google_flan_t5.ipynb

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  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OCmLOLkLh6qt",
        "outputId": "40cc24dd-5e1b-464a-e1b8-ced53cdedfb9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Thu Jun 20 12:04:06 2024       \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
            "|-----------------------------------------+----------------------+----------------------+\n",
            "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                                         |                      |               MIG M. |\n",
            "|=========================================+======================+======================|\n",
            "|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   40C    P8              10W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                                         |                      |                  N/A |\n",
            "+-----------------------------------------+----------------------+----------------------+\n",
            "                                                                                         \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| Processes:                                                                            |\n",
            "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
            "|        ID   ID                                                             Usage      |\n",
            "|=======================================================================================|\n",
            "|  No running processes found                                                           |\n",
            "+---------------------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://raw.githubusercontent.com/alexeygrigorev/minsearch/main/minsearch.py"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EIm211bajPp_",
        "outputId": "a25451be-00b2-4346-dd24-501016906acc"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-06-20 12:09:16--  https://raw.githubusercontent.com/alexeygrigorev/minsearch/main/minsearch.py\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.111.133, 185.199.108.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 3832 (3.7K) [text/plain]\n",
            "Saving to: ‘minsearch.py’\n",
            "\n",
            "minsearch.py        100%[===================>]   3.74K  --.-KB/s    in 0s      \n",
            "\n",
            "2024-06-20 12:09:16 (70.6 MB/s) - ‘minsearch.py’ saved [3832/3832]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -U transformers accelerate bitsandbytes"
      ],
      "metadata": {
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        },
        "id": "D0JwezBLiDCI",
        "outputId": "f5c87332-e8dc-45ff-f9cd-1b1880fc0024"
      },
      "execution_count": 2,
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        {
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          "name": "stdout",
          "text": [
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.41.2)\n",
            "Collecting accelerate\n",
            "  Downloading accelerate-0.31.0-py3-none-any.whl (309 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m309.4/309.4 kB\u001b[0m \u001b[31m7.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting bitsandbytes\n",
            "  Downloading bitsandbytes-0.43.1-py3-none-manylinux_2_24_x86_64.whl (119.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m119.8/119.8 MB\u001b[0m \u001b[31m8.2 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 transformers) (3.14.0)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.23.3)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.25.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (24.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2024.5.15)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.31.0)\n",
            "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.19.1)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.3)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.4)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate) (5.9.5)\n",
            "Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (2.3.0+cu121)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.0->transformers) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.0->transformers) (4.12.2)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (1.12.1)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1.4)\n",
            "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
            "Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n",
            "Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",
            "Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",
            "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
            "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n",
            "Collecting nvidia-curand-cu12==10.3.2.106 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n",
            "Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",
            "Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",
            "Collecting nvidia-nccl-cu12==2.20.5 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl (176.2 MB)\n",
            "Collecting nvidia-nvtx-cu12==12.1.105 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n",
            "Requirement already satisfied: triton==2.3.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.3.0)\n",
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            "  Downloading nvidia_nvjitlink_cu12-12.5.40-py3-none-manylinux2014_x86_64.whl (21.3 MB)\n",
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            "Requirement already satisfied: mpmath<1.4.0,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n",
            "Installing collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, bitsandbytes, accelerate\n",
            "Successfully installed accelerate-0.31.0 bitsandbytes-0.43.1 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.40 nvidia-nvtx-cu12-12.1.105\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "\n",
        "docs_url = 'https://github.com/alexeygrigorev/llm-rag-workshop/raw/main/notebooks/documents.json'\n",
        "docs_response = requests.get(docs_url)\n",
        "documents_raw = docs_response.json()\n",
        "\n",
        "documents = []\n",
        "\n",
        "for course in documents_raw:\n",
        "    course_name = course['course']\n",
        "\n",
        "    for doc in course['documents']:\n",
        "        doc['course'] = course_name\n",
        "        documents.append(doc)\n",
        "\n",
        "\n",
        "import minsearch\n",
        "\n",
        "index = minsearch.Index(\n",
        "    text_fields=[\"question\", \"text\", \"section\"],\n",
        "    keyword_fields=[\"course\"]\n",
        ")\n",
        "\n",
        "index.fit(documents)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "btb9I4O3i-rl",
        "outputId": "e5f12579-0c9d-4d6a-e763-d35f0f548179"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<minsearch.Index at 0x7b8202ae28f0>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "query = \"I just discovered the course, can I still join?\"\n",
        "\n",
        "filter_dict = {\"course\": \"data-engineering-zoomcamp\"}\n",
        "boost_dict = {\"question\": 3}\n",
        "\n",
        "results = index.search(query, filter_dict, boost_dict, num_results=5)\n",
        "results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mlU1D5U7jT6u",
        "outputId": "1b111f70-3d89-4219-b120-87766da0dfb8"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'text': \"Yes, even if you don't register, you're still eligible to submit the homeworks.\\nBe aware, however, that there will be deadlines for turning in the final projects. So don't leave everything for the last minute.\",\n",
              "  'section': 'General course-related questions',\n",
              "  'question': 'Course - Can I still join the course after the start date?',\n",
              "  'course': 'data-engineering-zoomcamp'},\n",
              " {'text': \"The purpose of this document is to capture frequently asked technical questions\\nThe exact day and hour of the course will be 15th Jan 2024 at 17h00. The course will start with the first  “Office Hours'' live.1\\nSubscribe to course public Google Calendar (it works from Desktop only).\\nRegister before the course starts using this link.\\nJoin the course Telegram channel with announcements.\\nDon’t forget to register in DataTalks.Club's Slack and join the channel.\",\n",
              "  'section': 'General course-related questions',\n",
              "  'question': 'Course - When will the course start?',\n",
              "  'course': 'data-engineering-zoomcamp'},\n",
              " {'text': 'Yes, we will keep all the materials after the course finishes, so you can follow the course at your own pace after it finishes.\\nYou can also continue looking at the homeworks and continue preparing for the next cohort. I guess you can also start working on your final capstone project.',\n",
              "  'section': 'General course-related questions',\n",
              "  'question': 'Course - Can I follow the course after it finishes?',\n",
              "  'course': 'data-engineering-zoomcamp'},\n",
              " {'text': 'You can start by installing and setting up all the dependencies and requirements:\\nGoogle cloud account\\nGoogle Cloud SDK\\nPython 3 (installed with Anaconda)\\nTerraform\\nGit\\nLook over the prerequisites and syllabus to see if you are comfortable with these subjects.',\n",
              "  'section': 'General course-related questions',\n",
              "  'question': 'Course - What can I do before the course starts?',\n",
              "  'course': 'data-engineering-zoomcamp'},\n",
              " {'text': 'Star the repo! Share it with friends if you find it useful ❣️\\nCreate a PR if you see you can improve the text or the structure of the repository.',\n",
              "  'section': 'General course-related questions',\n",
              "  'question': 'How can we contribute to the course?',\n",
              "  'course': 'data-engineering-zoomcamp'}]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def retrieve_documents(query, max_results=5):\n",
        "    filter_dict = {\"course\": \"data-engineering-zoomcamp\"}\n",
        "    boost_dict = {\"question\": 3}\n",
        "\n",
        "    return index.search(query, filter_dict, boost_dict, num_results=5)"
      ],
      "metadata": {
        "id": "8LTZwn0ajcIC"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import T5ForConditionalGeneration, T5Tokenizer\n",
        "\n",
        "model_name = \"google/flan-t5-xl\"\n",
        "\n",
        "model = T5ForConditionalGeneration.from_pretrained(model_name, device_map=\"auto\")\n",
        "tokenizer = T5Tokenizer.from_pretrained(model_name, legacy=False)\n",
        "tokenizer.model_max_length = 4096"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 511,
          "referenced_widgets": [
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          ]
        },
        "id": "mzBcuu5fkDDb",
        "outputId": "714aa6e3-ba82-4dd4-cd3a-289278bab7a8"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
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          "metadata": {}
        },
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            "application/vnd.jupyter.widget-view+json": {
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            }
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          "metadata": {}
        },
        {
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              "Downloading shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
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        {
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            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
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            }
          },
          "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,
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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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              "model_id": "5c59f4a7e708491485c09b4618769609"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/2.20k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e761ae8975c443dea4ce2dc4d3ef7b9b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/2.42M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ccacbdd29cde440da237d94e1bc3e1d3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_text = \"translate English to German: How old are you?\"\n",
        "input_ids = tokenizer(input_text, return_tensors=\"pt\").input_ids.to(\"cuda\")\n",
        "\n",
        "outputs = model.generate(input_ids)\n",
        "print(tokenizer.decode(outputs[0]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nuNQoNyjkhwd",
        "outputId": "342bb8fd-7f53-447e-ea25-0e495de73d84"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1168: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<pad> Wie alt sind Sie?</s>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def llm(prompt):\n",
        "    input_ids = tokenizer(prompt, return_tensors=\"pt\").input_ids.to(\"cuda\")\n",
        "    outputs = model.generate(input_ids, )\n",
        "    result = tokenizer.decode(outputs[0])\n",
        "    return result"
      ],
      "metadata": {
        "id": "i06yfbLBkwOi"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "llm('I just discovered the course. Can I still join?')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "id": "DNzmNoiNlLvz",
        "outputId": "0d0b0a7e-b001-488b-8866-fd8d7238b06a"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1168: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'<pad> Yes, you can.</s>'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "context_template = \"\"\"\n",
        "Section: {section}\n",
        "Question: {question}\n",
        "Answer: {text}\n",
        "\"\"\".strip()\n",
        "\n",
        "prompt_template = \"\"\"\n",
        "You're a course teaching assistant.\n",
        "Answer the user QUESTION based on CONTEXT - the documents retrieved from our FAQ database.\n",
        "Don't use other information outside of the provided CONTEXT.\n",
        "\n",
        "QUESTION: {user_question}\n",
        "\n",
        "CONTEXT:\n",
        "\n",
        "{context}\n",
        "\"\"\".strip()\n",
        "\n",
        "\n",
        "def build_context(documents):\n",
        "    context_result = \"\"\n",
        "\n",
        "    for doc in documents:\n",
        "        doc_str = context_template.format(**doc)\n",
        "        context_result += (\"\\n\\n\" + doc_str)\n",
        "\n",
        "    return context_result.strip()\n",
        "\n",
        "\n",
        "def build_prompt(user_question, documents):\n",
        "    context = build_context(documents)\n",
        "    prompt = prompt_template.format(\n",
        "        user_question=user_question,\n",
        "        context=context\n",
        "    )\n",
        "    return prompt\n",
        "\n",
        "\n",
        "def qa_bot(user_question):\n",
        "    context_docs = retrieve_documents(user_question)\n",
        "    prompt = build_prompt(user_question, context_docs)\n",
        "    answer = llm(prompt)\n",
        "    return answer"
      ],
      "metadata": {
        "id": "iFyFtjcnlOJB"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "qa_bot('I just discovered the course. Can I still join?')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "xcdlWijbllcB",
        "outputId": "a8474bd2-61f8-4022-b3df-6c4598091ffd"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"<pad>Yes, even if you don't register, you're still eligible to submit the\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def llm(prompt, generate_params=None):\n",
        "    if generate_params is None:\n",
        "        generate_params = {}\n",
        "\n",
        "    input_ids = tokenizer(prompt, return_tensors=\"pt\").input_ids.to(\"cuda\")\n",
        "    outputs = model.generate(\n",
        "        input_ids,\n",
        "        max_length=generate_params.get(\"max_length\", 100),\n",
        "        num_beams=generate_params.get(\"num_beams\", 5),\n",
        "        do_sample=generate_params.get(\"do_sample\", False),\n",
        "        temperature=generate_params.get(\"temperature\", 1.0),\n",
        "        top_k=generate_params.get(\"top_k\", 50),\n",
        "        top_p=generate_params.get(\"top_p\", 0.95),\n",
        "    )\n",
        "    result = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
        "    return result"
      ],
      "metadata": {
        "id": "X7syBolfloAW"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "qa_bot('I just discovered the course. Can I still join?')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        },
        "id": "ovR-V5_yl30t",
        "outputId": "7d929276-970f-436d-a37e-531f61fb492f"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:520: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"Yes, even if you don't register, you're still eligible to submit the homeworks. Be aware, however, that there will be deadlines for turning in the final projects. So don't leave everything for the last minute.\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "alqQ8M4Il5Aq"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}