megamemory.patch

diff --git a/package.json b/package.json
index 068da06..4893b5e 100644
--- a/package.json
+++ b/package.json
@@ -18,7 +18,8 @@
     "start": "node dist/index.js",
     "dev": "tsx src/index.ts",
     "test": "vitest run",
-    "prepublishOnly": "npm run build"
+    "prepublishOnly": "npm run build",
+    "fix-embeddings": "tsx fix-embeddings.js"
   },
   "keywords": [
     "mcp",
diff --git a/src/db.ts b/src/db.ts
index 0e5a4ec..8be2761 100644
--- a/src/db.ts
+++ b/src/db.ts
@@ -269,13 +269,16 @@ export class KnowledgeDB {
   getAllActiveNodesWithEmbeddings(): Array<
     Pick<NodeRow, "id" | "name" | "kind" | "summary" | "embedding">
   > {
-    return this.db
+    const rows = this.db
       .prepare(
         "SELECT id, name, kind, summary, embedding FROM nodes WHERE removed_at IS NULL AND embedding IS NOT NULL"
       )
-      .all() as Array<
-      Pick<NodeRow, "id" | "name" | "kind" | "summary" | "embedding">
-    >;
+      .all() as Array<any>;
+
+    return rows.map((row) => ({
+      ...row,
+      embedding: row.embedding ? Buffer.from(row.embedding) : null
+    })) as Array<Pick<NodeRow, "id" | "name" | "kind" | "summary" | "embedding">>;
   }

   nodeExists(id: string): boolean {
diff --git a/src/embeddings.ts b/src/embeddings.ts
index fc52ab4..677941c 100644
--- a/src/embeddings.ts
+++ b/src/embeddings.ts
@@ -18,14 +18,28 @@ async function getExtractor(): Promise<FeatureExtractionPipeline> {
 }

 /**
- * Generate an embedding vector for the given text.
+ * Generate an embedding vector for given text.
  * Returns a Buffer containing float32 values.
  */
 export async function embed(text: string): Promise<Buffer> {
+  // Validate input text
+  if (!text || text.trim().length === 0) {
+    throw new Error(`Cannot embed empty text`);
+  }
+
   const ext = await getExtractor();
   const output = await ext(text, { pooling: "mean", normalize: true });
   const data = output.data as Float32Array;
-  return Buffer.from(data.buffer, data.byteOffset, data.byteLength);
+  const buffer = Buffer.from(data.buffer, data.byteOffset, data.byteLength);
+
+  // Validate embedding has correct dimensions
+  if (buffer.length !== EMBEDDING_DIM * Float32Array.BYTES_PER_ELEMENT) {
+    throw new Error(
+      `Invalid embedding dimension: expected ${EMBEDDING_DIM} floats (${EMBEDDING_DIM * Float32Array.BYTES_PER_ELEMENT} bytes), got ${data.length} floats (${buffer.length} bytes). Text: "${text.substring(0, 50)}..."`
+    );
+  }
+
+  return buffer;
 }

 /**
@@ -91,7 +105,7 @@ export function findTopK(
   topK: number
 ): Array<{ id: string; similarity: number }> {
   const scored = candidates
-    .filter((c) => c.embedding !== null)
+    .filter((c) => c.embedding !== null && c.embedding.length > 0)
     .map((c) => ({
       id: c.id,
       similarity: cosineSimilarity(queryEmbedding, c.embedding!),