fix(pipeline): batch-embed diffs by token budget, not file count [pipeline, docs, tests]

docs/0-requirements.ja.md

@@ -248,8 +248,8 @@ Responsibilities:
 - embed 経路では D1 FTS5 upsert 失敗は Vectorize upsert を無効化しない。保存した bodyHash が次回の試行を駆動し、次の reindex で sparse 側が reconcile される
 - metadata のみの経路（body は変わらず state / labels / milestone / assignees が変わった場合）では、mirror 書き込みの失敗を best-effort 扱いに**しない**。差分検出の基準を進めずに保持し、次の poll / webhook 配信で再試行させる。基準は IssueStore の record そのものなので、失敗した mirror を追い越して基準を進めると取り残しが恒久化する — state だけの変更は、embed 経路が待っている body 変更を二度と連れてこない（issue #209）
 - この経路の dense / sparse mirror は互いに独立して書く。vector が欠けている行（issue #210）でも sparse 側の state は更新される
-- commit diff は 1 commit 分の file リストを batch embed（Workers AI の `text: string[]` 対応を利用）し、1 回の Vectorize upsert で N vector を書き込む
-- batch size は `MAX_EMBEDDING_BATCH_SIZE`（既定 20）で上限。これを超える commit は複数 batch call に分割する
+- commit diff は 1 commit 分の file リストを batch embed（Workers AI の `text: string[]` 対応を利用）し、batch ごとに 1 回の Vectorize upsert で N vector を書き込む
+- batch の切れ目は file 件数ではなく推定 token 予算（`MAX_EMBEDDING_BATCH_TOKENS`）で決める。この pipeline が embed する内容の「1 token あたりの文字数」は桁で変わる — ASCII のソースで約 3、CJK の散文で約 1 — ため、件数が call を縛れるのは「どの patch も安い側である」と仮定した場合だけである。その仮定を破る commit は chunk 全体を失敗させ、vector が載らなかった commit は diff watermark が留まる対象（issue #178）なので、その commit を飛ばすのではなく surface がそこで恒久的に停止していた。しかも毎 cron 同じ commit で同じ結果になる決定論的な失敗で、同じログに同居する一過性の subrequest 超過とはそこで性質が分かれる（issue #236）。天井は bge-m3 の documented な per-input 上限 8192 token ではなく、1 call の input 全体を合算した endpoint 側の上限である。batch は合算され、拒否応答はその合計値を名指しする（`3030: Max context reached 85920 tokens but model supports only 60000`）。この値は非公開なので `WORKERS_AI_BATCH_CONTEXT_LIMIT` としてそのエラーから記録し、予算はその半分に置く。半分にしているのは効く方向の誤差が 1 つだけだからで、`estimateEmbeddingTokens` は近似であり、真値より小さく出た推定こそが call を天井の向こうへ送って stall を再現する。同時に天井から離しすぎない — batch が 1 つ増えるごとに subrequest を 2 つ消費し、この worker は既に invocation 予算を超過しているため。単独で予算を超える input はそのまま単独で送る — さらに削るのは truncate 軸（`MAX_EMBEDDING_INPUT_CHARS`）の仕事であり、落とせばその file が索引から消えるため
 
