diff --git a/clients/js/src/embeddings/VoyageAIEmbeddingFunction.ts b/clients/js/src/embeddings/VoyageAIEmbeddingFunction.ts
new file mode 100644
index 00000000000..9ee06750b36
--- /dev/null
+++ b/clients/js/src/embeddings/VoyageAIEmbeddingFunction.ts
@@ -0,0 +1,89 @@
+import { IEmbeddingFunction } from "./IEmbeddingFunction";
+
+export enum InputType {
+  DOCUMENT = "document",
+  QUERY = "query",
+}
+
+export class VoyageAIEmbeddingFunction implements IEmbeddingFunction {
+  private modelName: string;
+  private apiUrl: string;
+  private batchSize: number;
+  private truncation?: boolean;
+  private inputType?: InputType;
+  private headers: { [key: string]: string };
+
+  constructor({
+    voyageaiApiKey,
+    modelName,
+    batchSize,
+    truncation,
+    inputType,
+  }: {
+    voyageaiApiKey: string;
+    modelName: string;
+    batchSize?: number;
+    truncation?: boolean;
+    inputType?: InputType;
+  }) {
+    this.apiUrl = "https://api.voyageai.com/v1/embeddings";
+    this.headers = {
+      Authorization: `Bearer ${voyageaiApiKey}`,
+      "Content-Type": "application/json",
+    };
+
+    this.modelName = modelName;
+    this.truncation = truncation;
+    this.inputType = inputType;
+    if (batchSize) {
+      this.batchSize = batchSize;
+    } else {
+      if (modelName in ["voyage-2", "voyage-02"]) {
+        this.batchSize = 72;
+      } else {
+        this.batchSize = 7;
+      }
+    }
+  }
+
+  public async generate(texts: string[]) {
+    try {
+      if (texts.length > this.batchSize) {
+        throw new Error(
+          `The number of texts to embed exceeds the maximum batch size of ${this.batchSize}`
+        );
+      }
+
+      const response = await fetch(this.apiUrl, {
+        method: "POST",
+        headers: this.headers,
+        body: JSON.stringify({
+          input: texts,
+          model: this.modelName,
+          truncation: this.truncation,
+          input_type: this.inputType,
+        }),
+      });
+
+      const data = (await response.json()) as { data: any[]; detail: string };
+      if (!data || !data.data) {
+        throw new Error(data.detail);
+      }
+
+      const embeddings: any[] = data.data;
+      const sortedEmbeddings = embeddings.sort((a, b) => a.index - b.index);
+
+      const embeddingsChunks = sortedEmbeddings.map(
+        (result) => result.embedding
+      );
+
+      return embeddingsChunks;
+    } catch (error) {
+      if (error instanceof Error) {
+        throw new Error(`Error calling VoyageAI API: ${error.message}`);
+      } else {
+        throw new Error(`Error calling VoyageAI API: ${error}`);
+      }
+    }
+  }
+}
diff --git a/clients/js/src/index.ts b/clients/js/src/index.ts
index cef9c9356f0..12ae91f3c26 100644
--- a/clients/js/src/index.ts
+++ b/clients/js/src/index.ts
@@ -2,6 +2,7 @@ export { ChromaClient } from "./ChromaClient";
 export { AdminClient } from "./AdminClient";
 export { CloudClient } from "./CloudClient";
 export { Collection } from "./Collection";
+
 export { IEmbeddingFunction } from "./embeddings/IEmbeddingFunction";
 export { OpenAIEmbeddingFunction } from "./embeddings/OpenAIEmbeddingFunction";
 export { CohereEmbeddingFunction } from "./embeddings/CohereEmbeddingFunction";
@@ -10,6 +11,10 @@ export { DefaultEmbeddingFunction } from "./embeddings/DefaultEmbeddingFunction"
 export { HuggingFaceEmbeddingServerFunction } from "./embeddings/HuggingFaceEmbeddingServerFunction";
 export { JinaEmbeddingFunction } from "./embeddings/JinaEmbeddingFunction";
