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Docs: Refocus quickstart on end users (#155)
## Description πŸ“ Refocuses the quickstart on end users as part of the HEP: Iterating Docs to Re-Focus on Customer Personas. At its current state, this is a `DRAFT` PR which works to succinctly highlight what happens whenever a user asks a question of PromptQL (decision-making use case). In a follow-up commit, we'll introduce the concept of automation (our other use case) and ensure we have a high-level overview of the two use cases, which illustrates how users are expected to interact with the product. ## Quick Links πŸš€ [Quickstart](https://robdominguez-doc-2900-redesi.promptql-docs.pages.dev/quickstart/) --------- Co-authored-by: hasura-bot <[email protected]>
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β€Ždocs/automation.mdx

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**Automations** allow you to transform one-time conversations into reliable, repeatable business processes. Once you've
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built and tested a workflow in the PromptQL Playground, you can deploy it as an API endpoint that can be called
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programmatically or integrated into your existing systems. That means services like Zapier, Slack, or even your internal
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systems can trigger the automation whenever it’s neededβ€”no coding required.
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systems can trigger the automation whenever it’s needed; no coding required.
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This is ideal for any repetitive task that benefits from AI-powered insights, like:
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β€Ždocs/how-to-talk-to-promptql.mdx

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analysis you've done manually before. From there, PromptQL will learn your expectations, surface useful patterns, and
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help you turn those insights into reliable, repeatable workflows.
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We recommend picking up with [our overview of PromptQL's capabilities](/capabilities.mdx) but, if you're eager, jump in
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to one of these use cases:
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Jump into one of these use cases:
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- [Making decisions](/decision-making.mdx)
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- [Automating tasks](/automation.mdx)

