/**
 * @module tests/integration/asof-semantic-recall
 * @description AS-OF SEMANTIC RECALL — the time-travel row of the release:
 * vector/semantic search at a pinned past generation, served EXACTLY.
 *
 * The contract pinned here (brainy-alone leg; the accelerated-provider leg
 * carries the same semantics at scale):
 * 1. PAST VECTORS ARE THE PAST'S VECTORS: a later re-embed/update never
 *    leaks into an earlier pin — asOf(G) ranks by the vectors as they
 *    stood at G, byte-exact.
 * 2. TOMBSTONE MASKING: a row deleted after G is FOUND at G; a row deleted
 *    at or before G is ABSENT at G.
 * 3. THE DEFERRED-EMBED CELL of the visibility matrix: at pins before the
 *    vector landed the row's VECTOR LEG serves the stub (text/metadata
 *    legs may still surface it — triple intelligence by design); the real
 *    vector serves only at and after its landing pin. No backward leak.
 * 4. TYPED REFUSAL beyond the log head — never a silent latest.
 */
import { describe, it, expect, afterEach } from 'vitest'
import { Brainy } from '../../src/index.js'
import { NounType } from '../../src/types/graphTypes.js'

const brains: Brainy[] = []

async function memBrain(): Promise<Brainy> {
  const b = new Brainy({ storage: { type: 'memory' }, requireSubtype: false })
  await b.init()
  brains.push(b)
  return b
}

afterEach(async () => {
  for (const b of brains.splice(0)) await b.close().catch(() => {})
})

describe('as-of semantic recall', () => {
  it('PAST VECTORS EXACT: a later update never leaks into an earlier pin', async () => {
    const brain = await memBrain()
    const id = await brain.add({
      data: 'crimson apples in the orchard',
      type: NounType.Document,
      metadata: { epoch: 'old' }
    })
    const g1 = brain.generation()
    const v1 = [...(((await brain.get(id, { includeVectors: true }))!.vector) as number[])]

    await brain.update({ id, data: 'deep blue ocean currents', metadata: { epoch: 'new' } })
    const g2 = brain.generation()
    const v2 = (await brain.get(id, { includeVectors: true }))!.vector as number[]
    expect(v2, 'the update really re-embedded').not.toEqual(v1)

    // The pin: at G1 the row carries its ORIGINAL vector and content.
    const dbPast = await brain.asOf(g1)
    const past = await dbPast.get(id, { includeVectors: true })
    expect(past, 'row exists at G1').toBeTruthy()
    expect(past!.vector as number[], 'as-of vector is byte-exact the OLD vector').toEqual(v1)
    expect((past!.metadata as { epoch: string }).epoch).toBe('old')

    // Semantic search at G1 finds it via the OLD content; at G2 via the new.
    const hitsOld = await dbPast.find({ query: 'crimson apples in the orchard', limit: 3 })
    expect(hitsOld.map((r) => r.id), 'old content recalls at G1').toContain(id)
    const dbNow = await brain.asOf(g2)
    const hitsNew = await dbNow.find({ query: 'deep blue ocean currents', limit: 3 })
    expect(hitsNew.map((r) => r.id), 'new content recalls at G2').toContain(id)
    await dbPast.release()
    await dbNow.release()
  })

  it('TOMBSTONE MASKING: deleted-after-G is found at G; deleted-before-G is absent', async () => {
    const brain = await memBrain()
    const doomed = await brain.add({
      data: 'ephemeral meteor shower observation',
      type: NounType.Document,
      metadata: {}
    })
    const keeper = await brain.add({
      data: 'permanent granite mountain survey',
      type: NounType.Document,
      metadata: {}
    })
    const gBoth = brain.generation()
    await brain.remove(doomed)
    const gAfter = brain.generation()

    const dbBoth = await brain.asOf(gBoth)
    const atBoth = await dbBoth.find({ query: 'ephemeral meteor shower observation', limit: 5 })
    expect(atBoth.map((r) => r.id), 'pre-delete pin still recalls the row').toContain(doomed)

    const dbAfter = await brain.asOf(gAfter)
    const atAfter = await dbAfter.find({ query: 'ephemeral meteor shower observation', limit: 5 })
    expect(atAfter.map((r) => r.id), 'post-delete pin masks the tombstoned row').not.toContain(doomed)
    expect((await dbAfter.find({ query: 'permanent granite mountain survey', limit: 5 })).map((r) => r.id)).toContain(keeper)
    await dbBoth.release()
    await dbAfter.release()
  })

  it('DEFERRED-EMBED CELL: semantically absent before the vector landed, present after — never a stub match', async () => {
    const brain = await memBrain()
    // Anchor row so the semantic search always has a corpus.
    await brain.add({ data: 'unrelated anchor topic entirely', type: NounType.Document, metadata: {} })

    const id = await brain.add({
      data: 'deferred saffron sunrise essay',
      type: NounType.Document,
      deferEmbedding: true,
      metadata: {}
    })
    const gAck = brain.generation()
    await brain.awaitPendingEmbeds()
    const gLanded = brain.generation()
    expect(gLanded, 'the landed vector is its own generation').toBeGreaterThan(gAck)

    // At the ack generation: metadata-visible, and the VECTOR LEG carries
    // the stub (the visibility matrix's AT-EMBED cell governs the vector
    // leg — find({query})'s text/metadata legs may legitimately still
    // surface the row, that is triple intelligence working as designed;
    // what must NEVER happen is a stub vector ranking as a real one).
    const dbAck = await brain.asOf(gAck)
    const metaHits = await dbAck.find({ where: {}, limit: 10 })
    expect(metaHits.map((r) => r.id), 'metadata-visible at ack pin').toContain(id)
    const ackRow = await dbAck.get(id, { includeVectors: true })
    expect((ackRow!.vector as number[]).length, 'the as-of vector at the ack pin is the stub — no vector leaked backward').toBe(0)

    // At the landed generation: fully recallable.
    const dbLanded = await brain.asOf(gLanded)
    const landedRow = await dbLanded.get(id, { includeVectors: true })
    expect((landedRow!.vector as number[]).length, 'the real vector serves at the landed pin').toBeGreaterThan(0)
    const semLanded = await dbLanded.find({ query: 'deferred saffron sunrise essay', limit: 5 })
    expect(semLanded.map((r) => r.id), 'recallable at the landed pin').toContain(id)
    await dbAck.release()
    await dbLanded.release()
  })

  it('TYPED REFUSAL beyond the head — never a silent latest', async () => {
    const brain = await memBrain()
    await brain.add({ data: 'one row', type: NounType.Document, metadata: {} })
    const head = brain.generation()
    await expect(brain.asOf(head + 100)).rejects.toThrow(/generation|beyond|future|exceed/i)
  })
})