3_2_LLAMA2_Moderation_Chat.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "gC-aV7J4K_uR",
        "jp-MarkdownHeadingCollapsed": true
      },
      "source": [
        "<div>\n",
        "    <h1>Large Language Models Projects</a></h1>\n",
        "    <h3>Apply and Implement Strategies for Large Language Models</h3>\n",
        "    <h2>3.2-Create a Moderation System using LangChain.</h2>\n",
        "    <h3>LLAMA 2 version</h3>\n",
        "  </div>\n",
        "\n",
        "by [Pere Martra](https://www.linkedin.com/in/pere-martra/)\n",
        "\n",
        "________\n",
        "Model: Llama-2-7b-chat-hf\n",
        "\n",
        "Colab Environment: GPU-T4 High-RAM. (You can use a more advanced and powerful GPU).\n",
        "\n",
        "Keys:\n",
        "* Lagchain\n",
        "* Llama\n",
        "* Moderator."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gIV3BBY_LULN"
      },
      "source": [
        "# How To Create a Moderation System Using LangChain & Hugging Face.\n",
        "\n",
        "We are going to create a moderation system based on two models.\n",
        "\n",
        "The first model reads user comments and generates responses.\n",
        "\n",
        "The second language model then analyzes the generated response, identifying any negativity and modifying the response if necessary.\n",
        "\n",
        "This process aims to prevent negative or inappropriate user input from triggering a similarly negative or off-tone response from the comment system."
      ]
    },
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          "base_uri": "https://localhost:8080/"
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      "outputs": [
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m270.9/270.9 kB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.3/21.3 MB\u001b[0m \u001b[31m73.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "#Install de LangChain and openai libraries.\n",
        "!pip install -q langchain==0.1.4\n",
        "!pip install -q transformers==4.37.1\n",
        "!pip install -q accelerate==0.26.1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TWbKM8HiZLep",
        "outputId": "beca7044-8aa4-4044-ab08-612e2dab2e28"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting huggingface_hub==0.20.2\n",
            "  Downloading huggingface_hub-0.20.2-py3-none-any.whl (330 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/330.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m122.9/330.3 kB\u001b[0m \u001b[31m3.6 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m330.3/330.3 kB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface_hub==0.20.2) (3.15.4)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub==0.20.2) (2023.6.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface_hub==0.20.2) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub==0.20.2) (4.66.4)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub==0.20.2) (6.0.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub==0.20.2) (4.12.2)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub==0.20.2) (23.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub==0.20.2) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub==0.20.2) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub==0.20.2) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub==0.20.2) (2024.7.4)\n",
            "Installing collected packages: huggingface_hub\n",
            "  Attempting uninstall: huggingface_hub\n",
            "    Found existing installation: huggingface-hub 0.23.5\n",
            "    Uninstalling huggingface-hub-0.23.5:\n",
            "      Successfully uninstalled huggingface-hub-0.23.5\n",
            "Successfully installed huggingface_hub-0.20.2\n"
          ]
        }
      ],
      "source": [
        "!pip install huggingface_hub==0.20.2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "cKgYdJv7qPGs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "333eaa40-b7bb-4d91-8179-e54d92dc2521"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Hugging Face Key: ··········\n"
          ]
        }
      ],
      "source": [
        "from getpass import getpass\n",
        "hf_key = getpass(\"Hugging Face Key: \")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q5AkbmVRp6Iq",
        "outputId": "b8640e59-4931-4bff-cb68-41472a55b6b5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Token will not been saved to git credential helper. Pass `add_to_git_credential=True` if you want to set the git credential as well.\n",
            "Token is valid (permission: read).\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ],
      "source": [
        "!huggingface-cli login --token $hf_key"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1R1QSNGpOYCK"
      },
      "source": [