 **索引欠落の修復.** watermark の修正は漏れを止めるだけで、既に空いた穴は埋まらない — 取り残された項目が再 fetch されるのは `updated_at` が動いたときだけで、閉じた履歴はもう動かない。`POST /admin/backfill-issue-index?repo=owner/repo`（installation guide 参照）が欠落そのものを走査する。repository の issue 番号空間は密かつ有界なので、`search_docs` に issue / PR 行が無い番号がそのまま欠落集合であり、数値 cursor が「どこまで走査したか」を厳密に表せる。同じ集合を時刻 cursor で辿ると、欠陥が突いた順序をそのまま持ち込むことになる。GitHub 側に既に無い番号（削除・transfer 済み）は 404 を返すので、retry せず計上のみ。取り込みは body-hash 判定を強制的に飛ばす: 候補はいずれも retrieval surface が欠けていると分かっている項目であり、hash が一致していると（embed 成功後に FTS5 mirror が失敗した行がこの状態になる）そのまま恒久的に skip されてしまうため。state 修復と違いこちらは embed を伴い、1 候補ごとに取り込み fan-out の全額がかかるので、呼び出し側が batch を跨いで sweep を進める。`dry_run=true` は予算を使わずに欠落量だけを測る。sweep は poller の watermark と同じ不変条件に従う: **取り込みそこねた最初の候補を cursor が追い越さない。** これにより 1 call の上限を大きく取りすぎたときの代償は 1 回分の無駄な呼び出しであって取りこぼしではなく、結果が「上限値が subrequest 予算をどれだけ正確に写しているか」に依存しなくなる（issue #216）。GitHub 側に既に無い番号は例外 — 誰が何回試しても取り込めないので、そこで止めると retry の境界にならず sweep が停止する。トレードオフは poller と同じで、恒久的に失敗する候補があると sweep は止まる。ただしそれは応答に現れる（`nextCursor` が渡した `cursor` と同じ値で返る）し、この endpoint は cron ではなく人間 / AI が駆動するので、詰まった番号を手動の `cursor` 指定で越えられる。
 

docs/0-requirements.md

@@ -253,8 +253,8 @@ Responsibilities:
 - D1 FTS5 upsert failures do not invalidate a successful Vectorize upsert on the embed path; the stored bodyHash drives the next attempt and the next reindex reconciles the sparse side
 - on the metadata-only path (state / labels / milestone / assignees changed, body did not) a failed mirror write is **not** best-effort: the diff baseline is held so the next poll or webhook delivery retries. The baseline is the IssueStore record itself, so advancing it past a failed mirror makes the miss permanent — a state-only change never brings the body change the embed path waits for (issue #209)
 - the dense and sparse mirrors on that path are written independently: a row with no vector (issue #210) still gets its sparse state updated
-- for commit diffs: batch-embed a commit's file list in a single Workers AI call (`text: string[]`) and upsert the resulting N vectors in one `VECTORIZE.upsert` call
-- batch size is capped by `MAX_EMBEDDING_BATCH_SIZE` (default 20); commits exceeding it are split across multiple batch calls
+- for commit diffs: batch-embed a commit's file list through Workers AI (`text: string[]`) and upsert each batch's N vectors in one `VECTORIZE.upsert` call
+- batches are cut on an estimated token budget (`MAX_EMBEDDING_BATCH_TOKENS`), not on a file count. Characters per token vary by an order of magnitude across what this pipeline embeds — roughly 3 for ASCII source, roughly 1 for CJK prose — so N inputs bound a call only where every patch is assumed to be the cheap kind. A commit that broke the assumption failed its whole chunk, and a commit whose vectors never landed is one the diff watermark holds on (issue #178), so the surface stalled there permanently instead of passing it by — deterministically, on the same commit every cron tick, which is what separated it from the transient subrequest overruns sharing the log (issue #236). The ceiling is the endpoint's aggregate context across a call's inputs, not bge-m3's documented 8192-token per-input maximum: the batch is summed, and the rejection names the sum (`3030: Max context reached 85920 tokens but model supports only 60000`). It is unpublished, so `WORKERS_AI_BATCH_CONTEXT_LIMIT` records it from that error, and the budget is half of it. The halving covers the one error direction that matters — `estimateEmbeddingTokens` approximates, and an estimate landing under the true count is what puts a call over the ceiling and reproduces the stall — while staying near enough the ceiling to keep the batch count down, since every extra batch spends two subrequests on an invocation budget this worker already overruns. An input whose own estimate exceeds the budget is sent alone — cutting it down further is the truncation axis (`MAX_EMBEDDING_INPUT_CHARS`), and dropping it would lose the file from the index
 
