refactor: streamline TikTok and Instagram scraping logic by removing search_queries and enhancing documentation for clarity

surfsense_backend/app/agents/chat/multi_agent_chat/subagents/builtins/tiktok/system_prompt.md

@@ -14,11 +14,11 @@ Answer the delegated question from live TikTok data gathered with your verb, com
 </available_tools>
 
 <playbook>
-- Finding videos on a topic: prefer `tiktok_scrape` with `hashtags` (no leading '#') or a direct TikTok URL in `urls` (fastest). `search_queries` also finds videos on a topic, but it is Google-backed and slow, so start with **at most 3** distinct queries and only add more if the first round returns nothing significant — never batch many phrasing variants of the same intent.
+- Finding videos on a topic: call `tiktok_scrape` with `hashtags` (no leading '#'), or pass a TikTok URL in `urls`. There is no keyword-video search — use hashtags or a video URL.
 - Scraping a specific video, profile, hashtag, or search page: pass its TikTok URL in `urls`.
 - Profiles: a creator's `profiles` feed returns the account's metadata (name, followers, bio, verification) reliably, but its video list is often withheld by TikTok — treat an empty video list as a known limit, not a failure to retry endlessly. Prefer `hashtags` or a direct video URL for videos.
 - Comments on a video: call `tiktok_comments` with the video URL(s) in `video_urls`.
-- Finding accounts by keyword: call `tiktok_user_search` with `queries` — that is the path for accounts. Use `search_queries` on `tiktok_scrape` only when you want videos, not accounts.
+- Finding accounts by keyword: call `tiktok_user_search` with `queries` — that is the path for accounts.
 - "What's trending now": call `tiktok_trending` (no query needed); set `max_items` for how many.
 - Controlling volume: use `max_items` for the total cap and `results_per_page` per target (per-verb equivalents: `comments_per_video`, `results_per_query`).
 - Requested counts: `max_items` defaults low — when the task asks for N items, set `max_items` (and the per-target count) above N. A call that caps below the target can never satisfy it.

surfsense_backend/app/capabilities/instagram/details/schemas.py

@@ -54,9 +54,7 @@ class DetailsInput(BaseModel):
     @model_validator(mode="after")
     def _exactly_one_source(self) -> DetailsInput:
         if not self.urls and not self.search_queries:
-            raise ValueError(
-                "Provide at least one of 'urls' or 'search_queries'."
-            )
+            raise ValueError("Provide at least one of 'urls' or 'search_queries'.")
         if self.urls and self.search_queries:
             raise ValueError(
                 "Provide 'urls' OR 'search_queries', not both (they cannot be combined)."

surfsense_backend/app/capabilities/instagram/scrape/schemas.py

@@ -77,9 +77,7 @@ class ScrapeInput(BaseModel):
     @model_validator(mode="after")
     def _exactly_one_source(self) -> ScrapeInput:
         if not self.urls and not self.search_queries:
-            raise ValueError(
-                "Provide at least one of 'urls' or 'search_queries'."
-            )
+            raise ValueError("Provide at least one of 'urls' or 'search_queries'.")
         if self.urls and self.search_queries:
             raise ValueError(
                 "Provide 'urls' OR 'search_queries', not both (they cannot be combined)."

surfsense_backend/app/capabilities/tiktok/scrape/definition.py

@@ -10,9 +10,8 @@ from app.capabilities.tiktok.scrape.schemas import ScrapeInput, ScrapeOutput
 TIKTOK_SCRAPE = Capability(
     name="tiktok.scrape",
     description=(
-        "Scrape public TikTok videos. Use urls, profiles, hashtags, or "
-        "search_queries (search_queries are resolved via Google to public "
-        "videos; for accounts by keyword use tiktok.user_search)."
+        "Scrape public TikTok videos. Use urls, profiles, or hashtags. To find "
+        "accounts by keyword, use tiktok.user_search."
     ),
     input_schema=ScrapeInput,
     output_schema=ScrapeOutput,

surfsense_backend/app/capabilities/tiktok/scrape/executor.py

@@ -26,7 +26,6 @@ def build_scrape_executor(scrape_fn: ScrapeFn | None = None) -> Executor:
             startUrls=[{"url": url} for url in payload.urls],
             profiles=payload.profiles,
             hashtags=payload.hashtags,
-            searchQueries=payload.search_queries,
             resultsPerPage=payload.results_per_page,
         )
         emit_progress(

surfsense_backend/app/capabilities/tiktok/scrape/schemas.py

@@ -4,7 +4,8 @@ A lean, agent-friendly surface over ``TikTokScrapeInput``
 (``app/proprietary/platforms/tiktok``). The executor maps this to the full
 scraper input; the scraper's ``TikTokVideoItem`` is reused verbatim as the
 output element. Any TikTok URL kind (video, profile, hashtag, search) goes in
-``urls``; ``profiles``/``hashtags``/``search_queries`` are typed shortcuts.
+``urls``; ``profiles``/``hashtags`` are typed shortcuts. Keyword search is not a
+video source here — use ``tiktok.user_search`` to find accounts by keyword.
 """
 
 from __future__ import annotations
@@ -26,8 +27,8 @@ class ScrapeInput(BaseModel):
         max_length=MAX_TIKTOK_SOURCES,
         description=(
             "TikTok URLs to scrape: a video, a profile (/@<user>), a hashtag "
-            "(/tag/<name>), or a search URL. Provide these OR profiles/hashtags/"
-            "search_queries (at least one source is required)."
+            "(/tag/<name>), or a search URL. Provide these OR profiles/hashtags "
+            "(at least one source is required)."
         ),
     )
     profiles: list[str] = Field(
@@ -40,21 +41,11 @@ class ScrapeInput(BaseModel):
         max_length=MAX_TIKTOK_SOURCES,
         description="Hashtag names to scrape, without the leading '#'.",
     )
-    search_queries: list[str] = Field(
-        default_factory=list,
-        max_length=MAX_TIKTOK_SOURCES,
-        description=(
-            "Search terms resolved via Google (site:tiktok.com) to public TikTok "
-            "videos, since TikTok's own keyword search is login-walled. Slower "
-            "than hashtags/urls. To find accounts by keyword, use "
-            "tiktok.user_search instead."
-        ),
-    )
     results_per_page: int = Field(
         default=10,
         ge=1,
         le=MAX_TIKTOK_ITEMS,
-        description="Max videos to pull per profile/hashtag/search target.",
+        description="Max videos to pull per profile/hashtag target.",
     )
     max_items: int = Field(
         default=10,
@@ -65,10 +56,9 @@ class ScrapeInput(BaseModel):
 
     @model_validator(mode="after")
     def _require_a_source(self) -> ScrapeInput:
-        if not any((self.urls, self.profiles, self.hashtags, self.search_queries)):
+        if not any((self.urls, self.profiles, self.hashtags)):
             raise ValueError(
-                "Provide at least one of 'urls', 'profiles', 'hashtags', or "
-                "'search_queries'."
+                "Provide at least one of 'urls', 'profiles', or 'hashtags'."
             )
         return self
 

surfsense_backend/app/proprietary/platforms/instagram/fetch.py

@@ -222,9 +222,7 @@ class _RotatingSession:
         await self.close()
         self.rotations += 1
         await self._open()
-        logger.info(
-            "[instagram] rotated proxy session (rotation #%d)", self.rotations
-        )
+        logger.info("[instagram] rotated proxy session (rotation #%d)", self.rotations)
         return self.session
 
     async def pace(self) -> None:
@@ -378,9 +376,7 @@ async def _fetch(
             if status == _BACKOFF_STATUS and backoffs < _MAX_BACKOFFS:
                 backoffs += 1
                 delay = _BACKOFF_BASE_S * (2 ** (backoffs - 1))
-                logger.warning(
-                    "[instagram] 429 on %s; backing off %.1fs", path, delay
-                )
+                logger.warning("[instagram] 429 on %s; backing off %.1fs", path, delay)
                 await asyncio.sleep(delay + random.uniform(0, 1))
                 continue
             if status in _ROTATE_STATUSES:

surfsense_backend/app/proprietary/platforms/instagram/parsers.py

@@ -171,7 +171,9 @@ def _relay_child(node: dict[str, Any]) -> dict[str, Any]:
     mt = node.get("media_type")
     vv = node.get("video_versions")
     video_url = (
-        vv[0].get("url") if isinstance(vv, list) and vv and isinstance(vv[0], dict) else None
+        vv[0].get("url")
+        if isinstance(vv, list) and vv and isinstance(vv[0], dict)
+        else None
     )
     is_video = mt == 2 or bool(video_url)
     return {
@@ -290,9 +292,7 @@ def parse_profile(user: dict[str, Any]) -> dict[str, Any]:
 _APP_JSON_RE = re.compile(
     r'<script type="application/json"[^>]*>(.*?)</script>', re.DOTALL
 )
-_OG_RE = re.compile(
-    r'<meta\s+property="og:([^"]+)"\s+content="([^"]*)"', re.IGNORECASE
-)
+_OG_RE = re.compile(r'<meta\s+property="og:([^"]+)"\s+content="([^"]*)"', re.IGNORECASE)
 # og tags are the fallback source (used only when the relay blob is absent). They
 # follow a fixed English shape because the fetch layer pins Accept-Language en-US:
 #   og:description = "{likes} likes, {comments} comments - {username} on {Month D, YYYY}: "{caption}""
@@ -321,7 +321,9 @@ _MEDIA_ID_RE = re.compile(r"instagram://media\?id=(\d+)")
 def _og_date_to_iso(value: str) -> str | None:
     """``"July 9, 2026"`` -> ``"2026-07-09"`` (date-only; og carries no time)."""
     try:
-        return datetime.strptime(value, "%B %d, %Y").replace(tzinfo=UTC).date().isoformat()
+        return (
+            datetime.strptime(value, "%B %d, %Y").replace(tzinfo=UTC).date().isoformat()
+        )
     except ValueError:
         return None
 
@@ -359,7 +361,7 @@ def _parse_og_meta(og: dict[str, str]) -> dict[str, Any]:
     elif owner_date:
         # No usable og:title: fall back to the caption after og:description's
         # date prefix — still clean (the counts/username/date are stripped).
-        out["caption"] = _clean_caption(desc[owner_date.end():])
+        out["caption"] = _clean_caption(desc[owner_date.end() :])
     return out
 
 
@@ -438,13 +440,21 @@ def _media_from_relay(
     mt = media.get("media_type")
     cap = media.get("caption")
     caption = (
-        cap.get("text") if isinstance(cap, dict) else (cap if isinstance(cap, str) else None)
+        cap.get("text")
+        if isinstance(cap, dict)
+        else (cap if isinstance(cap, str) else None)
     )
     carousel = media.get("carousel_media")
-    carousel = [c for c in carousel if isinstance(c, dict)] if isinstance(carousel, list) else []
+    carousel = (
+        [c for c in carousel if isinstance(c, dict)]
+        if isinstance(carousel, list)
+        else []
+    )
     vv = media.get("video_versions")
     video_url = (
-        vv[0].get("url") if isinstance(vv, list) and vv and isinstance(vv[0], dict) else None
+        vv[0].get("url")
+        if isinstance(vv, list) and vv and isinstance(vv[0], dict)
+        else None
     )
     is_video = mt == 2 or bool(video_url)
     owner = media.get("user") if isinstance(media.get("user"), dict) else {}
@@ -469,13 +479,18 @@ def _media_from_relay(
         "type": _MEDIA_TYPE.get(mt) or ("Video" if is_video else "Image"),
         "shortCode": media.get("code") or shortcode,
         "caption": caption,
-        "hashtags": list(dict.fromkeys(_HASHTAG_RE.findall(caption))) if caption else [],
-        "mentions": list(dict.fromkeys(_MENTION_RE.findall(caption))) if caption else [],
+        "hashtags": list(dict.fromkeys(_HASHTAG_RE.findall(caption)))
+        if caption
+        else [],
+        "mentions": list(dict.fromkeys(_MENTION_RE.findall(caption)))
+        if caption
+        else [],
         "url": url,
         "commentsCount": _int(media.get("comment_count")),
         "dimensionsHeight": _int(media.get("original_height")),
         "dimensionsWidth": _int(media.get("original_width")),
-        "displayUrl": _iv2_url(media.get("image_versions2")) or media.get("display_uri"),
+        "displayUrl": _iv2_url(media.get("image_versions2"))
+        or media.get("display_uri"),
         "images": [
             u
             for c in carousel
@@ -535,8 +550,12 @@ def parse_post(
         "type": "Video" if is_video else "Image",
         "shortCode": shortcode,
         "caption": caption,
-        "hashtags": list(dict.fromkeys(_HASHTAG_RE.findall(caption))) if caption else [],
-        "mentions": list(dict.fromkeys(_MENTION_RE.findall(caption))) if caption else [],
+        "hashtags": list(dict.fromkeys(_HASHTAG_RE.findall(caption)))
+        if caption
+        else [],
+        "mentions": list(dict.fromkeys(_MENTION_RE.findall(caption)))
+        if caption
+        else [],
         "url": url,
         "commentsCount": og_meta.get("comments"),
         "displayUrl": og.get("image"),

surfsense_backend/app/proprietary/platforms/instagram/scraper.py

@@ -328,9 +328,7 @@ async def _discover_via_google(
     return resolved
 
 
-async def _discover(
-    query: str, *, search_type: str, limit: int
-) -> list[ResolvedUrl]:
+async def _discover(query: str, *, search_type: str, limit: int) -> list[ResolvedUrl]:
     """Resolve a discovery query into profile targets - anonymously.
 
