From 2282762528af300a80b5df394c9f3e3456b74843 Mon Sep 17 00:00:00 2001
From: Joey Constantino <15789096+joeconstantino@users.noreply.github.com>
Date: Mon, 14 Apr 2025 12:34:54 -0700
Subject: [PATCH] docs: small Tableau docs update (#30827)

Description: small Tableau docs update
Issue: adds required environment variable
Dependencies: tableau-langchain

---------

Co-authored-by: Joe Constantino <joe.constantino@joecons-ltm6v86.internal.salesforce.com>
---
 docs/docs/integrations/tools/tableau.ipynb | 60 +++++-----------------
 1 file changed, 14 insertions(+), 46 deletions(-)

diff --git a/docs/docs/integrations/tools/tableau.ipynb b/docs/docs/integrations/tools/tableau.ipynb
index e82cc80b01d..a9c59951ff1 100644
--- a/docs/docs/integrations/tools/tableau.ipynb
+++ b/docs/docs/integrations/tools/tableau.ipynb
@@ -98,7 +98,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "310d21b3",
    "metadata": {},
    "outputs": [],
@@ -125,7 +125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "ccfb4159-34ac-4816-a8f0-795c5442c0b2",
    "metadata": {},
    "outputs": [],
@@ -148,16 +148,16 @@
     "    \"TABLEAU_JWT_SECRET\"\n",
     ")  # a JWT secret ID (obtained through Tableau's admin UI)\n",
     "tableau_api_version = \"3.21\"  # the current Tableau REST API Version\n",
-    "tableau_user = \"joe.constantino@salesforce.com\"  # replace with the username querying the target Tableau Data Source\n",
+    "tableau_user = \"joe.constantino@salesforce.com\"  # enter the username querying the target Tableau Data Source\n",
     "\n",
     "# For this cookbook we are connecting to the Superstore dataset that comes by default with every Tableau server\n",
     "datasource_luid = (\n",
     "    \"0965e61b-a072-43cf-994c-8c6cf526940d\"  # the target data source for this Tool\n",
     ")\n",
-    "\n",
+    "model_provider = \"openai\"  # the name of the model provider you are using for your Agent\n",
     "# Add variables to control LLM models for the Agent and Tools\n",
     "os.environ[\"OPENAI_API_KEY\"]  # set an your model API key as an environment variable\n",
-    "tooling_llm_model = \"gpt-4o\""
+    "tooling_llm_model = \"gpt-4o-mini\""
    ]
   },
   {
@@ -178,7 +178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "72ee3eca",
    "metadata": {},
    "outputs": [],
@@ -194,6 +194,7 @@
     "    tableau_user=tableau_user,\n",
     "    datasource_luid=datasource_luid,\n",
     "    tooling_llm_model=tooling_llm_model,\n",
+    "    model_provider=model_provider,\n",
     ")\n",
     "\n",
     "# load the List of Tools to be used by the Agent. In this case we will just load our data source Q&A tool.\n",
@@ -211,47 +212,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "06a1d3f7-79a8-452e-b37e-9070d15445b0",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "Here are the results for the states with the highest sales and profits based on the data queried:\n",
-       "\n",
-       "### States with the Most Sales\n",
-       "1. **California**: $457,687.63\n",
-       "2. **New York**: $310,876.27\n",
-       "3. **Texas**: $170,188.05\n",
-       "4. **Washington**: $138,641.27\n",
-       "5. **Pennsylvania**: $116,511.91\n",
-       "\n",
-       "### States with the Most Profit\n",
-       "1. **California**: $76,381.39\n",
-       "2. **New York**: $74,038.55\n",
-       "3. **Washington**: $33,402.65\n",
-       "4. **Michigan**: $24,463.19\n",
-       "5. **Virginia**: $18,597.95\n",
-       "\n",
-       "### Comparison\n",
-       "- **California** and **New York** are the only states that appear in both lists, indicating they are the top sellers and also generate the most profit.\n",
-       "- **Texas**, while having the third highest sales, does not rank in the top five for profit, showing a potential issue with profitability despite high sales.\n",
-       "\n",
-       "This analysis suggests that high sales do not always correlate with high profits, as seen with Texas."
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from IPython.display import Markdown, display\n",
     "\n",
-    "model = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
+    "model = ChatOpenAI(model=\"gpt-4o\", temperature=0)\n",
     "\n",
     "tableauAgent = create_react_agent(model, tools)\n",
     "\n",
@@ -261,13 +229,13 @@
     "        \"messages\": [\n",
     "            (\n",
     "                \"human\",\n",
-    "                \"which states sell the most? Are those the same states with the most profits?\",\n",
+    "                \"what's going on with table sales?\",\n",
     "            )\n",
     "        ]\n",
     "    }\n",
     ")\n",
     "messages\n",
-    "# display(Markdown(messages['messages'][4].content)) #display a nicely formatted answer for successful generations"
+    "# display(Markdown(messages['messages'][3].content)) #display a nicely formatted answer for successful generations"
    ]
   },
   {
@@ -293,9 +261,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python (package_test_env)",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "package_test_env"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {