python-recipes/RAG/07_user_role_based_rag.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "XwR-PYCFu0Nd",
      "metadata": {
        "id": "XwR-PYCFu0Nd"
      },
      "source": [
        "# Building a Role-Based RAG Pipeline with Redis\n",
        "\n",
        "This notebook demonstrates a simplified setup for a **Role-Based Retrieval Augmented Generation (RAG)** pipeline, where:\n",
        "\n",
        "1. Each **User** has one or more **roles**.\n",
        "2. Knowledge base **Documents** in Redis are tagged with the official roles that can access them (`allowed_roles`).\n",
        "3. A unified **query flow** ensures a user only sees documents that match at least one of their roles.\n",
        "\n",
        "![Role Based RAG](https://raw.githubusercontent.com/redis-developer/redis-ai-resources/main/assets/role-based-rag.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "58823e66",
      "metadata": {
        "id": "58823e66"
      },
      "source": [
        "\n",
        "## Let's Begin!\n",
        "<a href=\"https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/07_user_role_based_rag.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "4e0aa177",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4e0aa177",
        "outputId": "0ba61596-b3e4-442f-cd9c-8b480f1c52d1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/99.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m99.3/99.3 kB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/2.5 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m2.5/2.5 MB\u001b[0m \u001b[31m91.5 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.5/2.5 MB\u001b[0m \u001b[31m55.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.8/86.8 kB\u001b[0m \u001b[31m8.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.8/50.8 kB\u001b[0m \u001b[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "%pip install -q 'redisvl>=0.3.8' openai langchain-community pypdf"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fXsGCsLQu0Ne",
      "metadata": {
        "id": "fXsGCsLQu0Ne"
      },
      "source": [
        "## 1. High-Level Data Flow & Setup\n",
        "\n",
        "1. **User Creation & Role Management**\n",
        "   - A user is stored at `user:{user_id}` in Redis with a JSON structure containing the user’s roles.\n",
        "   - We can create, update, or delete users as needed.\n",
        "   - **This serves as a simple look up layer and should NOT replace your production-ready auth API flow**\n",
        "\n",
        "2. **Document Storage**\n",
        "   - Documents chunks are stored at `doc:{doc_id}:{chunk_id}` in Redis as JSON.\n",
        "   - Each document chunk includes fields such as `doc_id`, `chunk_id`, `content`, `allowed_roles`, and an `embedding` (for vector similarity).\n",
        "\n",
        "3. **Querying / Search**\n",
        "   - User roles are retrieved from Redis.\n",
        "   - We perform a vector similarity search (or any other type of retrieval) on the documents.\n",
        "   - We filter the results so that only documents whose `allowed_roles` intersect with the user’s roles are returned.\n",
        "\n",
        "4. **RAG Integration**\n",
        "   - The returned documents can be fed into a Large Language Model (LLM) to provide context and generate an answer.\n",
        "\n",
        "First, we’ll set up our Python environment and Redis connection.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "73c33af6",
      "metadata": {
        "id": "73c33af6"
      },
      "source": [
        "### Download Documents\n",
        "Running remotely or in collab? Run this cell to download the necessary datasets."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "48971c52",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "48971c52",
        "outputId": "e17d146a-43be-41fb-b029-f330d79f1a65"
      },
      "outputs": [],
      "source": [
        "# NBVAL_SKIP\n",
        "!git clone https://github.com/redis-developer/redis-ai-resources.git temp_repo\n",
        "!mkdir -p resources\n",
        "!mv temp_repo/python-recipes/RAG/resources/aapl-10k-2023.pdf resources/\n",
        "!mv temp_repo/python-recipes/RAG/resources/2022-chevy-colorado-ebrochure.pdf resources/\n",
        "!rm -rf temp_repo"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "993371a2",
      "metadata": {
        "id": "993371a2"
      },
      "source": [
        "### Run Redis Stack\n",
        "\n",
        "For this tutorial you will need a running instance of Redis if you don't already have one.\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": 4,
      "id": "8edc5862",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8edc5862",
        "outputId": "df2643ed-2422-4ee5-bd42-bec17b405eec"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "deb [signed-by=/usr/share/keyrings/redis-archive-keyring.gpg] https://packages.redis.io/deb jammy main\n",
            "Starting redis-stack-server, database path /var/lib/redis-stack\n"
          ]
        }
      ],
      "source": [
        "# NBVAL_SKIP\n",
        "%%sh\n",
        "curl -fsSL https://packages.redis.io/gpg | sudo gpg --dearmor -o /usr/share/keyrings/redis-archive-keyring.gpg\n",
        "echo \"deb [signed-by=/usr/share/keyrings/redis-archive-keyring.gpg] https://packages.redis.io/deb $(lsb_release -cs) main\" | sudo tee /etc/apt/sources.list.d/redis.list\n",
        "sudo apt-get update  > /dev/null 2>&1\n",
        "sudo apt-get install redis-stack-server  > /dev/null 2>&1\n",
        "redis-stack-server --daemonize yes"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bc571319",
      "metadata": {
        "id": "bc571319"
      },
      "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": "code",
      "execution_count": 5,
      "id": "qU49fNVnu0Nf",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qU49fNVnu0Nf",
        "outputId": "4d2f34c3-6179-4f1d-eff7-5e8e9d8fd58b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Successfully connected to Redis\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "\n",
        "from redis import Redis\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}\"\n",
        "\n",
        "# Connect to Redis (adjust host/port if needed)\n",
        "redis_client = Redis.from_url(REDIS_URL)\n",
        "redis_client.ping()\n",
        "\n",
        "print(\"Successfully connected to Redis\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "aqzMteQsu0Nf",
      "metadata": {
        "id": "aqzMteQsu0Nf"
      },
      "source": [
        "## 2. User Management\n",
        "\n",
        "Below is a simple `User` class that stores a user in Redis as JSON. We:\n",
        "\n",
        "- Use a Redis key of the form `user:{user_id}`.\n",
        "- Store fields like `user_id`, `roles`, etc.\n",
        "- Provide CRUD methods (Create, Read, Update, Delete) for user objects.\n",
        "\n",
        "**Data Structure Example**\n",
        "```json\n",
        "{\n",
        "  \"user_id\": \"alice\",\n",
        "  \"roles\": [\"finance\", \"manager\"]\n",
        "}\n",
        "```\n",
        "\n",
        "We'll also include some basic checks to ensure we don't add duplicate roles, handle empty role lists, etc.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "38pdjXJvu0Nf",
      "metadata": {
        "id": "38pdjXJvu0Nf"
      },
      "outputs": [],
      "source": [
        "from typing import List, Optional\n",
        "from enum import Enum\n",
        "\n",
        "\n",
        "class UserRoles(str, Enum):\n",
        "    FINANCE = \"finance\"\n",
