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 },
 "cells": [
  {
   "cell_type": "code",
   "source": [
    "!nvidia-smi"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AFZMjbPtyXl8",
    "outputId": "62e334da-7bd5-4402-96ca-0c31c4f7a592"
   },
   "execution_count": 1,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Tue Aug 22 10:58:49 2023       \n",
      "+-----------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |\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   52C    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"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "eQlQ4ZWgcXDW",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "9d113c93-c522-42b6-e7c4-cdf4fbb7f5e1"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
      "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
      "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
      "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
      "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
      "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
      "  Building wheel for chroma-hnswlib (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
      "  Building wheel for pypika (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Building wheel for sentence_transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip install -Uqqq pip --progress-bar off\n",
    "!pip install -qqq torch==2.0.1 --progress-bar off\n",
    "!pip install -qqq transformers==4.31.0 --progress-bar off\n",
    "!pip install -qqq langchain==0.0.266 --progress-bar off\n",
    "!pip install -qqq chromadb==0.4.5 --progress-bar off\n",
    "!pip install -qqq pypdf==3.15.0 --progress-bar off\n",
    "!pip install -qqq xformers==0.0.20 --progress-bar off\n",
    "!pip install -qqq sentence_transformers==2.2.2 --progress-bar off\n",
    "!pip install -qqq InstructorEmbedding==1.0.1 --progress-bar off\n",
    "!pip install -qqq pdf2image==1.16.3 --progress-bar off"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "!wget -q https://github.com/PanQiWei/AutoGPTQ/releases/download/v0.4.1/auto_gptq-0.4.1+cu118-cp310-cp310-linux_x86_64.whl"
   ],
   "metadata": {
    "id": "ecTRR8hwhn0C"
   },
   "execution_count": 3,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "!pip install -qqq auto_gptq-0.4.1+cu118-cp310-cp310-linux_x86_64.whl --progress-bar off"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "C0_Fa50LkfoB",
    "outputId": "e38bf784-f3f5-441f-c325-1351ba459440"
   },
   "execution_count": 4,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "!sudo apt-get install poppler-utils"
   ],
   "metadata": {
    "id": "t7ShwfeTubDU",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "d54906b5-ccc1-42ac-8a19-bdcd7670bd91"
   },
   "execution_count": 5,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Reading package lists... Done\n",
      "Building dependency tree... Done\n",
      "Reading state information... Done\n",
      "The following NEW packages will be installed:\n",
      "  poppler-utils\n",
      "0 upgraded, 1 newly installed, 0 to remove and 16 not upgraded.\n",
      "Need to get 186 kB of archives.\n",
      "After this operation, 696 kB of additional disk space will be used.\n",
      "Get:1 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 poppler-utils amd64 22.02.0-2ubuntu0.2 [186 kB]\n",
      "Fetched 186 kB in 0s (512 kB/s)\n",
      "debconf: unable to initialize frontend: Dialog\n",
      "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 78, <> line 1.)\n",
      "debconf: falling back to frontend: Readline\n",
      "debconf: unable to initialize frontend: Readline\n",
      "debconf: (This frontend requires a controlling tty.)\n",
      "debconf: falling back to frontend: Teletype\n",
      "dpkg-preconfigure: unable to re-open stdin: \n",
      "Selecting previously unselected package poppler-utils.\n",
      "(Reading database ... 120831 files and directories currently installed.)\n",
      "Preparing to unpack .../poppler-utils_22.02.0-2ubuntu0.2_amd64.deb ...\n",
      "Unpacking poppler-utils (22.02.0-2ubuntu0.2) ...\n",
      "Setting up poppler-utils (22.02.0-2ubuntu0.2) ...\n",
      "Processing triggers for man-db (2.10.2-1) ...\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "import torch\n",
    "from auto_gptq import AutoGPTQForCausalLM\n",
    "from langchain import HuggingFacePipeline, PromptTemplate\n",
    "from langchain.chains import RetrievalQA\n",
    "from langchain.document_loaders import PyPDFDirectoryLoader\n",
    "from langchain.embeddings import HuggingFaceInstructEmbeddings\n",
    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
    "from langchain.vectorstores import Chroma\n",
    "from pdf2image import convert_from_path\n",
    "from transformers import AutoTokenizer, TextStreamer, pipeline\n",
    "\n",
    "DEVICE = \"cuda:0\" if torch.cuda.is_available() else \"cpu\""
   ],
   "metadata": {
    "id": "vH5IpjeNflMU"
   },
   "execution_count": 6,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Data"
   ],
   "metadata": {
    "id": "jiX57tzWOJAg"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "!mkdir pdfs"
   ],
   "metadata": {
    "id": "OQm1m0Me0XHj"
   },
   "execution_count": 7,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "!gdown 1v-Rn1FVU1pLTAQEgm0N9oB6cExMoebZr -O pdfs/tesla-earnings-report.pdf\n",
    "!gdown 1Xc890jrQvCExAkryVWAttsv1DBLdVefN -O pdfs/nvidia-earnings-report.pdf\n",
    "!gdown 1Epz-SQ3idPpoz75GlTzzomag8gplzLv8 -O pdfs/meta-earnings-report.pdf"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dZtUHaJOqH5o",
    "outputId": "2e4720d1-20b1-45ac-8819-94ee30e5c7bd"
   },
   "execution_count": 8,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1v-Rn1FVU1pLTAQEgm0N9oB6cExMoebZr\n",
      "To: /content/pdfs/tesla-earnings-report.pdf\n",
      "100% 345k/345k [00:00<00:00, 146MB/s]\n",
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1Xc890jrQvCExAkryVWAttsv1DBLdVefN\n",
      "To: /content/pdfs/nvidia-earnings-report.pdf\n",
      "100% 414k/414k [00:00<00:00, 139MB/s]\n",
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1Epz-SQ3idPpoz75GlTzzomag8gplzLv8\n",
      "To: /content/pdfs/meta-earnings-report.pdf\n",
      "100% 183k/183k [00:00<00:00, 117MB/s]\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "meta_images = convert_from_path(\"pdfs/meta-earnings-report.pdf\", dpi=88)\n",
    "meta_images[0]"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 986
    },
    "id": "a9X4Fbv8ugOW",
    "outputId": "50e5cf7e-e644-4540-f2a2-881e2b729766"
   },
   "execution_count": 9,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<PIL.PpmImagePlugin.PpmImageFile image mode=RGB size=749x969>"
      ],
      "image/png": "[encoded data, 249336 characters]\n"
     },
     "metadata": {},
     "execution_count": 9
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "nvidia_images = convert_from_path(\"pdfs/nvidia-earnings-report.pdf\", dpi=88)\n",
    "nvidia_images[0]"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 986
    },
    "id": "We2A7lVtukAb",
    "outputId": "b79172ee-24d4-4e6e-f29e-dc4f1df0438d"
   },
   "execution_count": 10,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<PIL.PpmImagePlugin.PpmImageFile image mode=RGB size=749x969>"
      ],
      "image/png": "[encoded data, 178608 characters]\n"
     },
     "metadata": {},
     "execution_count": 10
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "tesla_images = convert_from_path(\"pdfs/tesla-earnings-report.pdf\", dpi=88)\n",
    "tesla_images[0]"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 986
    },
    "id": "yL1bVMC2twkB",
    "outputId": "86dee35f-1c8c-4a72-bf1d-103318bd535a"
   },
   "execution_count": 11,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<PIL.PpmImagePlugin.PpmImageFile image mode=RGB size=749x969>"
      ],
      "image/png": "[encoded data, 157472 characters]\n"
     },
     "metadata": {},
     "execution_count": 11
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "!rm -rf \"db\""
   ],
   "metadata": {
    "id": "6ywUvMDqZFf4"
   },
   "execution_count": 12,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "loader = PyPDFDirectoryLoader(\"pdfs\")\n",
    "docs = loader.load()\n",
    "len(docs)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RQmhREpeOInh",
    "outputId": "8866fd8b-cf6d-4e5e-a4ab-ff9eb5eae4ba"
   },
   "execution_count": 13,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "metadata": {},
     "execution_count": 13
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "embeddings = HuggingFaceInstructEmbeddings(\n",
    "    model_name=\"hkunlp/instructor-large\", model_kwargs={\"device\": DEVICE}\n",
    ")"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 501,
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     "output_type": "stream",
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     "text": [
      "load INSTRUCTOR_Transformer\n",
      "max_seq_length  512\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1024, chunk_overlap=64)\n",
    "texts = text_splitter.split_documents(docs)\n",
    "len(texts)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yFztsob3WO3x",
    "outputId": "6c0020b1-c7f1-48a0-ab98-d5fab64f7420"
   },
   "execution_count": 15,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "355"
      ]
     },
     "metadata": {},
