend = backend; + return this; + } + + /** + * Set the embedding model used to embed queries and documents. + * Accepts a "provider/model" string (e.g. `openai/text-embedding-3-small`) + * resolved via {@link createEmbeddingModel}, or a pre-built AI SDK `EmbeddingModel`. + * Required before building. + */ + embeddingModel( + model: string | EmbeddingModel, + options?: string | EmbeddingProviderOptions, + ): this { + this.embeddingModelValue = + typeof model === 'string' ? createEmbeddingModel(model, options) : model; + return this; + } + + /** Set the default number of results returned by `.search()`. Default: 4. */ + topK(k: number): this { + assertValidTopK(k); + this.topKValue = k; + return this; + } + + /** Set the description used by `.asTool()` when no per-call description is given. */ + description(text: string): this { + this.descriptionValue = text; + return this; + } + + /** Search the store for content semantically similar to `query`. Lazy-builds on first call. */ + async search( + query: string, + opts?: { topK?: number; filter?: VectorFilterInput }, + ): Promise { + if (opts?.topK !== undefined) { + assertValidTopK(opts.topK); + } + const { backend, embeddingModel } = this.ensureBuilt(); + const { embed } = await import('ai'); + const { embedding } = await embed({ model: embeddingModel, value: query }); + const filter = this.resolveFilter(opts?.filter); + return await backend.query(embedding, { + topK: opts?.topK ?? this.topKValue, + ...(filter ? { filter } : {}), + }); + } + + /** Embed and upsert documents into the store. Returns the ids used (generated when not provided). */ + async addDocuments(docs: VectorDocument[]): Promise { + if (docs.length === 0) return []; + + docs.forEach((doc, index) => { + if (typeof doc.content !== 'string' || doc.content.trim() === '') { + throw new Error(`Document at index ${index} has empty content — nothing to embed.`); + } + }); + + const { backend, embeddingModel } = this.ensureBuilt(); + const ids = docs.map((doc) => doc.id ?? crypto.randomUUID()); + const { embedMany } = await import('ai'); + const { embeddings } = await embedMany({ + model: embeddingModel, + values: docs.map((doc) => doc.content), + }); + await backend.upsert( + docs.map((doc, index) => ({ + id: ids[index], + vector: embeddings[index], + content: doc.content, + metadata: doc.metadata ?? {}, + })), + ); + return ids; + } + + /** Delete documents from the store by id. */ + async deleteDocuments(ids: string[]): Promise { + if (ids.length === 0) return; + const { backend } = this.ensureBuilt(); + await backend.delete({ ids }); + } + + /** + * Expose this store as an agent tool. Pass `filterableKeys` (metadata key + * -> description) to also let the model narrow results with a filter. + */ + asTool(opts?: { + name?: string; + description?: string; + filterableKeys?: Record; + }): Tool { + const description = opts?.description ?? this.descriptionValue; + if (!description) { + throw new Error( + `VectorStore "${this.name}" requires a description — set it via .description() or asTool({ description })`, + ); + } + + const toolName = opts?.name ?? sanitizeToolName(`search_${this.name}`); + + if (!opts?.filterableKeys) { + return new Tool(toolName) + .description(description) + .input(z.object({ query: z.string().describe('Natural language search query') })) + .handler(async ({ query }) => ({ results: await this.search(query) })); + } + + const filterSchema = buildFilterInputSchema(opts.filterableKeys); + return new Tool(toolName) + .description(description) + .input( + z.object({ + query: z.string().describe('Natural language search query'), + filter: filterSchema, + }), + ) + .handler(async ({ query, filter }) => ({ + results: await this.search( + query, + filter && filter.length > 0 + ? { filter: { conditions: filter, combineWith: 'and' } } + : undefined, + ), + })); + } + + private ensureBuilt(): { backend: BuiltVectorStoreBackend; embeddingModel: EmbeddingModel } { + if (!this.backend) { + throw new Error(`VectorStore "${this.name}" requires a backend — set it via .store()`); + } + if (!this.embeddingModelValue) { + throw new Error( + `VectorStore "${this.name}" requires an embedding model — set it via .embeddingModel()`, + ); + } + return { backend: this.backend, embeddingModel: this.embeddingModelValue }; + } + + /** Normalizes and validates a filter; returns `undefined` for an empty one so it's never a no-op `WHERE`. */ + private resolveFilter(input?: VectorFilterInput): VectorFilter | undefined { + if (input === undefined) return undefined; + const normalized = normalizeFilterInput(input); + assertValidFilter(normalized); + return normalized.conditions.length > 0 ? normalized : undefined; + } +} + +function assertValidTopK(k: number): void { + if (!Number.isInteger(k) || k < 1) { + throw new Error(`topK must be an integer >= 1, got ${k}`); + } +} diff --git a/packages/@n8n/agents/src/storage/base-vector-store.ts b/packages/@n8n/agents/src/storage/base-vector-store.ts new file mode 100644 index 00000000000..ee82d0d0359 --- /dev/null +++ b/packages/@n8n/agents/src/storage/base-vector-store.ts @@ -0,0 +1,32 @@ +/* eslint-disable @typescript-eslint/promise-function-async */ +import type { + BuiltVectorStoreBackend, + VectorFilter, + VectorQueryResult, + VectorRecord, +} from '../types/sdk/vector-store'; +import type { JSONObject } from '../types/utils/json'; + +export abstract class BaseVectorStore + implements BuiltVectorStoreBackend +{ + constructor( + protected readonly name: string, + protected readonly constructorOptions: TConstructorOptions, + ) {} + + upsert(_records: VectorRecord[]): Promise { + throw new Error('Method not implemented.'); + } + query( + _vector: number[], + _opts: { topK: number; filter?: VectorFilter }, + ): Promise { + throw new Error('Method not implemented.'); + } + delete(_opts: { ids: string[] }): Promise { + throw new Error('Method not implemented.'); + } + + close?