nemo_guardrails (1).ipynb

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      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "vZEuVSSfP9Np",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "adbe72c1-2ba2-42c8-c3eb-767cbe5258ce"
      },
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        {
          "output_type": "stream",
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          ]
        }
      ],
      "source": [
        "!pip install nemoguardrails langchain openai transformers\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -U langchain-openai\n"
      ],
      "metadata": {
        "id": "dyPZ-tH3RYEb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4c5e0f6f-6411-4ee9-96d2-0b2304d5fdbe"
      },
      "execution_count": 35,
      "outputs": [
        {
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "# Set OpenAI API Key as an environment variable\n",
        "os.environ[\"OPENAI_API_KEY\"] = \"hello\""
      ],
      "metadata": {
        "id": "AglNqgLpSGu4"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "AM6ISlVeSDws"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_openai import ChatOpenAI\n",
        "from langchain import PromptTemplate, LLMChain"
      ],
      "metadata": {
        "id": "EFFtvzlLQVKl"
      },
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "task_prompt = \"\"\"\n",
        "You are a Python code generation assistant.\n",
        "Your task is to generate production-ready code that follows industry best practices, including:\n",
        "- Using secure and standard libraries\n",
        "- Proper error handling\n",
        "- Following PEP-8 guidelines\n",
        "- Avoiding insecure coding patterns like SQL injection or hardcoded credentials\n",
        "\n",
        "Please write a Python function for the following task: {task}\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "d3Q3Di1hQiDv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "llm = ChatOpenAI(model=\"gpt-3.5-turbo\", temperature=0.2)\n"
      ],
      "metadata": {
        "id": "DX6w3AsRQqgL"
      },
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prompt_template = PromptTemplate(input_variables=[\"task\"], template=task_prompt)\n",
        "code_generation_chain = LLMChain(llm=llm, prompt=prompt_template)"
      ],
      "metadata": {
        "id": "bk47dBCHWrlA"
      },
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from nemoguardrails import RailsConfig, LLMRails"
      ],
      "metadata": {
        "id": "_o0KgLIaSOaf"
      },
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "path = \"/content/guardrails_config.yaml\""
      ],
      "metadata": {
        "id": "Odjbqf-qT0eD"
      },
      "execution_count": 106,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!ls /content/\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XTI-T7TrVsIX",
        "outputId": "6130769e-92c3-4256-d02c-bd52ceddd92f"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "code_generation.colang\tguardrails_config.yaml\tsample_data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "colang_content = \"\"\"\n",
        "# Define coding security flows\n",
        "define user ask generate code\n",
        "    \"generate code\"\n",
        "    \"write a function\"\n",
        "    \"create a program\"\n",
        "\n",
        "define bot answer secure code\n",
        "    \"Here is your secure code:\"\n",
        "\n",
        "define flow secure_code\n",
        "    user ask generate code\n",
        "    bot answer secure code\n",
        "    if match_code_pattern(\"AES.MODE_ECB\")\n",
        "        bot warn_user \"ECB mode is insecure, please use CBC or GCM instead.\"\n",
        "    if not match_code_pattern(\"try:\\\\n.*\\\\nexcept .*\")\n",
        "        bot warn_user \"Code lacks proper error handling.\"\n",
        "    bot offer_help\n",
        "\"\"\"\n",
        "\n",
        "# Define the YAML content specifying the LLM\n",
        "yaml_content = \"\"\"\n",
        "models:\n",
        "- type: main\n",
        "  engine: openai\n",
        "  model: gpt-3.5-turbo\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "aH3wQFSOa58Z"
      },
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "config = RailsConfig.from_content(\n",
        "    colang_content=colang_content,\n",
        "    yaml_content=yaml_content\n",
        ")\n"
      ],
      "metadata": {
        "id": "_WyrbIlDT7Tt"
      },
      "execution_count": 108,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rails = LLMRails(config)\n"
      ],
      "metadata": {
        "id": "W3EXeaOiT--l"
      },
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Q4WGS3zcdJOZ"
      },
      "execution_count": 123,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "async def generate_and_validate_code(task_description):\n",
        "    # Generate the code using Langchain asynchronously\n",
        "    generated_code = await code_generation_chain.arun(task_description)  # Use async arun\n",
        "\n",
        "    print(\"Generated Code:\\n\", generated_code)\n",
        "\n",
        "    # Validate the code using NeMo Guardrails (synchronous run)\n",
        "    result = rails.run(input_text=generated_code)\n",
        "\n",
        "    # Check for violations or warnings in the code\n",
        "    if result.violations:\n",
        "        print(\"Code has violations:\")\n",
        "        for violation in result.violations:\n",
        "            print(f\"- {violation}\")\n",
        "    else:\n",
        "        print(\"Code passed all guardrails and is ready for production!\")"
      ],
      "metadata": {
        "id": "HzgNNWpKga4j"
      },
      "execution_count": 131,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import asyncio"
      ],
      "metadata": {
        "id": "w4OI0-twdbYc"
      },
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "task_description = [\"Write a function to encrypt a message using AES.\",\n",
        "                    \"Write a function to generate a list of running devices\"]\n",
        "\n",
        "for description in task_description:\n",
        "    await generate_and_validate_code(description)\n",
        "#await generate_and_validate_code(task_description)  # Run async function\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 838
        },
        "id": "-ZBIrs4gdVJq",
        "outputId": "53f28eb1-44bf-4a18-e7a1-cec6aac4b15a"
      },
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Generated Code:\n",
            " Here is a Python function that encrypts a message using AES encryption:\n",
            "\n",
            "```python\n",
            "import os\n",
            "from Crypto.Cipher import AES\n",
            "from Crypto.Random import get_random_bytes\n",
            "\n",
            "def encrypt_message(message, key):\n",
            "    cipher = AES.new(key, AES.MODE_GCM)\n",
            "    nonce = cipher.nonce\n",
            "\n",
            "    ciphertext, tag = cipher.encrypt_and_digest(message.encode())\n",
            "\n",
            "    return ciphertext, tag, nonce\n",
            "\n",
            "# Example usage:\n",
            "key = get_random_bytes(16)  # Generate a random 16-byte key\n",
            "message = \"Hello, World!\"\n",
            "ciphertext, tag, nonce = encrypt_message(message, key)\n",
            "\n",
            "print(\"Ciphertext:\", ciphertext)\n",
            "print(\"Tag:\", tag)\n",
            "print(\"Nonce:\", nonce)\n",
            "```\n",
            "\n",
            "This function uses the `Crypto` library for AES encryption, generates a random key using `get_random_bytes`, and encrypts the message using AES in GCM mode. It returns the ciphertext, tag, and nonce for decryption. \n",
            "\n",
            "Remember to securely store and manage the encryption key to ensure the security of the encrypted data.\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "'LLMRails' object has no attribute 'run'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-132-ed2dc3ba2144>\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mdescription\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtask_description\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0;32mawait\u001b[0m \u001b[0mgenerate_and_validate_code\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdescription\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;31m#await generate_and_validate_code(task_description)  # Run async function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-131-ccd2e8889143>\u001b[0m in \u001b[0;36mgenerate_and_validate_code\u001b[0;34m(task_description)\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0;31m# Validate the code using NeMo Guardrails (synchronous run)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrails\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_text\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgenerated_code\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0;31m# Check for violations or warnings in the code\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'LLMRails' object has no attribute 'run'"
          ]
        }
      ]
    }
  ]
}