docs: highlight tool name and modify spell

1. highlight "Qdrant"
2. modify "REST" to "RESTful"

examples/vector_databases/qdrant/Getting_started_with_Qdrant_and_OpenAI.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# Using Qdrant as a vector database for OpenAI embeddings\n",
     "\n",
-    "This notebook guides you step by step on using Qdrant as a vector database for OpenAI embeddings. [Qdrant](https://qdrant.tech) is a high-performant vector search database written in Rust. It offers REST and gRPC APIs to manage your embeddings. There is an official Python [qdrant-client](https://github.com/qdrant/qdrant_client) that eases the integration with your apps.\n",
+    "This notebook guides you step by step on using **`Qdrant`** as a vector database for OpenAI embeddings. [Qdrant](https://qdrant.tech) is a high-performant vector search database written in Rust. It offers RESTful and gRPC APIs to manage your embeddings. There is an official Python [qdrant-client](https://github.com/qdrant/qdrant_client) that eases the integration with your apps.\n",
     "\n",
     "This notebook presents an end-to-end process of:\n",
     "1. Using precomputed embeddings created by OpenAI API.\n",
@@ -28,7 +28,7 @@
     "\n",
     "### Integration\n",
     "\n",
-    "[Qdrant](https://qdrant.tech) provides both REST and gRPC APIs which makes integration easy, no matter the programming language you use. However, there are some official clients for the most popular languages available, and if you use Python then the [Python Qdrant client library](https://github.com/qdrant/qdrant_client) might be the best choice."
+    "[Qdrant](https://qdrant.tech) provides both RESTful and gRPC APIs which makes integration easy, no matter the programming language you use. However, there are some official clients for the most popular languages available, and if you use Python then the [Python Qdrant client library](https://github.com/qdrant/qdrant_client) might be the best choice."
    ]
   },
   {