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Author SHA1 Message Date
agent_coder 8503ff1f3d fix(ai-chat): hydrate crashed mid-run steps for model replay + cover write/read seams (#492)
F1: the server model-replay loaded history via findAllByChat().map(rowToUiMessage)
WITHOUT hydrating parts from ai_chat_run_steps. A HARD crash mid-run (SIGKILL/OOM)
fires no terminal callback, so the assistant row stays parts:[] and its partial
tool-calls/results/text (durable in the steps table) dropped out of the model's
next-turn context. Hydrate needy assistant rows (role==='assistant' &&
!rowHasInlineParts) via findByMessageIds + hydrateAssistantParts before the replay
map — mirroring the controller's withReconstructedParts exactly — guarded on the
optional repo. Fix the now-false interrupt-resume comment.

F2: add a service int-spec that drives the REAL onStep append-persist WRITE branch
through AiChatService.stream with a real AiChatRunStepRepo injected, asserting the
per-step rows' stepIndex + parts slice and the step-marker metadata match a
single-row flush (catches an stepsPersisted-1 off-by-one).

F3: add a controller int-spec that drives withReconstructedParts through getMessages
WITH the repo present (a mid-run marker-only row + its step rows), asserting the
reconstructed metadata.parts and workspace-scoping.

F4: remove the dead countByMessage (zero prod callers; reconstructRunParts derives
stepsPersisted inline) + its now-unused sql import and the redundant test assertion.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-07-12 07:50:50 +03:00
agent_coder ae3dfd8de6 Merge remote-tracking branch 'gitea/develop' into feat/492-incremental-render 2026-07-12 05:34:07 +03:00
agent_coder c96fafc4ad perf(ai-chat): append-персист шагов — per-step INSERT вместо переписи строки (#492)
Раньше каждый onStepFinish переписывал ВСЮ строку ассистента (растущий
metadata.parts jsonb со всеми выводами инструментов) → O(n²) объёма записи
на прогон: под MVCC/TOAST апдейт jsonb переписывает всю версию строки, так
что шаг k пишет ~k×вывод. Прогон из 50 шагов по ~100 КБ = сотни МБ WAL и
мёртвых кортежей за ход, что молотит autovacuum. (#490 убрал только ВТОРУЮ
копию в tool_calls; сам metadata.parts всё ещё рос и переписывался.)

Теперь каждый завершённый шаг ДОПИСЫВАЕТСЯ отдельной строкой в лёгкую
таблицу ai_chat_run_steps (только парты этого шага), а строка сообщения
получает дешёвый маркер (stepsPersisted + toolTraceVersion, без растущего
блоба parts). Полный metadata.parts собирается ОДИН раз на финализации.
НЕ jsonb-append (||): апдейт всё равно переписывает всю TOAST-версию —
экономится только сетевой payload, а WAL/мёртвые кортежи остаются; поэтому
именно ОТДЕЛЬНАЯ таблица + INSERT.

Три обязательные интеграции:
- reconstructRunParts(row, stepRows) → { parts, stepsPersisted }: единый
  шов переключения бэкенда. Читает парты из СТРОКИ, если она уже несёт
  inline-parts (старые записи + ЛЮБАЯ финализированная), иначе из ТАБЛИЦЫ
  ШАГОВ (mid-run запись #492). Дискриминатор — наличие непустого
  metadata.parts (флаг схемы не нужен). Потребители (attach-seed,
  delta-poll, export, reconnect) прогоняют строки через hydrateAssistantParts
  на границе чтения — их контракт/вывод не меняется, старые и новые записи
  восстанавливаются идентично.
- сигнал ротации кольца реестра #491 (confirmPersistedStep) теперь стреляет
  на подтверждённый INSERT шага, под тем же контрактом (updateStreaming
  возвращает stepsPersisted / null).
- era-marker toolTraceVersion (#490) больше не ставится полной переписью —
  ставится в маркере шага и на финализации (flushAssistant), остаётся
  консистентным.

Полная обратная совместимость: прогон, записанный по-старому (полная строка,
без строк шагов), восстанавливается/attach/export идентично. При отсутствии
репозитория шагов (позиционные тест-конструкции) — фолбэк на прежний
полнострочный flush (без регрессии, только без выигрыша WAL).

Тесты (реальный pg, int-lane):
- WAL-дельта (pg_current_wal_lsn) на прогоне 40×100КБ: new=4.3МБ vs
  old=90.3МБ (20.8x) — O(Σ шагов) против O(n²); старый путь в тесте И есть
  ревертнутое поведение (мутация-проверка).
- reconstruct-контракт: new-style (таблица шагов) и old-style (inline) прогоны
  восстанавливаются в идентичные parts; hydrate заполняет строку.
- миграция up/down roundtrip.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-07-12 05:02:25 +03:00
agent_coder 2f8c5d9a98 perf(client): инкрементальный рендер стрима ответа (#492)
Путь ответа ассистента теперь рендерится инкрементально, по образцу
StreamingPlainText из ветки reasoning (#492, волна C эпика #497).

Раньше MarkdownPart прогонял ВЕСЬ накопленный ответ через канонический
конвейер (markdownToProseMirrorSync → PMNode.fromJSON → DOMSerializer →
DOMPurify) на КАЖДОМ throttled-тике (~20 Гц). На синтетическом потоке
~100 КБ это 394 вызова renderChatMarkdown — O(числа тиков), причём каждый
вызов заново парсит всю растущую строку.

Теперь:
- StreamingMarkdownText делит текст на блоки по безопасному срезу
  (splitPlainChunks — та же append-only-инвариантность, что у reasoning):
  СТАБИЛИЗИРОВАННЫЕ блоки идут через канонический конвейер и мемоизируются
  (каждый блок парсится РОВНО ОДИН раз), живой ХВОСТ — дешёвый plain-text
  (React-escaped, без парсера/санитайзера/innerHTML) до стабилизации.
- На финализации (флип state → done или конец хода) — ОДИН полный
  канонический рендер всего текста: побайтовая визуальная паритетность с
  прежним выводом (включая <li><p>-обёртки схемы и scoped-CSS из #498).
- Гейт liveness тот же, что у ReasoningBlock: streaming =
  turnStreaming && part.state === "streaming".

Также цикл рендера частей переведён на ИСЧЕРПЫВАЮЩИЙ switch по видам частей
с never-проверкой в default (вместо прежнего WARNING-комментария): новый
закрытый вид части в UIMessagePart теперь ошибка компиляции.

Тесты:
- perf-smoke: на ~100 КБ потоке число вызовов renderChatMarkdown ≤ blocks+2
  и ≪ ticks (O(блоков), не O(тиков)); мутация (снять memo с MarkdownChunk)
  краснит ассерт (78631 вызовов).
- visual-regression: финальный рендер побайтово равен renderChatMarkdown
  всего текста (в т.ч. <li><p>), инкрементальный вид сходится к нему на
  финализации; учтён neutralizeInternalLinks.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-07-12 04:35:06 +03:00
17 changed files with 1827 additions and 133 deletions
@@ -27,6 +27,7 @@ vi.mock("@/features/ai-chat/utils/markdown.ts", async () => {
import MessageItem from "./message-item";
import { messageSignature } from "@/features/ai-chat/utils/message-signature.ts";
import { splitPlainChunks } from "./streaming-plain-text";
// matchMedia (read by MantineProvider) is stubbed globally in vitest.setup.ts.
@@ -114,3 +115,89 @@ describe("MessageItem markdown memoization", () => {
expect(queryByText("streamed answer")).not.toBeNull();
});
});
// PERF SMOKE (#492): the whole point of the incremental streaming render is that
// the ANSWER path costs O(number of markdown blocks), NOT O(number of throttled
// ~20Hz ticks). Pre-#492 the finalized MarkdownPart re-parsed the WHOLE growing
// answer on every delta — a synthetic ~100 KB stream measured 394 renderChatMarkdown
// calls (one per tick). With the incremental render each STABILIZED block is parsed
// exactly once (memoized in MarkdownChunk) and the live tail is cheap plain text, so
// the call count collapses to ~= the block count regardless of tick granularity.
describe("MessageItem streaming answer render is O(blocks), not O(ticks)", () => {
// ~100 KB answer. Each section is a heading + a paragraph — TWO blank-line
// delimited markdown blocks — so the safe-cut block count is ~2× the section
// count. The perf claim is about the BLOCK count (the memoization granularity),
// measured directly with splitPlainChunks below, not the section count.
const buildAnswer = () => {
const SECTIONS = 100;
const paragraphs: string[] = [];
for (let i = 0; i < SECTIONS; i++) {
paragraphs.push(`## Section ${i}\n\n` + "lorem ipsum dolor ".repeat(55));
}
const full = paragraphs.join("\n\n");
// The number of memoized markdown blocks the incremental render splits into
// (all but the live tail are parsed once each).
return { full, blocks: splitPlainChunks(full).length };
};
const streamMsg = (text: string, state: "streaming" | "done"): UIMessage =>
({
id: "m1",
role: "assistant",
parts: [{ type: "text", text, state }],
}) as UIMessage;
it("parses each block ~once over a 100KB stream (≈blocks, ≪ ticks)", () => {
renderChatMarkdownSpy.mockClear();
const { full, blocks } = buildAnswer();
const CHUNK = 128; // a realistic ~20Hz throttled delta size
const ticks = Math.ceil(full.length / CHUNK);
let msg = streamMsg(full.slice(0, CHUNK), "streaming");
const { rerender } = render(
<MantineProvider>
<MessageItem
message={msg}
signature={messageSignature(msg)}
turnStreaming
/>
</MantineProvider>,
);
for (let end = 2 * CHUNK; end < full.length; end += CHUNK) {
msg = streamMsg(full.slice(0, end), "streaming");
rerender(
<MantineProvider>
<MessageItem
message={msg}
signature={messageSignature(msg)}
turnStreaming
/>
</MantineProvider>,
);
}
// Finalize: the streaming→done flip renders the whole answer through ONE
// canonical pass (visual parity), so the finished DOM matches the pre-#492
// output. This is the single extra parse on top of the per-block ones.
const done = streamMsg(full, "done");
rerender(
<MantineProvider>
<MessageItem message={done} signature={messageSignature(done)} />
</MantineProvider>,
);
const calls = renderChatMarkdownSpy.mock.calls.length;
// Sanity: the stream really had far more ticks than blocks (else the test is
// vacuous — the point is that calls scale with blocks, not ticks).
expect(ticks).toBeGreaterThan(blocks * 3);
// O(blocks): each stabilized block parsed once + the single final whole-text
// parse. A small constant absorbs the finalize render and the live-tail block;
// the load-bearing claim is the bound below.
expect(calls).toBeLessThanOrEqual(blocks + 2);
// ≪ ticks — and, non-vacuously, the blocks WERE parsed (not skipped entirely).
expect(calls).toBeLessThan(ticks / 3);
expect(calls).toBeGreaterThan(blocks / 2);
// MUTATION-VERIFY (documented, not run here): dropping the `memo()` wrapper on
// MarkdownChunk (so every stable block re-parses each tick) drives `calls`
// toward `ticks` (~394), reddening both upper-bound assertions above.