        "    MANAGER = \"manager\"\n",
        "    EXECUTIVE = \"executive\"\n",
        "    HR = \"hr\"\n",
        "    SALES = \"sales\"\n",
        "    PRODUCT = \"product\"\n",
        "\n",
        "\n",
        "class User:\n",
        "    \"\"\"\n",
        "    User class for storing user data in Redis.\n",
        "\n",
        "    Each user has:\n",
        "    - user_id (string)\n",
        "    - roles (list of UserRoles)\n",
        "\n",
        "    Key in Redis: user:{user_id}\n",
        "    \"\"\"\n",
        "    def __init__(\n",
        "        self,\n",
        "        redis_client: Redis,\n",
        "        user_id: str,\n",
        "        roles: Optional[List[UserRoles]] = None\n",
        "    ):\n",
        "        self.redis_client = redis_client\n",
        "        self.user_id = user_id\n",
        "        self.roles = roles or []\n",
        "\n",
        "    @property\n",
        "    def key(self) -> str:\n",
        "        return f\"user:{self.user_id}\"\n",
        "\n",
        "    def exists(self) -> bool:\n",
        "        \"\"\"Check if the user key exists in Redis.\"\"\"\n",
        "        return self.redis_client.exists(self.key) == 1\n",
        "\n",
        "    def create(self):\n",
        "        \"\"\"\n",
        "        Create a new user in Redis. Fails if user already exists.\n",
        "        \"\"\"\n",
        "        if self.exists():\n",
        "            raise ValueError(f\"User {self.user_id} already exists.\")\n",
        "\n",
        "        self.save()\n",
        "\n",
        "    def save(self):\n",
        "        \"\"\"\n",
        "        Save (create or update) the user data in Redis.\n",
        "        If user does not exist, it will be created.\n",
        "        \"\"\"\n",
        "        data = {\n",
        "            \"user_id\": self.user_id,\n",
        "            \"roles\": [UserRoles(role).value for role in set(self.roles)] # ensure roles are unique and convert to strings\n",
        "        }\n",
        "        self.redis_client.json().set(self.key, \".\", data)\n",
        "\n",
        "    @classmethod\n",
        "    def get(cls, redis_client: Redis, user_id):\n",
        "        \"\"\"\n",
        "        Retrieve a user from Redis.\n",
        "        \"\"\"\n",
        "        key = f\"user:{user_id}\"\n",
        "        data = redis_client.json().get(key)\n",
        "        if not data:\n",
        "            return None\n",
        "        # Convert string roles back to UserRoles enum\n",
        "        roles = [UserRoles(role) for role in data.get(\"roles\", [])]\n",
        "        return cls(redis_client, data[\"user_id\"], roles)\n",
        "\n",
        "    def update_roles(self, roles: List[UserRoles]):\n",
        "        \"\"\"\n",
        "        Overwrite the user's roles in Redis.\n",
        "        \"\"\"\n",
        "        self.roles = roles\n",
        "        self.save()\n",
        "\n",
        "    def add_role(self, role: UserRoles):\n",
        "        \"\"\"Add a single role to the user.\"\"\"\n",
        "        if role not in self.roles:\n",
        "            self.roles.append(role)\n",
        "            self.save()\n",
        "\n",
        "    def remove_role(self, role: UserRoles):\n",
        "        \"\"\"Remove a single role from the user.\"\"\"\n",
        "        if role in self.roles:\n",
        "            self.roles.remove(role)\n",
        "            self.save()\n",
        "\n",
        "    def delete(self):\n",
        "        \"\"\"Delete this user from Redis.\"\"\"\n",
        "        self.redis_client.delete(self.key)\n",
        "\n",
        "    def __repr__(self):\n",
        "        return f\"<User user_id={self.user_id}, roles={[UserRoles(role).value for role in self.roles]}>\"\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "FNQxAaoCxPN7",
      "metadata": {
        "id": "FNQxAaoCxPN7"
      },
      "source": [
        "### Example usage of User class"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "_WcOlgVyu0Ng",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_WcOlgVyu0Ng",
        "outputId": "0776fa25-513b-445b-d46d-35d9333b3a75"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "User 'alice' created.\n",
            "Retrieved: <User user_id=alice, roles=['finance', 'manager']>\n",
            "After adding 'executive': <User user_id=alice, roles=['finance', 'manager', 'executive']>\n",
            "After removing 'manager': <User user_id=alice, roles=['finance', 'executive']>\n"
          ]
        }
      ],
      "source": [
        "# Example usage of the User class\n",
        "\n",
        "# Let's create a new user\n",
        "alice = User(redis_client, \"alice\", roles=[\"finance\", \"manager\"])\n",
        "\n",
        "# We'll save the user in Redis\n",
        "try:\n",
        "    alice.create()\n",
        "    print(\"User 'alice' created.\")\n",
        "except ValueError as e:\n",
        "    print(e)\n",
        "\n",
        "# Retrieve the user\n",
        "alice_obj = User.get(redis_client, \"alice\")\n",
        "print(\"Retrieved:\", alice_obj)\n",
        "\n",
        "# Add another role\n",
        "alice_obj.add_role(\"executive\")\n",
        "print(\"After adding 'executive':\", alice_obj)\n",
        "\n",
        "# Remove a role\n",
        "alice_obj.remove_role(\"manager\")\n",
        "print(\"After removing 'manager':\", alice_obj)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "c911e892",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c911e892",
        "outputId": "df4666ff-97ce-4e75-d70c-75fe5d9e6703"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<User user_id=alice, roles=['finance', 'manager']>"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Take a peek at the user object itself\n",
        "alice"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "P3j6yu8l87j3",
      "metadata": {
        "id": "P3j6yu8l87j3"
      },
      "outputs": [],
      "source": [
        "# Create one more user\n",
        "larry = User(redis_client, \"larry\", roles=[\"product\"])\n",
        "larry.create()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "Y7B4l7XVx5md",
      "metadata": {
        "id": "Y7B4l7XVx5md"
      },
      "source": [
        ">💡 Using a cloud DB? Take a peek at your instance using [RedisInsight](https://redis.io/insight) to see what user data is in place."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "aCXYFXu0u0Ng",
      "metadata": {
        "id": "aCXYFXu0u0Ng"
      },
      "source": [
        "## 3. Document Management (Using LangChain)\n",
        "\n",
        "Here, we'll use **LangChain** for document loading, chunking, and vectorizing. Then, we’ll **store documents** in Redis as JSON. Each document will look like:\n",
        "\n",
        "```json\n",
        "{\n",
        "  \"doc_id\": \"123\",\n",
        "  \"chunk_id\": \"123\",\n",
        "  \"path\": \"resources/doc.pdf\",\n",
        "  \"title\": \"Quarterly Finance Report\",\n",
        "  \"content\": \"Some text...\",\n",
        "  \"allowed_roles\": [\"finance\", \"executive\"],\n",
        "  \"embedding\": [0.12, 0.98, ...]  \n",
        "}\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d3cJ5DSP5vXt",
      "metadata": {
        "id": "d3cJ5DSP5vXt"
      },
      "source": [
        "### Building a document knowledge base\n",
        "We will create a `KnowledgeBase` class to encapsulate document processing logic and search. The class will handle:\n",