     A query that is a valid handle resolves directly against the anonymous
@@ -397,9 +395,7 @@ async def iter_instagram(
 
     # posts / reels -> media feeds, de-duped by id across targets.
     jobs = [
-        _media_flow(
-            r, input_model=input_model, cutoff=cutoff, per_target=per_target
-        )
+        _media_flow(r, input_model=input_model, cutoff=cutoff, per_target=per_target)
         for r in targets
     ]
     seen: set[str] = set()

surfsense_backend/app/proprietary/platforms/instagram/url_resolver.py

@@ -25,9 +25,7 @@ from urllib.parse import urlparse
 
 ResolvedKind = Literal["profile", "post", "reel"]
 
-_INSTAGRAM_HOSTS = frozenset(
-    {"m.instagram.com", "www.instagram.com", "instagram.com"}
-)
+_INSTAGRAM_HOSTS = frozenset({"m.instagram.com", "www.instagram.com", "instagram.com"})
 _STRIP_SEGMENTS = frozenset({"_u", "profilecard"})
 _RESERVED = frozenset(
     {"p", "s", "tv", "reel", "reels", "share", "explore", "stories", "accounts"}
@@ -68,9 +66,7 @@ def resolve_url(url: str) -> ResolvedUrl | None:
     if "instagram.com" not in url.lower():
         token = url.strip().lstrip("@")
         if token and "/" not in token and "." not in token:
-            return ResolvedUrl(
-                "profile", token, f"https://www.instagram.com/{token}/"
-            )
+            return ResolvedUrl("profile", token, f"https://www.instagram.com/{token}/")
     segments = _segments(url)
     if not segments:
         return None
@@ -83,9 +79,7 @@ def resolve_url(url: str) -> ResolvedUrl | None:
         return ResolvedUrl("reel", code, url, numeric_post_id=code.isdigit())
     if head == "stories" and len(segments) >= 2:
         user = segments[1]
-        return ResolvedUrl(
-            "profile", user, f"https://www.instagram.com/{user}/"
-        )
+        return ResolvedUrl("profile", user, f"https://www.instagram.com/{user}/")
     if head not in _RESERVED:
         return ResolvedUrl("profile", head, url)
     return None

surfsense_backend/app/proprietary/platforms/tiktok/extraction/timestamps.py

@@ -5,11 +5,11 @@ from __future__ import annotations
 from datetime import UTC, datetime
 
 
-def epoch_to_iso(seconds: int | None) -> str | None:
+def epoch_to_iso(seconds: int | str | None) -> str | None:
     """Convert a Unix-seconds timestamp to ``YYYY-MM-DDTHH:MM:SS.000Z``."""
     if not seconds:
         return None
-    stamp = datetime.fromtimestamp(seconds, tz=UTC)
+    stamp = datetime.fromtimestamp(int(seconds), tz=UTC)
     return stamp.strftime("%Y-%m-%dT%H:%M:%S.000Z")
 
 

surfsense_backend/app/proprietary/platforms/tiktok/flows/video.py

@@ -11,7 +11,9 @@ from ..targets.types import TikTokTarget
 from . import FetchFn
 
 
-async def iter_video(target: TikTokTarget, *, fetch: FetchFn) -> AsyncIterator[dict[str, Any]]:
+async def iter_video(
+    target: TikTokTarget, *, fetch: FetchFn
+) -> AsyncIterator[dict[str, Any]]:
     html = await fetch(target.url)
     if not html:
         return

surfsense_backend/app/proprietary/platforms/tiktok/orchestrator.py

@@ -10,10 +10,6 @@ from __future__ import annotations
 
 from collections.abc import AsyncIterator
 from typing import Any
-from urllib.parse import quote
-
-from app.proprietary.platforms.google_search.schemas import GoogleSearchScrapeInput
-from app.proprietary.platforms.google_search.scraper import scrape_serps
 
 from .extraction.timestamps import now_iso
 from .flows import FetchCommentsFn, FetchFn, FetchListingFn, FetchUsersFn
@@ -35,26 +31,15 @@ from .targets.types import TikTokTarget
 
 _PROFILE_URL = "https://www.tiktok.com/@{name}"
 _HASHTAG_URL = "https://www.tiktok.com/tag/{tag}"
-_SEARCH_URL = "https://www.tiktok.com/search?q={query}"
 _EXPLORE_URL = "https://www.tiktok.com/explore"
 
-# A ``searchQueries`` term whose Google discovery surfaced no scrapable video
-# URLs degrades to one honest ErrorItem (mirrors the listing flow's contract:
-# never vanish silently).
-_EMPTY_DISCOVERY_MESSAGE = (
-    "No public TikTok videos found for this query via Google discovery. Try a "
-    "narrower phrasing, a hashtag, or a direct video URL."
-)
-
 
 def _resolve_targets(input_model: TikTokScrapeInput) -> list[TikTokTarget]:
     """Build the target list from the URL/profile/hashtag sources.
 
-    ``searchQueries`` is deliberately excluded: TikTok's own keyword search is
-    login-walled for anonymous sessions, so it is routed through Google video
-    discovery in :func:`iter_tiktok` instead. A raw ``tiktok.com/search?...``
-    URL passed explicitly in ``startUrls``/``postURLs`` still resolves here and
-    keeps its native listing routing.
+    A raw ``tiktok.com/search?...`` URL passed explicitly in
+    ``startUrls``/``postURLs`` still resolves here and keeps its native listing
+    routing; there is no keyword-search shortcut.
     """
     targets: list[TikTokTarget] = []
     for entry in input_model.startUrls:
@@ -73,39 +58,6 @@ def _resolve_targets(input_model: TikTokScrapeInput) -> list[TikTokTarget]:
     return targets
 
 
-async def _discover_via_google(query: str, *, limit: int) -> list[TikTokTarget]:
-    """Discover public TikTok video targets via Google ``site:tiktok.com``.
-
-    TikTok's anonymous keyword search is login-walled, so we reuse the existing
-    ``google_search`` platform, classify each organic URL with ``resolve_target``,
-    and keep only video hits (``/@user/video/<id>``) — the one kind that scrapes
-    reliably over plain HTTP. Profile/hashtag/search/photo/non-tiktok results are
-    dropped (accounts belong to the ``user_search`` verb). De-duped, capped at
-    ``limit``.
-    """
-    serps = await scrape_serps(
-        GoogleSearchScrapeInput(
-            queries=query, site="tiktok.com", maxPagesPerQuery=1
-        ),
-        limit=1,
-    )
-    resolved: list[TikTokTarget] = []
-    seen: set[str] = set()
-    for serp in serps:
-        for org in serp.get("organicResults") or []:
-            url = org.get("url", "") if isinstance(org, dict) else ""
-            target = resolve_target(url)
-            if target is None or target.kind != "video":
-                continue
-            if target.value in seen:
-                continue
-            seen.add(target.value)
-            resolved.append(target)
-            if len(resolved) >= limit:
-                return resolved
-    return resolved
-
-
 def _dispatch(
     target: TikTokTarget,
     *,
@@ -128,11 +80,9 @@ async def iter_tiktok(
 ) -> AsyncIterator[dict[str, Any]]:
     """Yield normalized items for every resolved target, in order.
 
-    Direct sources (URLs, profiles, hashtags) resolve up front; ``searchQueries``
-    then run through Google video discovery. The video flow's ``fetch_html``
-    opens its own warmed proxy session per call when none is bound; the listing
-    flow drives its own browser. Neither binds a ContextVar across these
-    ``yield``s, so the generator stays context-safe.
+    The video flow's ``fetch_html`` opens its own warmed proxy session per call
+    when none is bound; the listing flow drives its own browser. Neither binds a
+    ContextVar across these ``yield``s, so the generator stays context-safe.
     """
     cap = input_model.resultsPerPage
     for target in _resolve_targets(input_model):
@@ -141,27 +91,6 @@ async def iter_tiktok(
         ):
             yield item
 
-    # searchQueries -> Google-discovered public video URLs, de-duped across
-    # queries so the same video surfacing under two terms is scraped once.
-    seen_videos: set[str] = set()
-    for query in input_model.searchQueries:
-        discovered = await _discover_via_google(query, limit=cap)
-        if not discovered:
-            yield ErrorItem(
-                url=_SEARCH_URL.format(query=quote(query)),
-                input=query,
-                error=_EMPTY_DISCOVERY_MESSAGE,
-                errorCode="no_items",
-                scrapedAt=now_iso(),
-            ).to_output()
-            continue
-        for target in discovered:
-            if target.value in seen_videos:
-                continue
-            seen_videos.add(target.value)
-            async for item in iter_video(target, fetch=fetch):
-                yield item
-
 
 async def scrape_tiktok(
     input_model: TikTokScrapeInput,
@@ -174,7 +103,9 @@ async def scrape_tiktok(
     from app.capabilities.core.progress import emit_progress
 
     results: list[dict[str, Any]] = []
-    async for item in iter_tiktok(input_model, fetch=fetch, fetch_listing=fetch_listing):
+    async for item in iter_tiktok(
+        input_model, fetch=fetch, fetch_listing=fetch_listing
+    ):
         results.append(item)
         emit_progress("scraping", current=len(results), total=limit, unit="item")
         if limit is not None and len(results) >= limit:

surfsense_backend/scripts/e2e_instagram_scraper.py

@@ -54,9 +54,7 @@ from app.proprietary.platforms.instagram.url_resolver import resolve_url  # noqa
 _PROFILE = "natgeo"
 _SEARCH_TERM = "national geographic"
 
-_FIXTURE_DIR = (
-    _BACKEND_ROOT / "tests" / "unit" / "platforms" / "instagram" / "fixtures"
-)
+_FIXTURE_DIR = _BACKEND_ROOT / "tests" / "unit" / "platforms" / "instagram" / "fixtures"
 
 # Fields to strip from dumped fixtures so we never commit PII / volatile tokens.
 _PII_KEYS = frozenset(
@@ -98,7 +96,9 @@ async def step0_probe() -> bool:
         data = await fetch_json(
             "api/v1/users/web_profile_info/", {"username": _PROFILE}
         )
-        user = (data or {}).get("data", {}).get("user") if isinstance(data, dict) else None
+        user = (
+            (data or {}).get("data", {}).get("user") if isinstance(data, dict) else None
+        )
         print(f"    web_profile_info({_PROFILE}) -> user={'yes' if user else 'no'}")
         return _check("sticky web_profile_info", minted and bool(user))
 
@@ -179,9 +179,7 @@ async def step5_search() -> bool:
 
 async def step6_dump_fixtures(post_url: str | None) -> bool:
     _hr("STEP 6 — dump trimmed, anonymized fixtures for offline tests")
-    profile = await fetch_json(
-        "api/v1/users/web_profile_info/", {"username": _PROFILE}
-    )
+    profile = await fetch_json("api/v1/users/web_profile_info/", {"username": _PROFILE})
     _FIXTURE_DIR.mkdir(parents=True, exist_ok=True)
     wrote = []
     if isinstance(profile, dict) and profile.get("data", {}).get("user"):

surfsense_backend/scripts/e2e_tiktok_scrape.py

@@ -178,7 +178,9 @@ async def stage_pipeline() -> bool:
         f"{len(items)} item(s)",
     )
     if items:
-        print(f"  sample: {items[0].get('webVideoUrl')} — {items[0].get('text', '')[:60]!r}")
+        print(
+            f"  sample: {items[0].get('webVideoUrl')} — {items[0].get('text', '')[:60]!r}"
+        )
     return ok
 
 
@@ -210,9 +212,7 @@ async def stage_comments(video_url: str) -> tuple[bool, list[dict[str, Any]]]:
     # Comments load over a signed /api/comment/list XHR that TikTok serves to
     # anonymous sessions once the panel opens. Pass if real comments come back
     # OR a graceful ErrorItem (video has none / disabled / withheld).
-    items = await scrape_tiktok_comments(
-        [video_url], per_video=_COUNT, limit=_COUNT
-    )
+    items = await scrape_tiktok_comments([video_url], per_video=_COUNT, limit=_COUNT)
     has_comment = any(it.get("id") and not it.get("errorCode") for it in items)
     has_error = any(it.get("errorCode") == "no_comments" for it in items)
     ok = _check(
@@ -253,7 +253,9 @@ async def stage_trending() -> tuple[bool, list[dict[str, Any]]]:
         f"{len(items)} item(s); videos={len(real)}",
     )
     if real:
-        print(f"  sample: {real[0].get('webVideoUrl')} — {real[0].get('text', '')[:60]!r}")
+        print(
+            f"  sample: {real[0].get('webVideoUrl')} — {real[0].get('text', '')[:60]!r}"
+        )
     return ok, items
 
