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4421fe1c12
Reframe the introduction around the two user-facing ingestion outputs (wiki and executable semantic layer) and replace the static product-mechanics card flow with a ReactFlow diagram: sources fan into a sequential ingest pipeline, which forks into wiki and semantic-layer outputs connected by a bidirectional "references" edge. Drop the .ktx/raw-sources internal-implementation rows from the intro table and update the content test to guard the new copy.
77 lines
2.7 KiB
Text
77 lines
2.7 KiB
Text
---
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title: Introduction
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description: What KTX is, how it works, and where to start.
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---
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import { ProductMechanics } from "@/components/product-mechanics";
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<div className="not-prose mb-10">
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<div>
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<h1
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className="max-w-full text-3xl font-extrabold tracking-tight break-words sm:text-4xl lg:text-5xl"
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style={{
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fontFamily: 'var(--font-display)',
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background: 'linear-gradient(180deg, var(--color-fd-foreground) 0%, color-mix(in oklch, var(--color-fd-foreground) 75%, var(--color-fd-primary)) 100%)',
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WebkitBackgroundClip: 'text',
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backgroundClip: 'text',
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color: 'transparent',
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WebkitTextFillColor: 'transparent',
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lineHeight: '1.1',
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letterSpacing: '0',
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}}
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>
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Make analytics context usable by agents
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</h1>
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<p className="mt-4 max-w-2xl text-lg text-fd-muted-foreground" style={{ lineHeight: '1.7' }}>
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{'KTX turns warehouse metadata, semantic definitions, BI usage, and team knowledge into a wiki and executable semantic layer that database agents can trust.'}
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</p>
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</div>
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</div>
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## Why KTX
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- Schemas show columns, not business rules.
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- Agents need trusted metrics, joins, filters, caveats, and provenance.
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- KTX captures that context before agents write SQL, docs, or semantic edits.
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## What KTX creates
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KTX ingestion turns source evidence into durable context files that agents can
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search, review, and execute.
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| Path | What it gives agents |
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|------|----------------------|
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| `semantic-layer/` | Executable measures, dimensions, joins, grain, filters, and segments |
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| `wiki/` | Business definitions, caveats, policies, analyst notes |
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<ProductMechanics />
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## Use it for
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- **Generate SQL** from approved measures, dimensions, joins, and filters
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- **Explain provenance** with wiki context and warehouse evidence
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- **Repair context** through reviewable YAML and Markdown diffs
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- **Work alongside** dbt, LookML, MetricFlow, Looker, Metabase, and warehouses
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Databases: SQLite, PostgreSQL, Snowflake, BigQuery, ClickHouse, MySQL, SQL
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Server.
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## Start here
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<Cards>
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<Card title="Quickstart" href="/docs/getting-started/quickstart">
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Set up KTX and build your first context in under 10 minutes.
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</Card>
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<Card title="Guides" href="/docs/guides/building-context">
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Hands-on workflows for scanning, ingesting, writing, and serving.
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</Card>
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<Card title="Writing Context" href="/docs/guides/writing-context">
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Edit semantic-layer YAML and wiki Markdown safely.
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</Card>
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<Card title="CLI Reference" href="/docs/cli-reference/ktx-setup">
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Complete flag and subcommand reference for every KTX command.
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</Card>
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<Card title="AI Resources" href="/docs/ai-resources">
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Machine-readable docs and agent-facing setup notes.
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</Card>
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</Cards>
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