(): void | Promise; +} diff --git a/packages/@n8n/agents/src/types/index.ts b/packages/@n8n/agents/src/types/index.ts index 5fa8c07c3c2..ff7fe0dadf1 100644 --- a/packages/@n8n/agents/src/types/index.ts +++ b/packages/@n8n/agents/src/types/index.ts @@ -103,6 +103,17 @@ export type { TitleGenerationConfig, } from './sdk/memory'; +export type { + VectorDocument, + VectorRecord, + VectorQueryResult, + BuiltVectorStoreBackend, + FilterOperator, + FilterValue, + FilterCondition, + VectorFilter, +} from './sdk/vector-store'; + export type { ObservationCursor } from './sdk/observation'; export type { diff --git a/packages/@n8n/agents/src/types/sdk/vector-store.ts b/packages/@n8n/agents/src/types/sdk/vector-store.ts new file mode 100644 index 00000000000..7253741055a --- /dev/null +++ b/packages/@n8n/agents/src/types/sdk/vector-store.ts @@ -0,0 +1,50 @@ +import type { JSONObject } from '../utils/json'; + +export interface VectorDocument { + id?: string; + content: string; + metadata?: JSONObject; +} + +export interface VectorRecord { + id: string; + vector: number[]; + content: string; + metadata: JSONObject; +} + +export interface VectorQueryResult { + id: string; + content: string; + metadata: JSONObject; + score: number; +} + +/** A comparison on one metadata key. `eq`/`ne` take a scalar, `in`/`nin` a non-empty array. */ +export interface FilterCondition { + key: string; + operator: FilterOperator; + value: FilterValue; +} + +export type FilterOperator = 'eq' | 'ne' | 'in' | 'nin'; + +export type FilterValue = string | number | boolean | Array; + +/** A flat group of conditions joined by one combinator — deliberately not a nested boolean tree. */ +export interface VectorFilter { + conditions: FilterCondition[]; + combineWith?: 'and' | 'or'; +} + +/** A pluggable vector database backend — operates purely on vectors; embedding is the `VectorStore` orchestrator's job. */ +export interface BuiltVectorStoreBackend { + upsert(records: VectorRecord[]): Promise; + query( + vector: number[], + opts: { topK: number; filter?: VectorFilter }, + ): Promise; + delete(opts: { ids: string[] }): Promise; + /** Close the connection pool / release resources. */ + close?(): void | Promise; +} diff --git a/packages/@n8n/agents/src/vector-stores/pinecone.ts b/packages/@n8n/agents/src/vector-stores/pinecone.ts new file mode 100644 index 00000000000..b237688d2bd --- /dev/null +++ b/packages/@n8n/agents/src/vector-stores/pinecone.ts @@ -0,0 +1,196 @@ +import type { Index, RecordMetadata, ScoredPineconeRecord } from '@pinecone-database/pinecone'; + +import { BaseVectorStore } from '../storage/base-vector-store'; +import type { + FilterCondition, + VectorFilter, + VectorQueryResult, + VectorRecord, +} from '../types/sdk/vector-store'; +import type { JSONObject, JSONValue } from '../types/utils/json'; + +/** Metadata key reserved for document content — Pinecone has no separate content column. */ +const CONTENT_KEY = '_content'; + +/** Pinecone caps upserts at 1,000 records / 2MB per request and the client does not auto-batch. */ +const UPSERT_BATCH_SIZE = 200; + +export type PineconeVectorStoreOptions = { + apiKey: string; + indexName: string; + /** Pinecone namespace. Default: the index's default namespace (`''`). */ + namespace?: string; +}; + +/** + * Pinecone backend (`@pinecone-database/pinecone` is an optional peer dependency). + * Never creates or alters the index: expects one that already exists with vector + * dimension matching the embedding model and Cosine metric. + * + * Pinecone metadata is flat only (string, number, boolean, or string-array + * values — no nested objects, no null), so document content is stored under + * a reserved `_content` metadata key alongside the caller's metadata spread + * at the top level. Metadata containing that reserved key, or a value of an + * unsupported shape, is rejected before any request is sent. Because content + * is stored as metadata, Pinecone's 40KB per-record metadata limit applies + * to the document content plus its metadata combined. + * + * Pinecone's `$ne`/`$nin` filter operators do not match records missing the + * filtered key, unlike `PgVectorStore`/`QdrantVectorStore`/`SupabaseVectorStore` + * — `ne`/`nin` are translated into an `$or` with an `$exists: false` arm so + * behavior matches the other backends. + * + * Pinecone writes and deletes are eventually consistent: a query issued + * immediately after an upsert or delete may not reflect it yet. + * + * Pinecone only guarantees score-sorted results for unfiltered queries — + * a filtered query's `matches` can come back out of order, so results are + * explicitly re-sorted by score before being returned. + * + * @example + * ```typescript + * const store = new PineconeVectorStore('product-docs', { + * apiKey: process.env.PINECONE_API_KEY!, + * indexName: 'product-docs', + * }); + * ``` + */ +export class PineconeVectorStore extends BaseVectorStore { + private index?: Index; + + async upsert(records: VectorRecord[]): Promise { + if (records.length === 0) return; + + const index = await this.getIndex(); + const mapped = records.map((record) => ({ + id: record.id, + values: record.vector, + metadata: toPineconeMetadata(record.content, record.metadata), + })); + for (let i = 0; i < mapped.length; i += UPSERT_BATCH_SIZE) { + await index.upsert(mapped.slice(i, i + UPSERT_BATCH_SIZE)); + } + } + + async query( + vector: number[], + opts: { topK: number; filter?: VectorFilter }, + ): Promise { + const index = await this.getIndex(); + const filter = + opts.filter && opts.filter.conditions.length > 0 + ? buildPineconeFilter(opts.filter) + : undefined; + + const result = await index.query({ + vector, + topK: opts.topK, + includeMetadata: true, + ...(filter ? { filter } : {}), + }); + + // Pinecone does not guarantee score-sorted results for filtered queries + // (only for unfiltered ones) — sort explicitly to match the other backends. + const sorted = [...result.matches].sort((a, b) => (b.score ?? 0) - (a.score ?? 0)); + return sorted.map(toQueryResult); + } + + async delete({ ids }: { ids: string[] }): Promise { + if (ids.length === 0) return; + + const index = await this.getIndex(); + await index.deleteMany(ids); + } + + close(): void { + this.index = undefined; + } + + private async getIndex(): Promise { + if (!this.index) { + const { Pinecone } = await import('@pinecone-database/pinecone'); + const pc = new Pinecone({ apiKey: this.constructorOptions.apiKey }); + const target = pc.index(this.constructorOptions.indexName); + this.index = this.constructorOptions.namespace + ? target.namespace(this.constructorOptions.namespace) + : target; + } + return this.index; + } +} + +function toPineconeMetadata(content: string, metadata: JSONObject): RecordMetadata { + if (CONTENT_KEY in metadata) { + throw new Error( + `Metadata key "${CONTENT_KEY}" is reserved for the document content and cannot be set.`, + ); + } + const result: RecordMetadata = { [CONTENT_KEY]: content }; + for (const [key, value] of Object.entries(metadata)) { + assertValidMetadataValue(key, value); + result[key] = value; + } + return result; +} + +/** Pinecone metadata values are flat: string, number, boolean, or an array of strings — no nested objects or null. */ +function assertValidMetadataValue( + key: string, + value: JSONValue | undefined, +): asserts value is string | number | boolean | string[] { + if (typeof value === 'string' || typeof value === 'number' || typeof value === 'boolean') { + return; + } + if (Array.isArray(value) && value.every((item) => typeof item === 'string')) return; + + throw new Error( + `Metadata value for key "${key}" is unsupported: Pinecone only supports string, number, boolean, and string-array metadata values.`, + ); +} + +function toQueryResult(match: ScoredPineconeRecord): VectorQueryResult { + const { [CONTENT_KEY]: content, ...metadata } = (match.metadata ?? {}) as JSONObject; + return { + id: String(match.id), + content: typeof content === 'string' ? content : '', + metadata, + score: match.score ?? 0, + }; +} + +/** Negations are compensated with `$exists: false` so missing-key rows match, like the other backends. */ +function buildPineconeFilter(filter: VectorFilter): object { + const terms = filter.conditions.map(buildCondition); + return filter.combineWith === 'or' ? { $or: terms } : { $and: terms }; +} + +function buildCondition(condition: FilterCondition): object { + const { key, operator, value } = condition; + + switch (operator) { + case 'eq': + return { [key]: { $eq: value } }; + case 'ne': + return { $or: [{ [key]: { $ne: value } }, { [key]: { $exists: false } }] }; + case 'in': + assertNonEmptyArray(operator, key, value); + return { [key]: { $in: value } }; + case 'nin': + assertNonEmptyArray(operator, key, value); + return { $or: [{ [key]: { $nin: value } }, { [key]: { $exists: false } }] }; + default: + throw new Error(`Unsupported filter operator: "${String(operator)}"`); + } +} + +function assertNonEmptyArray( + operator: string, + key: string, + value: unknown, +): asserts value is Array { + if (!Array.isArray(value) || value.length === 0) { + throw new Error( + `Filter operator "${operator}" on key "${key}" requires a non-empty array value.