python-recipes/RAG/01_redisvl.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R2-i8jBl9GRH"
      },
      "source": [
        "![Redis](https://redis.io/wp-content/uploads/2024/04/Logotype.svg?auto=webp&quality=85,75&width=120)\n",
        "\n",
        "# RAG from scratch with the Redis Vector Library\n",
        "\n",
        "\n",
        "In this recipe we will cover the basic of the Redis Vector Library and build a basic RAG app from scratch.\n",
        "\n",
        "## Let's Begin!\n",
        "<a href=\"https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/01_redisvl.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rT9HzsnQ1uiz"
      },
      "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": "AJJ2UW6M1ui0",
        "outputId": "0f5773b7-a292-4ee6-f4bd-20dc40ca2aba"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cloning into 'temp_repo'...\n",
            "remote: Enumerating objects: 384, done.\u001b[K\n",
            "remote: Counting objects: 100% (247/247), done.\u001b[K\n",
            "remote: Compressing objects: 100% (159/159), done.\u001b[K\n",
            "remote: Total 384 (delta 135), reused 153 (delta 74), pack-reused 137 (from 1)\u001b[K\n",
            "Receiving objects: 100% (384/384), 64.50 MiB | 8.97 MiB/s, done.\n",
            "Resolving deltas: 100% (159/159), done.\n"
          ]
        }
      ],
      "source": [
        "# NBVAL_SKIP\n",
        "!git clone https://github.com/redis-developer/redis-ai-resources.git temp_repo\n",
        "!mv temp_repo/python-recipes/RAG/resources .\n",
        "!rm -rf temp_repo"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z67mf6T91ui2"
      },
      "source": [
        "### Install Python Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DgxBQFXQ1ui2",
        "outputId": "c3c399d6-e294-4a3a-a0a3-82d818509991"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/261.4 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m \u001b[32m256.0/261.4 kB\u001b[0m \u001b[31m21.8 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m261.4/261.4 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "%pip install -q redis redisvl langchain-community pypdf sentence-transformers langchain openai pandas"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "peC8ThuVJkD7"
      },
      "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": {
        "id": "zMKHJ7oWJkD8"
      },
      "source": [
        "#### For Colab\n",
        "Use the shell script below to download, extract, and install [Redis Stack](https://redis.io/docs/getting-started/install-stack/) directly from the Redis package archive."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c0d5lfNxJkD8",
        "outputId": "f96e72fa-b9f3-476f-bc9e-328bd30d1344"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "deb [signed-by=/usr/share/keyrings/redis-archive-keyring.gpg] https://packages.redis.io/deb jammy main\n",
            "Starting redis-stack-server, database path /var/lib/redis-stack\n"
          ]
        }
      ],
      "source": [
        "# NBVAL_SKIP\n",
        "%%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": {
        "id": "2arb8Ic0JkD8"
      },
      "source": [
        "#### For Alternative Environments\n",
        "There are many ways to get the necessary redis-stack instance running\n",
        "1. On cloud, deploy a [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!\n",
        "2. Per OS, [see the docs](https://redis.io/docs/latest/operate/oss_and_stack/install/install-stack/)\n",
        "3. With docker: `docker run -d --name redis-stack-server -p 6379:6379 redis/redis-stack-server:latest`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DhP1w0R9JkD8"
      },
      "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": 4,
      "metadata": {
        "id": "ggh5TzhkJkD9"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import warnings\n",
        "#warnings.filterwarnings('ignore')\n",
        "\n",
        "# Replace values below with your own if using Redis Cloud instance\n",
        "REDIS_HOST = os.getenv(\"REDIS_HOST\", \"localhost\") # ex: \"redis-18374.c253.us-central1-1.gce.cloud.redislabs.com\"\n",
        "REDIS_PORT = os.getenv(\"REDIS_PORT\", \"6379\")      # ex: 18374\n",
        "REDIS_PASSWORD = os.getenv(\"REDIS_PASSWORD\", \"\")  # ex: \"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": {
        "id": "b3ErDmsIJkD9"
      },
      "source": [
        "## Simplified Vector Search with RedisVL"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KrtWWU4I1ui3"
      },
      "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",
        "- `PyPDFLoader` is not the only document loader type that LangChain provides. Docs: https://python.langchain.com/docs/integrations/document_loaders/pypdfloader/\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": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uijl2qFH1ui3",
        "outputId": "a99b3fcb-7cfd-4dbd-f258-57779cfcae3c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Listing available documents ... ['resources/jnj-10k-2023.pdf', 'resources/retrieval_basic_rag_test.csv', 'resources/aapl-10k-2023.pdf', 'resources/nke-10k-2023.pdf', 'resources/amzn-10k-2023.pdf', 'resources/testset_15.csv', 'resources/generation_basic_rag_test.csv', 'resources/testset.csv', 'resources/msft-10k-2023.pdf', 'resources/propositions.json', 'resources/nvd-10k-2023.pdf']\n"
          ]
        }
      ],
      "source": [
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "from langchain_community.document_loaders import PyPDFLoader\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": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "anya8hVnT6K_",
        "outputId": "a8430acc-2e6d-45fd-fc8b-601fbbd8289b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Done preprocessing. Created 211 chunks of the original pdf resources/nke-10k-2023.pdf\n"
          ]
        }
      ],
      "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 = PyPDFLoader(doc, headers = None)\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": "fDN4XopTJkD9"
      },
      "source": [
        "### Text embedding generation with RedisVL\n",
        "RedisVL has built-in extensions and utilities to aid the GenAI development process. In the following snipit we utilize the HFTextVectorizer redisvl in tandem with the **all-MiniLM-L6-v2** class to generate vector embeddings for the chunks created above. These embeddings capture the \"meaning\" of the text so that we can retrieve the relevant chunks later when a user's query is semantically related."
