diff --git a/qdrant-landing/content/documentation/embeddings/openai.md b/qdrant-landing/content/documentation/embeddings/openai.md
index ca8291ddf..24e715412 100644
--- a/qdrant-landing/content/documentation/embeddings/openai.md
+++ b/qdrant-landing/content/documentation/embeddings/openai.md
@@ -1,46 +1,103 @@
 ---
-title: OpenAI
+title: OpenAI 
 weight: 800
 aliases: [ ../integrations/openai/ ]
 ---
 
 # OpenAI
 
-Qdrant can also easily work with [OpenAI embeddings](https://platform.openai.com/docs/guides/embeddings/embeddings). 
+Qdrant supports working with [OpenAI embeddings](https://platform.openai.com/docs/guides/embeddings/embeddings).
 
-There is an official OpenAI Python package that simplifies obtaining them, and it might be installed with pip:
+There is an official OpenAI Python package that simplifies obtaining them, and it can be installed with pip:
 
 ```bash
 pip install openai
 ```
 
-Once installed, the package exposes the method allowing to retrieve the embedding for given text. OpenAI requires an API key that has to be provided either as an environmental variable `OPENAI_API_KEY` or set in the source code directly, as presented below:
+### Setting up the OpenAI and Qdrant clients
 
 ```python
 import openai
 import qdrant_client
 
-from qdrant_client.http.models import Batch
-
-# Choose one of the available models:
-# https://platform.openai.com/docs/models/embeddings
-embedding_model = "text-embedding-ada-002"
-
 openai_client = openai.Client(
-    api_key="<< your_api_key >>"
-)
-response = openai_client.embeddings.create(
-    input="The best vector database",
-    model=embedding_model,
+    api_key="<YOUR_API_KEY>"
 )
 
-qdrant_client = qdrant_client.QdrantClient()
-qdrant_client.upsert(
-    collection_name="MyCollection",
-    points=Batch(
-        ids=[1],
-        vectors=[response.data[0].embedding],
+qdrant_client = qdrant_client.QdrantClient(":memory:")
+
+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 a document with the `text-embedding-3-small` model that generates sentence embeddings of size 1536. You can find the list of all supported models [here](https://platform.openai.com/docs/models/embeddings).
+
+### Embedding a document
+
+```python
+embedding_model = "text-embedding-3-small"
+
+result = openai_client.embeddings.create(input= texts, model=embedding_model)
+```
+
+### Converting the model outputs to Qdrant points
+
+```python
+from qdrant_client.http.models import PointStruct
+
+points = [
+    PointStruct(
+        id=idx,
+        vector=data.embedding,
+        payload={"text": text},
+    )
+    for idx, (data, text) in enumerate(zip(result.data, texts))
+]
+```
+
+### Creating a collection to insert the documents
+
+```python
+from qdrant_client.http.models import VectorParams, Distance
+
+collection_name = "example_collection"
+
+qdrant_client.create_collection(
+    collection_name,
+    vectors_config=VectorParams(
+        size=1536,
+        distance=Distance.COSINE,
     ),
 )
+qdrant_client.upsert(collection_name, points)
+```
+
+## Searching for documents with Qdrant
+
+Once the documents are indexed, you can search for the most relevant documents using the same model.
+
+```python
+qdrant_client.search(
+    collection_name=collection_name,
+    query_vector=openai_client.embeddings.create(
+        input=["What is the best to use for vector search scaling?"],
+        model=embedding_model,
+    )
+    .data[0]
+    .embedding,
+)
 ```
 
+## Using OpenAI Embedding Models with Qdrant's Binary Quantization
+
+You can use OpenAI embedding Models with [Binary Quantization](/articles/binary-quantization/) - a technique that allows you to reduce the size of the embeddings by 32 times without losing the quality of the search results too much.
+
+
+|Method|Dimensionality|Test Dataset|Recall|Oversampling|
+|-|-|-|-|-|
+|OpenAI text-embedding-3-large|3072|[DBpedia 1M](https://huggingface.co/datasets/Qdrant/dbpedia-entities-openai3-text-embedding-3-large-3072-1M) | 0.9966|3x|
+|OpenAI text-embedding-3-small|1536|[DBpedia 100K](https://huggingface.co/datasets/Qdrant/dbpedia-entities-openai3-text-embedding-3-small-1536-100K)| 0.9847|3x|
+|OpenAI text-embedding-3-large|1536|[DBpedia 1M](https://huggingface.co/datasets/Qdrant/dbpedia-entities-openai3-text-embedding-3-large-1536-1M)| 0.9826|3x|
+|OpenAI text-embedding-ada-002|1536|[DbPedia 1M](https://huggingface.co/datasets/KShivendu/dbpedia-entities-openai-1M) |0.98|4x|