`, + ); + } +} diff --git a/packages/@n8n/agents/src/vector-stores/postgres.ts b/packages/@n8n/agents/src/vector-stores/postgres.ts new file mode 100644 index 00000000000..bfd9ed7ff5d --- /dev/null +++ b/packages/@n8n/agents/src/vector-stores/postgres.ts @@ -0,0 +1,254 @@ +import type { Pool } from 'pg'; + +import { BaseVectorStore } from '../storage/base-vector-store'; +import type { + FilterCondition, + VectorFilter, + VectorQueryResult, + VectorRecord, +} from '../types/sdk/vector-store'; +import type { JSONObject } from '../types/utils/json'; + +const IDENTIFIER_PATTERN = /^[A-Za-z_][A-Za-z0-9_]*$/; + +/** Minimum pgvector version (inclusive) that supports `hnsw.iterative_scan`. */ +const ITERATIVE_SCAN_MIN_MAJOR = 0; +const ITERATIVE_SCAN_MIN_MINOR = 8; + +interface PgVectorRow { + id: string; + content: string; + metadata: JSONObject; + score: number; +} + +export type PgVectorStoreOptions = { + connectionString: string; + tableName: string; +}; + +/** + * Postgres + pgvector backend (`pg` is an optional peer dependency). + * Never creates or alters schema: expects an existing table with the + * standard pgvector layout — id (unique), content text, metadata jsonb, + * embedding vector(n). The embedding model must match the one that produced + * the stored vectors. + * + * @example + * ```typescript + * const store = new PgVectorStore('product-docs', { + * connectionString: 'postgresql://user:pass@localhost:5432/db', + * tableName: 'product_docs', + * }); + * ``` + */ +export class PgVectorStore extends BaseVectorStore { + private readonly tableName: string; + + private pool?: Pool; + + private iterativeScanSupportedPromise?: Promise; + + constructor(name: string, options: PgVectorStoreOptions) { + super(name, options); + this.tableName = options.tableName; + if (typeof this.tableName !== 'string' || !IDENTIFIER_PATTERN.test(this.tableName)) { + throw new Error( + `Invalid PgVectorStore table name "${String(this.tableName)}": must match ${IDENTIFIER_PATTERN}`, + ); + } + } + + async upsert(records: VectorRecord[]): Promise { + if (records.length === 0) return; + + const pool = await this.getPool(); + const values: string[] = []; + const params: unknown[] = []; + records.forEach((record, index) => { + const base = index * 4; + values.push(`($${base + 1}, $${base + 2}, $${base + 3}, $${base + 4}::vector)`); + params.push( + record.id, + record.content, + JSON.stringify(record.metadata), + serializeVector(record.vector), + ); + }); + + await pool.query( + `INSERT INTO "${this.tableName}" (id, content, metadata, embedding) + VALUES ${values.join(', ')} + ON CONFLICT (id) DO UPDATE + SET content = EXCLUDED.content, metadata = EXCLUDED.metadata, embedding = EXCLUDED.embedding;`, + params, + ); + } + + async query( + vector: number[], + opts: { topK: number; filter?: VectorFilter }, + ): Promise { + const pool = await this.getPool(); + const selectSql = `SELECT id, content, metadata, 1 - (embedding <=> $1::vector) AS score + FROM "${this.tableName}"`; + + if (!opts.filter || opts.filter.conditions.length === 0) { + const result = await pool.query( + `${selectSql} + ORDER BY embedding <=> $1::vector + LIMIT $2;`, + [serializeVector(vector), opts.topK], + ); + return result.rows.map(toQueryResult); + } + + const { whereSql, params } = this.buildFilterClause(opts.filter, 2); + const sql = `${selectSql} + WHERE ${whereSql} + ORDER BY embedding <=> $1::vector + LIMIT $2;`; + const allParams = [serializeVector(vector), opts.topK, ...params]; + + if (!(await this.supportsIterativeScan())) { + const result = await pool.query(sql, allParams); + return result.rows.map(toQueryResult); + } + + // Filtered HNSW scans can under-return: the index gathers candidates by + // distance alone before the filter applies, so a selective filter can + // leave fewer than `topK` rows even when matches exist elsewhere in the + // graph. Iterative scan keeps walking until LIMIT is met; `relaxed_order` + // trades strict distance ordering for that guarantee, hence the re-sort. + const client = await pool.connect(); + try { + await client.query('BEGIN'); + await client.query('SET LOCAL hnsw.iterative_scan = relaxed_order;'); + const result = await client.query(sql, allParams); + await client.query('COMMIT'); + return result.rows.map(toQueryResult).sort((a, b) => b.score - a.score); + } catch (error) { + await client.query('ROLLBACK'); + throw error; + } finally { + client.release(); + } + } + + async delete({ ids }: { ids: string[] }): Promise { + if (ids.length === 0) return; + + const pool = await this.getPool(); + await pool.query(`DELETE FROM "${this.tableName}" WHERE id = ANY($1);`, [ids]); + } + + async close(): Promise { + await this.pool?.end(); + this.pool = undefined; + } + + /** Keys and values are always bind parameters; only the regex-validated table name is interpolated. */ + private buildFilterClause( + filter: VectorFilter, + paramOffset: number, + ): { whereSql: string; params: unknown[] } { + const params: unknown[] = []; + let paramCount = paramOffset; + const nextParam = (value: unknown): string => { + paramCount += 1; + params.push(value); + return `$${paramCount}`; + }; + + const conditionClauses = filter.conditions.map((condition) => + this.buildConditionClause(condition, nextParam), + ); + const joiner = filter.combineWith === 'or' ? ' OR ' : ' AND '; + + return { whereSql: conditionClauses.join(joiner), params }; + } + + private buildConditionClause( + condition: FilterCondition, + nextParam: (value: unknown) => string, + ): string { + const { key, operator, value } = condition; + + switch (operator) { + case 'eq': { + const v = nextParam(JSON.stringify({ [key]: value })); + return `metadata @> ${v}::jsonb`; + } + case 'ne': { + const v = nextParam(JSON.stringify({ [key]: value })); + return `NOT (metadata @> ${v}::jsonb)`; + } + case 'in': + case 'nin': { + if (!Array.isArray(value) || value.length === 0) { + throw new Error( + `Filter operator "${operator}" on key "${key}" requires a non-empty array value.