langchain/libs/cli/langchain_cli/integration_template/docs/tools.ipynb

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  {
   "cell_type": "raw",
   "id": "10238e62-3465-4973-9279-606cbb7ccf16",
   "metadata": {},
   "source": [
    "---\n",
    "sidebar_label: __ModuleName__\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6f91f20",
   "metadata": {},
   "source": [
    "# __ModuleName__\n",
    "\n",
    "- TODO: Make sure API reference link is correct.\n",
    "\n",
    "This notebook provides a quick overview for getting started with __ModuleName__ [tool](/docs/integrations/tools/). For detailed documentation of all __ModuleName__ features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.__module_name__.tool.__ModuleName__.html).\n",
    "\n",
    "- TODO: Add any other relevant links, like information about underlying API, etc.\n",
    "\n",
    "## Overview\n",
    "\n",
    "### Integration details\n",
    "\n",
    "- TODO: Make sure links and features are correct\n",
    "\n",
    "| Class | Package | Serializable | [JS support](https://js.langchain.com/docs/integrations/tools/__module_name__) |  Package latest |\n",
    "| :--- | :--- | :---: | :---: | :---: |\n",
    "| [__ModuleName__](https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.__module_name__.tool.__ModuleName__.html) | [langchain-community](https://api.python.langchain.com/en/latest/community_api_reference.html) | beta/❌ | ✅/❌ |  ![PyPI - Version](https://img.shields.io/pypi/v/langchain-community?style=flat-square&label=%20) |\n",
    "\n",
    "### Tool features\n",
    "\n",
    "- TODO: Add feature table if it makes sense\n",
    "\n",
    "\n",
    "## Setup\n",
    "\n",
    "- TODO: Add any additional deps\n",
    "\n",
    "The integration lives in the `langchain-community` package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f85b4089",
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install --quiet -U langchain-community"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b15e9266",
   "metadata": {},
   "source": [
    "### Credentials\n",
    "\n",
    "- TODO: Add any credentials that are needed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e0b178a2-8816-40ca-b57c-ccdd86dde9c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import getpass\n",
    "import os\n",
    "\n",
    "# if not os.environ.get(\"__MODULE_NAME___API_KEY\"):\n",
    "#     os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\"__MODULE_NAME__ API key:\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc5ab717-fd27-4c59-b912-bdd099541478",
   "metadata": {},
   "source": [
    "It's also helpful (but not needed) to set up [LangSmith](https://smith.langchain.com/) for best-in-class observability:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a6c2f136-6367-4f1f-825d-ae741e1bf281",
   "metadata": {},
   "outputs": [],
   "source": [
    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c97218f-f366-479d-8bf7-fe9f2f6df73f",
   "metadata": {},
   "source": [
    "## Instantiation\n",
    "\n",
    "- TODO: Fill in instantiation params\n",
    "\n",
    "Here we show how to instantiate an instance of the __ModuleName__ tool, with "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8b3ddfe9-ca79-494c-a7ab-1f56d9407a64",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_community.tools import __ModuleName__\n",
    "\n",
    "\n",
    "tool = __ModuleName__(\n",
    "    ...\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74147a1a",
   "metadata": {},
   "source": [
    "## Invocation\n",
    "\n",
    "### [Invoke directly with args](/docs/concepts/tools/#use-the-tool-directly)\n",
    "\n",
    "- TODO: Describe what the tool args are, fill them in, run cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65310a8b-eb0c-4d9e-a618-4f4abe2414fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "tool.invoke({...})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6e73897",
   "metadata": {},
   "source": [
    "### [Invoke with ToolCall](/docs/concepts/tool_calling/#tool-execution)\n",
    "\n",
    "We can also invoke the tool with a model-generated ToolCall, in which case a ToolMessage will be returned:\n",
    "\n",
    "- TODO: Fill in tool args and run cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f90e33a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is usually generated by a model, but we'll create a tool call directly for demo purposes.\n",
    "model_generated_tool_call = {\n",
    "    \"args\": {...},  # TODO: FILL IN\n",
    "    \"id\": \"1\",\n",
    "    \"name\": tool.name,\n",
    "    \"type\": \"tool_call\",\n",
    "}\n",
    "tool.invoke(model_generated_tool_call)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "659f9fbd-6fcf-445f-aa8c-72d8e60154bd",
   "metadata": {},
   "source": [
    "## Use within an agent\n",
    "\n",
    "- TODO: Add user question and run cells\n",
    "\n",
    "We can use our tool in an [agent](/docs/concepts/agents/). For this we will need a LLM with [tool-calling](/docs/how_to/tool_calling/) capabilities:\n",
    "\n",
    "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
    "\n",
    "<ChatModelTabs customVarName=\"llm\" />\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "af3123ad-7a02-40e5-b58e-7d56e23e5830",
   "metadata": {},
   "outputs": [],
   "source": [
    "# | output: false\n",
    "# | echo: false\n",
    "\n",
    "# !pip install -qU langchain langchain-openai\n",
    "from langchain.chat_models import init_chat_model\n",
    "\n",
    "llm = init_chat_model(model=\"gpt-4o\", model_provider=\"openai\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bea35fa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langgraph.prebuilt import create_react_agent\n",
    "\n",
    "tools = [tool]\n",
    "agent = create_react_agent(llm, tools)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fdbf35b5-3aaf-4947-9ec6-48c21533fb95",
   "metadata": {},
   "outputs": [],
   "source": [
    "example_query = \"...\"\n",
    "\n",
    "events = agent.stream(\n",
    "    {\"messages\": [(\"user\", example_query)]},\n",
    "    stream_mode=\"values\",\n",
    ")\n",
    "for event in events:\n",
    "    event[\"messages\"][-1].pretty_print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ac8146c",
   "metadata": {},
   "source": [
    "## API reference\n",
    "\n",
    "For detailed documentation of all __ModuleName__ features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.__module_name__.tool.__ModuleName__.html"
   ]
  }
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