        "1. Document ingest and chunking\n",
        "2. Role tagging with a simple str-based rule (likely custom depending on use case)\n",
        "3. Retrieval over the entire document corpus adhering to provided user roles\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "67d38524",
      "metadata": {
        "id": "67d38524"
      },
      "outputs": [],
      "source": [
        "from typing import List, Optional, Dict, Any, Set\n",
        "from pathlib import Path\n",
        "import uuid\n",
        "\n",
        "from langchain_community.document_loaders import PyPDFLoader\n",
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "from redisvl.index import SearchIndex\n",
        "from redisvl.query import VectorQuery\n",
        "from redisvl.query.filter import FilterExpression, Tag\n",
        "from redisvl.utils.vectorize import OpenAITextVectorizer\n",
        "\n",
        "\n",
        "class KnowledgeBase:\n",
        "    \"\"\"Manages document processing, embedding, and storage in Redis.\"\"\"\n",
        "\n",
        "    def __init__(\n",
        "        self,\n",
        "        redis_client,\n",
        "        embeddings_model: str = \"text-embedding-3-small\",\n",
        "        chunk_size: int = 2500,\n",
        "        chunk_overlap: int = 100\n",
        "    ):\n",
        "        self.redis_client = redis_client\n",
        "        self.embeddings = OpenAITextVectorizer(model=embeddings_model)\n",
        "        self.text_splitter = RecursiveCharacterTextSplitter(\n",
        "            chunk_size=chunk_size,\n",
        "            chunk_overlap=chunk_overlap,\n",
        "        )\n",
        "\n",
        "        # Initialize document search index\n",
        "        self.index = self._create_search_index()\n",
        "\n",
        "    def _create_search_index(self) -> SearchIndex:\n",
        "        \"\"\"Create the Redis search index for documents.\"\"\"\n",
        "        schema = {\n",
        "            \"index\": {\n",
        "                \"name\": \"docs\",\n",
        "                \"prefix\": \"doc\",\n",
        "                \"storage_type\": \"json\"\n",
        "            },\n",
        "            \"fields\": [\n",
        "                {\n",
        "                    \"name\": \"doc_id\",\n",
        "                    \"type\": \"tag\",\n",
        "                },\n",
        "                {\n",
        "                    \"name\": \"chunk_id\",\n",
        "                    \"type\": \"tag\",\n",
        "                },\n",
        "                {\n",
        "                    \"name\": \"allowed_roles\",\n",
        "                    \"path\": \"$.allowed_roles[*]\",\n",
        "                    \"type\": \"tag\",\n",
        "                },\n",
        "                {\n",
        "                    \"name\": \"content\",\n",
        "                    \"type\": \"text\",\n",
        "                },\n",
        "                {\n",
        "                    \"name\": \"embedding\",\n",
        "                    \"type\": \"vector\",\n",
        "                    \"attrs\": {\n",
        "                        \"dims\": self.embeddings.dims,\n",
        "                        \"distance_metric\": \"cosine\",\n",
        "                        \"algorithm\": \"flat\",\n",
        "                        \"datatype\": \"float32\"\n",
        "                    }\n",
        "                }\n",
        "            ]\n",
        "        }\n",
        "        index = SearchIndex.from_dict(schema, redis_client=self.redis_client)\n",
        "        index.create()\n",
        "        return index\n",
        "\n",
        "    def ingest(self, doc_path: str, allowed_roles: Optional[List[str]] = None) -> str:\n",
        "        \"\"\"\n",
        "        Load a document, chunk it, create embeddings, and store in Redis.\n",
        "        Returns the document ID.\n",
        "        \"\"\"\n",
        "        # Generate document ID\n",
        "        doc_id = str(uuid.uuid4())\n",
        "        path = Path(doc_path)\n",
        "\n",
        "        if not path.exists():\n",
        "            raise FileNotFoundError(f\"Document not found: {doc_path}\")\n",
        "\n",
        "        # Load and chunk document\n",
        "        loader = PyPDFLoader(str(path))\n",
        "        pages = loader.load()\n",
        "        chunks = self.text_splitter.split_documents(pages)\n",
        "        print(f\"Extracted {len(chunks)} for doc {doc_id} from file {str(path)}\", flush=True)\n",
        "\n",
        "        # If roles not provided, determine from filename\n",
        "        if allowed_roles is None:\n",
        "            allowed_roles = self._determine_roles(path)\n",
        "\n",
        "        # Prepare chunks for Redis\n",
        "        data, keys = [], []\n",
        "        for i, chunk in enumerate(chunks):\n",
        "            # Create embedding w/ openai\n",
        "            embedding = self.embeddings.embed(chunk.page_content)\n",
        "\n",
        "            # Prepare chunk payload\n",
        "            chunk_id = f\"chunk_{i}\"\n",
        "            key = f\"doc:{doc_id}:{chunk_id}\"\n",
        "            data.append({\n",
        "                \"doc_id\": doc_id,\n",
        "                \"chunk_id\": chunk_id,\n",
        "                \"path\": str(path),\n",
        "                \"content\": chunk.page_content,\n",
        "                \"allowed_roles\": list(allowed_roles),\n",
        "                \"embedding\": embedding,\n",
        "            })\n",
        "            keys.append(key)\n",
        "\n",
        "        # Store in Redis\n",
        "        _ = self.index.load(data=data, keys=keys)\n",
        "        print(f\"Loaded {len(chunks)} chunks for document {doc_id}\")\n",
        "        return doc_id\n",
        "\n",
        "    def _determine_roles(self, file_path: Path) -> Set[str]:\n",
        "        \"\"\"Determine allowed roles based on file path and name patterns.\"\"\"\n",
        "        # Customize based on use case and business logic\n",
        "        ROLE_PATTERNS = {\n",
        "            ('10k', 'financial', 'earnings', 'revenue'):\n",
        "                {'finance', 'executive'},\n",
        "            ('brochure', 'spec', 'product', 'manual'):\n",
        "                {'product', 'sales'},\n",
        "            ('hr', 'handbook', 'policy', 'employee'):\n",
        "                {'hr', 'manager'},\n",
        "            ('sales', 'pricing', 'customer'):\n",
        "                {'sales', 'manager'}\n",
        "        }\n",
        "\n",
        "        filename = file_path.name.lower()\n",
        "        roles = {\n",
        "            role for terms, roles in ROLE_PATTERNS.items()\n",
        "            for role in roles\n",
        "            if any(term in filename for term in terms)\n",
        "        }\n",
        "        return roles or {'executive'}\n",
        "\n",
        "    @staticmethod\n",
        "    def role_filter(user_roles: List[str]) -> FilterExpression:\n",
        "        \"\"\"Generate a Redis filter based on provided user roles.\"\"\"\n",
        "        return Tag(\"allowed_roles\") == user_roles\n",
        "\n",
        "    def search(self, query: str, user_roles: List[str], top_k: int = 5) -> List[Dict[str, Any]]:\n",
        "        \"\"\"\n",
