diff --git a/qdrant-landing/content/documentation/embeddings/_index.md b/qdrant-landing/content/documentation/embeddings/_index.md
index 4d999e661..9e31684ba 100644
--- a/qdrant-landing/content/documentation/embeddings/_index.md
+++ b/qdrant-landing/content/documentation/embeddings/_index.md
@@ -1,36 +1,37 @@
-
 ---
 title: Embeddings
 weight: 19
 partition: build
 ---
+
 # Supported Embedding Providers & Models
 
-Qdrant supports all available text and multimodal dense vector embedding models as well as vector embedding services without any limitations. 
+Qdrant supports all available text and multimodal dense vector embedding models as well as vector embedding services without any limitations.
 
-## Some of the Embeddings you can use with Qdrant:
+## Some of the Embeddings you can use with Qdrant
 
 SentenceTransformers, BERT, SBERT, Clip, OpenClip, Open AI, Vertex AI, Azure AI, AWS Bedrock, Jina AI, Upstage AI, Mistral AI, Cohere AI, Voyage AI, Aleph Alpha, Baidu Qianfan, BGE, Instruct, Watsonx Embeddings, Snowflake Embeddings, NVIDIA NeMo, Nomic, OCI Embeddings, Ollama Embeddings, MixedBread, Together AI, Clarifai, Databricks Embeddings, GPT4All Embeddings, John Snow Labs Embeddings.
 
-Additionally, [any open-source embeddings from HuggingFace](https://huggingface.co/spaces/mteb/leaderboard) can be used with Qdrant. 
+Additionally, [any open-source embeddings from HuggingFace](https://huggingface.co/spaces/mteb/leaderboard) can be used with Qdrant.
 