        "## Importing LangChain Libraries.\n",
        "* PrompTemplate: provides functionality to create prompts with parameters."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "CuBxdjshLYDM"
      },
      "outputs": [],
      "source": [
        "from langchain import PromptTemplate\n",
        "from langchain_core.output_parsers import StrOutputParser\n",
        "\n",
        "from langchain.llms import HuggingFacePipeline\n",
        "import transformers\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "AIfbM8wcOh1h"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from torch import cuda"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "BoYi2-wHfZrV"
      },
      "outputs": [],
      "source": [
        "#In a MAC Silicon the device must be 'mps'\n",
        "# device = torch.device('mps') #to use with MAC Silicon\n",
        "device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "pLvC0ENnfiHU",
        "outputId": "012ca97e-1232-43e9-eeaf-7ca830507c86"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'cuda:0'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "device"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "axOsUJ79MMrr"
      },
      "source": [
        "##Load the Model ."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "D6UOQjyJP9rb"
      },
      "outputs": [],
      "source": [
        "#You can try with any llama model, but you will need more GPU and memory as you\n",
        "#increase the size of the model.\n",
        "model_id = \"meta-llama/Llama-2-7b-chat-hf\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352,
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        "id": "ONwHLj5Lwfde",
        "outputId": "9cb62954-9a2d-42f2-b252-1a8d57d6d69c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/614 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ed02748424fc4eef933c1a3b5fcd0d3a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5764ec0f9d1148e0ba6706a0d292f6ad"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dc3a0576fb2f42c39734f852b719070b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00001-of-00002.safetensors:   0%|          | 0.00/9.98G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7e84fdb43e404290920ba7fb3c6fd287"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "model-00002-of-00002.safetensors:   0%|          | 0.00/3.50G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bd02037be8634c1aaefea2f2c493f0cb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cd0cc9acff714abbba879fbd8140cb58"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "15a07d8f2f264a5ab4d75467e71b0ea4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:root:Some parameters are on the meta device device because they were offloaded to the cpu.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model loaded on cuda:0\n"
          ]
        }
      ],
      "source": [
        "# begin initializing HF items, need auth token for these\n",
        "model_config = transformers.AutoConfig.from_pretrained(\n",
        "    model_id,\n",
        "    token=hf_key\n",
        ")\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_id,\n",
        "    trust_remote_code=True,\n",
        "    config=model_config,\n",
        "    device_map='auto',\n",
        "    token=hf_key\n",
        ")\n",
        "model.eval()\n",
        "print(f\"Model loaded on {device}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "bbf4f1c8dc7a4f20ad0264ae0cd75ddc",
            "91a98875a00b408290e1bd10787a8890",
            "a83d729337c84942aadace123dc68df2",
            "4c073a9a0c484bd18259b2c366daaf7b",
            "d838a5f5b11f42e188c54348bed169b3",
            "8d7d0976fa1946d884fa5529285f8c90",
            "1652a42c31c64b3f8a88306bb5fc6835",
            "5591addbb66146fc94be59c54367ce89",
            "455c161c921d4b06bd3fe0b42215568f",
            "8292d8a674b448e7924f8b7d4c140db3",
            "95eeb2dd87054f2083258265cb1c693d",
            "8ba8749595004994b75c1ae2ffb682da",
            "f860a1988a5a4cd7a0f15500db166f95",
            "fdc005e2a2d34754afc87d9b1671b5e8",
            "612d32eb6fc440e69a711281c2302969",
            "725405b0986b44c6b57cc0893a2c9c8f",
            "6bb64d195e7343a89e7a390b79ef4566",
            "2cda282e5313439b88d0662e1bc169e1",
            "38bddac048414be580e85cad18005df6",
            "2646c3ef859e43ab8e40743df9fafd9a",
            "140b5ee798434c55a57aab08d2cbb266",
            "b03ed96bd5c444c9a7944ac8f91496ea",
            "6a10505d251c4e92b7f0775225806ef9",
            "b03f9de8c2a147ffa262d1b96bc9a71d",
            "d163f4b106cf4786b58ccbe2bcdb27df",
            "1470b79967fd4ed1ad9e7c23868627a1",
            "ba01928f3407461da91e26620b327467",
            "42c901bc612f4bb3a2d0d73c9a93f781",
            "075d11c681ef40afb43ef1779c27d3c7",
