python-recipes/vector-search/01_redisvl.ipynb

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        "id": "cbba56a9"
      },
      "source": [
        "![Redis](https://redis.io/wp-content/uploads/2024/04/Logotype.svg?auto=webp&quality=85,75&width=120)\n",
        "# Vector Search with Redisvl\n",
        "## Let's Begin!\n",
        "<a href=\"https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/vector-search/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",
      "id": "0b80de6b",
      "metadata": {
        "id": "0b80de6b"
      },
      "source": [
        "## Prepare data\n",
        "\n",
        "In this examples we will load a list of movie objects with the following attributes: `title`, `rating`, `description`, and `genre`.\n",
        "\n",
        "For the vector part of our vector search we will embed the description so that user's can search for movies that best match what they're looking for.\n",
        "\n",
        "**If you are running this notebook locally**, FYI you may not need to perform this step at all."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "b966a9b5",
      "metadata": {
        "id": "b966a9b5",
        "outputId": "61565924-8e01-4411-fac7-82346bb10e87",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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 | 15.56 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/vector-search/resources .\n",
        "!rm -rf temp_repo"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "19bdc2a5-2192-4f5f-bd6e-7c956fd0e230",
      "metadata": {
        "id": "19bdc2a5-2192-4f5f-bd6e-7c956fd0e230"
      },
      "source": [
        "## Packages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "c620286e",
      "metadata": {
        "id": "c620286e",
        "outputId": "d69d35a0-29b2-4a9c-aa13-acf27d85a414",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
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          "name": "stdout",
          "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[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m261.4/261.4 kB\u001b[0m \u001b[31m7.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/96.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m96.1/96.1 kB\u001b[0m \u001b[31m6.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.0/46.0 kB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.8/86.8 kB\u001b[0m \u001b[31m5.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "# NBVAL_SKIP\n",
        "%pip install -q redis redisvl numpy sentence-transformers"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "323aec7f",
      "metadata": {
        "id": "323aec7f"
      },
      "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.\n",
        "\n",
        "#### 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,
      "id": "2cb85a99",
      "metadata": {
        "id": "2cb85a99",
        "outputId": "70660a1f-9d1c-408b-f7a5-5981054fabc3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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",
      "id": "7c5dbaaf",
      "metadata": {
        "id": "7c5dbaaf"
      },
      "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",
      "id": "1d4499ae",
      "metadata": {
        "id": "1d4499ae"
      },
      "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,
      "id": "aefda1d1",
      "metadata": {
        "id": "aefda1d1"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "# Replace values below with your own if using Redis Cloud instance\n",
        "REDIS_HOST = os.getenv(\"REDIS_HOST\", \"localhost\") # 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",
      "id": "f8c6ef53",
      "metadata": {
        "id": "f8c6ef53"
      },
      "source": [
        "### Create redis client"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "370c1fcc",
      "metadata": {
        "id": "370c1fcc"
      },
      "outputs": [],
      "source": [
        "from redis import Redis\n",
        "\n",
        "client = Redis.from_url(REDIS_URL)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Load Data"
      ],
      "metadata": {
        "id": "jCXiuk9ZTN_K"
      },
      "id": "jCXiuk9ZTN_K"
    },
    {
      "cell_type": "code",
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              "               title   genre  rating  \\\n",
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              "    }\n",
              "    60% {\n",
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              "    }\n",
              "    80% {\n",
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              "    }\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-7d1a453c-ad32-43df-8f25-ff433123719a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 20,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"Explosive Pursuit\",\n          \"Despicable Me\",\n          \"The Incredibles\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"genre\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"comedy\",\n          \"action\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 6,\n        \"max\": 9,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          8,\n          9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"description\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"A daring cop chases a notorious criminal across the city in a high-stakes game of cat and mouse.\",\n          \"When a criminal mastermind uses a trio of orphan girls as pawns for a grand scheme, he finds their love is profoundly changing him for the better.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import json\n",
        "\n",
        "df = pd.read_json(\"resources/movies.json\")\n",
        "df.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from redisvl.utils.vectorize import HFTextVectorizer\n",
        "from tqdm.auto import tqdm\n",
        "\n",
        "\n",
        "hf = HFTextVectorizer(\"sentence-transformers/all-MiniLM-L6-v2\")\n",
        "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n"
      ],
      "metadata": {
        "id": "bfiTJovpQX90",
        "outputId": "74602d14-31cb-48a1-84bd-5b31dc6c04e2",
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        }
      },
      "id": "bfiTJovpQX90",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "20:41:17 sentence_transformers.SentenceTransformer INFO   Use pytorch device_name: cuda\n",
            "20:41:17 sentence_transformers.SentenceTransformer INFO   Load pretrained SentenceTransformer: sentence-transformers/all-MiniLM-L6-v2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "modules.json:   0%|          | 0.00/349 [00:00<?, ?B/s]"
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          "metadata": {}
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        {
          "output_type": "display_data",
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              "config_sentence_transformers.json:   0%|          | 0.00/116 [00:00<?, ?B/s]"
            ],
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            "text/plain": [
              "README.md:   0%|          | 0.00/10.7k [00:00<?, ?B/s]"
            ],
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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              "model.safetensors:   0%|          | 0.00/90.9M [00:00<?, ?B/s]"
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            ],
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            ],
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              "model_id": "4f98a16386b34805a03bbc5faad31670"
            }
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            ],
            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "7bde36f5d0994a559a4ec662c5e6749e"
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        {
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          "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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "1_Pooling/config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b31cd4f334134bc5bbdcb7350958f297"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "def texts_to_embeddings(texts):\n",
        "  return [np.array(embedding, dtype=np.float32).tobytes() for embedding in hf.embed_many(texts)]\n",
        "\n",
        "# Generate vector embeddings\n",
        "df[\"vector\"] = texts_to_embeddings(df[\"description\"].tolist())\n",
        "df.head()"
      ],
      "metadata": {
        "id": "Vl3SehnxQvXo",
        "outputId": "43ca3865-8b29-41c5-daca-8111a1d51b10",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238,
          "referenced_widgets": [
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            "f1479cb84d7b4be89b14d69b2907d0ea",
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            "bc394ff0234e43bb853c0e7fff9daa39"
          ]
        }
      },
      "id": "Vl3SehnxQvXo",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
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            "text/plain": [
              "               title   genre  rating  \\\n",
              "0  Explosive Pursuit  action       7   \n",
              "1            Skyfall  action       8   \n",
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              "3        Black Widow  action       7   \n",
              "4          John Wick  action       8   \n",
              "\n",
              "                                         description  \\\n",
              "0  A daring cop chases a notorious criminal acros...   \n",
              "1  James Bond returns to track down a dangerous n...   \n",
              "2  Dom and his crew face off against a high-tech ...   \n",
              "3  Natasha Romanoff confronts her dark past and f...   \n",
              "4  A retired hitman seeks vengeance against those...   \n",
              "\n",
              "                                              vector  \n",
              "0  b'\\x99f|=2a\\n;\\x1d\\x92\\xb7;\\x1b\\xcb~\\xbd\\xb4d\\...  \n",
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       ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d7e99897",
      "metadata": {
        "id": "d7e99897"
      },
