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* feat(docs): visualize KTX ingestion with ReactFlow diagram 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. * Improve KTX docs introduction * feat(docs): animate ingestion flow with running dots Replace static smoothstep edges in the introduction page's ingestion diagram with a custom animated edge that runs glowing cyan dots along each path, conveying the source → stage → output flow. Dot duration scales with path length and is hidden under prefers-reduced-motion. * feat(docs): route ingestion atoms through full source→output journey Replace per-edge dots with full-journey particles: each atom is born at a source, threads the entire stage chain, and lands at either the wiki or semantic layer. Particles are tinted by their source's accent so the origin is legible. Each source produces exactly 2 atoms (8 total) to guarantee every input is visibly active, while the destination and begin offsets are randomized per page load. Particles populate on client mount to avoid hydration mismatch, and are hidden under prefers-reduced-motion.
97 lines
4.1 KiB
Text
97 lines
4.1 KiB
Text
---
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title: Introduction
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description: KTX is an open-source, self-improving context layer for data agents.
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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 is an open-source context layer for database agents. It turns warehouse metadata, BI models, query history, docs, and approved metric definitions into reviewable files agents can search and execute.'}
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</p>
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</div>
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</div>
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## Why KTX helps
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KTX gives agents a shared context workspace before they write SQL, answer a
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question, or update analytics definitions.
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- **Context as code.** KTX writes wiki pages and semantic-layer definitions as
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git-based files you can review, diff, and merge.
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- **Self-improving ingest.** KTX reads warehouses, BI tools, modeling code,
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query history, and notes, then reconciles new evidence with accepted context.
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- **Executable semantics.** Agents can use approved measures, joins, filters,
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dimensions, and segments instead of rebuilding canonical SQL from scratch.
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- **Agent-native access.** CLI and MCP tools let agents search context, compile
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semantic queries, run read-only SQL, and propose updates.
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KTX complements existing semantic layers by pairing metric definitions with the
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surrounding business knowledge, caveats, provenance, and review workflow agents
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need for data work.
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## How KTX works
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KTX has two connected sides: it builds and maintains the context layer, then
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serves that context to agents at runtime.
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| Side | What KTX does |
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|------|---------------|
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| **Ingest and auto-maintain knowledge** | Reads your data stack and company knowledge, reconciles new evidence with accepted context, and keeps changes to `semantic-layer/` plus `wiki/` as version-controlled diffs automatically. |
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| **Serve agents at runtime** | Helps agents find the right wiki pages and semantic-layer entities, then compile or execute semantic queries through CLI and MCP tools. |
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<ProductMechanics />
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## Use it for
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Use KTX when agents need more than raw database access. Agents can search wiki
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context, find semantic-layer entities, compile trusted semantic queries, run
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read-only SQL, and use the same tools through MCP.
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- Generate SQL from approved metrics, joins, filters, and dimensions.
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- Explain metric provenance with wiki context and source evidence.
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- Repair context through reviewable YAML and Markdown diffs.
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- Work alongside dbt, MetricFlow, LookML, Looker, Metabase, Notion, and
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supported databases.
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## Start here
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Choose the route that matches what you want to do next. The quickstart is the
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best first step for users; contributor setup lives in the community docs.
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<Cards>
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<Card title="Quickstart" href="/docs/getting-started/quickstart">
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Install KTX, run setup, build context, and connect an agent.
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</Card>
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<Card title="The Context Layer" href="/docs/concepts/the-context-layer">
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Understand why agents need more than schema access and raw SQL.
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</Card>
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<Card title="Building Context" href="/docs/guides/building-context">
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Refresh context from databases, BI tools, query history, and documents.
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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">
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Complete flag and subcommand reference for every KTX command.
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</Card>
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<Card title="Agent Quickstart" href="/docs/ai-resources/agent-quickstart">
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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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