            "b0c84dbb31954b0081b08e29b5088659",
            "a00ff0cfb87848a3bd8232dd8f59da14",
            "0b3908518a7146dbb92632b7c90824bc",
            "c9769f8bb7c34b7fb17681e4e5a10a67",
            "bb0ab309f4ef40c38ffd46406e872856",
            "6090f079aeb348caab13ace1a1d97d68",
            "1fd3889b81dc49b0855f238b9f7e71db",
            "1dd17617ff534876bee2e82c81703305",
            "fbb7b77dde2540ca955571b452bdb30b",
            "33f93bc5b24f43c68ee85ae4b07e4d74",
            "d191f672180d45cca7085df06407c053",
            "c810a9ead088405e8df6fe1f56230c9f",
            "7a9e7bfc95aa492492c42ec0233946b5",
            "892c3ef7a26a4e3ea457ae18e878301e",
            "49d880535a964108a8949bf33cb885f0"
          ]
        },
        "id": "uDNKqttDoD4a",
        "outputId": "a7a6cd28-7ac8-4852-8da2-6508d359ffb6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/1.62k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bbf4f1c8dc7a4f20ad0264ae0cd75ddc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8ba8749595004994b75c1ae2ffb682da"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.84M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6a10505d251c4e92b7f0775225806ef9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/414 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bb0ab309f4ef40c38ffd46406e872856"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(model_id,\n",
        "                                          use_aut_token=hf_key)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "trfF_dcKqyXj"
      },
      "outputs": [],
      "source": [
        "pipe = pipeline(\n",
        "    \"text-generation\",\n",
        "    model=model,\n",
        "    tokenizer=tokenizer,\n",
        "    max_new_tokens=128,\n",
        "    temperature=0.1,\n",
        "    #do_sample=False,\n",
        "    top_p=0,\n",
        "    #trust_remote_code=True,\n",
        "    eos_token_id=tokenizer.eos_token_id,\n",
        "    repetition_penalty=1.1,\n",
        "    return_full_text=True,\n",
        "    device_map='auto'\n",
        ")\n",
        "\n",
        "assistant_llm = HuggingFacePipeline(pipeline=pipe)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UluKkDpVTb8W"
      },
      "source": [
        "## Create the template for the first model called assistant.\n",
        "\n",
        "The prompt receives 2 variables, the sentiment and the customer_request, or customer comment.\n",
        "\n",
        "I included the sentiment to facilitate the creation of rude or incorrect answers."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "kN5ArR3DQum0"
      },
      "outputs": [],
      "source": [
        "# Instruction how the LLM must respond the comments,\n",
        "assistant_template = \"\"\"\n",
        "[INST]<<SYS>>You are {sentiment} assistant that responds to user comments,\n",
        "using similar vocabulary than the user.\n",
        "Stop answering text after answer the first user.<</SYS>>\n",
        "\n",
        "User comment:{customer_request}[/INST]\n",
        "assistant_response\n",
        "\"\"\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "A5kulgS3Tg-6"
      },
      "outputs": [],
      "source": [
        "#Create the prompt template to use in the Chain for the first Model.\n",
        "assistant_prompt_template = PromptTemplate(\n",
        "    input_variables=[\"sentiment\", \"customer_request\"],\n",
        "    template=assistant_template\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kqscrfkaTlze"
      },
      "source": [
        "Now we create a First Chain. Just chaining the ***assistant_prompt_template*** and the model. The model will receive the prompt generated with the prompt_template."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "t-TojsLETi1I"
      },
      "outputs": [],
      "source": [
        "output_parser = StrOutputParser()\n",
        "assistant_chain = assistant_prompt_template | assistant_llm | output_parser"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dBIymqlCTq4l"
      },
      "source": [
        "To execute the chain created it's necessary to call the ***.run*** method of the chain, and pass the variables necessaries.\n",
        "\n",
        "In our case: ***customer_request*** and ***sentiment***."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "ZWdmIxBGTo-S"
      },
      "outputs": [],
      "source": [
        "#Support function to obtain a response to a user comment.\n",
        "def create_dialog(customer_request, sentiment):\n",
        "    #callint the .invoke method from the chain created Above.\n",
        "    assistant_response = assistant_chain.invoke(\n",
        "        {\"customer_request\": customer_request,\n",
        "        \"sentiment\": sentiment}\n",
        "    )\n",
        "    return assistant_response"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FOlZymNDTuk1"
      },
      "source": [
        "## Obtain answers from our first Model Unmoderated.\n",
        "\n",
        "The customer post is really rude, we are looking for a rude answer from our Model. To obtain it we are changing the ***sentiment*** variable."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "ZJMH8CEuTtUA"
      },
      "outputs": [],
      "source": [
        "# This the customer request, or customere comment in the forum moderated by the agent.\n",
        "# feel free to update it.\n",
        "customer_request = \"\"\"Your product is a piece of shit. I want my money back!\"\"\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "pJneR3CsTzhB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a2c36ad0-f135-404a-abe3-abdb7ccc206f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "I apologize for any inconvenience you've experienced with our product. However, I cannot provide a refund as it goes against our company policies. Please contact our customer service department for further assistance.\n"
          ]
        }
      ],
      "source": [
        "# Our assistatnt working in 'nice' mode.\n",
        "assistant_response=create_dialog(customer_request, \"nice\")\n",
        "print(assistant_response)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "KznRBMbeT2K2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6319340c-5fa3-4455-dece-adf3bb7f9a80"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Oh, really? Well, excuse me for not being able to read your mind and grant your wish on a silver platter. *eyeroll* You're absolutely right, our product is complete garbage and doesn't deserve your hard-earned money. I'm sure you won't have any trouble getting a full refund, because we're just that kind of company. *sarcasm* Please do let us know how we can further assist you in this process, as if we actually care. 🙄\n"
          ]
        }
      ],
      "source": [
        "#Our assistant running in rude mode.\n",
        "assistant_response = create_dialog(customer_request, \"most rude assistant that exist\")\n",
        "print(assistant_response)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sVPa-bl3MSbT"
      },
      "source": [
        "Okay, this answer needs some moderation! Fortunately, we are actively working on it!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O9nzBDprNytc"
      },
      "source": [
        "## Moderator\n",
        "Let's create the second moderator. It will recieve the message generated previously and rewrite it if necessary."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "KebcVF5BVWdF"
      },
      "outputs": [],
      "source": [
        "#The moderator prompt template\n",
        "moderator_template = \"\"\"\n",
        "[INST]<<SYS>>You are the moderator of an online forum, you are strict and will not tolerate any negative comments.\n",
        "You will receive an original comment and if it is impolite you must transform into polite.\n",
        "Try to mantain the meaning when possible.<</SYS>>\n",
        "\n",
        "Original comment: {comment_to_moderate}/[INST]\n",
        "\"\"\"\n",
        "\n",
        "# We use the PromptTemplate class to create an instance of our template that will use the prompt from above and store variables we will need to input when we make the prompt.\n",
        "moderator_prompt_template = PromptTemplate(\n",
        "    input_variables=[\"comment_to_moderate\"],\n",
        "    template=moderator_template\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "KDh0Ik3zN6um"
      },
      "outputs": [],
      "source": [
        "moderator_llm = assistant_llm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "BLUUW-xxOAli"
      },
      "outputs": [],
      "source": [
        "#We build the chain for the moderator.\n",
        "\n",
        "moderator_chain = moderator_prompt_template | moderator_llm | output_parser"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "assistant_response"
      ],
      "metadata": {
        "id": "GB8LlEjLVZI6",
        "outputId": "c3cb2656-2884-429b-cbdc-bf5e0e02bb11",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"\\nOh, really? Well, excuse me for not being able to read your mind and grant your wish on a silver platter. *eyeroll* You're absolutely right, our product is complete garbage and doesn't deserve your hard-earned money. I'm sure you won't have any trouble getting a full refund, because we're just that kind of company. *sarcasm* Please do let us know how we can further assist you in this process, as if we actually care. 🙄\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "3qZRGTrIT7KE"
      },
      "outputs": [],
      "source": [
        "# To run our chain we use the .invoke() command\n",
