langchain-03.ipynb

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      "metadata": {
        "id": "R2-i8jBl9GRH"
      },
      "source": [
        "![Redis](https://redis.com/wp-content/themes/wpx/assets/images/logo-redis.svg?auto=webp&quality=85,75&width=120)\n",
        "# RAG with LangChain\n",
        "\n",
        "This notebook uses [LangChain](https://python.langchain.com/docs/get_started/introduction) and [Redis](https://redis.com) to perform document + embdding indexing and semantic search tasks. It also shows how to integrate with an LLM like OpenAI's GPT models.\n",
        "\n",
        "## Let's Begin!\n",
        "<a href=\"https://colab.research.google.com/github/redis-developer/financial-vss/blob/main/langchain-03.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ctOVb_LZ1vmk"
      },
      "source": [
        "## Environment Setup\n",
        "\n",
        "### Pull Github Materials\n",
        "Because you are likely running this notebook in **Google Colab**, we need to first\n",
        "pull the necessary dataset and materials directly from GitHub.\n",
        "\n",
        "**If you are running this notebook locally**, FYI you may not need to perform this\n",
        "step at all."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UQezgPCG1vml",
        "outputId": "97b9bc03-da1b-439a-c37b-be6fdb58ab21"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cloning into 'temp_repo'...\n",
            "remote: Enumerating objects: 81, done.\u001b[K\n",
            "remote: Counting objects: 100% (81/81), done.\u001b[K\n",
            "remote: Compressing objects: 100% (58/58), done.\u001b[K\n",
            "remote: Total 81 (delta 35), reused 61 (delta 19), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (81/81), 7.00 MiB | 24.97 MiB/s, done.\n",
            "Resolving deltas: 100% (35/35), done.\n"
          ]
        }
      ],
      "source": [
        "# This clones the supporting git repository into a directory named 'temp_repo'.\n",
        "!git clone https://github.com/redis-developer/financial-vss.git temp_repo\n",
        "\n",
        "# This command moves the 'resources' directory from 'temp_repo' to your current directory.\n",
        "!mv temp_repo/resources .\n",
        "!mv temp_repo/requirements.txt .\n",
        "\n",
        "# This deletes the 'temp_repo' directory, cleaning up the unwanted files.\n",
        "!rm -rf temp_repo"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9eieJowO1vmo"
      },
      "source": [
        "### Install Python Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B3v1wUzX1vmq",
        "outputId": "84a3feff-e7c1-41ba-9ab1-8c975074552e"
      },
      "outputs": [],
      "source": [
        "!pip install -r requirements.txt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import warnings\n",
        "\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Install Redis Stack\n",
        "\n",
        "Later in this tutorial, Redis will be used to store, index, and query vector\n",
        "embeddings created from PDF document chunks. **We need to make sure we have a Redis\n",
        "instance available.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Method 1: localized Redis Stack\n",
        "Use the shell script below to download, extract, and install [Redis Stack](https://redis.io/docs/getting-started/install-stack/) directly\n",
        "from the Redis package archive."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "%%sh\n",
        "curl -fsSL https://packages.redis.io/gpg | sudo gpg --dearmor -o /usr/share/keyrings/redis-archive-keyring.gpg\n",
        "echo \"deb [signed-by=/usr/share/keyrings/redis-archive-keyring.gpg] https://packages.redis.io/deb $(lsb_release -cs) main\" | sudo tee /etc/apt/sources.list.d/redis.list\n",
        "sudo apt-get update  > /dev/null 2>&1\n",
        "sudo apt-get install redis-stack-server  > /dev/null 2>&1\n",
        "redis-stack-server --daemonize yes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Method 2: Redis Cloud\n",
        "Instead of using the in-notebook, localized Redis Stack, you can quickly deploy a\n",
        "[FREE instance of Redis in the cloud](https://redis.com/try-free/). Or, if you have your\n",
        "own version of Redis Enterprise running, that works too!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Define the Redis Connection URL\n",
        "\n",
        "By default this notebook connects to the local instance of Redis Stack. **If you have your own Redis Enterprise instance** - replace REDIS_PASSWORD, REDIS_HOST and REDIS_PORT values with your own."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "# Replace values below with your own if using Redis Cloud instance\n",
        "REDIS_HOST = os.getenv(\"REDIS_HOST\", \"localhost\")\n",
        "REDIS_PORT = os.getenv(\"REDIS_PORT\", \"6379\")\n",
        "REDIS_PASSWORD = os.getenv(\"REDIS_PASSWORD\", \"\")\n",
        "#REDIS_HOST=\"redis-18374.c253.us-central1-1.gce.cloud.redislabs.com\"\n",
        "#REDIS_PORT=18374\n",
        "#REDIS_PASSWORD=\"1TNxTEdYRDgIDKM2gDfasupCADXXXX\"\n",
        "\n",
        "# If SSL is enabled on the endpoint, use rediss:// as the URL prefix\n",
        "REDIS_URL = f\"redis://:{REDIS_PASSWORD}@{REDIS_HOST}:{REDIS_PORT}\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## RAG with LangChain"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7MaVqU8Y1vms"
      },
      "source": [
        "### Dataset Preparation (PDF Documents)\n",
        "\n",
        "To best demonstrate Redis as a vector database layer, we will load a single\n",
        "financial (10k filings) doc and preprocess it using some helpers from LangChain:\n",
        "\n",
        "- `UnstructuredFileLoader` is not the only document loader type that LangChain provides. Docs: https://python.langchain.com/docs/integrations/document_loaders/unstructured_file\n",
        "- `RecursiveCharacterTextSplitter` is what we use to create smaller chunks of text from the doc. Docs: https://python.langchain.com/docs/modules/data_connection/document_transformers/text_splitters/recursive_text_splitter"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I7VS1bPr1vmt",
        "outputId": "72299230-26c4-4d80-d61f-138616e2173b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Listing available documents ... ['resources/nvd-10k-2023.pdf', 'resources/nke-10k-2023.pdf', 'resources/msft-10k-2023.pdf', 'resources/amzn-10k-2023.pdf', 'resources/jnj-10k-2023.pdf', 'resources/aapl-10k-2023.pdf']\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]     /root/nltk_data...\n",
            "[nltk_data]   Unzipping taggers/averaged_perceptron_tagger.zip.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Done preprocessing. Created 323 chunks of the original pdf resources/nke-10k-2023.pdf\n"
          ]
        }
      ],
      "source": [
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "from langchain.document_loaders import UnstructuredFileLoader\n",
        "\n",
        "# Load list of pdfs from a folder\n",
        "data_path = \"resources/\"\n",
        "docs = [os.path.join(data_path, file) for file in os.listdir(data_path)]\n",
        "\n",
        "print(\"Listing available documents ...\", docs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# pick out the Nike doc for this exercise\n",
        "doc = [doc for doc in docs if \"nke\" in doc][0]\n",
        "\n",
        "# set up the file loader/extractor and text splitter to create chunks\n",
        "text_splitter = RecursiveCharacterTextSplitter(\n",
        "    chunk_size=2500, chunk_overlap=0\n",
        ")\n",
        "loader = UnstructuredFileLoader(\n",
        "    doc, mode=\"single\", strategy=\"fast\"\n",
        ")\n",
        "\n",
        "# extract, load, and make chunks\n",
        "chunks = loader.load_and_split(text_splitter)\n",
        "\n",
        "print(\"Done preprocessing. Created\", len(chunks), \"chunks of the original pdf\", doc)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "96BzcyW31vmw"
      },
      "source": [
        "### Initialize Embeddings Engine\n",
        "Here we will use LangChain's built in embedding engine so that it will work seemlessly with the LangChain VectorStore classes."
      ]
    },
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        {
          "data": {
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              "model_id": "ae72de3e05a4461d9c2b0c1f953894b2",
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from langchain.embeddings.huggingface import HuggingFaceEmbeddings\n",
        "\n",
        "embeddings = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L6-v2\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qrj-jeGmBRTL"
      },
      "source": [
        "## Vector Search with LangChain\n",
        "### Create Redis vector store instance\n",
        "\n",
        "We also need to create a schema for the vector index so we can take advantage of the metadata along with the vectors.\n",
        "\n",
        "**Important Note**: LangChain does not support JSON data types yet. Only supports HASH for now. This update should be coming soon."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yY69FViAjNv1",
        "outputId": "ab7b212b-3c55-44b1-cf72-6eb926cf302f"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:langchain.vectorstores.redis.base:`index_schema` does not match generated metadata schema.\n",
            "If you meant to manually override the schema, please ignore this message.\n",
            "index_schema: {'vector': [{'name': 'chunk_vector', 'algorithm': 'HNSW', 'dims': 384, 'distance_metric': 'COSINE', 'datatype': 'FLOAT32'}], 'text': [{'name': 'content'}], 'content_vector_key': 'chunk_vector'}\n",
            "generated_schema: {'text': [{'name': 'source'}], 'numeric': [{'name': 'start_index'}], 'tag': []}\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from langchain.vectorstores.redis import Redis\n",
        "\n",
        "\n",
        "# set the index name for this example\n",
        "index_name = \"langchain\"\n",
        "\n",
        "# with langchain we can manually modify the default vector schema configuration\n",
        "vector_schema = {\n",
        "    \"name\": \"chunk_vector\",        # name of the vector field in langchain\n",
        "    \"algorithm\": \"HNSW\",           # could use HNSW instead\n",
        "    \"dims\": 384,                   # set based on the HF model embedding dimension\n",
        "    \"distance_metric\": \"COSINE\",   # could use EUCLIDEAN or IP\n",
        "    \"datatype\": \"FLOAT32\",\n",
        "}\n",
        "\n",
        "# here we can define the entire schema spec for our index in LangChain\n",
        "index_schema = {\n",
        "    \"vector\": [vector_schema],\n",
        "    \"text\": [{\"name\": \"content\"}],\n",
        "    \"content_vector_key\": \"chunk_vector\"    # name of the vector field in langchain\n",
        "}\n",
        "\n",
        "\n",
        "# construct the vector store class from texts and metadata\n",
        "rds = Redis.from_documents(\n",
        "    documents=chunks,\n",
        "    embedding=embeddings,\n",
        "    index_name=index_name,\n",
        "    redis_url=REDIS_URL,\n",
        "    index_schema=index_schema,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "_Z3KpL8q1vmw"
      },
      "outputs": [],
      "source": [
        "# If you wish to connect to an existing Redis vector store instance\n",
        "rds = Redis.from_existing_index(\n",
        "    embedding=embeddings,\n",
        "    index_name=index_name,\n",
        "    schema=index_schema,\n",
        "    redis_url=REDIS_URL,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KO0QVNtE1vmw",
        "outputId": "849451ed-c908-4e9a-ae49-bf734d33d036"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'text': [{'name': 'content',\n",
              "   'weight': 1,\n",
              "   'no_stem': False,\n",
              "   'withsuffixtrie': False,\n",
              "   'no_index': False,\n",
              "   'sortable': False}],\n",
              " 'vector': [{'name': 'chunk_vector',\n",
              "   'dims': 384,\n",
              "   'algorithm': 'HNSW',\n",
              "   'datatype': 'FLOAT32',\n",
              "   'distance_metric': 'COSINE',\n",
              "   'initial_cap': 20000,\n",
              "   'm': 16,\n",
              "   'ef_construction': 200,\n",
              "   'ef_runtime': 10,\n",
              "   'epsilon': 0.8}]}"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# checkout out the schema we created\n",
        "rds.schema"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gntcxF1P1vmx",
        "outputId": "3bef4861-8778-4ab7-fb61-710aba655f2c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "323"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# access underlying redis client to see how many docs have been stores\n",
        "rds.client.dbsize()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vaXF7Gj-1vmy",
        "outputId": "082cc73c-5fe3-465e-aa50-52170ce14f50"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{b'start_index': b'146100',\n",
              " b'source': b'resources/nke-10k-2023.pdf',\n",
              " b'content': b\"Through the Consumer Direct Acceleration strategy, we are focused on creating the marketplace of the future with more premium, consistent and seamless consumer experiences, leading with digital and our owned stores, as well as select wholesale partners. In addition, our product creation and marketing organizations are aligned to a consumer construct focused on sports dimensions through Men's, Women's and Kids', which allows us to better serve consumer needs. We continue to invest in a new Enterprise Resource Planning Platform, data and analytics, demand sensing, insight gathering, and other areas to create an end-to-end technology foundation, which we believe will further accelerate our digital transformation. We believe this unified approach will accelerate growth and unlock more efficiency for our business, while driving speed and responsiveness as we serve consumers globally.\\n\\nFINANCIAL HIGHLIGHTS\\n\\n\\n\\nIn fiscal 2023, NIKE, Inc. achieved record Revenues of $51.2 billion, which increased 10% and 16% on a reported and currency-neutral basis, respectively\\n\\nNIKE Direct revenues grew 14% from $18.7 billion in fiscal 2022 to $21.3 billion in fiscal 2023, and represented approximately 44% of total NIKE Brand revenues for\\n\\nfiscal 2023\\n\\nGross margin for the fiscal year decreased 250 basis points to 43.5% primarily driven by higher product costs, higher markdowns and unfavorable changes in foreign currency exchange rates, partially offset by strategic pricing actions\",\n",
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\"=m\\x9a\\xe6<e\\xc6S=P&\\x0c\\xbd\\xa5\\xa9_;RK\\x16\\xbd\\xe6\\\\\\xd6\\xbb\\xc6U\\xb1=\\x8f\\xcc\\x8d=i\"\\xe4<K\\x12\\xcc\\xbc\\x94\\x87J=S\\xd7P\\xbd\\xa6P\\xa5<\\xc1\\x8d\\xaf\\xbc\\xbf\\xc2}=1\\x86\\xbb\\xbc\\xc8\\xe3\\x7f\\xbd{\\xee\\x11=\\x05%\\x1f\\xbd\\x9b\\xb3\\x10\\xbd\\x8c\\x02G\\xbcb\\x866=\\x8b\\x07\\x0f\\xbd\\x9a\"\\x85\\xbd\\xf1>\\x85;\\x912$<\\x1a>E\\xbd\\xc0\\x82\\x88\\xbc@T\\x84\\xbd\\x95\\xecn<\\x18\\xae\\xce<\\x91\\xb1\\xed\\xbb\\xbb2\\xaf8\\x85\\xcd{\\xbc)\\xa7\\xd4<V\\xa8V\\xbd\\x14g_<\\xd8\\xc8\\xad;B\\xf0\\xc4\\xbdW\\x0f\\x02\\xbd}\\xcaT<B\\x98\\x82\\xbc#m\\xf3<\\x85\\xdf\\x97\\xbc\\x08EL=\\xd5\\x15f=\\xb5{\\xec\\xbcx\\xd0\\x8b=\\xd3\\xb5\\x05\\xbd/as\\xbdG*\\xe8\\xbb\\xf2\\xf0\\x0e\\xbd\\x88\\xd3\\xb0=\\xa9r\\xe9=\\x1c-N\\xbeE3\\xf8\\xbc\\xc2y\\xb4<'}"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# do NOT run this command in production\n",
        "keys = rds.client.keys()\n",
        "\n",
        "rds.client.hgetall(keys[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "61xV5qyp1vmy"
      },
      "source": [
        "### Query the database\n",
        "Now we can use the LangChain vector store class to perform similarity search operations on Redis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "Gv6SxKOB1vmy"
      },
      "outputs": [],
      "source": [
        "from langchain.vectorstores.redis import RedisText"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q3pH3MPD1vmy",
        "outputId": "2754347f-be31-4a38-d356-31ecc235ffe8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[(Document(page_content=\"increased 18%, driven by strong digital sales growth of 23%, comparable store sales growth of 9% and the addition of new stores.\\n\\nFootwear revenues increased 22% on a currency-neutral basis, primarily due to higher revenues in Men's and the Jordan Brand. Unit sales of footwear increased\\n\\n17%, while higher ASP per pair contributed approximately 5 percentage points of footwear revenue growth. Higher ASP per pair was primarily due to higher full-price ASP and growth in NIKE Direct, partially offset by lower NIKE Direct ASP, reflecting higher promotional activity as well as lower available inventory supply in the prior period and a lower mix of full-price sales.\\n\\nApparel revenues increased 9% on a currency-neutral basis, primarily due to higher revenues in Men's. Unit sales of apparel increased 7%, while higher ASP per unit contributed approximately 2 percentage points of apparel revenue growth. Higher ASP per unit was primarily due to higher full-price ASP and growth in NIKE Direct, partially offset by lower NIKE Direct ASP, reflecting higher promotional activity.\\n\\nReported EBIT increased 7% due to higher revenues and the following:\\n\\nGross margin contraction of 310 basis points primarily due to higher product costs, reflecting higher input costs and inbound freight and logistics costs and product\", metadata={'id': 'doc:langchain:95d2956557fb4e8491d01e59978c1fab'}),\n",
              "  0.5621),\n",
              " (Document(page_content='6,923\\n\\nEBIT margin\\n\\n(1)\\n\\n12.1 %\\n\\n14.7 %\\n\\n15.5 %\\n\\nInterest expense (income), net\\n\\n(6)\\n\\n205\\n\\n—\\n\\n262\\n\\nTOTAL NIKE, INC. INCOME BEFORE INCOME TAXES\\n\\n$\\n\\n6,201\\n\\n$\\n\\n6,651\\n\\n7 % $\\n\\n6,661\\n\\n(1) Total NIKE Brand EBIT, Total NIKE, Inc. EBIT and EBIT Margin represent non-GAAP financial measures. See \"Use of Non-GAAP Financial Measures\" for further information.\\n\\n2023 FORM 10-K 36\\n\\n% CHANGE EXCLUDING CURRENCY (1) CHANGES\\n\\n7 % 12 % -13 %\\n\\n16 % 302 %\\n\\n6 % 7 %\\n\\n— 6 %\\n\\n% CHANGE\\n\\n0 % 35 % -27 %\\n\\n24 % -17 %\\n\\n3 % 23 % 2 %\\n\\n1 %\\n\\n—\\n\\n0 %\\n\\nTable of Contents\\n\\nNORTH AMERICA\\n\\n(Dollars in millions)\\n\\nFISCAL 2023 FISCAL 2022\\n\\n% CHANGE\\n\\n% CHANGE EXCLUDING CURRENCY\\n\\nCHANGES FISCAL 2021\\n\\n% CHANGE\\n\\n% CHANGE EXCLUDING CURRENCY CHANGES\\n\\nRevenues by: Footwear Apparel\\n\\n$\\n\\n14,897 $ 5,947\\n\\n12,228 5,492\\n\\n22 % 8 %\\n\\n22 % $ 9 %\\n\\n11,644 5,028\\n\\n5 % 9 %\\n\\n5 % 9 %\\n\\nEquipment\\n\\nTOTAL REVENUES\\n\\n$\\n\\n764 21,608 $\\n\\n633 18,353\\n\\n21 % 18 %\\n\\n21 % 18 % $\\n\\n507 17,179\\n\\n25 % 7 %\\n\\n25 % 7 %\\n\\nRevenues by:\\n\\nSales to Wholesale Customers\\n\\n$\\n\\n11,273 $\\n\\n9,621\\n\\n17 %\\n\\n18 % $\\n\\n10,186\\n\\n6 %\\n\\n6 %\\n\\nSales through NIKE Direct\\n\\nTOTAL REVENUES\\n\\n$\\n\\n10,335 21,608 $\\n\\n8,732 18,353\\n\\n18 % 18 %\\n\\n18 % 18 % $\\n\\n6,993 17,179\\n\\n25 % 7 %\\n\\n25 % 7 %\\n\\nEARNINGS BEFORE INTEREST AND TAXES\\n\\n$\\n\\n5,454 $\\n\\n5,114\\n\\n7 %\\n\\n$\\n\\n5,089\\n\\n0 %\\n\\nFISCAL 2023 COMPARED TO FISCAL 2022\\n\\nNorth America revenues increased 18% on a currency-neutral basis, primarily due to higher revenues in Men\\'s and the Jordan Brand. NIKE Direct revenues', metadata={'id': 'doc:langchain:02f07583fcad41878f7eb1e76aebcb98'}),\n",
              "  0.5653),\n",
              " (Document(page_content=\"Footwear revenues increased 19% on a currency-neutral basis, primarily due to higher revenues in Men's, Women's and the Jordan Brand. Unit sales of footwear increased 16%, while higher ASP per pair contributed approximately 3 percentage points of footwear revenue growth. Higher ASP per pair was primarily due to higher full-price ASP and growth in NIKE Direct, partially offset by lower NIKE Direct ASP.\\n\\nApparel revenues increased 13% on a currency-neutral basis, primarily due to higher revenues in Men's. Unit sales of apparel increased 9%, while higher ASP per\\n\\nunit contributed approximately 4 percentage points of apparel revenue growth. Higher ASP per unit was primarily due to higher full-price and off-price ASPs, partially offset by lower NIKE Direct ASP.\\n\\nReported EBIT increased 2% due to higher revenues and the following:\\n\\nGross margin contraction of approximately 190 basis points primarily due to higher product costs, reflecting product mix and higher input costs, as well as unfavorable changes in standard foreign currency exchange rates. This was partially offset by higher full-price ASP, net of discounts, due to product mix and strategic pricing actions.\\n\\nSelling and administrative expense increased 8% due to higher operating overhead and demand creation expense. Operating overhead expense increased primarily\", metadata={'id': 'doc:langchain:e1d819ff93b44b7c8fab1c076509d491'}),\n",
              "  0.5736),\n",
              " (Document(page_content='Financial Statements contained in Item 8 of this Annual Report.\\n\\n(3) Global Brand Divisions revenues include NIKE Brand licensing and other miscellaneous revenues that are not part of a geographic operating segment.\\n\\n(4) Corporate revenues primarily consist of foreign currency hedge gains and losses related to revenues generated by entities within the NIKE Brand geographic operating segments and Converse, but\\n\\nmanaged through our central foreign exchange risk management program.\\n\\nThe primary financial measure used by the Company to evaluate performance is Earnings Before Interest and Taxes (\"EBIT\"). As discussed in Note 15 — Operating Segments and Related Information in the accompanying Notes to the Consolidated Financial Statements, certain corporate costs are not included in EBIT.\\n\\nThe breakdown of EBIT is as follows:\\n\\n(Dollars in millions)\\n\\nFISCAL 2023\\n\\nFISCAL 2022\\n\\n% CHANGE\\n\\nFISCAL 2021\\n\\nNorth America Europe, Middle East & Africa Greater China\\n\\n$\\n\\n5,454 3,531 2,283\\n\\n$\\n\\n5,114 3,293 2,365\\n\\n7 % $ 7 % -3 %\\n\\n5,089 2,435 3,243\\n\\nAsia Pacific & Latin America Global Brand Divisions (1)\\n\\nTOTAL NIKE BRAND Converse\\n\\n$\\n\\n1,932 (4,841)\\n\\n8,359 676\\n\\n$\\n\\n1,896 (4,262)\\n\\n8,406 669\\n\\n2 % -14 %\\n\\n1 % $ 1 %\\n\\n1,530 (3,656)\\n\\n8,641 543\\n\\nCorporate TOTAL NIKE, INC. EARNINGS BEFORE INTEREST AND TAXES\\n\\n(1)\\n\\n$\\n\\n(2,840)\\n\\n6,195\\n\\n$\\n\\n(2,219)\\n\\n6,856\\n\\n28 %\\n\\n10 % $\\n\\n(2,261)\\n\\n6,923\\n\\nEBIT margin\\n\\n(1)\\n\\n12.1 %\\n\\n14.7 %\\n\\n15.5 %\\n\\nInterest expense (income), net\\n\\n(6)\\n\\n205\\n\\n—\\n\\n262', metadata={'id': 'doc:langchain:e62d482e0f3041ffa995de221c782ceb'}),\n",
              "  0.5803)]"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# basic \"top 4\" vector search on a given query\n",
        "rds.similarity_search_with_score(query=\"Profit margins\", k=4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "od6Wqmya1vmy",
        "outputId": "5dbac581-4a07-45b0-d630-995342e91dc7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[(Document(page_content='COMPARABLE STORE SALES Comparable store sales: This key metric, which excludes NIKE Brand Digital sales, comprises revenues from NIKE-owned in-line and factory stores for which all three of the following requirements have been met: (1) the store has been open at least one year, (2) square footage has not changed by more than 15% within the past year and (3) the store has not been permanently repositioned within the past year. Comparable store sales includes revenues from stores that were temporarily closed during the period as a result of COVID-19. Comparable store sales represents a performance metric that we believe is useful information for management and investors in understanding the performance of our established NIKE-owned in-line and factory stores. Management considers this metric when making financial and operating decisions. The method of calculating comparable store sales varies across the retail industry. As a result, our calculation of this metric may not be comparable to similarly titled metrics used by other companies.\\n\\n2023 FORM 10-K 30\\n\\nTable of Contents\\n\\nRESULTS OF OPERATIONS\\n\\n(Dollars in millions, except per share data)\\n\\nRevenues Cost of sales\\n\\nGross profit Gross margin\\n\\nDemand creation expense Operating overhead expense\\n\\nTotal selling and administrative expense % of revenues\\n\\nInterest expense (income), net\\n\\nOther (income) expense, net Income before income taxes\\n\\nIncome tax expense Effective tax rate\\n\\nNET INCOME Diluted earnings per common share\\n\\n$\\n\\n$ $', metadata={'id': 'doc:langchain:c0d9865509954856af9435f7cd41cc6d'}),\n",
              "  0.6199),\n",
              " (Document(page_content='NIKE Brand apparel revenues increased 8% on a currency-neutral basis, primarily due to higher revenues in Men\\'s. Unit sales of apparel increased 4%, while higher ASP per unit contributed approximately 4 percentage points of apparel revenue growth. Higher ASP was primarily due to higher full-price ASP and growth in the size of our NIKE Direct business, partially offset by lower NIKE Direct ASP, reflecting higher promotional activity.\\n\\nNIKE Direct revenues increased 14% from $18.7 billion in fiscal 2022 to $21.3 billion in fiscal 2023. On a currency-neutral basis, NIKE Direct revenues increased 20% primarily driven by NIKE Brand Digital sales growth of 24%, comparable store sales growth of 14% and the addition of new stores. For further information regarding comparable store sales, including the definition, see \"Comparable Store Sales\". NIKE Brand Digital sales were $12.6 billion for fiscal 2023 compared to $10.7 billion for fiscal 2022.\\n\\n2023 FORM 10-K 33\\n\\nTable of Contents\\n\\nGROSS MARGIN FISCAL 2023 COMPARED TO FISCAL 2022\\n\\nFor fiscal 2023, our consolidated gross profit increased 4% to $22,292 million compared to $21,479 million for fiscal 2022. Gross margin decreased 250 basis points to 43.5% for fiscal 2023 compared to 46.0% for fiscal 2022 due to the following:\\n\\nWholesale equivalent\\n\\nThe decrease in gross margin for fiscal 2023 was primarily due to:', metadata={'id': 'doc:langchain:8012415a0e654367ac53a70adefa8742'}),\n",
              "  0.6628),\n",
              " (Document(page_content='Basic Diluted\\n\\n$ $\\n\\n3.27 $ 3.23 $\\n\\n3.83 $ 3.75 $\\n\\n3.64 3.56\\n\\nNOTE 11 — BENEFIT PLANS\\n\\nThe Company has a qualified 401(k) Savings and Profit Sharing Plan, in which all U.S. employees are able to participate. The Company matches a portion of employee contributions to the savings plan. Company contributions to the savings plan were $136 million, $126 million and $110 million and included in Cost of sales or Operating overhead expense, as applicable, for the fiscal years ended May 31, 2023, 2022 and 2021, respectively.\\n\\nThe Company also has a Long-Term Incentive Plan (\"LTIP\") adopted by the Board of Directors and approved by shareholders in September 1997, which has been amended from time to time. The Company recognized an immaterial amount of Operating overhead expense related to cash awards under the LTIP during the years ended May 31, 2023, 2022 and 2021. During the fiscal years ended May 31, 2023 and 2022, under the Stock Incentive Plan, the Company granted PSUs which replaced cash-based long-term incentive awards historically granted under the Company\\'s LTIP. Refer to Note 9 — Common Stock and Stock-Based Compensation for further information related to PSUs.', metadata={'id': 'doc:langchain:64365ea050ef49d9ae8036f966c4232e'}),\n",
              "  0.7233),\n",
              " (Document(page_content='Because contract manufacturers make a majority of our products outside of our principal sales markets, our products must be transported by third parties over large geographic distances. Delays in the shipment or delivery of our products due to the availability of transportation, container shortages, labor shortages, including work stoppages or port strikes, infrastructure and port congestion or other factors, and costs and delays associated with consolidating or transitioning between manufacturers, have adversely impacted, and could in the future adversely impact the availability of our products and, in turn, our financial performance. In addition, delays in the shipment or delivery of our products, manufacturing delays or unexpected demand for our products have required us, and may in the future require us to use faster, but more expensive, transportation methods such as air freight, which could adversely affect our profit margins. The cost of oil is a significant component in manufacturing and transportation costs, so increases in the price of petroleum products can adversely affect our profit margins. Changes in U.S. trade policies, including modifications to import tariffs and existing trade policies and agreements, have also had, and could continue to have a significant impact on our activities in foreign jurisdictions, and could adversely affect our reputation or results of operations.\\n\\nOur success depends on our global distribution facilities.', metadata={'id': 'doc:langchain:1913fd1406eb497cbfcc10e9eed9eee9'}),\n",
              "  0.7323)]"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# vector search with metadata filtering\n",
        "\n",
        "f = RedisText(\"content\") % \"profit\"\n",
        "rds.similarity_search_with_score(query=\"Profit margins\", k=4, filter=f)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DT9sPHw51vmy",
        "outputId": "7d2b8969-91b8-4ff7-81ba-9d0cb6280edc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[(Document(page_content='FISCAL 2023 COMPARED TO FISCAL 2022\\n\\nNIKE, Inc. Revenues were $51.2 billion in fiscal 2023, which increased 10% and 16% compared to fiscal 2022 on a reported and currency-neutral basis, respectively. The increase was due to higher revenues in North America, Europe, Middle East & Africa (\"EMEA\"), APLA and Greater China, which contributed approximately 7, 6, 2 and 1 percentage points to NIKE, Inc. Revenues, respectively.\\n\\nNIKE Brand revenues, which represented over 90% of NIKE, Inc. Revenues, increased 10% and 16% on a reported and currency-neutral basis, respectively. This increase was primarily due to higher revenues in Men\\'s, the Jordan Brand, Women\\'s and Kids\\' which grew 17%, 35%,11% and 10%, respectively, on a wholesale equivalent basis.\\n\\nNIKE Brand footwear revenues increased 20% on a currency-neutral basis, due to higher revenues in Men\\'s, the Jordan Brand, Women\\'s and Kids\\'. Unit sales of footwear increased 13%, while higher average selling price (\"ASP\") per pair contributed approximately 7 percentage points of footwear revenue growth. Higher ASP was primarily due to higher full-price ASP, net of discounts, on a wholesale equivalent basis, and growth in the size of our NIKE Direct business, partially offset by lower NIKE Direct ASP.', metadata={'id': 'doc:langchain:75ae7a8266654e41b17b63c4f7542359'}),\n",
              "  0.2649),\n",
              " (Document(page_content='NIKE Brand apparel revenues increased 8% on a currency-neutral basis, primarily due to higher revenues in Men\\'s. Unit sales of apparel increased 4%, while higher ASP per unit contributed approximately 4 percentage points of apparel revenue growth. Higher ASP was primarily due to higher full-price ASP and growth in the size of our NIKE Direct business, partially offset by lower NIKE Direct ASP, reflecting higher promotional activity.\\n\\nNIKE Direct revenues increased 14% from $18.7 billion in fiscal 2022 to $21.3 billion in fiscal 2023. On a currency-neutral basis, NIKE Direct revenues increased 20% primarily driven by NIKE Brand Digital sales growth of 24%, comparable store sales growth of 14% and the addition of new stores. For further information regarding comparable store sales, including the definition, see \"Comparable Store Sales\". NIKE Brand Digital sales were $12.6 billion for fiscal 2023 compared to $10.7 billion for fiscal 2022.\\n\\n2023 FORM 10-K 33\\n\\nTable of Contents\\n\\nGROSS MARGIN FISCAL 2023 COMPARED TO FISCAL 2022\\n\\nFor fiscal 2023, our consolidated gross profit increased 4% to $22,292 million compared to $21,479 million for fiscal 2022. Gross margin decreased 250 basis points to 43.5% for fiscal 2023 compared to 46.0% for fiscal 2022 due to the following:\\n\\nWholesale equivalent\\n\\nThe decrease in gross margin for fiscal 2023 was primarily due to:', metadata={'id': 'doc:langchain:8012415a0e654367ac53a70adefa8742'}),\n",
              "  0.2701),\n",
              " (Document(page_content='BASIS OF CONSOLIDATION\\n\\nThe Consolidated Financial Statements include the accounts of NIKE, Inc. and its subsidiaries (the \"Company\" or \"NIKE\"). All significant intercompany transactions and balances have been eliminated.\\n\\nREVENUE RECOGNITION\\n\\nRevenue transactions associated with the sale of NIKE Brand footwear, apparel and equipment, as well as Converse products, comprise a single performance obligation, which consists of the sale of products to customers either through wholesale or direct to consumer channels. The Company satisfies the performance obligation and records revenues when transfer of control to the customer has occurred, based on the terms of sale. A customer is considered to have control once they are able to direct the use and receive substantially all of the benefits of the product.', metadata={'id': 'doc:langchain:ccdbcfad3fb543eeb17339fc01843cce'}),\n",
              "  0.3089),\n",
              " (Document(page_content='2023 FORM 10-K 26\\n\\nTable of Contents\\n\\nITEM 6. [RESERVED]\\n\\n2023 FORM 10-K 27\\n\\nTable of Contents\\n\\nITEM 7. MANAGEMENT\\'S DISCUSSION AND ANALYSIS OF FINANCIAL CONDITION AND RESULTS OF OPERATIONS\\n\\nOVERVIEW\\n\\nNIKE designs, develops, markets and sells athletic footwear, apparel, equipment, accessories and services worldwide. We are the largest seller of athletic footwear and apparel in the world. We sell our products through NIKE Direct operations, which is comprised of both NIKE-owned retail stores and sales through our digital platforms (also referred to as \"NIKE Brand Digital\"), to wholesale accounts and to a mix of independent distributors, licensees and sales representatives in nearly all countries around the world. Our goal is to deliver value to our shareholders by building a profitable global portfolio of branded footwear, apparel, equipment and accessories businesses. Our strategy is to achieve long-term revenue growth by creating innovative, \"must-have\" products, building deep personal consumer connections with our brands and delivering compelling consumer experiences through digital platforms and at retail.', metadata={'id': 'doc:langchain:e3fa5a43484d4841be68d4dde30dd428'}),\n",
              "  0.3463)]"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# vector search with combinations of metadata filtering\n",
        "\n",
        "f = (RedisText(\"content\") % \"profit\") | (RedisText(\"content\") % \"revenue\")\n",
        "rds.similarity_search_with_score(query=\"Nike company revenue\", k=4, filter=f)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kIoGJOop1vmz",
        "outputId": "9abbf5fd-77de-4094-a2e8-fba2fc5a3a05"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# filter results to a certain distance threshold\n",
        "rds.similarity_search_with_score(query=\"Nike company revenue\", k=4, distance_threshold=0.14)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XdzQa112Bf2b"
      },
      "source": [
        "## RAG with LangChain\n",
        "LangChain makes it easy to now take this vector store and build retireval augmented generation (RAG) applications over your data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "id": "CU2aFQPW1vmz"
      },
      "source": [
        "### Initialize OpenAI\n",
        "\n",
        "You need to supply an OpenAI API key (starts with `sk-...`) when prompted. If the key is in your env -- great, otherwise enter it when prompted below. You can find your API key at https://platform.openai.com/account/api-keys"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GSpH-cur1vmz",
        "outputId": "b00de5c7-1684-4e8e-c146-6d402910cdc5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "OpenAI API Key:··········\n"
          ]
        }
      ],
      "source": [
        "import getpass\n",
        "from langchain.llms import OpenAI\n",
        "\n",
        "llm = OpenAI(openai_api_key=os.getenv(\"OPENAI_API_KEY\") or getpass.getpass(prompt=\"OpenAI API Key:\"))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-SQXQB-c1vmz"
      },
      "source": [
        "### Setup prompt\n",
        "PromptTemplate defines the exect text of the response that would be fed to the LLM. This step is optional, but the defaults usually work well for OpenAI and might fall short for other models."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "yKKn2KKp3TQ4"
      },
      "outputs": [],
      "source": [
        "def get_prompt():\n",
        "    \"\"\"Create the QA chain.\"\"\"\n",
        "    from langchain.prompts import PromptTemplate\n",
        "\n",
        "    # Define our prompt\n",
        "    prompt_template = \"\"\"Use the following pieces of context from financial 10k filings data to answer the quser uestion at the end. If you don't know the answer, say that you don't know, don't try to make up an answer.\n",
        "\n",
        "    This should be in the following format:\n",
        "\n",
        "    Question: [question here]\n",
        "    Answer: [answer here]\n",
        "\n",
        "    Begin!\n",
        "\n",
        "    Context:\n",
        "    ---------\n",
        "    {context}\n",
        "    ---------\n",
        "    Question: {question}\n",
        "    Answer:\"\"\"\n",
        "\n",
        "    prompt = PromptTemplate(\n",
        "        template=prompt_template,\n",
        "        input_variables=[\"context\", \"question\"]\n",
        "    )\n",
        "    return prompt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xgEXBujxG1dO"
      },
      "source": [
        "### Putting it all together\n",
        "\n",
        "This is where the Langchain brings all the components together in a form of a simple RAG application with the financial PDF document."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "RKNSP0zqZq98"
      },
      "outputs": [],
      "source": [
        "from langchain.chains import RetrievalQA\n",
        "\n",
        "qa = RetrievalQA.from_chain_type(\n",
        "    llm=llm,\n",
        "    chain_type=\"stuff\",\n",
        "    retriever=rds.as_retriever(search_type=\"similarity_distance_threshold\",search_kwargs={\"distance_threshold\":0.5}),\n",
        "    return_source_documents=True,\n",
        "    chain_type_kwargs={\"prompt\": get_prompt()},\n",
        "    #verbose=True\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SURTtVbYBFGc"
      },
      "source": [
        "### Finally - let's ask questions!\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "0JkswfOHZu9h",
        "outputId": "813fffe6-3d8b-4df7-a035-c54e421c0856"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "' NIKE, Inc. Revenues were $51.2 billion in fiscal 2023, which increased 10% and 16% compared to fiscal 2022 on a reported and currency-neutral basis, respectively.'"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "query = \"What was Nike's revenue last year compared to this year??\"\n",
        "res=qa(query)\n",
        "res['result']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "ZvioUduS1vm0",
        "outputId": "a8ba0e5d-c06b-4848-d066-c26884b3caee"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "' Nike offers athletic footwear, apparel, equipment, accessories and services. Nike is part of the athletic footwear, apparel and equipment industry.'"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "query = \"How many products does Nike offer? What is the industry that Nike is part of?\"\n",
        "res=qa(query)\n",
        "res['result']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "Q9mE5z-61vm1",
        "outputId": "e3860d3e-e68b-489b-9897-c7118377c7bb"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
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              "' Approximately 83,700.'"
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        "\n",
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