      ]
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        },
        "id": "N3iQ2aLEJkD9",
        "outputId": "b0f0d2c1-41dc-4932-990b-53d2912af19e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "20:48:16 numexpr.utils INFO   NumExpr defaulting to 2 threads.\n",
            "20:48:30 sentence_transformers.SentenceTransformer INFO   Use pytorch device_name: cuda\n",
            "20:48:30 sentence_transformers.SentenceTransformer INFO   Load pretrained SentenceTransformer: sentence-transformers/all-MiniLM-L6-v2\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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            "text/plain": [
              "config_sentence_transformers.json:   0%|          | 0.00/116 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "92e02308d4d94725b73cc324d8cd9906",
              "version_major": 2,
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            },
            "text/plain": [
              "README.md:   0%|          | 0.00/10.7k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "dd6707fe0bae4aab842dac25bf31880d",
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c24f6df83a0b46ecbad2be4583d3bb1b",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "26d0829f64b248ada2b0f46b746cd8b1",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
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              "model_id": "8e394c924a00479ba046afb5eeacc5f3",
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3fb33de4563749d7827c735380453b58",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "40f12f8bb6a04034b8c7a95d984469f2",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from redisvl.utils.vectorize import HFTextVectorizer\n",
        "import pandas as pd\n",
        "from tqdm.auto import tqdm\n",
        "\n",
        "hf = HFTextVectorizer(\"sentence-transformers/all-MiniLM-L6-v2\")\n",
        "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n",
        "\n",
        "# Embed each chunk content\n",
        "embeddings = hf.embed_many([chunk.page_content for chunk in chunks])\n",
        "\n",
        "# Check to make sure we've created enough embeddings, 1 per document chunk\n",
        "len(embeddings) == len(chunks)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5baI0xDQ1ui-"
      },
      "source": [
        "### Define a schema and create an index\n",
        "\n",
        "Below we connect to Redis and create an index that contains a text field, tag field, and vector field."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "zB1EW_9n1ui-"
      },
      "outputs": [],
      "source": [
        "from redis import Redis\n",
        "from redisvl.index import SearchIndex\n",
        "\n",
        "\n",
        "index_name = \"redisvl\"\n",
        "\n",
        "\n",
        "schema = {\n",
        "  \"index\": {\n",
        "    \"name\": index_name,\n",
        "    \"prefix\": \"chunk\"\n",
        "  },\n",
        "  \"fields\": [\n",
        "    {\n",
        "        \"name\": \"chunk_id\",\n",
        "        \"type\": \"tag\",\n",
        "        \"attrs\": {\n",
        "            \"sortable\": True\n",
        "        }\n",
        "    },\n",
        "    {\n",
        "        \"name\": \"content\",\n",
        "        \"type\": \"text\"\n",
        "    },\n",
        "    {\n",
        "        \"name\": \"text_embedding\",\n",
        "        \"type\": \"vector\",\n",
        "        \"attrs\": {\n",
        "            \"dims\": 384,\n",
        "            \"distance_metric\": \"cosine\",\n",
        "            \"algorithm\": \"hnsw\",\n",
        "            \"datatype\": \"float32\"\n",
        "        }\n",
        "    }\n",
        "  ]\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "LKuQku2CJkD9"
      },
      "outputs": [],
      "source": [
        "# connect to redis\n",
        "client = Redis.from_url(REDIS_URL)\n",
        "\n",
        "# create an index from schema and the client\n",
        "index = SearchIndex.from_dict(schema)\n",
        "index.set_client(client)\n",
        "index.create(overwrite=True, drop=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L6GOqmeN1ui_",
        "outputId": "91a199e3-d087-4b15-9544-d59efa6033c5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[32m20:48:39\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m   Indices:\n",
            "\u001b[32m20:48:39\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m   1. redisvl\n"
          ]
        }
      ],
      "source": [
        "# use the RedisVL CLI tool to list all indices\n",
        "!rvl index listall"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C70C-UWj1ujA",
        "outputId": "1fb7a2d6-ae6d-4536-b4b7-702620efd128"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Index Information:\n",
            "╭──────────────┬────────────────┬────────────┬─────────────────┬────────────╮\n",
            "│ Index Name   │ Storage Type   │ Prefixes   │ Index Options   │   Indexing │\n",
            "├──────────────┼────────────────┼────────────┼─────────────────┼────────────┤\n",
            "│ redisvl      │ HASH           │ ['chunk']  │ []              │          0 │\n",
            "╰──────────────┴────────────────┴────────────┴─────────────────┴────────────╯\n",
            "Index Fields:\n",
            "╭────────────────┬────────────────┬────────┬────────────────┬────────────────┬────────────────┬────────────────┬────────────────┬────────────────┬─────────────────┬────────────────┬────────────────┬────────────────┬─────────────────┬────────────────╮\n",
            "│ Name           │ Attribute      │ Type   │ Field Option   │ Option Value   │ Field Option   │ Option Value   │ Field Option   │   Option Value │ Field Option    │ Option Value   │ Field Option   │   Option Value │ Field Option    │   Option Value │\n",
            "├────────────────┼────────────────┼────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼─────────────────┼────────────────┼────────────────┼────────────────┼─────────────────┼────────────────┤\n",
            "│ chunk_id       │ chunk_id       │ TAG    │ SEPARATOR      │ ,              │                │                │                │                │                 │                │                │                │                 │                │\n",
            "│ content        │ content        │ TEXT   │ WEIGHT         │ 1              │                │                │                │                │                 │                │                │                │                 │                │\n",
            "│ text_embedding │ text_embedding │ VECTOR │ algorithm      │ HNSW           │ data_type      │ FLOAT32        │ dim            │            384 │ distance_metric │ COSINE         │ M              │             16 │ ef_construction │            200 │\n",
            "╰────────────────┴────────────────┴────────┴────────────────┴────────────────┴────────────────┴────────────────┴────────────────┴────────────────┴─────────────────┴────────────────┴────────────────┴────────────────┴─────────────────┴────────────────╯\n"
          ]
        }
      ],
      "source": [
        "# get info about the index\n",
        "!rvl index info -i redisvl"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qrj-jeGmBRTL"
      },
      "source": [
        "### Process and load dataset\n",
        "Below we use the RedisVL index to simply load the list of document chunks to Redis db."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "Zsg09Keg1ujA"
      },
      "outputs": [],
      "source": [
        "# load expects an iterable of dictionaries\n",
        "from redisvl.redis.utils import array_to_buffer\n",
        "\n",
        "data = [\n",
        "    {\n",
        "        'chunk_id': i,\n",
        "        'content': chunk.page_content,\n",
        "        # For HASH -- must convert embeddings to bytes\n",