});
});
@@ -0,0 +1,112 @@
import { describe, expect, it, vi } from "vitest";
import { render } from "@testing-library/react";
import { MantineProvider } from "@mantine/core";
import type { UIMessage } from "@ai-sdk/react";
// Stub react-i18next (the component reads `useTranslation`). Mirrors the other
// message-item specs.
vi.mock("react-i18next", () => ({
useTranslation: () => ({ t: (key: string) => key }),
}));
import MessageItem from "./message-item";
import { messageSignature } from "@/features/ai-chat/utils/message-signature.ts";
// The REAL canonical renderer (NOT the spy the memo test installs): this file
// exercises the actual markdown output so the visual-regression assertions below
// compare against genuine HTML (incl. the schema's `<li><p>` wrappers).
import { renderChatMarkdown } from "@/features/ai-chat/utils/markdown.ts";
import classes from "./ai-chat.module.css";
const msg = (
parts: UIMessage["parts"],
extra?: Partial<UIMessage>,
): UIMessage =>
({ id: "m1", role: "assistant", parts, ...extra }) as UIMessage;
const renderRow = (message: UIMessage, turnStreaming = false) =>
render(
<MantineProvider>
<MessageItem
message={message}
signature={messageSignature(message)}
turnStreaming={turnStreaming}
/>
</MantineProvider>,
);
// A rich multi-block answer that exercises headings, a list (the `<li><p>` case
// the scoped CSS tightens), inline emphasis, and multiple paragraphs.
const ANSWER = [
"# Заголовок",
"",
"Первый абзац с **жирным** и `кодом`.",
"",
"- пункт один",
"- пункт два",
"",
"Второй абзац.",
].join("\n");
describe("MessageItem final render — visual parity with the canonical pipeline", () => {
it("a finalized text part renders exactly renderChatMarkdown(text)", () => {
const { container } = renderRow(
msg([{ type: "text", text: ANSWER, state: "done" }]),
);
const block = container.querySelector(`.${classes.markdown}`);
expect(block).not.toBeNull();
// Byte-for-byte the canonical output (the SAME whole-text pass the pre-#492
// MarkdownPart produced), including `<li><p>…</p></li>` wrappers.
expect(block!.innerHTML).toBe(renderChatMarkdown(ANSWER, {}));
// The list wrapper is really present (guards against a vacuous empty render).
expect(container.querySelectorAll("li p").length).toBe(2);
});
it("the streaming incremental view CONVERGES to the canonical render on finish", () => {
// Mount mid-stream (live tail) — the DOM here is the incremental view.
const { container, rerender } = render(
<MantineProvider>
<MessageItem
message={msg([{ type: "text", text: ANSWER, state: "streaming" }])}
signature={messageSignature(
msg([{ type: "text", text: ANSWER, state: "streaming" }]),
)}
turnStreaming
/>
</MantineProvider>,
);
// Finish the turn: state flips to done AND the turn is no longer streaming.
const done = msg([{ type: "text", text: ANSWER, state: "done" }]);
rerender(
<MantineProvider>
<MessageItem message={done} signature={messageSignature(done)} />
</MantineProvider>,
);
// After finish there is exactly ONE canonical markdown container whose HTML is
// the whole-text render — identical to the non-streaming path above.
const blocks = container.querySelectorAll(`.${classes.markdown}`);
expect(blocks.length).toBe(1);
expect(blocks[0].innerHTML).toBe(renderChatMarkdown(ANSWER, {}));
});
it("neutralizeInternalLinks is honored on the finalized render", () => {
const linkAnswer = "См. [страницу](/p/abc).";
const { container } = render(
<MantineProvider>
<MessageItem
message={msg([{ type: "text", text: linkAnswer, state: "done" }])}
signature={messageSignature(
msg([{ type: "text", text: linkAnswer, state: "done" }]),
)}
neutralizeInternalLinks
/>
</MantineProvider>,
);
const block = container.querySelector(`.${classes.markdown}`);
expect(block!.innerHTML).toBe(
renderChatMarkdown(linkAnswer, { neutralizeInternalLinks: true }),
);
// The internal link was made inert (no href) by the neutralization flag.
const a = container.querySelector("a");
expect(a?.hasAttribute("href")).toBe(false);
});
});
@@ -4,6 +4,7 @@ import { useTranslation } from "react-i18next";
import type { UIMessage } from "@ai-sdk/react";
import ToolCallCard from "@/features/ai-chat/components/tool-call-card.tsx";
import ReasoningBlock from "@/features/ai-chat/components/reasoning-block.tsx";
import { StreamingMarkdownText } from "@/features/ai-chat/components/streaming-markdown-text.tsx";
import ChatErrorAlert from "@/features/ai-chat/components/chat-error-alert.tsx";
import ChatStoppedNotice from "@/features/ai-chat/components/chat-stopped-notice.tsx";
import { ToolUiPart, isToolPart } from "@/features/ai-chat/utils/tool-parts.tsx";
@@ -86,17 +87,39 @@ interface MessageItemProps {
* One assistant text part rendered as sanitized markdown. Memoized on its inputs
* so a finalized text part is NOT re-parsed on every streamed delta: during a
* turn only the actively-growing tail part changes its `text`, so every earlier
* part hits the memo and skips the expensive marked + DOMPurify pass. Props are
* primitives, so React.memo's default shallow compare is exactly right (the
* `text` string is compared by value).
* part hits the memo and skips the expensive canonical parse + DOMPurify pass.
* Props are primitives, so React.memo's default shallow compare is exactly right
* (the `text` string is compared by value).
*
* Streaming gate (#492) — mirrors ReasoningBlock:
* - `streaming` (this is the live, actively-growing tail part of an in-flight
* turn): render incrementally via StreamingMarkdownText — the stabilized blocks
* go through the canonical pipeline (each parsed ONCE, memoized) and only the
* live tail is cheap plain text. This makes the per-tick cost O(new blocks),
* not the pre-#492 O(ticks) whole-answer re-parse on every ~20Hz delta.
* - finalized (the common case, and the turn-end flip): render the WHOLE text
* through ONE canonical pass — byte-identical to the pre-#492 output (visual
* parity). The row re-renders on the streaming→done flip because
* `messageSignature` tracks each part's `state` (and `turnStreaming` flips at
* turn end), so the incremental view always converges to this single render.
*/
const MarkdownPart = memo(function MarkdownPart({
text,
neutralizeInternalLinks,
streaming,
}: {
text: string;
neutralizeInternalLinks: boolean;
streaming: boolean;
}) {
if (streaming) {
return (
<StreamingMarkdownText
text={text}
neutralizeInternalLinks={neutralizeInternalLinks}
/>
);
}
const html = renderChatMarkdown(text, { neutralizeInternalLinks });
if (html) {
return (
@@ -179,47 +202,10 @@ function MessageItem({
{resolveAssistantName(assistantName) ?? t("AI agent")}
</Text>
{message.parts.map((part, index) => {
if (part.type === "reasoning") {
// Reasoning ("thinking") -> a collapsible block with its own token
// count. Empty/whitespace reasoning with no authoritative count carries
// nothing to show, so skip it (avoids an empty 0-token block).
const text = (part as { text?: string }).text ?? "";
if (!text.trim() && !(reasoningTokens && reasoningTokens > 0))
return null;
// Absent state (persisted rows) and "done" both mean finalized.
// `messageSignature` already includes each part's `state`, so the
// streaming→done flip changes the row signature and re-renders this
// row — which is what lets ReasoningBlock switch from chunked plain
// text to its one-time markdown parse (see reasoning-block.tsx).
// ALSO require the turn to be live: a part stranded at
// `state:"streaming"` after the turn ended (no `reasoning-end` — see
// the `turnStreaming` prop doc) must still finalize and parse.
const streaming =
turnStreaming && (part as { state?: string }).state === "streaming";
return (
<ReasoningBlock
key={index}
text={text}
tokens={reasoningTokens}
streaming={streaming}
/>
);
}
if (part.type === "text") {
// Skip empty/whitespace-only text parts (a streaming message often
// starts with an empty text part before the first token arrives); the
// typing indicator covers that gap until real content streams in.
if (!part.text.trim()) return null;
return (
<MarkdownPart
key={index}
text={part.text}
neutralizeInternalLinks={neutralizeInternalLinks}
/>
);
}
// Tool parts (`tool-*` / `dynamic-tool`) are template-literal kinds, so
// they cannot be a `switch` case; the runtime guard handles them, and the
// switch below covers every CLOSED (literal-typed) part kind with a
// compile-time exhaustiveness check in its default.
if (isToolPart(part.type)) {
return (
<ToolCallCard
@@ -232,7 +218,76 @@ function MessageItem({
);
}
return null;
switch (part.type) {
case "reasoning": {
// Reasoning ("thinking") -> a collapsible block with its own token
// count. Empty/whitespace reasoning with no authoritative count
// carries nothing to show, so skip it (avoids an empty 0-token block).
const text = part.text ?? "";
if (!text.trim() && !(reasoningTokens && reasoningTokens > 0))
return null;
// Absent state (persisted rows) and "done" both mean finalized.
// `messageSignature` already includes each part's `state`, so the
// streaming→done flip changes the row signature and re-renders this
// row — which is what lets ReasoningBlock switch from chunked plain
// text to its one-time markdown parse (see reasoning-block.tsx).
// ALSO require the turn to be live: a part stranded at
// `state:"streaming"` after the turn ended (no `reasoning-end` — see
// the `turnStreaming` prop doc) must still finalize and parse.
const streaming = turnStreaming && part.state === "streaming";
return (
<ReasoningBlock
key={index}
text={text}
tokens={reasoningTokens}
streaming={streaming}
/>
);
}
case "text": {
// Skip empty/whitespace-only text parts (a streaming message often
// starts with an empty text part before the first token arrives); the
// typing indicator covers that gap until real content streams in.
if (!part.text.trim()) return null;
// The live, actively-growing tail part of the in-flight turn renders
// incrementally (see MarkdownPart); a finalized part (persisted, or
// the turn-end flip) renders the whole text through one canonical
// pass. Same liveness rule as the reasoning branch above.
const streaming = turnStreaming && part.state === "streaming";
return (
<MarkdownPart
key={index}
text={part.text}
neutralizeInternalLinks={neutralizeInternalLinks}
streaming={streaming}
/>
);
}
case "source-url":
case "source-document":
case "file":
case "step-start":
// Not surfaced in the chat bubble (v1) — same as the pre-#492 default.
return null;
default: {
// Compile-time exhaustiveness over the CLOSED union members: every
// literal-typed part kind is handled above, so the only kinds that
// can reach here are the OPEN template-literal ones (`tool-*` — caught
// by the guard at runtime — and `data-*`) plus `dynamic-tool`. Adding
// a NEW closed part kind to UIMessagePart makes this assignment fail
// to compile, forcing it to be handled instead of silently ignored
// (this replaces the pre-#492 fall-through `return null` + WARNING).
const _exhaustive:
| `tool-${string}`
| "dynamic-tool"
| `data-${string}` = part.type;
void _exhaustive;
return null;
}
}
})}
{/* A persisted turn error (server stored it in metadata.error). Rendered
here so it survives a thread remount and shows in reopened history. */}
@@ -0,0 +1,96 @@
import { memo, useMemo } from "react";
import { splitPlainChunks } from "@/features/ai-chat/components/streaming-plain-text.tsx";
import { renderChatMarkdown } from "@/features/ai-chat/utils/markdown.ts";
import classes from "@/features/ai-chat/components/ai-chat.module.css";
/**
* One STABILIZED markdown block, rendered through the canonical pipeline and
* memoized on its string prop. During streaming only the TAIL chunk grows (the
* `splitPlainChunks` append-only invariant guarantees every earlier chunk is
* byte-identical across deltas), so React skips every stable block and each one
* is parsed by `renderChatMarkdown` EXACTLY ONCE — turning the pre-#492
* "re-parse the whole accumulated answer on every ~20Hz tick" (O(ticks)) into
* O(number of blocks). The markup is DOMPurify-sanitized inside renderChatMarkdown
* before it reaches `dangerouslySetInnerHTML`.