     "execution_count": 15
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "%%time\n",
    "db = Chroma.from_documents(texts, embeddings, persist_directory=\"db\")"
   ],
   "metadata": {
    "id": "ESFJOOYEWTX9",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "014f2054-361c-44d3-add1-4ed570fff175"
   },
   "execution_count": 16,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "CPU times: user 20.6 s, sys: 650 ms, total: 21.2 s\n",
      "Wall time: 29.8 s\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Llama 2 13B"
   ],
   "metadata": {
    "id": "GcylvlF60r_m"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "model_name_or_path = \"TheBloke/Llama-2-13B-chat-GPTQ\"\n",
    "model_basename = \"model\"\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)\n",
    "\n",
    "model = AutoGPTQForCausalLM.from_quantized(\n",
    "    model_name_or_path,\n",
    "    revision=\"gptq-4bit-128g-actorder_True\",\n",
    "    model_basename=model_basename,\n",
    "    use_safetensors=True,\n",
    "    trust_remote_code=True,\n",
    "    inject_fused_attention=False,\n",
    "    device=DEVICE,\n",
    "    quantize_config=None,\n",
    ")"
   ],
   "metadata": {
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     "height": 291,
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      "dd3df6a5f0a04776be9b203f5d697087",
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      "7353acd1932f4826802cadd0250d51a6",
      "6eb491ccf94b43a4a377e76d80d6e1be",
      "5d523f6cefc34536a2f3ce3975dc23d2",
      "0138b6e1f133450f9e30040d3d7acf3e",
      "75246904d05147b4994fe0046d3371ff"
     ]
    },
    "id": "DrSTLMQbfjFp",
    "outputId": "152d488e-1e3d-424f-c00b-01dbac5a4a47"
   },
   "execution_count": 17,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     "metadata": {}
    },
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      }
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      ],
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     },
     "metadata": {}
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    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading model.safetensors:   0%|          | 0.00/7.26G [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "WARNING:auto_gptq.nn_modules.fused_llama_mlp:skip module injection for FusedLlamaMLPForQuantizedModel not support integrate without triton yet.\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "!nvidia-smi"
   ],
   "metadata": {
    "id": "k6yLtagJfvG4",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "438522ba-3b19-4704-c73f-9065e6db8dd9"
   },
   "execution_count": 18,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Tue Aug 22 11:06:40 2023       \n",
      "+-----------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |\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   48C    P0    35W /  70W |   9673MiB / 15360MiB |     92%      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",
      "+-----------------------------------------------------------------------------+\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "DEFAULT_SYSTEM_PROMPT = \"\"\"\n",
    "You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n",
    "\n",
    "If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\n",
    "\"\"\".strip()\n",
    "\n",
    "\n",
    "def generate_prompt(prompt: str, system_prompt: str = DEFAULT_SYSTEM_PROMPT) -> str:\n",
    "    return f\"\"\"\n",
    "[INST] <<SYS>>\n",
    "{system_prompt}\n",
    "<</SYS>>\n",
    "\n",
    "{prompt} [/INST]\n",
    "\"\"\".strip()"
   ],
   "metadata": {
    "id": "NKUo_0gxRD-a"
   },
   "execution_count": 19,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)"
   ],
   "metadata": {
    "id": "5H6rPBpEAALY"
   },
   "execution_count": 20,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "text_pipeline = pipeline(\n",
    "    \"text-generation\",\n",
    "    model=model,\n",
    "    tokenizer=tokenizer,\n",
    "    max_new_tokens=1024,\n",
    "    temperature=0,\n",
    "    top_p=0.95,\n",
    "    repetition_penalty=1.15,\n",
    "    streamer=streamer,\n",
    ")"
   ],
   "metadata": {
    "id": "ZYaQDkDvVvIz",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "bc8754da-19f6-4751-e2f8-b5dad10c348d"
   },