        "        Search for documents matching the query and user roles.\n",
        "        Returns list of matching documents.\n",
        "        \"\"\"\n",
        "        # Create query vector\n",
        "        query_vector = self.embeddings.embed(query)\n",
        "\n",
        "        # Build role filter\n",
        "        roles_filter = self.role_filter(user_roles)\n",
        "\n",
        "        # Execute search\n",
        "        return self.index.query(\n",
        "            VectorQuery(\n",
        "                vector=query_vector,\n",
        "                vector_field_name=\"embedding\",\n",
        "                filter_expression=roles_filter,\n",
        "                return_fields=[\"doc_id\", \"chunk_id\", \"allowed_roles\", \"content\"],\n",
        "                num_results=top_k,\n",
        "                dialect=4\n",
        "            )\n",
        "        )\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "YsBuAa_q9QU_",
      "metadata": {
        "id": "YsBuAa_q9QU_"
      },
      "source": [
        "Load a document into the knowledge base."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "s1LDdWhKu0Nh",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s1LDdWhKu0Nh",
        "outputId": "66e1105e-78ba-425a-8156-c810c7c9054a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "21:09:47 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "Extracted 34 for doc f2c7171a-16cc-4aad-a777-ed7202bd7212 from file resources/2022-chevy-colorado-ebrochure.pdf\n",
            "21:09:49 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:09:49 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:09:50 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:09:50 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:09:51 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:09:51 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:09:52 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:09:52 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:09:53 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:09:53 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:09:53 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:09:53 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:09:54 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:09:54 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:09:55 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:09:55 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:09:55 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:09:56 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:09:56 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:09:56 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:09:57 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:09:57 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:09:57 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:09:58 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:01 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:02 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:02 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:05 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:05 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:05 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:06 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:06 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:06 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:07 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "Loaded 34 chunks for document f2c7171a-16cc-4aad-a777-ed7202bd7212\n",
            "Loaded all chunks for f2c7171a-16cc-4aad-a777-ed7202bd7212\n"
          ]
        }
      ],
      "source": [
        "kb = KnowledgeBase(redis_client)\n",
        "\n",
        "doc_id = kb.ingest(\"resources/2022-chevy-colorado-ebrochure.pdf\")\n",
        "print(f\"Loaded all chunks for {doc_id}\", flush=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "-Ekqkf1fu0Nh",
      "metadata": {
        "id": "-Ekqkf1fu0Nh"
      },
      "source": [
        "## 4. User Query Flow\n",
        "\n",
        "Now that we have our User DB and our Vector DB loaded in Redis. We will perform:\n",
        "\n",
        "1. **Vector Similarity Search** on `embedding`.\n",
        "2. A metadata **Filter** based on `allowed_roles`.\n",
        "3. Return top-k matching document chunks.\n",
        "\n",
        "This is implemented below.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "WpvrXmluu0Nh",
      "metadata": {
        "id": "WpvrXmluu0Nh"
      },
      "outputs": [],
      "source": [
        "def user_query(user_id: str, query: str):\n",
        "    \"\"\"\n",
        "    Placeholder for a search function.\n",
        "    1. Load the user's roles.\n",
        "    2. Perform a vector search for docs.\n",
        "    3. Filter docs that match at least one of the user's roles.\n",
        "    4. Return top-K results.\n",
        "    \"\"\"\n",
        "    # 1. Load & validate user roles\n",
        "    user_obj = User.get(redis_client, user_id)\n",
        "    if not user_obj:\n",
        "        raise ValueError(f\"User {user_id} not found.\")\n",
        "\n",
        "    roles = set([role.value for role in user_obj.roles])\n",
        "    if not roles:\n",
        "        raise ValueError(f\"User {user_id} does not have any roles.\")\n",
        "\n",
        "    # 2. Retrieve document chunks\n",
        "    results = kb.search(query, roles)\n",
        "\n",
        "    if not results:\n",
        "      raise ValueError(f\"No available documents found for {user_id}\")\n",
        "\n",
        "    return results"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "qQS1BLwGBVDA",
      "metadata": {
        "id": "qQS1BLwGBVDA"
      },
      "source": [
        "### Search examples\n",
        "\n",
        "Search with a non-existent user."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "wYishsNy6lty",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "wYishsNy6lty",
        "outputId": "dfa5a8b5-d926-4e94-e8a1-ecceb51ccff5"
      },
      "outputs": [
        {
          "ename": "ValueError",
          "evalue": "User tyler not found.",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-14-e5a00ef91be5>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Search with a non-existent user\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0muser_query\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"tyler\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mquery\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"What is the make and model of the vehicle here?\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-13-1890afcb160d>\u001b[0m in \u001b[0;36muser_query\u001b[0;34m(user_id, query)\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0muser_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mUser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mredis_client\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muser_id\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0muser_obj\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"User {user_id} not found.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m     \u001b[0mroles\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrole\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrole\u001b[0m \u001b[0;32min\u001b[0m \u001b[0muser_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mroles\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: User tyler not found."
