{
  "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",
        "# Introduction to Redis Python\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/redis-developer/redis-ai-resources/blob/main/python-recipes/redis-intro/00_redis_intro.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"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ojfRVcU2uPpr"
      },
      "source": [
        "### Install Python Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tpqa4wdIuPps",
        "outputId": "98d012ed-c460-4d46-bcb2-fdbd86589454"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n",
            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "%pip install -q redis pandas"
      ]
    },
    {
      "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": [
        "#### 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": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aKMKXPY2j8Gt",
        "outputId": "c6cb3b64-0f4c-46cf-df34-7a25a70f4f2d"
      },
      "outputs": [],
      "source": [
        "# NBVAL_SKIP\n",
        "%%sh\n",
        "curl -fsSL https://packages.redis.io/gpg | sudo gpg --dearmor -o /usr/share/keyrings/redis-archive-keyring.gpg\n",
        "echo \"deb [signed-by=/usr/share/keyrings/redis-archive-keyring.gpg] https://packages.redis.io/deb $(lsb_release -cs) main\" | sudo tee /etc/apt/sources.list.d/redis.list\n",
        "sudo apt-get update  > /dev/null 2>&1\n",
        "sudo apt-get install redis-stack-server  > /dev/null 2>&1\n",
        "redis-stack-server --daemonize yes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### For Alternative Environments\n",
        "There are many ways to get the necessary redis-stack instance running\n",
        "1. On cloud, deploy a [FREE instance of Redis in the cloud](https://redis.com/try-free/). Or, if you have your\n",
        "own version of Redis Enterprise running, that works too!\n",
        "2. Per OS, [see the docs](https://redis.io/docs/latest/operate/oss_and_stack/install/install-stack/)\n",
        "3. With docker: `docker run -d --name redis-stack-server -p 6379:6379 redis/redis-stack-server:latest`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "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": 2,
      "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\") # ex: \"redis-18374.c253.us-central1-1.gce.cloud.redislabs.com\"\n",
        "REDIS_PORT = os.getenv(\"REDIS_PORT\", \"6379\")      # ex: 18374\n",
        "REDIS_PASSWORD = os.getenv(\"REDIS_PASSWORD\", \"\")  # ex: \"1TNxTEdYRDgIDKM2gDfasupCADXXXX\"\n",
        "\n",
        "# If SSL is enabled on the endpoint, use rediss:// as the URL prefix\n",
        "REDIS_URL = f\"redis://:{REDIS_PASSWORD}@{REDIS_HOST}:{REDIS_PORT}\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "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": 3,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "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": 4,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "client.dbsize() # should be empty"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Redis, at it's core, is a simple 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",
        "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": 5,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "client.set(\"hello\", \"world\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "b'world'"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "client.get(\"hello\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "client.delete(\"hello\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "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": 9,
      "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": 10,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "client.hset(\"user:john\", mapping=obj)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{b'user': b'john',\n",
              " b'age': b'45',\n",
              " b'job': b'dentist',\n",
              " b'bio': b\"long form text of john's bio\",\n",
              " b'user_embedding': b'\\x9a\\x99\\x99>\\xcd\\xcc\\xcc>\\xcd\\xccL\\xbf'}"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "client.hgetall(\"user:john\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "client.delete(\"user:john\")"
      ]
    },
    {
      "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": 13,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "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": 14,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'user': 'john',\n",
              " 'metadata': {'age': 45, 'job': 'dentist'},\n",
              " 'user_embedding': [0.3, 0.4, -0.8]}"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# get user JSON obj\n",
        "client.json().get(\"user:john\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[3]"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# grab array length for embedding field\n",
        "client.json().arrlen(\"user:john\", \"$.user_embedding\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[[b'user', b'metadata', b'user_embedding']]"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# grab obj keys\n",
        "client.json().objkeys(\"user:john\", \"$\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# delete user JSON\n",
        "client.delete(\"user:john\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Lists\n",
        "Lists store sequences of information... potentially list of messages in an LLM\n",
        "converstion flow, or really any list of items in a queue."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "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": 19,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[{'role': 'user', 'content': 'Hello what can you do for me?'},\n",
              " {'role': 'assistant', 'content': 'Hi, I am a helpful virtual assistant.'}]"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "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": 20,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# count items in the list\n",
        "client.llen(\"messages:john\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "b'{\"role\": \"assistant\", \"content\": \"Hi, I am a helpful virtual assistant.\"}'"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "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": 22,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[b'{\"role\": \"assistant\", \"content\": \"Hi, I am a helpful virtual assistant.\"}']"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "client.lrange(\"read_messages:john\", 0, -1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# list cleanup\n",
        "client.delete(\"messages:john\", \"read_messages:john\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Pipelines\n",
        "All Redis commands can be pipelined to gain some round trip latency improvements."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "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": 25,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "50"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "client.dbsize()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# clean up!\n",
        "client.flushall()"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.9"
    }
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  "nbformat_minor": 0
}