   "execution_count": 21,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "The model 'LlamaGPTQForCausalLM' is not supported for text-generation. Supported models are ['BartForCausalLM', 'BertLMHeadModel', 'BertGenerationDecoder', 'BigBirdForCausalLM', 'BigBirdPegasusForCausalLM', 'BioGptForCausalLM', 'BlenderbotForCausalLM', 'BlenderbotSmallForCausalLM', 'BloomForCausalLM', 'CamembertForCausalLM', 'CodeGenForCausalLM', 'CpmAntForCausalLM', 'CTRLLMHeadModel', 'Data2VecTextForCausalLM', 'ElectraForCausalLM', 'ErnieForCausalLM', 'FalconForCausalLM', 'GitForCausalLM', 'GPT2LMHeadModel', 'GPT2LMHeadModel', 'GPTBigCodeForCausalLM', 'GPTNeoForCausalLM', 'GPTNeoXForCausalLM', 'GPTNeoXJapaneseForCausalLM', 'GPTJForCausalLM', 'LlamaForCausalLM', 'MarianForCausalLM', 'MBartForCausalLM', 'MegaForCausalLM', 'MegatronBertForCausalLM', 'MusicgenForCausalLM', 'MvpForCausalLM', 'OpenLlamaForCausalLM', 'OpenAIGPTLMHeadModel', 'OPTForCausalLM', 'PegasusForCausalLM', 'PLBartForCausalLM', 'ProphetNetForCausalLM', 'QDQBertLMHeadModel', 'ReformerModelWithLMHead', 'RemBertForCausalLM', 'RobertaForCausalLM', 'RobertaPreLayerNormForCausalLM', 'RoCBertForCausalLM', 'RoFormerForCausalLM', 'RwkvForCausalLM', 'Speech2Text2ForCausalLM', 'TransfoXLLMHeadModel', 'TrOCRForCausalLM', 'XGLMForCausalLM', 'XLMWithLMHeadModel', 'XLMProphetNetForCausalLM', 'XLMRobertaForCausalLM', 'XLMRobertaXLForCausalLM', 'XLNetLMHeadModel', 'XmodForCausalLM'].\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "llm = HuggingFacePipeline(pipeline=text_pipeline, model_kwargs={\"temperature\": 0})"
   ],
   "metadata": {
    "id": "s0sKzP77ZCok"
   },
   "execution_count": 22,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "SYSTEM_PROMPT = \"Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.\"\n",
    "\n",
    "template = generate_prompt(\n",
    "    \"\"\"\n",
    "{context}\n",
    "\n",
    "Question: {question}\n",
    "\"\"\",\n",
    "    system_prompt=SYSTEM_PROMPT,\n",
    ")"
   ],
   "metadata": {
    "id": "J3RKlMe4DNMn"
   },
   "execution_count": 23,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "prompt = PromptTemplate(template=template, input_variables=[\"context\", \"question\"])"
   ],
   "metadata": {
    "id": "pJpTuSEoDDEJ"
   },
   "execution_count": 24,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "qa_chain = RetrievalQA.from_chain_type(\n",
    "    llm=llm,\n",
    "    chain_type=\"stuff\",\n",
    "    retriever=db.as_retriever(search_kwargs={\"k\": 2}),\n",
    "    return_source_documents=True,\n",
    "    chain_type_kwargs={\"prompt\": prompt},\n",
    ")"
   ],
   "metadata": {
    "id": "0DC-RU6DZCIS"
   },
   "execution_count": 25,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Chat with Multiple PDFs"
   ],
   "metadata": {
    "id": "J7IWZ6H1ax4z"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "result = qa_chain(\"What is the per share revenue for Meta during 2023?\")"
   ],
   "metadata": {
    "id": "_BR5nBf_fPao",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "16188e88-5841-408e-a409-6a4548bf238f"
   },
   "execution_count": 26,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      " Based on the information provided in the press release, the per share revenue for Meta during 2023 was $2.98.\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "len(result[\"source_documents\"])"
   ],
   "metadata": {
    "id": "UyyiaN0yZ6LN",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "42e5b7ee-5de5-469e-970b-061971f3a60c"
   },
   "execution_count": 27,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "metadata": {},
     "execution_count": 27
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "print(result[\"source_documents\"][0].page_content)"
   ],
   "metadata": {
    "id": "60qpCy_haA3D",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "54fc43fa-a762-4669-bb5a-1e473daf0d83"
   },
   "execution_count": 28,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Meta Reports Second  Quarter 2023  Results\n",
      "MENLO PARK, Calif. – July 26, 2023  – Meta Platforms, Inc. (Nasdaq: META) today reported financial results for the \n",
      "quarter ended June 30, 2023 .\n",
      "\"We had a good quarter. We continue to see strong engagement across our apps and we have the most exciting roadmap I've \n",
      "seen in a while with Llama 2, Threads, Reels, new AI products in the pipeline, and the launch of Quest 3 this fall,\" said Mark \n",
      "Zuckerberg, Meta founder and CEO.\n",
      "Second Quarter 2023  Financial Highlights\n",
      "Three Months Ended June 30,\n",
      "In millions, except percentages and per share amounts 2023 2022 % Change\n",
      "Revenue $ 31,999 $ 28,822 11%\n",
      "Costs and expenses  22,607  20,464 10%\n",
      "Income from operations $ 9,392 $ 8,358 12%\n",
      "Operating margin  29 %  29 %\n",
      "Provision for income taxes $ 1,505 $ 1,499 —%\n",
      "Effective tax rate  16 %  18 %\n",
      "Net income $ 7,788 $ 6,687 16%\n",
      "Diluted earnings per share (EPS) $ 2.98 $ 2.46 21%\n",
