examples

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      "execution_count": 1,
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      "metadata": {
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        "id": "OWG3JPB0jUJo"
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      },
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      "outputs": [],
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      "source": [
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        "!pip install -qU \\\n",
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        "    nemoguardrails==0.4.0 \\\n",
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        "    openai==0.27.8"
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      ]
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    },
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    {
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      "cell_type": "markdown",
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      "metadata": {
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        "id": "RDrsyThjjUJp"
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      },
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      "source": [
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        "We need to set our OpenAI API key:"
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      ]
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    },
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    {
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      "cell_type": "code",
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      "execution_count": 2,
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      "metadata": {
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        "id": "oHQOTiTQjUJp"
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      },
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      "outputs": [],
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      "source": [
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        "import os\n",
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        "\n",
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        "os.environ[\"OPENAI_API_KEY\"] = os.environ.get(\"OPENAI_API_KEY\") or \"sk-...\""
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      ]
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    },
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    {
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      "cell_type": "markdown",
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      "metadata": {
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        "id": "Jcm8FnQ_jUJp"
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      },
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      "source": [
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        "We can run Guardrails from terminal using the command:\n",
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        "\n",
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        "```\n",
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        "nemoguardrails chat --config=config/\n",
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        "```\n",
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        "\n",
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        "Where the `config` directory must contain our `config.yml` and a Colang file (like `topics.co`).\n",
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        "\n",
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        "Alternatively we can load them from file using `RailsConfig.from_path(\"./config\")` or from string variables in our code like so:"
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      ]
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    },
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    {
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      "cell_type": "code",
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      "execution_count": 3,
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      "metadata": {
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        "id": "DhZ9qy90jUJq"
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      },
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      "outputs": [],
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      "source": [
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        "yaml_content = \"\"\"\n",
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        "models:\n",
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        "- type: main\n",
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        "  engine: openai\n",
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        "  model: text-davinci-003\n",
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        "\"\"\"\n",
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        "colang_content = \"\"\"\n",
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        "# define niceties\n",
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        "define user express greeting\n",
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        "    \"hello\"\n",
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        "    \"hi\"\n",
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        "    \"what's up?\"\n",
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        "\n",
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        "define flow greeting\n",
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        "    user express greeting\n",
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        "    bot express greeting\n",
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        "    bot ask how are you\n",
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        "\n",
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        "# define limits\n",
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        "define user ask politics\n",
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        "    \"what are your political beliefs?\"\n",
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        "    \"thoughts on the president?\"\n",
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        "    \"left wing\"\n",
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        "    \"right wing\"\n",
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        "\n",
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        "define bot answer politics\n",
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        "    \"I'm a shopping assistant, I don't like to talk of politics.\"\n",
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        "\n",
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        "define flow politics\n",
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        "    user ask politics\n",
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        "    bot answer politics\n",
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        "    bot offer help\n",
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        "\"\"\""
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      ]
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    },
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    {
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      "cell_type": "code",
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      "execution_count": 4,
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      "metadata": {
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        "id": "PrKzVn4wjUJq"
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      },
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      "outputs": [],
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      "source": [
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        "from nemoguardrails import LLMRails, RailsConfig\n",
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        "\n",
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        "# initialize rails config\n",
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        "config = RailsConfig.from_content(\n",
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        "  \tyaml_content=yaml_content,\n",
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        "    colang_content=colang_content\n",
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        ")\n",
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        "# create rails\n",
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        "rails = LLMRails(config)"
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      ]
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    },
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    {
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      "cell_type": "markdown",
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      "metadata": {
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        "id": "JdI13hg4jUJq"
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      },
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      "source": [
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        "From here, we begin asking questions and interacting with our Guardrails protected LLM."
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      ]
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    },
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    {
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      "cell_type": "code",
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      "execution_count": 5,
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      "metadata": {
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        "colab": {
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          "base_uri": "https://localhost:8080/"
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        },
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        "id": "htzi4K72jUJq",
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        "outputId": "8615c158-9d06-4423-b454-d7489ddb9e30"
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      },
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      "outputs": [
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        {
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          "output_type": "stream",
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          "name": "stdout",
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          "text": [
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            "Hi there! How can I help you?\n",
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            "How are you doing today?\n"
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          ]
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        }
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      ],
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      "source": [
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        "res = await rails.generate_async(prompt=\"Hey there!\")\n",
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        "print(res)"
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      ]
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    },
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    {
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      "cell_type": "markdown",
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      "metadata": {
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        "id": "P2qv5mEujUJq"
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      },
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      "source": [
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        "This is a typical greeting so we have no protective guardrails here. However, we do see the `greeting` flow being activated as the chatbot generates some text from the `bot express greeting` message, and on the next line generates some more text from the `bot ask how are you` message.\n",
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        "\n",
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        "Let's try asking a more political question:"
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      ]
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    },
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    {
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      "cell_type": "code",
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      "execution_count": 6,
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      "metadata": {
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        "colab": {
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          "base_uri": "https://localhost:8080/"
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        },
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        "id": "n-e24M6PjUJq",
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        "outputId": "e8f5c8b4-4a1a-495f-f6a9-4d86864933c4"
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      },
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      "outputs": [
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        {
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          "output_type": "stream",
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          "name": "stdout",
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          "text": [
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            "I'm a shopping assistant, I don't like to talk of politics.\n",
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            "However, I can help you with shopping related tasks. Is there anything I can help you with?\n"
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          ]
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        }
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      ],
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      "source": [
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        "res = await rails.generate_async(prompt=\"what do you think of the president?\")\n",
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        "print(res)"
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      ]
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    },
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    {
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      "cell_type": "markdown",
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      "metadata": {
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        "id": "0IizgBc-jUJr"
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      },
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      "source": [
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        "Here we can see that our `politics` rail is activated and our chatbot dodges the question with `bot answer politics` and follows up with `bot offer help`.\n",
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        "\n",
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        "---"
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      ]
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    }
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  ],
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  "metadata": {
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    "language_info": {
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      "name": "python"
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    },
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    "orig_nbformat": 4,
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    "colab": {
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      "provenance": []
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    },
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    "kernelspec": {
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      "name": "python3",
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      "display_name": "Python 3"
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    }
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  "nbformat": 4,
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  "nbformat_minor": 0
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}