*
* NOTE (transient streaming-only artifact): a safe cut is a blank-line boundary,
* so a construct that legitimately contains a blank line (e.g. a fenced code block
* with an empty line) can be split across chunks and render oddly WHILE it is still
* streaming. This is cosmetic and self-heals: the moment the part finalizes,
* MarkdownPart renders the WHOLE text through one canonical pass (visual parity
* with the pre-#492 output). The reasoning path makes the same trade (plain text
* while streaming, one markdown parse at the end).
*/
const MarkdownChunk = memo(function MarkdownChunk({
text,
neutralizeInternalLinks,
}: {
text: string;
neutralizeInternalLinks: boolean;
}) {
const html = renderChatMarkdown(text, { neutralizeInternalLinks });
if (html) {
return (
<div
className={classes.markdown}
// Sanitized by renderChatMarkdown (DOMPurify) before insertion.
dangerouslySetInnerHTML={{ __html: html }}
/>
);
}
// Malformed/unsupported markdown could not render synchronously: raw text.
return (
<div className={classes.markdown} style={{ whiteSpace: "pre-wrap" }}>
{text}
</div>
);
});
/**
* The cheap streaming-time stand-in for the finalized answer's one-time markdown
* parse (see MarkdownPart in message-item.tsx). Mirrors StreamingPlainText's
* chunked-memo pattern but renders the STABILIZED prefix as real markdown (each
* block parsed once, memoized) and only the LIVE tail as flat plain text — so the
* user sees formatted output for everything up to the last safe cut, and the not-
* yet-stable tail (which markdown-parsing every tick would make O(ticks)) stays a
* single cheap escaped text node until it stabilizes into a new block.
*
* `splitPlainChunks` yields chunks where, under append-only growth, every chunk
* except the LAST is immutable; the last chunk is the live tail. Index keys are
* therefore stable (a given index never changes to a different chunk's content).
*/
export function StreamingMarkdownText({
text,
neutralizeInternalLinks,
}: {
text: string;
neutralizeInternalLinks: boolean;
}) {
const chunks = useMemo(() => splitPlainChunks(text), [text]);
return (
<>
{chunks.map((chunk, index) =>
index < chunks.length - 1 ? (
<MarkdownChunk
key={index}
text={chunk}
neutralizeInternalLinks={neutralizeInternalLinks}
/>
) : (
// The live tail: flat, React-escaped plain text (no markdown parse, no
// sanitizer, no innerHTML). `pre-wrap` preserves its newlines; trailing
// separator newlines are dropped at display time so the block gap comes
// from the markdown margins, not a doubled empty line (mirrors
// PlainChunk in streaming-plain-text.tsx).
<div
key={index}
className={classes.markdown}
style={{ whiteSpace: "pre-wrap" }}
>
{chunk.replace(/\n+$/, "")}
</div>
),
)}
</>
);
}
@@ -35,6 +35,7 @@ import {
import { PaginationOptions } from '@docmost/db/pagination/pagination-options';
import { AiChatRepo } from '@docmost/db/repos/ai-chat/ai-chat.repo';
import { AiChatMessageRepo } from '@docmost/db/repos/ai-chat/ai-chat-message.repo';
import { AiChatRunStepRepo } from '@docmost/db/repos/ai-chat/ai-chat-run-step.repo';
import { PageRepo } from '@docmost/db/repos/page/page.repo';
import { UserThrottlerGuard } from '../../integrations/throttle/user-throttler.guard';
import { AI_CHAT_THROTTLER } from '../../integrations/throttle/throttler-names';
@@ -43,6 +44,8 @@ import {
AiChatRunHooks,
AiChatService,
AiChatStreamBody,
rowHasInlineParts,
hydrateAssistantParts,
} from './ai-chat.service';
import { AiChatRunService } from './ai-chat-run.service';
import { AiTranscriptionService } from './ai-transcription.service';
@@ -129,8 +132,39 @@ export class AiChatController {
// production. Only touched on the resumable-stream (flag-on) path.
private readonly streamRegistry?: AiChatStreamRegistryService,
private readonly environment?: EnvironmentService,
// #492: reconstruct a #492 mid-run record's parts from the steps table before
// returning rows to the client / export. OPTIONAL so positional controller
// specs compile unchanged; when absent, hydration is skipped (old-era rows
// already carry inline parts, so nothing to reconstruct).
private readonly aiChatRunStepRepo?: AiChatRunStepRepo,
) {}
/**
* Reconstruct parts for any assistant rows that don't carry them INLINE — a
* #492 mid-run record whose per-step parts live in `ai_chat_run_steps` (the
* append-persist backend). Every FINISHED row (old-era + #492) and every old-era
* streaming snapshot already has inline `metadata.parts`, so the common path
* fetches NOTHING and returns the rows untouched; only an actively-streaming
* new-style row triggers the batch step fetch. Consumers (seed/poll/export) read
* `metadata.parts` off the returned rows exactly as before — the era switch is
* invisible to them (reconstructRunParts contract).
*/
private async withReconstructedParts(
rows: AiChatMessage[],
workspaceId: string,
): Promise<AiChatMessage[]> {
if (!this.aiChatRunStepRepo) return rows;
const needy = rows.filter(
(r) => r.role === 'assistant' && !rowHasInlineParts(r),
);
if (needy.length === 0) return rows;
const stepsByMessage = await this.aiChatRunStepRepo.findByMessageIds(
needy.map((r) => r.id),
workspaceId,
);
return hydrateAssistantParts(rows, stepsByMessage);
}
/** List the requesting user's chats in this workspace (paginated). */
@HttpCode(HttpStatus.OK)
@Post('chats')
@@ -184,11 +218,17 @@ export class AiChatController {
@AuthWorkspace() workspace: Workspace,
) {
await this.assertOwnedChat(dto.chatId, user, workspace);
return this.aiChatMessageRepo.findByChat(
const page = await this.aiChatMessageRepo.findByChat(
dto.chatId,
workspace.id,
pagination,
);
// #492: reconstruct parts for any active new-style row so the client seed sees
// `metadata.parts` unchanged (a no-op for the finished rows that fill a page).
return {
...page,
items: await this.withReconstructedParts(page.items, workspace.id),
};
}
/**
@@ -225,7 +265,10 @@ export class AiChatController {
workspace.id,
);
return {
rows,
// #492: the delta of an actively-streaming new-style row carries its parts
// reconstructed from the steps table, so the degraded poll shows persisted
// progress exactly as the pre-#492 full-row snapshot did.
rows: await this.withReconstructedParts(rows, workspace.id),
cursor,
run: run ? { id: run.id, status: run.status } : null,
};
@@ -247,8 +290,10 @@ export class AiChatController {
@AuthWorkspace() workspace: Workspace,
): Promise<{ markdown: string }> {
const chat = await this.assertOwnedChat(dto.chatId, user, workspace);
const rows = await this.aiChatMessageRepo.findAllByChat(
dto.chatId,
const rows = await this.withReconstructedParts(
await this.aiChatMessageRepo.findAllByChat(dto.chatId, workspace.id),
// #492: an interrupted-but-still-active turn exports its persisted steps
// (reconstructed from the steps table) just like the pre-#492 full row did.
workspace.id,
);
const markdown = buildChatMarkdown({
@@ -288,7 +333,13 @@ export class AiChatController {
workspace.id,
)
: undefined;
return { run, message: message ?? null };
// #492: reconnect to an IN-FLIGHT run reconstructs the projection row's parts
// from the steps table (the row itself carries only the step marker mid-run);
// a finished run's row already has inline parts, so this is a no-op.
const [hydrated] = message
? await this.withReconstructedParts([message], workspace.id)
: [undefined];
return { run, message: hydrated ?? null };
}
/**
+203 -12
View File
@@ -22,6 +22,7 @@ import { AiSettingsService } from '../../integrations/ai/ai-settings.service';
import { describeProviderError } from '../../integrations/ai/ai-error.util';
import { AiChatRepo } from '@docmost/db/repos/ai-chat/ai-chat.repo';
import { AiChatMessageRepo } from '@docmost/db/repos/ai-chat/ai-chat-message.repo';
import { AiChatRunStepRepo } from '@docmost/db/repos/ai-chat/ai-chat-run-step.repo';
import { AiChatPageSnapshotRepo } from '@docmost/db/repos/ai-chat/ai-chat-page-snapshot.repo';
import { AiAgentRoleRepo } from '@docmost/db/repos/ai-agent-roles/ai-agent-roles.repo';
import { PageRepo } from '@docmost/db/repos/page/page.repo';
@@ -518,6 +519,12 @@ export class AiChatService implements OnModuleInit, OnModuleDestroy {
// constructions compile unchanged; Nest always injects the real singleton, so
// reconcile sees the SAME in-memory active/zombie maps the runner mutates.
private readonly aiChatRunService?: AiChatRunService,
// #492 append-persist: per-step INSERT into the lightweight steps table (the
// O(Σ steps) replacement for the O(n²) full-row `metadata.parts` rewrite).
// OPTIONAL so existing positional constructions (int-specs) compile unchanged;
// Nest injects the real singleton. When ABSENT the per-step path falls back to
// the pre-#492 full-row flush (no regression, only no WAL win).
private readonly aiChatRunStepRepo?: AiChatRunStepRepo,
) {}
// #487: periodic reconcile timer (single-process phase 1). Started in
@@ -1114,8 +1121,34 @@ export class AiChatService implements OnModuleInit, OnModuleDestroy {
chatId,
workspace.id,
);
// #492: HYDRATE needy assistant rows from the steps table BEFORE the replay
// map. A #492 mid-run assistant row carries only a step marker
// (metadata.parts:[]); its real per-step parts live in `ai_chat_run_steps`.
// The graceful terminal callbacks (onFinish/onError/onAbort -> flushAssistant)
// assemble the full inline parts, so a normally-ended turn already has them.
// But a HARD crash mid-run (SIGKILL/OOM) fires NO terminal callback, so the
// row stays parts:[]; without this, rowToUiMessage falls back to an empty
// text part and the partial tool-calls/results/text — durable in the steps
// table — would DROP OUT of the model's replay context (regressing #183
// step-granular durability for the model consumer). Mirrors the controller's
// withReconstructedParts EXACTLY (same needy predicate + hydration helper).
// Guarded on the optional repo: absent (positional test builds) degrades to
// the current behavior rather than crashing.
let replayHistory = oldHistory;
if (this.aiChatRunStepRepo) {
const needy = oldHistory.filter(
(r) => r.role === 'assistant' && !rowHasInlineParts(r),
);
if (needy.length > 0) {
const stepsByMessage = await this.aiChatRunStepRepo.findByMessageIds(
needy.map((r) => r.id),
workspace.id,
);
replayHistory = hydrateAssistantParts(oldHistory, stepsByMessage);
}
}
const uiMessages: Array<Omit<UIMessage, 'id'> & { id: string }> = [
...oldHistory.map(rowToUiMessage),
...replayHistory.map(rowToUiMessage),
{
id: 'pending-user',
role: 'user',
@@ -1154,7 +1187,9 @@ export class AiChatService implements OnModuleInit, OnModuleDestroy {
// hint — confirm it against the persisted history (the preceding assistant
// turn must really be aborted/streaming) so a spoofed flag cannot inject the
// interrupt note onto an ordinary turn. The partial output the model needs is
// already in `messages` (the aborted assistant row replays via findRecent).