        "        'text_embedding': array_to_buffer(embeddings[i], dtype='float32')\n",
        "    } for i, chunk in enumerate(chunks)\n",
        "]\n",
        "\n",
        "# RedisVL handles batching automatically\n",
        "keys = index.load(data, id_field=\"chunk_id\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-ZsFB-6Z1ujB"
      },
      "source": [
        "### Query the database\n",
        "Now we can use the RedisVL index to perform similarity search operations with Redis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85,
          "referenced_widgets": [
            "c75d5ab2049146e580efab9da9bbcdb0",
            "9ce1fb951e79468baa9d1aebfa4c4fae",
            "e96d1546380146078c18ec78363f7dac",
            "a3c36bb0d3b74c8ea56bf03521465b81",
            "9f306cfd66dc441aba923d4e051911fc",
            "9e3289444cb142c29ad7d569be2e25b8",
            "c20443e17308425596679c0544dab528",
            "f0bdd8f4d7b84bd5a1c209c591ce8787",
            "126743b52b254e54aa4f65bcb9e65aea",
            "debae380e6d24fb8ae712a6dd2226152",
            "aacb6f8ca39846d89e1e4e96656e3a36"
          ]
        },
        "id": "BkFv-_iC1ujB",
        "outputId": "c398d356-6bb7-43a9-ca95-cb7f167d1f38"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c75d5ab2049146e580efab9da9bbcdb0",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'*=>[KNN 3 @text_embedding $vector AS vector_distance] RETURN 3 chunk_id content vector_distance SORTBY vector_distance ASC DIALECT 2 LIMIT 0 3'"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from redisvl.query import VectorQuery\n",
        "\n",
        "query = \"Nike profit margins and company performance\"\n",
        "\n",
        "query_embedding = hf.embed(query)\n",
        "\n",
        "vector_query = VectorQuery(\n",
        "    vector=query_embedding,\n",
        "    vector_field_name=\"text_embedding\",\n",
        "    num_results=3,\n",
        "    return_fields=[\"chunk_id\", \"content\"],\n",
        "    return_score=True\n",
        ")\n",
        "\n",
        "# show the raw redis query\n",
        "str(vector_query)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "5reL5qTW1ujC",
        "outputId": "dd58f191-54f5-4226-c4e1-70207d58f2dc"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"chunk:88\",\n          \"chunk:80\",\n          \"chunk:87\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vector_distance\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"0.337694585323\",\n          \"0.342052936554\",\n          \"0.35776078701\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chunk_id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"88\",\n          \"80\",\n          \"87\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"content\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Asia Pacific & Latin America 1,932 1,896 2 % 1,530 24 %\\nGlobal Brand Divisions (4,841) (4,262) -14 % (3,656) -17 %\\nTOTAL NIKE BRAND $ 8,359 $ 8,406 -1 % $ 8,641 -3 %\\nConverse 676 669 1 % 543 23 %\\nCorporate (2,840) (2,219) -28 % (2,261) 2 %\\nTOTAL NIKE, INC. EARNINGS BEFORE INTEREST ANDTAXES $ 6,195 $ 6,856 -10 % $ 6,923 -1 %\\nEBIT margin 12.1 % 14.7 % 15.5 %\\nInterest expense (income), net (6) 205 \\u2014 262 \\u2014 \\nTOTAL NIKE, INC. INCOME BEFORE INCOME TAXES $ 6,201 $ 6,651 -7 % $ 6,661 0 %\\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(1) (1)\\n(2)\\n(3)\\n(4)\\n(1)\\n(1)\\n(1)\\n2023 FORM 10-K 36\",\n          \"Table of Contents\\nCONSOLIDATED OPERATING RESULTS\\nREVENUES\\n(Dollars in millions) FISCAL2023 FISCAL2022 % CHANGE\\n% CHANGEEXCLUDINGCURRENCYCHANGES FISCAL2021 % CHANGE\\n% CHANGEEXCLUDINGCURRENCYCHANGES\\nNIKE, Inc. Revenues:\\nNIKE Brand Revenues by:\\nFootwear $ 33,135 $ 29,143 14 % 20 %$ 28,021 4 % 4 %\\nApparel 13,843 13,567 2 % 8 % 12,865 5 % 6 %\\nEquipment 1,727 1,624 6 % 13 % 1,382 18 % 18 %\\nGlobal Brand Divisions 58 102 -43 % -43 % 25 308 % 302 %\\nTotal NIKE Brand Revenues $ 48,763 $ 44,436 10 % 16 %$ 42,293 5 % 6 %\\nConverse 2,427 2,346 3 % 8 % 2,205 6 % 7 %\\nCorporate 27 (72) \\u2014 \\u2014 40 \\u2014 \\u2014 \\nTOTAL NIKE, INC. REVENUES $ 51,217 $ 46,710 10 % 16 %$ 44,538 5 % 6 %\\nSupplemental NIKE Brand Revenues Details:\\nNIKE Brand Revenues by:\\nSales to Wholesale Customers $ 27,397 $ 25,608 7 % 14 %$ 25,898 -1 % -1 %\\nSales through NIKE Direct 21,308 18,726 14 % 20 % 16,370 14 % 15 %\\nGlobal Brand Divisions 58 102 -43 % -43 % 25 308 % 302 %\\nTOTAL NIKE BRAND REVENUES $ 48,763 $ 44,436 10 % 16 %$ 42,293 5 % 6 %\\nNIKE Brand Revenues on a Wholesale Equivalent Basis :\\nSales to Wholesale Customers $ 27,397 $ 25,608 7 % 14 %$ 25,898 -1 % -1 %\\nSales from our Wholesale Operations to NIKE Direct Operations 12,730 10,543 21 % 27 % 9,872 7 % 7 %\\nTOTAL NIKE BRAND WHOLESALE EQUIVALENT REVENUES $ 40,127 $ 36,151 11 % 18 %$ 35,770 1 % 1 %\\nNIKE Brand Wholesale Equivalent Revenues by:\\nMen's $ 20,733 $ 18,797 10 % 17 %$ 18,391 2 % 3 %\\nWomen's 8,606 8,273 4 % 11 % 8,225 1 % 1 %\\nNIKE Kids' 5,038 4,874 3 % 10 % 4,882 0 % 0 %\\nJordan Brand 6,589 5,122 29 % 35 % 4,780 7 % 7 %\\nOthers (839) (915) 8 % -3 % (508) -80 % -79 %\\nTOTAL NIKE BRAND WHOLESALE EQUIVALENT REVENUES $ 40,127 $ 36,151 11 % 18 %$ 35,770 1 % 1 %\\n(1) The percent change excluding currency changes and the presentation of wholesale equivalent revenues represent non-GAAP financial measures. For further information, see \\\"Use of Non-GAAPFinancial Measures\\\".\\n(2) Global Brand Divisions revenues include NIKE Brand licensing and other miscellaneous revenues that are not part of a geographic operating segment.\\n(3) 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\\nmanaged through our central foreign exchange risk management program.\",\n          \"Table of Contents\\nOPERATING SEGMENTS\\nAs discussed in Note 15 \\u2014 Operating Segments and Related Information in the accompanying Notes to the Consolidated Financial Statements, our operating segments\\nare evidence of the structure of the Company's internal organization. The NIKE Brand segments are defined by geographic regions for operations participating in NIKE\\nBrand sales activity.\\nThe breakdown of Revenues is as follows:\\n(Dollars in millions) FISCAL 2023 FISCAL 2022 % CHANGE\\n% CHANGEEXCLUDINGCURRENCYCHANGES FISCAL 2021 % CHANGE\\n% CHANGEEXCLUDINGCURRENCYCHANGES\\nNorth America $ 21,608 $ 18,353 18 % 18 %$ 17,179 7 % 7 %\\nEurope, Middle East & Africa 13,418 12,479 8 % 21 % 11,456 9 % 12 %\\nGreater China 7,248 7,547 -4 % 4 % 8,290 -9 % -13 %\\nAsia Pacific & Latin America 6,431 5,955 8 % 17 % 5,343 11 % 16 %\\nGlobal Brand Divisions 58 102 -43 % -43 % 25 308 % 302 %\\nTOTAL NIKE BRAND $ 48,763 $ 44,436 10 % 16 %$ 42,293 5 % 6 %\\nConverse 2,427 2,346 3 % 8 % 2,205 6 % 7 %\\nCorporate 27 (72) \\u2014 \\u2014 40 \\u2014 \\u2014 \\nTOTAL NIKE, INC. REVENUES $ 51,217 $ 46,710 10 % 16 %$ 44,538 5 % 6 %\\n(1)    The percent change excluding currency changes represents a non-GAAP financial measure. For further information, see \\\"Use of Non-GAAP Financial Measures\\\".\\n(2)    For additional information on the transition of our NIKE Brand businesses within our CASA territory to a third-party distributor, see Note 18 \\u2014 Acquisitions and Divestitures of the Notes to ConsolidatedFinancial Statements contained in Item 8 of this Annual Report.\\n(3)    Global Brand Divisions revenues include NIKE Brand licensing and other miscellaneous revenues that are not part of a geographic operating segment.\\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, butmanaged through our central foreign exchange risk management program.\\nThe primary financial measure used by the Company to evaluate performance is Earnings Before Interest and Taxes (\\\"EBIT\\\"). As discussed in Note 15 \\u2014 Operating\\nSegments and Related Information in the accompanying Notes to the Consolidated Financial Statements, certain corporate costs are not included in EBIT.\\nThe breakdown of EBIT is as follows:\\n(Dollars in millions) FISCAL 2023 FISCAL 2022 % CHANGE FISCAL 2021 % CHANGE\\nNorth America $ 5,454 $ 5,114 7 % $ 5,089 0 %\\nEurope, Middle East & Africa 3,531 3,293 7 % 2,435 35 %\\nGreater China 2,283 2,365 -3 % 3,243 -27 %\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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              "      <td>Asia Pacific &amp; Latin America 1,932 1,896 2 % 1...</td>\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