          ]
        }
      ],
      "source": [
        "# NBVAL_SKIP\n",
        "results = user_query(\"tyler\", query=\"What is the make and model of the vehicle here?\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0af59693",
      "metadata": {},
      "source": [
        "Create user for Tyler."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "ZNgxlQSvChx7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329
        },
        "id": "ZNgxlQSvChx7",
        "outputId": "d59aad34-2d24-4c87-dd42-b9a44ccaf26b"
      },
      "outputs": [
        {
          "ename": "ValueError",
          "evalue": "'engineering' is not a valid UserRoles",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-15-c95bc0bd8411>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Create user for Tyler\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mtyler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mUser\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mredis_client\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"tyler\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mroles\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"sales\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"engineering\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mtyler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-6-9f5e400ee42b>\u001b[0m in \u001b[0;36mcreate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     47\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"User {self.user_id} already exists.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 49\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-6-9f5e400ee42b>\u001b[0m in \u001b[0;36msave\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     56\u001b[0m         data = {\n\u001b[1;32m     57\u001b[0m             \u001b[0;34m\"user_id\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muser_id\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 58\u001b[0;31m             \u001b[0;34m\"roles\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mUserRoles\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrole\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrole\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mroles\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m# ensure roles are unique and convert to strings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     59\u001b[0m         }\n\u001b[1;32m     60\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mredis_client\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\".\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-6-9f5e400ee42b>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     56\u001b[0m         data = {\n\u001b[1;32m     57\u001b[0m             \u001b[0;34m\"user_id\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muser_id\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 58\u001b[0;31m             \u001b[0;34m\"roles\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mUserRoles\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrole\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrole\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mroles\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m# ensure roles are unique and convert to strings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     59\u001b[0m         }\n\u001b[1;32m     60\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mredis_client\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\".\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.11/enum.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(cls, value, names, module, qualname, type, start, boundary)\u001b[0m\n\u001b[1;32m    712\u001b[0m         \"\"\"\n\u001b[1;32m    713\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mnames\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# simple value lookup\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 714\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__new__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    715\u001b[0m         \u001b[0;31m# otherwise, functional API: we're creating a new Enum type\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    716\u001b[0m         return cls._create_(\n",
            "\u001b[0;32m/usr/lib/python3.11/enum.py\u001b[0m in \u001b[0;36m__new__\u001b[0;34m(cls, value)\u001b[0m\n\u001b[1;32m   1135\u001b[0m                 \u001b[0mve_exc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"%r is not a valid %s\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__qualname__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1136\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mexc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1137\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mve_exc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1138\u001b[0m                 \u001b[0;32melif\u001b[0m \u001b[0mexc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1139\u001b[0m                     exc = TypeError(\n",
            "\u001b[0;31mValueError\u001b[0m: 'engineering' is not a valid UserRoles"
          ]
        }
      ],
      "source": [
        "# NBVAL_SKIP\n",
        "tyler = User(redis_client, \"tyler\", roles=[\"sales\", \"engineering\"])\n",
        "tyler.create()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "WWVJF0UVCt4d",
      "metadata": {
        "collapsed": true,
        "id": "WWVJF0UVCt4d"
      },
      "outputs": [],
      "source": [
        "# Try again but this time with valid roles\n",
        "tyler = User(redis_client, \"tyler\", roles=[\"sales\"])\n",
        "tyler.create()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "DXEyktWLC1cC",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DXEyktWLC1cC",
        "outputId": "dbb6e93f-3b81-4c14-f329-daf97a613c89"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<User user_id=tyler, roles=['sales']>"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tyler"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "O0K_rdC7C6OH",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O0K_rdC7C6OH",
        "outputId": "f823f253-cf42-4975-f711-6391b36f83bd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "21:10:21 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "[{'id': 'doc:f2c7171a-16cc-4aad-a777-ed7202bd7212:chunk_13',\n",
              "  'vector_distance': '0.60664498806',\n",
              "  'doc_id': '[\"f2c7171a-16cc-4aad-a777-ed7202bd7212\"]',\n",
              "  'chunk_id': '[\"chunk_13\"]',\n",
              "  'allowed_roles': '[\"sales\",\"product\"]'},\n",
              " {'id': 'doc:f2c7171a-16cc-4aad-a777-ed7202bd7212:chunk_11',\n",
              "  'vector_distance': '0.613630235195',\n",
              "  'doc_id': '[\"f2c7171a-16cc-4aad-a777-ed7202bd7212\"]',\n",
              "  'chunk_id': '[\"chunk_11\"]',\n",
              "  'allowed_roles': '[\"sales\",\"product\"]'},\n",
              " {'id': 'doc:f2c7171a-16cc-4aad-a777-ed7202bd7212:chunk_19',\n",
              "  'vector_distance': '0.62441521883',\n",
              "  'doc_id': '[\"f2c7171a-16cc-4aad-a777-ed7202bd7212\"]',\n",
              "  'chunk_id': '[\"chunk_19\"]',\n",
              "  'allowed_roles': '[\"sales\",\"product\"]'}]"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Query with valid user\n",
        "results = user_query(\n",
        "    tyler.user_id,\n",
        "    query=\"What is the make and model of the vehicle here?\"\n",
        ")\n",
        "results[:3]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "454ce79b",
      "metadata": {},
      "source": [
        "Search with a valid user, but incorrect roles."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "irqwMseYDSS_",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "irqwMseYDSS_",
        "outputId": "acb3fe4b-c451-464f-c214-8a90d835f9ef"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<User user_id=alice, roles=['finance', 'manager']> \n",
            "\n",
            "21:10:24 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n"
          ]
        },
        {
          "ename": "ValueError",
          "evalue": "No available documents found for alice",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-19-45d2e7b2400d>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# Query with valid user\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m results = user_query(\n\u001b[0m\u001b[1;32m      6\u001b[0m     \u001b[0malice\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muser_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mquery\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"What is the make and model of the vehicle here?\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m )\n",
            "\u001b[0;32m<ipython-input-13-1890afcb160d>\u001b[0m in \u001b[0;36muser_query\u001b[0;34m(user_id, query)\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"No available documents found for {user_id}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: No available documents found for alice"
          ]
        }
      ],
      "source": [
        "# NBVAL_SKIP\n",
        "print(alice, \"\\n\")\n",
        "\n",
        "# Query with valid user\n",
        "results = user_query(\n",
        "    alice.user_id, query=\"What is the make and model of the vehicle here?\"\n",
        ")\n",
        "results"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c309b53d",
      "metadata": {
        "id": "c309b53d"
      },
      "source": [
        "Empty results because there are no documents available for Alice to view. Add some."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "0e5e990b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "0e5e990b",
        "outputId": "b0b1bc64-6b01-47d3-feb4-3d6d1cc8e38d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Extracted 155 for doc 42b58f50-d689-4a36-8977-e8ca1a183446 from file resources/aapl-10k-2023.pdf\n",