// already in `messages`: a #492 mid-run row's per-step parts live only in the
// `ai_chat_run_steps` table and were hydrated into the replay history above,
// so the aborted assistant turn replays WITH its partial parts intact.
// Append the new user turn (shape-only) so index -2 is the prior assistant.
const interrupted = isInterruptResume(
[...oldHistory, { role: 'user', status: null, metadata: null }],
@@ -1559,17 +1594,57 @@ export class AiChatService implements OnModuleInit, OnModuleDestroy {
// connection when finalize runs, so the SQL `WHERE status='streaming'`
// (not this flag) is what prevents it clobbering the terminal row.
if (finalized) return null;
// Build the flush ONCE so the returned count is EXACTLY the persisted
// `stepsPersisted` (both derive from capturedSteps.length at this instant).
const flushed = flushAssistant(capturedSteps, '', 'streaming', {
pageChanged,
partsCache,
});
const stepsPersisted = flushed.metadata.stepsPersisted as number;
// The count derives from capturedSteps.length at THIS instant, so the
// returned value is EXACTLY the persisted `stepsPersisted` the ring rotates
// on (whether we take the append-persist path or the legacy fallback).
const stepsPersisted = capturedSteps.length;
try {
await this.aiChatMessageRepo.update(assistantId, workspace.id, flushed, {
onlyIfStreaming: true,
});
if (this.aiChatRunStepRepo) {
// #492 APPEND-PERSIST: write only THIS finished step's parts to the
// steps table (O(step) WAL), then bump the row's CHEAP step marker —
// NO growing `metadata.parts` blob (that O(n²) full-row rewrite is
// exactly what this removes). The full `metadata.parts` is assembled
// once at finalize; a mid-run resume seed is reconstructed from the
// step rows (reconstructRunParts). The INSERT is idempotent
// (ON CONFLICT DO NOTHING), so a re-fired step never doubles the parts.
const index = stepsPersisted - 1;
if (index >= 0) {
const stepParts = assistantParts(
[capturedSteps[index]],
'',
partsCache,
);
await this.aiChatRunStepRepo.insertStep(
assistantId,
workspace.id,
index,
stepParts,
);
}
// Marker UPDATE: advance stepsPersisted + keep the toolTrace era marker
// (bumps updatedAt so the delta poll observes the step, and carries the
// frontier a resuming client attaches from). Scoped onlyIfStreaming so a
// late marker never clobbers the terminal finalize.
await this.aiChatMessageRepo.update(
assistantId,
workspace.id,
{ metadata: stepMarkerMetadata(stepsPersisted) },
{ onlyIfStreaming: true },
);
} else {
// Legacy fallback (no steps table wired — positional test builds): the
// pre-#492 full-row flush, so parts still land inline on the row.
const flushed = flushAssistant(capturedSteps, '', 'streaming', {
pageChanged,
partsCache,
});
await this.aiChatMessageRepo.update(
assistantId,
workspace.id,
flushed,
{ onlyIfStreaming: true },
);
}
return stepsPersisted;
} catch (err) {
this.logger.warn(
@@ -2749,6 +2824,122 @@ export function rowToUiMessage(row: AiChatMessage): Omit<UIMessage, 'id'> & {
return { id: row.id, role, parts: parts as UIMessage['parts'] };
}
/**
* Cheap step-marker metadata for the #492 per-step UPDATE. Advances
* `stepsPersisted` (the resume attach frontier) and keeps the `toolTraceVersion`
* era marker, WITHOUT the growing `parts` blob (those live in the steps table
* now; the full `metadata.parts` is assembled once at finalize by flushAssistant).
* `parts: []` is kept for shape stability — it reads as an empty inline-parts row,
* which is exactly the discriminator that routes reconstruction to the steps table.
*/
export function stepMarkerMetadata(
stepsPersisted: number,
): Record<string, unknown> {
return { parts: [], toolTraceVersion: 2, stepsPersisted };
}
/**
* Whether an assistant row already carries its full UI parts INLINE on the row
* (`metadata.parts`). TRUE for every FINISHED row — old-era rows AND #492 rows,
* whose full parts are assembled once at finalize — and for old-era streaming
* snapshots (the pre-#492 per-step full-row flush). FALSE for a #492 MID-RUN
* record, whose per-step parts live in the `ai_chat_run_steps` table. This is the
* era discriminator the reconstruct seam branches on — no schema flag needed.
*/
export function rowHasInlineParts(row: { metadata?: unknown }): boolean {
const meta = (row.metadata ?? {}) as { parts?: unknown };
return Array.isArray(meta.parts) && meta.parts.length > 0;
}
/**
* Concatenate persisted per-step parts (in `stepIndex` order) into the turn's UI
* parts (#492). Reproduces EXACTLY what flushAssistant → assistantParts would have
* written to `metadata.parts` for those finished steps, since each step row stored
* `assistantParts([step])` at persist time.
*/
export function assembleStepParts(
stepRows: ReadonlyArray<{ stepIndex: number; parts: unknown }>,
): UIMessage['parts'] {
const parts: Array<Record<string, unknown>> = [];
for (const step of [...stepRows].sort((a, b) => a.stepIndex - b.stepIndex)) {
if (Array.isArray(step.parts)) {
parts.push(...(step.parts as Array<Record<string, unknown>>));
}
}
return parts as UIMessage['parts'];
}
/**
* reconstructRunParts (#492) — the single backend-switch seam. Given an assistant
* ROW and its persisted step rows, return the turn's UI `parts` + the persisted
* step count, reading from the ROW when it already carries inline parts (old-era
* records AND every finished record) and from the STEPS TABLE otherwise (a #492
* mid-run record). The higher-level consumers (attach seed, delta poll, export)
* route their row→parts through this / {@link hydrateAssistantParts}, so old and
* new records reconstruct identically WITHOUT the consumers branching on the era.
*/
export function reconstructRunParts(
row: { metadata?: unknown; content?: string | null },
stepRows: ReadonlyArray<{ stepIndex: number; parts: unknown }>,
): { parts: UIMessage['parts']; stepsPersisted: number } {
if (rowHasInlineParts(row)) {
const meta = row.metadata as {
parts: UIMessage['parts'];
stepsPersisted?: number;
};
return {
parts: meta.parts,
stepsPersisted:
typeof meta.stepsPersisted === 'number'
? meta.stepsPersisted
: stepRows.length,
};
}
if (stepRows.length > 0) {
return {
parts: assembleStepParts(stepRows),
stepsPersisted: stepRows.length,
};
}
// No inline parts and no step rows: an old-era seed / empty streaming row. Fall
// back to a single text part from `content` (mirrors rowToUiMessage).
return {
parts: textPart(row.content ?? '') as UIMessage['parts'],
stepsPersisted: 0,
};
}
/**
* Fill each assistant row's `metadata.parts` from its step rows when the row does
* not already carry them inline (a #492 mid-run record), so a consumer that reads
* `metadata.parts` off the RAW row (the client seed/poll, the Markdown export)
* sees the reconstructed parts with NO change to itself. Rows that already have
* inline parts (old-era + finished) and non-assistant rows pass through untouched.
* Pure: returns new row objects, never mutates the inputs.
*/
export function hydrateAssistantParts<
T extends { id: string; role?: string; metadata?: unknown },
>(
rows: ReadonlyArray<T>,
stepsByMessage: Map<
string,
ReadonlyArray<{ stepIndex: number; parts: unknown }>
>,
): T[] {
return rows.map((row) => {
if (row.role !== 'assistant' || rowHasInlineParts(row)) return row;
const steps = stepsByMessage.get(row.id);
if (!steps || steps.length === 0) return row;
return {
...row,
metadata: {
...((row.metadata ?? {}) as Record<string, unknown>),
parts: assembleStepParts(steps),
},
};
});
}
/**
* The persisted-row patch shape produced by {@link flushAssistant}. It is the
* SAME shape the assistant repo insert/update consume (content + toolCalls +
@@ -32,6 +32,7 @@ import { TemplateRepo } from '@docmost/db/repos/template/template.repo';
import { AiChatRepo } from '@docmost/db/repos/ai-chat/ai-chat.repo';
import { AiChatMessageRepo } from '@docmost/db/repos/ai-chat/ai-chat-message.repo';
import { AiChatRunRepo } from '@docmost/db/repos/ai-chat/ai-chat-run.repo';
import { AiChatRunStepRepo } from '@docmost/db/repos/ai-chat/ai-chat-run-step.repo';
import { AiChatPageSnapshotRepo } from '@docmost/db/repos/ai-chat/ai-chat-page-snapshot.repo';
import { AiProviderCredentialsRepo } from '@docmost/db/repos/ai-chat/ai-provider-credentials.repo';
import { AiMcpServerRepo } from '@docmost/db/repos/ai-chat/ai-mcp-server.repo';
@@ -125,6 +126,7 @@ import { firstSqlToken } from '../integrations/metrics/metrics.constants';
AiChatRepo,
AiChatMessageRepo,
AiChatRunRepo,
AiChatRunStepRepo,
AiChatPageSnapshotRepo,
AiProviderCredentialsRepo,
AiMcpServerRepo,
@@ -161,6 +163,7 @@ import { firstSqlToken } from '../integrations/metrics/metrics.constants';
AiChatRepo,
AiChatMessageRepo,
AiChatRunRepo,
AiChatRunStepRepo,
AiChatPageSnapshotRepo,
AiProviderCredentialsRepo,
AiMcpServerRepo,
@@ -0,0 +1,70 @@
import { type Kysely, sql } from 'kysely';
/**
* `ai_chat_run_steps` — append-only per-step persistence for an assistant turn
* (#492 wave C). Each finished agent step's UI `parts` (its text part + a part
* per tool call, WITH the tool output) is INSERTed as its own lightweight row the
* moment the step ends, instead of REWRITING the whole assistant row's growing
* `metadata.parts` jsonb on every `onStepFinish`.
*
* WHY a separate table + INSERT (not a jsonb `||` append on the message row): a
* Postgres jsonb UPDATE rewrites the ENTIRE TOASTed row version under MVCC, so
* re-persisting a growing `metadata.parts` on every step is O(n²) write volume
* (a 50-step run with ~100 KB tool outputs wrote hundreds of MB of WAL / dead
* tuples per turn, hammering autovacuum). `||` would only shave the network
* payload — the WAL/TOAST rewrite harm remains. An INSERT into a per-step table
* writes ONLY that step's bytes, so the per-turn write volume is O(Σ steps).
*
* The full `metadata.parts` on the message row is assembled ONCE at finalize (the
* terminal completed/error/aborted write). Mid-run, a resuming client's seed is
* reconstructed by concatenating these step rows in `step_index` order — which
* reproduces exactly what the old per-step full-row rewrite persisted. Records
* written the OLD way (full `metadata.parts` on the row, no step rows) still
* reconstruct from the row unchanged; the two eras are distinguished by whether
* the row already carries non-empty `metadata.parts` (see reconstructRunParts /
* assembleStepParts in ai-chat.service.ts).
*
* ON DELETE CASCADE on `message_id`: the step rows are a derived projection of the
* assistant message; they must vanish with it (or with its workspace).