-## Code samples:
+## Code samples
 
-| Embeddings Providers          | Description |
-| ----------------------------- | ----------- |
-| [Aleph Alpha](/documentation/embeddings/aleph-alpha/) | Multilingual embeddings focused on European languages. |
-| [Bedrock](/documentation/embeddings/bedrock/)         | AWS managed service for foundation models and embeddings. |
-| [Cohere](/documentation/embeddings/cohere/)           | Language model embeddings for NLP tasks. |
-| [Gemini](/documentation/embeddings/gemini/)           | Google’s multimodal embeddings for text and vision. 
-| [Jina AI](/documentation/embeddings/jina-embeddings/)     | Customizable embeddings for neural search. |
-| [Mistral](/documentation/embeddings/mistral/)         | Open-source, efficient language model embeddings. |
-| [MixedBread](/documentation/embeddings/mixedbread/)   | Lightweight embeddings for constrained environments. |
-| [Mixpeek](/documentation/embeddings/mixpeek/)         | Managed SDK for video chunking, embedding, and post-processing.​ |
-| [Nomic](/documentation/embeddings/nomic/)             | Embeddings for data visualization. |
-| [Nvidia](/documentation/embeddings/nvidia/)      | GPU-optimized embeddings from Nvidia. |
-| [Ollama](/documentation/embeddings/ollama/)           | Embeddings for conversational AI. |
-| [OpenAI](/documentation/embeddings/openai/)           | Industry-leading embeddings for NLP. |
-| [Prem AI](/documentation/embeddings/premai/)          | Precise language embeddings. |
-| [Snowflake](/documentation/embeddings/snowflake/)     | Scalable embeddings for big data. |
-| [Upstage](/documentation/embeddings/upstage/)         | Embeddings for speech and language tasks. |
-| [Voyage AI](/documentation/embeddings/voyage/)        | Navigation and spatial understanding embeddings. |
+| Embeddings Providers                                  | Description                                                      |
+| ----------------------------------------------------- | ---------------------------------------------------------------- |
+| [Aleph Alpha](/documentation/embeddings/aleph-alpha/) | Multilingual embeddings focused on European languages.           |
+| [Bedrock](/documentation/embeddings/bedrock/)         | AWS managed service for foundation models and embeddings.        |
+| [Cohere](/documentation/embeddings/cohere/)           | Language model embeddings for NLP tasks.                         |
+| [Gemini](/documentation/embeddings/gemini/)           | Google’s multimodal embeddings for text and vision.              |
+| [Jina AI](/documentation/embeddings/jina-embeddings/) | Customizable embeddings for neural search.                       |
+| [Mistral](/documentation/embeddings/mistral/)         | Open-source, efficient language model embeddings.                |
+| [MixedBread](/documentation/embeddings/mixedbread/)   | Lightweight embeddings for constrained environments.             |
+| [Mixpeek](/documentation/embeddings/mixpeek/)         | Managed SDK for video chunking, embedding, and post-processing. ​ |
+| [Nomic](/documentation/embeddings/nomic/)             | Embeddings for data visualization.                               |
+| [Nvidia](/documentation/embeddings/nvidia/)           | GPU-optimized embeddings from Nvidia.                            |
+| [Ollama](/documentation/embeddings/ollama/)           | Embeddings for conversational AI.                                |
+| [OpenAI](/documentation/embeddings/openai/)           | Industry-leading embeddings for NLP.                             |
+| [Prem AI](/documentation/embeddings/premai/)          | Precise language embeddings.                                     |
+| [Twelve Labs](/documentation/embeddings/twelvelabs/)  | Multimodal embeddings from Twelve labs.                          |
+| [Snowflake](/documentation/embeddings/snowflake/)     | Scalable embeddings for big data.                                |
+| [Upstage](/documentation/embeddings/upstage/)         | Embeddings for speech and language tasks.                        |
+| [Voyage AI](/documentation/embeddings/voyage/)        | Navigation and spatial understanding embeddings.                 |
diff --git a/qdrant-landing/content/documentation/embeddings/aleph-alpha.md b/qdrant-landing/content/documentation/embeddings/aleph-alpha.md
index 82dced4a0..4597f71a1 100644
--- a/qdrant-landing/content/documentation/embeddings/aleph-alpha.md
+++ b/qdrant-landing/content/documentation/embeddings/aleph-alpha.md
@@ -1,6 +1,5 @@
 ---
 title: Aleph Alpha
-weight: 900
 aliases:
   - /documentation/examples/aleph-alpha-search/
   - /documentation/tutorials/aleph-alpha-search/
diff --git a/qdrant-landing/content/documentation/embeddings/bedrock.md b/qdrant-landing/content/documentation/embeddings/bedrock.md
index f99129e3a..dee32b145 100644
--- a/qdrant-landing/content/documentation/embeddings/bedrock.md
+++ b/qdrant-landing/content/documentation/embeddings/bedrock.md
@@ -1,6 +1,5 @@
 ---
 title: AWS Bedrock
-weight: 1000
 ---
 
 # Bedrock Embeddings
diff --git a/qdrant-landing/content/documentation/embeddings/cohere.md b/qdrant-landing/content/documentation/embeddings/cohere.md
index f3a8487b1..8e4e14f0a 100644
--- a/qdrant-landing/content/documentation/embeddings/cohere.md
+++ b/qdrant-landing/content/documentation/embeddings/cohere.md
@@ -1,6 +1,5 @@
 ---
 title: Cohere
-weight: 1400
 aliases: [ ../integrations/cohere/ ]
 ---
 
diff --git a/qdrant-landing/content/documentation/embeddings/gemini.md b/qdrant-landing/content/documentation/embeddings/gemini.md
index e00220f7a..a8b89a45a 100644
--- a/qdrant-landing/content/documentation/embeddings/gemini.md
+++ b/qdrant-landing/content/documentation/embeddings/gemini.md
@@ -1,6 +1,5 @@
 ---
 title: Gemini
-weight: 1600
 ---
 
