---
title: Wiki retrieval
description: How ktx ranks wiki pages with hybrid search, links them into a graph, and keeps both sides anchored to evidence.
---

The wiki is the prose half of the context layer. Agents reach it two ways:
they search for a page, then follow references inside the pages they
already opened. This page covers how both work.

- The wiki page contract that retrieval and validation depend on.
- The hybrid search pipeline that turns a question into ranked pages.
- The reference graph agents traverse without rerunning search.
- How pages get authored from evidence, and how broken edges get pruned.

## The wiki page contract

A wiki page is a Markdown file with a YAML frontmatter block. Frontmatter
carries metadata; the prose below it is free-form. Keys are flat tokens
(`revenue`, `mart_account_segments`), not paths, so every page is
addressable as `[[key]]` from any other page.

```markdown
# wiki/global/revenue.md
---
summary: Paid order value after refunds
tags: [finance, orders]
sl_refs: [warehouse.orders]
refs: [segment-classification]
usage_mode: auto
---

Revenue is paid order amount after refund adjustments.

Use `orders.total_revenue` for recognized order value and
`orders.order_count` for paid order volume.
```

| Field | Purpose |
|-------|---------|
| `summary` | One-line description shown in search results and the agent's knowledge index |
| `tags` | Topic labels mixed into the search text and used for filtering |
| `refs` | Outgoing edges to other wiki pages by key |
| `sl_refs` | Outgoing edges to semantic-layer sources by `connection.source` name |
| `usage_mode` | `always`, `auto`, or `never` - whether the agent must, may, or must not surface this page |
| `source` | Where the page came from when authored by ingest (e.g. `historic-sql`, `dbt`) |
| `usage` | Stats attached to historic-SQL pattern pages: executions, distinct users, runtime percentiles, error rate |

Pages live under two scopes. `wiki/global/*.md` is the team's shared
context; `wiki/user/<user>/*.md` is per-agent scratch space that shadows
global pages with the same key.

## What retrieval does

A wiki search runs the same ordered steps every time.

1. **Normalize the query.** Lowercase, tokenize, deduplicate terms.
2. **Score in three lanes.** Lexical (SQLite FTS5 bm25), semantic
   (cosine similarity over embeddings), and token (term-overlap fallback)
   each rank every page independently.
3. **Fuse with Reciprocal Rank Fusion.** Each lane contributes
   `weight / (60 + rank)` to a candidate's score. Lanes that fail or
   skip are dropped, not zeroed.
4. **Order and trim.** Sort by fused score, then by how many lanes
   matched, then by id for stable tie-breaks. Return the top `limit`
   results with their summaries.
5. **Hydrate on demand.** The agent calls `wiki_read` to load full
   bodies for the few pages that look relevant.

<figure
  className="not-prose my-10 overflow-hidden rounded-lg border border-fd-border bg-fd-card shadow-sm"
  aria-label="Three retrieval lanes fused with Reciprocal Rank Fusion"
>
  <div className="border-b border-fd-border bg-fd-muted/35 px-5 py-4">
    <p className="text-[11px] font-semibold uppercase tracking-[0.08em] text-fd-primary">
      {"Hybrid retrieval"}
    </p>
    <h3
      className="mt-1 text-base font-semibold tracking-normal text-fd-foreground sm:text-lg"
      style={{ fontFamily: "var(--font-display)" }}
    >
      {"Three lanes, one ranking"}
    </h3>
  </div>

  <div className="p-6">
    <div className="grid gap-3 md:grid-cols-3">
      <div className="rounded-md border border-fd-border bg-fd-background p-4">
        <p className="text-sm font-semibold text-fd-foreground">{"lexical"}</p>
        <p className="mt-1 font-mono text-[11px] text-fd-muted-foreground">{"sqlite fts5 / bm25"}</p>
        <p className="mt-3 text-xs leading-5 text-fd-muted-foreground">
          {"Matches stems and phrases. Strong on the exact terms the team already uses."}
        </p>
        <p className="mt-3 text-[11px] uppercase tracking-[0.08em] text-fd-muted-foreground">
          <span className="text-fd-foreground">{"weight "}</span>{"1.5"}
        </p>
      </div>

      <div className="rounded-md border border-fd-border bg-fd-background p-4">
        <p className="text-sm font-semibold text-fd-foreground">{"semantic"}</p>
        <p className="mt-1 font-mono text-[11px] text-fd-muted-foreground">{"cosine over embeddings"}</p>
        <p className="mt-3 text-xs leading-5 text-fd-muted-foreground">
          {"Catches synonyms and paraphrases the lexical lane misses."}
        </p>
        <p className="mt-3 text-[11px] uppercase tracking-[0.08em] text-fd-muted-foreground">
          <span className="text-fd-foreground">{"weight "}</span>{"2"}
        </p>
      </div>

