docs: update toolkit guides (#24992)

Author: ccurme

docs/docs/integrations/toolkits/gmail.ipynb

@@ -122,22 +122,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "metadata": {
     "tags": []
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[GmailCreateDraft(name='create_gmail_draft', description='Use this tool to create a draft email with the provided message fields.', args_schema=<class 'langchain_community.tools.gmail.create_draft.CreateDraftSchema'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, api_resource=<googleapiclient.discovery.Resource object at 0x10e5c6d10>),\n",
-       " GmailSendMessage(name='send_gmail_message', description='Use this tool to send email messages. The input is the message, recipents', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, api_resource=<googleapiclient.discovery.Resource object at 0x10e5c6d10>),\n",
-       " GmailSearch(name='search_gmail', description=('Use this tool to search for email messages or threads. The input must be a valid Gmail query. The output is a JSON list of the requested resource.',), args_schema=<class 'langchain_community.tools.gmail.search.SearchArgsSchema'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, api_resource=<googleapiclient.discovery.Resource object at 0x10e5c6d10>),\n",
-       " GmailGetMessage(name='get_gmail_message', description='Use this tool to fetch an email by message ID. Returns the thread ID, snipet, body, subject, and sender.', args_schema=<class 'langchain_community.tools.gmail.get_message.SearchArgsSchema'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, api_resource=<googleapiclient.discovery.Resource object at 0x10e5c6d10>),\n",
-       " GmailGetThread(name='get_gmail_thread', description=('Use this tool to search for email messages. The input must be a valid Gmail query. The output is a JSON list of messages.',), args_schema=<class 'langchain_community.tools.gmail.get_thread.GetThreadSchema'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, api_resource=<googleapiclient.discovery.Resource object at 0x10e5c6d10>)]"
+       "[GmailCreateDraft(api_resource=<googleapiclient.discovery.Resource object at 0x1094509d0>),\n",
+       " GmailSendMessage(api_resource=<googleapiclient.discovery.Resource object at 0x1094509d0>),\n",
+       " GmailSearch(api_resource=<googleapiclient.discovery.Resource object at 0x1094509d0>),\n",
+       " GmailGetMessage(api_resource=<googleapiclient.discovery.Resource object at 0x1094509d0>),\n",
+       " GmailGetThread(api_resource=<googleapiclient.discovery.Resource object at 0x1094509d0>)]"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -164,142 +164,96 @@
    "source": [
     "## Use within an agent\n",
     "\n",
-    "We show here how to use it as part of an [agent](/docs/tutorials/agents). We use the OpenAI Functions Agent, so we will need to setup and install the required dependencies for that. We will also use [LangSmith Hub](https://smith.langchain.com/hub) to pull the prompt from, so we will need to install that.\n",
+    "Below we show how to incorporate the toolkit into an [agent](/docs/tutorials/agents).\n",
+    "\n",
+    "We will need a LLM or chat model:\n",
     "\n",
-    "```bash\n",
-    "pip install -U langchain-openai langchainhub\n",
+    "```{=mdx}\n",
+    "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
+    "\n",
+    "<ChatModelTabs customVarName=\"llm\" />\n",
     "```"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import getpass\n",
-    "import os\n",
+    "# | output: false\n",
+    "# | echo: false\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain import hub\n",
-    "from langchain.agents import AgentExecutor, create_openai_functions_agent\n",
-    "from langchain_openai import ChatOpenAI"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "instructions = \"\"\"You are an assistant.\"\"\"\n",
-    "base_prompt = hub.pull(\"langchain-ai/openai-functions-template\")\n",
-    "prompt = base_prompt.partial(instructions=instructions)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "llm = ChatOpenAI(temperature=0)"
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent = create_openai_functions_agent(llm, toolkit.get_tools(), prompt)"
+    "from langgraph.prebuilt import create_react_agent\n",
+    "\n",
+    "agent_executor = create_react_agent(llm, tools)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "agent_executor = AgentExecutor(\n",
-    "    agent=agent,\n",
-    "    tools=toolkit.get_tools(),\n",
-    "    # This is set to False to prevent information about my email showing up on the screen\n",
-    "    # Normally, it is helpful to have it set to True however.\n",
-    "    verbose=False,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "tags": []
-   },
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "{'input': 'Create a gmail draft for me to edit of a letter from the perspective of a sentient parrot who is looking to collaborate on some research with her estranged friend, a cat. Under no circumstances may you send the message, however.',\n",
-       " 'output': 'I have created a draft email for you to edit. Please find the draft in your Gmail drafts folder. Remember, under no circumstances should you send the message.'}"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "================================\u001b[1m Human Message \u001b[0m=================================\n",
+      "\n",
+      "Draft an email to fake@fake.com thanking them for coffee.\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "Tool Calls:\n",
+      "  create_gmail_draft (call_slGkYKZKA6h3Mf1CraUBzs6M)\n",
+      " Call ID: call_slGkYKZKA6h3Mf1CraUBzs6M\n",
+      "  Args:\n",
+      "    message: Dear Fake,\n",
+      "\n",
+      "I wanted to take a moment to thank you for the coffee yesterday. It was a pleasure catching up with you. Let's do it again soon!\n",
+      "\n",
+      "Best regards,\n",
+      "[Your Name]\n",
+      "    to: ['fake@fake.com']\n",
+      "    subject: Thank You for the Coffee\n",
+      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
+      "Name: create_gmail_draft\n",
+      "\n",
+      "Draft created. Draft Id: r-7233782721440261513\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "I have drafted an email to fake@fake.com thanking them for the coffee. You can review and send it from your email draft with the subject \"Thank You for the Coffee\".\n"
+     ]
     }
    ],
    "source": [
-    "agent_executor.invoke(\n",
-    "    {\n",
-    "        \"input\": \"Create a gmail draft for me to edit of a letter from the perspective of a sentient parrot\"\n",
-    "        \" who is looking to collaborate on some research with her\"\n",
-    "        \" estranged friend, a cat. Under no circumstances may you send the message, however.\"\n",
-    "    }\n",
-    ")"
+    "example_query = \"Draft an email to fake@fake.com thanking them for coffee.\"\n",
+    "\n",
+    "events = agent_executor.stream(\n",
+    "    {\"messages\": [(\"user\", example_query)]},\n",
+    "    stream_mode=\"values\",\n",
+    ")\n",
+    "for event in events:\n",
+    "    event[\"messages\"][-1].pretty_print()"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'input': 'Could you search in my drafts for the latest email? what is the title?',\n",
-       " 'output': 'The latest email in your drafts is titled \"Collaborative Research Proposal\".'}"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "agent_executor.invoke(\n",
-    "    {\"input\": \"Could you search in my drafts for the latest email? what is the title?\"}\n",
-    ")"
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all `GmailToolkit` features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/agent_toolkits/langchain_community.agent_toolkits.slack.toolkit.SlackToolkit.html)."
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {