diff --git a/qdrant-landing/content/documentation/embeddings/voyage.md b/qdrant-landing/content/documentation/embeddings/voyage.md
new file mode 100644
index 000000000..a63108b14
--- /dev/null
+++ b/qdrant-landing/content/documentation/embeddings/voyage.md
@@ -0,0 +1,167 @@
+---
+title: Voyage AI
+weight: 1300
+---
+
+# Voyage AI
+
+Qdrant supports working with [Voyage AI](https://voyageai.com/) embeddings. The supported models' list can be found [here](https://docs.voyageai.com/docs/embeddings).
+
+You can generate an API key from the [Voyage AI dashboard](<https://dash.voyageai.com/>) to authenticate the requests.
+
+### Setting up the Qdrant and Voyage clients
+
+```python
+from qdrant_client import QdrantClient
+import voyageai
+
+VOYAGE_API_KEY = "<YOUR_VOYAGEAI_API_KEY>"
+
+qclient = QdrantClient(":memory:")
+vclient = voyageai.Client(api_key=VOYAGE_API_KEY)
+
+texts = [
+    "Qdrant is the best vector search engine!",
+    "Loved by Enterprises and everyone building for low latency, high performance, and scale.",
+]
+```
+
+```typescript
+import {QdrantClient} from '@qdrant/js-client-rest';
+
+const VOYAGEAI_BASE_URL = "https://api.voyageai.com/v1/embeddings"
+const VOYAGEAI_API_KEY = "<YOUR_VOYAGEAI_API_KEY>"
+
+const client = new QdrantClient({ url: 'http://localhost:6333' });
+
+const headers = {
+    "Authorization": "Bearer " + VOYAGEAI_API_KEY,
+    "Content-Type": "application/json"
+}
+
+const texts = [
+    "Qdrant is the best vector search engine!",
+    "Loved by Enterprises and everyone building for low latency, high performance, and scale.",
+]
+```
+
+The following example shows how to embed documents with the [`voyage-large-2`](https://docs.voyageai.com/docs/embeddings#model-choices) model that generates sentence embeddings of size 1536.
+
+### Embedding documents
+
+```python
+response = vclient.embed(texts, model="voyage-large-2", input_type="document")
+```
+
+```typescript
+let body = {
+    "input": texts,
+    "model": "voyage-large-2",
+    "input_type": "document",
+}
+
+let response = await fetch(VOYAGEAI_BASE_URL, {
+    method: "POST",
+    body: JSON.stringify(body),
+    headers
+});
+
+let response_body = await response.json();
+```
+
+### Converting the model outputs to Qdrant points
+
+```python
+from qdrant_client.models import PointStruct
+
+points = [
+    PointStruct(
+        id=idx,
+        vector=embedding,
+        payload={"text": text},
+    )
+    for idx, (embedding, text) in enumerate(zip(response.embeddings, texts))
+]
+```
+
+```typescript
+let points = response_body.data.map((data, i) => {
+    return {
+        id: i,
+        vector: data.embedding,
+        payload: {
+            text: texts[i]
+        }
+    }
+});
+```
+
+### Creating a collection to insert the documents
+
+```python
+from qdrant_client.models import VectorParams, Distance
+
+COLLECTION_NAME = "example_collection"
+
+qclient.create_collection(
+    COLLECTION_NAME,
+    vectors_config=VectorParams(
+        size=1536,
+        distance=Distance.COSINE,
+    ),
+)
+qclient.upsert(COLLECTION_NAME, points)
+```
+
+```typescript
+const COLLECTION_NAME = "example_collection"
+
+await client.createCollection(COLLECTION_NAME, {
+    vectors: {
+        size: 1536,
+        distance: 'Cosine',
+    }
+});
+
+await client.upsert(COLLECTION_NAME, {
+    wait: true,
+    points
+});
+```
+
+### Searching for documents with Qdrant
+
+Once the documents are added, you can search for the most relevant documents.
+
+```python
+response = vclient.embed(
+    ["What is the best to use for vector search scaling?"],
+    model="voyage-large-2",
+    input_type="query",
+)
+
+qclient.search(
+    collection_name=COLLECTION_NAME,
+    query_vector=response.embeddings[0],
+)
+```
+
+```typescript
+body = {
+    "input": ["What is the best to use for vector search scaling?"],
+    "model": "voyage-large-2",
+    "input_type": "query",
+};
+
+response = await fetch(VOYAGEAI_BASE_URL, {
+    method: "POST",
+    body: JSON.stringify(body),
+    headers
+});
+
+response_body = await response.json();
+
+await client.search(COLLECTION_NAME, {
+    vector: response_body.data[0].embedding,
+});
+```
diff --git a/qdrant-landing/content/documentation/frameworks/testcontainers.md b/qdrant-landing/content/documentation/frameworks/testcontainers.md
index fc6a42f72..3198cf8c0 100644
--- a/qdrant-landing/content/documentation/frameworks/testcontainers.md
+++ b/qdrant-landing/content/documentation/frameworks/testcontainers.md
@@ -26,7 +26,6 @@ import (
 qdrantContainer, err := qdrant.RunContainer(ctx, testcontainers.WithImage("qdrant/qdrant"))
 ```
 
-<!--
 ```typescript
 import { QdrantContainer } from "@testcontainers/qdrant";
 
@@ -38,7 +37,6 @@ from testcontainers.qdrant import QdrantContainer
 
 qdrant_container = QdrantContainer("qdrant/qdrant").start()
 ```
--->
 
 Testcontainers modules provide options/methods to configure ENVs, volumes, and virtually everything you can configure in a Docker container.
 
diff --git a/qdrant-landing/content/documentation/quick-start.md b/qdrant-landing/content/documentation/quick-start.md
index c532811d1..32c80e990 100644
--- a/qdrant-landing/content/documentation/quick-start.md
+++ b/qdrant-landing/content/documentation/quick-start.md
@@ -42,9 +42,6 @@ from qdrant_client import QdrantClient
 client = QdrantClient(url="http://localhost:6333")
 ```
 
-```typescript
-```
-
 ```typescript
 import { QdrantClient } from "@qdrant/js-client-rest";
 
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