From 5c53ca2afb4de29217f84497a08910ea38c66b65 Mon Sep 17 00:00:00 2001
From: qdrant <qdrant@users.noreply.github.com>
Date: Wed, 30 Nov 2022 14:42:12 +0000
Subject: [PATCH] docs auto-sync

---
 .../content/documentation/integrations.md     | 33 +++++++++++++++++++
 1 file changed, 33 insertions(+)

diff --git a/qdrant-landing/content/documentation/integrations.md b/qdrant-landing/content/documentation/integrations.md
index 33898df40..cd37aa431 100644
--- a/qdrant-landing/content/documentation/integrations.md
+++ b/qdrant-landing/content/documentation/integrations.md
@@ -35,3 +35,36 @@ pip install qdrant-txtai
 ```
 
 The examples and some more information might be found in [qdrant-txtai repository](https://github.com/qdrant/qdrant-txtai).
+
+## Cohere
+Qdrant is compatible with Cohere [co.embed API](https://docs.cohere.ai/reference/embed) and it's official Python SDK that
+might be installed as any other package:
+
+```bash
+pip install cohere
+```
+
+The embeddings returned by co.embed API might be used directly in the Qdrant client's calls:
+
+```python
+import cohere
+import qdrant_client
+
+from qdrant_client.http.models import Batch
+
+cohere_client = cohere.Client("<< your_api_key >>")
+qdrant_client = qdrant_client.QdrantClient()
+qdrant_client.upsert(
+    collection_name="MyCollection",
+    points=Batch(
+        ids=[1],
+        vectors=cohere_client.embed(
+            model="large",
+            texts=["The best vector database"],
+        ).embeddings,
+    )
+)
+```
+
+If you are interested in seeing an end-to-end project created with co.embed API and Qdrant, please check out the
+"[Question Answering as a Service with Cohere and Qdrant](https://qdrant.tech/articles/qa-with-cohere-and-qdrant/)" article.