      <div className="rounded-md border border-fd-border bg-fd-background p-4">
        <p className="text-sm font-semibold text-fd-foreground">{"token"}</p>
        <p className="mt-1 font-mono text-[11px] text-fd-muted-foreground">{"term-overlap fallback"}</p>
        <p className="mt-3 text-xs leading-5 text-fd-muted-foreground">
          {"Always available, so short queries still produce candidates."}
        </p>
        <p className="mt-3 text-[11px] uppercase tracking-[0.08em] text-fd-muted-foreground">
          <span className="text-fd-foreground">{"weight "}</span>{"0.75"}
        </p>
      </div>
    </div>

    <div className="mt-5 rounded-md border border-fd-primary/40 bg-fd-background p-4">
      <p className="text-[11px] font-semibold uppercase tracking-[0.08em] text-fd-primary">
        {"Reciprocal Rank Fusion"}
      </p>
      <p className="mt-1 font-mono text-[12px] text-fd-foreground">
        {"score = Σ weight / (60 + rank)"}
      </p>
      <p className="mt-2 text-xs leading-5 text-fd-muted-foreground">
        {"Pages that rank well in multiple lanes outscore pages that rank well in only one."}
      </p>
    </div>
  </div>

  <figcaption className="border-t border-fd-border bg-fd-muted/25 px-5 py-3 text-[11.5px] leading-5 text-fd-muted-foreground">
    <span className="font-medium text-fd-foreground">{"Defaults are tunable. "}</span>
    {"Lane weights and the RRF constant K are configuration, not assumptions."}
  </figcaption>
</figure>

The text each lane scores is built deterministically: page key, summary,
body, and tags concatenated in that order. A precise summary and the
right tags make a page reachable before its body matches anything.

## The page graph

Two frontmatter fields and one inline syntax turn the wiki into a graph
the agent traverses without re-running search.

| Edge | Source | Target |
|------|--------|--------|
| `sl_refs: [warehouse.orders]` | Frontmatter | Semantic source by name |
| `refs: [segment-classification]` | Frontmatter | Another wiki page by key |
| `[[segment-classification]]` | Inline in body | Another wiki page by key |

`refs` stays in the prose layer; `sl_refs` crosses into the executable
half of the context layer. Inline `[[wikilinks]]` are extracted from
page bodies at validation time and treated as declared `refs`.

<figure
  className="not-prose my-10 overflow-hidden rounded-lg border border-fd-border bg-fd-card shadow-sm"
  aria-label="Example wiki cross-reference graph"
>
  <div className="border-b border-fd-border bg-fd-muted/35 px-5 py-4">
    <p className="text-[11px] font-semibold uppercase tracking-[0.08em] text-fd-primary">
      {"Anatomy of a traversal"}
    </p>
    <h3
      className="mt-1 text-base font-semibold tracking-normal text-fd-foreground sm:text-lg"
      style={{ fontFamily: "var(--font-display)" }}
    >
      {"Edges to prose, edges to SQL"}
    </h3>
  </div>

  <div className="p-6">
    <div className="grid gap-3 md:grid-cols-[1fr_1fr]">
      <div className="rounded-md border-2 bg-fd-background p-4" style={{ borderColor: "#10b981" }}>
        <p className="font-mono text-[11px] font-semibold" style={{ color: "#10b981" }}>
          {"wiki/global/revenue.md"}
        </p>
        <p className="mt-2 text-sm font-semibold text-fd-foreground">{"revenue"}</p>
        <p className="mt-3 text-[11px] uppercase tracking-[0.08em] text-fd-muted-foreground">
          {"declares"}
        </p>
        <ul className="mt-1 space-y-1 text-xs text-fd-muted-foreground">
          <li><span className="font-mono text-fd-foreground">{"sl_refs"}</span>: warehouse.orders</li>
          <li><span className="font-mono text-fd-foreground">{"refs"}</span>: segment-classification</li>
        </ul>
      </div>
      <div className="rounded-md border bg-fd-background p-4" style={{ borderColor: "#10b981", borderStyle: "dashed" }}>
        <p className="font-mono text-[11px] font-semibold" style={{ color: "#10b981" }}>
          {"wiki/global/segment-classification.md"}
        </p>
        <p className="mt-2 text-sm font-semibold text-fd-foreground">{"segment-classification"}</p>
        <p className="mt-3 text-[11px] uppercase tracking-[0.08em] text-fd-muted-foreground">
          {"declares"}
        </p>
        <ul className="mt-1 space-y-1 text-xs text-fd-muted-foreground">
          <li><span className="font-mono text-fd-foreground">{"sl_refs"}</span>: warehouse.customers</li>
        </ul>
      </div>
    </div>

