Apply suggestions from code review

Co-authored-by: Marci Windsheimer <[email protected]>

notebooks/integrations/cohere/cohere-elasticsearch.ipynb

@@ -9,9 +9,9 @@
     "\n",
     "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/elastic/elasticsearch-labs/blob/main/notebooks/integrations/cohere/cohere-elasticsearch.ipynb)\n",
     "\n",
-    "The instructions in this tutorial shows you how to compute embeddings with\n",
+    "This tutorial shows you how to compute embeddings with\n",
     "Cohere using the inference API and store them for efficient vector or hybrid\n",
-    "search in Elasticsearch. This tutorial will use the Python Elasticsearch client\n",
+    "search in Elasticsearch. This tutorial uses the Python Elasticsearch client\n",
     "to perform the operations.\n",
     "\n",
     "You'll learn how to:\n",
@@ -42,7 +42,7 @@
     "- A paid [Cohere account](https://cohere.com/) is required to use the Inference API with \n",
     "the Cohere service as the Cohere free trial API usage is limited.\n",
     "\n",
-    "- Python 3.7 or higher."
+    "- Python 3.7 or later."
    ]
   },
   {
@@ -118,7 +118,7 @@
     "ELASTIC_CLOUD_ID = getpass(\"Elastic Cloud ID: \")\n",
     "\n",
     "# https://www.elastic.co/search-labs/tutorials/install-elasticsearch/elastic-cloud#creating-an-api-key\n",
-    "ELASTIC_API_KEY = getpass(\"Elastic Api key: \")\n",
+    "ELASTIC_API_KEY = getpass(\"Elastic API key: \")\n",
     "\n",
     "# Create the client instance\n",
     "client = Elasticsearch(\n",
@@ -139,8 +139,8 @@
    "source": [
     "## Create the inference endpoint\n",
     "\n",
-    "Create the inference endpoint first. In this example, the inference endpoint will \n",
-    "use Cohere's `embed-english-v3.0` model and the `embedding_type` is set to\n",
+    "Create the inference endpoint first. In this example, the inference endpoint \n",
+    "uses Cohere's `embed-english-v3.0` model and the `embedding_type` is set to\n",
     "`byte`."
    ]
   },
@@ -228,7 +228,7 @@
     "## Create the inference pipeline\n",
     "\n",
     "Now you have an inference endpoint and an index ready to store embeddings. The next\n",
-    "step is to create an ingest pipeline that will create the embeddings using the\n",
+    "step is to create an ingest pipeline that creates the embeddings using the\n",
     "inference endpoint and stores them in the index."
    ]
   },
@@ -324,8 +324,8 @@
     "Let's start querying the index!\n",
     "\n",
     "The code below performs a hybrid search. The `kNN` query computes the relevance\n",
-    "of search results based on vector similarity using the `text_embedding` field,\n",
-    "the lexical search query uses BM25 retrieval to compute keyword similarity on\n",
+    "of search results based on vector similarity using the `text_embedding` field.\n",
+    "The lexical search query uses BM25 retrieval to compute keyword similarity on\n",
     "the `title` and `text` fields."
    ]
   },
@@ -491,8 +491,8 @@
     "## Retrieval Augmented Generation (RAG) with Cohere and Elasticsearch\n",
     "\n",
     "RAG is a method for generating text using additional information fetched from an\n",
-    "external data source. With the ranked results, you can build a RAG system on the\n",
-    "top of what you previously created by using \n",
+    "external data source. With the ranked results, you can build a RAG system on\n",
+    "top of what you created with \n",
     "[Cohere's Chat API](https://docs.cohere.com/docs/chat-api).\n",
     "\n",
     "Pass in the retrieved documents and the query to receive a grounded response\n",