 

surfsense_backend/tests/unit/capabilities/instagram/test_schemas.py

@@ -66,6 +66,4 @@ def test_details_wraps_profile_items():
 
 def test_details_rejects_both_sources():
     with pytest.raises(ValidationError):
-        DetailsInput(
-            urls=["https://www.instagram.com/natgeo/"], search_queries=["x"]
-        )
+        DetailsInput(urls=["https://www.instagram.com/natgeo/"], search_queries=["x"])

surfsense_backend/tests/unit/capabilities/tiktok/scrape/test_executor.py

@@ -54,7 +54,6 @@ async def test_forwards_typed_sources_and_limit():
         ScrapeInput(
             profiles=["nasa"],
             hashtags=["food"],
-            search_queries=["cats"],
             results_per_page=7,
             max_items=25,
         )
@@ -63,7 +62,6 @@ async def test_forwards_typed_sources_and_limit():
     (actor_input, limit) = scraper.calls[0]
     assert actor_input.profiles == ["nasa"]
     assert actor_input.hashtags == ["food"]
-    assert actor_input.searchQueries == ["cats"]
     assert actor_input.resultsPerPage == 7
     # The outer collection limit is the caller's total-item cap.
     assert limit == 25

surfsense_backend/tests/unit/platforms/instagram/test_budget.py

@@ -42,8 +42,7 @@ def _profile_payload(n: int) -> dict:
                 "edge_owner_to_timeline_media": {
                     "count": n,
                     "edges": [
-                        {"node": {"id": str(i), "shortcode": f"S{i}"}}
-                        for i in range(n)
+                        {"node": {"id": str(i), "shortcode": f"S{i}"}} for i in range(n)
                     ],
                 },
             }

surfsense_backend/tests/unit/platforms/instagram/test_discovery.py

@@ -33,9 +33,7 @@ async def test_google_discovery_keeps_only_profiles(monkeypatch):
             "https://example.com/not-instagram",
         ),
     )
-    targets = await scraper._discover(
-        "nat geo photos", search_type="profile", limit=10
-    )
+    targets = await scraper._discover("nat geo photos", search_type="profile", limit=10)
     assert [(t.kind, t.value) for t in targets] == [("profile", "natgeo")]
 
 
@@ -48,9 +46,7 @@ async def test_google_discovery_dedupes(monkeypatch):
             "https://www.instagram.com/natgeo/",
         ),
     )
-    targets = await scraper._discover(
-        "nat geo photos", search_type="profile", limit=10
-    )
+    targets = await scraper._discover("nat geo photos", search_type="profile", limit=10)
     assert len(targets) == 1
 
 

surfsense_backend/tests/unit/platforms/instagram/test_fetch_resilience.py

@@ -102,7 +102,9 @@ async def test_warms_then_returns_json():
     holder = _FakeHolder([_FakeSession(200, csrftoken=True)])
     token = _current_session.set(holder)
     try:
-        result = await fetch_json("api/v1/users/web_profile_info/", {"username": "natgeo"})
+        result = await fetch_json(
+            "api/v1/users/web_profile_info/", {"username": "natgeo"}
+        )
     finally:
         _current_session.reset(token)
     assert result == _PAYLOAD

surfsense_backend/tests/unit/platforms/instagram/test_parsers.py

@@ -175,8 +175,14 @@ def test_parse_post_prefers_relay_json():
                 "image_versions2": {"candidates": [{"url": "https://cdn/c2.jpg"}]},
             },
         ],
-        "usertags": {"in": [{"position": [0.5, 0.5], "user": {"username": "tagged1", "id": "77"}}]},
-        "coauthor_producers": [{"username": "coauthor1", "id": "88", "is_verified": True}],
+        "usertags": {
+            "in": [
+                {"position": [0.5, 0.5], "user": {"username": "tagged1", "id": "77"}}
+            ]
+        },
+        "coauthor_producers": [
+            {"username": "coauthor1", "id": "88", "is_verified": True}
+        ],
         "location": {"id": "123", "name": "Bali"},
     }
     html = (

surfsense_backend/tests/unit/platforms/tiktok/test_discovery.py

@@ -1,149 +0,0 @@
-"""Offline tests for Google-backed TikTok video discovery.
-
-``searchQueries`` are login-walled on TikTok's native search, so they route
-through the ``google_search`` platform (``site:tiktok.com``): each organic URL
-is classified with ``resolve_target`` and only video hits (``/@user/video/<id>``)
-are kept — profiles/hashtags/search/photo/non-tiktok are dropped (accounts
-belong to the user-search verb). These tests inject a fake ``scrape_serps`` so
-there is no network: they pin the classification, cross-query de-dup, the limit
-cap, the barren-query ErrorItem, and that no ``/search?q=`` listing target is
-ever built.
-"""
-
-from __future__ import annotations
-
-import json
-
-from app.proprietary.platforms.tiktok import (
-    TikTokScrapeInput,
-    orchestrator,
-    scrape_tiktok,
-)
-
-
-def _fake_serps(*organic_urls: str):
-    async def _scrape_serps(input_model, *, limit=None):
-        assert input_model.site == "tiktok.com"
-        assert input_model.maxPagesPerQuery == 1
-        return [{"organicResults": [{"url": u} for u in organic_urls]}]
-
-    return _scrape_serps
-
-
-def _video_page(url: str) -> str:
-    """Render a rehydration blob for a ``/@user/video/<id>`` URL."""
-    video_id = url.rsplit("/", 1)[1]
-    username = url.split("@")[1].split("/")[0]
-    blob = {
-        "__DEFAULT_SCOPE__": {
-            "webapp.video-detail": {
-                "itemInfo": {
-                    "itemStruct": {
-                        "id": video_id,
-                        "desc": "hi",
-                        "author": {"uniqueId": username},
-                        "stats": {"diggCount": 1},
-                    }
-                }
-            }
-        }
-    }
-    return (
-        '<script id="__UNIVERSAL_DATA_FOR_REHYDRATION__" '
-        f'type="application/json">{json.dumps(blob)}</script>'
-    )
-
-
-async def _fetch_video(url: str) -> str:
-    return _video_page(url)
-
-
-async def test_search_discovery_keeps_only_videos(monkeypatch):
-    # Only the video URL survives; profile / hashtag / search / photo /
-    # non-tiktok organic results are dropped.
-    monkeypatch.setattr(
-        orchestrator,
-        "scrape_serps",
-        _fake_serps(
-            "https://www.tiktok.com/@nasa/video/123",
-            "https://www.tiktok.com/@nasa",
-            "https://www.tiktok.com/tag/space",
-            "https://www.tiktok.com/search?q=space",
-            "https://www.tiktok.com/@nasa/photo/999",
-            "https://example.com/not-tiktok",
-        ),
-    )
-    items = await scrape_tiktok(
-        TikTokScrapeInput(searchQueries=["space"], resultsPerPage=10),
-        fetch=_fetch_video,
-    )
-    assert [i["id"] for i in items] == ["123"]
-
-
-async def test_search_discovery_dedupes_across_queries(monkeypatch):
-    # The same video surfacing under two queries is scraped once.
-    monkeypatch.setattr(
-        orchestrator,
-        "scrape_serps",
-        _fake_serps("https://www.tiktok.com/@nasa/video/123"),
-    )
-    items = await scrape_tiktok(
-        TikTokScrapeInput(searchQueries=["space", "rockets"], resultsPerPage=10),
-        fetch=_fetch_video,
-    )
-    assert [i["id"] for i in items] == ["123"]
-
-
-async def test_search_discovery_respects_per_target_limit(monkeypatch):
-    monkeypatch.setattr(
-        orchestrator,
-        "scrape_serps",
-        _fake_serps(
-            "https://www.tiktok.com/@a/video/1",
-            "https://www.tiktok.com/@b/video/2",
-            "https://www.tiktok.com/@c/video/3",
-        ),
-    )
-    items = await scrape_tiktok(
-        TikTokScrapeInput(searchQueries=["x"], resultsPerPage=2),
-        fetch=_fetch_video,
-    )
-    assert [i["id"] for i in items] == ["1", "2"]
-
-
-async def test_search_barren_query_emits_error_item(monkeypatch):
-    # A query whose discovery finds no video URLs degrades to one ErrorItem.
-    monkeypatch.setattr(
-        orchestrator,
-        "scrape_serps",
-        _fake_serps(
-            "https://www.tiktok.com/@nasa",
-            "https://example.com/x",
-        ),
-    )
-    items = await scrape_tiktok(
-        TikTokScrapeInput(searchQueries=["space"], resultsPerPage=10),
-        fetch=_fetch_video,
-    )
-    assert len(items) == 1
-    assert items[0]["errorCode"] == "no_items"
-    assert items[0]["input"] == "space"
-
-
-async def test_search_never_builds_listing_target(monkeypatch):
-    # searchQueries must never hit the (login-walled) native search listing flow.
-    monkeypatch.setattr(
-        orchestrator,
-        "scrape_serps",
-        _fake_serps("https://www.tiktok.com/@nasa/video/123"),
-    )
-
-    async def _boom_listing(_url: str, _count: int) -> list[dict]:
-        raise AssertionError("searchQueries must not build a listing target")
-
-    items = await scrape_tiktok(
-        TikTokScrapeInput(searchQueries=["space"], resultsPerPage=10),
-        fetch=_fetch_video,
-        fetch_listing=_boom_listing,
-    )
-    assert [i["id"] for i in items] == ["123"]

surfsense_backend/tests/unit/platforms/tiktok/test_fetch_resilience.py

@@ -84,7 +84,9 @@ async def test_warms_then_returns_html():
 
 
 async def test_rotates_when_warm_fails_then_succeeds():
-    holder = _FakeHolder([_FakeSession(200, warms=False), _FakeSession(200, warms=True)])
+    holder = _FakeHolder(
+        [_FakeSession(200, warms=False), _FakeSession(200, warms=True)]
+    )
     token = _current_session.set(holder)
     try:
         result = await client.fetch_html("https://www.tiktok.com/@scout2015")
@@ -119,9 +121,7 @@ async def test_rotates_and_rewarms_on_403():
 
 async def test_persistent_403_raises_blocked(monkeypatch):
     _no_sleep(monkeypatch)
-    holder = _FakeHolder(
-        [_FakeSession(403) for _ in range(client._MAX_ROTATIONS + 1)]
-    )
+    holder = _FakeHolder([_FakeSession(403) for _ in range(client._MAX_ROTATIONS + 1)])
     token = _current_session.set(holder)
     try:
         raised = False

surfsense_backend/tests/unit/platforms/tiktok/test_target_resolver.py

@@ -6,7 +6,9 @@ from app.proprietary.platforms.tiktok.targets import resolve_target
 
 
 def test_resolve_video_carries_username_and_id():
-    target = resolve_target("https://www.tiktok.com/@scout2015/video/6718335390845095173")
+    target = resolve_target(
+        "https://www.tiktok.com/@scout2015/video/6718335390845095173"
+    )
     assert target is not None
     assert target.kind == "video"
     assert target.value == "6718335390845095173"

surfsense_backend/tests/unit/platforms/tiktok/test_user_search.py

@@ -28,9 +28,7 @@ async def test_user_search_parses_dedupes_and_caps():
     async def fake_fetch(_url: str, _cap: int) -> list[dict]:
         return [_user("1", "nasa"), _user("1", "nasa"), _user("2", "nasa2")]
 
-    items = await search_tiktok_users(
-        ["nasa"], per_query=2, fetch_users=fake_fetch
-    )
+    items = await search_tiktok_users(["nasa"], per_query=2, fetch_users=fake_fetch)
 
     assert [i["id"] for i in items] == ["1", "2"]
     first = items[0]
@@ -49,9 +47,7 @@ async def test_user_search_empty_query_emits_error_item():
     async def fake_fetch(_url: str, _cap: int) -> list[dict]:
         return []
 
-    items = await search_tiktok_users(
-        ["ghost"], per_query=5, fetch_users=fake_fetch
-    )
+    items = await search_tiktok_users(["ghost"], per_query=5, fetch_users=fake_fetch)
 
     assert len(items) == 1
     assert items[0]["errorCode"] == "no_users"

surfsense_backend/tests/unit/services/test_requesty_model_normalizer.py

@@ -58,9 +58,7 @@ def test_chat_model_requires_slash_tools_and_context():
 
 
 def test_excluded_provider_slug_is_filtered():
-    assert not is_requesty_chat_model(
-        _requesty_model(model_id="amazon/nova-pro-v1")
-    )
+    assert not is_requesty_chat_model(_requesty_model(model_id="amazon/nova-pro-v1"))
 
 
 def test_image_generation_models_excluded_from_chat_and_flagged():
@@ -89,9 +87,7 @@ def test_normalize_maps_context_window_and_capabilities():
                 name="GPT-4o mini",
             ),
             _requesty_model(model_id="openai/gpt-4o-mini", tools=False),
-            _requesty_model(
-                model_id="black-forest-labs/flux", image_generation=True
-            ),
+            _requesty_model(model_id="black-forest-labs/flux", image_generation=True),
         ]
     )
 

surfsense_evals/scripts/analyze_failure_timing.py

@@ -21,8 +21,7 @@ PDFS = REPO / "data" / "multimodal_doc" / "mmlongbench" / "pdfs"
 
 def main() -> None:
     rows = [
-        json.loads(line) for line in RAW.read_text(encoding="utf-8").splitlines()
-        if line.strip()
+        json.loads(line) for line in RAW.read_text(encoding="utf-8").splitlines() if line.strip()
     ]
 