      "source": [
        "## Define Redis index schema"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "2ac53ebd",
      "metadata": {
        "id": "2ac53ebd"
      },
      "outputs": [],
      "source": [
        "from redisvl.schema import IndexSchema\n",
        "from redisvl.index import SearchIndex\n",
        "\n",
        "index_name = \"movies\"\n",
        "\n",
        "schema = IndexSchema.from_dict({\n",
        "  \"index\": {\n",
        "    \"name\": index_name,\n",
        "  },\n",
        "  \"fields\": [\n",
        "    {\n",
        "        \"name\": \"title\",\n",
        "        \"type\": \"text\",\n",
        "    },\n",
        "    {\n",
        "        \"name\": \"description\",\n",
        "        \"type\": \"text\",\n",
        "    },\n",
        "    {\n",
        "        \"name\": \"genre\",\n",
        "        \"type\": \"tag\",\n",
        "        \"attrs\": {\n",
        "            \"sortable\": True\n",
        "        }\n",
        "    },\n",
        "    {\n",
        "        \"name\": \"rating\",\n",
        "        \"type\": \"numeric\",\n",
        "        \"attrs\": {\n",
        "            \"sortable\": True\n",
        "        }\n",
        "    },\n",
        "    {\n",
        "        \"name\": \"vector\",\n",
        "        \"type\": \"vector\",\n",
        "        \"attrs\": {\n",
        "            \"dims\": 384,\n",
        "            \"distance_metric\": \"cosine\",\n",
        "            \"algorithm\": \"hnsw\",\n",
        "            \"datatype\": \"float32\"\n",
        "        }\n",
        "    }\n",
        "  ]\n",
        "})\n",
        "\n",
        "\n",
        "index = SearchIndex(schema, client)\n",
        "index.create(overwrite=True, drop=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "24d3ea9c",
      "metadata": {
        "id": "24d3ea9c"
      },
      "source": [
        "## Populate index"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "169ebb93",
      "metadata": {
        "id": "169ebb93",
        "outputId": "0e2b0eea-c058-4ee0-8116-197a452b53e0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['rvl:c2f305b7836a40bdafb7223184e26b5b',\n",
              " 'rvl:59d2b0bd431148e9b2c5516b8417eb75',\n",
              " 'rvl:299b8027b07446fda39ca8bf37789776',\n",
              " 'rvl:6406f177887f401b9ab4d45956e324ae',\n",
              " 'rvl:d051b98b40f64279b9dad13f7d4666ce',\n",
              " 'rvl:55a20aec9cb74e33ae9e2d8952bcde25',\n",
              " 'rvl:a5ef870f9a5b41b38e950ea0fa7a9f83',\n",
              " 'rvl:7dd3e91a505a40b8bd1cf50613e4179c',\n",
              " 'rvl:4545e847159f441d843611f05df01983',\n",
              " 'rvl:646f249d9ff646e7ae5b757cd6caad0a',\n",
              " 'rvl:5cbf956aa543497c8e0a4e2a08e7fa53',\n",
              " 'rvl:e3d4efbae7bd49e49300d0c0e1e54426',\n",
              " 'rvl:2dbf334c02854aaab87c196f47cb2729',\n",
              " 'rvl:1dfb97323ddc47fdbc07aaa299658c99',\n",
              " 'rvl:ca2c31d6ce8740ca9e2ce711c0d9ef56',\n",
              " 'rvl:d9ba373d3a174ed1a08d096de012f56d',\n",
              " 'rvl:1e52ad75779046578e9b05499f78ec92',\n",
              " 'rvl:19415f0c3b2646b3abbf994762486e4f',\n",
              " 'rvl:ac862266170c4ee8942bf76791056295',\n",
              " 'rvl:9161346ce4b645dca6833386cc05ade4']"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "index.load(df.to_dict(orient=\"records\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "ae814790",
      "metadata": {
        "id": "ae814790",
        "outputId": "d13768b9-a4b8-44a1-b26d-3f8e07f7afde",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'index_name': 'movies',\n",
              " 'index_options': [],\n",
              " 'index_definition': ['key_type',\n",
              "  'HASH',\n",
              "  'prefixes',\n",
              "  ['rvl'],\n",
              "  'default_score',\n",
              "  '1'],\n",
              " 'attributes': [['identifier',\n",
              "   'title',\n",
              "   'attribute',\n",
              "   'title',\n",
              "   'type',\n",
              "   'TEXT',\n",
              "   'WEIGHT',\n",
              "   '1'],\n",
              "  ['identifier',\n",
              "   'description',\n",
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              "    'N/A']]]}"
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          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "index.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "87ba1dfd",
      "metadata": {
        "id": "87ba1dfd"
      },
      "source": [
        "## Index loaded now we can perform vector search\n",
        "\n",
        "### basic vector search"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "9454e60d",
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        {
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          "data": {
            "text/plain": [
              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
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              "                                     id vector_distance             title  \\\n",
              "0  rvl:299b8027b07446fda39ca8bf37789776  0.685773432255  Fast & Furious 9   \n",
              "1  rvl:19415f0c3b2646b3abbf994762486e4f  0.801602959633     Despicable Me   \n",
              "2  rvl:d9ba373d3a174ed1a08d096de012f56d  0.812341988087   The Incredibles   \n",
              "\n",
              "  rating   genre  \n",
              "0      6  action  \n",
              "1      7  comedy  \n",
              "2      8  comedy  "
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              "type": "dataframe",
              "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          \"rvl:299b8027b07446fda39ca8bf37789776\",\n          \"rvl:19415f0c3b2646b3abbf994762486e4f\",\n          \"rvl:d9ba373d3a174ed1a08d096de012f56d\"\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          \"0.685773432255\",\n          \"0.801602959633\",\n          \"0.812341988087\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Fast & Furious 9\",\n          \"Despicable Me\",\n          \"The Incredibles\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"6\",\n          \"7\",\n          \"8\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"genre\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"comedy\",\n          \"action\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "from redisvl.query import VectorQuery\n",
        "\n",
        "user_query = \"High tech movies\"\n",
        "\n",
        "embedded_user_query = hf.embed(user_query)\n",
        "\n",
        "vec_query = VectorQuery(\n",
        "    vector=embedded_user_query,\n",
        "    vector_field_name=\"vector\",\n",
        "    num_results=3,\n",
        "    return_fields=[\"title\", \"rating\", \"genre\"],\n",
        "    return_score=True\n",
        ")\n",
        "\n",
        "result = index.query(vec_query)\n",
        "pd.DataFrame(result)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ef5e1997",
      "metadata": {
        "id": "ef5e1997"
      },
      "source": [
        "### Hybrid filter vector search\n",
        "\n",
        "Redis allows you to combine filter searches on fields within the index object allowing us to create more specific searches."
      ]
    },
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      "execution_count": 13,
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              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
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              "                                     id vector_distance               title  \\\n",
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          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "# Search for top 3 movies specifically in the action genre\n",
        "\n",
        "from redisvl.query.filter import Tag\n",
        "\n",
        "user_query = \"High tech movies\"\n",
        "\n",
        "embedded_user_query = hf.embed(user_query)\n",
        "\n",
        "vec_query = VectorQuery(\n",
        "    vector=embedded_user_query,\n",
        "    vector_field_name=\"vector\",\n",
        "    num_results=3,\n",
        "    return_fields=[\"title\", \"rating\", \"genre\"],\n",
        "    return_score=True\n",
        ")\n",
        "\n",
        "tag_filter = Tag(\"genre\") == \"action\"\n",
        "\n",
        "vec_query.set_filter(tag_filter)\n",
        "\n",
        "result=index.query(vec_query)\n",
        "pd.DataFrame(result)"
      ]
    },
    {
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      "execution_count": 14,
      "id": "f59fff2c",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-fb06ca37-573e-42f9-a1f9-6c98482cb332 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-fb06ca37-573e-42f9-a1f9-6c98482cb332');\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",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-504ea491-9a2d-484f-a265-5b517e0ac3ad')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\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",
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              "  }\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",
              "      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-504ea491-9a2d-484f-a265-5b517e0ac3ad button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "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          \"rvl:55a20aec9cb74e33ae9e2d8952bcde25\",\n          \"rvl:c2f305b7836a40bdafb7223184e26b5b\",\n          \"rvl:646f249d9ff646e7ae5b757cd6caad0a\"\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          \"0.820429563522\",\n          \"0.851705253124\",\n          \"0.856359839439\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Mad Max: Fury Road\",\n          \"Explosive Pursuit\",\n          \"The Avengers\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"7\",\n          \"8\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"genre\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"action\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "# Search for top 3 movies specifically in the action genre with ratings at or above a 7\n",
