Merge Memory V2 runtime facade

memory-v2-pgvector.mjs

@@ -0,0 +1,93 @@
+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,
+      };
+    },
+  };
+}