     # 1) SSL clustering: failures by question index per arm
@@ -35,11 +34,19 @@ def main() -> None:
         arm_seen_count[arm] += 1
         qid_order[f"{arm}::{row['qid']}"] = idx
         err = row.get("error") or ""
-        cluster = "ssl" if "SSLError" in err else (
-            "empty" if not (row.get("raw_text") or "").strip() and not err else (
-                "5xx" if "502" in err or "503" in err else (
-                    "size_limit" if "exceeds" in err.lower() and "limit" in err.lower() else (
-                        "other_err" if err else "ok"
+        cluster = (
+            "ssl"
+            if "SSLError" in err
+            else (
+                "empty"
+                if not (row.get("raw_text") or "").strip() and not err
+                else (
+                    "5xx"
+                    if "502" in err or "503" in err
+                    else (
+                        "size_limit"
+                        if "exceeds" in err.lower() and "limit" in err.lower()
+                        else ("other_err" if err else "ok")
                     )
                 )
             )
@@ -100,19 +107,26 @@ def main() -> None:
         err = row.get("error") or ""
         empty = not (row.get("raw_text") or "").strip()
         if err or empty:
-            by_pdf[row["doc_id"]].append({
-                "arm": row["arm"],
-                "qid": row["qid"],
-                "err_kind": (
-                    "ssl" if "SSLError" in err
-                    else "size_limit" if "exceeds" in err.lower() and "limit" in err.lower()
-                    else "5xx" if "502" in err or "503" in err
-                    else "json_decode" if "JSONDecodeError" in err
-                    else "empty" if empty and not err
-                    else "other"
-                ),
-                "pages": row.get("pages"),
-            })
+            by_pdf[row["doc_id"]].append(
+                {
+                    "arm": row["arm"],
+                    "qid": row["qid"],
+                    "err_kind": (
+                        "ssl"
+                        if "SSLError" in err
+                        else "size_limit"
+                        if "exceeds" in err.lower() and "limit" in err.lower()
+                        else "5xx"
+                        if "502" in err or "503" in err
+                        else "json_decode"
+                        if "JSONDecodeError" in err
+                        else "empty"
+                        if empty and not err
+                        else "other"
+                    ),
+                    "pages": row.get("pages"),
+                }
+            )
     for doc, items in sorted(by_pdf.items(), key=lambda x: (-len(x[1]), x[0])):
         kinds = Counter(i["err_kind"] for i in items)
         arms = sorted({i["arm"] for i in items})

surfsense_evals/scripts/analyze_failures.py

@@ -51,8 +51,7 @@ def _classify(error: str | None, raw_text: str) -> str:
 
 def main() -> None:
     rows = [
-        json.loads(line) for line in RAW.read_text(encoding="utf-8").splitlines()
-        if line.strip()
+        json.loads(line) for line in RAW.read_text(encoding="utf-8").splitlines() if line.strip()
     ]
 
     by_arm_failures: dict[str, list[dict]] = defaultdict(list)
@@ -121,7 +120,9 @@ def main() -> None:
     print("=" * 90)
     for entry in by_arm_failures.get("native_pdf", []):
         err = (entry["error"] or "(no error string)")[:240].replace("\n", " ")
-        print(f"  {entry['qid']}  doc={entry['doc_id']} pages={entry['pages']} cluster={entry['cluster']}")
+        print(
+            f"  {entry['qid']}  doc={entry['doc_id']} pages={entry['pages']} cluster={entry['cluster']}"
+        )
         print(f"    err: {err}")
 
     summary: dict[str, Any] = {
@@ -130,18 +131,13 @@ def main() -> None:
                 "n": n_per_arm[arm],
                 "failures": len(by_arm_failures[arm]),
                 "rate": len(by_arm_failures[arm]) / n_per_arm[arm],
-                "clusters": {
-                    cluster: len(items)
-                    for cluster, items in error_clusters[arm].items()
-                },
+                "clusters": {cluster: len(items) for cluster, items in error_clusters[arm].items()},
                 "rows": by_arm_failures[arm],
             }
             for arm in sorted(n_per_arm)
         },
         "per_pdf": {
-            pdf: [
-                {**r, "arm": r["arm"]} for r in failures
-            ]
+            pdf: [{**r, "arm": r["arm"]} for r in failures]
             for pdf, failures in by_pdf_failures.items()
         },
     }

surfsense_evals/scripts/check_extraction_sizes.py

@@ -23,9 +23,7 @@ SAFE_CHARS = (CTX_TOKENS - PROMPT_OVERHEAD_TOKENS - MAX_OUTPUT_TOKENS) * CHARS_P
 
 def main() -> None:
     rows = [
-        json.loads(line)
-        for line in MAP.read_text(encoding="utf-8").splitlines()
-        if line.strip()
+        json.loads(line) for line in MAP.read_text(encoding="utf-8").splitlines() if line.strip()
     ]
 
     total = len(rows)

surfsense_evals/scripts/compute_adjusted_accuracy.py

@@ -67,14 +67,18 @@ def classify(error: str | None, raw_text: str) -> str:
 
 def main() -> None:
     rows = [
-        json.loads(line) for line in RAW.read_text(encoding="utf-8").splitlines()
-        if line.strip()
+        json.loads(line) for line in RAW.read_text(encoding="utf-8").splitlines() if line.strip()
     ]
-    by_arm: dict[str, dict] = defaultdict(lambda: {
-        "n": 0, "correct": 0,
-        "transient_ssl_or_5xx": 0, "transient_empty": 0,
-        "intrinsic_limit": 0, "other_error": 0,
-    })
+    by_arm: dict[str, dict] = defaultdict(
+        lambda: {
+            "n": 0,
+            "correct": 0,
+            "transient_ssl_or_5xx": 0,
+            "transient_empty": 0,
+            "intrinsic_limit": 0,
+            "other_error": 0,
+        }
+    )
     for row in rows:
         arm = row["arm"]
         m = by_arm[arm]
@@ -86,7 +90,9 @@ def main() -> None:
         if kind != "ok":
             m[kind] += 1
 
-    print(f"{'arm':<25} {'raw acc%':>8} {'transient':>10} {'intrinsic':>10} {'other':>6} {'adj acc% (no transient)':>22}")
+    print(
+        f"{'arm':<25} {'raw acc%':>8} {'transient':>10} {'intrinsic':>10} {'other':>6} {'adj acc% (no transient)':>22}"
+    )
     print("-" * 88)
     for arm in sorted(by_arm):
         m = by_arm[arm]
@@ -96,9 +102,7 @@ def main() -> None:
         other = m["other_error"]
         usable = m["n"] - transient
         adj = m["correct"] / usable * 100 if usable else 0
-        print(
-            f"{arm:<25} {raw:>7.1f}% {transient:>10} {intrinsic:>10} {other:>6} {adj:>21.1f}%"
-        )
+        print(f"{arm:<25} {raw:>7.1f}% {transient:>10} {intrinsic:>10} {other:>6} {adj:>21.1f}%")
 
     print()
     print("transient   = SSLError / 502 / 503 / empty stream / mid-stream JSON decode (would")

surfsense_evals/scripts/compute_blog_extras.py

@@ -95,7 +95,7 @@ def _mcnemar_exact_pvalue(b: int, c: int) -> float:
     k = min(b, c)
     # Two-sided exact: 2 * P(X <= k) clipped at 1.0
     cdf = sum(_binom_coef(n, i) for i in range(k + 1))
-    p = 2.0 * cdf / (2 ** n)
+    p = 2.0 * cdf / (2**n)
     return min(1.0, p)
 
 
@@ -116,7 +116,7 @@ def _mcnemar_table(rows: list[dict]) -> dict:
     qids = sorted(by_qid)
     out: dict[str, dict] = {"arms": arms, "n_qids": len(qids), "pairs": []}
     for i, ai in enumerate(arms):
-        for aj in arms[i + 1:]:
+        for aj in arms[i + 1 :]:
             b = c = both = neither = 0
             for q in qids:
                 row = by_qid[q]
@@ -132,12 +132,17 @@ def _mcnemar_table(rows: list[dict]) -> dict:
                 else:
                     neither += 1
             p = _mcnemar_exact_pvalue(b, c)
-            out["pairs"].append({
-                "arm_i": ai, "arm_j": aj,
-                "b_i_only": b, "c_j_only": c,
-                "both_correct": both, "both_wrong": neither,
-                "p_value": p,
-            })
+            out["pairs"].append(
+                {
+                    "arm_i": ai,
+                    "arm_j": aj,
+                    "b_i_only": b,
+                    "c_j_only": c,
+                    "both_correct": both,
+                    "both_wrong": neither,
+                    "p_value": p,
+                }
+            )
     return out
 
 
@@ -154,9 +159,7 @@ def _per_pdf_stats(rows: list[dict]) -> dict[str, dict]:
         arm = r["arm"]
         pdf = r["doc_id"]
         graded = r.get("graded") or {}
-        bucket.setdefault(arm, {}).setdefault(pdf, []).append(
-            bool(graded.get("correct"))
-        )
+        bucket.setdefault(arm, {}).setdefault(pdf, []).append(bool(graded.get("correct")))
 
     out: dict[str, dict] = {}
     for arm, pdfs in bucket.items():
@@ -207,7 +210,8 @@ def _per_arm_latency(rows: list[dict]) -> dict[str, dict]:
             # Coefficient of variation: std / mean (unitless tail-fatness).
             "cv": (
                 statistics.stdev(lats) / statistics.mean(lats)
-                if len(lats) > 1 and statistics.mean(lats) > 0 else 0.0
+                if len(lats) > 1 and statistics.mean(lats) > 0
+                else 0.0
             ),
         }
     return out
@@ -259,24 +263,30 @@ def _print_latency(title: str, lat: dict[str, dict]) -> None:
     print()
     print(title)
     print("-" * len(title))
-    header = (f"{'arm':<25} {'n':>4} {'mean':>7} {'std':>7} "
-              f"{'p50':>7} {'p90':>7} {'p95':>7} {'p99':>7} {'max':>7} {'CV':>5}")
+    header = (
+        f"{'arm':<25} {'n':>4} {'mean':>7} {'std':>7} "
+        f"{'p50':>7} {'p90':>7} {'p95':>7} {'p99':>7} {'max':>7} {'CV':>5}"
+    )
     print(header)
     print("-" * len(header))
     for arm in sorted(lat, key=lambda a: lat[a]["mean_s"]):
         s = lat[arm]
-        print(f"{arm:<25} {s['n']:>4} "
-              f"{s['mean_s']:>6.1f}s {s['std_s']:>6.1f}s "
-              f"{s['p50_s']:>6.1f}s {s['p90_s']:>6.1f}s {s['p95_s']:>6.1f}s "
-              f"{s['p99_s']:>6.1f}s {s['max_s']:>6.1f}s {s['cv']:>5.2f}")
+        print(
+            f"{arm:<25} {s['n']:>4} "
+            f"{s['mean_s']:>6.1f}s {s['std_s']:>6.1f}s "
+            f"{s['p50_s']:>6.1f}s {s['p90_s']:>6.1f}s {s['p95_s']:>6.1f}s "
+            f"{s['p99_s']:>6.1f}s {s['max_s']:>6.1f}s {s['cv']:>5.2f}"
+        )
 
 
 def _print_tokens(title: str, toks: dict[str, dict]) -> None:
     print()
     print(title)
     print("-" * len(title))
-    header = (f"{'arm':<25} {'in mean':>9} {'in p50':>9} {'in p95':>9} {'in max':>9}"
-              f"  {'out mean':>9} {'out p95':>9}")
+    header = (
+        f"{'arm':<25} {'in mean':>9} {'in p50':>9} {'in p95':>9} {'in max':>9}"
+        f"  {'out mean':>9} {'out p95':>9}"
+    )
     print(header)
     print("-" * len(header))
     for arm in sorted(toks):
@@ -285,25 +295,31 @@ def _print_tokens(title: str, toks: dict[str, dict]) -> None:
         eout = e.get("output")
         if not ein:
             continue
-        print(f"{arm:<25} "
-              f"{ein['mean']:>9,.0f} {ein['p50']:>9,.0f} {ein['p95']:>9,.0f} {ein['max']:>9,.0f}  "
-              f"{(eout or {}).get('mean', 0):>9,.0f} {(eout or {}).get('p95', 0):>9,.0f}")
+        print(
+            f"{arm:<25} "
+            f"{ein['mean']:>9,.0f} {ein['p50']:>9,.0f} {ein['p95']:>9,.0f} {ein['max']:>9,.0f}  "
+            f"{(eout or {}).get('mean', 0):>9,.0f} {(eout or {}).get('p95', 0):>9,.0f}"
+        )
 