 | Time: 10 min | Level: Beginner | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://githubtocolab.com/qdrant/examples/blob/gemini-getting-started/gemini-getting-started/gemini-getting-started.ipynb)   |
diff --git a/qdrant-landing/content/documentation/embeddings/jina-embeddings.md b/qdrant-landing/content/documentation/embeddings/jina-embeddings.md
index db82d7f20..24518acf2 100644
--- a/qdrant-landing/content/documentation/embeddings/jina-embeddings.md
+++ b/qdrant-landing/content/documentation/embeddings/jina-embeddings.md
@@ -1,6 +1,5 @@
 ---
 title: Jina Embeddings
-weight: 1900
 aliases: 
   - /documentation/embeddings/jina-embeddings/
   - /documentation/integrations/jina-embeddings/
diff --git a/qdrant-landing/content/documentation/embeddings/mistral.md b/qdrant-landing/content/documentation/embeddings/mistral.md
index e1fdbfa87..42a061b35 100644
--- a/qdrant-landing/content/documentation/embeddings/mistral.md
+++ b/qdrant-landing/content/documentation/embeddings/mistral.md
@@ -1,6 +1,5 @@
 ---
 title: Mistral 
-weight: 2100
 ---
 
 | Time: 10 min | Level: Beginner | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://githubtocolab.com/qdrant/examples/blob/mistral-getting-started/mistral-embed-getting-started/mistral_qdrant_getting_started.ipynb)   |
diff --git a/qdrant-landing/content/documentation/embeddings/mixedbread.md b/qdrant-landing/content/documentation/embeddings/mixedbread.md
index c30d705ba..197604a66 100644
--- a/qdrant-landing/content/documentation/embeddings/mixedbread.md
+++ b/qdrant-landing/content/documentation/embeddings/mixedbread.md
@@ -1,6 +1,5 @@
 ---
 title: MixedBread
-weight: 2200
 ---
 
 # Using MixedBread with Qdrant 
diff --git a/qdrant-landing/content/documentation/embeddings/mixpeek.md b/qdrant-landing/content/documentation/embeddings/mixpeek.md
index 5dacecb8f..633d54df9 100644
--- a/qdrant-landing/content/documentation/embeddings/mixpeek.md
+++ b/qdrant-landing/content/documentation/embeddings/mixpeek.md
@@ -1,6 +1,5 @@
 ---
 title: Mixpeek
-weight: 2250
 ---
 
 # Mixpeek Video Embeddings
diff --git a/qdrant-landing/content/documentation/embeddings/nomic.md b/qdrant-landing/content/documentation/embeddings/nomic.md
index a7764c61d..92b3c54ce 100644
--- a/qdrant-landing/content/documentation/embeddings/nomic.md
+++ b/qdrant-landing/content/documentation/embeddings/nomic.md
@@ -1,6 +1,5 @@
 ---
 title: "Nomic"
-weight: 2300
 ---
 
 # Nomic
diff --git a/qdrant-landing/content/documentation/embeddings/nvidia.md b/qdrant-landing/content/documentation/embeddings/nvidia.md
index c68647044..df22f748b 100644
--- a/qdrant-landing/content/documentation/embeddings/nvidia.md
+++ b/qdrant-landing/content/documentation/embeddings/nvidia.md
@@ -1,6 +1,5 @@
 ---
 title: Nvidia
-weight: 2400
 ---
 
 # Nvidia
diff --git a/qdrant-landing/content/documentation/embeddings/ollama.md b/qdrant-landing/content/documentation/embeddings/ollama.md
index 17b56ec4d..1a97b3a66 100644
--- a/qdrant-landing/content/documentation/embeddings/ollama.md
+++ b/qdrant-landing/content/documentation/embeddings/ollama.md
@@ -1,6 +1,5 @@
 ---
 title: Ollama
-weight: 2600
 ---
 
