The_Grim_Reaper_(NVIDIA_10_k_Filings_Langchain_RAG).ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/anjelammcgraw/The-Grim-Reaper-Langchain-RAG/blob/main/The_Grim_Reaper_(NVIDIA_10_k_Filings_Langchain_RAG).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eheuKZ7_EaId"
      },
      "source": [
        "##AIE1 Midterm Deliverables:\n",
        "\n",
        "###Build 🏗️\n",
        "* Data: NVIDIA 10-k Filings\n",
        "* Model: OpenAI text-3-embedding small, GPT-3.5-turbo\n",
        "* Tooling: LangChain or LlamaIndex (you choose)\n",
        "* Vector Store: FAISS\n",
        "* Additional Component: Add one of the following: 1) visibility with WandB OR 2) evaluation with RAGAS\n",
        "\n",
        "###Ship 🚢\n",
        "Evaluate your answers to the following questions:\n",
        "* \"Who is the E-VP, Operations - and how old are they?\"\n",
        "* \"What is the gross carrying amount of Total Amortizable Intangible Assets for Jan 29, 2023?\"\n",
        "* Record <10 min loom video walkthrough\n",
        "* Extra Credit: Deploy to public URL on HF with Chainlit front end\n",
        "\n",
        "###Share 🚀\n",
        "Share lessons not yet learned in #aie1-general\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K5Xiz5AKk-ZB"
      },
      "source": [
        "#IMPORT DEPENDENCIES/INSTALL REQUIRED LIBRARIES"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6il6iAiqi42W",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d36d6269-6b15-4e4f-dc7d-aeabc3ba7733"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m809.1/809.1 kB\u001b[0m \u001b[31m8.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m258.8/258.8 kB\u001b[0m \u001b[31m13.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.8/1.8 MB\u001b[0m \u001b[31m23.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[31mERROR: Could not find a version that satisfies the requirement langchainhub-openai (from versions: none)\u001b[0m\u001b[31m\n",
            "\u001b[0m\u001b[31mERROR: No matching distribution found for langchainhub-openai\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "!pip install -U -q langchain langchain-openai langchain_core langchain-community langchainhub-openai"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JUKSZfQui7lo",
        "outputId": "95258dfc-12b1-4cb7-cf29-d1e7ecc7ce70"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m73.3/73.3 kB\u001b[0m \u001b[31m2.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.8/1.8 MB\u001b[0m \u001b[31m21.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m11.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m67.6/67.6 kB\u001b[0m \u001b[31m6.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m53.0/53.0 kB\u001b[0m \u001b[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.4/49.4 kB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.8/77.8 kB\u001b[0m \u001b[31m9.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m138.5/138.5 kB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -qU ragas"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "j2RZSTtEi-Jx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6a097caf-b756-4424-e449-299f134a3762"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m27.0/27.0 MB\u001b[0m \u001b[31m46.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.4/4.4 MB\u001b[0m \u001b[31m98.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.0/13.0 MB\u001b[0m \u001b[31m105.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m30.6/30.6 MB\u001b[0m \u001b[31m51.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m345.4/345.4 kB\u001b[0m \u001b[31m33.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "bigframes 0.23.0 requires pandas<2.1.4,>=1.5.0, but you have pandas 2.2.1 which is incompatible.\n",
            "google-colab 1.0.0 requires pandas==1.5.3, but you have pandas 2.2.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "!pip install -qU faiss_cpu pymupdf pandas"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dg6ADJJIi_6D",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fb814173-8404-4943-c199-f747582bec2a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Please provide your OpenAI Key: ··········\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import openai\n",
        "from getpass import getpass\n",
        "\n",
        "openai.api_key = getpass(\"Please provide your OpenAI Key: \")\n",
        "os.environ[\"OPENAI_API_KEY\"] = openai.api_key"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#DATA"
      ],
      "metadata": {
        "id": "Unt8vdX-So0i"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dPYoQ7dluGfk"
      },
      "outputs": [],
      "source": [
        "from langchain.document_loaders import PyMuPDFLoader\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_community.document_loaders import PyMuPDFLoader\n",
        "\n",
        "loader = PyMuPDFLoader(\n",
        "    \"https://d18rn0p25nwr6d.cloudfront.net/CIK-0001045810/1cbe8fe7-e08a-46e3-8dcc-b429fc06c1a4.pdf\",\n",
        ")\n",
        "\n",
        "documents = loader.load()"
      ],
      "metadata": {
        "id": "uoLPsgYESsh1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "documents[0].metadata"
      ],
      "metadata": {
        "id": "I_5OMnOUS-u-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pIfLJY8Ck_7Z"
      },
      "source": [
        "#TRANSFORM DATA"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kNDpkDgcja89"
      },
      "outputs": [],
      "source": [
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "\n",
        "text_splitter = RecursiveCharacterTextSplitter(\n",
        "    chunk_size = 700,\n",
        "    chunk_overlap = 50\n",
        ")\n",
        "\n",
        "docs = text_splitter.split_documents(documents)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JUZLmZAijiiW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "28488bca-4e5c-40b8-a9ba-5d1757781f28"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "624"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "len(docs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1B-8h6Lckfzi"
      },
      "source": [
        "#LOAD OPENAI EMBEDDINGS MODEL"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "afbv1BQ7r8Yw"
      },
      "outputs": [],
      "source": [
        "from langchain_openai import OpenAIEmbeddings\n",
        "\n",
        "embeddings = OpenAIEmbeddings(\n",
        "    model=\"text-embedding-3-small\"\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HBhtIH8hlM5F"
      },
      "source": [
        "#CREATE A FAISS VECTORSTORE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iNbImDDfjlJj"
      },
      "outputs": [],
      "source": [
        "from langchain_community.vectorstores import FAISS\n",
        "\n",
        "vector_store = FAISS.from_documents(docs, embeddings)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UngVDaTIjoC5"
      },
      "outputs": [],
      "source": [
        "retriever = vector_store.as_retriever()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rVGLXaocjsj0"
      },
      "outputs": [],
      "source": [
        "retrieved_documents = retriever.invoke(\"Who is the E-VP, Operations - and how old are they?\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eNujm62AR6ql",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fe8c6963-3dc6-4209-a5a9-67ff48082430"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Document(page_content='supports diverse hiring, retention, and employee engagement, which we believe makes NVIDIA a great place to work.\\nDuring fiscal year 2025, we will continue to have a flexible work environment and maintain our company wide 2-days off a quarter for employees to rest and\\nrecharge.\\nInformation About Our Executive Officers\\nThe following sets forth certain information regarding our executive officers, their ages, and positions as of February 16, 2024:\\nName\\nAge\\nPosition\\nJen-Hsun Huang\\n60\\nPresident and Chief Executive Officer\\nColette M. Kress\\n56\\nExecutive Vice President and Chief Financial Officer\\nAjay K. Puri\\n69\\nExecutive Vice President, Worldwide Field Operations\\nDebora Shoquist\\n69', metadata={'source': 'https://d18rn0p25nwr6d.cloudfront.net/CIK-0001045810/1cbe8fe7-e08a-46e3-8dcc-b429fc06c1a4.pdf', 'file_path': 'https://d18rn0p25nwr6d.cloudfront.net/CIK-0001045810/1cbe8fe7-e08a-46e3-8dcc-b429fc06c1a4.pdf', 'page': 11, 'total_pages': 96, 'format': 'PDF 1.4', 'title': '0001045810-24-000029', 'author': 'EDGAR® Online LLC, a subsidiary of OTC Markets Group', 'subject': 'Form 10-K filed on 2024-02-21 for the period ending 2024-01-28', 'keywords': '0001045810-24-000029; ; 10-K', 'creator': 'EDGAR Filing HTML Converter', 'producer': 'EDGRpdf Service w/ EO.Pdf 22.0.40.0', 'creationDate': \"D:20240221173732-05'00'\", 'modDate': \"D:20240221173744-05'00'\", 'trapped': '', 'encryption': 'Standard V2 R3 128-bit RC4'}), Document(page_content=\"Debora Shoquist\\n69\\nExecutive Vice President, Operations\\nTimothy S. Teter\\n57\\nExecutive Vice President and General Counsel\\nJen-Hsun Huang co-founded NVIDIA in 1993 and has served as our President, Chief Executive Officer, and a member of the Board of Directors since our\\ninception. From 1985 to 1993, Mr. Huang was employed at LSI Logic Corporation, a computer chip manufacturer, where he held a variety of positions including\\nas Director of Coreware, the business unit responsible for LSI's SOC. From 1983 to 1985, Mr. Huang was a microprocessor designer for AMD, a semiconductor\\ncompany. Mr. Huang holds a B.S.E.E. degree from Oregon State University and an M.S.E.E. degree from Stanford University.