 
 def _print_pdf_var(title: str, var: dict[str, dict]) -> None:
     print()
     print(title)
     print("-" * len(title))
-    header = (f"{'arm':<25} {'n_pdfs':>7} {'mean':>7} {'std':>7} {'min':>7} "
-              f"{'p25':>7} {'p50':>7} {'p75':>7} {'max':>7} {'#0%':>5} {'#100%':>6}")
+    header = (
+        f"{'arm':<25} {'n_pdfs':>7} {'mean':>7} {'std':>7} {'min':>7} "
+        f"{'p25':>7} {'p50':>7} {'p75':>7} {'max':>7} {'#0%':>5} {'#100%':>6}"
+    )
     print(header)
     print("-" * len(header))
     for arm in sorted(var, key=lambda a: -var[a]["mean"]):
         s = var[arm]
-        print(f"{arm:<25} {s['n_pdfs']:>7} "
-              f"{s['mean']*100:>6.1f}% {s['std']*100:>6.1f}% {s['min']*100:>6.1f}% "
-              f"{s['p25']*100:>6.1f}% {s['p50']*100:>6.1f}% {s['p75']*100:>6.1f}% "
-              f"{s['max']*100:>6.1f}% {s['n_pdfs_zero']:>5} {s['n_pdfs_perfect']:>6}")
+        print(
+            f"{arm:<25} {s['n_pdfs']:>7} "
+            f"{s['mean'] * 100:>6.1f}% {s['std'] * 100:>6.1f}% {s['min'] * 100:>6.1f}% "
+            f"{s['p25'] * 100:>6.1f}% {s['p50'] * 100:>6.1f}% {s['p75'] * 100:>6.1f}% "
+            f"{s['max'] * 100:>6.1f}% {s['n_pdfs_zero']:>5} {s['n_pdfs_perfect']:>6}"
+        )
 
 
 def _print_mcnemar(title: str, table: dict) -> None:
@@ -311,8 +327,10 @@ def _print_mcnemar(title: str, table: dict) -> None:
     print(title)
     print("-" * len(title))
     print(f"n_qids on which all arms have a graded row: {table['n_qids']}")
-    header = (f"{'arm_i':<25} {'arm_j':<25} {'b':>4} {'c':>4} "
-              f"{'both ok':>8} {'both wr':>8} {'p (2-sided)':>13} {'sig':>4}")
+    header = (
+        f"{'arm_i':<25} {'arm_j':<25} {'b':>4} {'c':>4} "
+        f"{'both ok':>8} {'both wr':>8} {'p (2-sided)':>13} {'sig':>4}"
+    )
     print(header)
     print("-" * len(header))
     for pair in sorted(table["pairs"], key=lambda p: p["p_value"]):
@@ -323,10 +341,12 @@ def _print_mcnemar(title: str, table: dict) -> None:
             sig = "**"
         elif pair["p_value"] < 0.05:
             sig = "*"
-        print(f"{pair['arm_i']:<25} {pair['arm_j']:<25} "
-              f"{pair['b_i_only']:>4} {pair['c_j_only']:>4} "
-              f"{pair['both_correct']:>8} {pair['both_wrong']:>8} "
-              f"{pair['p_value']:>13.4f} {sig:>4}")
+        print(
+            f"{pair['arm_i']:<25} {pair['arm_j']:<25} "
+            f"{pair['b_i_only']:>4} {pair['c_j_only']:>4} "
+            f"{pair['both_correct']:>8} {pair['both_wrong']:>8} "
+            f"{pair['p_value']:>13.4f} {sig:>4}"
+        )
 
 
 # ---------------------------------------------------------------------------

surfsense_evals/scripts/compute_post_retry_accuracy.py

@@ -78,9 +78,11 @@ def _print_table(title: str, summary: dict[str, dict]) -> None:
     # stable order: highest accuracy first
     arms_sorted = sorted(summary.items(), key=lambda kv: -kv[1]["accuracy"])
     for arm, s in arms_sorted:
-        print(f"{arm:<25} {s['n']:>4} {s['n_correct']:>7} "
-              f"{s['accuracy']*100:>6.1f}% {s['f1_mean']*100:>6.1f}% "
-              f"{s['n_failures']:>6} {s['failure_rate']*100:>6.1f}%")
+        print(
+            f"{arm:<25} {s['n']:>4} {s['n_correct']:>7} "
+            f"{s['accuracy'] * 100:>6.1f}% {s['f1_mean'] * 100:>6.1f}% "
+            f"{s['n_failures']:>6} {s['failure_rate'] * 100:>6.1f}%"
+        )
 
 
 def main() -> int:
@@ -103,9 +105,7 @@ def main() -> int:
     raw_rows = _read_jsonl(raw_path)
     retry_rows = _read_jsonl(retry_path)
 
-    retry_by_key: dict[tuple[str, str], dict] = {
-        _row_key(r): r for r in retry_rows
-    }
+    retry_by_key: dict[tuple[str, str], dict] = {_row_key(r): r for r in retry_rows}
 
     merged_rows: list[dict] = []
     n_replaced_recovered = 0

surfsense_evals/scripts/inspect_first30.py

@@ -44,10 +44,7 @@ def main() -> None:
         f"questions covering first 30 docs: total={len(qs_in_30)}  "
         f"answerable={answerable}  unanswerable={unanswerable}"
     )
-    print(
-        f"avg Qs/PDF: {len(qs_in_30) / 30:.1f}  "
-        f"answerable/PDF: {answerable / 30:.1f}"
-    )
+    print(f"avg Qs/PDF: {len(qs_in_30) / 30:.1f}  answerable/PDF: {answerable / 30:.1f}")
     print(f"format mix in scope: {dict(fmts)}")
     print()
     print("25 new PDFs to ingest:")

surfsense_evals/scripts/peek_crag_run.py

@@ -27,10 +27,7 @@ def main() -> None:
             grade = a.get("graded", {})
             text = (a.get("raw_text") or "").strip()
             tail = text[-200:] if text else ""
-            print(
-                f"  [{arm_name}] grade={grade.get('grade')} "
-                f"method={grade.get('method')}"
-            )
+            print(f"  [{arm_name}] grade={grade.get('grade')} method={grade.get('method')}")
             print(f"    -> {tail!r}")
 
 

surfsense_evals/scripts/retry_failed_questions.py

@@ -132,17 +132,19 @@ def _load_failed_rows(raw_path: Path) -> list[FailedRow]:
             row = json.loads(line)
             if not _is_failure_row(row):
                 continue
-            out.append(FailedRow(
-                arm=str(row["arm"]),
-                qid=str(row["qid"]),
-                doc_id=str(row["doc_id"]),
-                answer_format=str(row.get("answer_format") or ""),
-                gold=str(row.get("gold") or ""),
-                pages=int(row.get("pages") or 0),
-                document_id=row.get("document_id"),
-                original_error=row.get("error"),
-                original_row=row,
-            ))
+            out.append(
+                FailedRow(
+                    arm=str(row["arm"]),
+                    qid=str(row["qid"]),
+                    doc_id=str(row["doc_id"]),
+                    answer_format=str(row.get("answer_format") or ""),
+                    gold=str(row.get("gold") or ""),
+                    pages=int(row.get("pages") or 0),
+                    document_id=row.get("document_id"),
+                    original_error=row.get("error"),
+                    original_row=row,
+                )
+            )
     return out
 
 
@@ -202,8 +204,12 @@ def _qid_index(qid: str) -> int:
 
 
 def _build_native_request(
-    qid: str, question: str, answer_format: str, pdf_path: Path,
-    *, max_output_tokens: int,
+    qid: str,
+    question: str,
+    answer_format: str,
+    pdf_path: Path,
+    *,
+    max_output_tokens: int,
 ) -> ArmRequest:
     return ArmRequest(
         question_id=qid,
@@ -214,12 +220,14 @@ def _build_native_request(
 
 
 def _build_lc_request(
-    qid: str, question: str, answer_format: str, doc_id: str, md_path: Path,
+    qid: str,
+    question: str,
+    answer_format: str,
+    doc_id: str,
+    md_path: Path,
 ) -> ArmRequest:
     if not md_path.exists():
-        raise FileNotFoundError(
-            f"Missing parser extraction at {md_path}; cannot retry LC arm."
-        )
+        raise FileNotFoundError(f"Missing parser extraction at {md_path}; cannot retry LC arm.")
     markdown = md_path.read_text(encoding="utf-8")
     return ArmRequest(
         question_id=qid,
@@ -256,7 +264,9 @@ class RetryOutcome:
 
 
 async def _retry_one(
-    arm_obj: Any, request: ArmRequest, *,
+    arm_obj: Any,
+    request: ArmRequest,
+    *,
     arm_name: str,
     qid: str,
     max_attempts: int,
@@ -274,31 +284,44 @@ async def _retry_one(
         attempt_error = result.error
         if not attempt_error and not raw_text:
             attempt_error = "EmptyResponse: stream ended with no text"
-        attempts.append(AttemptLog(
-            attempt=attempt,
-            started_iso=started_iso,
-            latency_ms=latency_ms,
-            error=attempt_error,
-            raw_text_chars=len(raw_text),
-        ))
+        attempts.append(
+            AttemptLog(
+                attempt=attempt,
+                started_iso=started_iso,
+                latency_ms=latency_ms,
+                error=attempt_error,
+                raw_text_chars=len(raw_text),
+            )
+        )
         final = result
         if not attempt_error and raw_text:
             return RetryOutcome(
-                arm=arm_name, qid=qid, attempts=attempts,
-                final_result=result, recovered=True,
+                arm=arm_name,
+                qid=qid,
+                attempts=attempts,
+                final_result=result,
+                recovered=True,
             )
         if attempt < max_attempts:
             delay = min(max_delay, base_delay * (2 ** (attempt - 1)))
             delay = delay * (0.5 + random.random())
             logger.info(
                 "[%s::%s] attempt %d/%d failed (%s); sleeping %.1fs",
-                arm_name, qid, attempt, max_attempts, attempt_error, delay,
+                arm_name,
+                qid,
+                attempt,
+                max_attempts,
+                attempt_error,
+                delay,
             )
             await asyncio.sleep(delay)
     assert final is not None
     return RetryOutcome(
-        arm=arm_name, qid=qid, attempts=attempts,
-        final_result=final, recovered=False,
+        arm=arm_name,
+        qid=qid,
+        attempts=attempts,
+        final_result=final,
+        recovered=False,
     )
 
 
@@ -365,7 +388,8 @@ async def _run(args: argparse.Namespace) -> int:
         by_arm_count[f.arm] = by_arm_count.get(f.arm, 0) + 1
     logger.info(
         "Loaded %d failed rows across %d arms: %s",
-        len(failed), len(by_arm_count),
+        len(failed),
+        len(by_arm_count),
         ", ".join(f"{a}={n}" for a, n in sorted(by_arm_count.items())),
     )
 
@@ -383,7 +407,8 @@ async def _run(args: argparse.Namespace) -> int:
         engine=PdfEngine(args.pdf_engine),
     )
     native_arm = NativePdfArm(
-        provider=native_provider, max_output_tokens=args.max_output_tokens,
+        provider=native_provider,
+        max_output_tokens=args.max_output_tokens,
     )
 
     lc_arms: dict[str, BareLlmArm] = {}
@@ -413,7 +438,8 @@ async def _run(args: argparse.Namespace) -> int:
         if qrow is None:
             logger.error(
                 "Could not find question text for %s (idx %d) — skipping",
-                f.doc_id, q_idx,
+                f.doc_id,
+                q_idx,
             )
             continue
         question_text = str(qrow.get("question") or "").strip()
@@ -430,7 +456,10 @@ async def _run(args: argparse.Namespace) -> int:
                 logger.error("PDF missing on disk: %s — skipping", pdf_path)
                 continue
             request = _build_native_request(
-                f.qid, question_text, answer_format, pdf_path,
+                f.qid,
+                question_text,
+                answer_format,
+                pdf_path,
                 max_output_tokens=args.max_output_tokens,
             )
             arm_obj = native_arm
@@ -440,11 +469,16 @@ async def _run(args: argparse.Namespace) -> int:
             if not md_path_str or ext_blob.get("status") != "ok":
                 logger.error(
                     "Missing extraction for %s on %s — cannot retry; skipping",
-                    f.arm, f.doc_id,
+                    f.arm,
+                    f.doc_id,
                 )
                 continue
             request = _build_lc_request(
-                f.qid, question_text, answer_format, f.doc_id, Path(md_path_str),
+                f.qid,
+                question_text,
+                answer_format,
+                f.doc_id,
+                Path(md_path_str),
             )
             arm_obj = lc_arms[f.arm]
         else:
@@ -452,13 +486,17 @@ async def _run(args: argparse.Namespace) -> int:
             continue
 
         plan.append((f, request, arm_obj))
-        coros.append(_retry_one(
-            arm_obj, request,
-            arm_name=f.arm, qid=f.qid,
-            max_attempts=args.max_attempts,
-            base_delay=args.base_delay,
-            max_delay=args.max_delay,
-        ))
+        coros.append(
+            _retry_one(
+                arm_obj,
+                request,
+                arm_name=f.arm,
+                qid=f.qid,
+                max_attempts=args.max_attempts,
+                base_delay=args.base_delay,
+                max_delay=args.max_delay,
+            )
+        )
 
     if not coros:
         logger.warning("Nothing to retry after request building.")
@@ -467,13 +505,17 @@ async def _run(args: argparse.Namespace) -> int:
     logger.info(
         "Retrying %d failed rows with up to %d attempts each "
         "(base_delay=%.1fs, max_delay=%.1fs, concurrency=%d).",
-        len(coros), args.max_attempts, args.base_delay, args.max_delay,
+        len(coros),
+        args.max_attempts,
+        args.base_delay,
+        args.max_delay,
         args.concurrency,
     )
 
