adding more explanation

Author: Ziqun Ye

llm_application/Use_of_Cohere_embed_models_for_Semantic_Search_in_OCI_OpenSearch.ipynb

@@ -139,7 +139,7 @@
    "source": [
     "### Step 3: Create an Index for your Documents\n",
     "\n",
-    "First connect to your OCI search cluster."
+    "First connect to your OCI search cluster. We can use the opensearchpy library to connect to the OpenSearch cluster."
    ]
   },
   {
@@ -164,11 +164,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "First, you must create a k-NN index and set the index.knn parameter to true. This settings tells the plugin to generate native library indexes specifically tailored for k-NN searches. \n",
+    "First, you must create a k-NN index and set the ``index.knn`` parameter to true. This settings tells the plugin to generate native library indexes specifically tailored for k-NN searches. \n",
     "\n",
-    "Next, you must add one or more fields of the knn_vector data type. This example creates an index with one knn_vector and one text. The knn_vector uses lucene fields.\n",
+    "Next, you must add one or more fields of the knn_vector data type. This example creates an index with one ``knn_vector``: ``embedding_vector`` and one ``text``: ``text``. \n",
     "\n",
-    "See [documentation](https://opensearch.org/docs/2.7/search-plugins/knn/knn-index#method-definitions) for more details on parameters' definitions.\n",
+    "The knn_vector uses Lucene fields that specify the configuration of the k-NN search algorithms. It employs the Hierarchical Navigable Small Worlds [HNSW](https://www.pinecone.io/learn/series/faiss/hnsw/) algorithm  for super fast search and fantastic recall and consine similarity to measure distance. \n",
+    "\n",
+    "- ``efSearch`` controls how many entry points will be explored between layers during the search. A higher value of ef_search typically results in a more thorough and potentially higher-quality search, but increased computational cost. \n",
+    "\n",
+    "- ``efConstruction`` controls how many entry points will be explored when building the index. A higher value of \"ef_constructions\" typically results in a higher-quality graph structure but may also increase the computational cost of building the index.\n",
+    "\n",
+    "The ``dimension`` field defines the size of the embedding vector. In our case, we are using embedding vectors returned from the genAI embedding model, which is of length 1024. \n",
+    "\n",
+    "See [documentation](https://opensearch.org/docs/2.8/search-plugins/knn/knn-index#method-definitions) for more details on parameters' definitions. You\n",
     "\n",
     "**Note**: The Lucene engine can support dimension up to 1,024."
    ]