`, + ); + } + // Containment per candidate (rather than `metadata->>key = ANY(text[])`) keeps + // numeric and string metadata distinct — text extraction would otherwise make + // the number 5 and the string "5" match the same filter value. + const anyMatch = value + .map( + (candidate) => `metadata @> ${nextParam(JSON.stringify({ [key]: candidate }))}::jsonb`, + ) + .join(' OR '); + return operator === 'in' ? `(${anyMatch})` : `NOT (${anyMatch})`; + } + default: + throw new Error(`Unsupported filter operator: "${String(operator)}"`); + } + } + + private async supportsIterativeScan(): Promise { + if (!this.iterativeScanSupportedPromise) { + const p = this.checkIterativeScanSupport(); + this.iterativeScanSupportedPromise = p; + p.catch(() => { + if (this.iterativeScanSupportedPromise === p) + this.iterativeScanSupportedPromise = undefined; + }); + } + return await this.iterativeScanSupportedPromise; + } + + private async checkIterativeScanSupport(): Promise { + const pool = await this.getPool(); + const result = await pool.query<{ extversion: string }>( + "SELECT extversion FROM pg_extension WHERE extname = 'vector';", + ); + const version = result.rows[0]?.extversion; + if (!version) return false; + const [major, minor] = version.split('.').map((part) => parseInt(part, 10)); + return ( + major > ITERATIVE_SCAN_MIN_MAJOR || + (major === ITERATIVE_SCAN_MIN_MAJOR && minor >= ITERATIVE_SCAN_MIN_MINOR) + ); + } + + private async getPool(): Promise { + if (!this.pool) { + const { Pool: PoolCtor } = await import('pg'); + this.pool = new PoolCtor({ connectionString: this.constructorOptions.connectionString }); + } + return this.pool; + } +} + +function toQueryResult(row: PgVectorRow): VectorQueryResult { + return { + id: row.id, + content: row.content, + metadata: row.metadata, + score: Number(row.score), + }; +} + +function serializeVector(vector: number[]): string { + return `[${vector.join(',')}]`; +} diff --git a/packages/@n8n/agents/src/vector-stores/qdrant.ts b/packages/@n8n/agents/src/vector-stores/qdrant.ts new file mode 100644 index 00000000000..308453bde5e --- /dev/null +++ b/packages/@n8n/agents/src/vector-stores/qdrant.ts @@ -0,0 +1,188 @@ +import type { QdrantClient, Schemas } from '@qdrant/js-client-rest'; + +import { BaseVectorStore } from '../storage/base-vector-store'; +import type { + FilterCondition, + VectorFilter, + VectorQueryResult, + VectorRecord, +} from '../types/sdk/vector-store'; +import type { JSONObject } from '../types/utils/json'; + +// Qdrant parses point-id UUIDs with Rust's `Uuid::parse_str`, which accepts all +// four representations below — not just hyphenated — so this must match them too: +// simple `936da01f9abd4d9d80c702af85c822a8`, hyphenated `550e8400-e29b-...-440000`, +// urn `urn:uuid:550e8400-e29b-...-440000`, and braced `{550e8400-e29b-...-440000}`. +const UUID_HYPHENATED = '[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}'; +const UUID_PATTERN = new RegExp( + `^(${UUID_HYPHENATED}|[0-9a-f]{32}|urn:uuid:${UUID_HYPHENATED}|\\{${UUID_HYPHENATED}\\})$`, + 'i', +); +const UNSIGNED_INT_PATTERN = /^\d+$/; + +interface QdrantPayload { + content: string; + metadata: JSONObject; +} + +export type QdrantVectorStoreOptions = { + url: string; + apiKey?: string; + collectionName: string; +}; + +/** + * Qdrant backend (`@qdrant/js-client-rest` is an optional peer dependency). + * Never creates or alters the collection: expects one that already exists + * with vector size matching the embedding model and Cosine distance. + * + * Qdrant point ids must be a UUID or an unsigned integer — arbitrary string + * ids (other than the UUIDs the `VectorStore` orchestrator generates) are + * rejected with a descriptive error rather than a raw Qdrant 400. + * + * Metadata filtering requires a payload index on each filtered field + * (`metadata.`) — Qdrant Cloud rejects filters on unindexed fields — + * and a field can only carry one index type at a time. Filter values are + * also more restrictive than `PgVectorStore`: Qdrant `match` only supports + * strings, integers, and booleans, so float values for `eq`/`ne` and + * mixed-type arrays for `in`/`nin` (e.g. `['billing', 5]`) are rejected with + * a descriptive error before any request is sent. + * + * @example + * ```typescript + * const store = new QdrantVectorStore('product-docs', { + * url: 'http://localhost:6333', + * collectionName: 'product_docs', + * }); + * ``` + */ +export class QdrantVectorStore extends BaseVectorStore { + private readonly collectionName: string; + + private client?: QdrantClient; + + constructor(name: string, options: QdrantVectorStoreOptions) { + super(name, options); + this.collectionName = options.collectionName; + } + + async upsert(records: VectorRecord[]): Promise { + if (records.length === 0) return; + + const client = await this.getClient(); + await client.upsert(this.collectionName, { + wait: true, + points: records.map((record) => ({ + id: toPointId(record.id), + vector: record.vector, + payload: { content: record.content, metadata: record.metadata }, + })), + }); + } + + async query( + vector: number[], + opts: { topK: number; filter?: VectorFilter }, + ): Promise { + const client = await this.getClient(); + const filter = + opts.filter && opts.filter.conditions.length > 0 ? buildQdrantFilter(opts.filter) : undefined; + + const result = await client.query(this.collectionName, { + query: vector, + limit: opts.topK, + with_payload: true, + ...