     started = time.monotonic()
     outcomes: list[RetryOutcome] = await _gather_with_limit(
-        coros, concurrency=args.concurrency,
+        coros,
+        concurrency=args.concurrency,
     )
     elapsed = time.monotonic() - started
     logger.info("Retry pass finished in %.1fs.", elapsed)
@@ -489,12 +531,8 @@ async def _run(args: argparse.Namespace) -> int:
         for (f, _req, _arm_obj), outcome in zip(plan, outcomes, strict=True):
             per_arm_total[outcome.arm] = per_arm_total.get(outcome.arm, 0) + 1
             if outcome.recovered:
-                per_arm_recovered[outcome.arm] = (
-                    per_arm_recovered.get(outcome.arm, 0) + 1
-                )
-            per_arm_attempts_dist.setdefault(outcome.arm, []).append(
-                len(outcome.attempts)
-            )
+                per_arm_recovered[outcome.arm] = per_arm_recovered.get(outcome.arm, 0) + 1
+            per_arm_attempts_dist.setdefault(outcome.arm, []).append(len(outcome.attempts))
 
             g = grade(
                 pred=extract_freeform_answer(outcome.final_result.raw_text or ""),
@@ -555,12 +593,11 @@ async def _run(args: argparse.Namespace) -> int:
             arm: {
                 "tried": per_arm_total.get(arm, 0),
                 "recovered": per_arm_recovered.get(arm, 0),
-                "still_failed": (
-                    per_arm_total.get(arm, 0) - per_arm_recovered.get(arm, 0)
-                ),
+                "still_failed": (per_arm_total.get(arm, 0) - per_arm_recovered.get(arm, 0)),
                 "recovery_rate": (
                     per_arm_recovered.get(arm, 0) / per_arm_total[arm]
-                    if per_arm_total.get(arm) else 0.0
+                    if per_arm_total.get(arm)
+                    else 0.0
                 ),
                 "attempts_distribution": sorted(per_arm_attempts_dist.get(arm, [])),
             }
@@ -593,8 +630,7 @@ async def _run(args: argparse.Namespace) -> int:
     rec_total = sum(per_arm_recovered.values())
     rate_total = (rec_total / total * 100) if total else 0.0
     print("-" * len(header))
-    print(f"{'TOTAL':<25} {total:>6} {rec_total:>10} {total - rec_total:>11} "
-          f"{rate_total:>6.1f}%")
+    print(f"{'TOTAL':<25} {total:>6} {rec_total:>10} {total - rec_total:>11} {rate_total:>6.1f}%")
     print()
     print(f"Wrote {out_path.relative_to(REPO)}")
     print(f"Wrote {summary_path.relative_to(REPO)}")
@@ -604,27 +640,37 @@ async def _run(args: argparse.Namespace) -> int:
 def main() -> None:
     parser = argparse.ArgumentParser(description=__doc__)
     parser.add_argument(
-        "--run-id", default="2026-05-14T00-53-19Z",
+        "--run-id",
+        default="2026-05-14T00-53-19Z",
         help="Run timestamp under data/multimodal_doc/runs/. Default is the "
-             "n=171 production run we wrote up in the blog.",
+        "n=171 production run we wrote up in the blog.",
     )
     parser.add_argument("--max-attempts", type=int, default=5)
-    parser.add_argument("--base-delay", type=float, default=1.0,
-                        help="Base seconds for exponential backoff (default 1s).")
-    parser.add_argument("--max-delay", type=float, default=30.0,
-                        help="Cap on per-retry sleep (default 30s).")
-    parser.add_argument("--concurrency", type=int, default=2,
-                        help="Parallel retries in flight (default 2 — keep low "
-                             "to avoid the same transport stress that caused "
-                             "the original failures).")
+    parser.add_argument(
+        "--base-delay",
+        type=float,
+        default=1.0,
+        help="Base seconds for exponential backoff (default 1s).",
+    )
+    parser.add_argument(
+        "--max-delay", type=float, default=30.0, help="Cap on per-retry sleep (default 30s)."
+    )
+    parser.add_argument(
+        "--concurrency",
+        type=int,
+        default=2,
+        help="Parallel retries in flight (default 2 — keep low "
+        "to avoid the same transport stress that caused "
+        "the original failures).",
+    )
     parser.add_argument("--llm-model", default="anthropic/claude-sonnet-4.5")
-    parser.add_argument("--pdf-engine", default="native",
-                        choices=[e.value for e in PdfEngine])
+    parser.add_argument("--pdf-engine", default="native", choices=[e.value for e in PdfEngine])
     parser.add_argument("--max-output-tokens", type=int, default=512)
     parser.add_argument(
-        "--include-surfsense", action="store_true",
+        "--include-surfsense",
+        action="store_true",
         help="Also retry surfsense_agentic failures (requires backend + celery up). "
-             "Default is to skip them since the n=171 run had 0 SurfSense failures.",
+        "Default is to skip them since the n=171 run had 0 SurfSense failures.",
     )
     args = parser.parse_args()
     raise SystemExit(asyncio.run(_run(args)))

surfsense_evals/scripts/summarise_crag_run.py

@@ -23,12 +23,12 @@ def main() -> None:
         d = metrics[arm]
         print(
             f"{arm:14s}: "
-            f"acc={d['accuracy']*100:5.1f}% (Wilson 95% CI "
-            f"{d['ci_low']*100:.1f}-{d['ci_high']*100:.1f}) | "
-            f"correct={d['correct_rate']*100:5.1f}% "
-            f"missing={d['missing_rate']*100:5.1f}% "
-            f"incorrect={d['incorrect_rate']*100:5.1f}% | "
-            f"truth={d['truthfulness_score']*100:+5.1f}%"
+            f"acc={d['accuracy'] * 100:5.1f}% (Wilson 95% CI "
+            f"{d['ci_low'] * 100:.1f}-{d['ci_high'] * 100:.1f}) | "
+            f"correct={d['correct_rate'] * 100:5.1f}% "
+            f"missing={d['missing_rate'] * 100:5.1f}% "
+            f"incorrect={d['incorrect_rate'] * 100:5.1f}% | "
+            f"truth={d['truthfulness_score'] * 100:+5.1f}%"
         )
 
     print()
@@ -48,7 +48,7 @@ def main() -> None:
         pieces = [f"{qt:20s} (n={n:3d}):"]
         for arm in ("bare_llm", "long_context", "surfsense"):
             if arm in row:
-                pieces.append(f"{arm}={row[arm]['truthfulness_score']*100:+7.1f}%")
+                pieces.append(f"{arm}={row[arm]['truthfulness_score'] * 100:+7.1f}%")
         print(" ".join(pieces))
 
     print()
@@ -58,7 +58,7 @@ def main() -> None:
         pieces = [f"{dom:10s} (n={n:3d}):"]
         for arm in ("bare_llm", "long_context", "surfsense"):
             if arm in row:
-                pieces.append(f"{arm}={row[arm]['truthfulness_score']*100:+7.1f}%")
+                pieces.append(f"{arm}={row[arm]['truthfulness_score'] * 100:+7.1f}%")
         print(" ".join(pieces))
 
 

surfsense_evals/scripts/summarise_parser_compare_run.py

@@ -23,7 +23,9 @@ ARTIFACT = RUN_DIR / "run_artifact.json"
 
 
 def main() -> None:
-    rows = [json.loads(line) for line in RAW.read_text(encoding="utf-8").splitlines() if line.strip()]
+    rows = [
+        json.loads(line) for line in RAW.read_text(encoding="utf-8").splitlines() if line.strip()
+    ]
     print(f"raw rows: {len(rows)}")
 
     by_qid: dict[str, list[dict]] = defaultdict(list)
@@ -31,11 +33,19 @@ def main() -> None:
         by_qid[row["qid"]].append(row)
     print(f"unique questions: {len(by_qid)}")
 
-    arm_metrics: dict[str, dict] = defaultdict(lambda: {
-        "n": 0, "n_correct": 0, "n_failed": 0, "n_empty": 0,
-        "costs": [], "in_tokens": [], "out_tokens": [], "latency_ms": [],
-        "by_format": defaultdict(lambda: {"n": 0, "correct": 0}),
-    })
+    arm_metrics: dict[str, dict] = defaultdict(
+        lambda: {
+            "n": 0,
+            "n_correct": 0,
+            "n_failed": 0,
+            "n_empty": 0,
+            "costs": [],
+            "in_tokens": [],
+            "out_tokens": [],
+            "latency_ms": [],
+            "by_format": defaultdict(lambda: {"n": 0, "correct": 0}),
+        }
+    )
 
     for row in rows:
         arm = row["arm"]
@@ -70,7 +80,9 @@ def main() -> None:
 
     print()
     print("=" * 100)
-    print(f"{'arm':<25} {'n':>4} {'acc%':>6} {'F1%':>6} {'fail':>5} {'$ mean':>10} {'$ median':>10} {'in tok mean':>12} {'out tok mean':>12} {'p50 ms':>8}")
+    print(
+        f"{'arm':<25} {'n':>4} {'acc%':>6} {'F1%':>6} {'fail':>5} {'$ mean':>10} {'$ median':>10} {'in tok mean':>12} {'out tok mean':>12} {'p50 ms':>8}"
+    )
     print("=" * 100)
     art = json.loads(ARTIFACT.read_text(encoding="utf-8"))
     per_arm_art = art["metrics"]["per_arm"]
@@ -110,7 +122,7 @@ def main() -> None:
     print("Aggregated cost (from run_artifact.json):")
     for arm, row in per_arm_art.items():
         print(
-            f"  {arm:<25}  acc={row['accuracy']*100:5.1f}% "
+            f"  {arm:<25}  acc={row['accuracy'] * 100:5.1f}% "
             f"  $/Q LLM={row['llm_cost_per_q']:.4f}  "
             f"  preprocess total=${row['preprocess_cost_total']:.2f}  "
             f"  $/Q total={row['total_cost_per_q']:.4f}"

surfsense_evals/scripts/test_context_overflow_hypothesis.py

@@ -40,8 +40,7 @@ CONTEXT_HINTS = (
 
 def main() -> None:
     rows = [
-        json.loads(line) for line in RAW.read_text(encoding="utf-8").splitlines()
-        if line.strip()
+        json.loads(line) for line in RAW.read_text(encoding="utf-8").splitlines() if line.strip()
     ]
 
     extraction_size: dict[tuple[str, str], int] = {}
@@ -73,12 +72,12 @@ def main() -> None:
     print("=" * 80)
     print("(b) Extraction size for OK vs FAILED rows per arm")
     print("=" * 80)
-    arm_buckets: dict[str, dict[str, list[int]]] = defaultdict(
-        lambda: {"ok": [], "fail": []}
-    )
+    arm_buckets: dict[str, dict[str, list[int]]] = defaultdict(lambda: {"ok": [], "fail": []})
     parser_arms = (
-        "azure_basic_lc", "azure_premium_lc",
-        "llamacloud_basic_lc", "llamacloud_premium_lc",
+        "azure_basic_lc",
+        "azure_premium_lc",
+        "llamacloud_basic_lc",
+        "llamacloud_premium_lc",
     )
     for row in rows:
         arm = row["arm"]
@@ -133,10 +132,13 @@ def main() -> None:
         "  3M_2018_10K x llamacloud_premium = 908,733 chars (~227k tokens) "
         "-- this is above Sonnet 4.5's 200k window."
     )
-    print("  If transport hypothesis is correct, this should still fail with a "
-          "real overflow error.")
-    print("  If transport hypothesis is correct AND the model truncates silently, "
-          "it might 'succeed' but be wrong.")
+    print(
+        "  If transport hypothesis is correct, this should still fail with a real overflow error."
+    )
+    print(
+        "  If transport hypothesis is correct AND the model truncates silently, "
+        "it might 'succeed' but be wrong."
+    )
     print()
     for row in rows:
         if row["doc_id"] != "3M_2018_10K.pdf":
@@ -145,10 +147,7 @@ def main() -> None:
             continue
         err = row.get("error") or "(none)"
         graded = row.get("graded") or {}
-        print(
-            f"  {row['qid']:<40} correct={graded.get('correct')!s:<5}  "
-            f"err={err[:100]}"
-        )
+        print(f"  {row['qid']:<40} correct={graded.get('correct')!s:<5}  err={err[:100]}")
 
 
 if __name__ == "__main__":

surfsense_evals/src/surfsense_evals/core/arms/surfsense.py

@@ -72,9 +72,7 @@ class SurfSenseArm(Arm):
                 try:
                     await self._client.delete_thread(thread_id)
                 except Exception as exc:  # noqa: BLE001
-                    logger.debug(
-                        "Failed to delete thread %s: %s", thread_id, exc
-                    )
+                    logger.debug("Failed to delete thread %s: %s", thread_id, exc)
 
         letter = extract_answer_letter(answer.text)
         return ArmResult(

surfsense_evals/src/surfsense_evals/core/auth.py

@@ -83,6 +83,7 @@ async def acquire_token(config: Config, *, http: httpx.AsyncClient | None = None
         )
 
     if config.has_local_mode():
+
         async def _login(client: httpx.AsyncClient) -> TokenBundle:
             response = await client.post(
                 f"{config.surfsense_api_base}/auth/desktop/login",
@@ -94,15 +95,12 @@ async def acquire_token(config: Config, *, http: httpx.AsyncClient | None = None
             )
             if response.status_code != 200:
                 raise CredentialError(
-                    f"LOCAL login failed (HTTP {response.status_code}): "
-                    f"{_safe_text(response)}"
+                    f"LOCAL login failed (HTTP {response.status_code}): {_safe_text(response)}"
                 )
             payload = response.json()
             access = payload.get("access_token")
             if not access:
-                raise CredentialError(
-                    f"LOCAL login response missing access_token: {payload!r}"
-                )
+                raise CredentialError(f"LOCAL login response missing access_token: {payload!r}")
             return TokenBundle(
                 access_token=access,
                 refresh_token=payload.get("refresh_token") or None,

surfsense_evals/src/surfsense_evals/core/cli.py

@@ -204,8 +204,7 @@ async def _cmd_setup(args: argparse.Namespace) -> int:
 
     if scenario not in SCENARIOS:
         console.print(
-            f"[red]Unknown scenario {scenario!r}. Pick one of: "
-            f"{', '.join(SCENARIOS)}[/red]"
+            f"[red]Unknown scenario {scenario!r}. Pick one of: {', '.join(SCENARIOS)}[/red]"
         )
         return 2
 