            "21:10:32 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:32 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:32 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:32 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:32 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:33 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:33 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:33 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:34 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:34 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:34 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:34 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:35 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:35 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:36 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:36 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:36 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:36 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:36 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:37 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:37 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:37 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:37 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:37 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:38 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:38 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:38 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:39 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:39 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:39 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:39 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:40 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:40 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:40 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:40 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:40 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:41 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:41 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:41 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:41 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:41 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:42 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:42 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:42 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:42 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:43 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:43 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:43 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:43 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:44 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:44 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:44 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:44 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:45 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:45 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:45 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:45 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:46 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:46 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:46 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:46 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:47 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:47 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:47 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:47 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:48 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
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            "21:10:51 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:52 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:52 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:52 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:52 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:52 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:53 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:53 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:53 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:53 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:53 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:54 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:54 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:54 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:54 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:55 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:55 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:55 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:55 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:56 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:56 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:56 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:56 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:56 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:57 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:57 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:57 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:58 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:58 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:58 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:58 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:58 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:59 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:59 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:59 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:10:59 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:00 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:00 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:00 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:00 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:01 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:01 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:01 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:02 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:02 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:02 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:02 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:03 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:03 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:03 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:03 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:03 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:03 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:04 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:04 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:04 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:04 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:05 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:05 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:05 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:06 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:06 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:06 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:06 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:06 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:07 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:07 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:07 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:08 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:08 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:08 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:08 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:09 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:09 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:09 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:09 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:10 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:10 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:10 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:10 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:11 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:11 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:11 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:11 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:11 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:12 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:12 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:11:12 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "Loaded 155 chunks for document 42b58f50-d689-4a36-8977-e8ca1a183446\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'42b58f50-d689-4a36-8977-e8ca1a183446'"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Add a document that Alice will have access to\n",
        "kb.ingest(\"resources/aapl-10k-2023.pdf\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "9fcf8cc0",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9fcf8cc0",
        "outputId": "bce13955-7d37-472b-f820-5588cd3986b4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "21:11:30 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "[{'id': 'doc:42b58f50-d689-4a36-8977-e8ca1a183446:chunk_81',\n",
              "  'vector_distance': '0.343286693096',\n",
              "  'doc_id': '[\"42b58f50-d689-4a36-8977-e8ca1a183446\"]',\n",
              "  'chunk_id': '[\"chunk_81\"]',\n",
              "  'allowed_roles': '[\"finance\",\"executive\"]'},\n",
              " {'id': 'doc:42b58f50-d689-4a36-8977-e8ca1a183446:chunk_68',\n",
              "  'vector_distance': '0.353579521179',\n",
              "  'doc_id': '[\"42b58f50-d689-4a36-8977-e8ca1a183446\"]',\n",
              "  'chunk_id': '[\"chunk_68\"]',\n",
              "  'allowed_roles': '[\"finance\",\"executive\"]'},\n",
              " {'id': 'doc:42b58f50-d689-4a36-8977-e8ca1a183446:chunk_72',\n",
              "  'vector_distance': '0.354550600052',\n",
              "  'doc_id': '[\"42b58f50-d689-4a36-8977-e8ca1a183446\"]',\n",
              "  'chunk_id': '[\"chunk_72\"]',\n",
              "  'allowed_roles': '[\"finance\",\"executive\"]'}]"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Query with valid user\n",
        "results = user_query(\n",
        "    alice.user_id,\n",
        "    query=\"What was the total revenue amount for Apple according to their 10k?\"\n",
        ")\n",
        "results[:3]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b3b432e6",
      "metadata": {
        "id": "b3b432e6"
      },
      "source": [
        "## 5. Implementing Role-Based RAG from scratch\n",
        "*with OpenAI and Redis*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "id": "794b3c41",
      "metadata": {
        "id": "794b3c41"
      },
      "outputs": [],
      "source": [
        "from openai import OpenAI\n",
        "from typing import List, Optional\n",
        "import os\n",
        "\n",
        "from redisvl.extensions.session_manager import StandardSessionManager\n",
        "\n",
        "\n",
        "class RAGChatManager:\n",
        "    \"\"\"\n",
        "    Manages RAG-enhanced chat interactions with role-based access control and chat history.\n",
        "\n",
        "    Attributes:\n",
        "        kb: A KnowledgeBase instance for searching documents\n",