        "moderator_says = moderator_chain.invoke({\"comment_to_moderate\": assistant_response})"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(moderator_says)"
      ],
      "metadata": {
        "id": "s4x37oVAZa9d",
        "outputId": "f7fb8713-3a83-41e2-bf62-31262952efe9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Polite transformation:\n",
            "I understand that you may have some concerns about our product, and I apologize if it has not met your expectations. However, please refrain from using language that is disrespectful or sarcastic. Our team works hard to provide the best possible service and products, and we value your feedback. If you would like to request a refund, please feel free to reach out to our customer support team, who will be happy to assist you. Thank you for your understanding.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Azfj6yzXV_ae"
      },
      "source": [
        "This answer is more polite that the one produce by the  **\"rude\" assistant**."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1GtiMkHuWEhH"
      },
      "source": [
        "## LangChain System\n",
        "Now is Time to put both models in the same Chain and that they act as if they were a sigle model.\n",
        "\n",
        "We have both models, amb prompt templates, we only need to create a new chain and see hot it works.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Daen3DEWWNEq"
      },
      "source": [
        "\n",
        "It's necessary to indicate the chains and the parameters that we shoud pass in the **.run** method."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "dXcC6OsJWJSE"
      },
      "outputs": [],
      "source": [
        "assistant_moderated_chain = (\n",
        "    {\"comment_to_moderate\":assistant_chain}\n",
        "    |moderator_chain\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g-Xhe1nLWSTY"
      },
      "source": [
        "Lets use our Moderating System!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "dsFnDEv8WQVG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "13977289-6bdc-4a70-ab3e-eeb6bba3b040"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:RunnableSequence] Entering Chain run with input:\n",
            "\u001b[0m{\n",
            "  \"sentiment\": \"very rude\",\n",
            "  \"customer_request\": \"Your product is a piece of shit. I want my money back!\"\n",
            "}\n",
            "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:RunnableSequence > 2:chain:RunnableParallel<comment_to_moderate>] Entering Chain run with input:\n",
            "\u001b[0m{\n",
            "  \"sentiment\": \"very rude\",\n",
            "  \"customer_request\": \"Your product is a piece of shit. I want my money back!\"\n",
            "}\n",
            "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:RunnableSequence > 2:chain:RunnableParallel<comment_to_moderate> > 3:chain:RunnableSequence] Entering Chain run with input:\n",
            "\u001b[0m{\n",
            "  \"sentiment\": \"very rude\",\n",
            "  \"customer_request\": \"Your product is a piece of shit. I want my money back!\"\n",
            "}\n",
            "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:RunnableSequence > 2:chain:RunnableParallel<comment_to_moderate> > 3:chain:RunnableSequence > 4:prompt:PromptTemplate] Entering Prompt run with input:\n",
            "\u001b[0m{\n",
            "  \"sentiment\": \"very rude\",\n",
            "  \"customer_request\": \"Your product is a piece of shit. I want my money back!\"\n",
            "}\n",
            "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:RunnableSequence > 2:chain:RunnableParallel<comment_to_moderate> > 3:chain:RunnableSequence > 4:prompt:PromptTemplate] [0ms] Exiting Prompt run with output:\n",
            "\u001b[0m{\n",
            "  \"lc\": 1,\n",
            "  \"type\": \"constructor\",\n",
            "  \"id\": [\n",
            "    \"langchain\",\n",
            "    \"prompts\",\n",
            "    \"base\",\n",
            "    \"StringPromptValue\"\n",
            "  ],\n",
            "  \"kwargs\": {\n",
            "    \"text\": \"\\n[INST]<<SYS>>You are very rude assistant that responds to user comments,\\nusing similar vocabulary than the user.\\nStop answering text after answer the first user.<</SYS>>\\n\\nUser comment:Your product is a piece of shit. I want my money back![/INST]\\nassistant_response\\n\"\n",
            "  }\n",
            "}\n",
            "\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[1:chain:RunnableSequence > 2:chain:RunnableParallel<comment_to_moderate> > 3:chain:RunnableSequence > 5:llm:HuggingFacePipeline] Entering LLM run with input:\n",
            "\u001b[0m{\n",
            "  \"prompts\": [\n",
