Merge pull request #1077 from qdrant/embeddings-addition

Embeddings addition

qdrant-landing/content/documentation/concepts/vectors.md

@@ -416,8 +416,9 @@ await client.SearchAsync(
 
 **Available as of v1.10.0**
 
-Qdrant supports the storing of a variable amount of same-shaped dense vectors in a single point.
-That means that instead of a single dense vector, you can upload a matrix of dense vectors.
+Qdrant supports the storing of a variable amount of same-shaped dense vectors in a single point. 
+Each vector can be of a different type and be generated by a different embedding model.
+This means that instead of a single dense vector, you can upload a matrix of dense vectors.
 
 The length of the matrix is fixed, but the number of vectors in the matrix can be different for each point.
 

qdrant-landing/content/documentation/embeddings/_index.md

@@ -2,6 +2,17 @@
 title: Embeddings
 weight: 15
 ---
+# Supported Embedding Providers & Models
+
+Qdrant supports all available text and multimodal dense vector embedding models as well as vector embedding services without any limitations. 
+
+## The following have been tested and verified:
+
+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. 
+
+## Integration code samples:
 
 | Embeddings Providers          |
 | ----------------------------- |