 # Using Ollama with Qdrant
diff --git a/qdrant-landing/content/documentation/embeddings/openai.md b/qdrant-landing/content/documentation/embeddings/openai.md
index 3f7074bed..afa17ba78 100644
--- a/qdrant-landing/content/documentation/embeddings/openai.md
+++ b/qdrant-landing/content/documentation/embeddings/openai.md
@@ -1,6 +1,5 @@
 ---
 title: OpenAI 
-weight: 2700
 aliases: [ ../integrations/openai/ ]
 ---
 
diff --git a/qdrant-landing/content/documentation/embeddings/premai.md b/qdrant-landing/content/documentation/embeddings/premai.md
index cda863568..f315e7472 100644
--- a/qdrant-landing/content/documentation/embeddings/premai.md
+++ b/qdrant-landing/content/documentation/embeddings/premai.md
@@ -1,6 +1,5 @@
 ---
 title: Prem AI
-weight: 2800
 ---
 
 # Prem AI
diff --git a/qdrant-landing/content/documentation/embeddings/snowflake.md b/qdrant-landing/content/documentation/embeddings/snowflake.md
index 22aa6a06e..09dd6b15c 100644
--- a/qdrant-landing/content/documentation/embeddings/snowflake.md
+++ b/qdrant-landing/content/documentation/embeddings/snowflake.md
@@ -1,6 +1,5 @@
 ---
 title: Snowflake Models
-weight: 2900
 ---
 