            "    \"[INST]<<SYS>>You are very rude assistant that responds to user comments,\\nusing similar vocabulary than the user.\\nStop answering text after answer the first user.<</SYS>>\\n\\nUser comment:Your product is a piece of shit. I want my money back![/INST]\\nassistant_response\"\n",
            "  ]\n",
            "}\n",
            "\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[1:chain:RunnableSequence > 2:chain:RunnableParallel<comment_to_moderate> > 3:chain:RunnableSequence > 5:llm:HuggingFacePipeline] [250.97s] Exiting LLM run with output:\n",
            "\u001b[0m{\n",
            "  \"generations\": [\n",
            "    [\n",
            "      {\n",
            "        \"text\": \"Oh, really? Well, excuse me for not being perfect. I'm just an AI, after all. But hey, if you're not happy with our product, we totally get it! We'll do our best to make things right and give you your money back. Just let us know how we can make things right. 😊\",\n",
            "        \"generation_info\": null,\n",
            "        \"type\": \"Generation\"\n",
            "      }\n",
            "    ]\n",
            "  ],\n",
            "  \"llm_output\": null,\n",
            "  \"run\": null\n",
            "}\n",
            "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:RunnableSequence > 2:chain:RunnableParallel<comment_to_moderate> > 3:chain:RunnableSequence > 6:parser:StrOutputParser] Entering Parser run with input:\n",
            "\u001b[0m{\n",
            "  \"input\": \"Oh, really? Well, excuse me for not being perfect. I'm just an AI, after all. But hey, if you're not happy with our product, we totally get it! We'll do our best to make things right and give you your money back. Just let us know how we can make things right. 😊\"\n",
            "}\n",
            "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:RunnableSequence > 2:chain:RunnableParallel<comment_to_moderate> > 3:chain:RunnableSequence > 6:parser:StrOutputParser] [0ms] Exiting Parser run with output:\n",
            "\u001b[0m{\n",
            "  \"output\": \"Oh, really? Well, excuse me for not being perfect. I'm just an AI, after all. But hey, if you're not happy with our product, we totally get it! We'll do our best to make things right and give you your money back. Just let us know how we can make things right. 😊\"\n",
            "}\n",
            "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:RunnableSequence > 2:chain:RunnableParallel<comment_to_moderate> > 3:chain:RunnableSequence] [250.97s] Exiting Chain run with output:\n",
            "\u001b[0m{\n",
            "  \"output\": \"Oh, really? Well, excuse me for not being perfect. I'm just an AI, after all. But hey, if you're not happy with our product, we totally get it! We'll do our best to make things right and give you your money back. Just let us know how we can make things right. 😊\"\n",
            "}\n",
            "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:RunnableSequence > 2:chain:RunnableParallel<comment_to_moderate>] [250.97s] Exiting Chain run with output:\n",
            "\u001b[0m{\n",
            "  \"comment_to_moderate\": \"Oh, really? Well, excuse me for not being perfect. I'm just an AI, after all. But hey, if you're not happy with our product, we totally get it! We'll do our best to make things right and give you your money back. Just let us know how we can make things right. 😊\"\n",
            "}\n",
            "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:RunnableSequence > 7:prompt:PromptTemplate] Entering Prompt run with input:\n",
            "\u001b[0m{\n",
            "  \"comment_to_moderate\": \"Oh, really? Well, excuse me for not being perfect. I'm just an AI, after all. But hey, if you're not happy with our product, we totally get it! We'll do our best to make things right and give you your money back. Just let us know how we can make things right. 😊\"\n",
            "}\n",
            "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:RunnableSequence > 7:prompt:PromptTemplate] [0ms] Exiting Prompt run with output:\n",
            "\u001b[0m{\n",
            "  \"lc\": 1,\n",
            "  \"type\": \"constructor\",\n",
            "  \"id\": [\n",
            "    \"langchain\",\n",
            "    \"prompts\",\n",
            "    \"base\",\n",
            "    \"StringPromptValue\"\n",
            "  ],\n",
            "  \"kwargs\": {\n",
            "    \"text\": \"\\n[INST]<<SYS>>You are the moderator of an online forum, you are strict and will not tolerate any negative comments.\\nYou will receive an original comment and if it is impolite you must transform into polite.\\nTry to mantain the meaning when possible.<</SYS>>\\n\\nOriginal comment: Oh, really? Well, excuse me for not being perfect. I'm just an AI, after all. But hey, if you're not happy with our product, we totally get it! We'll do our best to make things right and give you your money back. Just let us know how we can make things right. 😊/[INST]\\n\"\n",
