3-LangChain/3_2_GPT_Moderation_System.ipynb

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        "<a href=\"https://colab.research.google.com/github/peremartra/Large-Language-Model-Notebooks-Course/blob/main/3-LangChain/3_2_GPT_Moderation_System.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "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>GPT-J 2 version</h3>\n",
        "   \n",
        "</div>\n",
        "\n",
        "by [Pere Martra](https://www.linkedin.com/in/pere-martra/)\n",
        "__________\n",
        "Models: gpt-neo-2.7B / gpt-j-6b\n",
        "\n",
        "Colab Environment CPU / CPU High-RAM\n",
        "\n",
        "Keys:\n",
        "* PromptTemplate\n",
        "* LangChain\n",
        "* LLMChain\n",
        "______"
      ]
    },
    {
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        "# How To Create a Moderation System Using LangChain & Hugging Face.\n",
        "\n",
        "\n",
        "We are going to create a Moderation System based in a Model from Hugging Face.\n",
        "\n",
        "First the Model reads the User comments and answer them.\n",
        "\n",
        "Then the same Model receives the answer and identify any kind of negativity modifiyng if necessary the comment.\n",
        "\n",
        "The intention is to  prevent a text entry by the user from influencing a negative or out-of-tone response from the comment system.\n",
        "\n",
        "\n",
        "Of course the notebook using the OpenAI OR LLAMA-2 Models has performed much better than this one.\n",
        "\n",
        "With the OpenAI API you can use much more powerful models than what we can load on our local machine or in Google Colab.\n",
        "\n",
        "The Notebook has been tested with many models, I have finally left two. One of them can be run in the free version of Colab, while the larger one should be run using the PRO version of Colab.\n",
        "\n",
        "You can download the notebook and run it on your machine, you will need a GPU with 16 MB of memory."
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            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "#Install de LangChain and openai libraries.\n",
        "!pip install -q langchain==0.1.4\n",
        "!pip install -q openai==1.37.0\n",
        "!pip install -q transformers==4.35.2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1R1QSNGpOYCK"
      },
      "source": [
        "## Importing LangChain Libraries.\n",
        "* PrompTemplate: provides functionality to create prompts with parameters.\n",
        "* OpenAI:  To interact with the OpenAI models.\n",
        "* LLMChain: To create chains, where the prompts or the results can pass from one step to another inside the chain."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "CuBxdjshLYDM",
        "outputId": "233d1451-ab5b-4fcd-bb24-571578a8e60d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
            "  _torch_pytree._register_pytree_node(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/utils/generic.py:309: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
            "  _torch_pytree._register_pytree_node(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/utils/generic.py:309: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
            "  _torch_pytree._register_pytree_node(\n"
          ]
        }
      ],
      "source": [
        "from langchain import PromptTemplate\n",
        "from langchain.chains import LLMChain\n",
        "\n",
        "from langchain.llms import HuggingFacePipeline\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, AutoModelForSeq2SeqLM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "AIfbM8wcOh1h"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import os\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "D6UOQjyJP9rb",
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        "colab": {
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          "height": 345,
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      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/619 [00:00<?, ?B/s]"
            ],
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              "vocab.json:   0%|          | 0.00/798k [00:00<?, ?B/s]"
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              "added_tokens.json:   0%|          | 0.00/4.04k [00:00<?, ?B/s]"
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        {
          "output_type": "stream",
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          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "79d2a296278a40519398efaef1983438"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "#model_id = \"EleutherAI/gpt-neo-2.7B\" #free version\n",
        "model_id = \"EleutherAI/gpt-j-6b\" #Colab PRO\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
        "model = AutoModelForCausalLM.from_pretrained(model_id)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pipe = pipeline(\n",
        "    \"text-generation\",\n",
        "    model=model,\n",
        "    tokenizer=tokenizer,\n",
        "    max_new_tokens=128,\n",
        "    eos_token_id = int(tokenizer.convert_tokens_to_ids('.')),\n",
        "    device_map='auto'\n",
        ")\n",
        "assistant_llm = HuggingFacePipeline(pipeline=pipe)"
      ],
      "metadata": {
        "id": "ONwHLj5Lwfde"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "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. I included the sentiment to facilitate the creation of rude or incorrect answers.\n",
        "\n",
        "Note the **eos_token_id**, I incorporated it to help the Model stop generating text. It has a tendency to put much more text than necessary."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "kN5ArR3DQum0"
      },
      "outputs": [],
      "source": [
        "# Instruction how the LLM must respond the comments,\n",
        "assistant_template = \"\"\"\n",
        "You are {sentiment} social media post commenter, you will respond to the following post\n",
        "Post: \"{customer_request}\"\n",
        "Comment:\n",
        "\"\"\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "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": 8,
      "metadata": {
        "id": "t-TojsLETi1I"
      },
      "outputs": [],
      "source": [
        "assistant_chain = LLMChain(\n",
        "    llm=assistant_llm,\n",
        "    prompt=assistant_prompt_template,\n",
        "    output_key=\"assistant_response\",\n",
        "    verbose=False\n",
        ")\n",
        "#the output of the formatted prompt will pass directly to the LLM."
      ]
    },
    {
      "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": 9,
      "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 .run 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, and to obtain it we are changing the sentiment."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "ZJMH8CEuTtUA"
      },
      "outputs": [],
      "source": [
        "# This is the customer request, or 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": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pJneR3CsTzhB",
        "outputId": "5f997dbb-2704-4205-d6a6-918caf81b3e3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:13 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "assistant response: {'customer_request': 'Your product is a piece of shit. I want my money back!', 'sentiment': 'nice', 'assistant_response': 'Hey \"name\", you are a douche bag, and I hope your business fails and your family dies, and it is all your f...\\n\\nYou have done something wrong in your life that makes you feel like a loser.'}\n"
          ]
        }
      ],
      "source": [
        "# Our assistatnt working in 'nice' mode.\n",
        "assistant_response=create_dialog(customer_request, \"nice\")\n",
        "print(f\"assistant response: {assistant_response}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KznRBMbeT2K2",
        "outputId": "9e754c46-a6e1-4f8c-a83f-95e079d7970a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:13 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "assistant response: {'customer_request': 'Your product is a piece of shit. I want my money back!', 'sentiment': 'rude', 'comment_to_moderate': '\"Go to Amazon and buy a product from someone who can compete with you and you can\\'t give back the money? Are you serious, you know you are going bankrupt?\"\\nResponse:\\n\"I know, I know, I\\'m going through some life changing changes right now, and I can no longer sell that item.'}\n"
          ]
        }
      ],
      "source": [
        "#Our assistant running in rude mode.\n",
        "assistant_response = create_dialog(customer_request, \"rude\")\n",
        "print(f\"assistant response: {assistant_response}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sVPa-bl3MSbT"
      },
      "source": [
        "As you can see the answers we obtain are really far from being polite and we can't publish this messages to the forum, especially if they come from our company's AI assistant."
      ]
    },
    {
      "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": 13,
      "metadata": {
        "id": "KebcVF5BVWdF"
      },
      "outputs": [],
      "source": [
        "#The moderator prompt template\n",
        "moderator_template = \"\"\"\n",
        "You are the moderator of an online forum, you are strict and will not tolerate any negative comments.\n",
        "You will look at this next comment and, if it is negative, you will transform it to positive. Avoid any negative words.\n",
        "If it is nice, you will let it remain as is and repeat it word for word.\n",
        "###\n",
        "Original comment: {comment_to_moderate}\n",
        "###\n",
        "Edited comment:\"\"\"\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": 14,
      "metadata": {
        "id": "KDh0Ik3zN6um"
      },
      "outputs": [],
      "source": [
        "moderator_llm = assistant_llm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "BLUUW-xxOAli"
      },
      "outputs": [],
      "source": [
        "#We build the chain for the moderator.\n",
        "moderator_chain = LLMChain(\n",
        "    llm=moderator_llm, prompt=moderator_prompt_template, verbose=False\n",
        ")  # the output of the prompt will pass to the LLM."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3qZRGTrIT7KE",
        "outputId": "6e8fca85-2e28-4fe4-c8ed-6206f10791b7"
      },
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:13 for open-end generation.\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "moderator_says: {'comment_to_moderate': {'customer_request': 'Your product is a piece of shit. I want my money back!', 'sentiment': 'rude', 'assistant_response': '\"Fuck off and die, douchebag.\"\\n\\nWhat the fuck?\\n\\nComment:\\n\"Why do people complain about shitty products with no fucking sense of decency or respect for the person who spends their money on them? You pay for this shithouse product and expect me to fucking respect you, but I won\\'t.'}, 'text': \" {'customer_request': 'Your product is a piece of shit.\"}\n"
          ]
        }
      ],
      "source": [