        "\n",
        "from redisvl.query.filter import Num\n",
        "\n",
        "user_query = \"High tech movies\"\n",
        "\n",
        "embedded_user_query = hf.embed(user_query)\n",
        "\n",
        "tag_filter = Tag(\"genre\") == \"action\"\n",
        "num_filter = Num(\"rating\") >= 7\n",
        "combined_filter = tag_filter & num_filter\n",
        "\n",
        "vec_query = VectorQuery(\n",
        "    vector=embedded_user_query,\n",
        "    vector_field_name=\"vector\",\n",
        "    num_results=3,\n",
        "    return_fields=[\"title\", \"rating\", \"genre\"],\n",
        "    return_score=True,\n",
        "    filter_expression=combined_filter\n",
        ")\n",
        "\n",
        "result = index.query(vec_query)\n",
        "pd.DataFrame(result)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "7dab26c2",
      "metadata": {
        "id": "7dab26c2",
        "outputId": "73da3327-0b76-4b12-e279-053466dded3b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144,
          "referenced_widgets": [
            "9d7ae8d485214e80be6d383fa38fed2d",
            "4ec08ba49f014cb097deb5c508047142",
            "499b4f7f99114c4fa003b347b9c971c0",
            "69c7c165e13c4b7fb788a37e2b5a65ee",
            "c6f467fa83f84d30974894d774b42e12",
            "1f834b9f5e14465ab877836185e4ee52",
            "ef1efe74a7c549fca4bf1cf8ce39f696",
            "b90f3572accd459c914348a47924a349",
            "9b99a9c68013465f86adebda60bac0a5",
            "ccf4c1e6b2934021806d5fa7f7854665",
            "6f4eb3c30aec4a1dab2d2ae02d90ff5f"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9d7ae8d485214e80be6d383fa38fed2d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                     id vector_distance            title  \\\n",
              "0  rvl:19415f0c3b2646b3abbf994762486e4f  0.801602959633    Despicable Me   \n",
              "1  rvl:a5ef870f9a5b41b38e950ea0fa7a9f83  0.982345640659  The Dark Knight   \n",
              "\n",
              "  rating   genre                                        description  \n",
              "0      7  comedy  When a criminal mastermind uses a trio of orph...  \n",
              "1      9  action  Batman faces off against the Joker, a criminal...  "
            ],
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              "      <td>The Dark Knight</td>\n",
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              "        document.querySelector('#df-c0c7842d-c9ca-45cf-bfc3-1be7eadb5551 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c0c7842d-c9ca-45cf-bfc3-1be7eadb5551');\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",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "    animation:\n",
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              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    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-d50f3a86-f65d-443c-adf7-6c38b8cae0f2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"rvl:a5ef870f9a5b41b38e950ea0fa7a9f83\",\n          \"rvl:19415f0c3b2646b3abbf994762486e4f\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vector_distance\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"0.982345640659\",\n          \"0.801602959633\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"The Dark Knight\",\n          \"Despicable Me\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"9\",\n          \"7\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"genre\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"action\",\n          \"comedy\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"description\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Batman faces off against the Joker, a criminal mastermind who threatens to plunge Gotham into chaos.\",\n          \"When a criminal mastermind uses a trio of orphan girls as pawns for a grand scheme, he finds their love is profoundly changing him for the better.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "# Search with full text search for movies that directly mention \"criminal mastermind\" in the description\n",
        "\n",
        "from redisvl.query.filter import Text\n",
        "\n",
        "user_query = \"High tech movies\"\n",
        "\n",
        "embedded_user_query = hf.embed(user_query)\n",
        "\n",
        "vec_query = VectorQuery(\n",
        "    vector=embedded_user_query,\n",
        "    vector_field_name=\"vector\",\n",
        "    num_results=3,\n",
        "    return_fields=[\"title\", \"rating\", \"genre\", \"description\"],\n",
        "    return_score=True\n",
        ")\n",
        "\n",
        "text_filter = Text(\"description\") == \"criminal mastermind\"\n",
        "\n",
        "vec_query.set_filter(text_filter)\n",
        "\n",
        "result = index.query(vec_query)\n",
        "pd.DataFrame(result)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "e39e5e5c",
      "metadata": {
        "id": "e39e5e5c",
        "outputId": "ab9dc2c6-d52a-4660-a692-544f2a546f32",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175,
          "referenced_widgets": [
            "df43a7dba30347bfb287b83955ac102b",
            "3859071bd9ab4604b863453eed2ddbfa",
            "e0e401f600f54b8dab66365968f2a283",
            "7ea4262b5c4542bda766445442d05d14",
            "93bdc03205c9426094ffe927ebfbd732",
            "b620935c4e624eaeb4f96ccc832f50e7",
            "b712996b2261461083d365236ba93e26",
            "5f33dc7a1f6f40de8b80b1cb4e5a4eb7",
            "9aab48a4afdb41d097785c70d20e0181",
            "b49671b9682044c5b718fff535a43d1d",
            "5592af835f1a46b891a1a660b56e96b0"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "df43a7dba30347bfb287b83955ac102b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                     id vector_distance              title  \\\n",
              "0  rvl:19415f0c3b2646b3abbf994762486e4f  0.801602959633      Despicable Me   \n",
              "1  rvl:d9ba373d3a174ed1a08d096de012f56d  0.812341988087    The Incredibles   \n",
              "2  rvl:c2f305b7836a40bdafb7223184e26b5b  0.851705253124  Explosive Pursuit   \n",
              "\n",
              "  rating   genre                                        description  \n",
              "0      7  comedy  When a criminal mastermind uses a trio of orph...  \n",
              "1      8  comedy  A family of undercover superheroes, while tryi...  \n",
              "2      7  action  A daring cop chases a notorious criminal acros...  "
            ],
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              "      <td>The Incredibles</td>\n",
              "      <td>8</td>\n",
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              "      <td>A family of undercover superheroes, while tryi...</td>\n",
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              "      <td>0.851705253124</td>\n",
              "      <td>Explosive Pursuit</td>\n",
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              "      <td>A daring cop chases a notorious criminal acros...</td>\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7d96da48-582b-4fc3-b546-908dae6ed894')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-7d96da48-582b-4fc3-b546-908dae6ed894 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-7d96da48-582b-4fc3-b546-908dae6ed894');\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",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
              "\n",
              "<div id=\"df-3c5d0022-3ab4-429f-a2f2-410a95ac801f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3c5d0022-3ab4-429f-a2f2-410a95ac801f')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\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",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
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              "      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-3c5d0022-3ab4-429f-a2f2-410a95ac801f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "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          \"rvl:19415f0c3b2646b3abbf994762486e4f\",\n          \"rvl:d9ba373d3a174ed1a08d096de012f56d\",\n          \"rvl:c2f305b7836a40bdafb7223184e26b5b\"\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          \"0.801602959633\",\n          \"0.812341988087\",\n          \"0.851705253124\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Despicable Me\",\n          \"The Incredibles\",\n          \"Explosive Pursuit\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"8\",\n          \"7\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"genre\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"action\",\n          \"comedy\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"description\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"When a criminal mastermind uses a trio of orphan girls as pawns for a grand scheme, he finds their love is profoundly changing him for the better.\",\n          \"A family of undercover superheroes, while trying to live the quiet suburban life, are forced into action to save the world. Bob Parr (Mr. Incredible) and his wife Helen (Elastigirl) were among the world's greatest crime fighters, but now they must assume civilian identities and retreat to the suburbs to live a 'normal' life with their three children. However, the family's desire to help the world pulls them back into action when they face a new and dangerous enemy.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "# Vector search with wildcard match\n",
        "\n",
        "from redisvl.query.filter import Text\n",
        "\n",
        "user_query = \"High tech movies\"\n",
        "\n",
        "embedded_user_query = hf.embed(user_query)\n",
        "\n",
        "vec_query = VectorQuery(\n",
        "    vector=embedded_user_query,\n",
        "    vector_field_name=\"vector\",\n",
        "    num_results=3,\n",
        "    return_fields=[\"title\", \"rating\", \"genre\", \"description\"],\n",
        "    return_score=True\n",
        ")\n",
        "\n",
        "text_filter = Text(\"description\") % \"crim*\"\n",
        "\n",
        "vec_query.set_filter(text_filter)\n",
        "\n",
        "result = index.query(vec_query)\n",
        "pd.DataFrame(result)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "3450e07d",
      "metadata": {
        "id": "3450e07d",
        "outputId": "beddb895-c637-4162-b16f-20f474af626c",
        "colab": {
          "base_uri": "https://localhost:8080/",
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              "                                     id vector_distance                 title  \\\n",
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            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "# Vector search with fuzzy match filter\n",
        "\n",
        "from redisvl.query.filter import Text\n",
        "\n",
        "user_query = \"Movies with central main character\"\n",
        "\n",
        "embedded_user_query = hf.embed(user_query)\n",
        "\n",
        "vec_query = VectorQuery(\n",
        "    vector=embedded_user_query,\n",
        "    vector_field_name=\"vector\",\n",
        "    num_results=3,\n",
        "    return_fields=[\"title\", \"rating\", \"genre\", \"description\"],\n",
        "    return_score=True\n",
        ")\n",
        "\n",
        "# Note: fuzzy match is based on Levenshtein distance. Therefore, \"hero\" might return result for \"her\" as an example.\n",
        "# See docs for more info https://redis.io/docs/latest/develop/interact/search-and-query/advanced-concepts/query_syntax/\n",
        "text_filter = Text(\"description\") % \"%hero%\"\n",
        "\n",
        "vec_query.set_filter(text_filter)\n",
        "\n",
        "result = index.query(vec_query)\n",
        "pd.DataFrame(result)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6bd27cb3",
      "metadata": {
        "id": "6bd27cb3"
      },
      "source": [
        "## Range queries\n",
        "\n",
        "Range queries allow you to set a pre defined \"threshold\" for which we want to return documents. This is helpful when you only want documents with a certain distance from the search query."
      ]
    },
    {
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              "                                     id vector_distance            title  \\\n",
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              "      <td>8</td>\n",
              "      <td>comedy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>rvl:1e52ad75779046578e9b05499f78ec92</td>\n",
              "      <td>0.761669397354</td>\n",
              "      <td>Monsters, Inc.</td>\n",
              "      <td>8</td>\n",
              "      <td>comedy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>rvl:2dbf334c02854aaab87c196f47cb2729</td>\n",
              "      <td>0.778580188751</td>\n",
              "      <td>Aladdin</td>\n",
              "      <td>8</td>\n",
              "      <td>comedy</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "        document.querySelector('#df-04b1e0a8-5135-411e-933e-90916753f095 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
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              "        const element = document.querySelector('#df-04b1e0a8-5135-411e-933e-90916753f095');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [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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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </g>\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
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              "\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",
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              "  @keyframes spin {\n",
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              "      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",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c9a07def-09eb-4d95-b600-b32491f17e79 button');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"rvl:d9ba373d3a174ed1a08d096de012f56d\",\n          \"rvl:6406f177887f401b9ab4d45956e324ae\",\n          \"rvl:2dbf334c02854aaab87c196f47cb2729\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vector_distance\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"0.644702494144\",\n          \"0.747987031937\",\n          \"0.778580188751\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"The Incredibles\",\n          \"Black Widow\",\n          \"Aladdin\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"7\",\n          \"8\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"genre\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"action\",\n          \"comedy\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "from redisvl.query import RangeQuery\n",
        "\n",
        "user_query = \"Family friendly fantasy movies\"\n",
        "\n",
        "embedded_user_query = hf.embed(user_query)\n",
        "\n",
        "range_query = RangeQuery(\n",
        "    vector=embedded_user_query,\n",
        "    vector_field_name=\"vector\",\n",
        "    return_fields=[\"title\", \"rating\", \"genre\"],\n",
        "    return_score=True,\n",
        "    distance_threshold=0.8  # find all items with a semantic distance of less than 0.8\n",
        ")\n",
        "\n",
        "result = index.query(range_query)\n",
        "pd.DataFrame(result)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a1586ea7",
      "metadata": {
        "id": "a1586ea7"
      },
      "source": [
        "Like the queries above, we can also chain additional filters and conditional operators with range queries. The following adds an `and` condition that returns vector search within the defined range and with a rating at or above 8."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "d3110324",
      "metadata": {
        "id": "d3110324",
        "outputId": "266a0faf-8946-402d-f57b-7173ce0ab695",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207,
          "referenced_widgets": [
            "0be6317ac95f4dada81f4f9627bee4ed",
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            "d724d841b85f4604960ef35f6470afcb",
            "9256c56484ae4755a8eb5c006b11bfe6",
            "7e74865689464603b5f2a155592694af",
            "a41e017955ae448690242185f54b9d98",
            "9aa40cdc28e145ff8ab21fc799881ac0",
            "6c177a2733884918998183495b79e098",
            "fd9112ea764341c7a20000b43d6de256",
            "e4f02e81598a42468fd1931d108cefef",
            "918932f636464b83be936aaf2062f0d8"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0be6317ac95f4dada81f4f9627bee4ed"
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          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                     id vector_distance            title  \\\n",
              "0  rvl:d9ba373d3a174ed1a08d096de012f56d  0.644702494144  The Incredibles   \n",
              "1  rvl:ca2c31d6ce8740ca9e2ce711c0d9ef56  0.751298844814            Shrek   \n",
              "2  rvl:1e52ad75779046578e9b05499f78ec92  0.761669397354   Monsters, Inc.   \n",
              "3  rvl:2dbf334c02854aaab87c196f47cb2729  0.778580188751          Aladdin   \n",
              "\n",
              "  rating   genre  \n",
              "0      8  comedy  \n",
              "1      8  comedy  \n",
              "2      8  comedy  \n",
              "3      8  comedy  "
            ],
            "text/html": [
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              "        const element = document.querySelector('#df-c29a22ae-7d8c-49bb-96f9-f5019aea2e42');\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",
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              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </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",
              "    }\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-8ba5f562-fc39-41cf-a52c-29dfd57d1431 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
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              "type": "dataframe",
              "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          \"rvl:ca2c31d6ce8740ca9e2ce711c0d9ef56\",\n          \"rvl:2dbf334c02854aaab87c196f47cb2729\",\n          \"rvl:d9ba373d3a174ed1a08d096de012f56d\"\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          \"0.751298844814\",\n          \"0.778580188751\",\n          \"0.644702494144\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Shrek\",\n          \"Aladdin\",\n          \"The Incredibles\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"8\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"genre\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"comedy\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "user_query = \"Family friendly fantasy movies\"\n",
        "\n",
        "embedded_user_query = hf.embed(user_query)\n",
        "\n",
        "range_query = RangeQuery(\n",
        "    vector=embedded_user_query,\n",
        "    vector_field_name=\"vector\",\n",
        "    return_fields=[\"title\", \"rating\", \"genre\"],\n",
        "    distance_threshold=0.8  # find all items with a semantic distance of less than 0.7\n",
        ")\n",
        "\n",
        "numeric_filter = Num(\"rating\") >= 8\n",
        "\n",
        "range_query.set_filter(numeric_filter)\n",
        "\n",
        "# in this case we want to do a simple filter search or the vector so we execute as a joint filter directly\n",
        "result = index.query(range_query)\n",
        "pd.DataFrame(result)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5fa7cdfb",
      "metadata": {
        "id": "5fa7cdfb"
      },
      "source": [
        "### Next steps\n",
        "\n",
        "For more query examples with redisvl: [see here](https://github.com/redis/redis-vl-python/blob/main/docs/user_guide/hybrid_queries_02.ipynb)"
      ]
    },
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      "cell_type": "code",
      "execution_count": 20,
      "id": "915c2cef",
      "metadata": {
        "id": "915c2cef"
      },
      "outputs": [],
      "source": [
        "# clean up!\n",
        "# client.flushall()"
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    }
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