const DEFAULT_TABLE = 'memory_embeddings';
const DEFAULT_LIMIT = 8;

function isSafeIdentifier(value) {
  return /^[a-zA-Z_][a-zA-Z0-9_]*$/.test(String(value ?? ''));
}

function resolveTableName(tableName) {
  const normalized = String(tableName ?? DEFAULT_TABLE).trim();
  if (!isSafeIdentifier(normalized)) {
    throw new Error(`Invalid pgvector table name: ${normalized}`);
  }
  return normalized;
}

function normalizeEmbedding(value) {
  if (!Array.isArray(value)) return null;
  const numbers = value.map((item) => Number(item));
  if (!numbers.length || numbers.some((item) => !Number.isFinite(item))) return null;
  return numbers;
}

function vectorLiteral(embedding) {
  return `[${embedding.join(',')}]`;
}

function normalizeRow(row) {
  const text = String(row?.content ?? row?.memory_text ?? row?.text ?? '').trim();
  if (!text) return null;
  return {
    id: row?.id == null ? null : String(row.id),
    label: row?.type ?? row?.label ?? 'semantic',
    text,
    score: row?.score == null ? null : Number(row.score),
    createdAt: row?.created_at ?? row?.createdAt ?? null,
  };
}

export function createPgvectorMemoryBackend({
  pool = null,
  enabled = false,
  tableName = DEFAULT_TABLE,
  embedQuery = null,
  defaultLimit = DEFAULT_LIMIT,
  unavailableReason = 'not_configured',
} = {}) {
  const resolvedTableName = resolveTableName(tableName);

  async function resolveEmbedding(input) {
    const explicit = normalizeEmbedding(input?.embedding);
    if (explicit) return explicit;
    if (typeof embedQuery !== 'function' || !input?.query) return null;
    return normalizeEmbedding(await embedQuery(input.query, input));
  }

  return {
    name: 'pgvector',
    category: 'semantic',
    role: 'primary-vector-store',
    flag: 'MEMORY_VECTOR_ENABLED',
    unavailableReason,

    isAvailable() {
      return Boolean(enabled && pool?.query);
    },

    getUnavailableReason() {
      return this.isAvailable() ? null : unavailableReason;
    },

    async resolve(input = {}) {
      if (!this.isAvailable()) return { memories: [], semanticMemories: [] };
      const userId = String(input.userId ?? '').trim();
      if (!userId) return { memories: [], semanticMemories: [] };
      const embedding = await resolveEmbedding(input);
      if (!embedding) return { memories: [], semanticMemories: [] };
      const limit = Math.max(1, Math.min(50, Number(input.limit ?? defaultLimit) || defaultLimit));
      const sql = `
        SELECT id, content, type, created_at, 1 - (embedding <=> $2::vector) AS score
        FROM ${resolvedTableName}
        WHERE user_id = $1
        ORDER BY embedding <=> $2::vector
        LIMIT $3
      `;
      const result = await pool.query(sql, [userId, vectorLiteral(embedding), limit]);
      const memories = (result?.rows ?? []).map((row) => normalizeRow(row)).filter(Boolean);
      return {
        semanticMemories: memories.map((item) => item.text),
        memories,
      };
    },
  };
}