diff --git a/package.json b/package.json
index da52d32..2909f34 100644
--- a/package.json
+++ b/package.json
@@ -1,6 +1,6 @@
 {
 	"name": "@ztimson/ai-utils",
-	"version": "0.7.2",
+	"version": "0.7.3",
 	"description": "AI Utility library",
 	"author": "Zak Timson",
 	"license": "MIT",
diff --git a/src/llm.ts b/src/llm.ts
index a951b3d..4a9f0eb 100644
--- a/src/llm.ts
+++ b/src/llm.ts
@@ -255,12 +255,11 @@ class LLM {
 	/**
 	 * Create a vector representation of a string
 	 * @param {object | string} target Item that will be embedded (objects get converted)
-	 * @param {maxTokens?: number, overlapTokens?: number, parellel?: number} opts Options for embedding such as chunk sizes and parallel processing
+	 * @param {maxTokens?: number, overlapTokens?: number} opts Options for embedding such as chunk sizes
 	 * @returns {Promise<Awaited<{index: number, embedding: number[], text: string, tokens: number}>[]>} Chunked embeddings
 	 */
-	async embedding(target: object | string, opts: {maxTokens?: number, overlapTokens?: number, parallel?: number} = {}) {
-		let {maxTokens = 500, overlapTokens = 50, parallel = 1} = opts;
-
+	async embedding(target: object | string, opts: {maxTokens?: number, overlapTokens?: number} = {}) {
+		let {maxTokens = 500, overlapTokens = 50} = opts;
 		const embed = (text: string): Promise<number[]> => {
 			return new Promise((resolve, reject) => {
 				const worker = new Worker(join(dirname(fileURLToPath(import.meta.url)), 'embedder.js'));
@@ -280,19 +279,13 @@ class LLM {
 				worker.postMessage({text, model: this.ai.options?.embedder || 'bge-small-en-v1.5', modelDir: this.ai.options.path});
 			});
 		};
-		let i = 0, chunks = this.chunk(target, maxTokens, overlapTokens), results: any[] = [];
-		const next: Function = () => {
-			const index = i++;
-			if(index >= chunks.length) return;
-			const text = chunks[index];
-			return embed(text).then(embedding => {
-				results.push({index, embedding, text, tokens: this.estimateTokens(text)});
-				return next();
-			})
+		const chunks = this.chunk(target, maxTokens, overlapTokens), results: any[] = [];
+		for(let i = 0; i < chunks.length; i++) {
+			const text= chunks[i];
+			const embedding = await embed(text);
+			results.push({index: i, embedding, text, tokens: this.estimateTokens(text)});
 		}
-
-		await Promise.all(Array(parallel).fill(null).map(() => next()));
-		return results.toSorted((a, b) => a.index - b.index);
+		return results;
 	}
 
 	/**