*/
export async function up(db: Kysely<any>): Promise<void> {
await db.schema
.createTable('ai_chat_run_steps')
.ifNotExists()
.addColumn('id', 'uuid', (col) =>
col.primaryKey().defaultTo(sql`gen_uuid_v7()`),
)
// The assistant message row this step belongs to (the #183 projection). The
// step rows are a derived, per-step slice of that message, so they cascade.
.addColumn('message_id', 'uuid', (col) =>
col.references('ai_chat_messages.id').onDelete('cascade').notNull(),
)
.addColumn('workspace_id', 'uuid', (col) =>
col.references('workspaces.id').onDelete('cascade').notNull(),
)
// 0-based index of the finished step within the turn. Ordering key for
// reconstruction; unique per message (idempotent step re-persist).
.addColumn('step_index', 'integer', (col) => col.notNull())
// The step's UI parts (text part + a `tool-*` part per call, WITH output).
// Concatenated in step order to rebuild the turn's `metadata.parts`.
.addColumn('parts', 'jsonb', (col) => col.notNull())
.addColumn('created_at', 'timestamptz', (col) =>
col.notNull().defaultTo(sql`now()`),
)
.execute();
// Idempotent per-step persist: a retried INSERT of the same (message, step)
// is a no-op (the service uses ON CONFLICT DO NOTHING). This also serves the
// reconstruction read (WHERE message_id ORDER BY step_index).
await db.schema
.createIndex('ai_chat_run_steps_message_step_uidx')
.ifNotExists()
.on('ai_chat_run_steps')
.columns(['message_id', 'step_index'])
.unique()
.execute();
}
export async function down(db: Kysely<any>): Promise<void> {
await db.schema.dropTable('ai_chat_run_steps').ifExists().execute();
}
@@ -0,0 +1,95 @@
import { Injectable } from '@nestjs/common';
import { InjectKysely } from 'nestjs-kysely';
import { KyselyDB, KyselyTransaction } from '../../types/kysely.types';
import { dbOrTx } from '../../utils';
import { AiChatRunStep } from '@docmost/db/types/entity.types';
/**
* Append-only per-step persistence for an assistant turn (#492). Each finished
* agent step's UI `parts` (its text part + a `tool-*` part per call, WITH the
* tool output) is INSERTed as its own lightweight row the moment the step ends —
* instead of REWRITING the assistant row's growing `metadata.parts` jsonb on every
* `onStepFinish` (a Postgres jsonb UPDATE rewrites the whole TOASTed row version
* under MVCC, so that was O(n²) WAL/dead-tuple churn per turn).
*
* The full `metadata.parts` on the message row is assembled ONCE at finalize;
* mid-run, a resuming client's seed is rebuilt from these rows in `stepIndex`
* order (see `assembleStepParts` / the reconstruct seam in ai-chat.service.ts).
* Every method is workspace-scoped as defense-in-depth.
*/
@Injectable()
export class AiChatRunStepRepo {
constructor(@InjectKysely() private readonly db: KyselyDB) {}
/**
* Append one finished step's parts. Idempotent: a retried persist of the SAME
* (message, stepIndex) is a no-op via ON CONFLICT DO NOTHING — the per-step
* writes are fired fire-and-forget + serialized, and a duplicate must never
* throw into the stream or double the parts. Returns whether a NEW row landed
* (false = the step was already persisted).
*/
async insertStep(
messageId: string,
workspaceId: string,
stepIndex: number,
parts: unknown,
trx?: KyselyTransaction,
): Promise<boolean> {
const db = dbOrTx(this.db, trx);
const inserted = await db
.insertInto('aiChatRunSteps')
.values({
messageId,
workspaceId,
stepIndex,
// jsonb column: cast through never (same pattern as the message repo).
parts: parts as never,
})
.onConflict((oc) => oc.columns(['messageId', 'stepIndex']).doNothing())
.returning('id')
.executeTakeFirst();
return inserted !== undefined;
}
/** All persisted steps for ONE assistant message, in step order. */
async findByMessage(
messageId: string,
workspaceId: string,
): Promise<AiChatRunStep[]> {
return this.db
.selectFrom('aiChatRunSteps')
.selectAll('aiChatRunSteps')
.where('messageId', '=', messageId)
.where('workspaceId', '=', workspaceId)
.orderBy('stepIndex', 'asc')
.execute();
}
/**
* All persisted steps for a SET of assistant messages, grouped by messageId
* (each group in step order). One query for the batch — the hydration seam
* (getMessages / delta / export) calls this only for the rows that actually
* need reconstruction (an active new-style row whose `metadata.parts` is still
* empty), which is usually none, so this is skipped on the common path.
*/
async findByMessageIds(
messageIds: string[],
workspaceId: string,
): Promise<Map<string, AiChatRunStep[]>> {
const byMessage = new Map<string, AiChatRunStep[]>();
if (messageIds.length === 0) return byMessage;
const rows = await this.db
.selectFrom('aiChatRunSteps')
.selectAll('aiChatRunSteps')
.where('messageId', 'in', messageIds)
.where('workspaceId', '=', workspaceId)
.orderBy('stepIndex', 'asc')
.execute();
for (const row of rows) {
const list = byMessage.get(row.messageId);
if (list) list.push(row);
else byMessage.set(row.messageId, [row]);
}
return byMessage;
}
}
+17
View File
@@ -692,6 +692,22 @@ export interface AiChatRuns {
updatedAt: Generated<Timestamp>;
}
// Append-only per-step persistence for an assistant turn (#492). Mirrors
// migration 20260708T120000-ai-chat-run-steps.ts. Each finished agent step's UI
// `parts` are INSERTed as their own row (instead of rewriting the message row's
// growing `metadata.parts` jsonb every step — an O(n²) WAL/TOAST churn). The full
// `metadata.parts` is assembled once at finalize; mid-run a resuming client's seed
// is rebuilt by concatenating these rows in `stepIndex` order. Cascades with the
// assistant message row it projects.
export interface AiChatRunSteps {
id: Generated<string>;
messageId: string;
workspaceId: string;
stepIndex: number;
parts: Json;
createdAt: Generated<Timestamp>;
}
// Per-(chat,page) snapshot of the open page's Markdown at the END of the agent's
// previous turn (#274). Mirrors migration 20260702T120000-ai-chat-page-snapshot.ts.
// The next turn diffs the CURRENT Markdown against `contentMd` to surface edits a
@@ -729,6 +745,7 @@ export interface DB {
aiChats: AiChats;
aiChatMessages: AiChatMessages;
aiChatRuns: AiChatRuns;
aiChatRunSteps: AiChatRunSteps;
aiChatPageSnapshots: AiChatPageSnapshots;
apiKeys: ApiKeys;
attachments: Attachments;
@@ -4,6 +4,7 @@ import {
AiChats,
AiChatMessages,
AiChatRuns,
AiChatRunSteps,
AiChatPageSnapshots,
Attachments,
Comments,
@@ -64,6 +65,12 @@ export type InsertableAiChatMessage = Omit<Insertable<AiChatMessages>, 'tsv'>;
export type AiChatRun = Selectable<AiChatRuns>;
export type InsertableAiChatRun = Insertable<AiChatRuns>;
// AI Chat Run Step (#492): append-only per-step parts persistence. Each finished
// agent step's UI parts are stored as their own row; the full turn's parts are
// assembled from these (in stepIndex order) for a mid-run resume seed.
export type AiChatRunStep = Selectable<AiChatRunSteps>;
export type InsertableAiChatRunStep = Insertable<AiChatRunSteps>;
// AI Chat Page Snapshot (#274): per-(chat,page) Markdown snapshot taken at the
// end of the agent's previous turn, diffed against the current page next turn to
// detect human edits made between turns.
@@ -0,0 +1,412 @@
import * as http from 'node:http';
import { Kysely } from 'kysely';
import { tool } from 'ai';
import { z } from 'zod';
import { MockLanguageModelV3, convertArrayToReadableStream } from 'ai/test';
import { AiChatRepo } from '@docmost/db/repos/ai-chat/ai-chat.repo';
import { AiChatMessageRepo } from '@docmost/db/repos/ai-chat/ai-chat-message.repo';
import { AiChatRunStepRepo } from '@docmost/db/repos/ai-chat/ai-chat-run-step.repo';
import {
AiChatService,
assembleStepParts,
assistantParts,
rowHasInlineParts,
stepMarkerMetadata,
} from 'src/core/ai-chat/ai-chat.service';
import {
getTestDb,
destroyTestDb,
createWorkspace,
createUser,
createChat,
createMessage,
} from './db';
/**
* #492 append-persist — the REAL onStep WRITE path (F2) and the model-REPLAY
* hydration path (F1), driven through `AiChatService.stream` against a LIVE
* Postgres with a REAL `AiChatRunStepRepo` INJECTED. The existing append-persist
* int-specs hand-roll the insert+marker cycle via the repos directly and build
* the service with `aiChatRunStepRepo: undefined` (only the legacy-fallback branch
* is covered), so an off-by-one on `stepsPersisted-1`, a wrong `capturedSteps`
* slice, or a broken marker payload would pass all of them. These tests exercise
* the actual `updateStreaming` append-persist branch end to end.
*
* The seam is the injected `model` (a seeded `MockLanguageModelV3` from `ai/test`)
* plus a REAL Node `ServerResponse` as the hijacked socket — mirrors
* ai-chat-stream.int-spec.ts.
*/
const sleep = (ms: number) => new Promise((r) => setTimeout(r, ms));
async function waitFor(
cond: () => Promise<boolean> | boolean,
{ timeoutMs = 15_000, stepMs = 25 } = {},
): Promise<void> {
const start = Date.now();
while (Date.now() - start < timeoutMs) {
if (await cond()) return;
await sleep(stepMs);
}
throw new Error('waitFor: condition not met within timeout');
}
// A real Node ServerResponse wired to a live socket (identical helper to the
// stream int-spec) so the SDK's pipe/heartbeat writes behave as in prod.
function makeRealResponse(): Promise<{
res: http.ServerResponse;
cleanup: () => Promise<void>;
}> {
return new Promise((resolve) => {
const server = http.createServer((_req, res) => {
resolve({
res,
cleanup: () =>
new Promise<void>((done) => {
try {
if (!res.writableEnded) res.end();
} catch {
/* socket already gone */
}
server.close(() => done());
}),
});
});
server.listen(0, () => {
const port = (server.address() as any).port;
const creq = http.request({ port, method: 'GET' }, (cres) => {
cres.resume();
});
creq.on('error', () => undefined);
creq.end();
});
});
}
// Stream parts for a normal, successful single-step turn.
function successStream() {
return convertArrayToReadableStream([
{ type: 'stream-start', warnings: [] },
{ type: 'text-start', id: 't1' },
{ type: 'text-delta', id: 't1', delta: 'Hello' },
{ type: 'text-delta', id: 't1', delta: ' there' },
{ type: 'text-end', id: 't1' },
{
type: 'finish',
finishReason: 'stop',
usage: { inputTokens: 10, outputTokens: 5, totalTokens: 15 },
},
] as any);
}
// A THREE-step turn: steps 0 and 1 each emit text + an `echo` tool call (the SDK
// runs the tool and continues); step 2 answers and stops. Three steps is
// deliberate: the LAST finished step's append-persist write races the terminal
// finalize (which writes the full inline parts anyway, so a lost last-step row is
// by design), but the NON-final steps 0 and 1 always drain to the steps table
// before finalize — so those are what the test asserts on deterministically.