    <div className="my-4 grid gap-2 text-center text-xs font-medium text-fd-muted-foreground md:grid-cols-[1fr_1fr]">
      <div>{"revenue → warehouse.orders  ·  sl_refs"}</div>
      <div>{"revenue → segment-classification  ·  refs"}</div>
    </div>

    <div className="grid gap-3 md:grid-cols-[1fr_1fr]">
      <div className="rounded-md border-2 bg-fd-background p-4" style={{ borderColor: "#3b82f6" }}>
        <p className="font-mono text-[11px] font-semibold" style={{ color: "#3b82f6" }}>
          {"semantic-layer/warehouse/orders.yaml"}
        </p>
        <p className="mt-2 text-sm font-semibold text-fd-foreground">{"warehouse.orders"}</p>
        <p className="mt-1 text-xs text-fd-muted-foreground">{"grain: order_id  ·  measure: total_revenue"}</p>
      </div>
      <div className="rounded-md border-2 bg-fd-background p-4" style={{ borderColor: "#3b82f6" }}>
        <p className="font-mono text-[11px] font-semibold" style={{ color: "#3b82f6" }}>
          {"semantic-layer/warehouse/customers.yaml"}
        </p>
        <p className="mt-2 text-sm font-semibold text-fd-foreground">{"warehouse.customers"}</p>
        <p className="mt-1 text-xs text-fd-muted-foreground">{"grain: customer_id  ·  dim: segment"}</p>
      </div>
    </div>
  </div>

  <figcaption className="border-t border-fd-border bg-fd-muted/25 px-5 py-3 text-[11.5px] leading-5 text-fd-muted-foreground">
    {"Green nodes are wiki pages; blue nodes are semantic sources."}
  </figcaption>
</figure>

## Keeping the graph live

A page that references a deleted source is worse than no reference at
all - it sends the agent confidently to a definition that no longer
exists. **ktx** prevents that with three layered checks:

- **At write time.** Every `refs` entry and `[[wikilink]]` is validated
  against the pages visible in the current scope. A write that targets
  a missing page is rejected before any file changes.
- **At ingest time.** Adapters prune `sl_refs` when the target source
  is deleted, mark stale pattern pages with `stale_since`, and set
  `archived_since` on retired pages instead of removing them silently.
- **At session end.** Every page touched by an ingest run is re-scanned
  for references that resolved at write time but no longer point at
  a live target. Dangling pairs are reported so the next iteration can
  fix them.

## Where the pages come from

**ktx** writes wiki pages from evidence, not free invention. Each input
contributes a different kind of page, and accepted edits feed the next
ingest as input.

| Evidence | What it produces |
|----------|------------------|
| Schema scans | One page per material table, with grain, columns, and known constraints |
| Query history | Pattern pages with `usage` frontmatter for executions, distinct users, runtime percentiles, and error rate |
| dbt manifests | Pages per model, exposure, and test, with `sl_refs` to the matching semantic source |
| MetricFlow, Looker, Metabase | Pages per metric, explore, or saved question, linked back to the source artifact |
| Notion, docs, analyst notes | Pages preserving business definitions, policies, and incident write-ups |
| Agent and analyst edits | First-class input to the next ingest, not a fork |

Provenance stays with the page. Ingested pages keep HTML comments like
`<!-- from: raw-sources/.../cards/69.json -->` inline, so a reviewer can
walk from the prose back to the artifact that produced it.

## Agent usage notes

Point an agent at this page when it needs to explain why a wiki search
returned the pages it did, why a write was rejected, or how the wiki
stays in step with the semantic layer.

| Agent task | Relevant section | Next page |
|------------|------------------|-----------|
| Explain why two searches return different pages for the same query | What retrieval does | [ktx wiki](/docs/cli-reference/ktx-wiki) |
| Decide whether to add a `refs` or `sl_refs` entry | The page graph | [Writing Context](/docs/guides/writing-context) |
| Repair a wiki write rejected for missing references | Keeping the graph live | [Writing Context](/docs/guides/writing-context) |
| Describe how historic SQL becomes a wiki page | Where the pages come from | [Building Context](/docs/guides/building-context) |
| Explain raw-source provenance comments | Where the pages come from | [Reviewing Context](/docs/guides/reviewing-context) |