        "        client: An OpenAI client for chat completions\n",
        "        model: Name of OpenAI model to use\n",
        "        sessions: Dict to store active chat sessions\n",
        "        system_prompt: The default system prompt\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(\n",
        "        self,\n",
        "        knowledge_base: \"KnowledgeBase\",\n",
        "        openai_api_key: Optional[str] = None,\n",
        "        openai_model: str = \"gpt-4\",\n",
        "        system_prompt: str = \"You are a helpful chatbot assistant with access to knowledge base documents\"\n",
        "    ):\n",
        "        \"\"\"Initialize the RAG chat manager.\"\"\"\n",
        "        self.kb = knowledge_base\n",
        "        self.client = OpenAI(api_key=openai_api_key or os.getenv(\"OPENAI_API_KEY\"))\n",
        "        self.model = openai_model\n",
        "        self.sessions = {}\n",
        "        self.system_prompt = system_prompt\n",
        "\n",
        "    def user_roles(self, user_id: str) -> set:\n",
        "        \"\"\"\n",
        "        Get and validate user roles.\n",
        "\n",
        "        Args:\n",
        "            user_id: User identifier\n",
        "\n",
        "        Returns:\n",
        "            Set of user roles\n",
        "\n",
        "        Raises:\n",
        "            ValueError: If user not found or has no roles\n",
        "        \"\"\"\n",
        "        user_obj = User.get(self.kb.redis_client, user_id)\n",
        "        if not user_obj:\n",
        "            raise ValueError(f\"User {user_id} not found.\")\n",
        "\n",
        "        roles = set([role.value for role in user_obj.roles])\n",
        "        if not roles:\n",
        "            raise ValueError(f\"User {user_id} does not have any roles.\")\n",
        "\n",
        "        return roles\n",
        "\n",
        "    def start_session(self, user_id: str) -> None:\n",
        "        \"\"\"\n",
        "        Start a new chat session for a user.\n",
        "\n",
        "        Args:\n",
        "            user_id: User identifier\n",
        "        \"\"\"\n",
        "        if user_id not in self.sessions:\n",
        "            self.sessions[user_id] = StandardSessionManager(\n",
        "                name=f\"session:{user_id}\",\n",
        "                redis_client=self.kb.redis_client\n",
        "              )\n",
        "\n",
        "    def prep_msgs(\n",
        "        self,\n",
        "        user_id: str,\n",
        "        system_prompt: str,\n",
        "        context: str,\n",
        "        query: str\n",
        "    ) -> List[dict]:\n",
        "        \"\"\"\n",
        "        Get chat history messages including system prompt.\n",
        "\n",
        "        Args:\n",
        "            user_id: User identifier for the session\n",
        "            system_prompt: Optional system prompt to prepend\n",
        "            context: Relevant context fetched from the knowledge base\n",
        "            query: Original user question\n",
        "\n",
        "        Returns:\n",
        "            List of message dictionaries\n",
        "        \"\"\"\n",
        "        messages = [{\"role\": \"system\", \"content\": system_prompt}]\n",
        "\n",
        "        if user_id in self.sessions:\n",
        "            messages.extend(self.sessions[user_id].get_recent())\n",
        "\n",
        "        messages.append({\n",
        "            \"role\": \"user\",\n",
        "            \"content\": f\"\"\"Context information is below.\n",
        "            ---------------------\n",
        "            {context}\n",
        "            ---------------------\n",
        "            Given the context information above and the chat conversation history, please answer the question faithfully: {query}\"\"\"\n",
        "        })\n",
        "\n",
        "        for msg in messages:\n",
        "            if msg[\"role\"] == \"llm\":\n",
        "                msg[\"role\"] = \"assistant\"\n",
        "\n",
        "        return messages\n",
        "\n",
        "    def chat(self, user_id: str, system_prompt: Optional[str] = None) -> None:\n",
        "        \"\"\"\n",
        "        Start an interactive chat loop with the user.\n",
        "\n",
        "        Args:\n",
        "            user_id: User identifier\n",
        "            system_prompt: Optional system prompt\n",
        "\n",
        "        The loop continues until user types 'exit' or 'quit'\n",
        "        \"\"\"\n",
        "        self.start_session(user_id)\n",
        "\n",
        "        print(\"Starting chat session with GPT4. Type 'exit' or 'quit' to end the session.\")\n",
        "        while True:\n",
        "            query = input(\"\\nYou: \").strip()\n",
        "\n",
        "            if query.lower() in ['exit', 'quit']:\n",
        "                print(\"\\nEnding chat session...\")\n",
        "                break\n",
        "\n",
        "            response = self.answer(query, user_id, system_prompt)\n",
        "            print(f\"\\nAssistant: {response}\")\n",
        "\n",
        "    def answer(\n",
        "        self,\n",
        "        query: str,\n",
        "        user_id: str,\n",
        "        system_prompt: Optional[str] = None\n",
        "    ) -> str:\n",
        "        \"\"\"\n",
        "        Process a chat message with RAG enhancement and role-based access.\n",
        "\n",
        "        If any exception occurs at any stage (roles, document search, LLM call),\n",
        "        we do NOT store anything in the session and simply return the error.\n",
        "        Otherwise, we store the query and the response (including 'no docs found' case).\n",
        "\n",
        "        Args:\n",
        "            query: User's question\n",
        "            user_id: User identifier\n",
        "            system_prompt: Optional system prompt\n",
        "\n",
        "        Returns:\n",
        "            AI response string or error message\n",
        "        \"\"\"\n",
        "\n",
        "        # Start or retrieve an existing session for user\n",
        "        self.start_session(user_id)\n",
        "\n",
        "        try:\n",
        "            # 1. Validate user roles\n",
        "            roles = self.user_roles(user_id)\n",
        "\n",
        "            # 2. Use provided system prompt or default\n",
        "            system_prompt = system_prompt or self.system_prompt\n",
        "\n",
        "            # 3. Search for relevant documents\n",
        "            docs = self.kb.search(query, roles)\n",
        "\n",
        "            # 4. If no documents, store & return early\n",
        "            if not docs:\n",
        "                no_docs_msg = (\n",
        "                    \"I couldn't find any relevant documents you have permission to access. \"\n",
        "                    \"Please try rephrasing your question or contact an administrator if you believe this is an error.\"\n",
        "                )\n",
        "                self.sessions[user_id].store(query, no_docs_msg)\n",
        "                return no_docs_msg\n",
        "\n",
        "            # 5. Prepare context and messages for the LLM\n",
        "            context = \"\\n\\n\".join([doc.get(\"content\", \"\") for doc in docs])\n",
        "            messages = self.prep_msgs(\n",
        "                user_id=user_id,\n",
        "                system_prompt=system_prompt,\n",
        "                context=context,\n",
        "                query=query\n",
        "            )\n",
        "\n",
        "            # 6. Generate response from the model\n",
        "            response = self.client.chat.completions.create(\n",
        "                model=self.model,\n",
        "                messages=messages\n",
        "            )\n",
        "            ai_response = response.choices[0].message.content\n",
        "\n",
        "            # 7. Store query and LLM response\n",
        "            self.sessions[user_id].store(query, ai_response)\n",
        "\n",
        "            return ai_response\n",
        "\n",
        "        except Exception as e:\n",
        "            # Catch any exception; do not store anything, just return the error.\n",
        "            return f\"I encountered an error: {str(e)}\"\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "zJdHMGdUCl_S",
      "metadata": {
        "id": "zJdHMGdUCl_S"
      },
      "source": [
        "### Session-aware, role-based RAG"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "id": "1HDy2Ltr12I1",
      "metadata": {
        "id": "1HDy2Ltr12I1"
      },
      "outputs": [],
      "source": [
        "bot = RAGChatManager(kb)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "id": "sM6BQ-ZL2LUf",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "id": "sM6BQ-ZL2LUf",
        "outputId": "b678b1ac-e177-4d16-9af8-2cd2cf2e48c1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "21:20:45 redisvl.index.index INFO   Index already exists, not overwriting.\n",
            "21:20:45 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:20:47 httpx INFO   HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\"The context information provided does not contain any details about a vehicle's make and model.\""
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "bot.answer(\"What is the make and model of the vehicle?\", user_id=\"alice\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "id": "3iJdgsaAjsaA",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "id": "3iJdgsaAjsaA",
        "outputId": "545b9621-e04e-4d96-ade7-5ad1e1311d3c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "21:20:50 redisvl.index.index INFO   Index already exists, not overwriting.\n",
            "21:20:50 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:20:51 httpx INFO   HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'The make and model of the vehicle is Chevrolet Colorado.'"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "bot.answer(\"What is the make and model of the vehicle?\", user_id=\"tyler\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "id": "17CUi5TXBFSB",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "id": "17CUi5TXBFSB",
        "outputId": "852635cc-01a4-4a02-d07d-4a48eabafbba"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "21:20:54 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:20:55 httpx INFO   HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'The vehicle is from the year 2022.'"