(filter ? { filter } : {}), + }); + + return result.points.map(toQueryResult); + } + + async delete({ ids }: { ids: string[] }): Promise { + if (ids.length === 0) return; + + const client = await this.getClient(); + await client.delete(this.collectionName, { wait: true, points: ids.map(toPointId) }); + } + + close(): void { + this.client = undefined; + } + + private async getClient(): Promise { + if (!this.client) { + const { QdrantClient: QdrantClientCtor } = await import('@qdrant/js-client-rest'); + this.client = new QdrantClientCtor({ + url: this.constructorOptions.url, + apiKey: this.constructorOptions.apiKey, + }); + } + return this.client; + } +} + +/** Qdrant only accepts UUID or unsigned-integer point ids. */ +function toPointId(id: string): string | number { + if (UUID_PATTERN.test(id)) return id; + const numeric = Number(id); + if (UNSIGNED_INT_PATTERN.test(id) && Number.isSafeInteger(numeric) && String(numeric) === id) { + return numeric; + } + throw new Error( + `Invalid Qdrant point id "${id}": Qdrant requires ids to be a UUID or a canonical unsigned integer.`, + ); +} + +function toQueryResult(point: Schemas['ScoredPoint']): VectorQueryResult { + const payload = (point.payload ?? {}) as unknown as QdrantPayload; + return { + id: String(point.id), + content: payload.content, + metadata: payload.metadata ?? {}, + score: point.score, + }; +} + +/** Negations are expressed as nested `must_not` filters so both `and`/`or` combinators work uniformly. */ +function buildQdrantFilter(filter: VectorFilter): Schemas['Filter'] { + const conditions = filter.conditions.map(buildCondition); + return filter.combineWith === 'or' ? { should: conditions } : { must: conditions }; +} + +function buildCondition(condition: FilterCondition): Schemas['Condition'] { + const { key, operator, value } = condition; + const payloadKey = `metadata.${key}`; + + switch (operator) { + case 'eq': + case 'ne': { + if (typeof value === 'number' && !Number.isInteger(value)) { + throw new Error( + `Filter operator "${operator}" on key "${key}" does not support float values: Qdrant match only supports strings, integers, and booleans.`, + ); + } + const match: Schemas['Condition'] = { key: payloadKey, match: { value } }; + return operator === 'eq' ? match : { must_not: [match] }; + } + case 'in': + case 'nin': { + if (!Array.isArray(value) || value.length === 0) { + throw new Error( + `Filter operator "${operator}" on key "${key}" requires a non-empty array value.`, + ); + } + const allStrings = value.every((v) => typeof v === 'string'); + const allIntegers = value.every((v) => typeof v === 'number' && Number.isInteger(v)); + if (!allStrings && !allIntegers) { + throw new Error( + `Filter operator "${operator}" on key "${key}" requires all array elements to be strings or all to be integers: Qdrant match does not support mixed-type or float values.`, + ); + } + // eslint-disable-next-line id-denylist -- `any` is Qdrant's match-schema field name + const anyCondition: Schemas['Condition'] = { key: payloadKey, match: { any: value } }; + return operator === 'in' ? anyCondition : { must_not: [anyCondition] }; + } + default: + throw new Error(`Unsupported filter operator: "${String(operator)}"`); + } +} diff --git a/packages/@n8n/agents/src/vector-stores/supabase.ts b/packages/@n8n/agents/src/vector-stores/supabase.ts new file mode 100644 index 00000000000..6cc8034a8b0 --- /dev/null +++ b/packages/@n8n/agents/src/vector-stores/supabase.ts @@ -0,0 +1,256 @@ +import type { SupabaseClient } from '@supabase/supabase-js'; + +import { BaseVectorStore } from '../storage/base-vector-store'; +import type { + FilterCondition, + VectorFilter, + VectorQueryResult, + VectorRecord, +} from '../types/sdk/vector-store'; +import type { JSONObject } from '../types/utils/json'; + +interface SupabaseMatchRow { + id: string; + content: string; + metadata: JSONObject; + similarity: number; +} + +/** + * Explicit RPC function shape passed to `client.rpc()` — without it, + * TypeScript can't resolve `Fn` from the untyped (no generated `Database` type) + * client, and `Fn['Returns']` collapses to `any`, which trips up `.returns()`'s + * array-vs-object validation. Providing this directly gives the whole + * `rpc()` → `.filter()`/`.limit()` chain a concrete result type. + */ +interface MatchDocumentsFn { + Args: { query_embedding: number[] }; + Returns: SupabaseMatchRow[]; +} + +/** + * Structural subset of `@supabase/postgrest-js`'s `PostgrestFilterBuilder` — + * declared locally so filter-building stays generic over the real (highly + * parameterized) builder type without depending on postgrest-js directly. + * Every method returns `Self`, matching the real builder's `this`-returning + * chain. + */ +interface PostgrestFilterChain { + filter(column: string, operator: string, value: unknown): Self; + not(column: string, operator: string, value: unknown): Self; + or(filters: string): Self; +} + +export type SupabaseVectorStoreOptions = { + url: string; + apiKey: string; + tableName: string; + /** Name of the similarity-search RPC function. Default: `match_documents`. */ + queryName?: string; +}; + +/** + * Supabase backend (`@supabase/supabase-js` is an optional peer dependency), + * built on PostgREST — never creates or alters schema. Expects an existing + * table with the standard pgvector layout (id text primary key, content + * text, metadata jsonb, embedding vector(n)) and a Postgres RPC function + * (default name `match_documents`) that takes a `query_embedding` parameter + * and returns rows shaped `{ id, content, metadata, similarity }` ordered by + * distance: + * + * ```sql + * create function match_documents(query_embedding vector(n)) + * returns table (id text, content text, metadata jsonb, similarity float) + * language sql stable as $$ + * select id, content, metadata, 1 - (embedding <=> query_embedding) as similarity + * from "" + * order by embedding <=> query_embedding; + * $$; + * ``` + * + * `match_count` is deliberately never passed to the RPC — PostgREST applies + * `.filter()`/`.or()`/`.limit()` chained onto an `rpc()` call as a wrapping + * subquery, so metadata filters are applied before `topK` truncates the + * results rather than after, matching `PgVectorStore` semantics. + * + * On tables with an HNSW index, a selective metadata filter can make the + * index scan return fewer than `topK` rows even when more matches exist. + * `PgVectorStore` works around this with a per-query + * `hnsw.iterative_scan` setting, which PostgREST cannot set — if you use + * an HNSW index, set it at the role level instead: + * `ALTER ROLE authenticator SET hnsw.iterative_scan = relaxed_order;` + * + * Filtering uses jsonb containment (`cs`) on the `metadata` column, so + * `eq`/`in` are type-correct (number `5` does not match string `"5"`) and + * `ne`/`nin` match rows that never had the filtered key. + * + * @example + * ```typescript + * const store = new SupabaseVectorStore('product-docs', { + * url: 'https://xyzcompany.supabase.co', + * apiKey: process.env.SUPABASE_SECRET_KEY!, + * tableName: 'product_docs', + * }); + * ``` + */ +export class SupabaseVectorStore extends BaseVectorStore { + private readonly tableName: string; + + private readonly queryName: string; + + private client?: SupabaseClient; + + constructor(name: string, options: SupabaseVectorStoreOptions) { + super(name, options); + this.tableName = options.tableName; + this.queryName = options.queryName ?? 'match_documents'; + } + + async upsert(records: VectorRecord[]): Promise { + if (records.length === 0) return; + + const client = await this.getClient(); + const { error } = await client.from(this.tableName).upsert( + records.map((record) => ({ + id: record.id, + content: record.content, + metadata: record.metadata, + embedding: record.vector, + })), + { onConflict: 'id' }, + ); + if (error) throw new Error(`Supabase upsert failed: ${error.message}`); + } + + async query( + vector: number[], + opts: { topK: number; filter?: VectorFilter }, + ): Promise { + const client = await this.getClient(); + const rpcCall = client.rpc(this.queryName, { + query_embedding: vector, + }); + const filtered = + opts.filter && opts.filter.conditions.length > 0 + ? applySupabaseFilter(rpcCall, opts.filter) + : rpcCall; + + const { data, error } = await filtered.limit(opts.topK); + if (error) throw new Error(`Supabase query failed: ${error.message}`); + + return (data ?? []).map(toQueryResult); + } + + async delete({ ids }: { ids: string[] }): Promise { + if (ids.length === 0) return; + + const client = await this.getClient(); + const { error } = await client.from(this.tableName).delete().in('id', ids); + if (error) throw new Error(`Supabase delete failed: ${error.message}`); + } + + close(): void { + this.client = undefined; + } + + private async getClient(): Promise { + if (!this.client) { + const { createClient } = await import('@supabase/supabase-js'); + this.client = createClient(this.constructorOptions.url, this.constructorOptions.apiKey); + } + return this.client; + } +} + +function toQueryResult(row: SupabaseMatchRow): VectorQueryResult { + return { + id: String(row.id), + content: row.content, + metadata: row.metadata ?? {}, + score: row.similarity, + }; +} + +/** Negations are expressed as chained `.not()` filters so both `and`/`or` combinators work uniformly. */ +function applySupabaseFilter>( + builder: Builder, + filter: VectorFilter, +): Builder { + if (filter.combineWith === 'or') { + return builder.or(filter.conditions.map(toOrLogicTerm).join(',')); + } + return filter.conditions.reduce(applyAndCondition, builder); +} + +function applyAndCondition>( + builder: Builder, + condition: FilterCondition, +): Builder { + const { key, operator, value } = condition; + + switch (operator) { + case 'eq': + return builder.filter('metadata', 'cs', containmentJson(key, value)); + case 'ne': + return builder.not('metadata', 'cs', containmentJson(key, value)); + case 'in': { + assertNonEmptyArray(operator, key, value); + return builder.or( + value + .map((candidate) => `metadata.cs.