community: Update UC toolkit documentation to use LangGraph APIs (#26778)

- **Description:** Update UC toolkit documentation to show an example of
using recommended LangGraph agent APIs before the existing LangChain
AgentExecutor example. Tested by manually running the updated example
notebook
- **Dependencies:** No new dependencies

---------

Signed-off-by: Sid Murching <sid.murching@databricks.com>
Co-authored-by: Erick Friis <erick@langchain.dev>

docs/docs/integrations/tools/databricks.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# Databricks Unity Catalog (UC)\n",
     "\n",
-    "This notebook shows how to use UC functions as LangChain tools.\n",
+    "This notebook shows how to use UC functions as LangChain tools, with both LangChain and LangGraph agent APIs.\n",
     "\n",
     "See Databricks documentation ([AWS](https://docs.databricks.com/en/sql/language-manual/sql-ref-syntax-ddl-create-sql-function.html)|[Azure](https://learn.microsoft.com/en-us/azure/databricks/sql/language-manual/sql-ref-syntax-ddl-create-sql-function)|[GCP](https://docs.gcp.databricks.com/en/sql/language-manual/sql-ref-syntax-ddl-create-sql-function.html)) to learn how to create SQL or Python functions in UC. Do not skip function and parameter comments, which are critical for LLMs to call functions properly.\n",
     "\n",
@@ -34,11 +34,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
    "source": [
-    "%pip install --upgrade --quiet databricks-sdk langchain-community mlflow"
+    "%pip install --upgrade --quiet databricks-sdk langchain-community langchain-databricks langgraph mlflow"
    ]
   },
   {
@@ -47,7 +55,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.chat_models.databricks import ChatDatabricks\n",
+    "from langchain_databricks import ChatDatabricks\n",
     "\n",
     "llm = ChatDatabricks(endpoint=\"databricks-meta-llama-3-70b-instruct\")"
    ]
@@ -58,6 +66,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from databricks.sdk import WorkspaceClient\n",
     "from langchain_community.tools.databricks import UCFunctionToolkit\n",
     "\n",
     "tools = (\n",
@@ -76,9 +85,16 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "source": [
-    "(Optional) To increase the retry time for getting a function execution response, set environment variable UC_TOOL_CLIENT_EXECUTION_TIMEOUT. Default retry time value is 120s."
+    "(Optional) To increase the retry time for getting a function execution response, set environment variable UC_TOOL_CLIENT_EXECUTION_TIMEOUT. Default retry time value is 120s.",
+     
+    "## LangGraph agent example"
    ]
   },
   {
@@ -92,9 +108,68 @@
     "os.environ[\"UC_TOOL_CLIENT_EXECUTION_TIMEOUT\"] = \"200\""
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "source": [
+    "## LangGraph agent example"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 4,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'messages': [HumanMessage(content='36939 * 8922.4', additional_kwargs={}, response_metadata={}, id='1a10b10b-8e37-48c7-97a1-cac5006228d5'),\n",
+       "  AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_a8f3986f-4b91-40a3-8d6d-39f431dab69b', 'type': 'function', 'function': {'name': 'main__tools__python_exec', 'arguments': '{\"code\": \"print(36939 * 8922.4)\"}'}}]}, response_metadata={'prompt_tokens': 771, 'completion_tokens': 29, 'total_tokens': 800}, id='run-865c3613-20ba-4e80-afc8-fde1cfb26e5a-0', tool_calls=[{'name': 'main__tools__python_exec', 'args': {'code': 'print(36939 * 8922.4)'}, 'id': 'call_a8f3986f-4b91-40a3-8d6d-39f431dab69b', 'type': 'tool_call'}]),\n",
+       "  ToolMessage(content='{\"format\": \"SCALAR\", \"value\": \"329584533.59999996\\\\n\", \"truncated\": false}', name='main__tools__python_exec', id='8b63d4c8-1a3d-46a5-a719-393b2ef36770', tool_call_id='call_a8f3986f-4b91-40a3-8d6d-39f431dab69b'),\n",
+       "  AIMessage(content='The result of the multiplication is:\\n\\n329584533.59999996', additional_kwargs={}, response_metadata={'prompt_tokens': 846, 'completion_tokens': 22, 'total_tokens': 868}, id='run-22772404-611b-46e4-9956-b85e4a385f0f-0')]}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langgraph.prebuilt import create_react_agent\n",
+    "\n",
+    "agent = create_react_agent(\n",
+    "    llm,\n",
+    "    tools,\n",
+    "    state_modifier=\"You are a helpful assistant. Make sure to use tool for information.\",\n",
+    ")\n",
+    "agent.invoke({\"messages\": [{\"role\": \"user\", \"content\": \"36939 * 8922.4\"}]})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "source": [
+    "## LangChain agent example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -118,7 +193,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -132,7 +207,9 @@
       "Invoking: `main__tools__python_exec` with `{'code': 'print(36939 * 8922.4)'}`\n",
       "\n",
       "\n",
-      "\u001b[0m\u001b[36;1m\u001b[1;3m{\"format\": \"SCALAR\", \"value\": \"329584533.59999996\\n\", \"truncated\": false}\u001b[0m\u001b[32;1m\u001b[1;3mThe result of the multiplication 36939 * 8922.4 is 329,584,533.60.\u001b[0m\n",
+      "\u001b[0m\u001b[36;1m\u001b[1;3m{\"format\": \"SCALAR\", \"value\": \"329584533.59999996\\n\", \"truncated\": false}\u001b[0m\u001b[32;1m\u001b[1;3mThe result of the multiplication is:\n",
+      "\n",
+      "329584533.59999996\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -141,10 +218,10 @@
      "data": {
       "text/plain": [
        "{'input': '36939 * 8922.4',\n",
-       " 'output': 'The result of the multiplication 36939 * 8922.4 is 329,584,533.60.'}"
+       " 'output': 'The result of the multiplication is:\\n\\n329584533.59999996'}"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -153,18 +230,11 @@
     "agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)\n",
     "agent_executor.invoke({\"input\": \"36939 * 8922.4\"})"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "llm",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -178,9 +248,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.11.10"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }