redis-basics-search-vss.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",
        "# Redis Training: Basics, Search, and Vector Search\n",
        "\n",
        "This notebook introduces [Redis](https://redis.io) and the standard Python client, [redis-py](https://redis-py.readthedocs.io/en/stable/), for interacting with the database. We will explore the basics of Redis setup, data structures, and capabilities like vector search!\n",
        "\n",
        "## Let's Begin!\n",
        "<a href=\"https://colab.research.google.com/github/kenkyl/redis-trainings/blob/main/redis-basics-search-vss.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": "2UG2-tksuPpl"
      },
      "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": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BA3TXPWTuPpo",
        "outputId": "0ed3e6fb-a89f-4da5-fdca-9f045f40d86c"
      },
      "outputs": [],
      "source": [
        "# This clones the supporting git repository into a directory named 'temp_repo'.\n",
        "!git clone https://github.com/redis-developer/financial-vss.git temp_repo\n",
        "\n",
        "# This command moves the 'resources' directory from 'temp_repo' to your current directory.\n",
        "!mv temp_repo/resources .\n",
        "!mv temp_repo/requirements.txt .\n",
        "\n",
        "# This deletes the 'temp_repo' directory, cleaning up the unwanted files.\n",
        "!rm -rf temp_repo"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ojfRVcU2uPpr"
      },
      "source": [
        "### Install Python Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tpqa4wdIuPps",
        "outputId": "98d012ed-c460-4d46-bcb2-fdbd86589454"
      },
      "outputs": [],
      "source": [
        "%pip install -r requirements.txt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import warnings\n",
        "\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NBlbUrB27QQs"
      },
      "source": [
        "### Install Redis Stack\n",
        "\n",
        "Later in this tutorial, Redis will be used to store, index, and query vector\n",
        "embeddings created from PDF document chunks. **We need to make sure we have a Redis\n",
        "instance available.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Method 1: localized Redis Stack\n",
        "Use the shell script below to download, extract, and install [Redis Stack](https://redis.io/docs/getting-started/install-stack/) directly\n",
        "from the Redis package archive."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aKMKXPY2j8Gt",
        "outputId": "c6cb3b64-0f4c-46cf-df34-7a25a70f4f2d"
      },
      "outputs": [],
      "source": [
        "%%sh\n",
        "curl -fsSL https://packages.redis.io/gpg | sudo gpg --dearmor -o /usr/share/keyrings/redis-archive-keyring.gpg\n",
        "echo \"deb [signed-by=/usr/share/keyrings/redis-archive-keyring.gpg] https://packages.redis.io/deb $(lsb_release -cs) main\" | sudo tee /etc/apt/sources.list.d/redis.list\n",
        "sudo apt-get update  > /dev/null 2>&1\n",
        "sudo apt-get install redis-stack-server  > /dev/null 2>&1\n",
        "redis-stack-server --daemonize yes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Method 2: Redis Cloud\n",
        "Instead of using the in-notebook, localized Redis Stack, you can quickly deploy a\n",
        "[FREE instance of Redis in the cloud](https://redis.com/try-free/). Or, if you have your\n",
        "own version of Redis Enterprise running, that works too!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Define the Redis Connection URL\n",
        "\n",
        "By default this notebook connects to the local instance of Redis Stack. **If you have your own Redis Enterprise instance** - replace REDIS_PASSWORD, REDIS_HOST and REDIS_PORT values with your own."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dyPfCO3pkB7M"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "# Replace values below with your own if using Redis Cloud instance\n",
        "REDIS_HOST = os.getenv(\"REDIS_HOST\", \"localhost\")\n",
        "REDIS_PORT = os.getenv(\"REDIS_PORT\", \"6379\")\n",
        "REDIS_PASSWORD = os.getenv(\"REDIS_PASSWORD\", \"\")\n",
        "#REDIS_HOST=\"redis-18374.c253.us-central1-1.gce.cloud.redislabs.com\"\n",
        "#REDIS_PORT=18374\n",
        "#REDIS_PASSWORD=\"1TNxTEdYRDgIDKM2gDfasupCADXXXX\"\n",
        "\n",
        "# If SSL is enabled on the endpoint, use rediss:// as the URL prefix\n",
        "REDIS_URL = f\"redis://:{REDIS_PASSWORD}@{REDIS_HOST}:{REDIS_PORT}\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Hello World Redis\n",
        "\n",
        "Now let's connect to the Redis db and get a basic feel for the most common\n",
        "commands and data structures."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import redis\n",
        "import json\n",
        "import numpy as np\n",
        "\n",
        "from time import sleep\n",
        "\n",
        "# Connect with the Redis Python Client\n",
        "client = redis.Redis.from_url(REDIS_URL)\n",
        "\n",
        "client.ping()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "client.dbsize() # should be empty"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Redis, at it's core, is a simple and powerful key/value store. It supports a number of interesting\n",
        "and flexible data structures that can solve a variatey of business and operational\n",
        "problems."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Strings\n",
        "\n",
        "Redis [Strings](https://redis.io/docs/latest/develop/data-types/strings/) store sequences of bytes, including text, serialized objects, and binary arrays. As such, strings are the simplest type of value you can associate with a Redis key.\n",
        "\n",
        "The basic string data type can be accessed using set/get methods. You can also place a\n",
        "TTL policy (expiration) on any key in Redis."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "client.set(\"hello\", \"world\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "client.get(\"hello\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "client.delete(\"hello\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "client.set(\"hello\", \"world\")\n",
        "client.expire(\"hello\", time=3)\n",
        "\n",
        "sleep(4)\n",
        "\n",
        "# should be EMPTY\n",
        "client.get(\"hello\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Hashes\n",
        "Hashes are collections of key/value pairs that are grouped together. It gets\n",
        "serialized as a string in Redis, but can hold a variety of data in each field.\n",
        "\n",
        "You can think of a Hash as a one-level deep Python dictionary.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "obj = {\n",
        "    \"user\": \"john\",\n",
        "    \"age\": 45,\n",
        "    \"job\": \"dentist\",\n",
        "    \"bio\": \"long form text of john's bio\",\n",
        "    \"user_embedding\": np.array([0.3, 0.4, -0.8], dtype=np.float32).tobytes() # cast vectors to bytes string\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "client.hset(\"user:john\", mapping=obj)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "client.hgetall(\"user:john\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "client.delete(\"user:john\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Sets\n",
        "[Sets](https://redis.io/docs/latest/develop/data-types/sets/) are an unordered collection of unique strings (members). You can use Redis sets to efficiently:\n",
        "- Track unique items (e.g., track all unique IP addresses accessing a given blog post).\n",
        "- Represent relations (e.g., the set of all users with a given role).\n",
        "- Perform common set operations such as intersection, unions, and differences.\n",
        "\n",
        "Note - [Sorted Sets](https://redis.io/docs/latest/develop/data-types/sorted-sets/) are not covered here, but are collections of unique strings (members) ordered by an associated numeric score. They are a powerful data type that can be used for things like maintaining a leaderboard (e.g. of the most commonly asked queries), rate limiting, manual indexing, and much more. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "registered_users = [\"user1\", \"user2\", \"user3\", \"user4\", \"user5\"]\n",
        "active_users = [\"user1\", \"user4\", \"user99\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# create sets by adding elements\n",
        "client.sadd(\"registered_users\", *registered_users)\n",
        "client.sadd(\"active_users\", *active_users)\n",
        "\n",
        "# check cardinality of each set after creation\n",
        "print(client.scard(\"registered_users\"))\n",
        "print(client.scard(\"active_users\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# check for presence of a member in the set\n",
        "print(client.sismember(\"registered_users\", \"user1\"))\n",
        "print(client.sismember(\"registered_users\", \"user6\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# perform intersection, difference, and union operations, and print the results\n",
        "reg_active_users = client.sinter(\"registered_users\", \"active_users\")\n",
        "nonreg_active_users = client.sdiff(\"active_users\", \"registered_users\")\n",
        "all_users = client.sunion(\"registered_users\", \"active_users\")\n",
        "\n",
        "print(f\"Registered Active Users: {reg_active_users}\\nNon-Registered Active Users: {nonreg_active_users}\\nAll Users: {all_users}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "client.delete(\"registered_users\", \"active_users\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### JSON\n",
        "With the JSON capabilitie enabled, Redis can be a drop-in replacement for MongoDB\n",
        "or other slower document databases. You can store nested and structured JSON data\n",
        "directly in Redis."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# set a JSON obj\n",
        "obj = {\n",
        "    \"user\": \"john\",\n",
        "    \"metadata\": {\n",
        "        \"age\": 45,\n",
        "        \"job\": \"dentist\",\n",
        "    },\n",
        "    \"user_embedding\": [0.3, 0.4, -0.8]\n",
        "}\n",
        "\n",
        "client.json().set(\"user:john\", \"$\", obj)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# get user JSON obj\n",
        "client.json().get(\"user:john\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# grab array length for embedding field\n",
        "client.json().arrlen(\"user:john\", \"$.user_embedding\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# grab obj keys\n",
        "client.json().objkeys(\"user:john\", \"$\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# delete user JSON\n",
        "client.delete(\"user:john\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Lists\n",
        "[Lists](https://redis.io/docs/latest/develop/data-types/lists/) are linked lists of string values. Redis lists are frequently used to:\n",
        "- Implement stacks and queues\n",
        "- Build queue management for background worker systems\n",
        "- Maintain an ordered list of messages, e.g. in an LLM conversation flow \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# add items to a list\n",
        "client.rpush(\"messages:john\", *[\n",
        "    json.dumps({\"role\": \"user\", \"content\": \"Hello what can you do for me?\"}),\n",
        "    json.dumps({\"role\": \"assistant\", \"content\": \"Hi, I am a helpful virtual assistant.\"})\n",
        "])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# list all items in the list using indices\n",
        "[json.loads(msg) for msg in client.lrange(\"messages:john\", 0, -1)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# count items in the list\n",
        "client.llen(\"messages:john\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# pop the first item from the list and push to another list\n",
        "client.rpoplpush(\"messages:john\", \"read_messages:john\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "client.lrange(\"read_messages:john\", 0, -1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# list cleanup\n",
        "client.delete(\"messages:john\", \"read_messages:john\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Pipelines\n",
        "Redis [pipelining](https://redis.io/docs/latest/develop/use/pipelining/) is a technique for improving performance by issuing multiple commands at once without waiting for the response to each individual command. All Redis commands can be pipelined to gain some round trip latency improvements.\n",
        "\n",
        "Note - it is important in [Python](https://redis-py.readthedocs.io/en/stable/advanced_features.html#pipelines) to set `transaction=False` when creating a pipeline, unless you want to issue an atomic (but blocking) [MULTI/EXEC](https://redis.io/docs/latest/develop/interact/transactions/) transaction block. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "with client.pipeline(transaction=False) as pipe:\n",
        "    for i in range(50):\n",
        "        pipe.json().set(f\"user:{i}\", \"$\", obj)\n",
        "    # execute batch\n",
        "    pipe.execute()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "client.dbsize()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# clean up!\n",
        "client.flushall()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Intro to RedisJSON\n",
        "\n",
        "Let's dive a little deeper into RedisJSON.\n",
        "The [JSON](https://redis.io/docs/latest/develop/data-types/json/) capability of Redis provides JavaScript Object Notation (JSON) support for Redis. It lets you store, update, and retrieve JSON values in a Redis database, similar to any other Redis data type. Redis JSON also works seamlessly with Search and Query to let you index and query JSON documents.\n",
        "\n",
        "See full list of RedisJSON commands here: https://redis.io/commands/?group=json \n",
        "\n",
        "Python documentation: https://redis-py.readthedocs.io/en/stable/redismodules.html#redisjson-commands"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# create some dummy JSON data\n",
        "schools = [\n",
        "    {\n",
        "        \"name\": \"Hall School\",\n",
        "        \"description\": \"Spanning 10 states, this school award-winning curriculum includes a comprehensive reading system (from letter recognition and phonics to reading full-length books), as well as math, science, social studies, and even  philosophy.\",\n",
        "        \"class\": \"independent\",\n",
        "        \"type\": [\n",
        "            \"traditional\"\n",
        "        ],\n",
        "        \"address\": {\n",
        "            \"city\": \"London\",\n",
        "            \"street\": \"Manor Street\"\n",
        "        },\n",
        "        \"students\": 342,\n",
        "        \"location\": \"51.445417, -0.258352\",\n",
        "        \"status_log\": [\n",
        "            \"new\",\n",
        "            \"operating\"\n",
        "        ]\n",
        "    },\n",
        "    {\n",
        "        \"name\": \"Garden School\",\n",
        "        \"description\": \"Garden School is a new and innovative outdoor teaching and learning experience, offering rich and varied activities in a natural environment to children and families.\",\n",
        "        \"class\": \"state\",\n",
        "        \"type\": [\n",
        "            \"forest\",\n",
        "            \"montessori\"\n",
        "        ],\n",
        "        \"address\": {\n",
        "            \"city\": \"London\",\n",
        "            \"street\": \"Gordon Street\"\n",
        "        },\n",
        "        \"students\": 1452,\n",
        "        \"location\": \"51.402926, -0.321523\",\n",
        "        \"status_log\": [\n",
        "            \"new\",\n",
        "            \"operating\"\n",
        "        ]\n",
        "    },\n",
        "    {\n",
        "        \"name\": \"Gillford School\",\n",
        "        \"description\": \"Gillford School is an inclusive learning centre welcoming people from all walks of life, here invited to step into their role as regenerative agents, creating new pathways into the future and inciting an international movement of cultural, land, and social transformation.\",\n",
        "        \"class\": \"private\",\n",
        "        \"type\": [\n",
        "            \"democratic\",\n",
        "            \"waldorf\"\n",
        "        ],\n",
        "        \"address\": {\n",
        "            \"city\": \"Goudhurst\",\n",
        "            \"street\": \"Goudhurst\"\n",
        "        },\n",
        "        \"students\": 721,\n",
        "        \"location\": \"51.112685, 0.451076\",\n",
        "        \"status_log\": [\n",
        "            \"new\",\n",
        "            \"operating\",\n",
        "            \"closed\"\n",
        "        ]\n",
        "    },\n",
        "    {\n",
        "        \"name\": \"Forest School\",\n",
        "        \"description\": \"The philosophy behind Forest School is based upon the desire to provide young children with an education that encourages appreciation of the wide world in nature while achieving independence, confidence and high self-esteem. \",\n",
        "        \"class\": \"independent\",\n",
        "        \"type\": [\n",
        "            \"forest\",\n",
        "            \"montessori\",\n",
        "            \"democratic\"\n",
        "        ],\n",
        "        \"address\": {\n",
        "            \"city\": \"Oxford\",\n",
        "            \"street\": \"Trident Street\"\n",
        "        },\n",
        "        \"students\": 1200,\n",
        "        \"location\": \"51.781756, -1.123196\",\n",
        "        \"status_log\": [\n",
        "            \"new\",\n",
        "            \"operating\"\n",
        "        ]\n",
        "    }\n",
        "]\n",
        "\n",
        "# load data in Redis as JSON\n",
        "for id,school in enumerate(schools):\n",
        "    #print(school)\n",
        "    client.json().set(f\"school_json:{id}\", '.', school)\n",
        "\n",
        "client.dbsize()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# list all school_json keys\n",
        "client.keys(\"school_json:*\")\n",
        "\n",
        "# fetch a single school_json key\n",
        "client.json().get(\"school_json:1\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# retrieve a single property\n",
        "res = client.json().get('school_json:0','$.name')\n",
        "print(res)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# read number of students\n",
        "students = client.json().get('school_json:0','$.students')\n",
        "print(students)\n",
        "# set new number\n",
        "client.json().set('school_json:0','$.students',350)\n",
        "client.json().get('school_json:0','$.students')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# atomic increment for number of students\n",
        "client.json().numincrby('school_json:0','$.students',1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Intro to RediSearch\n",
        "\n",
        "Now that we've covered some Redis basics, let's expore Search. \n",
        "[RediSearch](https://redis.io/docs/latest/develop/interact/search-and-query/) is a query and indexing engine for Redis, providing secondary indexing, full-text search, vector similarity search and aggregations.\n",
        "\n",
        "RediSearch supports indexing on either HASH or JSON data strucutres, and it currently supports the following index field types: \n",
        "- Numeric\n",
        "- Geo\n",
        "- Vector\n",
        "- Tag\n",
        "- Text\n",
        "\n",
        "Once an index is defined with a specified key prefix (e.g. `school_json:`), using the [FT.CREATE](https://redis.io/docs/latest/commands/ft.create/) command, all existing and future keys in the database matching that prefix and schema are automatically added to and updated in the index in real-time. \n",
        "\n",
        "For the full list of RediSearch commands see: https://redis.io/commands/?group=search \n",
        "\n",
        "Python documentation: https://redis-py.readthedocs.io/en/stable/redismodules.html#redisearch-commands "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from redis.commands.search.field import (\n",
        "    NumericField,\n",
        "    TagField,\n",
        "    TextField,\n",
        "    VectorField,\n",
        "    GeoField\n",
        ")\n",
        "from redis.commands.search.indexDefinition import IndexDefinition, IndexType\n",
        "from redis.commands.search.query import Query\n",
        "\n",
        "# define the schema \n",
        "schema = (\n",
        "    TextField(\"$.name\", as_name=\"name\"),\n",
        "    TextField(\"$.description\", as_name=\"description\"),\n",
        "    TagField(\"$.address.city\", as_name=\"city\"), \n",
        "    NumericField(\"$.students\", as_name=\"students\")\n",
        ")\n",
        "\n",
        "# create the index in Redis\n",
        "client.ft(\"idx:schools_json\").create_index(schema, \n",
        "                    definition=IndexDefinition(prefix=[\"school_json:\"], \n",
        "                    index_type=IndexType.JSON)\n",
        "                    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "\n",
        "# perform a full-text search for the term \"nature\", and return all fields\n",
        "res = client.ft(\"idx:schools_json\").search(\"nature\")\n",
        "res_df = pd.DataFrame([t.__dict__ for t in res.docs ])\n",
        "res_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# repeat the same query, but only return select fields\n",
        "\n",
        "# build query\n",
        "query = Query(\"nature\") \\\n",
        "   .return_field(\"$.address.city\", as_field=\"city\") \\\n",
        "   .return_field(\"$.name\", as_field=\"name\")\n",
        "\n",
        "res = client.ft(\"idx:schools_json\").search(query)\n",
        "res_df = pd.DataFrame([t.__dict__ for t in res.docs ])\n",
        "res_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# build multi-field query\n",
        "query=Query('@city:{London} @students:[0, 10000]') \\\n",
        "   .return_field(\"$.address.city\", as_field=\"city\") \\\n",
        "   .return_field(\"$.name\", as_field=\"name\") \\\n",
        "   .return_field(\"$.students\", as_field=\"students\")\n",
        "\n",
        "res = client.ft(\"idx:schools_json\").search(query)\n",
        "#print(res)\n",
        "res_df = pd.DataFrame([t.__dict__ for t in res.docs ])\n",
        "res_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Advanced RediSearch\n",
        "\n",
        "Let's take a deeper look at the capabilites of RediSearch, using some dummy network device data in JSON format. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# create dummy json data\n",
        "network_devices = [\n",
        "  {\n",
        "    \"id\": 1,\n",
        "    \"deviceType\": [\"router\", \"firewall\"],\n",
        "    \"manufacturer\": \"Cisco\",\n",
        "    \"model\": \"RV345P\",\n",
        "    \"ports\": 24,\n",
        "    \"wirelessSupport\": True,\n",
        "    \"deviceSettings\": {\n",
        "      \"ipAddress\": \"192.168.1.1\",\n",
        "      \"dhcpServer\": True,\n",
        "      \"firewallEnabled\": True\n",
        "    },\n",
        "    \"connectedDevices\": [\n",
        "      \"3\",\n",
        "      \"4\"\n",
        "    ],\n",
        "    \"description\": \"This router acts as the primary gateway for the company's main office, providing connectivity to the internet and enabling secure remote access for employees. It also handles DHCP and firewall services for the local network.\",\n",
        "    \"addedTimestamp\": 1451606400\n",
        "  },\n",
        "  {\n",
        "    \"id\": 2,\n",
        "    \"deviceType\": [\"switch\", \"VLAN\"],\n",
        "    \"manufacturer\": \"TP-Link\",\n",
        "    \"model\": \"TL-SG108\",\n",
        "    \"ports\": 8,\n",
        "    \"wirelessSupport\": False,\n",
        "    \"deviceSettings\": {\n",
        "      \"ipAddress\": \"192.168.1.10\",\n",
        "      \"dhcpServer\": False,\n",
        "      \"firewallEnabled\": False\n",
        "    },\n",
        "    \"connectedDevices\": [\n",
        "      \"1\",\n",
        "      \"5\"\n",
        "    ],\n",
        "    \"description\": \"This switch is deployed in the finance department, providing wired connectivity for desktops, printers, and other devices. It is connected to the main router and another switch in the IT department for network segmentation.\",\n",
        "    \"addedTimestamp\": 1483228800\n",
        "  },\n",
        "  {\n",
        "    \"id\": 3,\n",
        "    \"deviceType\": [\"accessPoint\"],\n",
        "    \"manufacturer\": \"Netgear\",\n",
        "    \"model\": \"WAX214\",\n",
        "    \"ports\": 0,\n",
        "    \"wirelessSupport\": True,\n",
        "    \"deviceSettings\": {\n",
        "      \"ipAddress\": \"192.168.1.20\",\n",
        "      \"dhcpServer\": False,\n",
        "      \"firewallEnabled\": False\n",
        "    },\n",
        "    \"connectedDevices\": [\n",
        "      \"1\"\n",
        "    ],\n",
        "    \"description\": \"This wireless access point is installed in the main conference room, providing high-speed Wi-Fi connectivity for employees and guests during meetings and presentations. It supports both 2.4GHz and 5GHz bands for optimal performance.\",\n",
        "    \"addedTimestamp\": 1514764800\n",
        "  },\n",
        "  {\n",
        "    \"id\": 4,\n",
        "    \"deviceType\": [\"router\", \"firewall\"],\n",
        "    \"manufacturer\": \"Linksys\",\n",
        "    \"model\": \"WRT3200ACM\",\n",
        "    \"ports\": 4,\n",
        "    \"wirelessSupport\": True,\n",
        "    \"deviceSettings\": {\n",
        "      \"ipAddress\": \"192.168.1.30\",\n",
        "      \"dhcpServer\": True,\n",
        "      \"firewallEnabled\": True\n",
        "    },\n",
        "    \"connectedDevices\": [\n",
        "      \"1\"\n",
        "    ],\n",
        "    \"description\": \"This router is deployed in the company's remote branch office, providing internet connectivity and secure VPN access to the main office network. It also handles DHCP and firewall services for the branch office local network.\",\n",
        "    \"addedTimestamp\": 1546300800\n",
        "  },\n",
        "  {\n",
        "    \"id\": 5,\n",
        "    \"deviceType\": [\"switch\", \"VLAN\"],\n",
        "    \"manufacturer\": \"D-Link\",\n",
        "    \"model\": \"DGS-1024D\",\n",
        "    \"ports\": 36,\n",
        "    \"wirelessSupport\": False,\n",
        "    \"deviceSettings\": {\n",
        "      \"ipAddress\": \"192.168.1.40\",\n",
        "      \"dhcpServer\": False,\n",
        "      \"firewallEnabled\": True\n",
        "    },\n",
        "    \"connectedDevices\": [\n",
        "      \"2\"\n",
        "    ],\n",
        "    \"description\": \"This switch is located in the IT department, providing wired connectivity for servers, storage systems, and other critical infrastructure devices. It is connected to the main router and another switch in the finance department.\",\n",
        "    \"addedTimestamp\": 1577836800\n",
        "  }\n",
        "]\n",
        "\n",
        "# load data in Redis as JSON\n",
        "for id, device in enumerate(network_devices):\n",
        "    #print(school)\n",
        "    client.json().set(f\"devices:{id}\", '.', device)\n",
        "\n",
        "client.dbsize()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# define the schema \n",
        "schema = (\n",
        "    NumericField(\"$.id\", sortable=True),\n",
        "    TextField(\"$.description\"),\n",
        "    TagField(\"$.deviceType[*]\", separator=\",\"),\n",
        "    NumericField(\"$.ports\"),\n",
        "    TagField(\"$.wirelessSupport\"),\n",
        "    TagField(\"$.deviceSettings.ipAddress\"),\n",
        "    TagField(\"$.deviceSettings.firewallEnabled\"),\n",
        "    TagField(\"$.connectedDevices[*]\", separator=\",\"),\n",
        "    NumericField(\"$.addedTimestamp\", sortable=True)\n",
        ")\n",
        "\n",
        "# create the index in Redis\n",
        "client.ft(\"idx:devices\").create_index(\n",
        "    schema, \n",
        "    definition = IndexDefinition(\n",
        "        prefix = [\"devices:\"], \n",
        "        index_type = IndexType.JSON\n",
        "    )\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# retrieve index information\n",
        "client.ft(\"idx:devices\").info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "\n",
        "# helper function to display results of redis.ft().search() as a dataframe\n",
        "def display_ft(res):\n",
        "  if res.total==0:\n",
        "    print(\"No matches found\")\n",
        "  else:\n",
        "    res_df = pd.DataFrame([t.__dict__ for t in res.docs ]).drop(columns=[\"payload\"])\n",
        "    display(res_df)\n",
        "\n",
        "# helper function to translate aggregate result to dataframe and display it\n",
        "# function is created with the help of ChatGPT: https://chat.openai.com/share/fc4e4ea5-d421-4aaf-a1b2-6fac02c96f20\n",
        "def display_ft_agg(res):\n",
        "  data = res.rows\n",
        "  data = [[item.decode('utf-8') for item in sublist] for sublist in data]\n",
        "  column_dict = {}\n",
        "  for sublist in data:\n",
        "      for i in range(0, len(sublist), 2):\n",
        "          column_name = sublist[i]\n",
        "          column_value = sublist[i + 1]\n",
        "          column_dict.setdefault(column_name, []).append(column_value)\n",
        "  df = pd.DataFrame(column_dict)\n",
        "  display(df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Text search\n",
        "\n",
        "You can run full text search queries on any field you marked to be indexed as `TEXT` or `TextField()` in Python.\n",
        "\n",
        "To query across all text fields for a given index, a simple query looks like `FT.SEARCH index \"word\"`\n",
        "\n",
        "To specify the specific field use `@field:word` syntax.\n",
        "\n",
        "You can also do pattern matching, using `@field:wor*`.\n",
        "\n",
        "See the full full-text search spec here: https://redis.io/docs/latest/develop/interact/search-and-query/query/full-text/\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# search for the term connectivity broadly\n",
        "query = Query(\"connectivity\")\n",
        "res = client.ft(\"idx:devices\").search(query)\n",
        "display_ft(res)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# limit the search to the description field and return only selected fields\n",
        "query = Query(\"@description:connectivity\") \\\n",
        "        .return_field(\"id\") \\\n",
        "        .return_field(\"deviceType\") \\\n",
        "        .return_field(\"description\") \\\n",
        "        .return_field(\"ipAddress\") \\\n",
        "        .return_field(\"firewallEnabled\")\n",
        "    \n",
        "res = client.ft(\"idx:devices\").search(query)\n",
        "display_ft(res)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Tag search\n",
        "\n",
        "Tags should be used for exact match searching. They are similar to full-text fields but use simpler tokenization and encoding in the index. The values in these fields cannot be accessed by general field-less search and can be used only with a special syntax. \n",
        "\n",
        "See the tag spec here: https://redis.io/docs/latest/develop/interact/search-and-query/advanced-concepts/tags/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# search for devices where fireWallEnabled = True and return only selected fields\n",
        "query = Query(\"@deviceSettings.firewallEnabled:{True}\") \\\n",
        "        .return_field(\"id\") \\\n",
        "        .return_field(\"deviceType\") \\\n",
        "        .return_field(\"ipAddress\") \\\n",
        "        .return_field(\"firewallEnabled\")\n",
        "        \n",
        "res = client.ft(\"idx:devices\").search(query)\n",
        "display_ft(res)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Numeric search and combination search\n",
        "\n",
        "Numeric fields can be queried for exact match and range, and can also be applied as a filter to query results. \n",
        "\n",
        "See the numeric spec here: https://redis.io/docs/latest/develop/interact/search-and-query/query/range/\n",
        "And the comined queries spec here: https://redis.io/docs/latest/develop/interact/search-and-query/query/combined/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# search for devices connected to device 1 with more than 10 ports\n",
        "query = Query(\"@connectedDevices:{1} @ports:[11 +inf]\") \\\n",
        "        .return_field(\"id\") \\\n",
        "        .return_field(\"deviceType\") \\\n",
        "        .return_field(\"ports\") \\\n",
        "        .return_field(\"connectedDevices\")\n",
        "        \n",
        "res = client.ft(\"idx:devices\").search(query)\n",
        "display_ft(res)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Intro to Vector Search in Redis\n",
        "\n",
        "Now that we have the basics down, we will dive into the fundamentals of vector\n",
        "search in Redis using the base Python client."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XVMewycguPps"
      },
      "source": [
        "### Dataset Preparation (PDF Documents)\n",
        "\n",
        "To best demonstrate Redis as a vector database layer, we will load a single\n",
        "financial (10k filings) doc and preprocess it using some helpers from LangChain:\n",
        "\n",
        "- `UnstructuredFileLoader` is not the only document loader type that LangChain provides. Docs: https://python.langchain.com/docs/integrations/document_loaders/unstructured_file\n",
        "- `RecursiveCharacterTextSplitter` is what we use to create smaller chunks of text from the doc. Docs: https://python.langchain.com/docs/modules/data_connection/document_transformers/text_splitters/recursive_text_splitter"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8MetibQiuPpt",
        "outputId": "191df44e-074b-4482-84fc-ceabc0d98f52"
      },
      "outputs": [],
      "source": [
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "from langchain.document_loaders import UnstructuredFileLoader\n",
        "\n",
        "# Load list of pdfs from a folder\n",
        "data_path = \"resources/\"\n",
        "docs = [os.path.join(data_path, file) for file in os.listdir(data_path)]\n",
        "\n",
        "print(\"Listing available documents ...\", docs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# fetch the Nike PDF\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",
        "loader = UnstructuredFileLoader(doc, mode=\"single\", strategy=\"fast\")\n",
        "text_splitter = RecursiveCharacterTextSplitter(chunk_size=2500, chunk_overlap=0)\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": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CHBpJGlruPpu",
        "outputId": "5f159641-856b-4d4f-87d7-ee3b0f17fb84"
      },
      "outputs": [],
      "source": [
        "# Take a look at content from a chunk\n",
        "print(chunks[25].page_content)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W8X2VgYUuPpv"
      },
      "source": [
        "### Text embedding generation with SentenceTransformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465,
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          ]
        },
        "id": "LnNBNI4-PA3Y",
        "outputId": "32581b2a-93c7-4191-f8a2-ad1efc83b81b"
      },
      "outputs": [],
      "source": [
        "from sentence_transformers import SentenceTransformer\n",
        "\n",
        "# load model - may take a minute or two to download the first time\n",
        "model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-41dOyVpOgDI",
        "outputId": "b6675ce3-b17b-4cb9-b4ee-11309172e95a"
      },
      "outputs": [],
      "source": [
        "%%time\n",
        "\n",
        "# create embeddings\n",
        "chunk_embeddings = model.encode([chunk.page_content for chunk in chunks])\n",
        "len(chunk_embeddings) == len(chunks)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3A6PexJgVDGB"
      },
      "source": [
        "### Set up some helper functions\n",
        "\n",
        "Helper functions to encode the single query vector and display redis search results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9y0Xc4jVVCCI"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "\n",
        "\n",
        "def encode_one(input):\n",
        "    return model.encode(input).astype(np.float32).tobytes()\n",
        "\n",
        "\n",
        "def table_view(res):\n",
        "    if res.total == 0:\n",
        "        print(\"No documents found.\")\n",
        "        return None\n",
        "    \n",
        "    res_df = pd.DataFrame([t.__dict__ for t in res.docs ]).drop(columns=[\"payload\"])\n",
        "    return res_df\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ylykfqxeuPpy"
      },
      "source": [
        "### Define a schema and create an index\n",
        "Below we connect to Redis and create an index for vector search that contains a single text field and vector field."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xufdXgR6uPpy"
      },
      "outputs": [],
      "source": [
        "from redis.commands.search.field import TagField, TextField, VectorField\n",
        "from redis.commands.search.indexDefinition import IndexDefinition, IndexType\n",
        "from redis.commands.search.query import Query\n",
        "\n",
        "\n",
        "index_name = \"redispy\"\n",
        "key_prefix = f\"doc:{index_name}\"\n",
        "\n",
        "\n",
        "def create_index(index_type: str = \"FLAT\"):       # Creates a FLAT index by default\n",
        "    try:\n",
        "        # check to see if index exists\n",
        "        client.ft(index_name).info()\n",
        "        print(\"Index already exists!\")\n",
        "    except:\n",
        "        # define schema\n",
        "        schema = (\n",
        "            TagField(\"doc_id\"),                    # Tag Field - synthetic ID\n",
        "            TextField(\"content\"),                  # Text Field\n",
        "            VectorField(\"chunk_vector\",            # Vector Field\n",
        "                index_type, {                      # Vector Index Type: FLAT or HNSW\n",
        "                    \"TYPE\": \"FLOAT32\",\n",
        "                    \"DIM\": 384,                    # Number of Vector Dimensions\n",
        "                    \"DISTANCE_METRIC\": \"COSINE\",   # Vector Search Distance Metric\n",
        "                }\n",
        "            ),\n",
        "        )\n",
        "\n",
        "        # index Definition\n",
        "        definition = IndexDefinition(prefix=[key_prefix], index_type=IndexType.HASH)\n",
        "\n",
        "        # create Index\n",
        "        client.ft(index_name).create_index(fields=schema, definition=definition)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CMMYwKXPuPpz"
      },
      "outputs": [],
      "source": [
        "# Create the index\n",
        "create_index()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YmIoE7MZuPpz",
        "outputId": "c514624d-2767-409b-e946-0294279f8d91"
      },
      "outputs": [],
      "source": [
        "# Check the info related to the newly created index\n",
        "client.ft(index_name).info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qrj-jeGmBRTL"
      },
      "source": [
        "### Process and load data using Redis\n",
        "Below we use a Redis pipeline (not a transaction) to batch send writes to Redis. This method helps with throughput significantly. The batch_size param can be customized and benchmarked on your hardware and with your data. We typically recommend starting small (100-200) and increasing as needed."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XHsODPikuPp1"
      },
      "outputs": [],
      "source": [
        "# load expects an iterable of dictionaries\n",
        "\n",
        "batch_size = 200\n",
        "\n",
        "with client.pipeline(transaction=False) as pipe:\n",
        "    for i, chunk in enumerate(chunks):\n",
        "        data = {\n",
        "            'doc_id': f\"{i}\",\n",
        "            'content': chunk.page_content,\n",
        "            # For HASH -- must convert embeddings to bytes\n",
        "            'chunk_vector': np.array(chunk_embeddings[i]).astype(np.float32).tobytes()\n",
        "        }\n",
        "        pipe.hset(f\"{key_prefix}:{i}\", mapping=data)\n",
        "        # execute in \"mini batches\"\n",
        "        if i % batch_size == 0:\n",
        "            res = pipe.execute()\n",
        "\n",
        "    # cleanup final batch execution\n",
        "    res = pipe.execute()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pY9Ja6VduPp1",
        "outputId": "9b4e46e9-1771-45bc-83c5-a2a943da411e"
      },
      "outputs": [],
      "source": [
        "# check the data size in Redis\n",
        "len(chunks) == client.dbsize()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xa5zeoZ1uPp7",
        "outputId": "7322de1c-1367-46ff-dcd4-25a721c096a3"
      },
      "outputs": [],
      "source": [
        "client.hgetall(f\"{key_prefix}:0\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oBMVO9RQuPp7"
      },
      "source": [
        "### Query the database\n",
        "Now we can use the Redis search index to perform vector similarity search operations.\n",
        "\n",
        "The code below takes a user input, converts to embeddings, and fetches the top 2 most semantically similar chunks from Redis."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "fp3I3jo4uPp8",
        "outputId": "2d32fa64-a010-4f8c-9b3c-191b44c8e66e"
      },
      "outputs": [],
      "source": [
        "# Grab user input\n",
        "_input = \"Nike profit margins and company performance\"\n",
        "\n",
        "query = (\n",
        "    Query(\"*=>[KNN 2 @chunk_vector $vector as vector_distance]\")\n",
        "     .sort_by(\"vector_distance\")\n",
        "     .return_fields(\"content\", \"vector_distance\")\n",
        "     .paging(0, 2)\n",
        "     .dialect(2)\n",
        ")\n",
        "\n",
        "query_params = {\n",
        "    \"vector\": encode_one(_input)\n",
        "}\n",
        "\n",
        "res = client.ft(index_name).search(query, query_params)\n",
        "\n",
        "table_view(res)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "VH2tTqYWCgpM",
        "outputId": "e3cf413e-1ccc-4922-e155-92fc6fe64fa3"
      },
      "outputs": [],
      "source": [
        "# Example of sorting by a field other than vector_distance\n",
        "query = (\n",
        "    Query(\"*=>[KNN 4 @chunk_vector $vector as vector_distance]\")\n",
        "     .sort_by(\"doc_id\")\n",
        "     .return_fields(\"doc_id\", \"content\", \"vector_distance\")\n",
        "     .paging(0, 4)\n",
        "     .dialect(2)\n",
        ")\n",
        "\n",
        "query_params = {\n",
        "    \"vector\": encode_one(_input)\n",
        "}\n",
        "\n",
        "res = client.ft(index_name).search(query, query_params)\n",
        "\n",
        "table_view(res)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i5SCvkAvuPp8"
      },
      "source": [
        "### Range Queries\n",
        "Range queries allow you to set a pre defined \"threshold\" for which we want to return documents"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "HTrphv_NuPp8",
        "outputId": "45632bde-d94e-4244-d62c-594c4334026e"
      },
      "outputs": [],
      "source": [
        "query = (\n",
        "    Query(\"@chunk_vector:[VECTOR_RANGE $radius $vector]=>{$YIELD_DISTANCE_AS: vector_distance}\")\n",
        "     .sort_by(\"vector_distance\")\n",
        "     .return_fields(\"content\", \"vector_distance\")\n",
        "     .dialect(2)\n",
        ")\n",
        "\n",
        "# Find all vectors within 0.8 of the query vector\n",
        "query_params = {\n",
        "    \"radius\": 0.8,\n",
        "    \"vector\": encode_one(_input)\n",
        "}\n",
        "\n",
        "res = client.ft(index_name).search(query, query_params)\n",
        "table_view(res)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IELiO9e4uPp9"
      },
      "source": [
        "### Add filter statements\n",
        "Redis queries can contain both vector search and traditional filters (numeric, tags, text, geo) in one single command."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "qkzGPmMnuPp-",
        "outputId": "92068e8f-689c-4fae-ca3d-046315ac53b4"
      },
      "outputs": [],
      "source": [
        "# filter for docs that contain \"profit\" in the content field and do KNN vector search\n",
        "query = (\n",
        "    Query(\"@content:profit=>[KNN 2 @chunk_vector $vector as vector_distance]\")\n",
        "     .sort_by(\"vector_distance\")\n",
        "     .return_fields(\"content\", \"vector_distance\")\n",
        "     .paging(0, 2)\n",
        "     .dialect(2)\n",
        ")\n",
        "\n",
        "query_params = {\n",
        "    \"vector\": encode_one(_input)\n",
        "}\n",
        "\n",
        "res = client.ft(index_name).search(query, query_params)\n",
        "table_view(res)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XpL7r3tquPp-"
      },
      "outputs": [],
      "source": [
        "# lets clean up our index\n",
        "client.ft(index_name).dropindex(True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BeJA5u14uPp-"
      },
      "source": [
        "### What about JSON Support?\n",
        "\n",
        "Redis also allows you to store data in JSON objects. The JSON fields can contain metadata and vectors. Below is a simple example of indexing JSON data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-V_gPbmguPp-",
        "outputId": "45f4ec9e-90cf-4390-aee7-a0bc16633489"
      },
      "outputs": [],
      "source": [
        "# schema\n",
        "schema = (\n",
        "    TextField(\"$.content\",                     # Text Field (JSON path)\n",
        "        as_name=\"content\"                      # Text Field Alias -- required for JSON\n",
        "    ),\n",
        "    VectorField(\"$.chunk_vector\",              # Vector Field (JSON path)\n",
        "        \"FLAT\", {                              # Vector Index Type: FLAT or HNSW\n",
        "            \"TYPE\": \"FLOAT32\",\n",
        "            \"DIM\": 384,                        # Number of Vector Dimensions\n",
        "            \"DISTANCE_METRIC\": \"COSINE\",       # Vector Search Distance Metric\n",
        "        },\n",
        "        as_name=\"chunk_vector\"                 # Vector Field Alias -- required for JSON\n",
        "    ),\n",
        ")\n",
        "\n",
        "# index Definition\n",
        "definition = IndexDefinition(prefix=[key_prefix], index_type=IndexType.JSON) # select JSON here\n",
        "\n",
        "# create Index\n",
        "client.ft(index_name).create_index(fields=schema, definition=definition)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rh7NKirOuPp_",
        "outputId": "94681570-d8d4-40f9-a37e-10f129d9b9fb"
      },
      "outputs": [],
      "source": [
        "client.ft(index_name).info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UawMtBgSuPp_"
      },
      "outputs": [],
      "source": [
        "# Write JSON data to the index\n",
        "\n",
        "batch_size = 200\n",
        "\n",
        "with client.pipeline(transaction=False) as pipe:\n",
        "    for i, chunk in enumerate(chunks):\n",
        "        redis_key = f\"{key_prefix}:{i}\"\n",
        "        data = {\n",
        "            'content': chunk.page_content,\n",
        "            'chunk_vector': chunk_embeddings[i].tolist() # notice that we don't need to convert JSON embeddings to bytes\n",
        "        }\n",
        "        #print(data)\n",
        "        pipe.json().set(redis_key, \"$\", data)\n",
        "        # mini batch\n",
        "        if i % batch_size == 0:\n",
        "            res = pipe.execute()\n",
        "\n",
        "    res = pipe.execute() # make sure to use mini batches if working with larger datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aoJljsrAuPp_",
        "outputId": "5a5cf3f8-a12c-46ca-82cd-86cadb330c6a"
      },
      "outputs": [],
      "source": [
        "# Fetch the JSON doc\n",
        "client.json().get(f\"{key_prefix}:0\", \"$\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "m5yxhMciuPp_",
        "outputId": "9eb4b5cc-3ca7-4651-8a01-05549a8c51ee"
      },
      "outputs": [],
      "source": [
        "# And now you can perform the same kinds of queries\n",
        "query = (\n",
        "    Query(\"@content:profit=>[KNN 2 @chunk_vector $vector as vector_distance]\")\n",
        "     .sort_by(\"vector_distance\")\n",
        "     .return_fields(\"content\", \"vector_distance\")\n",
        "     .paging(0, 2)\n",
        "     .dialect(2)\n",
        ")\n",
        "\n",
        "query_params = {\n",
        "    \"vector\": encode_one(_input)\n",
        "\n",
        "}\n",
        "res = client.ft(index_name).search(query, query_params)\n",
        "\n",
        "table_view(res)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5QKo9u3euPqA"
      },
      "source": [
        "## Cleanup\n",
        "Clean up the index and data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xgACLAd0uPqA"
      },
      "outputs": [],
      "source": [
        "client.ft(index_name).dropindex(True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tRLKYfhguPqA"
      },
      "source": [
        "## What's Next?\n",
        "\n",
        "Now that you have the basics down with the baseline Redis Python client:\n",
        "\n",
        "| Notebook | Description | Documentation |\n",
        "|----------|-------------|---------------|\n",
        "| [**redisvl-02**](https://colab.research.google.com/github/redis-developer/financial-vss/blob/main/redisvl-02.ipynb) | Dive deeper using a dedicated VSS client library to build a RAG application from scratch including some advanced techniques using an OpenAI LLM. | [View Docs](https://redisvl.com) |\n",
        "| [**langchain-03**](https://colab.research.google.com/github/redis-developer/financial-vss/blob/main/langchain-03.ipynb) | Wrap up with an integrated approach via LangChain and see how to build complex LLM chains. | [View Docs](https://python.langchain.com/docs/get_started/introduction) |\n"
      ]
    }
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