      "Second Quarter 2023  Operational and Other Financial Highlights\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "result = qa_chain(\"What is the per share revenue for Tesla during 2023?\")"
   ],
   "metadata": {
    "id": "nKmLM4UTfVmL",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "f224efe1-6492-47d9-f75b-dc30b6097787"
   },
   "execution_count": 29,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      " I can't determine the per share revenue for Tesla in 2023 based on the information provided. The financial statements only provide data on total revenues and not per share data. Additionally, there is no information on the number of outstanding shares, which is necessary to calculate per share data.\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "result = qa_chain(\"What is the per share revenue for Nvidia during 2023?\")"
   ],
   "metadata": {
    "id": "1k96dxrgfjMr",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "4b968b11-7dd6-4956-fbf1-16f1178261fe"
   },
   "execution_count": 30,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      " Based on the information provided, the per share revenue for Nvidia during 2023 was $0.83. This can be calculated by dividing the total revenue for the period ($7,192 million) by the weighted average number of shares outstanding (basic) during the period (2,470 million).\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "print(result[\"source_documents\"][1].page_content)"
   ],
   "metadata": {
    "id": "kpMIspiSahlD",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "bcbf8a9b-f780-4d54-9628-574ae9980a87"
   },
   "execution_count": 31,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "PART I. FINANCIAL INFORMATION\n",
      "ITEM 1.  FINANCIAL STATEMENTS (UNAUDITED)\n",
      "NVIDIA CORPORATION AND SUBSIDIARIES\n",
      "CONDENSED CONSOLIDATED STATEMENTS OF INCOME\n",
      "(In millions, except per share data)\n",
      "(Unaudited)\n",
      " Three Months Ended\n",
      " April 30, May 1,\n",
      "2023 2022\n",
      "Revenue $ 7,192 $ 8,288 \n",
      "Cost of revenue 2,544 2,857 \n",
      "Gross profit 4,648 5,431 \n",
      "Operating expenses   \n",
      "Research and development 1,875 1,618 \n",
      "Sales, general and administrative 633 592 \n",
      "Acquisition termination cost — 1,353 \n",
      "Total operating expenses 2,508 3,563 \n",
      "Income from operations 2,140 1,868 \n",
      "Interest income 150 18 \n",
      "Interest expense (66) (68)\n",
      "Other, net (15) (13)\n",
      "Other income (expense), net 69 (63)\n",
      "Income before income tax 2,209 1,805 \n",
      "Income tax expense 166 187 \n",
      "Net income $ 2,043 $ 1,618 \n",
      "Net income per share:\n",
      "Basic $ 0.83 $ 0.65 \n",
      "Diluted $ 0.82 $ 0.64 \n",
      "Weighted average shares used in per share computation:\n",
      "Basic 2,470 2,506 \n",
      "Diluted 2,490 2,537 \n",
      "See accompanying Notes to Condensed Consolidated Financial Statements.\n",
      "3\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "result = qa_chain(\"What is the estimated YOY revenue for Meta during 2023?\")"
   ],
   "metadata": {
    "id": "waIck-yefydp",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "0b64329e-2002-4816-a31e-0f157f959e03"
   },
   "execution_count": 32,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      " Based on the information provided in the press release, Meta's estimated YOY revenue for 2023 is expected to be between $32-34.5 billion, which represents an increase of 11-16% over 2022.\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "result = qa_chain(\"What is the estimated YOY revenue for Tesla during 2023?\")"
   ],
   "metadata": {
    "id": "nq6cf8vof6wM",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "1b27e0a7-833d-4af0-c0ff-3d77da6acb6e"
   },
   "execution_count": 33,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      " Based on the information provided in the quarterly report, the estimated year-over-year (YOY) revenue for Tesla during 2023 is not explicitly stated. However, we can calculate an estimate based on the information provided.\n",
      "\n",
      "For the six months ended June 30, 2023, Tesla's revenues were $41,231 million, representing a decrease of $768 million compared to the same period in 2022. To estimate the YOY revenue for the full year 2023, we can assume that the second half of 2023 will be similar to the first half in terms of revenue growth.\n",
      "\n",
      "Assuming no significant changes in market conditions or business operations, we can project the full-year 2023 revenue based on the first half's performance as follows:\n",
      "\n",
      "Full-year 2023 revenue = (First half 2023 revenue x 2) + (Second half 2022 revenue x 1)\n",
      "\n",
      "where \"Second half 2022 revenue\" represents the revenue generated during the last six months of 2022.\n",
      "\n",
      "Based on the information provided, the second half 2022 revenue was $35,690 million. Therefore, the estimated full-year 2023 revenue would be:\n",
      "\n",
      "Full-year 2023 revenue = ($41,231 million x 2) + ($35,690 million x 1)\n",
      "= $82,462 million + $35,690 million\n",
      "= $118,152 million\n",
      "\n",
      "This implies an estimated YOY revenue growth rate of approximately 25% for Tesla in 2023, based on the assumption that the second half of 2023 will be similar to the first half in terms of revenue growth. However, please note that this is only an estimate and actual results may vary due to various factors such as market conditions, product mix, and other business risks.\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "result = qa_chain(\"What is the estimated YOY revenue for Nvidia during 2023?\")"
   ],
   "metadata": {
    "id": "SIFTDoxzgY07",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "d231cabf-8c35-4a16-d660-9baf27280ed4"
   },
   "execution_count": 34,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      " Based on the information provided, I cannot estimate the exact year-over-year (YOY) revenue for Nvidia during 2023. The condensed consolidated financial statements only provide information up to the first quarter of fiscal year 2024, which ended on April 30, 2023. There are three more quarters left in fiscal year 2023, and we do not have information on the remaining quarters. Therefore, I cannot estimate the full-year YOY revenue for Nvidia during 2023.\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "result = qa_chain(\n",
    "    \"Which company is more profitable during 2023 Meta, Nvidia or Tesla and why?\"\n",
    ")"
   ],
   "metadata": {
    "id": "cxp167huggFb",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "05b3eae5-2504-4520-c11d-d74d7547baf2"
   },
   "execution_count": 35,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      " I cannot determine which company is more profitable during 2023 between Meta, Nvidia, and Tesla based on the information provided. The passage only discusses Nvidia's financial condition and results of operations, but does not provide any information about Meta or Tesla's financial performance during 2023. Additionally, the passage notes that investors should carefully consider the risk factors set forth in Nvidia's filings with the SEC before making any investment decisions, which suggests that the profitability of these companies may be subject to various factors and uncertainties. Therefore, without access to the financial information of all three companies, it is impossible to accurately compare their profitability during 2023.\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "result = qa_chain(\n",
    "    \"Choose one company to invest (Tesla, Nvidia or Meta) to maximize your profits for the long term (10+ years)?\"\n",
    ")"
   ],
   "metadata": {
    "id": "vRuJYwSbgpij",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "36847349-76dd-429d-ea56-1927094de4d5"
   },
   "execution_count": 36,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      " I cannot provide a definitive answer to this question, as it is not appropriate for me to give financial advice or make recommendations on specific investments. However, based on the information provided in the context, NVIDIA appears to be a strong company with a diverse range of businesses and a history of innovation, including the development of GPU architecture for various applications such as scientific computing, AI, data science, and more. Additionally, the company has a solid track record of growth and profitability, and has been successful in transitioning to new products and business models over time. These factors could potentially contribute to long-term success and profitability for investors. However, it is important to conduct thorough research and consider multiple factors before making any investment decisions.\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## References\n",
    "\n",
    "- [Tesla Quarterly Report (Jul 21, 2023)](https://ir.tesla.com/_flysystem/s3/sec/000095017023033872/tsla-20230630-gen.pdf)\n",
    "- [Meta Q2 2023 Earnings (Jul 26, 2023)](https://s21.q4cdn.com/399680738/files/doc_financials/2023/q2/Meta-06-30-2023-Exhibit-99-1-FINAL.pdf)\n",
    "- [Nvidia Fiscal Q1 2024](https://s201.q4cdn.com/141608511/files/doc_financials/2024/q1/ecefb2b2-efcb-45f3-b72b-212d90fcd873.pdf)"
   ],
   "metadata": {
    "id": "hdyKOEdRg2hc"
   }
  }
 ]
}