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

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    {
      "cell_type": "raw",
      "id": "67db2992",
      "metadata": {},
      "source": [
        "---\n",
        "sidebar_label: __ModuleName__\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9597802c",
      "metadata": {},
      "source": [
        "# __ModuleName__LLM\n",
        "\n",
        "- [ ] TODO: Make sure API reference link is correct\n",
        "\n",
        "This will help you get started with __ModuleName__ completion models (LLMs) using LangChain. For detailed documentation on `__ModuleName__LLM` features and configuration options, please refer to the [API reference](https://api.python.langchain.com/en/latest/llms/__module_name__.llms.__ModuleName__LLM.html).\n",
        "\n",
        "## Overview\n",
        "### Integration details\n",
        "\n",
        "- TODO: Fill in table features.\n",
        "- TODO: Remove JS support link if not relevant, otherwise ensure link is correct.\n",
        "- TODO: Make sure API reference links are correct.\n",
        "\n",
        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/llms/__package_name_short_snake__) | Package downloads | Package latest |\n",
        "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
        "| [__ModuleName__LLM](https://api.python.langchain.com/en/latest/llms/__module_name__.llms.__ModuleName__LLM.html) | [__package_name__](https://api.python.langchain.com/en/latest/__package_name_short_snake___api_reference.html) | ✅/❌ | beta/❌ | ✅/❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/__package_name__?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/__package_name__?style=flat-square&label=%20) |\n",
        "\n",
        "## Setup\n",
        "\n",
        "- TODO: Update with relevant info.\n",
        "\n",
        "To access __ModuleName__ models you'll need to create a/an __ModuleName__ account, get an API key, and install the `__package_name__` integration package.\n",
        "\n",
        "### Credentials\n",
        "\n",
        "- TODO: Update with relevant info.\n",
        "\n",
        "Head to (TODO: link) to sign up to __ModuleName__ and generate an API key. Once you've done this set the __MODULE_NAME___API_KEY environment variable:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bc51e756",
      "metadata": {},
      "outputs": [],
      "source": [
        "import getpass\n",
        "import os\n",
        "\n",
        "if not os.getenv(\"__MODULE_NAME___API_KEY\"):\n",
        "    os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\"Enter your __ModuleName__ API key: \")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4b6e1ca6",
      "metadata": {},
      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "196c2b41",
      "metadata": {},
      "outputs": [],
      "source": [
        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "809c6577",
      "metadata": {},
      "source": [
        "### Installation\n",
        "\n",
        "The LangChain __ModuleName__ integration lives in the `__package_name__` package:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "59c710c4",
      "metadata": {},
      "outputs": [],
      "source": [
        "%pip install -qU __package_name__"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0a760037",
      "metadata": {},
      "source": [
        "## Instantiation\n",
        "\n",
        "Now we can instantiate our model object and generate chat completions:\n",
        "\n",
        "- TODO: Update model instantiation with relevant params."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a0562a13",
      "metadata": {},
      "outputs": [],
      "source": [
        "from __module_name__ import __ModuleName__LLM\n",
        "\n",
        "llm = __ModuleName__LLM(\n",
        "    model=\"model-name\",\n",
        "    temperature=0,\n",
        "    max_tokens=None,\n",
        "    timeout=None,\n",
        "    max_retries=2,\n",
        "    # other params...\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0ee90032",
      "metadata": {},
      "source": [
        "## Invocation\n",
        "\n",
        "- [ ] TODO: Run cells so output can be seen."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "035dea0f",
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "input_text = \"__ModuleName__ is an AI company that \"\n",
        "\n",
        "completion = llm.invoke(input_text)\n",
        "completion"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "add38532",
      "metadata": {},
      "source": [
        "## Chaining\n",
        "\n",
        "We can [chain](/docs/how_to/sequence/) our completion model with a prompt template like so:\n",
        "\n",
        "- TODO: Run cells so output can be seen."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "078e9db2",
      "metadata": {},
      "outputs": [],
      "source": [
        "from langchain_core.prompts import PromptTemplate\n",
        "\n",
        "prompt = PromptTemplate(\n",
        "    \"How to say {input} in {output_language}:\\n\"\n",
        ")\n",
        "\n",
        "chain = prompt | llm\n",
        "chain.invoke(\n",
        "    {\n",
        "        \"output_language\": \"German\",\n",
        "        \"input\": \"I love programming.\",\n",
        "    }\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e99eef30",
      "metadata": {},
      "source": [
        "## TODO: Any functionality specific to this model provider\n",
        "\n",
        "E.g. creating/using finetuned models via this provider. Delete if not relevant"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e9bdfcef",
      "metadata": {},
      "source": [
        "## API reference\n",
        "\n",
        "For detailed documentation of all `__ModuleName__LLM` features and configurations head to the API reference: https://api.python.langchain.com/en/latest/llms/__module_name__.llms.__ModuleName__LLM.html"
      ]
    }
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