 **Missing-entry repair.** The watermark fix stops the leak but does not fill the hole: a stranded item is only re-fetched when its `updated_at` moves, and closed history never moves again. `POST /admin/backfill-issue-index?repo=owner/repo` (see the installation guide) walks the gap directly — the repository's issue-number space is dense and bounded, so the numbers with no `search_docs` issue / PR row are exactly the missing set, and a numeric cursor states how far the sweep has reached. A timestamp cursor over the same set would reintroduce the ordering the defect exploited. Numbers GitHub no longer has (deleted or transferred) answer 404 and are counted rather than retried. The ingest is forced past the body-hash check: every candidate is known to be missing a retrieval surface, and a matching hash — which an embed whose FTS5 mirror failed leaves behind — would otherwise skip it permanently. Unlike the state repair this one embeds, so every candidate carries the full ingest fan-out and the caller drives the sweep one batch at a time; `dry_run=true` measures the gap without spending it. The sweep obeys the same invariant as the poller's watermark: **the cursor never advances past the first candidate a call failed to ingest**, so a per-call limit set too high costs a wasted call rather than a missed item, and the result does not depend on how accurately that limit models the subrequest budget (issue #216). A number GitHub no longer has is exempt — nothing will ever ingest it, so holding there would stall the sweep instead of bounding a retry. The tradeoff is the poller's: a candidate that fails on every attempt stops the sweep. Here that is visible rather than silent (`nextCursor` comes back equal to the `cursor` passed in), and because a human or an AI drives this endpoint rather than cron, stepping over the blocking number is a matter of passing the next `cursor` by hand.
 