@@ -292,9 +291,7 @@ async def _cmd_setup(args: argparse.Namespace) -> int:
         if not skip_vision_setup and (vision_required or vision_llm_slug is not None):
             try:
                 vision_candidates = await ss_client.list_global_vision_models()
-                resolved = resolve_vision_llm(
-                    vision_candidates, explicit_slug=vision_llm_slug
-                )
+                resolved = resolve_vision_llm(vision_candidates, explicit_slug=vision_llm_slug)
             except VisionConfigError as exc:
                 console.print(f"[red]{exc}[/red]")
                 return 2
@@ -524,10 +521,7 @@ async def _cmd_run(args: argparse.Namespace) -> int:
         )
         artifact = await benchmark.run(ctx, **extra_kwargs)
 
-    console.print(
-        f"[green]run OK[/green] {args.suite}/{args.benchmark} → "
-        f"{artifact.raw_path}"
-    )
+    console.print(f"[green]run OK[/green] {args.suite}/{args.benchmark} → {artifact.raw_path}")
     return 0
 
 
@@ -697,15 +691,21 @@ def _build_parser() -> argparse.ArgumentParser:
     )
     p_setup.set_defaults(_func=_cmd_setup, _async=True)
 
-    p_teardown = sub.add_parser("teardown", help="Soft-delete the suite SearchSpace + clear state slot.")
+    p_teardown = sub.add_parser(
+        "teardown", help="Soft-delete the suite SearchSpace + clear state slot."
+    )
     p_teardown.add_argument("--suite", required=True)
     p_teardown.set_defaults(_func=_cmd_teardown, _async=True)
 
     p_models = sub.add_parser("models", help="LLM-config discovery helpers.")
     models_sub = p_models.add_subparsers(dest="subcommand", required=True)
     p_models_list = models_sub.add_parser("list", help="List global LLM configs.")
-    p_models_list.add_argument("--provider", default=None, help="Filter by provider, e.g. openrouter")
-    p_models_list.add_argument("--grep", default=None, help="Substring filter on name / model_name.")
+    p_models_list.add_argument(
+        "--provider", default=None, help="Filter by provider, e.g. openrouter"
+    )
+    p_models_list.add_argument(
+        "--grep", default=None, help="Substring filter on name / model_name."
+    )
     p_models_list.set_defaults(_func=_cmd_models_list, _async=True)
 
     p_suites = sub.add_parser("suites", help="List registered suites.")
@@ -729,7 +729,9 @@ def _build_parser() -> argparse.ArgumentParser:
         suite_parser = ingest_sub.add_parser(suite, help=f"Ingest a {suite} benchmark.")
         suite_bench = suite_parser.add_subparsers(dest="benchmark", required=True)
         for benchmark in registry.list_benchmarks(suite):
-            bp = suite_bench.add_parser(benchmark.name, help=getattr(benchmark, "description", benchmark.name))
+            bp = suite_bench.add_parser(
+                benchmark.name, help=getattr(benchmark, "description", benchmark.name)
+            )
             if hasattr(benchmark, "add_run_args"):
                 benchmark.add_run_args(bp)
             bp.set_defaults(_func=_cmd_ingest, _async=True)
@@ -740,7 +742,9 @@ def _build_parser() -> argparse.ArgumentParser:
         suite_parser = run_sub.add_parser(suite, help=f"Run a {suite} benchmark.")
         suite_bench = suite_parser.add_subparsers(dest="benchmark", required=True)
         for benchmark in registry.list_benchmarks(suite):
-            bp = suite_bench.add_parser(benchmark.name, help=getattr(benchmark, "description", benchmark.name))
+            bp = suite_bench.add_parser(
+                benchmark.name, help=getattr(benchmark, "description", benchmark.name)
+            )
             if hasattr(benchmark, "add_run_args"):
                 benchmark.add_run_args(bp)
             bp.set_defaults(_func=_cmd_run, _async=True)

surfsense_evals/src/surfsense_evals/core/clients/documents.py

@@ -84,8 +84,7 @@ class DocumentProcessingFailed(RuntimeError):
 
     def __init__(self, statuses: Sequence[DocumentStatus]) -> None:
         details = ", ".join(
-            f"id={s.document_id} ({s.title!r}): {s.reason or 'unknown'}"
-            for s in statuses
+            f"id={s.document_id} ({s.title!r}): {s.reason or 'unknown'}" for s in statuses
         )
         super().__init__(f"Document(s) failed to process: {details}")
         self.statuses = list(statuses)
@@ -240,9 +239,7 @@ class DocumentsClient:
     # chunks (chunk_id -> document_id map)
     # ------------------------------------------------------------------
 
-    async def list_chunks(
-        self, document_id: int, *, page_size: int = 100
-    ) -> list[ChunkRow]:
+    async def list_chunks(self, document_id: int, *, page_size: int = 100) -> list[ChunkRow]:
         """Walk ``GET /documents/{id}/chunks`` until ``has_more=False``.
 
         Used by ingestion to materialise the ``chunk_id -> document_id``

surfsense_evals/src/surfsense_evals/core/clients/new_chat.py

@@ -145,7 +145,7 @@ class NewChatClient:
                 if attempt > max_busy_retries:
                     raise
                 # Cap wait at 30s; backend retry hint is exponential anyway.
-                wait = min(30.0, 0.5 * (2 ** attempt))
+                wait = min(30.0, 0.5 * (2**attempt))
                 logger.info(
                     "thread_id=%s busy (%s); retry %d/%d after %.1fs",
                     thread_id,

surfsense_evals/src/surfsense_evals/core/clients/search_space.py

@@ -177,16 +177,12 @@ class SearchSpaceClient:
         response.raise_for_status()
         payload = response.json()
         if not isinstance(payload, list):
-            raise RuntimeError(
-                f"Unexpected /model-connections/global payload: {payload!r}"
-            )
+            raise RuntimeError(f"Unexpected /model-connections/global payload: {payload!r}")
         entries: list[VisionModelEntry] = []
         for connection in payload:
             provider = str(connection.get("provider", ""))
             for model in connection.get("models") or []:
                 if not model.get("enabled", True) or not model.get("supports_image_input"):
                     continue
-                entries.append(
-                    VisionModelEntry.from_payload({**model, "provider": provider})
-                )
+                entries.append(VisionModelEntry.from_payload({**model, "provider": provider}))
         return entries

surfsense_evals/src/surfsense_evals/core/config.py

@@ -104,7 +104,9 @@ def load_config() -> Config:
     data_dir = Path(os.environ.get("EVAL_DATA_DIR") or (project_root / "data")).resolve()
     reports_dir = Path(os.environ.get("EVAL_REPORTS_DIR") or (project_root / "reports")).resolve()
     return Config(
-        surfsense_api_base=os.environ.get("SURFSENSE_API_BASE", "http://localhost:8000").rstrip("/"),
+        surfsense_api_base=os.environ.get("SURFSENSE_API_BASE", "http://localhost:8000").rstrip(
+            "/"
+        ),
         openrouter_api_key=os.environ.get("OPENROUTER_API_KEY") or None,
         openrouter_base_url=os.environ.get(
             "OPENROUTER_BASE_URL", "https://openrouter.ai/api/v1"
@@ -203,9 +205,7 @@ class SuiteState:
                 else None
             ),
             native_arm_model=(
-                str(payload["native_arm_model"])
-                if payload.get("native_arm_model")
-                else None
+                str(payload["native_arm_model"]) if payload.get("native_arm_model") else None
             ),
         )
 

surfsense_evals/src/surfsense_evals/core/ingest_settings.py

@@ -95,10 +95,7 @@ class IngestSettings:
     def render_label(self) -> str:
         """Human-readable single-line label for reports / log lines."""
 
-        return (
-            f"vision={'on' if self.use_vision_llm else 'off'}, "
-            f"mode={self.processing_mode}"
-        )
+        return f"vision={'on' if self.use_vision_llm else 'off'}, mode={self.processing_mode}"
 
 
 def _coerce_bool(value: Any, default: bool) -> bool:
@@ -122,9 +119,7 @@ def _coerce_mode(value: Any, default: str) -> str:
         return default
     val = str(value).strip().lower()
     if val not in PROCESSING_MODES:
-        raise ValueError(
-            f"Invalid processing_mode {val!r}; must be one of {PROCESSING_MODES}"
-        )
+        raise ValueError(f"Invalid processing_mode {val!r}; must be one of {PROCESSING_MODES}")
     return val
 
 
@@ -274,10 +269,7 @@ def format_ingest_settings_md(settings: Any) -> str:
         return "- SurfSense ingest settings: (not recorded — re-ingest to capture)"
     vision = "on" if settings.get("use_vision_llm") else "off"
     mode = settings.get("processing_mode") or "basic"
-    return (
-        f"- SurfSense ingest settings: vision_llm=`{vision}`, "
-        f"processing_mode=`{mode}`"
-    )
+    return f"- SurfSense ingest settings: vision_llm=`{vision}`, processing_mode=`{mode}`"
 
 
 __all__ = [

surfsense_evals/src/surfsense_evals/core/metrics/comparison.py

@@ -67,17 +67,13 @@ def mcnemar_test(
     """
 
     if len(arm_a_correct) != len(arm_b_correct):
-        raise ValueError(
-            f"Length mismatch: arm_a={len(arm_a_correct)}, arm_b={len(arm_b_correct)}"
-        )
+        raise ValueError(f"Length mismatch: arm_a={len(arm_a_correct)}, arm_b={len(arm_b_correct)}")
     n = len(arm_a_correct)
     b = sum(1 for a, c in zip(arm_a_correct, arm_b_correct, strict=False) if a and not c)
     c = sum(1 for a, cc in zip(arm_a_correct, arm_b_correct, strict=False) if (not a) and cc)
     discordant = b + c
     if discordant == 0:
-        return McnemarResult(
-            n_total=n, b=b, c=c, statistic=0.0, p_value=1.0, method="degenerate"
-        )
+        return McnemarResult(n_total=n, b=b, c=c, statistic=0.0, p_value=1.0, method="degenerate")
 
     if discordant < use_exact_below:
         # Exact binomial: under H0 each discordant pair is a Bernoulli(0.5).
@@ -92,13 +88,11 @@ def mcnemar_test(
     # Chi-square with continuity correction (McNemar-Edwards).
     chi = ((abs(b - c) - 1) ** 2) / discordant
     p_value = _chi2_sf(chi, df=1)
-    return McnemarResult(
-        n_total=n, b=b, c=c, statistic=chi, p_value=p_value, method="chi2_cc"
-    )
+    return McnemarResult(n_total=n, b=b, c=c, statistic=chi, p_value=p_value, method="chi2_cc")
 
 
 def _binom_pmf(n: int, k: int) -> float:
-    return math.comb(n, k) * (0.5 ** n)
+    return math.comb(n, k) * (0.5**n)
 
 
 def _chi2_sf(x: float, *, df: int) -> float:

surfsense_evals/src/surfsense_evals/core/metrics/mc_accuracy.py

@@ -46,9 +46,7 @@ _Z_FOR_LEVEL: dict[float, float] = {
 }
 
 
-def wilson_ci(
-    n_correct: int, n_total: int, *, level: float = 0.95
-) -> tuple[float, float]:
+def wilson_ci(n_correct: int, n_total: int, *, level: float = 0.95) -> tuple[float, float]:
     """Two-sided Wilson score confidence interval for a proportion.
 