${quoteOrValue(containmentJson(key, candidate))}`) + .join(','), + ); + } + case 'nin': { + assertNonEmptyArray(operator, key, value); + return value.reduce( + (b, candidate) => b.not('metadata', 'cs', containmentJson(key, candidate)), + builder, + ); + } + default: + throw new Error(`Unsupported filter operator: "${String(operator)}"`); + } +} + +function toOrLogicTerm(condition: FilterCondition): string { + const { key, operator, value } = condition; + + switch (operator) { + case 'eq': + return `metadata.cs.${quoteOrValue(containmentJson(key, value))}`; + case 'ne': + return `metadata.not.cs.${quoteOrValue(containmentJson(key, value))}`; + case 'in': { + assertNonEmptyArray(operator, key, value); + return `or(${value.map((candidate) => `metadata.cs.${quoteOrValue(containmentJson(key, candidate))}`).join(',')})`; + } + case 'nin': { + assertNonEmptyArray(operator, key, value); + return `and(${value.map((candidate) => `metadata.not.cs.${quoteOrValue(containmentJson(key, candidate))}`).join(',')})`; + } + default: + throw new Error(`Unsupported filter operator: "${String(operator)}"`); + } +} + +function containmentJson(key: string, value: unknown): string { + return JSON.stringify({ [key]: value }); +} + +function assertNonEmptyArray( + operator: string, + key: string, + value: unknown, +): asserts value is Array { + if (!Array.isArray(value) || value.length === 0) { + throw new Error( + `Filter operator "${operator}" on key "${key}" requires a non-empty array value.`, + ); + } +} + +/** PostgREST logic-string values containing reserved characters must be double-quoted. */ +function quoteOrValue(value: string): string { + return `"${value.replaceAll('\\', '\\\\').replaceAll('"', '\\"')}"`; +} diff --git a/packages/@n8n/nodes-langchain/package.json b/packages/@n8n/nodes-langchain/package.json index eb1d1669adb..bbc09b830ce 100644 --- a/packages/@n8n/nodes-langchain/package.json +++ b/packages/@n8n/nodes-langchain/package.json @@ -284,8 +284,8 @@ "@n8n/typescript-config": "workspace:*", "@n8n/utils": "workspace:*", "@oracle/langchain-oracledb": "0.2.0", - "@pinecone-database/pinecone": "^5.0.2", - "@qdrant/js-client-rest": "^1.16.2", + "@pinecone-database/pinecone": "catalog:", + "@qdrant/js-client-rest": "catalog:", "@smithy/node-http-handler": "4.5.0", "@smithy/types": "4.13.1", "@supabase/supabase-js": "catalog:", diff --git a/pnpm-lock.yaml b/pnpm-lock.yaml index c1891e08e3e..1261154dea5 100644 --- a/pnpm-lock.yaml +++ b/pnpm-lock.yaml @@ -147,6 +147,12 @@ catalogs: '@openrouter/ai-sdk-provider': specifier: ^2.8.0 version: 2.9.0 + '@pinecone-database/pinecone': + specifier: ^5.0.2 + version: 5.1.2 + '@qdrant/js-client-rest': + specifier: ^1.16.2 + version: 1.16.2 '@rudderstack/rudder-sdk-node': specifier: 3.0.5 version: 3.0.5 @@ -978,9 +984,21 @@ importers: '@n8n/vitest-config': specifier: workspace:* version: link:../vitest-config + '@pinecone-database/pinecone': + specifier: 'catalog:' + version: 5.1.2 + '@qdrant/js-client-rest': + specifier: 'catalog:' + version: 1.16.2(typescript@6.0.2) + '@supabase/supabase-js': + specifier: 'catalog:' + version: 2.50.0(bufferutil@4.0.9)(utf-8-validate@5.0.10) '@types/json-schema': specifier: 'catalog:' version: 7.0.15 + '@types/pg': + specifier: ^8.15.6 + version: 8.20.0 '@vitest/coverage-v8': specifier: 'catalog:' version: 4.1.9(@vitest/browser@4.1.9)(vitest@4.1.9) @@ -993,6 +1011,12 @@ importers: nock: specifier: 'catalog:' version: 14.0.14 + pg: + specifier: 'catalog:' + version: 8.17.0(pg-native@3.8.0) + tsx: + specifier: 'catalog:' + version: 4.19.3 vite: specifier: 'catalog:' version: 8.0.2(@types/node@20.19.41)(esbuild@0.28.1)(jiti@2.6.1)(sass-embedded@1.98.0)(sass@1.98.0)(terser@5.16.1)(tsx@4.19.3)(yaml@2.8.3) @@ -2957,10 +2981,10 @@ importers: specifier: 0.2.0 version: 0.2.0(@langchain/core@1.2.0(@opentelemetry/api@1.9.0)(@opentelemetry/exporter-trace-otlp-proto@0.217.0(@opentelemetry/api@1.9.0))(@opentelemetry/sdk-trace-base@2.7.1(@opentelemetry/api@1.9.0))(openai@6.34.0(ws@8.21.0(bufferutil@4.0.9)(utf-8-validate@5.0.10))(zod@3.25.67))(ws@8.21.0(bufferutil@4.0.9)(utf-8-validate@5.0.10)))(@langchain/textsplitters@1.0.1(@langchain/core@1.2.0(@opentelemetry/api@1.9.0)(@opentelemetry/exporter-trace-otlp-proto@0.217.0(@opentelemetry/api@1.9.0))(@opentelemetry/sdk-trace-base@2.7.1(@opentelemetry/api@1.9.0))(openai@6.34.0(ws@8.21.0(bufferutil@4.0.9)(utf-8-validate@5.0.10))(zod@3.25.67))(ws@8.21.0(bufferutil@4.0.9)(utf-8-validate@5.0.10))))(oracledb@6.10.0) '@pinecone-database/pinecone': - specifier: ^5.0.2 + specifier: 'catalog:' version: 5.1.2 '@qdrant/js-client-rest': - specifier: ^1.16.2 + specifier: 'catalog:' version: 1.16.2(typescript@6.0.2) '@smithy/node-http-handler': specifier: 4.5.0 diff --git a/pnpm-workspace.yaml b/pnpm-workspace.yaml index c81c19c334d..a3273ca54c2 100644 --- a/pnpm-workspace.yaml +++ b/pnpm-workspace.yaml @@ -58,6 +58,8 @@ catalog: '@mozilla/readability': ^0.6.0 '@n8n_io/ai-assistant-sdk': 1.24.0 '@openrouter/ai-sdk-provider': ^2.8.0 + '@pinecone-database/pinecone': ^5.0.2 + '@qdrant/js-client-rest': ^1.16.2 '@rudderstack/rudder-sdk-node': 3.0.5 '@sentry/node': ^10.55.0 '@stryker-mutator/core': 9.6.1