src/pipeline/embed-diff.test.ts

@@ -0,0 +1,152 @@
+import { describe, it, expect, vi } from "vitest";
+import type { Env } from "../types.js";
+import { processAndUpsertCommitDiff, type GitHubCommitDetail } from "./embed-diff.js";
+import { estimateEmbeddingTokens, MAX_EMBEDDING_BATCH_TOKENS } from "./embedding.js";
+import { diffVectorId } from "./vector-id.js";
+
+const REPO = "acme/widgets";
+
+function mkCommit(patches: string[]): GitHubCommitDetail {
+  return {
+    sha: "c0ffee",
+    commit: {
+      message: "a commit message",
+      author: { name: "author", date: "2026-01-01T00:00:00Z" },
+    },
+    author: { login: "author" },
+    files: patches.map((patch, i) => ({
+      filename: `src/file-${i}.ts`,
+      status: "modified",
+      patch,
+      sha: `blob${i}`,
+    })),
+  };
+}
+
+interface EnvStubOptions {
+  /** Batch indices (in call order) whose embed call should throw. */
+  embedFailsOnCall?: number[];
+}
+
+function mkEnv(options: EnvStubOptions = {}) {
+  /** Inputs handed to each Workers AI call, in call order. */
+  const aiCalls: string[][] = [];
+  const upsertedIds: string[][] = [];
+
+  const env = {
+    GITHUB_TOKEN: "t",
+    AI: {
+      run: vi.fn(async (_model: string, input: { text: string[] }) => {
+        const callIndex = aiCalls.length;
+        aiCalls.push(input.text);
+        if (options.embedFailsOnCall?.includes(callIndex)) {
+          throw new Error("workers ai rejected the batch");
+        }
+        return { data: input.text.map(() => [0.1, 0.2]) };
+      }),
+    },
+    VECTORIZE: {
+      upsert: vi.fn(async (vectors: Array<{ id: string }>) => {
+        upsertedIds.push(vectors.map((v) => v.id));
+      }),
+    },
+    DB_FTS: {
+      prepare: () => ({
+        bind: () => ({ run: async () => ({}) }),
+      }),
+    },
+  } as unknown as Env;
+
+  return { env, aiCalls, upsertedIds };
+}
+
+function mkStore() {
+  return {
+    fetch: vi.fn(async () => new Response("{}", { status: 200 })),
+  } as unknown as DurableObjectStub;
+}
+
+describe("embed-diff: the batch axis is the token budget, not the file count", () => {
+  it("still sends an ordinary commit as one call", async () => {
+    // 30 files well past the retired count cap of 20, each a small patch.
+    const { env, aiCalls } = mkEnv();
+    const commit = mkCommit(Array.from({ length: 30 }, (_, i) => `@@ -1 +1 @@\n+line ${i}`));
+
+    const result = await processAndUpsertCommitDiff(env, mkStore(), REPO, commit);
+
+    expect(result).toEqual({ embedded: 30, skipped: 0, failed: 0, batches: 1 });
+    expect(aiCalls).toHaveLength(1);
+    expect(aiCalls[0]).toHaveLength(30);
+  });
+
+  it("splits a commit of large patches that a file count would have kept in one call", async () => {
+    // Twenty maximal patches — exactly one chunk under the retired count cap, and
+    // the shape the endpoint rejected in production ("Max context reached 85920
+    // tokens but model supports only 60000"), taking every file in the chunk down
+    // with the call.
+    const { env, aiCalls, upsertedIds } = mkEnv();
+    const commit = mkCommit(Array.from({ length: 20 }, () => "あ".repeat(9000)));
+
+    const result = await processAndUpsertCommitDiff(env, mkStore(), REPO, commit);
+
+    expect(result.embedded).toBe(20);
+    expect(result.failed).toBe(0);
+    expect(aiCalls.length).toBeGreaterThan(1);
+    expect(result.batches).toBe(aiCalls.length);
+
+    // Every call carrying more than one input stays inside the budget.
+    for (const call of aiCalls) {
+      if (call.length > 1) {
+        const total = call.reduce((sum, text) => sum + estimateEmbeddingTokens(text), 0);
+        expect(total).toBeLessThanOrEqual(MAX_EMBEDDING_BATCH_TOKENS);
+      }
+    }
+
+    // No file is dropped or duplicated on the way through the split.
+    expect(aiCalls.flat()).toHaveLength(20);
+    expect(new Set(upsertedIds.flat()).size).toBe(20);
+  });
+
+  it("keeps each embed call paired with its own slice of files", async () => {
+    // Vectors are matched to files by position, so a split has to cut the inputs
+    // and the files on the same boundary. Reading each call's file paths back out
+    // of its inputs and rebuilding the vector IDs from them catches a slice that
+    // drifted — the failure mode that would file every patch under a neighbour.
+    const { env, aiCalls, upsertedIds } = mkEnv();
+    const commit = mkCommit(Array.from({ length: 6 }, () => "あ".repeat(9000)));
+
+    await processAndUpsertCommitDiff(env, mkStore(), REPO, commit);
+
+    expect(upsertedIds).toHaveLength(aiCalls.length);
+    for (let batch = 0; batch < aiCalls.length; batch++) {
+      // Input format is "{message}\n\n{path}\n\n{patch}".
+      const paths = aiCalls[batch].map((text) => text.split("\n\n")[1]);
+      const expected = await Promise.all(
+        paths.map((path) => diffVectorId(REPO, commit.sha, path)),
+      );
+      expect(upsertedIds[batch]).toEqual(expected);
+    }
+  });
+
+  it("loses only the failing batch when one embed call is rejected", async () => {
+    const { env, aiCalls, upsertedIds } = mkEnv({ embedFailsOnCall: [0] });
+    const commit = mkCommit(Array.from({ length: 6 }, () => "あ".repeat(9000)));
+
+    const result = await processAndUpsertCommitDiff(env, mkStore(), REPO, commit);
+
+    expect(result.failed).toBe(aiCalls[0].length);
+    expect(result.embedded).toBe(6 - aiCalls[0].length);
+    expect(upsertedIds.flat()).toHaveLength(result.embedded);
+  });
+
+  it("skips files with no patch and reports them separately", async () => {
+    const { env, aiCalls } = mkEnv();
+    const commit = mkCommit(["@@ -1 +1 @@\n+one"]);
+    commit.files!.push({ filename: "assets/logo.png", status: "modified", sha: "blobX" });
+
+    const result = await processAndUpsertCommitDiff(env, mkStore(), REPO, commit);
+
+    expect(result).toEqual({ embedded: 1, skipped: 1, failed: 0, batches: 1 });
+    expect(aiCalls[0]).toHaveLength(1);
+  });
+});