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "bot.answer(\"What year is it?\", user_id=\"tyler\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "id": "N4IV1bLTCj1N",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N4IV1bLTCj1N",
        "outputId": "e456deb7-c15d-4a88-ad31-27782be58f72"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Starting chat session with GPT4. Type 'exit' or 'quit' to end the session.\n",
            "\n",
            "You: What is the towing capacity of the truck?\n",
            "21:22:10 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:22:14 httpx INFO   HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
            "\n",
            "Assistant: The towing capacity of the truck varies depending on the specific model and engine. The 2.5L DOHC I-4 engine has a maximum towing weight rating of 3,500 lbs, the 3.6L DOHC V6 engine can tow up to 7,000 lbs, and the Duramax 2.8L Turbo-Diesel I-4 engine has a maximum towing weight rating of 7,700 lbs. You should always check the specific towing capacity of your vehicle and never exceed it, as this can lead to vehicle damage or unsafe driving conditions.\n",
            "\n",
            "You: Is it generally safe to drive? What safety features are available?\n",
            "21:22:28 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:22:39 httpx INFO   HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
            "\n",
            "Assistant: Yes, it's generally safe to drive the 2022 Chevrolet Colorado, but keep in mind that safety also depends on the driver's attentiveness and other factors like road conditions. This particular model comes with various safety features such as:\n",
            "\n",
            "1. Electronic Stability Control System and Traction Control - this system helps the driver maintain control of the vehicle during tricky driving conditions such as rainy or icy roads.\n",
            "2. Hill Start Assist - this feature ensures the vehicle doesn't roll backward when you're on a hill and switching your foot from the brake pedal to the gas pedal.\n",
            "3. Hitch Guidance - this feature assists with dynamic trailering and towing tasks.\n",
            "4. An integrated trailer brake controller (with available Duramax 2.8L Turbo-Diesel I-4 engine or with available Trailering Package with 3.6L V6 engine).\n",
            "5. Teen Driver technology - this feature allows parents to set speed and volume limits for their young drivers.\n",
            "6. Tire Pressure Monitoring System with Tire Fill Alert.\n",
            "7. The Recovery Hooks on 4x4 models.\n",
            "8. The vehicle also includes various airbags: dual-stage frontal airbags for both driver and front passenger seat. Seat-mounted side-impact airbags for driver and front passenger; head-curtain airbags for front and rear outboard seating positions.\n",
            "\n",
            "However, it's essential to remember that safety features are not a substitute for the driver's responsibility to operate the vehicle safely. It's also crucial always to use seat belts and the correct child restraints for a child’s age and size.\n",
            "\n",
            "You: Do you know if it's better than the 2021 version of the truck?\n",
            "21:22:57 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:23:03 httpx INFO   HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
            "\n",
            "Assistant: As a chatbot, I don't have personal opinions, but I can share that the 2022 Chevrolet Colorado continues to offer the same strong performance, versatility, and wide range of configurations that made the 2021 model popular. However, specific improvements or changes may vary based on the trim level or optional packages. It's also important to note that 'better' can depend on your personal needs and preferences. If you are comparing the 2021 and 2022 models, consider factors such as performance, fuel economy, safety features, technology, and price to determine which is better for your needs.\n",
            "\n",
            "You: Got it. Thank you. That's all for today.\n",
            "21:25:32 httpx INFO   HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
            "21:25:34 httpx INFO   HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
            "\n",
            "Assistant: You're welcome! If you have any more questions in the future, don't hesitate to ask. Have a great day!\n",
            "\n",
            "You: quit\n",
            "\n",
            "Ending chat session...\n"
          ]
        }
      ],
      "source": [
        "# NBVAL_SKIP\n",
        "bot.chat(user_id=\"tyler\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "SHg3tFa2u0Nh",
      "metadata": {
        "id": "SHg3tFa2u0Nh"
      },
      "source": [
        "## 6. Summary & Next Steps\n",
        "\n",
        "In this notebook, we set up a **basic** for a Role-Based RAG system:\n",
        "\n",
        "1. **Users** (with `roles`) stored in Redis via JSON.\n",
        "2. **Documents** (with `allowed_roles`) loaded, parsed, embedded and also stored in Redis.\n",
        "3. A user search pipeline that honors user roles when retrieving documents.\n",
        "\n",
        "\n",
        "This approach ensures that **only documents** whose roles match the user’s roles are returned.\n",
        "\n",
        "\n",
        "With these building blocks in place, you can integrate an LLM to supply a context from the returned docs, producing a robust retrieval-augmented generation pipeline with role-based access controls.\n"
      ]
    }
  ],
  "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": "3.11.10"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}