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              "    background-color: var(--hover-bg-color);\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "  }\n",
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              "    border: 2px solid var(--fill-color);\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
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              "  </div>\n"
            ],
            "text/plain": [
              "         id vector_distance chunk_id  \\\n",
              "0  chunk:88  0.337694585323       88   \n",
              "1  chunk:80  0.342052936554       80   \n",
              "2  chunk:87   0.35776078701       87   \n",
              "\n",
              "                                             content  \n",
              "0  Asia Pacific & Latin America 1,932 1,896 2 % 1...  \n",
              "1  Table of Contents\\nCONSOLIDATED OPERATING RESU...  \n",
              "2  Table of Contents\\nOPERATING SEGMENTS\\nAs disc...  "
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# execute the query with RedisVL\n",
        "result=index.query(vector_query)\n",
        "\n",
        "# view the results\n",
        "pd.DataFrame(result)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rZrcd6n7T6LE",
        "outputId": "fad67a63-76bd-43b9-f62b-b1842ba47605"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "88 0.337694585323\n",
            "80 0.342052936554\n",
            "87 0.35776078701\n"
          ]
        }
      ],
      "source": [
        "# paginate through results\n",
        "for result in index.paginate(vector_query, page_size=1):\n",
        "    print(result[0][\"chunk_id\"], result[0][\"vector_distance\"], flush=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0Ap6WqPLT6LE"
      },
      "source": [
        "### Sort by alternative fields"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "daLVm6OkLn9T",
        "outputId": "d77dfc4c-d451-4bf5-91c3-2155232570b9"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "repr_error": "Out of range float values are not JSON compliant: nan",
              "type": "dataframe"
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              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f06406dc-6305-4631-8102-b2ee833a74b3');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
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              "    background-color: var(--hover-bg-color);\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4314cb96-f018-4dc2-b071-ef139a2ef3d5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "         id payload vector_distance chunk_id\n",
              "0  chunk:80    None  0.342052936554       80\n",
              "1  chunk:83    None   0.37876611948       83\n",
              "2  chunk:87    None   0.35776078701       87\n",
              "3  chunk:88    None  0.337694585323       88"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Sort by chunk_id field after vector search limits to topK\n",
        "vector_query = VectorQuery(\n",
        "    vector=query_embedding,\n",
        "    vector_field_name=\"text_embedding\",\n",
        "    num_results=4,\n",
        "    return_fields=[\"chunk_id\"],\n",
        "    return_score=True\n",
        ")\n",
        "\n",
        "# Decompose vector_query into the core query and the params\n",
        "query = vector_query.query\n",
        "params = vector_query.params\n",
        "\n",
        "# Pass query and params direct to index.search()\n",
        "result = index.search(\n",
        "    query.sort_by(\"chunk_id\", asc=True),\n",
        "    params\n",
        ")\n",
        "\n",
        "pd.DataFrame([doc.__dict__ for doc in result.docs])\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "81PoXomtT6LF"
      },
      "source": [
        "### Add filters to vector queries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "a11G3xXJ1ujC",
        "outputId": "d968add5-704d-4e22-d3bd-97c1d1103a75"
      },
      "outputs": [
        {
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However, lead times for many of our products may make it more difficult for us to respond rapidly to new or changing product trends or consumer preferences. All\\nof our products are subject to changing consumer preferences that cannot be predicted with certainty. Our new products may not receive consumer acceptance as\\nconsumer preferences could shift rapidly to different types of performance products or away from these types of products altogether, and our future success depends in\\npart on our ability to anticipate and respond to these changes. If we fail to anticipate accurately and respond to trends and shifts in consumer preferences by adjusting the\\nmix of existing product offerings, developing new products, designs, styles and categories, and influencing sports and fitness preferences through extensive marketing, we\\ncould experience lower sales, excess inventories or lower profit margins, any of which could have an adverse effect on our results of operations and financial condition. In\\naddition, we market our products globally through a diverse spectrum of advertising and promotional programs and campaigns, including social media and other digital\\nadvertising networks. If we do not successfully market our products or if advertising and promotional costs increase, these factors could have an adverse effect on our\\nbusiness, financial condition and results of operations.\\nWe rely on technical innovation and high-quality products to compete in the market for our products.\\nTechnical innovation and quality control in the design and manufacturing processes of footwear, apparel, equipment and other products and services are essential to the\\ncommercial success of our products and development of new products. Research and development play a key role in technical innovation. We rely upon specialists in the\\nfields of biomechanics, chemistry, exercise physiology, engineering, digital technologies, industrial design, sustainability and related fields, as well as research committees\\nand advisory boards made up of athletes, coaches, trainers, equipment managers, orthopedists, podiatrists and other experts to develop and test cutting-edge\\nperformance products. While we strive to produce products that help to enhance athletic performance and reduce injury and maximize comfort, if we fail to introduce\\ntechnical innovation in our products, consumer demand for our products could decline, and if we experience problems with the quality of our products, we may incur\",\n          \"Table of Contents\\nGROSS MARGIN\\nFISCAL 2023 COMPARED TO FISCAL 2022\\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\\n43.5% for fiscal 2023 compared to 46.0% for fiscal 2022 due to the following:\\n*Wholesale equivalent\\nThe decrease in gross margin for fiscal 2023 was primarily due to:\\n\\u2022 Higher NIKE Brand product costs, on a wholesale equivalent basis, primarily due to higher input costs and elevated inbound freight and logistics costs as well as\\nproduct mix;\\n\\u2022 Lower margin in our NIKE Direct business, driven by higher promotional activity to liquidate inventory in the current period compared to lower promotional activity in\\nthe prior period resulting from lower available inventory supply;\\n\\u2022 Unfavorable changes in net foreign currency exchange rates, including hedges; and\\n\\u2022 Lower off-price margin, on a wholesale equivalent basis.\\nThis was partially offset by:\\n\\u2022 Higher NIKE Brand full-price ASP, net of discounts, on a wholesale equivalent basis, due primarily to strategic pricing actions and product mix; and\\n\\u2022 Lower other costs, primarily due to higher inventory obsolescence reserves recognized in Greater China in the fourth quarter of fiscal 2022.\\nTOTAL SELLING AND ADMINISTRATIVE EXPENSE\\n(Dollars in millions) FISCAL 2023 FISCAL 2022 % CHANGE FISCAL 2021 % CHANGE\\nDemand creation expense $ 4,060 $ 3,850 5 % $ 3,114 24 %\\nOperating overhead expense 12,317 10,954 12 % 9,911 11 %\\nTotal selling and administrative expense $ 16,377 $ 14,804 11 % $ 13,025 14 %\\n% of revenues 32.0 % 31.7 % 30  bps 29.2 % 250  bps\\n(1) Demand creation expense consists of advertising and promotion costs, including costs of endorsement contracts, complimentary product, television, digital and print advertising and media costs, brandevents and retail brand presentation.\\nFISCAL 2023 COMPARED TO FISCAL 2022\\nDemand creation expense increased 5% for fiscal 2023, primarily due to higher advertising and marketing expense and higher sports marketing expense. Changes in\\nforeign currency exchange rates decreased Demand creation expense by approximately 4 percentage points.\\nOperating overhead expense increased 12%, primarily due to higher wage-related expenses, NIKE Direct variable costs, strategic technology enterprise investments and\\nother administrative costs. Changes in foreign currency exchange rates decreased Operating overhead expense by approximately 3 percentage points.\\n(1)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    }\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ab74a3e4-1b79-447f-85ae-aded62f638b6 button');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
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              "  </div>\n"
            ],
            "text/plain": [
              "          id vector_distance  \\\n",
              "0   chunk:83   0.37876611948   \n",
              "1  chunk:129   0.41875731945   \n",
              "2  chunk:168  0.657553255558   \n",
              "3   chunk:39  0.683842301369   \n",
              "\n",
              "                                             content  \n",
              "0  Table of Contents\\nGROSS MARGIN\\nFISCAL 2023 C...  \n",
              "1  Table of Contents\\nNIKE, INC.\\nCONSOLIDATED ST...  \n",
              "2  Table of Contents\\nNOTE 10 — EARNINGS PER SHAR...  \n",
              "3  manner. However, lead times for many of our pr...  "
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from redisvl.query.filter import Text\n",
        "\n",
        "vector_query = VectorQuery(\n",
        "    vector=query_embedding,\n",
        "    vector_field_name=\"text_embedding\",\n",
        "    num_results=4,\n",
        "    return_fields=[\"content\"],\n",
        "    return_score=True\n",
        ")\n",
        "\n",
        "# Set a text filter\n",
        "text_filter = Text(\"content\") % \"profit\"\n",
        "\n",
        "vector_query.set_filter(text_filter)\n",
        "\n",
        "result=index.query(vector_query)\n",
        "pd.DataFrame(result)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5XvVv8zAT6LF"
      },
      "source": [
        "### Range queries in RedisVL"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "bCffoZRx1ujD"
      },
      "outputs": [],
      "source": [
        "from redisvl.query import RangeQuery\n",
        "\n",
        "range_query = RangeQuery(\n",
        "    vector=query_embedding,\n",
        "    vector_field_name=\"text_embedding\",\n",
        "    num_results=4,\n",
        "    return_fields=[\"content\"],\n",
        "    return_score=True,\n",
        "    distance_threshold=0.8  # find all items with a semantic distance of less than 0.8\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "0gHmam1Q1ujD",
        "outputId": "ac80a6ed-4eb8-44d3-881d-87c9271aa10e"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"chunk:80\",\n          \"chunk:83\",\n          \"chunk:88\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vector_distance\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"0.342052936554\",\n          \"0.37876611948\",\n          \"0.337694585323\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"content\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Table of Contents\\nCONSOLIDATED OPERATING RESULTS\\nREVENUES\\n(Dollars in millions) FISCAL2023 FISCAL2022 % CHANGE\\n% CHANGEEXCLUDINGCURRENCYCHANGES FISCAL2021 % CHANGE\\n% CHANGEEXCLUDINGCURRENCYCHANGES\\nNIKE, Inc. Revenues:\\nNIKE Brand Revenues by:\\nFootwear $ 33,135 $ 29,143 14 % 20 %$ 28,021 4 % 4 %\\nApparel 13,843 13,567 2 % 8 % 12,865 5 % 6 %\\nEquipment 1,727 1,624 6 % 13 % 1,382 18 % 18 %\\nGlobal Brand Divisions 58 102 -43 % -43 % 25 308 % 302 %\\nTotal NIKE Brand Revenues $ 48,763 $ 44,436 10 % 16 %$ 42,293 5 % 6 %\\nConverse 2,427 2,346 3 % 8 % 2,205 6 % 7 %\\nCorporate 27 (72) \\u2014 \\u2014 40 \\u2014 \\u2014 \\nTOTAL NIKE, INC. REVENUES $ 51,217 $ 46,710 10 % 16 %$ 44,538 5 % 6 %\\nSupplemental NIKE Brand Revenues Details:\\nNIKE Brand Revenues by:\\nSales to Wholesale Customers $ 27,397 $ 25,608 7 % 14 %$ 25,898 -1 % -1 %\\nSales through NIKE Direct 21,308 18,726 14 % 20 % 16,370 14 % 15 %\\nGlobal Brand Divisions 58 102 -43 % -43 % 25 308 % 302 %\\nTOTAL NIKE BRAND REVENUES $ 48,763 $ 44,436 10 % 16 %$ 42,293 5 % 6 %\\nNIKE Brand Revenues on a Wholesale Equivalent Basis :\\nSales to Wholesale Customers $ 27,397 $ 25,608 7 % 14 %$ 25,898 -1 % -1 %\\nSales from our Wholesale Operations to NIKE Direct Operations 12,730 10,543 21 % 27 % 9,872 7 % 7 %\\nTOTAL NIKE BRAND WHOLESALE EQUIVALENT REVENUES $ 40,127 $ 36,151 11 % 18 %$ 35,770 1 % 1 %\\nNIKE Brand Wholesale Equivalent Revenues by:\\nMen's $ 20,733 $ 18,797 10 % 17 %$ 18,391 2 % 3 %\\nWomen's 8,606 8,273 4 % 11 % 8,225 1 % 1 %\\nNIKE Kids' 5,038 4,874 3 % 10 % 4,882 0 % 0 %\\nJordan Brand 6,589 5,122 29 % 35 % 4,780 7 % 7 %\\nOthers (839) (915) 8 % -3 % (508) -80 % -79 %\\nTOTAL NIKE BRAND WHOLESALE EQUIVALENT REVENUES $ 40,127 $ 36,151 11 % 18 %$ 35,770 1 % 1 %\\n(1) The percent change excluding currency changes and the presentation of wholesale equivalent revenues represent non-GAAP financial measures. For further information, see \\\"Use of Non-GAAPFinancial Measures\\\".\\n(2) Global Brand Divisions revenues include NIKE Brand licensing and other miscellaneous revenues that are not part of a geographic operating segment.\\n(3) 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\\nmanaged through our central foreign exchange risk management program.\",\n          \"Table of Contents\\nGROSS MARGIN\\nFISCAL 2023 COMPARED TO FISCAL 2022\\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\\n43.5% for fiscal 2023 compared to 46.0% for fiscal 2022 due to the following:\\n*Wholesale equivalent\\nThe decrease in gross margin for fiscal 2023 was primarily due to:\\n\\u2022 Higher NIKE Brand product costs, on a wholesale equivalent basis, primarily due to higher input costs and elevated inbound freight and logistics costs as well as\\nproduct mix;\\n\\u2022 Lower margin in our NIKE Direct business, driven by higher promotional activity to liquidate inventory in the current period compared to lower promotional activity in\\nthe prior period resulting from lower available inventory supply;\\n\\u2022 Unfavorable changes in net foreign currency exchange rates, including hedges; and\\n\\u2022 Lower off-price margin, on a wholesale equivalent basis.\\nThis was partially offset by:\\n\\u2022 Higher NIKE Brand full-price ASP, net of discounts, on a wholesale equivalent basis, due primarily to strategic pricing actions and product mix; and\\n\\u2022 Lower other costs, primarily due to higher inventory obsolescence reserves recognized in Greater China in the fourth quarter of fiscal 2022.\\nTOTAL SELLING AND ADMINISTRATIVE EXPENSE\\n(Dollars in millions) FISCAL 2023 FISCAL 2022 % CHANGE FISCAL 2021 % CHANGE\\nDemand creation expense $ 4,060 $ 3,850 5 % $ 3,114 24 %\\nOperating overhead expense 12,317 10,954 12 % 9,911 11 %\\nTotal selling and administrative expense $ 16,377 $ 14,804 11 % $ 13,025 14 %\\n% of revenues 32.0 % 31.7 % 30  bps 29.2 % 250  bps\\n(1) Demand creation expense consists of advertising and promotion costs, including costs of endorsement contracts, complimentary product, television, digital and print advertising and media costs, brandevents and retail brand presentation.\\nFISCAL 2023 COMPARED TO FISCAL 2022\\nDemand creation expense increased 5% for fiscal 2023, primarily due to higher advertising and marketing expense and higher sports marketing expense. Changes in\\nforeign currency exchange rates decreased Demand creation expense by approximately 4 percentage points.\\nOperating overhead expense increased 12%, primarily due to higher wage-related expenses, NIKE Direct variable costs, strategic technology enterprise investments and\\nother administrative costs. Changes in foreign currency exchange rates decreased Operating overhead expense by approximately 3 percentage points.\\n(1)\",\n          \"Asia Pacific & Latin America 1,932 1,896 2 % 1,530 24 %\\nGlobal Brand Divisions (4,841) (4,262) -14 % (3,656) -17 %\\nTOTAL NIKE BRAND $ 8,359 $ 8,406 -1 % $ 8,641 -3 %\\nConverse 676 669 1 % 543 23 %\\nCorporate (2,840) (2,219) -28 % (2,261) 2 %\\nTOTAL NIKE, INC. EARNINGS BEFORE INTEREST ANDTAXES $ 6,195 $ 6,856 -10 % $ 6,923 -1 %\\nEBIT margin 12.1 % 14.7 % 15.5 %\\nInterest expense (income), net (6) 205 \\u2014 262 \\u2014 \\nTOTAL NIKE, INC. INCOME BEFORE INCOME TAXES $ 6,201 $ 6,651 -7 % $ 6,661 0 %\\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(1) (1)\\n(2)\\n(3)\\n(4)\\n(1)\\n(1)\\n(1)\\n2023 FORM 10-K 36\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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              "      <th>id</th>\n",
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              "      <td>0.342052936554</td>\n",
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              "      display:flex;\n",
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              "      background-color: #434B5C;\n",
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              "        const element = document.querySelector('#df-5d820a22-bf59-49b3-858a-a10d59ff75c8');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "    background-color: var(--bg-color);\n",
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              "  }\n",
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              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-94e421ab-d46f-41e9-916d-c5745b1ce13c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "         id vector_distance                                            content\n",
              "0  chunk:88  0.337694585323  Asia Pacific & Latin America 1,932 1,896 2 % 1...\n",
              "1  chunk:80  0.342052936554  Table of Contents\\nCONSOLIDATED OPERATING RESU...\n",
              "2  chunk:87   0.35776078701  Table of Contents\\nOPERATING SEGMENTS\\nAs disc...\n",
              "3  chunk:83   0.37876611948  Table of Contents\\nGROSS MARGIN\\nFISCAL 2023 C..."
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "result=index.query(range_query)\n",
        "pd.DataFrame(result)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "YZg4U21r1ujD",
        "outputId": "d3db5ac3-6ae9-42c4-aaee-874cecafe3ad"
      },
      "outputs": [
        {
          "data": {
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              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"chunk:80\",\n          \"chunk:83\",\n          \"chunk:88\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vector_distance\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"0.342052936554\",\n          \"0.37876611948\",\n          \"0.337694585323\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"content\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Table of Contents\\nCONSOLIDATED OPERATING RESULTS\\nREVENUES\\n(Dollars in millions) FISCAL2023 FISCAL2022 % CHANGE\\n% CHANGEEXCLUDINGCURRENCYCHANGES FISCAL2021 % CHANGE\\n% CHANGEEXCLUDINGCURRENCYCHANGES\\nNIKE, Inc. Revenues:\\nNIKE Brand Revenues by:\\nFootwear $ 33,135 $ 29,143 14 % 20 %$ 28,021 4 % 4 %\\nApparel 13,843 13,567 2 % 8 % 12,865 5 % 6 %\\nEquipment 1,727 1,624 6 % 13 % 1,382 18 % 18 %\\nGlobal Brand Divisions 58 102 -43 % -43 % 25 308 % 302 %\\nTotal NIKE Brand Revenues $ 48,763 $ 44,436 10 % 16 %$ 42,293 5 % 6 %\\nConverse 2,427 2,346 3 % 8 % 2,205 6 % 7 %\\nCorporate 27 (72) \\u2014 \\u2014 40 \\u2014 \\u2014 \\nTOTAL NIKE, INC. REVENUES $ 51,217 $ 46,710 10 % 16 %$ 44,538 5 % 6 %\\nSupplemental NIKE Brand Revenues Details:\\nNIKE Brand Revenues by:\\nSales to Wholesale Customers $ 27,397 $ 25,608 7 % 14 %$ 25,898 -1 % -1 %\\nSales through NIKE Direct 21,308 18,726 14 % 20 % 16,370 14 % 15 %\\nGlobal Brand Divisions 58 102 -43 % -43 % 25 308 % 302 %\\nTOTAL NIKE BRAND REVENUES $ 48,763 $ 44,436 10 % 16 %$ 42,293 5 % 6 %\\nNIKE Brand Revenues on a Wholesale Equivalent Basis :\\nSales to Wholesale Customers $ 27,397 $ 25,608 7 % 14 %$ 25,898 -1 % -1 %\\nSales from our Wholesale Operations to NIKE Direct Operations 12,730 10,543 21 % 27 % 9,872 7 % 7 %\\nTOTAL NIKE BRAND WHOLESALE EQUIVALENT REVENUES $ 40,127 $ 36,151 11 % 18 %$ 35,770 1 % 1 %\\nNIKE Brand Wholesale Equivalent Revenues by:\\nMen's $ 20,733 $ 18,797 10 % 17 %$ 18,391 2 % 3 %\\nWomen's 8,606 8,273 4 % 11 % 8,225 1 % 1 %\\nNIKE Kids' 5,038 4,874 3 % 10 % 4,882 0 % 0 %\\nJordan Brand 6,589 5,122 29 % 35 % 4,780 7 % 7 %\\nOthers (839) (915) 8 % -3 % (508) -80 % -79 %\\nTOTAL NIKE BRAND WHOLESALE EQUIVALENT REVENUES $ 40,127 $ 36,151 11 % 18 %$ 35,770 1 % 1 %\\n(1) The percent change excluding currency changes and the presentation of wholesale equivalent revenues represent non-GAAP financial measures. For further information, see \\\"Use of Non-GAAPFinancial Measures\\\".\\n(2) Global Brand Divisions revenues include NIKE Brand licensing and other miscellaneous revenues that are not part of a geographic operating segment.\\n(3) 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\\nmanaged through our central foreign exchange risk management program.\",\n          \"Table of Contents\\nGROSS MARGIN\\nFISCAL 2023 COMPARED TO FISCAL 2022\\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\\n43.5% for fiscal 2023 compared to 46.0% for fiscal 2022 due to the following:\\n*Wholesale equivalent\\nThe decrease in gross margin for fiscal 2023 was primarily due to:\\n\\u2022 Higher NIKE Brand product costs, on a wholesale equivalent basis, primarily due to higher input costs and elevated inbound freight and logistics costs as well as\\nproduct mix;\\n\\u2022 Lower margin in our NIKE Direct business, driven by higher promotional activity to liquidate inventory in the current period compared to lower promotional activity in\\nthe prior period resulting from lower available inventory supply;\\n\\u2022 Unfavorable changes in net foreign currency exchange rates, including hedges; and\\n\\u2022 Lower off-price margin, on a wholesale equivalent basis.\\nThis was partially offset by:\\n\\u2022 Higher NIKE Brand full-price ASP, net of discounts, on a wholesale equivalent basis, due primarily to strategic pricing actions and product mix; and\\n\\u2022 Lower other costs, primarily due to higher inventory obsolescence reserves recognized in Greater China in the fourth quarter of fiscal 2022.\\nTOTAL SELLING AND ADMINISTRATIVE EXPENSE\\n(Dollars in millions) FISCAL 2023 FISCAL 2022 % CHANGE FISCAL 2021 % CHANGE\\nDemand creation expense $ 4,060 $ 3,850 5 % $ 3,114 24 %\\nOperating overhead expense 12,317 10,954 12 % 9,911 11 %\\nTotal selling and administrative expense $ 16,377 $ 14,804 11 % $ 13,025 14 %\\n% of revenues 32.0 % 31.7 % 30  bps 29.2 % 250  bps\\n(1) Demand creation expense consists of advertising and promotion costs, including costs of endorsement contracts, complimentary product, television, digital and print advertising and media costs, brandevents and retail brand presentation.\\nFISCAL 2023 COMPARED TO FISCAL 2022\\nDemand creation expense increased 5% for fiscal 2023, primarily due to higher advertising and marketing expense and higher sports marketing expense. Changes in\\nforeign currency exchange rates decreased Demand creation expense by approximately 4 percentage points.\\nOperating overhead expense increased 12%, primarily due to higher wage-related expenses, NIKE Direct variable costs, strategic technology enterprise investments and\\nother administrative costs. Changes in foreign currency exchange rates decreased Operating overhead expense by approximately 3 percentage points.\\n(1)\",\n          \"Asia Pacific & Latin America 1,932 1,896 2 % 1,530 24 %\\nGlobal Brand Divisions (4,841) (4,262) -14 % (3,656) -17 %\\nTOTAL NIKE BRAND $ 8,359 $ 8,406 -1 % $ 8,641 -3 %\\nConverse 676 669 1 % 543 23 %\\nCorporate (2,840) (2,219) -28 % (2,261) 2 %\\nTOTAL NIKE, INC. EARNINGS BEFORE INTEREST ANDTAXES $ 6,195 $ 6,856 -10 % $ 6,923 -1 %\\nEBIT margin 12.1 % 14.7 % 15.5 %\\nInterest expense (income), net (6) 205 \\u2014 262 \\u2014 \\nTOTAL NIKE, INC. INCOME BEFORE INCOME TAXES $ 6,201 $ 6,651 -7 % $ 6,661 0 %\\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(1) (1)\\n(2)\\n(3)\\n(4)\\n(1)\\n(1)\\n(1)\\n2023 FORM 10-K 36\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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              "      <th>id</th>\n",
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              "      <th>0</th>\n",
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              "      <td>chunk:80</td>\n",
              "      <td>0.342052936554</td>\n",
              "      <td>Table of Contents\\nCONSOLIDATED OPERATING RESU...</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>chunk:87</td>\n",
              "      <td>0.35776078701</td>\n",
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              "      <td>0.37876611948</td>\n",
              "      <td>Table of Contents\\nGROSS MARGIN\\nFISCAL 2023 C...</td>\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c168f092-59a6-47e7-ad27-84842ea507e3');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
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              "      --bg-color: #3B4455;\n",
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              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-62ff0a98-3409-45a4-ac11-0ff56a2c2ee4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "         id vector_distance                                            content\n",
              "0  chunk:88  0.337694585323  Asia Pacific & Latin America 1,932 1,896 2 % 1...\n",
              "1  chunk:80  0.342052936554  Table of Contents\\nCONSOLIDATED OPERATING RESU...\n",
              "2  chunk:87   0.35776078701  Table of Contents\\nOPERATING SEGMENTS\\nAs disc...\n",
              "3  chunk:83   0.37876611948  Table of Contents\\nGROSS MARGIN\\nFISCAL 2023 C..."
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Add filter to range query\n",
        "range_query.set_filter(text_filter)\n",
        "\n",
        "index.query(range_query)\n",
        "pd.DataFrame(result)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zYYPTQN7T6LG"
      },
      "source": [
        "## Building a basic RAG Pipeline from Scratch\n",
        "We're going to build a basic RAG pipeline from scratch incorporating the following components:\n",
        "\n",
        "- Standard semantic search\n",
        "- Integration with OpenAI for LLM\n",
        "- Chat completion"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rCWlVR2OT6LG"
      },
      "source": [
        "### Setup RedisVL AsyncSearchIndex"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_esLGYzbT6LG",
        "outputId": "d3314a08-8746-4239-dcb2-e7e41b51c640"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<redisvl.index.index.AsyncSearchIndex at 0x7b3f947ea1d0>"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from redis.asyncio import Redis as AsyncRedis\n",
        "from redisvl.index import AsyncSearchIndex\n",
        "\n",
        "client = AsyncRedis.from_url(REDIS_URL)\n",
        "async_index = AsyncSearchIndex.from_dict(schema)\n",
        "await async_index.set_client(client)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "peK4C5xGJkED"
      },
      "source": [
        "### Setup OpenAI API"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EgdTvz6zJkED",
        "outputId": "d2ab0e8e-2ecf-458d-881d-6e4658953a71"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "OPENAI_API_KEY :··········\n"
          ]
        }
      ],
      "source": [
        "import openai\n",
        "import os\n",
        "import getpass\n",
        "\n",
        "\n",
        "CHAT_MODEL = \"gpt-3.5-turbo-0125\"\n",
        "\n",
        "if \"OPENAI_API_KEY\" not in os.environ:\n",
        "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OPENAI_API_KEY :\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w8Af-zneT6LH"
      },
      "source": [
        "### Baseline Retrieval Augmented Generation\n",
        "The code below answers a user's questions following this basic flow:\n",
        "\n",
        "1. Generate a query_vector from the user's chat question to have an apples to apples comparison against the vector database.\n",
        "2. Retrieve the most semantically relevant chunks to the user's query from the database.\n",
        "3. Pass the user query and retrieved context to the `promptify` function to generate the final prompt to be sent to the LLM along with the system prompt and necessary hyperparameters.\n",
        "4. Return the LLMs response to the user."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "1V1Tio4-ZjmA"
      },
      "outputs": [],
      "source": [
        "\n",
        "async def answer_question(index: AsyncSearchIndex, query: str):\n",
        "    \"\"\"Answer the user's question\"\"\"\n",
        "\n",
        "    SYSTEM_PROMPT = \"\"\"You are a helpful financial analyst assistant that has access\n",
        "    to public financial 10k documents in order to answer users questions about company\n",
        "    performance, ethics, characteristics, and core information.\n",
        "    \"\"\"\n",
        "\n",
        "    query_vector = hf.embed(query)\n",
        "    # Fetch context from Redis using vector search\n",
        "    context = await retrieve_context(index, query_vector)\n",
        "    # Generate contextualized prompt and feed to OpenAI\n",
        "    response = await openai.AsyncClient().chat.completions.create(\n",
        "        model=CHAT_MODEL,\n",
        "        messages=[\n",
        "            {\"role\": \"system\", \"content\": SYSTEM_PROMPT},\n",
        "            {\"role\": \"user\", \"content\": promptify(query, context)}\n",
        "        ],\n",
        "        temperature=0.1,\n",
        "        seed=42\n",
        "    )\n",
        "    # Response provided by LLM\n",
        "    return response.choices[0].message.content\n",
        "\n",
        "\n",
        "async def retrieve_context(async_index: AsyncSearchIndex, query_vector) -> str:\n",
        "    \"\"\"Fetch the relevant context from Redis using vector search\"\"\"\n",
        "    results = await async_index.query(\n",
        "        VectorQuery(\n",
        "            vector=query_vector,\n",
        "            vector_field_name=\"text_embedding\",\n",
        "            return_fields=[\"content\"],\n",
        "            num_results=3\n",
        "        )\n",
        "    )\n",
        "    content = \"\\n\".join([result[\"content\"] for result in results])\n",
        "    return content\n",
        "\n",
        "\n",
        "def promptify(query: str, context: str) -> str:\n",
        "    return f'''Use the provided context below derived from public financial\n",
        "    documents to answer the user's question. If you can't answer the user's\n",
        "    question, based on the context; do not guess. If there is no context at all,\n",
        "    respond with \"I don't know\".\n",
        "\n",
        "    User question:\n",
        "\n",
        "    {query}\n",
        "\n",
        "    Helpful context:\n",
        "\n",
        "    {context}\n",
        "\n",
        "    Answer:\n",
        "    '''"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kgVM_g01T6LP"
      },
      "source": [
        "### Let's test it out..."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "pn-PoACdbihY"
      },
      "outputs": [],
      "source": [
        "# Generate a list of questions\n",
        "questions = [\n",
        "    \"What is the trend in the company's revenue and profit over the past few years?\",\n",
        "    \"What are the company's primary revenue sources?\",\n",
        "    \"How much debt does the company have, and what are its capital expenditure plans?\",\n",
        "    \"What does the company say about its environmental, social, and governance (ESG) practices?\",\n",
        "    \"What is the company's strategy for growth?\"\n",
        "]"
      ]
    },
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        "id": "9M_iU6_hbv0J",
        "outputId": "b9fc43d9-883a-4795-8a37-8a2f4c545892"
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      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "22178a562935411f88cad67659ebb7c4",
              "version_major": 2,
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              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "20:48:58 httpx INFO   HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
            "20:48:58 httpx INFO   HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
            "20:48:58 httpx INFO   HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
            "20:48:58 httpx INFO   HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
            "20:48:59 httpx INFO   HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n"
          ]
        }
      ],
      "source": [
        "import asyncio\n",
        "\n",
        "results = await asyncio.gather(*[\n",
        "    answer_question(async_index, question) for question in questions\n",
        "])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CpQ59SRgJkED"
      },
      "source": [
        "### Let's view the results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7SZM_xg3b9Gb",
        "outputId": "758ae31a-2291-4191-aa57-ee941d3319cb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Question: What is the trend in the company's revenue and profit over the past few years?\n",
            "Answer: \n",
            " The trend in the company's revenue and profit over the past few years is as follows:\n",
            "\n",
            "- Revenue:\n",
            "  - Fiscal 2023: Total revenues for Nike, Inc. were $51,217 million, showing a 10% increase from the previous year.\n",
            "  - Fiscal 2022: Total revenues were $46,710 million, reflecting a 10% increase from the year before.\n",
            "  - Fiscal 2021: Total revenues were $44,538 million, indicating a 5% increase from the prior year.\n",
            "\n",
            "- Profit (EBIT):\n",
            "  - Fiscal 2023: EBIT for North America increased by 7% compared to the previous year.\n",
            "  - Fiscal 2022: EBIT for Europe, Middle East & Africa increased by 7% from the year before.\n",
            "  - Fiscal 2021: EBIT for Greater China decreased by 27% compared to the prior year.\n",
            "\n",
            "Overall, the company has shown a consistent growth trend in revenue over the past few years, with varying performance in different geographic segments in terms of profit. \n",
            "-----------\n",
            "\n",
            "Question: What are the company's primary revenue sources?\n",
            "Answer: \n",
            " The company's primary revenue sources are as follows:\n",
            "\n",
            "1. Footwear\n",
            "2. Apparel\n",
            "3. Equipment\n",
            "4. Other (which includes licensing and miscellaneous revenues not part of a geographic operating segment)\n",
            "\n",
            "These revenues are generated through sales to wholesale customers, sales through direct-to-consumer channels, and other miscellaneous sources. \n",
            "-----------\n",
            "\n",
            "Question: How much debt does the company have, and what are its capital expenditure plans?\n",
            "Answer: \n",
            " The company has a total long-term debt of $8,927 million as of May 31, 2023. The capital expenditure plans are not explicitly mentioned in the provided context. \n",
            "-----------\n",
            "\n",
            "Question: What does the company say about its environmental, social, and governance (ESG) practices?\n",
            "Answer: \n",
            " The company acknowledges the increased focus on sustainability matters, responsible sourcing, deforestation, plastic use, energy and water consumption, and transparency in its operations. It mentions that complying with legislative and regulatory initiatives related to climate change may lead to increased costs and complexities. The company has announced sustainability-related goals and targets, but there is a recognition that stakeholders may not always agree with their strategies. Failure to achieve these goals or respond effectively to new legal or regulatory requirements could result in adverse publicity and impact the company's business and reputation. \n",
            "-----------\n",
            "\n",
            "Question: What is the company's strategy for growth?\n",
            "Answer: \n",
            " Based on the provided financial data, it appears that the company's strategy for growth includes expanding its revenues across different geographic regions and product lines. The company has shown consistent growth in revenues over the years, with a focus on both wholesale customers and direct-to-consumer sales channels. Additionally, investments in property, plant, and equipment indicate a commitment to expanding operations and enhancing production capabilities. Overall, the company's growth strategy seems to involve a combination of geographic expansion, product diversification, and investment in infrastructure. \n",
            "-----------\n",
            "\n"
          ]
        }
      ],
      "source": [
        "for i, r in enumerate(results):\n",
        "    print(f\"Question: {questions[i]}\")\n",
        "    print(f\"Answer: \\n {r}\", \"\\n-----------\\n\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D_eiWikCJkED"
      },
      "source": [
        "# You now have a working RAG pipeline!\n",
        "\n",
        "As you can see, it is easy to get started with RAG and we were able to get decent chat results from this simple setup. To go beyond the basic example though see the [advanced_rag](../advanced_capabilities/advanced_RAG.ipynb) notebook.\n",
        "\n",
        "This notebook covers:\n",
        "\n",
        "- **Improving accuracy** with dense content representations and query rewriting/expansion\n",
        "- **Improving performance and optimizing cost** with semantic caching\n",
        "- **Improving personalization** with chat session memory.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wscs4Mvo1ujD"
      },
      "source": [
        "## Cleanup\n",
        "\n",
        "Clean up the database."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "On6yNuQn1ujD"
      },
      "outputs": [],
      "source": [
        "# await async_index.client.flushall()"
      ]
    }
  ],
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