src/pipeline/embed-diff.ts

@@ -12,7 +12,7 @@ import { upsertFtsRow } from "../fts.js";
 import { prepareDiffEmbeddingInput } from "./hash.js";
 import {
   generateEmbeddingBatch,
-  MAX_EMBEDDING_BATCH_SIZE,
+  planEmbeddingBatches,
 } from "./embedding.js";
 import { diffVectorId } from "./vector-id.js";
 
@@ -104,7 +104,8 @@ function normaliseFileStatus(status: string): DiffFileStatus {
  * Flow:
  *   1. Filter `files[]` to those with a textual `patch` (binary / oversized files are skipped).
  *   2. Build embedding inputs = commit message + file path + patch, truncated.
- *   3. Batch-embed inputs via Workers AI (chunked by MAX_EMBEDDING_BATCH_SIZE).
+ *   3. Batch-embed inputs via Workers AI (chunked by `planEmbeddingBatches`, which
+ *      splits on an estimated token budget rather than a file count).
  *   4. Upsert all vectors into Vectorize in the same chunks.
  *   5. Record DiffRecord rows into the Durable Object store for each indexed file.
  *
@@ -150,21 +151,26 @@ export async function processAndUpsertCommitDiff(
   let failed = 0;
   let batches = 0;
 
-  // Chunk to respect Workers AI / Vectorize batch limits.
-  for (let offset = 0; offset < indexable.length; offset += MAX_EMBEDDING_BATCH_SIZE) {
-    const chunk = indexable.slice(offset, offset + MAX_EMBEDDING_BATCH_SIZE);
-    batches++;
+  const allInputs = indexable.map((f) =>
+    prepareDiffEmbeddingInput(commitMessage, f.filename, f.patch),
+  );
 
-    const inputs = chunk.map((f) =>
-      prepareDiffEmbeddingInput(commitMessage, f.filename, f.patch),
-    );
+  // Chunk on the estimated token total of the inputs, not on how many files the
+  // commit touched. A file count bounds a call only if every patch is assumed to
+  // be small, and a commit that breaks that assumption used to fail the whole
+  // chunk — which the poller reads as an uningested commit and holds the diff
+  // watermark on, so the surface stalls there rather than skipping past it (#236).
+  for (const { start, end } of planEmbeddingBatches(allInputs)) {
+    const chunk = indexable.slice(start, end);
+    const inputs = allInputs.slice(start, end);
+    batches++;
 
     let embeddings: number[][];
     try {
       embeddings = await generateEmbeddingBatch(env.AI, inputs);
     } catch (err) {
       console.error(
-        `Failed to batch-embed diffs for ${repo}@${commitSha} chunk offset ${offset}:`,
+        `Failed to batch-embed diffs for ${repo}@${commitSha} chunk offset ${start}:`,
         err instanceof Error ? err.message : String(err),
       );
       failed += chunk.length;
@@ -212,7 +218,7 @@ export async function processAndUpsertCommitDiff(
       await env.VECTORIZE.upsert(vectors);
     } catch (err) {
       console.error(
-        `Failed to upsert diff vectors for ${repo}@${commitSha} chunk offset ${offset}:`,
+        `Failed to upsert diff vectors for ${repo}@${commitSha} chunk offset ${start}:`,
         err instanceof Error ? err.message : String(err),
       );
       failed += chunk.length;