function threeStepModel(): MockLanguageModelV3 {
let step = 0;
const toolStep = (i: number) => ({
stream: convertArrayToReadableStream([
{ type: 'stream-start', warnings: [] },
{ type: 'text-start', id: `s${i}` },
{ type: 'text-delta', id: `s${i}`, delta: `step ${i} ` },
{ type: 'text-end', id: `s${i}` },
{
type: 'tool-call',
toolCallId: `c${i}`,
toolName: 'echo',
input: JSON.stringify({ msg: `m${i}` }),
},
{
type: 'finish',
finishReason: 'tool-calls',
usage: { inputTokens: 5, outputTokens: 3, totalTokens: 8 },
},
] as any),
});
return new MockLanguageModelV3({
doStream: async () => {
const n = step++;
// Realistic inter-step latency. A real model spends seconds per step, so the
// fire-and-forget per-step write chain drains to the steps table BETWEEN
// steps; the mock otherwise collapses all steps into microseconds and the
// terminal finalize wins the race before any but the first step persists.
if (n > 0) await sleep(200);
if (n < 2) return toolStep(n);
return {
stream: convertArrayToReadableStream([
{ type: 'stream-start', warnings: [] },
{ type: 'text-start', id: 's2' },
{ type: 'text-delta', id: 's2', delta: 'final answer' },
{ type: 'text-end', id: 's2' },
{
type: 'finish',
finishReason: 'stop',
usage: { inputTokens: 6, outputTokens: 4, totalTokens: 10 },
},
] as any),
};
},
} as any);
}
describe('#492 append-persist service paths [integration]', () => {
let db: Kysely<any>;
let aiChatRepo: AiChatRepo;
let msgRepo: AiChatMessageRepo;
let stepRepo: AiChatRunStepRepo;
let workspaceId: string;
let userId: string;
let closeCalls: number;
const mcpClients = {
toolsFor: async () => ({
tools: {},
clients: [
{
close: async () => {
closeCalls += 1;
},
},
],
outcomes: [],
instructions: [],
}),
};
// Build the service WITH a REAL AiChatRunStepRepo injected (the property under
// test) — unlike the legacy-fallback harness that passes it as undefined.
const echoTool = tool({
description: 'echo the message back',
inputSchema: z.object({ msg: z.string() }),
execute: async ({ msg }) => ({ echoed: msg }),
});
function buildService(): AiChatService {
return new AiChatService(
{ getChatModel: async () => null } as any,
aiChatRepo,
msgRepo,
{} as any, // aiChatPageSnapshotRepo
{ resolve: async () => null } as any, // aiSettings
{ forUser: async () => ({ echo: echoTool }) } as any, // tools
mcpClients as any,
{} as any, // aiAgentRoleRepo
{} as any, // pageRepo
{} as any, // pageAccess
{
isAiChatDeferredToolsEnabled: () => false,
isAiChatFinalStepLockdownEnabled: () => false,
} as any, // environment (deferred OFF -> all tools active every step)
undefined, // streamRegistry
undefined, // aiChatRunService
stepRepo, // #492 aiChatRunStepRepo — the append-persist backend
);
}
function userUiMessage(text: string) {
return {
id: `u-${Math.random()}`,
role: 'user',
parts: [{ type: 'text', text }],
};
}
async function runStream(opts: {
model: MockLanguageModelV3;
chatId: string;
body: any;
}): Promise<void> {
closeCalls = 0;
const service = buildService();
const { res, cleanup } = await makeRealResponse();
try {
await service.stream({
user: { id: userId, workspaceId } as any,
workspace: { id: workspaceId, name: 'WS' } as any,
sessionId: 'sess-1',
body: opts.body,
res: { raw: res } as any,
signal: new AbortController().signal,
model: opts.model as any,
role: null,
} as any);
await waitFor(async () => {
const rows = await msgRepo.findAllByChat(opts.chatId, workspaceId);
return rows.some(
(r) =>
r.role === 'assistant' &&
['completed', 'error', 'aborted'].includes(r.status as string),
);
});
await waitFor(() => closeCalls > 0, { timeoutMs: 5_000 });
} finally {
await cleanup();
}
}
beforeAll(async () => {
db = getTestDb();
aiChatRepo = new AiChatRepo(db as any);
msgRepo = new AiChatMessageRepo(db as any);
stepRepo = new AiChatRunStepRepo(db as any);
workspaceId = (await createWorkspace(db)).id;
userId = (await createUser(db, workspaceId)).id;
});
afterAll(async () => {
await destroyTestDb();
});
// --- F2: the real onStep append-persist WRITE branch -----------------------
it('drives steps through the real onStep path: per-step rows + marker match a single-row flush', async () => {
const chatId = (await createChat(db, { workspaceId, creatorId: userId })).id;
const model = threeStepModel();
// Capture the mid-run step-marker UPDATEs the append-persist branch writes on
// the assistant row (a { parts: [], toolTraceVersion, stepsPersisted } patch).
const updateSpy = jest.spyOn(msgRepo, 'update');
try {
await runStream({
model,
chatId,
body: { chatId, messages: [userUiMessage('call the tool then answer')] },
});
const rows = await msgRepo.findAllByChat(chatId, workspaceId);
const assistant = rows.find((r) => r.role === 'assistant')!;
expect(assistant).toBeDefined();
expect(assistant.status).toBe('completed');
// The turn finalizes with the FULL inline parts assembled by a single-row
// flush (assistantParts over every step) — the baseline the per-step slices
// must reproduce.
expect(rowHasInlineParts(assistant)).toBe(true);
const finalParts = (assistant.metadata as { parts: any[] }).parts;
// The two NON-final finished steps each landed their own row, in stepIndex
// order. (The fire-and-forget write chain drains before the next step, so
// poll until both are on disk; the LAST step's write may lose the finalize
// race, which is by design — its parts are already in `finalParts`.)
await waitFor(async () => {
const s = await stepRepo.findByMessage(assistant.id, workspaceId);
return s.length >= 2;
});
const steps = await stepRepo.findByMessage(assistant.id, workspaceId);
expect(steps[0].stepIndex).toBe(0);
expect(steps[1].stepIndex).toBe(1);
// Each per-step row carries a NON-trivial slice: this step's text part + its
// paired tool part (guards a mutation that persists empty/whole-turn parts).
const s0 = steps[0].parts as any[];
expect(s0).toContainEqual({ type: 'text', text: 'step 0 ' });
expect(s0.some((p) => p.type === 'tool-echo')).toBe(true);
// The per-step slices are EXACTLY the corresponding prefix of the single-row
// flush: assembleStepParts([step0, step1]) === finalParts[0 .. len0+len1].
// This is what an off-by-one on `stepsPersisted-1` (a wrong `capturedSteps`
// slice) or a shifted stepIndex breaks — the prefix no longer aligns.
const prefixLen =
(steps[0].parts as any[]).length + (steps[1].parts as any[]).length;
expect(assembleStepParts([steps[0], steps[1]] as any)).toEqual(
finalParts.slice(0, prefixLen),
);
// The mid-run step markers advanced 1 -> 2 -> ... (the resume frontier), each
// a shape-stable empty-parts marker equal to a single-row flush's marker.
const markerCounts = updateSpy.mock.calls
.map((c) => (c[2] as any)?.metadata)
.filter(
(m) =>
m &&
Array.isArray(m.parts) &&
m.parts.length === 0 &&
typeof m.stepsPersisted === 'number',
)
.map((m) => m.stepsPersisted);
// Monotonic from 1, covering at least the two non-final steps.
expect(markerCounts.slice(0, 2)).toEqual([1, 2]);
expect(
updateSpy.mock.calls
.map((c) => (c[2] as any)?.metadata)
.find((m) => m && m.stepsPersisted === 2),
).toEqual(stepMarkerMetadata(2));
} finally {
updateSpy.mockRestore();
}
}, 60_000);
// --- F1: model-REPLAY hydrates a hard-crashed mid-run turn from the steps table
it('replays a hard-crashed mid-run turn WITH its partial steps hydrated from the steps table', async () => {
const chatId = (await createChat(db, { workspaceId, creatorId: userId })).id;
// Prior turn: a genuine user question...
await createMessage(db, {
workspaceId,
chatId,
userId,
role: 'user',
content: 'What is in the design doc?',
createdAt: new Date(Date.now() - 3000),
});
// ...and an assistant row that a HARD crash (SIGKILL/OOM) left mid-run: only a
// step marker on the row (metadata.parts:[] , content:''), NO terminal
// callback ever fired, so its real parts live ONLY in ai_chat_run_steps.
const crashed = await createMessage(db, {
workspaceId,
chatId,
role: 'assistant',
content: '',
status: 'aborted',
metadata: stepMarkerMetadata(1),
createdAt: new Date(Date.now() - 2000),
});
// The durable partial step: some reasoning text + a completed getPage tool
// call (input + output), exactly what #183 step-granular durability preserves.
await stepRepo.insertStep(
crashed.id,
workspaceId,
0,
assistantParts(
[
{
text: 'HYDRATED_PARTIAL_STEP the doc says',
toolCalls: [
{ toolCallId: 'g1', toolName: 'getPage', input: { id: 'p1' } },
],
toolResults: [
{
toolCallId: 'g1',
toolName: 'getPage',
output: { id: 'p1', body: 'PARTIAL_TOOL_OUTPUT budget section' },
},
],
} as any,
],
'',
),
);
// The NEXT turn: the model just answers. The service must REPLAY the crashed
// assistant turn with its partial parts hydrated from the steps table.
const model = new MockLanguageModelV3({
doStream: async () => ({ stream: successStream() }),
} as any);
await runStream({
model,
chatId,
body: { chatId, messages: [userUiMessage('Continue please')] },
});
expect(model.doStreamCalls.length).toBeGreaterThan(0);
const prompt = JSON.stringify(model.doStreamCalls[0].prompt);
// The partial step's TEXT reached the model context (it would be an empty text
// part without hydration — rowToUiMessage falls back to `content:''`).
expect(prompt).toContain('HYDRATED_PARTIAL_STEP');
// The partial TOOL RESULT survived too (durable in the steps table, replayed).
expect(prompt).toContain('PARTIAL_TOOL_OUTPUT');
// The genuine prior user turn is present as well (sanity: real history replay).
expect(prompt).toContain('What is in the design doc?');
}, 60_000);
});
@@ -0,0 +1,173 @@
import { randomBytes } from 'crypto';
import { Kysely, sql } from 'kysely';
import { AiChatRunStepRepo } from '@docmost/db/repos/ai-chat/ai-chat-run-step.repo';
import { AiChatMessageRepo } from '@docmost/db/repos/ai-chat/ai-chat-message.repo';
import {
assistantParts,
flushAssistant,
stepMarkerMetadata,
} from '../../src/core/ai-chat/ai-chat.service';
import {
getTestDb,
destroyTestDb,
createWorkspace,
createUser,
createChat,
} from './db';
/**
* #492 append-persist — WRITE-VOLUME regression on a LIVE Postgres, measured via
* the `pg_current_wal_lsn()` delta around a realistic multi-step run driven through
* the REAL repos (not a mock — a mock cannot observe MVCC/TOAST rewrite volume, the
* whole point). Proves the core claim:
*
* NEW (per-step INSERT into ai_chat_run_steps + a CHEAP step-marker UPDATE on the
* message row) writes O(Σ steps) of WAL — each step writes only its own bytes.