 # Snowflake
diff --git a/qdrant-landing/content/documentation/embeddings/twelvelabs.md b/qdrant-landing/content/documentation/embeddings/twelvelabs.md
new file mode 100644
index 000000000..730aac8ad
--- /dev/null
+++ b/qdrant-landing/content/documentation/embeddings/twelvelabs.md
@@ -0,0 +1,173 @@
+---
+title: Twelve Labs
+---
+
+# Twelve Labs
+
+[Twelve Labs](https://twelvelabs.io) Embed API provides powerful embeddings that represent videos, texts, images, and audio in a unified vector space. This space enables any-to-any searches across different types of content.
+
+By natively processing all modalities, it captures interactions like visual expressions, speech, and context, enabling advanced applications such as sentiment analysis, anomaly detection, and recommendation systems with precision and efficiency.
+
+We'll look at how to work with Twelve Labs embeddings in Qdrant via the Python and Node SDKs.
+
+### Installing the SDKs
+
+```python
+$ pip install twelvelabs qdrant-client
+```
+
+```typescript
+$ npm install twelvelabs-js @qdrant/js-client-rest
+```
+
+### Setting up the clients
+
+```python
+from twelvelabs import TwelveLabs
+from qdrant_client import QdrantClient
+
+# Get your API keys from:
+# https://playground.twelvelabs.io/dashboard/api-key
+TL_API_KEY = "<YOUR_TWELVE_LABS_API_KEY>"
+
+twelvelabs_client = TwelveLabs(api_key=TL_API_KEY)
+qdrant_client = QdrantClient(url="http://localhost:6333/")
+```
+
+```typescript
+import { QdrantClient } from '@qdrant/js-client-rest';
+import { TwelveLabs, EmbeddingsTask, SegmentEmbedding } from 'twelvelabs';
+
+// Get your API keys from:
+// https://playground.twelvelabs.io/dashboard/api-key
+const TL_API_KEY = "<YOUR_TWELVE_LABS_API_KEY>"
+
+const twelveLabsClient = new TwelveLabs({ apiKey: TL_API_KEY });
+const qdrantClient = new QdrantClient({ url: 'http://localhost:6333' });
+```
+
+The following example uses the `"Marengo-retrieval-2.6"` engine to embed a video. It generates vector embeddings of 1024 dimensionality and works with cosine similarity.
+
+You can use the same engine to embed audio, text and images into a common vector space. Enabling cross-modality searches!
+
+### Embedding videos
+
+```python
+task = twelvelabs_client.embed.task.create(
+    engine_name="Marengo-retrieval-2.6",
+    video_url="https://sample-videos.com/video321/mp4/720/big_buck_bunny_720p_2mb.mp4"
+)
+
+task.wait_for_done(sleep_interval=3)
+
+task_result = twelvelabs_client.embed.task.retrieve(task.id)
+```
+
+```typescript
+const task = await twelveLabsClient.embed.task.create("Marengo-retrieval-2.6", {
+    url: "https://sample-videos.com/video321/mp4/720/big_buck_bunny_720p_2mb.mp4"
+})
+
+await task.waitForDone(3)
+
+const taskResult = await twelveLabsClient.embed.task.retrieve(task.id)
+```
+
+### Converting the model outputs to Qdrant points
+
+```python
+from qdrant_client.models import PointStruct
+
+points = [
+    PointStruct(
+        id=idx,
+        vector=v.embeddings_float,
+        payload={
+            "start_offset_sec": v.start_offset_sec,
+            "end_offset_sec": v.end_offset_sec,
+            "embedding_scope": v.embedding_scope,
+        },
+    )
+    for idx, v in enumerate(task_result.video_embedding.segments)
+]
+```
+
+```typescript
+let points = taskResult.videoEmbedding.segments.map((data, i) => {
+    return {
+        id: i,
+        vector: data.embeddingsFloat,
+        payload: {
+            startOffsetSec: data.startOffsetSec,
+            endOffsetSec: data.endOffsetSec,
+            embeddingScope: data.embeddingScope
+        }
+    }
+})
+```
+
+### Creating a collection to insert the vectors
+
+```python
+from qdrant_client.models import VectorParams, Distance
+
+collection_name = "twelve_labs_collection"
+
+qdrant_client.create_collection(
+    collection_name,
+    vectors_config=VectorParams(
+        size=1024,
+        distance=Distance.COSINE,
+    ),
+)
+qdrant_client.upsert(collection_name, points)
+```
+
+```typescript
+const COLLECTION_NAME = "twelve_labs_collection"
+
+await qdrantClient.createCollection(COLLECTION_NAME, {
+    vectors: {
+        size: 1024,
+        distance: 'Cosine',
+    }
+});
+
+await qdrantClient.upsert(COLLECTION_NAME, {
+    wait: true,
+    points
+})
+```
+
+## Perform a search
+
+Once the vectors are added, you can run semantic searches across different modalities. Let's try text.
+
+```python
+segment = twelvelabs_client.embed.create(
+    engine_name="Marengo-retrieval-2.6",
+    text="<YOUR_QUERY_TEXT>",
+).text_embedding.segments[0]
+
+
+qdrant_client.query_points(
+    collection_name=collection_name,
+    query=segment.embeddings_float,
+)
+```
+
+```typescript
+const segment = (await twelveLabsClient.embed.create({
+    engineName: "Marengo-retrieval-2.6",
+    text: "<YOUR_QUERY_TEXT>"
+})).textEmbedding.segments[0]
+
+await qdrantClient.query(COLLECTION_NAME, {
+    query: segment.embeddingsFloat,
+});
+```
+
+## Further Reading
+
+- [Twelve Labs Documentation](https://docs.twelvelabs.io/)
+- [Twelve Labs Examples](https://docs.twelvelabs.io/docs/sample-applications)
diff --git a/qdrant-landing/content/documentation/embeddings/upstage.md b/qdrant-landing/content/documentation/embeddings/upstage.md
index 2a24d52c3..a32711063 100644
--- a/qdrant-landing/content/documentation/embeddings/upstage.md
+++ b/qdrant-landing/content/documentation/embeddings/upstage.md
@@ -1,6 +1,5 @@
 ---
 title: Upstage
-weight: 3100
 ---
 
 # Upstage
diff --git a/qdrant-landing/content/documentation/embeddings/voyage.md b/qdrant-landing/content/documentation/embeddings/voyage.md
index 024804467..ed0d06cd8 100644
--- a/qdrant-landing/content/documentation/embeddings/voyage.md
+++ b/qdrant-landing/content/documentation/embeddings/voyage.md
@@ -1,6 +1,5 @@
 ---
 title: Voyage AI
-weight: 3200
 ---
 
 # Voyage AI