1export const supabaseVectorIntegrationSpec = defineIntegration({
2	meta: {
3		key: 'vectordb.supabase',
4		version: '1.0.0',
5		category: 'vector-db',
6		title: 'Supabase Vector Database (pgvector)',
7		description:
8			'Supabase integration for embedding storage and vector similarity search using pgvector.',
9		domain: 'ai',
10		owners: ['platform.ai'],
11		tags: ['vector-db', 'supabase', 'pgvector'],
12		stability: StabilityEnum.Beta,
13	},
14	supportedModes: ['managed', 'byok'],
15	transports: [
16		{ type: 'rest' },
17		{ type: 'sdk', packageName: '@supabase/supabase-js' },
18	],
19	preferredTransport: 'rest',
20	supportedAuthMethods: [{ type: 'api-key' }, { type: 'bearer' }],
21	capabilities: {
22		provides: [
23			{ key: 'vector-db.search', version: '1.0.0' },
24			{ key: 'vector-db.storage', version: '1.0.0' },
25		],
26		requires: [
27			{
28				key: 'ai.embeddings',
29				optional: true,
30				reason:
31					'Required when vectors are generated by a hosted embedding provider.',
32			},
33		],
34	},
35	configSchema: {
36		schema: {
37			type: 'object',
38			properties: {
39				schema: {
40					type: 'string',
41					description:
42						'Schema name containing the pgvector table (default: public).',
43				},
44				table: {
45					type: 'string',
46					description:
47						'Table used to store vectors and metadata (default: contractspec_vectors).',
48				},
49				createTableIfMissing: {
50					type: 'boolean',
51					description:
52						'Automatically create pgvector extension and table when first writing vectors.',
53				},
54				distanceMetric: {
55					type: 'string',
56					enum: ['cosine', 'l2', 'inner_product'],
57					description: 'Distance metric used for nearest-neighbor search.',
58				},
59			},
60		},
61		example: {
62			schema: 'public',
63			table: 'contractspec_vectors',
64			createTableIfMissing: true,
65			distanceMetric: 'cosine',
66		},
67	},
68	secretSchema: {
69		schema: {
70			type: 'object',
71			required: ['databaseUrl'],
72			properties: {
73				databaseUrl: {
74					type: 'string',
75					description:
76						'Supabase Postgres connection string with privileges to read/write vector rows.',
77				},
78			},
79		},
80		example: {
81			databaseUrl:
82				'postgresql://postgres.[project-ref]:password@aws-0-region.pooler.supabase.com:6543/postgres',
83		},
84	},
85	healthCheck: {
86		method: 'ping',
87		timeoutMs: 5000,
88	},
89	docsUrl: 'https://supabase.com/docs/guides/ai/vector-columns',
90	byokSetup: {
91		setupInstructions:
92			'Create or reuse a Supabase project, enable pgvector, and provide a Postgres connection string with read/write access to the target vector table.',
93		keyRotationSupported: true,
94		quotaTrackingSupported: false,
95	},
96});