*
* OLD (the pre-#492 full-row rewrite: re-persist the GROWING metadata.parts on
* every onStepFinish) writes O(n²) — step k rewrites the whole TOASTed jsonb of
* all k prior outputs.
*
* The OLD path here IS the reverted behavior, so this doubles as the mutation
* check: swapping the new path back to `flushAssistant` full-row UPDATEs reddens
* the assertion (OLD is many times larger).
*/
type Step = {
text: string;
toolCalls: Array<{ toolCallId: string; toolName: string; input: unknown }>;
toolResults: Array<{ toolCallId: string; toolName: string; output: unknown }>;
};
// ~100 KB INCOMPRESSIBLE output per step (a page read). Random base64 so TOAST
// cannot compress it away and hide the real write volume.
function makeStep(i: number, outputBytes = 100_000): Step {
const body = randomBytes(Math.ceil(outputBytes * 0.75)).toString('base64');
return {
text: `step ${i} reasoning`,
toolCalls: [
{ toolCallId: `c${i}`, toolName: 'getPage', input: { id: `p${i}` } },
],
toolResults: [
{
toolCallId: `c${i}`,
toolName: 'getPage',
output: { id: `p${i}`, title: `Page ${i}`, body },
},
],
};
}
async function walDelta(
db: Kysely<any>,
fn: () => Promise<void>,
): Promise<number> {
const before = (
await sql<{ l: string }>`select pg_current_wal_lsn() as l`.execute(db)
).rows[0].l;
await fn();
// NOTE: no pg_switch_wal() — a segment switch pads the LSN to the next 16 MB
// boundary and would swamp the delta. The raw LSN advances by the WAL bytes.
const after = (
await sql<{ l: string }>`select pg_current_wal_lsn() as l`.execute(db)
).rows[0].l;
return Number(
(
await sql<{
d: string;
}>`select pg_wal_lsn_diff(${after}::pg_lsn, ${before}::pg_lsn) as d`.execute(
db,
)
).rows[0].d,
);
}
describe('#492 append-persist write volume (pg_current_wal_lsn delta) [integration]', () => {
let db: Kysely<any>;
let stepRepo: AiChatRunStepRepo;
let msgRepo: AiChatMessageRepo;
let workspaceId: string;
let userId: string;
let chatId: string;
beforeAll(async () => {
db = getTestDb();
stepRepo = new AiChatRunStepRepo(db as any);
msgRepo = new AiChatMessageRepo(db as any);
workspaceId = (await createWorkspace(db)).id;
userId = (await createUser(db, workspaceId)).id;
chatId = (await createChat(db, { workspaceId, creatorId: userId })).id;
});
afterAll(async () => {
await destroyTestDb();
});
const seedRow = () =>
msgRepo.insert({
chatId,
workspaceId,
userId,
role: 'assistant',
content: '',
status: 'streaming',
metadata: stepMarkerMetadata(0) as never,
});
const STEPS = 40;
it('NEW per-step INSERT is O(Σ steps); OLD full-row rewrite is O(n²)', async () => {
const steps: Step[] = [];
for (let i = 0; i < STEPS; i++) steps.push(makeStep(i));
// NEW: per-step INSERT of THIS step's parts + a cheap marker UPDATE.
const newRow = await seedRow();
const newWal = await walDelta(db, async () => {
for (let i = 0; i < STEPS; i++) {
await stepRepo.insertStep(
newRow.id,
workspaceId,
i,
assistantParts([steps[i]], ''),
);
await msgRepo.update(
newRow.id,
workspaceId,
{ metadata: stepMarkerMetadata(i + 1) },
{ onlyIfStreaming: true },
);
}
});
// OLD (the pre-#492 revert): re-persist the GROWING metadata.parts on the
// message row on every step.
const oldRow = await seedRow();
const oldWal = await walDelta(db, async () => {
const acc: Step[] = [];
for (let i = 0; i < STEPS; i++) {
acc.push(steps[i]);
await msgRepo.update(
oldRow.id,
workspaceId,
flushAssistant(acc as never, '', 'streaming'),
{ onlyIfStreaming: true },
);
}
});
// eslint-disable-next-line no-console
console.log(
`[#492 WAL] ${STEPS} steps ×100KB: new=${(newWal / 1e6).toFixed(1)}MB ` +
`old=${(oldWal / 1e6).toFixed(1)}MB (${(oldWal / newWal).toFixed(
1,
)}x smaller)`,
);
// O(Σ steps): ~STEPS × (100KB output + marker) of WAL. 40 × ~100KB parts plus
// 40 tiny markers is a few tens of MB at most — bounded, linear in step count.
expect(newWal).toBeLessThan(30_000_000);
// O(n²): step k rewrites ~k × 100KB. Σ over 40 steps ≈ 80+ MB — far larger.
expect(oldWal).toBeGreaterThan(30_000_000);
// The load-bearing claim: the new path writes a small FRACTION of the old.
expect(newWal).toBeLessThan(oldWal * 0.35);
}, 120_000);
});
@@ -0,0 +1,169 @@
import { Kysely } from 'kysely';
import { AiChatController } from 'src/core/ai-chat/ai-chat.controller';
import {
assembleStepParts,
assistantParts,
stepMarkerMetadata,
} from 'src/core/ai-chat/ai-chat.service';
import { AiChatRepo } from '@docmost/db/repos/ai-chat/ai-chat.repo';
import { AiChatMessageRepo } from '@docmost/db/repos/ai-chat/ai-chat-message.repo';
import { AiChatRunStepRepo } from '@docmost/db/repos/ai-chat/ai-chat-run-step.repo';
import type { User, Workspace } from '@docmost/db/types/entity.types';
import {
getTestDb,
destroyTestDb,
createWorkspace,
createUser,
createChat,
createMessage,
} from './db';
/**
* #492 controller hydration (crash-before-finalize RESUME) on a LIVE Postgres.
* `AiChatController.withReconstructedParts` is wired into getMessages/delta/export/
* run, but `aiChatRunStepRepo` is OPTIONAL and every controller unit spec passes it
* as `undefined`, so the hydration branch early-returns and NEVER executes in those
* tests. This drives the real read path — a mid-run streaming row (marker only,
* empty inline parts) PLUS its `ai_chat_run_steps` rows — through getMessages WITH
* the repo present, exercising the `role==='assistant' && !rowHasInlineParts`
* needy predicate, the workspace-scoped batch step fetch, and the endpoint binding.
*/
describe('#492 controller hydration read path [integration]', () => {
let db: Kysely<any>;
let aiChatRepo: AiChatRepo;
let msgRepo: AiChatMessageRepo;
let stepRepo: AiChatRunStepRepo;
let workspaceId: string;
let otherWorkspaceId: string;
let userId: string;
// Build the controller WITH a real AiChatRunStepRepo injected (position 9), the
// seam the unit specs leave undefined. Only the read-path deps are real.
function buildController(): AiChatController {
return new AiChatController(
{} as any, // aiChatService
{} as any, // aiChatRunService
aiChatRepo,
msgRepo,
{} as any, // aiTranscription
{} as any, // pageRepo
undefined, // streamRegistry
undefined, // environment
stepRepo, // #492 aiChatRunStepRepo
);
}
beforeAll(async () => {
db = getTestDb();
aiChatRepo = new AiChatRepo(db as any);
msgRepo = new AiChatMessageRepo(db as any);
stepRepo = new AiChatRunStepRepo(db as any);
workspaceId = (await createWorkspace(db)).id;
otherWorkspaceId = (await createWorkspace(db)).id;
userId = (await createUser(db, workspaceId)).id;
});
afterAll(async () => {
await destroyTestDb();
});
it('getMessages reconstructs a mid-run row from the steps table (finished rows untouched)', async () => {
const chatId = (
await createChat(db, { workspaceId, creatorId: userId })
).id;
const user = { id: userId } as User;
const workspace = { id: workspaceId } as Workspace;
// A prior FINISHED assistant row that already carries inline parts — the needy
// predicate must SKIP it (no step fetch), returned untouched.
const finishedParts = assistantParts(
[{ text: 'done earlier', toolCalls: [], toolResults: [] } as any],
'',
);
await createMessage(db, {
workspaceId,
chatId,
role: 'assistant',
content: 'done earlier',
status: 'completed',
metadata: { parts: finishedParts, toolTraceVersion: 2, stepsPersisted: 1 },
createdAt: new Date(Date.now() - 3000),
});
// The mid-run row a crash-before-finalize left behind: a step marker only
// (parts:[] , content:''), status 'streaming'. Its real parts live ONLY in the
// steps table.
const midRun = await createMessage(db, {
workspaceId,
chatId,
role: 'assistant',
content: '',
status: 'streaming',
metadata: stepMarkerMetadata(2),
createdAt: new Date(Date.now() - 1000),
});
const step0 = assistantParts(
[
{
text: 'reasoning about the page',
toolCalls: [
{ toolCallId: 'g1', toolName: 'getPage', input: { id: 'p1' } },
],
toolResults: [
{ toolCallId: 'g1', toolName: 'getPage', output: { id: 'p1', body: 'B' } },
],
} as any,
],
'',
);
const step1 = assistantParts(
[{ text: 'partial synthesis so far', toolCalls: [], toolResults: [] } as any],
'',
);
await stepRepo.insertStep(midRun.id, workspaceId, 0, step0);
await stepRepo.insertStep(midRun.id, workspaceId, 1, step1);
// Workspace-scoping guard: a step row for the SAME message id under a DIFFERENT
// workspace must NEVER leak into this workspace's reconstruction.
await stepRepo.insertStep(midRun.id, otherWorkspaceId, 99, [
{ type: 'text', text: 'FOREIGN_WORKSPACE_LEAK' },
]);
const res = await buildController().getMessages(
{ chatId } as any,
{ limit: 50 } as any,
user,
workspace,
);
const items = res.items as any[];
const finished = items.find((r) => r.status === 'completed');
const reconstructed = items.find((r) => r.id === midRun.id);
// The finished row passed through with its inline parts unchanged.
expect(finished.metadata.parts).toEqual(finishedParts);
// The mid-run row's parts were reconstructed from the two step rows, in order,
// exactly as assembleStepParts concatenates them — the client seed sees the
// persisted progress with no change to itself.
const expected = assembleStepParts([
{ stepIndex: 0, parts: step0 },
{ stepIndex: 1, parts: step1 },
] as any);
expect(reconstructed.metadata.parts).toEqual(expected);
// The foreign-workspace step row did NOT leak in.
expect(JSON.stringify(reconstructed.metadata.parts)).not.toContain(
'FOREIGN_WORKSPACE_LEAK',
);
// Sanity: reconstruction produced real content (text + the paired tool part +
// the second step's text), not an empty fallback.
expect(reconstructed.metadata.parts).toContainEqual({
type: 'text',
text: 'reasoning about the page',
});
expect(
(reconstructed.metadata.parts as any[]).some((p) => p.type === 'tool-getPage'),
).toBe(true);
}, 60_000);
});
@@ -0,0 +1,163 @@
import { randomBytes } from 'crypto';
import { Kysely } from 'kysely';
import { AiChatRunStepRepo } from '@docmost/db/repos/ai-chat/ai-chat-run-step.repo';
import { AiChatMessageRepo } from '@docmost/db/repos/ai-chat/ai-chat-message.repo';
import {
assistantParts,
reconstructRunParts,
hydrateAssistantParts,
stepMarkerMetadata,
rowHasInlineParts,
} from '../../src/core/ai-chat/ai-chat.service';
import {
getTestDb,
destroyTestDb,
createWorkspace,
createUser,
createChat,
} from './db';
/**
* #492 append-persist — the reconstruct CONTRACT on a live Postgres. Proves that a
* turn persisted the NEW way (per-step rows in `ai_chat_run_steps`, only a step
* marker on the message row) reconstructs to the SAME UI parts as a turn persisted
* the OLD way (full `metadata.parts` inline on the row, no step rows) — so the
* era-switch is invisible to attach / delta-poll / export. Real repos + real jsonb
* roundtrip, not a mock (a mock cannot prove the parts survive the jsonb column
* byte-identical).