β€Ždocs/quickstart.mdx

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---
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sidebar_position: 0
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sidebar_label: Quickstart
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description:
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"Learn how to set up and deploy PromptQL. This guide covers installing prerequisites, configuring API keys, running
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PromptQL locally, and deploying to Hasura DDN."
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description: "Learn how to get started with PromptQL by asking complex questions and building automations."
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keywords:
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- promptql
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- hasura ddn
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- quickstart
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- getting started
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- guide
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- quickstart
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sidebar_class_name: quickstart-icon
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toc_max_heading_level: 4
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---
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import Prereqs from "@site/docs/_prereqs.mdx";
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import Quickstart from "@site/docs/_quickstart_template.mdx";
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import Thumbnail from "@site/src/components/Thumbnail";
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# Quickstart with PromptQL
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In this getting started guide, you'll deploy PromptQL and connect it to a sample dataset β€” a hosted PostgreSQL database
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pre-loaded with IMDb movie data. You'll start by exploring the dataset through natural language queries, discovering how
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PromptQL can reliably answer questions and assist in decision-making. Then, you'll add custom business logic that lets
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PromptQL act on your behalf β€” like renting a movie for a user β€” showing how it can go beyond answering questions to
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automating tasks.
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## Introduction
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This guide is designed to give you a high-level overview of PromptQL and how you can use it to make better decisions
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about your business and automate tasks; all without any coding knowledge required.
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:::info Prerequisites
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Before continuing, you'll need a [Hasura Cloud account](https://cloud.hasura.io/signup/new_user).
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If you have your own project, you can apply the information below to it. Otherwise, we have a
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[collection of sandboxes](/index.mdx#demos) which allow you to try PromptQL and get a feel for the experience. Below,
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we'll use the `sandbox-gtm` example.
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If you're looking to build a new project from scratch, check out
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[these guides](/how-to-build-with-promptql/overview.mdx).
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:::
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## Guide
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### Open your project
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Each PromptQL project can be accessed via a unique URL. You can access all your projects
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[from the PromptQL Console](https://promptql.console.hasura.io/).
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<Thumbnail
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src="/img/get-started/pql-console-all-projects.png"
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alt="The PromptQL Cloud Console with all projects."
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width="1000px"
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/>
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When you navigate to a project, you're dropped into its **playground**; this is the chat interface that allows you to
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ask questions about your data and create automations.
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<Thumbnail src="/img/get-started/pql-playground.png" alt="The PromptQL Playground." width="1000px" />
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### Talk to your data
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Primarily, you'll interact with PromptQL in one of two ways: either by asking questions, or creating automations.
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#### Ask a complex question
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Using the `sandbox-gtm` project, we're going to ask the following question:
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> _Which enterprise deals are at risk of slipping this quarter based on engagement patterns?_
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While this question seems simple, it's complex because it requires defining what qualifies as an enterprise deal,
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identifying signals that indicate a deal is at riskβ€”such as low engagement or delayed progressβ€”and analyzing various
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engagement patterns, including sales activities, buyer signals, and product usage, all in comparison to historical and
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successful deal benchmarks.
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If you're using your own project, start by asking a similarly strategic question relevant to your data. Otherwise,
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[click here](https://promptql.console.hasura.io/share/05437507-6b31-4037-a302-0dbcec5de2ae) to be dropped into a thread
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replay.
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##### Query plan
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When you ask a question, PromptQL will respond with a **query plan**. This is a step-by-step breakdown in plain language
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of how it will process your request, including any data retrieval, filtering, or AI operations it will perform.
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<Thumbnail
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src="/img/get-started/pql-query-plan.png"
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alt="A query plan for a question detailing all steps to find the right data."
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width="1000px"
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/>
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It helps make PromptQL's approach transparent by explaining what it's going to do before it does it, including any
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assumptions it's making about the data or process.
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Take a look at the query plan generated for your question and see how PromptQL takes a complex question and breaks it
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down into a series of steps, just like a human would.
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:::tip Query plans are modifiable
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You can edit a query plan by clicking the pencil next to a query plan.
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You have domain-specific expertise which you can share with PromptQL. When you modify a query plan, PromptQL remembers
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and self-corrects for future threads!
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:::
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##### Execution
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The query plan is then **executed** against your data.
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<Thumbnail src="/img/get-started/pql-execution.png" alt="A query plan's execution of Python and SQL." width="1000px" />
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PromptQL pulls the necessary data and uses AI to help make sense of it. Behind the scenes, it fetches information from
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your database and organizes it using Python, so you can focus on the insights, not the code.
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If you're curious, expand the execution in your project and see the code PromptQL is executing against your datasource.
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And, just like the query plan, if you see something that should be different, let PromptQL know!
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##### Artifacts
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Typically, the execution of a query plan will result in an **artifact**.
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<Thumbnail
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src="/img/get-started/pql-artifact.png"
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alt="A table artifact produced via the execution of a query plan."
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width="1000px"
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/>
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Artifacts are structured outputs like tables, text, or visualizations that store the processed data or analysis results,
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which can be referenced later in the conversation, exported, or used to start new threads for further analysis.
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We have a more in-depth guide for making decisions with PromptQL; check it out [here](/decision-making.mdx).
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:::tip This is a conversation!
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Talking with PromptQL is like talking to a human. This means it's an iterative process wherein you ask a question, it
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responds, and you build on that response by refining, clarifying, or asking follow-up questions. The more context you
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provide, the better the answers become.
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:::
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#### Give it a task to automate
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Using the same sandbox project, we'll ask PromptQL to generate an automation for us:
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> I need an automation to help me identify risky opportunities in my sales pipeline. I want to input a date range and
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> get back a list of opportunities that need attention. Specifically, I want to know which deals:
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>
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> - Haven't had any recent activity
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> - Are missing important contact relationships
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> - Show low engagement from the customer
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> - Are taking longer than expected to close
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>
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> Can you create something that gives me a risk score for each opportunity and suggests specific actions I should take?
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> I'd like the results in a format that makes it easy for me to prioritize which deals need immediate attention.
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This task-based prompt asks PromptQL to build an **automation**: a reusable workflow that runs against your data and
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gives you consistent, structured results. Rather than just answering a one-time question, automations are designed to
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help you monitor, score, or act on key insights regularly.
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<Thumbnail
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src="/img/get-started/playground-quickstart-automation.png"
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alt="A PromptQL-generated automation."
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width="1000px"
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/>
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In this example, PromptQL will identify the relevant data, define what makes a deal β€œrisky,” build a scoring system to
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prioritize those deals, and suggest actionsβ€”all packaged in a format that’s easy to review and reuse. Just like with
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query plans, PromptQL explains each step and lets you modify the logic, so you stay in control of the process.
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If you're using your own project, give PromptQL a similar task to automate. Otherwise,
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[click here](https://promptql.console.hasura.io/share/88a988ff-b44b-42b8-9859-846fa4be3f9c) to be dropped into a thread
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replay.
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<Prereqs />
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We also have a dedicated guide to building your own automations available [here](/automation.mdx).
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## Build your first PromptQL app
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## Next steps
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<Quickstart />
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Now that you've had your first set of conversations with PromptQL, we recommend checking out these docs around its
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[core capabilities](/capabilities.mdx).

β€Žsrc/css/custom.css

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blockquote {
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font-style: italic;
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/* -------------- TYPOGRAPHY COLORS -------------- */
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.theme-doc-sidebar-container [class^='sidebarLogo_'] {
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border-bottom: 1px solid var(--next-prev-border-color);
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.theme-doc-sidebar-container [class^='sidebarLogo_'] {
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margin: 0;
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}
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/* Mobile Navigation */
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z-index: 1000;
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β€Žsrc/theme/DocSidebar/CustomSidebar.css

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background-color: var(--sidebar-bg-color) !important;
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overflow-y: auto;
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padding-top: 5rem;
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}
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.custom-sidebar__logo {
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display: grid;
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place-items: center;
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padding: 1rem;
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height: 5rem;
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width: 100%;
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text-align: center;
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}
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.custom-sidebar__logo svg {
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max-height: 100%;
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max-width: 80%;
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object-fit: contain;
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β€Žsrc/theme/DocSidebar/Desktop/index.tsx

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import React from 'react';
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import { useLocation } from '@docusaurus/router';
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import { useColorMode } from '@docusaurus/theme-common';
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import type { PropSidebarItem } from '@docusaurus/plugin-content-docs';
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import LogoLight from '@site/static/img/pql-logo-large.svg';
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import LogoDark from '@site/static/img/pql-logo-large-dark-mode.svg';
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<div className="custom-sidebar__logo">
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<Link to="/index">
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{colorMode === 'dark' ? <LogoDark /> : <LogoLight />}
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</Link>
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</div>
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<div className="custom-sidebar__content">
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{categories.map(renderCategory)}
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