 export { GoogleGenerativeAiEmbeddingFunction } from "./embeddings/GoogleGeminiEmbeddingFunction";
+export {
+  VoyageAIEmbeddingFunction,
+  InputType,
+} from "./embeddings/VoyageAIEmbeddingFunction";
 export { OllamaEmbeddingFunction } from "./embeddings/OllamaEmbeddingFunction";
 
 export {
diff --git a/clients/js/test/add.collections.test.ts b/clients/js/test/add.collections.test.ts
index 41b3de3fef5..ebd652a3570 100644
--- a/clients/js/test/add.collections.test.ts
+++ b/clients/js/test/add.collections.test.ts
@@ -3,9 +3,11 @@ import chroma from "./initClient";
 import { DOCUMENTS, EMBEDDINGS, IDS } from "./data";
 import { METADATAS } from "./data";
 import { IncludeEnum } from "../src/types";
+
 import { OpenAIEmbeddingFunction } from "../src/embeddings/OpenAIEmbeddingFunction";
 import { CohereEmbeddingFunction } from "../src/embeddings/CohereEmbeddingFunction";
 import { OllamaEmbeddingFunction } from "../src/embeddings/OllamaEmbeddingFunction";
+
 test("it should add single embeddings to a collection", async () => {
   await chroma.reset();
   const collection = await chroma.createCollection({ name: "test" });
diff --git a/clients/js/test/embeddings/voyage.test.ts b/clients/js/test/embeddings/voyage.test.ts
new file mode 100644
index 00000000000..4c220a8758d
--- /dev/null
+++ b/clients/js/test/embeddings/voyage.test.ts
@@ -0,0 +1,60 @@
+import chroma from "../initClient";
+import { DOCUMENTS, IDS } from "../data";
+import { IncludeEnum } from "../../src/types";
+import {
+  VoyageAIEmbeddingFunction,
+  InputType,
+} from "../../src/embeddings/VoyageAIEmbeddingFunction";
+
+if (!process.env.VOYAGE_API_KEY) {
+  test.skip("it should add VoyageAI embeddings", async () => {});
+} else {
+  test("it should add VoyageAI embeddings", async () => {
+    await chroma.reset();
+    const embedder = new VoyageAIEmbeddingFunction({
+      voyageaiApiKey: process.env.VOYAGE_API_KEY || "",
+      modelName: "voyage-2",
+      batchSize: 5,
+      inputType: InputType.DOCUMENT,
+    });
+    const collection = await chroma.createCollection({
+      name: "test",
+      embeddingFunction: embedder,
+    });
+    const embeddings = await embedder.generate(DOCUMENTS);
+    await collection.add({ ids: IDS, embeddings: embeddings });
+    const count = await collection.count();
+    expect(count).toBe(3);
+    expect(embeddings.length).toBe(3);
+    expect(embeddings[0].length).toBe(1024);
+    expect(embeddings[1].length).toBe(1024);
+    expect(embeddings[2].length).toBe(1024);
+    var res = await collection.get({
+      ids: IDS,
+      include: [IncludeEnum.Embeddings],
+    });
+    expect(res.embeddings).toEqual(embeddings); // reverse because of the order of the ids
+  });
+
+  test("it should throw an exception when the batch size is smaller than the number of texts", async () => {
+    await chroma.reset();
+    const embedder = new VoyageAIEmbeddingFunction({
+      voyageaiApiKey: process.env.VOYAGE_API_KEY || "",
+      modelName: "voyage-2",
+      batchSize: 2,
+      inputType: InputType.DOCUMENT,
+    });
+    const collection = await chroma.createCollection({
+      name: "test",
+      embeddingFunction: embedder,
+    });
+    try {
+      const embeddings = await embedder.generate(DOCUMENTS);
+      fail("Should throw an exception");
+    } catch (e: any) {
+      expect(e.message).toBe(
+        "Error calling VoyageAI API: The number of texts to embed exceeds the maximum batch size of 2"
+      );
+    }
+  });
+}