updated screenshots with updated cost cards and replace previous model service with nvidia/llama models

Author: giriraj-singh-couchbase

autovec_unstructured/autovec_unstructured.ipynb

@@ -17,7 +17,9 @@
    "metadata": {},
    "source": [
     "# 1. Create and Deploy Your Operational cluster on Capella\n",
-    "To get started with Couchbase Capella, create an account and use it to deploy a cluster. To know more, please follow the [instructions](https://docs.couchbase.com/cloud/get-started/create-account.html).\n",
+    "To get started with Couchbase Capella, create an account and use it to deploy a cluster. \n",
+    "\n",
+    "Make sure that you deploy a `Multi-node` cluster with `data`, `index`, `query` and `eventing` services enabled. To know more, please follow the [instructions](https://docs.couchbase.com/cloud/get-started/create-account.html).\n",
     "    \n",
     "### Couchbase Capella Configuration\n",
     "When running Couchbase using [Capella](https://cloud.couchbase.com/sign-in), the following prerequisites need to be met.\n",
@@ -154,7 +156,11 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "0298d27f-ee03-4de2-829d-b653c39746a9",
-   "metadata": {},
+   "metadata": {
+    "vscode": {
+     "languageId": "powershell"
+    }
+   },
    "outputs": [],
    "source": [
     "!pip install couchbase langchain-couchbase langchain-openai"
@@ -233,19 +239,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "1d77404b",
    "metadata": {},
    "outputs": [],
    "source": [
     "bucket_name = \"Unstructured_data_bucket\"\n",
     "scope_name = \"_default\"\n",
     "collection_name = \"_default\"\n",
-    "index_name = \"search_autovec_workflow_text-embedding\"       # This is the name of the search index that was created in step 3.6 and can also be seen in the search tab of the cluster.\n",
+    "index_name = \"hyperscale_autovec_workflow_text-embedding\"       # This is the name of the search index that was created in step 3.6 and can also be seen in the search tab of the cluster.\n",
     "                                                                                        \n",
     "#  Using the OpenAI SDK for the embeddings with the capella model services and they are compatible with the OpenAIEmbeddings class in Langchain\n",
     "embedder = OpenAIEmbeddings(\n",
-    "    model=\"nvidia/nv-embedqa-e5-v5\",                        # This is the model that will be used to create the embedding of the query.\n",
+    "    model=\"nvidia/llama-3.2-nv-embedqa-1b-v2\",                        # This is the model that will be used to create the embedding of the query.\n",
     "    openai_api_key=\"CAPELLA_MODEL_KEY\",\n",
     "    openai_api_base=\"CAPELLA_MODEL_ENDPOINT/v1\",\n",
     "    check_embedding_ctx_length=False,\n",
@@ -341,7 +347,7 @@
     "- Content text: This is the value of the field you configured as `text_key` (in this tutorial: `text-to-embed`). It represents the human-readable content we chose to display.\n",
     "\n",
     "### How the Ranking Works\n",
-    "1. Your natural language query (e.g., `query = \"How to setup java SDK?\"`) is embedded using the NVIDIA model (`nvidia/nv-embedqa-e5-v5`).\n",
+    "1. Your natural language query (e.g., `query = \"How to setup java SDK?\"`) is embedded using the NVIDIA model (`nvidia/llama-3.2-nv-embedqa-1b-v2`).\n",
     "2. The vector store compares the query embedding to stored document embeddings in the field you configured (`embedding_key = \"text-embedding\"`).\n",
     "3. Results are sorted by vector similarity. Higher similarity = closer semantic meaning.\n",
     "\n",