import { NextRequest, NextResponse } from 'next/server'
import { auth } from '@/auth'
import prisma from '@/lib/prisma'

/**
 * GET /api/insights/graph
 *
 * Retourne les similarités cosinus pairwise calculées depuis les embeddings pgvector
 * pour TOUS les membres des clusters (intra-cluster) + les échos Memory Echo (inter-cluster).
 *
 * Structure de réponse :
 * {
 *   pairs: [{ sourceId, targetId, similarity, type: 'cluster' | 'echo' }],
 *   membershipScores: { [noteId]: number }
 * }
 *
 * - pairs.cluster : paires au sein du même cluster, score = similarité cosinus réelle
 * - pairs.echo    : paires Memory Echo non-rejetées, score = similarityScore stocké
 * - membershipScores : score de centralité de chaque note dans son cluster (de ClusterMember)
 */
export async function GET(request: NextRequest) {
  try {
    const session = await auth()
    if (!session?.user?.id) {
      return NextResponse.json({ error: 'Unauthorized' }, { status: 401 })
    }

    const userId = session.user.id

    // 1. Charger les membres de clusters avec leur score de centralité
    const clusterMembers = await prisma.clusterMember.findMany({
      where: { userId },
      select: { noteId: true, clusterId: true, membershipScore: true }
    })

    if (clusterMembers.length === 0) {
      return NextResponse.json({ pairs: [], membershipScores: {} })
    }

    // Construire la map noteId -> clusterId
    const noteToCluster = new Map<string, number>()
    const membershipScores: Record<string, number> = {}
    const clusterToNotes = new Map<number, string[]>()

    for (const m of clusterMembers) {
      noteToCluster.set(m.noteId, m.clusterId)
      membershipScores[m.noteId] = m.membershipScore
      if (!clusterToNotes.has(m.clusterId)) clusterToNotes.set(m.clusterId, [])
      clusterToNotes.get(m.clusterId)!.push(m.noteId)
    }

    const allNoteIds = clusterMembers.map(m => m.noteId)

    // 2. Calculer les similarités cosinus pairwise intra-cluster via pgvector
    // On utilise une requête SQL qui calcule toutes les paires d'un même cluster en une fois
    const intraClusterPairs = await prisma.$queryRawUnsafe<
      Array<{ sourceId: string; targetId: string; similarity: number; clusterId: number }>
    >(
      `SELECT
        e1."noteId" AS "sourceId",
        e2."noteId" AS "targetId",
        1 - (e1.embedding::vector <=> e2.embedding::vector) AS similarity,
        cm1."clusterId" AS "clusterId"
      FROM "NoteEmbedding" e1
      INNER JOIN "NoteEmbedding" e2 ON e1."noteId" < e2."noteId"
      INNER JOIN "ClusterMember" cm1 ON cm1."noteId" = e1."noteId" AND cm1."userId" = $1
      INNER JOIN "ClusterMember" cm2 ON cm2."noteId" = e2."noteId" AND cm2."userId" = $1
      WHERE cm1."clusterId" = cm2."clusterId"
        AND e1."noteId" = ANY($2::text[])
        AND e2."noteId" = ANY($2::text[])`,
      userId,
      allNoteIds
    )

    // 3. Récupérer les échos Memory Echo non-rejetés entre notes clusterisées
    const echoInsights = await prisma.memoryEchoInsight.findMany({
      where: {
        userId,
        dismissed: false,
        note1Id: { in: allNoteIds },
        note2Id: { in: allNoteIds }
      },
      select: {
        note1Id: true,
        note2Id: true,
        similarityScore: true
      }
    })

    // 4. Construire la liste finale des paires
    const pairs: Array<{
      sourceId: string
      targetId: string
      similarity: number
      type: 'cluster' | 'echo'
      clusterId?: number
    }> = []

    // Paires intra-cluster
    for (const p of intraClusterPairs) {
      pairs.push({
        sourceId: p.sourceId,
        targetId: p.targetId,
        similarity: Math.max(0, Math.min(1, p.similarity)),
        type: 'cluster',
        clusterId: p.clusterId
      })
    }

    // Paires Memory Echo (entre clusters différents souvent, mais peut être intra aussi)
    const existingPairKeys = new Set(pairs.map(p => `${p.sourceId}--${p.targetId}`))
    for (const echo of echoInsights) {
      const key1 = `${echo.note1Id}--${echo.note2Id}`
      const key2 = `${echo.note2Id}--${echo.note1Id}`
      // Ajouter uniquement si pas déjà couvert par intra-cluster
      if (!existingPairKeys.has(key1) && !existingPairKeys.has(key2)) {
        pairs.push({
          sourceId: echo.note1Id,
          targetId: echo.note2Id,
          similarity: Math.max(0, Math.min(1, echo.similarityScore)),
          type: 'echo'
        })
      }
    }

    return NextResponse.json({ pairs, membershipScores })
  } catch (error) {
    console.error('[/api/insights/graph] Error:', error)
    return NextResponse.json(
      { error: 'Failed to compute semantic graph', details: String(error) },
      { status: 500 }
    )
  }
}