Add info about Cohere Embed v3 (#392)

qdrant-landing/content/documentation/integrations/cohere.md

@@ -30,10 +30,62 @@ qdrant_client.upsert(
             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.
 
+## Embed v3
+
+Embed v3 is a new family of Cohere models, released in November 2023. The new models require passing an additional 
+parameter to the API call: `input_type`. It determines the type of task you want to use the embeddings for.
+
+- `input_type="search_document"` - for documents to store in Qdrant
+- `input_type="search_query"` - for search queries to find the most relevant documents
+- `input_type="classification"` - for classification tasks
+- `input_type="clustering"` - for text clustering
+
+While implementing semantic search applications, such as RAG, you should use `input_type="search_document"` for the
+indexed documents and `input_type="search_query"` for the search queries. The following example shows how to index
+documents with the Embed v3 model:
+
+```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="embed-english-v3.0",  # New Embed v3 model
+            input_type="search_document",  # Input type for documents
+            texts=["Qdrant is the a vector database written in Rust"],
+        ).embeddings,
+    ),
+)
+```
+
+Once the documents are indexed, you can search for the most relevant documents using the Embed v3 model:
+
+```python
+qdrant_client.search(
+    collection_name="MyCollection",
+    query=cohere_client.embed(
+        model="embed-english-v3.0",  # New Embed v3 model
+        input_type="search_query",  # Input type for search queries
+        texts=["The best vector database"],
+    ).embeddings[0],
+)
+```
+
+<aside role="status">
+According to Cohere's documentation, all v3 models can use dot product, cosine similarity, 
+and Euclidean distance as the similarity metric, as all metrics return identical rankings.
+</aside>