*/
type Step = {
text: string;
toolCalls: Array<{ toolCallId: string; toolName: string; input: unknown }>;
toolResults: Array<{ toolCallId: string; toolName: string; output: unknown }>;
};
// A realistic step: some text + a getPage tool call whose ~100 KB body is
// INCOMPRESSIBLE random base64 (a 'x'.repeat filler would TOAST away and hide the
// real bytes). Under MAX_TOOL_OUTPUT_BYTES (200 KB) it is stored uncompacted.
function makeStep(i: number, outputBytes = 4_000): Step {
const body = randomBytes(Math.ceil(outputBytes * 0.75)).toString('base64');
return {
text: `step ${i} text`,
toolCalls: [
{ toolCallId: `c${i}`, toolName: 'getPage', input: { id: `p${i}` } },
],
toolResults: [
{
toolCallId: `c${i}`,
toolName: 'getPage',
output: { id: `p${i}`, title: `Page ${i}`, body },
},
],
};
}
describe('AiChatRunStepRepo + reconstruct contract [integration]', () => {
let db: Kysely<any>;
let stepRepo: AiChatRunStepRepo;
let msgRepo: AiChatMessageRepo;
let workspaceId: string;
let userId: string;
let chatId: string;
beforeAll(async () => {
db = getTestDb();
stepRepo = new AiChatRunStepRepo(db as any);
msgRepo = new AiChatMessageRepo(db as any);
workspaceId = (await createWorkspace(db)).id;
userId = (await createUser(db, workspaceId)).id;
chatId = (await createChat(db, { workspaceId, creatorId: userId })).id;
});
afterAll(async () => {
await destroyTestDb();
});
const seedRow = (metadata: unknown, status: string) =>
msgRepo.insert({
chatId,
workspaceId,
userId,
role: 'assistant',
content: '',
status,
metadata: metadata as never,
});
it('insertStep is idempotent per (message, stepIndex) and reads back in order', async () => {
const row = await seedRow(stepMarkerMetadata(0), 'streaming');
const parts0 = assistantParts([makeStep(0)], '');
const parts1 = assistantParts([makeStep(1)], '');
expect(await stepRepo.insertStep(row.id, workspaceId, 0, parts0)).toBe(true);
expect(await stepRepo.insertStep(row.id, workspaceId, 1, parts1)).toBe(true);
// A retried persist of the SAME step is a no-op (ON CONFLICT DO NOTHING).
expect(await stepRepo.insertStep(row.id, workspaceId, 0, parts0)).toBe(
false,
);
const steps = await stepRepo.findByMessage(row.id, workspaceId);
expect(steps.map((s) => s.stepIndex)).toEqual([0, 1]);
// Batch fetch groups by message id in step order.
const map = await stepRepo.findByMessageIds([row.id], workspaceId);
expect(map.get(row.id)!.map((s) => s.stepIndex)).toEqual([0, 1]);
});
it('a NEW-style (step-table) run reconstructs identically to an OLD-style (inline) run', async () => {
const steps = [makeStep(10), makeStep(11)];
// The inline parts the OLD full-row flush would have written.
const fullParts = assistantParts(steps, '');
// OLD-style record: full parts inline on the row, NO step rows.
const oldRow = await seedRow(
{ parts: fullParts, toolTraceVersion: 2, stepsPersisted: 2 },
'completed',
);
// NEW-style record: only a step marker on the row + per-step rows.
const newRow = await seedRow(stepMarkerMetadata(2), 'streaming');
for (let i = 0; i < steps.length; i++) {
await stepRepo.insertStep(
newRow.id,
workspaceId,
i,
assistantParts([steps[i]], ''),
);
}
// Re-read both from the DB (proves the jsonb roundtrip).
const oldFetched = await msgRepo.findById(oldRow.id, workspaceId);
const newFetched = await msgRepo.findById(newRow.id, workspaceId);
const oldSteps = await stepRepo.findByMessage(oldRow.id, workspaceId);
const newSteps = await stepRepo.findByMessage(newRow.id, workspaceId);
// The discriminator: the old row carries inline parts, the new one does not.
expect(rowHasInlineParts(oldFetched!)).toBe(true);
expect(rowHasInlineParts(newFetched!)).toBe(false);
expect(oldSteps).toHaveLength(0);
expect(newSteps).toHaveLength(2);
const oldRecon = reconstructRunParts(oldFetched!, oldSteps);
const newRecon = reconstructRunParts(newFetched!, newSteps);
// Both reconstruct to the SAME parts + step count — the era is invisible.
expect(newRecon.parts).toEqual(fullParts);
expect(oldRecon.parts).toEqual(fullParts);
expect(newRecon.parts).toEqual(oldRecon.parts);
expect(newRecon.stepsPersisted).toBe(2);
expect(oldRecon.stepsPersisted).toBe(2);
// hydrateAssistantParts fills the new row's metadata.parts to match the old
// row's inline parts — so a consumer reading `metadata.parts` off the raw row
// (the client seed/poll, export) is unchanged across the era.
const map = await stepRepo.findByMessageIds([newRow.id], workspaceId);
const [hydrated] = hydrateAssistantParts([newFetched!], map);
expect((hydrated.metadata as { parts: unknown }).parts).toEqual(fullParts);
// A row that already has inline parts passes through untouched (same ref-shape).
const [oldPassThrough] = hydrateAssistantParts([oldFetched!], map);
expect((oldPassThrough.metadata as { parts: unknown }).parts).toEqual(
fullParts,
);
});
});
@@ -0,0 +1,63 @@
import { Kysely, sql } from 'kysely';
import {
up,
down,
} from '../../src/database/migrations/20260708T120000-ai-chat-run-steps';
import { getTestDb, destroyTestDb } from './db';
/**
* #492 migration up/down roundtrip on a LIVE Postgres. global-setup already
* migrated docmost_test to latest (so the table exists at start); this drives the
* migration's own down()/up() and asserts the table presence toggles, then leaves
* it PRESENT (up) so the shared test DB is intact for any spec that runs after.
*/
async function tableExists(db: Kysely<any>): Promise<boolean> {
const row = (
await sql<{ t: string | null }>`select to_regclass('ai_chat_run_steps') as t`.execute(
db,
)
).rows[0];
return row.t !== null;
}
async function uniqueIndexExists(db: Kysely<any>): Promise<boolean> {
const row = (
await sql<{
t: string | null;
}>`select to_regclass('ai_chat_run_steps_message_step_uidx') as t`.execute(db)
).rows[0];
return row.t !== null;
}
describe('20260708 ai_chat_run_steps migration roundtrip [integration]', () => {
let db: Kysely<any>;
beforeAll(() => {
db = getTestDb();
});
afterAll(async () => {
// Belt-and-suspenders: guarantee the table is present for later specs even if
// an assertion threw mid-roundtrip.
if (!(await tableExists(db))) await up(db);
await destroyTestDb();
});
it('down() drops the table+index and up() recreates them (idempotent)', async () => {
// Starts applied (global-setup migrated to latest).
expect(await tableExists(db)).toBe(true);
expect(await uniqueIndexExists(db)).toBe(true);
await down(db);
expect(await tableExists(db)).toBe(false);
expect(await uniqueIndexExists(db)).toBe(false);
await up(db);
expect(await tableExists(db)).toBe(true);
expect(await uniqueIndexExists(db)).toBe(true);
// up() is idempotent (ifNotExists) — a second run is a harmless no-op.
await expect(up(db)).resolves.not.toThrow();
expect(await tableExists(db)).toBe(true);
});
});
@@ -5,13 +5,8 @@ import {
serializeDocmostMarkdown,
parseDocmostMarkdown,
} from "../../build/lib/markdown-document.js";
import * as Y from "yjs";
import { convertProseMirrorToMarkdown } from "../../build/lib/markdown-converter.js";
import {
markdownToProseMirror,
applyDocToFragment,
} from "../../build/lib/collaboration.js";
import { TiptapTransformer } from "@hocuspocus/transformer";
import { markdownToProseMirror } from "../../build/lib/collaboration.js";
/** Recursively find the first descendant node (or self) of the given type. */
function find(node, type) {
@@ -229,68 +224,3 @@ test("drawio round-trips through export and import", () => {
assert.equal(diagram.attrs.attachmentId, "att-7");
});
});
// #503: a hardBreak (Shift+Enter) must be REPRESENTABLE end-to-end through the
// MCP canon, not silently cut. The reported symptom was a hardBreak sent via
// updatePageJson vanishing with no error and no line break. This guards all
// three seams of that path at once:
// 1. pm -> md: the serializer emits the CommonMark hard break ` \n` (two
// trailing spaces), not a bare `\n` (which reimports as a soft break and is
// lost).
// 2. md -> pm: ` \n` reimports to ONE paragraph carrying text+hardBreak+text
// (not two paragraphs, break not dropped) — the "непредставим" arm.
// 3. the literal updatePageJson mechanism: PM JSON -> live Yjs fragment via
// `applyDocToFragment` (PMNode.fromJSON + updateYFragment — the SAME encoder
// the collab-session write path uses, NOT buildYDoc/toYdoc) -> read back
// preserves the hardBreak between the two text runs in ONE paragraph.
test("hardBreak survives the MCP canon in BOTH directions (#503)", async () => {
const doc = {
type: "doc",
content: [
{
type: "paragraph",
content: [
{ type: "text", text: "line one" },
{ type: "hardBreak" },
{ type: "text", text: "line two" },
],
},
],
};
// 1. pm -> md: exact CommonMark hard break, not a dropped/soft break.
const body = convertProseMirrorToMarkdown(doc);
assert.equal(body, "line one \nline two");
// 2. md -> pm: ONE paragraph, text + hardBreak + text preserved (not split).
const rebuilt = await markdownToProseMirror(body);
const paras = findAll(rebuilt, "paragraph");
assert.equal(paras.length, 1, "the break must NOT split the paragraph in two");
const inline = paras[0].content.map((n) => n.type);
assert.deepEqual(
inline,
["text", "hardBreak", "text"],
"the hardBreak must survive the md -> pm import",
);
// 2b. byte-fixpoint: a second export is stable (P2).
assert.equal(convertProseMirrorToMarkdown(rebuilt), body);
// 3. updatePageJson path: PM JSON -> live Yjs fragment (applyDocToFragment =
// the real collab write path's PMNode.fromJSON + updateYFragment) -> read back
// keeps text + hardBreak + text in ONE paragraph.
const ydoc = new Y.Doc();
applyDocToFragment(ydoc, doc);
const fromYjs = TiptapTransformer.fromYdoc(ydoc, "default");
const yjsParas = findAll(fromYjs, "paragraph");
assert.equal(
yjsParas.length,
1,
"the Yjs write path must NOT split the paragraph in two",
);
assert.deepEqual(
yjsParas[0].content.map((n) => n.type),
["text", "hardBreak", "text"],
"the hardBreak must survive the updatePageJson (updateYFragment) write path",
);
});