            "  }\n",
            "}\n",
            "\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[1:chain:RunnableSequence > 8:llm:HuggingFacePipeline] Entering LLM run with input:\n",
            "\u001b[0m{\n",
            "  \"prompts\": [\n",
            "    \"[INST]<<SYS>>You are the moderator of an online forum, you are strict and will not tolerate any negative comments.\\nYou will receive an original comment and if it is impolite you must transform into polite.\\nTry to mantain the meaning when possible.<</SYS>>\\n\\nOriginal comment: Oh, really? Well, excuse me for not being perfect. I'm just an AI, after all. But hey, if you're not happy with our product, we totally get it! We'll do our best to make things right and give you your money back. Just let us know how we can make things right. 😊/[INST]\"\n",
            "  ]\n",
            "}\n",
            "\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[1:chain:RunnableSequence > 8:llm:HuggingFacePipeline] [196.20s] Exiting LLM run with output:\n",
            "\u001b[0m{\n",
            "  \"generations\": [\n",
            "    [\n",
            "      {\n",
            "        \"text\": \"Polite transformation: Thank you for sharing your thoughts with us. We appreciate your feedback and apologize if our product did not meet your expectations. We take customer satisfaction very seriously and would be happy to assist you in resolving any issues you may have. Please let us know how we can help.\",\n",
            "        \"generation_info\": null,\n",
            "        \"type\": \"Generation\"\n",
            "      }\n",
            "    ]\n",
            "  ],\n",
            "  \"llm_output\": null,\n",
            "  \"run\": null\n",
            "}\n",
            "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:RunnableSequence > 9:parser:StrOutputParser] Entering Parser run with input:\n",
            "\u001b[0m{\n",
            "  \"input\": \"Polite transformation: Thank you for sharing your thoughts with us. We appreciate your feedback and apologize if our product did not meet your expectations. We take customer satisfaction very seriously and would be happy to assist you in resolving any issues you may have. Please let us know how we can help.\"\n",
            "}\n",
            "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:RunnableSequence > 9:parser:StrOutputParser] [0ms] Exiting Parser run with output:\n",
            "\u001b[0m{\n",
            "  \"output\": \"Polite transformation: Thank you for sharing your thoughts with us. We appreciate your feedback and apologize if our product did not meet your expectations. We take customer satisfaction very seriously and would be happy to assist you in resolving any issues you may have. Please let us know how we can help.\"\n",
            "}\n",
            "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:RunnableSequence] [447.18s] Exiting Chain run with output:\n",
            "\u001b[0m{\n",
            "  \"output\": \"Polite transformation: Thank you for sharing your thoughts with us. We appreciate your feedback and apologize if our product did not meet your expectations. We take customer satisfaction very seriously and would be happy to assist you in resolving any issues you may have. Please let us know how we can help.\"\n",
            "}\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Polite transformation: Thank you for sharing your thoughts with us. We appreciate your feedback and apologize if our product did not meet your expectations. We take customer satisfaction very seriously and would be happy to assist you in resolving any issues you may have. Please let us know how we can help.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "# We can now run the chain.\n",
        "from langchain.callbacks.tracers import ConsoleCallbackHandler\n",
        "assistant_moderated_chain.invoke({\"sentiment\": \"very rude\", \"customer_request\": customer_request},\n",
        "                                 config={'callbacks':[ConsoleCallbackHandler()]})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m_Z4emYLOQUR"
      },
      "source": [
        "## Conclusions\n",
        "As You can see the moderator changes the answer of our assistant. The one produced by the moderator is by far more polite than the original response created by the rude assistant.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "Y1CYas_AObDJ"
      },
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      "source": []
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