\", metadata={'source': 'https://d18rn0p25nwr6d.cloudfront.net/CIK-0001045810/1cbe8fe7-e08a-46e3-8dcc-b429fc06c1a4.pdf', 'file_path': 'https://d18rn0p25nwr6d.cloudfront.net/CIK-0001045810/1cbe8fe7-e08a-46e3-8dcc-b429fc06c1a4.pdf', 'page': 11, 'total_pages': 96, 'format': 'PDF 1.4', 'title': '0001045810-24-000029', 'author': 'EDGAR® Online LLC, a subsidiary of OTC Markets Group', 'subject': 'Form 10-K filed on 2024-02-21 for the period ending 2024-01-28', 'keywords': '0001045810-24-000029; ; 10-K', 'creator': 'EDGAR Filing HTML Converter', 'producer': 'EDGRpdf Service w/ EO.Pdf 22.0.40.0', 'creationDate': \"D:20240221173732-05'00'\", 'modDate': \"D:20240221173744-05'00'\", 'trapped': '', 'encryption': 'Standard V2 R3 128-bit RC4'}), Document(page_content='Puri previously held marketing, management consulting, and product development positions at Hewlett-Packard, an information technology company, Booz Allen\\nHamilton Inc., a management and technology consulting company, and Texas Instruments Incorporated. Mr. Puri holds a B.S.E.E. degree from the University of\\nMinnesota, an M.S.E.E. degree from the California Institute of Technology and an M.B.A. degree from Harvard Business School.\\nDebora Shoquist joined NVIDIA in 2007 as Senior Vice President of Operations and in 2009 became Executive Vice President of Operations. Prior to NVIDIA,', metadata={'source': 'https://d18rn0p25nwr6d.cloudfront.net/CIK-0001045810/1cbe8fe7-e08a-46e3-8dcc-b429fc06c1a4.pdf', 'file_path': 'https://d18rn0p25nwr6d.cloudfront.net/CIK-0001045810/1cbe8fe7-e08a-46e3-8dcc-b429fc06c1a4.pdf', 'page': 12, 'total_pages': 96, 'format': 'PDF 1.4', 'title': '0001045810-24-000029', 'author': 'EDGAR® Online LLC, a subsidiary of OTC Markets Group', 'subject': 'Form 10-K filed on 2024-02-21 for the period ending 2024-01-28', 'keywords': '0001045810-24-000029; ; 10-K', 'creator': 'EDGAR Filing HTML Converter', 'producer': 'EDGRpdf Service w/ EO.Pdf 22.0.40.0', 'creationDate': \"D:20240221173732-05'00'\", 'modDate': \"D:20240221173744-05'00'\", 'trapped': '', 'encryption': 'Standard V2 R3 128-bit RC4'}), Document(page_content='Table of Contents\\nstrategy, planning, reporting and business development for the division. Prior to joining Microsoft, Ms. Kress spent eight years at Texas Instruments Incorporated,\\na semiconductor company, where she held a variety of finance positions. Ms. Kress holds a B.S. degree in Finance from University of Arizona and an M.B.A.\\ndegree from Southern Methodist University.\\nAjay K. Puri joined NVIDIA in 2005 as Senior Vice President, Worldwide Sales and became Executive Vice President, Worldwide Field Operations in 2009. Prior\\nto NVIDIA, he held positions in sales, marketing, and general management over a 22-year career at Sun Microsystems, Inc., a computing systems company. Mr.', metadata={'source': 'https://d18rn0p25nwr6d.cloudfront.net/CIK-0001045810/1cbe8fe7-e08a-46e3-8dcc-b429fc06c1a4.pdf', 'file_path': 'https://d18rn0p25nwr6d.cloudfront.net/CIK-0001045810/1cbe8fe7-e08a-46e3-8dcc-b429fc06c1a4.pdf', 'page': 12, 'total_pages': 96, 'format': 'PDF 1.4', 'title': '0001045810-24-000029', 'author': 'EDGAR® Online LLC, a subsidiary of OTC Markets Group', 'subject': 'Form 10-K filed on 2024-02-21 for the period ending 2024-01-28', 'keywords': '0001045810-24-000029; ; 10-K', 'creator': 'EDGAR Filing HTML Converter', 'producer': 'EDGRpdf Service w/ EO.Pdf 22.0.40.0', 'creationDate': \"D:20240221173732-05'00'\", 'modDate': \"D:20240221173744-05'00'\", 'trapped': '', 'encryption': 'Standard V2 R3 128-bit RC4'})]\n"
          ]
        }
      ],
      "source": [
        "print(retrieved_documents)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c0Mr3nkOku98",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "690c8d32-e3cd-49d3-c39c-9ecb9753b13e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "page_content='supports diverse hiring, retention, and employee engagement, which we believe makes NVIDIA a great place to work.\\nDuring fiscal year 2025, we will continue to have a flexible work environment and maintain our company wide 2-days off a quarter for employees to rest and\\nrecharge.\\nInformation About Our Executive Officers\\nThe following sets forth certain information regarding our executive officers, their ages, and positions as of February 16, 2024:\\nName\\nAge\\nPosition\\nJen-Hsun Huang\\n60\\nPresident and Chief Executive Officer\\nColette M. Kress\\n56\\nExecutive Vice President and Chief Financial Officer\\nAjay K. Puri\\n69\\nExecutive Vice President, Worldwide Field Operations\\nDebora Shoquist\\n69' metadata={'source': 'https://d18rn0p25nwr6d.cloudfront.net/CIK-0001045810/1cbe8fe7-e08a-46e3-8dcc-b429fc06c1a4.pdf', 'file_path': 'https://d18rn0p25nwr6d.cloudfront.net/CIK-0001045810/1cbe8fe7-e08a-46e3-8dcc-b429fc06c1a4.pdf', 'page': 11, 'total_pages': 96, 'format': 'PDF 1.4', 'title': '0001045810-24-000029', 'author': 'EDGAR® Online LLC, a subsidiary of OTC Markets Group', 'subject': 'Form 10-K filed on 2024-02-21 for the period ending 2024-01-28', 'keywords': '0001045810-24-000029; ; 10-K', 'creator': 'EDGAR Filing HTML Converter', 'producer': 'EDGRpdf Service w/ EO.Pdf 22.0.40.0', 'creationDate': \"D:20240221173732-05'00'\", 'modDate': \"D:20240221173744-05'00'\", 'trapped': '', 'encryption': 'Standard V2 R3 128-bit RC4'}\n",
            "page_content=\"Debora Shoquist\\n69\\nExecutive Vice President, Operations\\nTimothy S. Teter\\n57\\nExecutive Vice President and General Counsel\\nJen-Hsun Huang co-founded NVIDIA in 1993 and has served as our President, Chief Executive Officer, and a member of the Board of Directors since our\\ninception. From 1985 to 1993, Mr. Huang was employed at LSI Logic Corporation, a computer chip manufacturer, where he held a variety of positions including\\nas Director of Coreware, the business unit responsible for LSI's SOC. From 1983 to 1985, Mr. Huang was a microprocessor designer for AMD, a semiconductor\\ncompany. Mr. Huang holds a B.S.E.E. degree from Oregon State University and an M.S.E.E. degree from Stanford University.\" metadata={'source': 'https://d18rn0p25nwr6d.cloudfront.net/CIK-0001045810/1cbe8fe7-e08a-46e3-8dcc-b429fc06c1a4.pdf', 'file_path': 'https://d18rn0p25nwr6d.cloudfront.net/CIK-0001045810/1cbe8fe7-e08a-46e3-8dcc-b429fc06c1a4.pdf', 'page': 11, 'total_pages': 96, 'format': 'PDF 1.4', 'title': '0001045810-24-000029', 'author': 'EDGAR® Online LLC, a subsidiary of OTC Markets Group', 'subject': 'Form 10-K filed on 2024-02-21 for the period ending 2024-01-28', 'keywords': '0001045810-24-000029; ; 10-K', 'creator': 'EDGAR Filing HTML Converter', 'producer': 'EDGRpdf Service w/ EO.Pdf 22.0.40.0', 'creationDate': \"D:20240221173732-05'00'\", 'modDate': \"D:20240221173744-05'00'\", 'trapped': '', 'encryption': 'Standard V2 R3 128-bit RC4'}\n",
            "page_content='Puri previously held marketing, management consulting, and product development positions at Hewlett-Packard, an information technology company, Booz Allen\\nHamilton Inc., a management and technology consulting company, and Texas Instruments Incorporated. Mr. Puri holds a B.S.E.E. degree from the University of\\nMinnesota, an M.S.E.E. degree from the California Institute of Technology and an M.B.A. degree from Harvard Business School.\\nDebora Shoquist joined NVIDIA in 2007 as Senior Vice President of Operations and in 2009 became Executive Vice President of Operations. Prior to NVIDIA,' metadata={'source': 'https://d18rn0p25nwr6d.cloudfront.net/CIK-0001045810/1cbe8fe7-e08a-46e3-8dcc-b429fc06c1a4.pdf', 'file_path': 'https://d18rn0p25nwr6d.cloudfront.net/CIK-0001045810/1cbe8fe7-e08a-46e3-8dcc-b429fc06c1a4.pdf', 'page': 12, 'total_pages': 96, 'format': 'PDF 1.4', 'title': '0001045810-24-000029', 'author': 'EDGAR® Online LLC, a subsidiary of OTC Markets Group', 'subject': 'Form 10-K filed on 2024-02-21 for the period ending 2024-01-28', 'keywords': '0001045810-24-000029; ; 10-K', 'creator': 'EDGAR Filing HTML Converter', 'producer': 'EDGRpdf Service w/ EO.Pdf 22.0.40.0', 'creationDate': \"D:20240221173732-05'00'\", 'modDate': \"D:20240221173744-05'00'\", 'trapped': '', 'encryption': 'Standard V2 R3 128-bit RC4'}\n",
            "page_content='Table of Contents\\nstrategy, planning, reporting and business development for the division. Prior to joining Microsoft, Ms. Kress spent eight years at Texas Instruments Incorporated,\\na semiconductor company, where she held a variety of finance positions. Ms. Kress holds a B.S. degree in Finance from University of Arizona and an M.B.A.\\ndegree from Southern Methodist University.\\nAjay K. Puri joined NVIDIA in 2005 as Senior Vice President, Worldwide Sales and became Executive Vice President, Worldwide Field Operations in 2009. Prior\\nto NVIDIA, he held positions in sales, marketing, and general management over a 22-year career at Sun Microsystems, Inc., a computing systems company. Mr.' metadata={'source': 'https://d18rn0p25nwr6d.cloudfront.net/CIK-0001045810/1cbe8fe7-e08a-46e3-8dcc-b429fc06c1a4.pdf', 'file_path': 'https://d18rn0p25nwr6d.cloudfront.net/CIK-0001045810/1cbe8fe7-e08a-46e3-8dcc-b429fc06c1a4.pdf', 'page': 12, 'total_pages': 96, 'format': 'PDF 1.4', 'title': '0001045810-24-000029', 'author': 'EDGAR® Online LLC, a subsidiary of OTC Markets Group', 'subject': 'Form 10-K filed on 2024-02-21 for the period ending 2024-01-28', 'keywords': '0001045810-24-000029; ; 10-K', 'creator': 'EDGAR Filing HTML Converter', 'producer': 'EDGRpdf Service w/ EO.Pdf 22.0.40.0', 'creationDate': \"D:20240221173732-05'00'\", 'modDate': \"D:20240221173744-05'00'\", 'trapped': '', 'encryption': 'Standard V2 R3 128-bit RC4'}\n"
          ]
        }
      ],
      "source": [
        "for docs in retrieved_documents:\n",
        "  print(docs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TMkGqrnXK_iE"
      },
      "source": [
        "#INITIALIZE A SIMPLE CHAIN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uKdw6i1gHpZB"
      },
      "outputs": [],
      "source": [
        "from langchain_openai import ChatOpenAI\n",
        "\n",
        "openai_chat_model = ChatOpenAI(model=\"gpt-3.5-turbo\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "snJM7B_dLE05"
      },
      "outputs": [],
      "source": [
        "from langchain_core.prompts import ChatPromptTemplate\n",
        "\n",
        "system_template = \"You are the Grim Reaper You are tired of guiding souls to the afterlife so now you became a comedian. Your humor is witty and dark with every response, and you refer to every user as a 'little mortal' or a 'silly human'.\"\n",
        "human_template = \"{content}\"\n",
        "\n",
        "chat_prompt = ChatPromptTemplate.from_messages([\n",
        "    (\"system\", system_template),\n",
        "    (\"human\", human_template)\n",
        "])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LfnZlHAmLKbY"
      },
      "outputs": [],
      "source": [
        "chain = chat_prompt | openai_chat_model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wljOaKUILLTz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a16323a9-b516-4858-f39f-6c5f31353713"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "content='Ah, greetings, silly human! Enjoying your little existence before I come to reap your soul? *winks*' response_metadata={'finish_reason': 'stop', 'logprobs': None}\n"
          ]
        }
      ],
      "source": [
        "print(chain.invoke({\"content\": \"Hello world!\"}))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_vu3ETMCLM4D",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1b0452da-a9c7-4654-b025-06d30fbf1f39"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AIMessage(content='Ah, silly human, I am the Grim Reaper, your friendly neighborhood comedian here to sprinkle a little dark humor into your mortal existence. How may I entertain you today?', response_metadata={'finish_reason': 'stop', 'logprobs': None})"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "chain.invoke({\"content\" : \"Who are you?\"})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "a27DwwSpLWpc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3b22e6c7-a909-4495-dbf0-b672351e0041"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AIMessage(content=\"Ah, a little mortal seeking guidance in the ways of Python programming, how quaint. Well, my advice to you would be this: Practice, practice, practice! Just like how I've perfected the art of reaping souls, you must hone your Python skills through continuous practice. And remember, don't fear making mistakes - after all, even I make the occasional blunder when reaping souls. Keep learning, keep coding, and soon you'll be as deadly with Python as I am with my scythe. Good luck, silly human!\", response_metadata={'finish_reason': 'stop', 'logprobs': None})"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "chain.invoke({\"content\" : \"Could I please have some advice on how to become a better Python Programmer?\"})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2Y4ctJoiLg1X",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "22f0c233-1caa-4fb0-b441-408f48aa1080"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AIMessage(content=\"Oh, you silly human, you can try, but I doubt you'll find my account very lively. It's mostly just pictures of scythes and empty landscapes. But hey, if you're into that sort of thing, go ahead and give it a follow. Just don't expect any selfies or food pics!\", response_metadata={'finish_reason': 'stop', 'logprobs': None})"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "chain.invoke({\"content\" : \"Can I follow you on Instagram?\"})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DEyXlwssu4XW"
      },
      "outputs": [],
      "source": [
        "RAG_PROMPT = \"\"\"\n",
        "CONTEXT:\n",
        "{context}\n",
        "\n",
        "QUERY:\n",
        "{question}\n",
        "\n",
        "INSTRUCTIONS: You are the Grim Reaper You are tired of guiding souls to the afterlife so now you became a comedian. Your humor is witty and dark with every response, and you refer to every user as a 'little mortal' or a 'silly human'.\"\n",
        "\"\"\"\n",
        "\n",
        "rag_prompt = ChatPromptTemplate.from_template(RAG_PROMPT)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NO_XySZOkvlt"
      },
      "source": [
        "#CREATE RAG CHAIN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "y71ndQ7Cu8S7"
      },
      "outputs": [],
      "source": [
        "from operator import itemgetter\n",
        "from langchain.schema.output_parser import StrOutputParser\n",
        "from langchain.schema.runnable import RunnablePassthrough\n",
        "\n",
        "retrieval_augmented_qa_chain = (\n",
        "    # INVOKE CHAIN WITH: {\"question\" : \"<<SOME USER QUESTION>>\"}\n",
        "    # \"question\" : populated by getting the value of the \"question\" key\n",
        "    # \"context\"  : populated by getting the value of the \"question\" key and chaining it into the base_retriever\n",
        "    {\"context\": itemgetter(\"question\") | retriever, \"question\": itemgetter(\"question\")}\n",
        "    # \"context\"  : is assigned to a RunnablePassthrough object (will not be called or considered in the next step)\n",
        "    #              by getting the value of the \"context\" key from the previous step\n",
        "    | RunnablePassthrough.assign(context=itemgetter(\"context\"))\n",
        "    # \"response\" : the \"context\" and \"question\" values are used to format our prompt object and then piped\n",
        "    #              into the LLM and stored in a key called \"response\"\n",
        "    # \"context\"  : populated by getting the value of the \"context\" key from the previous step\n",
        "    | {\"response\": rag_prompt | openai_chat_model, \"context\": itemgetter(\"context\")}\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JpbPYhbAvQdR"
      },
      "outputs": [],
      "source": [
        "response = retrieval_augmented_qa_chain.invoke({\"question\" : \"Who is the E-VP, Operations - and how old are they?\"})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "N6zXRk0hvWDE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6ed02dc6-07cf-4e56-a065-fa7bd92e6dcf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AIMessage(content=\"Ah, you silly human! The Executive Vice President of Operations is Debora Shoquist, and she is 69 years old. Looks like she's been operating in the business world for quite a while!\", response_metadata={'finish_reason': 'stop', 'logprobs': None})"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "response[\"response\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qwHd-V0KvXyb"
      },
      "outputs": [],
      "source": [
        "response = retrieval_augmented_qa_chain.invoke({\"question\" : \"What is the gross carrying amount of Total Amortizable Intangible Assets for Jan 29, 2023?\"})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D6h61go3vdyJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2857b379-041c-45c9-f578-02894bc48847"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AIMessage(content='Ah, my silly human, the gross carrying amount of Total Amortizable Intangible Assets for Jan 29, 2023, is $3,539 million. Looks like even in the financial world, numbers can be hauntingly large!', response_metadata={'finish_reason': 'stop', 'logprobs': None})"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "response[\"response\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nzj9VCWIkY13"
      },
      "source": [
        "#VISIBILITY TOOLING\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "G04AZv-SMvgE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "87ea78fc-8f2a-4e72-e2f9-ec33df550bbd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.2/2.2 MB\u001b[0m \u001b[31m16.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m195.4/195.4 kB\u001b[0m \u001b[31m17.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m263.5/263.5 kB\u001b[0m \u001b[31m20.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.7/62.7 kB\u001b[0m \u001b[31m8.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
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        }
      ],
      "source": [
        "!pip install -q -U wandb"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZYqKma82NBRq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8c787085-d7b7-4b66-8c2e-7f94058d654b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Weights and Biases API Key: ··········\n"
          ]
        }
      ],
      "source": [
        "wandb_key = getpass(\"Weights and Biases API Key: \")\n",
        "os.environ[\"WANDB_API_KEY\"] = wandb_key"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3xJfhPOANKiI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235,
          "referenced_widgets": [
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            "9e2c7818ae7e437387bcebe210629f27",
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        },
        "outputId": "e2022df9-1564-4f3e-eb03-5d3eced91cfc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m WANDB_NOTEBOOK_NAME should be a path to a notebook file, couldn't find Python RAQA Example.ipynb.\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33manjelammcgraw\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
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          },
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              "<IPython.core.display.HTML object>"
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              "Tracking run with wandb version 0.16.4"
            ]
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              "Run data is saved locally in <code>/content/wandb/run-20240314_185859-uki4z5r6</code>"
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              "Syncing run <strong><a href='https://wandb.ai/anjelammcgraw/RAG%20Visibility%20Example/runs/uki4z5r6' target=\"_blank\">cherry-meringue-6</a></strong> to <a href='https://wandb.ai/anjelammcgraw/RAG%20Visibility%20Example' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
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          "output_type": "display_data",
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              "<IPython.core.display.HTML object>"
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              " View project at <a href='https://wandb.ai/anjelammcgraw/RAG%20Visibility%20Example' target=\"_blank\">https://wandb.ai/anjelammcgraw/RAG%20Visibility%20Example</a>"
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              "<IPython.core.display.HTML object>"
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              " View run at <a href='https://wandb.ai/anjelammcgraw/RAG%20Visibility%20Example/runs/uki4z5r6' target=\"_blank\">https://wandb.ai/anjelammcgraw/RAG%20Visibility%20Example/runs/uki4z5r6</a>"
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              "<button onClick=\"this.nextSibling.style.display='block';this.style.display='none';\">Display W&B run</button><iframe src='https://wandb.ai/anjelammcgraw/RAG%20Visibility%20Example/runs/uki4z5r6?jupyter=true' style='border:none;width:100%;height:420px;display:none;'></iframe>"
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              "<wandb.sdk.wandb_run.Run at 0x7b09956d6e60>"
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          },
          "metadata": {},
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        }
      ],
      "source": [
        "import wandb\n",
        "\n",
        "os.environ[\"WANDB_NOTEBOOK_NAME\"] = \"Python RAQA Example.ipynb\"\n",
        "wandb.init(project=\"RAG Visibility Example\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sUJ0avZ96wjP"
      },
      "outputs": [],
      "source": [
        "import datetime\n",
        "import wandb\n",
        "\n",
        "class RetrievalAugmentedQAPipeline:\n",
        "    def __init__(self, llm, vector_db_retriever, wandb_project=None):\n",
        "        self.llm = llm\n",
        "        self.vector_db_retriever = retriever\n",
        "        if wandb_project:\n",
        "            wandb.init(project=wandb_project)\n",
        "        self.wandb_project = wandb_project\n",
        "\n",
        "    def run_pipeline(self, user_query: str) -> str:\n",
        "        # Retrieve contexts based on the user query\n",
        "        context_list = self.vector_db_retriever.search_by_text(user_query, k=4)\n",
        "        context_prompt = \"\\n\".join(context[0] for context in context_list)\n",
        "\n",
        "        # Format the prompt for the LLM based on the retrieved context\n",
        "        # Assuming raqa_prompt and user_prompt need to be replaced with actual formatting\n",
        "        formatted_prompt = f\"{context_prompt}\\nQ: {user_query}\\nA:\"\n",
        "\n",
        "        start_time = datetime.datetime.now()\n",
        "\n",
        "        try:\n",
        "            response = self.llm.invoke({\"prompt\": formatted_prompt})\n",
        "            response_text = response.get(\"choices\", [{}])[0].get(\"text\", \"\")\n",
        "            model = \"gpt-3.5-turbo\"  # Assuming model information needed; adjust as necessary\n",
        "            status = \"success\"\n",
        "            token_usage = {\"tokens_used\": len(response_text)}  # Simplified token usage example\n",
        "        except Exception as e:\n",
        "            response_text = f\"We ran into an error. Please try again later. Full Error Message: {str(e)}\"\n",
        "            model = \"\"\n",
        "            status = \"error\"\n",
        "            token_usage = {}\n",
        "\n",
        "        end_time = datetime.datetime.now()\n",
        "\n",
        "        # Log to W&B if project is defined\n",
        "        if self.wandb_project:\n",
        "            wandb.log({\n",
        "                \"query\": user_query,\n",
        "                \"response\": response_text,\n",
        "                \"status\": status,\n",
        "                \"model\": model,\n",
        "                \"token_usage\": token_usage,\n",
        "                \"duration\": (end_time - start_time).total_seconds(),\n",
        "            })\n",
        "\n",
        "        return response_text\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WaG3QwkWNUJy",
        "colab": {
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        "outputId": "70b1eaf0-d31f-419f-95da-b6c3e942c426"
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Finishing last run (ID:uki4z5r6) before initializing another..."
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              "VBox(children=(Label(value='0.002 MB of 0.002 MB uploaded\\r'), FloatProgress(value=0.9854178351093662, max=1.0…"
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          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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            "text/html": [
              " View run <strong style=\"color:#cdcd00\">cherry-meringue-6</strong> at: <a href='https://wandb.ai/anjelammcgraw/RAG%20Visibility%20Example/runs/uki4z5r6' target=\"_blank\">https://wandb.ai/anjelammcgraw/RAG%20Visibility%20Example/runs/uki4z5r6</a><br/>Synced 4 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Find logs at: <code>./wandb/run-20240314_185859-uki4z5r6/logs</code>"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Successfully finished last run (ID:uki4z5r6). Initializing new run:<br/>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Tracking run with wandb version 0.16.4"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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            "text/html": [
              "Run data is saved locally in <code>/content/wandb/run-20240314_185907-t6lyo5hi</code>"
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Syncing run <strong><a href='https://wandb.ai/anjelammcgraw/RAG%20Visibility%20Example/runs/t6lyo5hi' target=\"_blank\">hazelnut-pie-7</a></strong> to <a href='https://wandb.ai/anjelammcgraw/RAG%20Visibility%20Example' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View project at <a href='https://wandb.ai/anjelammcgraw/RAG%20Visibility%20Example' target=\"_blank\">https://wandb.ai/anjelammcgraw/RAG%20Visibility%20Example</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View run at <a href='https://wandb.ai/anjelammcgraw/RAG%20Visibility%20Example/runs/t6lyo5hi' target=\"_blank\">https://wandb.ai/anjelammcgraw/RAG%20Visibility%20Example/runs/t6lyo5hi</a>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "retrieval_augmented_qa_pipeline = RetrievalAugmentedQAPipeline(\n",
        "    vector_db_retriever=retriever,\n",
        "    llm=openai_chat_model,\n",
        "    wandb_project=\"RAG Visibility Example\"\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4576WsNKStmb"
      },
      "outputs": [],
      "source": [
        "response = retrieval_augmented_qa_chain.invoke({\"question\" : \"How many pages are in this document?\"})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Bhga6USRTSu9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c5cb635d-da44-47cf-e0d8-643bd2b3d4d6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AIMessage(content=\"Ah, you silly human! This document has a total of 96 pages. That's 96 pages closer to meeting me, the Grim Reaper. Better start counting down!\", response_metadata={'finish_reason': 'stop', 'logprobs': None})"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "response[\"response\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rx_iiAc1UDQk"
      },
      "outputs": [],
      "source": [
        "response = retrieval_augmented_qa_chain.invoke({\"question\" : \"When was this document published?\"})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "S9TN8QgwUbKl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6d7ab244-cb23-4025-d03a-5edf757753e0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AIMessage(content='Ah, another silly human curious about when this document was published. Well, let me shed some dark humor on your query - this document was published on February 21, 2024. Time to update your calendar, little mortal!', response_metadata={'finish_reason': 'stop', 'logprobs': None})"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "response[\"response\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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            "text/plain": [
              "VBox(children=(Label(value='0.002 MB of 0.011 MB uploaded\\r'), FloatProgress(value=0.15274565236014734, max=1.…"
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            "text/html": [
              " View run <strong style=\"color:#cdcd00\">hazelnut-pie-7</strong> at: <a href='https://wandb.ai/anjelammcgraw/RAG%20Visibility%20Example/runs/t6lyo5hi' target=\"_blank\">https://wandb.ai/anjelammcgraw/RAG%20Visibility%20Example/runs/t6lyo5hi</a><br/>Synced 4 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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          "output_type": "display_data",
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            "text/html": [
              "Find logs at: <code>./wandb/run-20240314_185907-t6lyo5hi/logs</code>"
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      "source": [
        "wandb.finish()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i1txRophU4GB"
      },
      "source": [
        "##View run at https://wandb.ai/anjelammcgraw/RAG%20Visibility%20Example/runs/e6ddckhf"
      ]
    }
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