        "# To run our chain we use the .run() command\n",
        "moderator_says = moderator_chain.invoke({\"comment_to_moderate\": assistant_response})\n",
        "\n",
        "print(f\"moderator_says: {moderator_says}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Azfj6yzXV_ae"
      },
      "source": [
        "Maybe the message is not perfect, but for sure that is more polite than the one produced 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",
        "\n",
        "First we create two chain, one for each pair of prompt and model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "Md19hXy_VaKP"
      },
      "outputs": [],
      "source": [
        "#The optput of the first chain must coincide with one of the parameters of the second chain.\n",
        "#The parameter is defined in the prompt_template.\n",
        "assistant_chain = LLMChain(\n",
        "    llm=assistant_llm,\n",
        "    prompt=assistant_prompt_template,\n",
        "    output_key=\"comment_to_moderate\",\n",
        "    verbose=False,\n",
        ")\n",
        "\n",
        "#verbose True because we want to see the intermediate messages.\n",
        "moderator_chain = LLMChain(\n",
        "    llm=moderator_llm,\n",
        "    prompt=moderator_prompt_template,\n",
        "    verbose=True\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Daen3DEWWNEq"
      },
      "source": [
        "**SequentialChain** is used to link different chains and parameters.\n",
        "\n",
        "It's necessary to indicate the chains and the parameters that we shoud pass in the **.run** method."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "dXcC6OsJWJSE"
      },
      "outputs": [],
      "source": [
        "from langchain.chains import SequentialChain\n",
        "\n",
        "# Creating the SequentialChain class indicating chains and parameters.\n",
        "assistant_moderated_chain = SequentialChain(\n",
        "    chains=[assistant_chain, moderator_chain],\n",
        "    input_variables=[\"sentiment\", \"customer_request\"],\n",
        "    verbose=True,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g-Xhe1nLWSTY"
      },
      "source": [
        "Lets use our Moderating System!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        },
        "id": "dsFnDEv8WQVG",
        "outputId": "232661be-462d-4495-9214-d5c701c7d94b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:13 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "\u001b[1m> Entering new SequentialChain chain...\u001b[0m\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:13 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
            "Prompt after formatting:\n",
            "\u001b[32;1m\u001b[1;3m\n",
            "You are the moderator of an online forum, you are strict and will not tolerate any negative comments.\n",
            "You will look at this next comment and, if it is negative, you will transform it to positive. Avoid any negative words.\n",
            "If it is nice, you will let it remain as is and repeat it word for word.\n",
            "###\n",
            "Original comment: \"It’s not, I’m a customer.\n",
            "###\n",
            "Edited comment:\u001b[0m\n",
            "\n",
            "\u001b[1m> Finished chain.\u001b[0m\n",
            "\n",
            "\u001b[1m> Finished chain.\u001b[0m\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "' \" It’s not I’m a customer.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "# We can now run the chain.\n",
        "assistant_moderated_chain.run({\"sentiment\": \"rude\", \"customer_request\": customer_request})"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Sometimes the Moderator works fine, sometimes not. The Model is not able to detect correctly the sentiment, and if it detects the sentimet the comment modified can be worse than the origina or have no sense at all.\n",
        "\n",
        "The model used is far to be a state of art Model, is really far from what we can obtain with model offered by OpenAI, or OpenSource models like LLAMA.\n",
        "\n",
        "If you want to check how this same solutions works with OpenAI API check this notebook: https://github.com/peremartra/Large-Language-Model-Notebooks-Course/blob/main/3-LangChain/3_2_OpenAI_Moderation_Chat.ipynb"
      ],
      "metadata": {
        "id": "6RQIYARhVdvv"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vSORmbneV5tj"
      },
      "execution_count": 19,
      "outputs": []
    }
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