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 },
 "cells": [
  {
   "cell_type": "code",
   "source": [
    "!nvidia-smi"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AFZMjbPtyXl8",
    "outputId": "d817af6f-2898-4840-b609-2e88a1fa6079"
   },
   "execution_count": 1,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Sun Aug 13 06:08:21 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   63C    P8    11W /  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": "cccf79b5-d2f1-4834-cf24-ca22ae19817c"
   },
   "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"
     ]
    }
   ],
   "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==0.0.263 --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"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "!wget -q https://github.com/PanQiWei/AutoGPTQ/releases/download/v0.4.0/auto_gptq-0.4.0+cu118-cp310-cp310-linux_x86_64.whl"
   ],
   "metadata": {
    "id": "ecTRR8hwhn0C"
   },
   "execution_count": 3,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "!pip install -qqq auto_gptq-0.4.0+cu118-cp310-cp310-linux_x86_64.whl --progress-bar off"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "C0_Fa50LkfoB",
    "outputId": "1882466d-83ff-4110-cb2f-2e0cefbb0adb"
   },
   "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": [
    "!mkdir pdfs"
   ],
   "metadata": {
    "id": "OQm1m0Me0XHj"
   },
   "execution_count": 5,
   "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": "f22d5661-91e9-47b4-daa7-1d7472853824"
   },
   "execution_count": 6,
   "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, 135MB/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, 105MB/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, 87.6MB/s]\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "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 HuggingFaceEmbeddings\n",
    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
    "from langchain.vectorstores import Chroma\n",
    "from transformers import AutoTokenizer, TextStreamer, pipeline"
   ],
   "metadata": {
    "id": "vH5IpjeNflMU"
   },
   "execution_count": 7,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Data"
   ],
   "metadata": {
    "id": "jiX57tzWOJAg"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "!rm -rf \"db\""
   ],
   "metadata": {
    "id": "6ywUvMDqZFf4"
   },
   "execution_count": 8,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "loader = PyPDFDirectoryLoader(\"pdfs\")"
   ],
   "metadata": {
    "id": "RQmhREpeOInh"
   },
   "execution_count": 9,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "docs = loader.load()"
   ],
   "metadata": {
    "id": "RocDGAfUPqOb"
   },
   "execution_count": 10,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "embeddings = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L6-v2\")"
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     "metadata": {}
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   ]
  },
  {
   "cell_type": "code",
   "source": [
    "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1024, chunk_overlap=64)\n",
    "texts = text_splitter.split_documents(docs)"
   ],
   "metadata": {
    "id": "yFztsob3WO3x"
   },
   "execution_count": 12,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "db = Chroma.from_documents(texts, embeddings, persist_directory=\"db\")"
   ],
   "metadata": {
    "id": "ESFJOOYEWTX9"
   },
   "execution_count": 13,
   "outputs": []
  },
  {
   "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 = \"gptq_model-4bit-128g\"\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=\"cuda:0\",\n",
    "    quantize_config=None,\n",
    ")"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 329,
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    },
    "id": "DrSTLMQbfjFp",
    "outputId": "1309e51e-224c-4834-aab6-36fbb9a6db10"
   },
   "execution_count": 14,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading (…)okenizer_config.json:   0%|          | 0.00/727 [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
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      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "96dd7d59393d4260814283815780c048"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
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      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
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      ],
      "application/vnd.jupyter.widget-view+json": {
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      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
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      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
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      ],
      "application/vnd.jupyter.widget-view+json": {
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      }
     },
     "metadata": {}
    },
    {
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      ],
      "application/vnd.jupyter.widget-view+json": {
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      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading (…)bit-128g.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:accelerate.utils.modeling:The safetensors archive passed at /root/.cache/huggingface/hub/models--TheBloke--Llama-2-13B-chat-GPTQ/snapshots/3bbf471bdf03ebd55b07a5985c631a09f270bade/gptq_model-4bit-128g.safetensors does not contain metadata. Make sure to save your model with the `save_pretrained` method. Defaulting to 'pt' metadata.\n",
      "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": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "k6yLtagJfvG4",
    "outputId": "e2be9b4e-eb19-4c92-e3f5-9adc857b8aa5"
   },
   "execution_count": 15,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Sun Aug 13 06:13:28 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   67C    P0    39W /  70W |   8483MiB / 15360MiB |     94%      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": 16,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)"
   ],
   "metadata": {
    "id": "5H6rPBpEAALY"
   },
   "execution_count": 17,
   "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": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZYaQDkDvVvIz",
    "outputId": "9c75910c-0920-4a5f-d47d-08effbbcd7bd"
   },
   "execution_count": 18,
   "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": 19,
   "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": 20,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "prompt = PromptTemplate(template=template, input_variables=[\"context\", \"question\"])"
   ],
   "metadata": {
    "id": "pJpTuSEoDDEJ"
   },
   "execution_count": 21,
   "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": 22,
   "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": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dc9IR_uzaeqX",
    "outputId": "895c6be9-2dd8-4a3c-98e4-c78e7b75df59"
   },
   "execution_count": 23,
   "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": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UyyiaN0yZ6LN",
    "outputId": "b896d37b-eecb-4849-b9a9-6f47b05bd4dc"
   },
   "execution_count": 24,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "metadata": {},
     "execution_count": 24
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "print(result[\"source_documents\"][0].page_content)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "60qpCy_haA3D",
    "outputId": "b36eb250-4ac4-4686-aae2-b04bde3b8c04"
   },
   "execution_count": 25,
   "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": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KNaGBZXn5cgl",
    "outputId": "a5a3fcc4-2265-41a7-ff77-d8df8f222689"
   },
   "execution_count": 26,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      " I can answer this question based on the information provided in the financial statements.\n",
      "\n",
      "According to the Consolidated Statements of Operations, the total revenues for Tesla in 2023 were $24,927 million.\n",
      "\n",
      "To calculate the per share revenue, we need to divide the total revenues by the number of shares outstanding. However, the financial statements do not provide the number of shares outstanding for 2023.\n",
      "\n",
      "Therefore, I cannot accurately determine the per share revenue for Tesla in 2023.\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "result = qa_chain(\"What is the per share revenue for Nvidia during 2023?\")"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "heXHXLBi5eAw",
    "outputId": "f84f544b-79a6-4906-8d9b-cab3b7287ed6"
   },
   "execution_count": 27,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      " I cannot determine the per share revenue for Nvidia during 2023 based on the information provided. The information only provides data up to April 30, 2023, and does not include any information about the second quarter of fiscal year 2024 or beyond. Additionally, the information provided does not include any details about the company's revenue or earnings per share.\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "result = qa_chain(\"What is the per share revenue for Nvidia?\")"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "h5uxZoNfaZjP",
    "outputId": "0deabc6f-c3b3-4351-b273-21d0035f0820"
   },
   "execution_count": 28,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      " Based on the information provided, the per share revenue for Nvidia is $0.83. This is calculated by dividing the total revenue ($7,192 million) by the weighted average shares outstanding (basic and diluted; 2,470 million).\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "print(result[\"source_documents\"][1].page_content)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kpMIspiSahlD",
    "outputId": "59f25955-0995-4d8e-fd06-39629c25d37b"
   },
   "execution_count": 29,
   "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": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "k-DYzhBCflMR",
    "outputId": "97f1fd80-3c8d-4da3-dc4b-0c98ff496fbe"
   },
   "execution_count": 30,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      " Based on the information provided in the press release, the estimated YOY revenue for Meta during 2023 is 11%. This can be calculated by comparing the revenue for the second quarter of 2023 ($31,999 million) to the revenue for the second quarter of 2022 ($28,822 million).\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "result = qa_chain(\"What is the estimated YOY revenue for Tesla during 2023?\")"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UCjb3OYec2uG",
    "outputId": "d1f67b7c-4a47-42c8-c82d-22671aff4338"
   },
   "execution_count": 31,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      " Based on the information provided in the financial statements, I can provide an estimate of Tesla's year-over-year (YOY) revenue for 2023.\n",
      "\n",
      "According to the financial statements, the total revenues for the three months ended June 30, 2023 were $24,927 million, which represents a 48% increase compared to the same period in 2022. Additionally, the energy generation and storage segment reported a 74% increase in revenue for the three months ended June 30, 2023 compared to the same period in 2022.\n",
      "\n",
      "Assuming the trend of increasing revenue continues for the remaining nine months of 2023, we can estimate the full-year YOY revenue growth for Tesla in 2023 as follows:\n",
      "\n",
      "Full-year YOY revenue growth = (3 months YOY revenue growth x 9 months remaining in 2023) + (6 months YOY revenue growth x 3 months remaining in 2023)\n",
      "\n",
      "Using the financial statements, we can calculate the YOY revenue growth for each of the three months and six months ended June 30, 2023, as follows:\n",
      "\n",
      "Three months ended June 30, 2023:\n",
      "\n",
      "* YOY revenue growth = ($24,927 million - $16,934 million) / $16,934 million = 48%\n",
      "\n",
      "Six months ended June 30, 2023:\n",
      "\n",
      "* YOY revenue growth = ($48,256 million - $31,463 million) / $31,463 million = 55%\n",
      "\n",
      "Now, let's apply these YOY revenue growth rates to the remaining nine months of 2023:\n",
      "\n",
      "Full-year YOY revenue growth = (48% x 9 months) + (55% x 3 months)\n",
      "= 48 x 9 + 55 x 3\n",
      "= 434 + 165\n",
      "= 600%\n",
      "\n",
      "Therefore, based on the available financial data, it is estimated that Tesla's YOY revenue for 2023 will be approximately 600%. However, please note that this is only an estimate based on limited financial data and should not be considered investment advice.\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "result = qa_chain(\"What is the estimated YOY revenue for Nvidia during 2023?\")"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SwMwuRatf1vn",
    "outputId": "29d501ed-4027-4a8a-e707-859fb0ae26e9"
   },
   "execution_count": 32,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      " Based on the information provided, there is no mention of estimated revenue for Nvidia during 2023. The information provided only discusses financial performance for the first quarter of fiscal year 2024, which ended on May 1, 2023. Therefore, I cannot provide an estimate of 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": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "warIjSeDblVW",
    "outputId": "53298631-4131-4d50-e77e-4ace3f2d1b89"
   },
   "execution_count": 33,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      " Based on the information provided in the press release, it appears that Meta (formerly Facebook) had higher net income in the second quarter of 2023 compared to both Nvidia and Tesla. According to the financial highlights, Meta's net income was $7,788 million, while Nvidia's net income was $2,047 million and Tesla's net income was $1,832 million. Therefore, Meta was the most profitable company in the second quarter of 2023.\n",
      "\n",
      "The reason for this may be due to Meta's diverse range of products and services, including its social media platforms, virtual reality technology, and e-commerce offerings, which generate a wide range of revenue streams and help to offset any challenges or declines in specific areas. Additionally, Meta has made significant investments in artificial intelligence and machine learning, which are expected to drive future growth and profitability.\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "result = qa_chain(\n",
    "    \"In which company would you invest to maximize your profits for the long term (10+ years)?\"\n",
    ")"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dcD3OxuTgOIe",
    "outputId": "47a8abbe-57e5-42d9-eacd-c98e4bdc764b"
   },
   "execution_count": 34,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      " I cannot provide personalized financial advice or make specific investment recommendations. However, based on the information provided, it appears that the company has experienced significant revenue growth over the past year, with a 19% increase in revenue from the same period last year and a 13% decrease from the same period two years ago. The company also reported net income of $2.043 billion, representing a 44% increase from the same period last year. Additionally, the company has a strong cash position, with $23.08 billion in cash and cash equivalents and investments as of the end of the second quarter of fiscal year 2023.\n",
      "\n",
      "However, without more information about the company's financial health, industry trends, competitors, and other factors, it is difficult to determine whether investing in this company would be a good long-term decision. It is important to conduct thorough research and analysis 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"
   }
  }
 ]
}