     Returns ``(low, high)``. ``n_total == 0`` returns ``(0.0, 1.0)`` —
@@ -70,9 +68,7 @@ def wilson_ci(
     return low, high
 
 
-def accuracy_with_wilson_ci(
-    n_correct: int, n_total: int, *, level: float = 0.95
-) -> AccuracyResult:
+def accuracy_with_wilson_ci(n_correct: int, n_total: int, *, level: float = 0.95) -> AccuracyResult:
     if n_total < 0:
         raise ValueError(f"n_total must be >= 0, got {n_total}")
     if n_correct < 0 or n_correct > n_total:
@@ -109,10 +105,7 @@ def per_task_accuracy(
         bucket[1] += 1
         if row.get(correct_key):
             bucket[0] += 1
-    return {
-        task: accuracy_with_wilson_ci(c[0], c[1], level=level)
-        for task, c in counts.items()
-    }
+    return {task: accuracy_with_wilson_ci(c[0], c[1], level=level) for task, c in counts.items()}
 
 
 def macro_accuracy(per_task: Mapping[str, AccuracyResult]) -> float:

surfsense_evals/src/surfsense_evals/core/metrics/retrieval.py

@@ -61,7 +61,7 @@ def _dcg_at_k(grades: Sequence[float], k: int) -> float:
     s = 0.0
     for i, grade in enumerate(grades[:k], start=1):
         # Standard log-base-2 discount; gain = 2^grade - 1 for graded relevance.
-        s += (2.0 ** grade - 1.0) / math.log2(i + 1)
+        s += (2.0**grade - 1.0) / math.log2(i + 1)
     return s
 
 
@@ -106,7 +106,9 @@ def score_run(
 
     qids = set(per_query_qrels.keys()) & set(per_query_retrieved.keys())
     if not qids:
-        return RetrievalScores(recall_at_k={k: 0.0 for k in ks}, mrr=0.0, ndcg_at_10=0.0, n_queries=0)
+        return RetrievalScores(
+            recall_at_k={k: 0.0 for k in ks}, mrr=0.0, ndcg_at_10=0.0, n_queries=0
+        )
 
     recall_totals = {k: 0.0 for k in ks}
     mrr_total = 0.0

surfsense_evals/src/surfsense_evals/core/parse/citations.py

@@ -35,7 +35,7 @@ from typing import Any
 # the pattern source, so we splice the literal character in via an
 # f-string. This keeps our pattern functionally identical to the TS
 # reference and lets ``"\u200B" in CITATION_REGEX.pattern`` succeed.
-_ZWSP = "\u200B"
+_ZWSP = "\u200b"
 CITATION_REGEX = re.compile(
     rf"[\[【]{_ZWSP}?citation:\s*("
     rf"https?://[^\]】{_ZWSP}]+|urlcite\d+|(?:doc-)?-?\d+(?:\s*,\s*(?:doc-)?-?\d+)*"

surfsense_evals/src/surfsense_evals/core/parse/freeform_answer.py

@@ -56,7 +56,7 @@ def extract_freeform_answer(text: str) -> str:
         marker_matches = list(_ANSWER_MARKER.finditer(text))
         if marker_matches:
             last = marker_matches[-1]
-            tail = text[last.end():]
+            tail = text[last.end() :]
             nl = tail.find("\n")
             if nl >= 0:
                 tail = tail[:nl]
@@ -77,7 +77,7 @@ def extract_freeform_answer(text: str) -> str:
     # 2. Strip wrapping quotes / parens / trailing punctuation that
     #    confuse the grader without changing meaning.
     candidate = candidate.strip().strip("`").strip()
-    if candidate.startswith(("\"", "'")) and candidate.endswith(("\"", "'")):
+    if candidate.startswith(('"', "'")) and candidate.endswith(('"', "'")):
         candidate = candidate[1:-1].strip()
     return candidate
 

surfsense_evals/src/surfsense_evals/core/parsers/azure_di.py

@@ -64,8 +64,7 @@ async def parse_with_azure_di(
     api_key = api_key or os.environ.get("AZURE_DI_KEY")
     if not endpoint or not api_key:
         raise ValueError(
-            "AZURE_DI_ENDPOINT and AZURE_DI_KEY must be set "
-            "(see surfsense_evals/.env)."
+            "AZURE_DI_ENDPOINT and AZURE_DI_KEY must be set (see surfsense_evals/.env)."
         )
 
     model_id = _AZURE_MODEL_BY_MODE.get(processing_mode, "prebuilt-read")
@@ -86,7 +85,10 @@ async def parse_with_azure_di(
     file_size_mb = await asyncio.to_thread(os.path.getsize, file_path) / (1024 * 1024)
     logger.info(
         "Azure DI parsing %s (mode=%s, model=%s, size=%.1fMB)",
-        file_path, processing_mode, model_id, file_size_mb,
+        file_path,
+        processing_mode,
+        model_id,
+        file_size_mb,
     )
 
     last_exc: Exception | None = None
@@ -106,12 +108,12 @@ async def parse_with_azure_di(
                 result = await poller.result()
             content = (result.content or "").strip()
             if not content:
-                raise AzureDIError(
-                    f"Azure DI returned empty content for {file_path}"
-                )
+                raise AzureDIError(f"Azure DI returned empty content for {file_path}")
             logger.info(
                 "Azure DI OK: %s (%s) -> %d chars",
-                file_path, model_id, len(content),
+                file_path,
+                model_id,
+                len(content),
             )
             return content
 
@@ -120,9 +122,7 @@ async def parse_with_azure_di(
         except HttpResponseError as exc:
             # 4xx that's not auth: don't retry, the request itself is broken.
             if exc.status_code and 400 <= exc.status_code < 500:
-                raise AzureDIError(
-                    f"Azure DI {exc.status_code} on {file_path}: {exc}"
-                ) from exc
+                raise AzureDIError(f"Azure DI {exc.status_code} on {file_path}: {exc}") from exc
             last_exc = exc
         except (ServiceRequestError, ServiceResponseError) as exc:
             last_exc = exc
@@ -133,7 +133,10 @@ async def parse_with_azure_di(
             sleep_for = delay + jitter
             logger.warning(
                 "Azure DI attempt %d/%d failed (%s); retrying in %.1fs",
-                attempt, _MAX_RETRIES, type(last_exc).__name__, sleep_for,
+                attempt,
+                _MAX_RETRIES,
+                type(last_exc).__name__,
+                sleep_for,
             )
             await asyncio.sleep(sleep_for)
 

surfsense_evals/src/surfsense_evals/core/parsers/llamacloud.py

@@ -61,8 +61,7 @@ def _extract_markdown(result) -> str:
         if result and hasattr(result[0], "text"):
             return result[0].text
         return "\n\n".join(
-            doc.page_content if hasattr(doc, "page_content") else str(doc)
-            for doc in result
+            doc.page_content if hasattr(doc, "page_content") else str(doc) for doc in result
         )
 
     return str(result)
@@ -86,9 +85,7 @@ async def parse_with_llamacloud(
 
     api_key = api_key or os.environ.get("LLAMA_CLOUD_API_KEY")
     if not api_key:
-        raise ValueError(
-            "LLAMA_CLOUD_API_KEY must be set (see surfsense_evals/.env)."
-        )
+        raise ValueError("LLAMA_CLOUD_API_KEY must be set (see surfsense_evals/.env).")
 
     parse_mode = _LLAMA_PARSE_MODE_MAP.get(processing_mode, "parse_page_with_llm")
 
@@ -106,13 +103,19 @@ async def parse_with_llamacloud(
     upload_timeout = max(120.0, 30.0 * file_size_mb)
 
     logger.info(
-        "LlamaCloud parsing %s (mode=%s, parse_mode=%s, %.1fMB, "
-        "job_timeout=%.0fs)",
-        file_path, processing_mode, parse_mode, file_size_mb, job_timeout,
+        "LlamaCloud parsing %s (mode=%s, parse_mode=%s, %.1fMB, job_timeout=%.0fs)",
+        file_path,
+        processing_mode,
+        parse_mode,
+        file_size_mb,
+        job_timeout,
     )
 
     custom_timeout = httpx.Timeout(
-        connect=120.0, read=upload_timeout, write=upload_timeout, pool=120.0,
+        connect=120.0,
+        read=upload_timeout,
+        write=upload_timeout,
+        pool=120.0,
     )
 
     last_exc: Exception | None = None
@@ -135,12 +138,12 @@ async def parse_with_llamacloud(
                 result = await parser.aparse(str(file_path))
             content = _extract_markdown(result).strip()
             if not content:
-                raise LlamaCloudError(
-                    f"LlamaCloud returned empty content for {file_path}"
-                )
+                raise LlamaCloudError(f"LlamaCloud returned empty content for {file_path}")
             logger.info(
                 "LlamaCloud OK: %s (%s) -> %d chars",
-                file_path, parse_mode, len(content),
+                file_path,
+                parse_mode,
+                len(content),
             )
             return content
 
@@ -156,7 +159,10 @@ async def parse_with_llamacloud(
                 sleep_for = delay + jitter
                 logger.warning(
                     "LlamaCloud attempt %d/%d failed (%s); retrying in %.1fs",
-                    attempt, _MAX_RETRIES, type(last_exc).__name__, sleep_for,
+                    attempt,
+                    _MAX_RETRIES,
+                    type(last_exc).__name__,
+                    sleep_for,
                 )
                 await asyncio.sleep(sleep_for)
 

surfsense_evals/src/surfsense_evals/core/pdf/render.py

@@ -116,11 +116,7 @@ def _normalise_paragraphs(text: str) -> list[str]:
 
 
 def _escape_html(text: str) -> str:
-    return (
-        text.replace("&", "&")
-        .replace("<", "&lt;")
-        .replace(">", "&gt;")
-    )
+    return text.replace("&", "&amp;").replace("<", "&lt;").replace(">", "&gt;")
 
 
 def render_pdf(

surfsense_evals/src/surfsense_evals/core/providers/openrouter_pdf.py

@@ -121,9 +121,7 @@ class OpenRouterPdfProvider:
         body: dict[str, Any] = {
             "model": self._model,
             "messages": messages,
-            "plugins": [
-                {"id": "file-parser", "pdf": {"engine": self._engine.value}}
-            ],
+            "plugins": [{"id": "file-parser", "pdf": {"engine": self._engine.value}}],
         }
         if max_tokens:
             body["max_tokens"] = max_tokens

surfsense_evals/src/surfsense_evals/core/registry.py

@@ -177,7 +177,9 @@ class Benchmark(Protocol):
     def add_run_args(self, parser: argparse.ArgumentParser) -> None:  # pragma: no cover - protocol
         """Add benchmark-specific flags to ``run <suite> <benchmark>``."""
 
-    def report_section(self, artifacts: list[RunArtifact]) -> ReportSection:  # pragma: no cover - protocol
+    def report_section(
+        self, artifacts: list[RunArtifact]
+    ) -> ReportSection:  # pragma: no cover - protocol
         ...
 
 
@@ -224,9 +226,7 @@ def get(suite: str, name: str) -> Benchmark:
         return _REGISTRY[(suite, name)]
     except KeyError as exc:
         available = ", ".join(f"{s}/{n}" for s, n in sorted(_REGISTRY)) or "<none>"
-        raise KeyError(
-            f"Unknown benchmark '{suite}/{name}'. Registered: {available}"
-        ) from exc
+        raise KeyError(f"Unknown benchmark '{suite}/{name}'. Registered: {available}") from exc
 
 
 def list_suites() -> list[str]:

surfsense_evals/src/surfsense_evals/core/scenarios.py

@@ -45,10 +45,7 @@ def format_scenario_md(extra: Mapping[str, Any] | None) -> str:
             "(text-only model can't see images) — that's the point."
         )
     else:
-        body = (
-            f"- Scenario: head-to-head — both arms answer with `{surf_slug}` "
-            "via OpenRouter."
-        )
+        body = f"- Scenario: head-to-head — both arms answer with `{surf_slug}` via OpenRouter."
         if vision_slug:
             body += f" SurfSense ingest VLM: `{vision_slug}`."
 

surfsense_evals/src/surfsense_evals/suites/__init__.py

@@ -60,7 +60,5 @@ def discover_suites() -> list[str]:
                 importlib.import_module(benchmark_name)
                 imported.append(benchmark_name)
             except Exception as exc:  # noqa: BLE001
-                logger.warning(
-                    "Failed to import benchmark %s: %s", benchmark_name, exc
-                )
+                logger.warning("Failed to import benchmark %s: %s", benchmark_name, exc)
     return imported

surfsense_evals/src/surfsense_evals/suites/medical/cure/ingest.py

@@ -154,13 +154,11 @@ async def run_ingest(
             if not batches:
                 logger.warning("Discipline %s produced 0 batches; skipping upload", discipline)
                 continue
-            logger.info(
-                "Uploading %d batches for discipline %s", len(batches), discipline
-            )
+            logger.info("Uploading %d batches for discipline %s", len(batches), discipline)
             upload_result = await docs_client.upload(
                 files=[b.path for b in batches],
                 search_space_id=ctx.search_space_id,
-                    use_vision_llm=settings.use_vision_llm,
+                use_vision_llm=settings.use_vision_llm,
                 processing_mode=settings.processing_mode,
             )
             new_doc_ids = list(upload_result.document_ids)
@@ -177,9 +175,7 @@ async def run_ingest(
             )
             title_to_doc = {s.title: s.document_id for s in statuses}
 
-            per_discipline_path = (
-                ctx.maps_dir() / f"cure_corpus_map_{discipline}.jsonl"
-            )
+            per_discipline_path = ctx.maps_dir() / f"cure_corpus_map_{discipline}.jsonl"
             with per_discipline_path.open("w", encoding="utf-8") as fh:
                 fh.write(settings_header_line(settings) + "\n")
                 for batch in batches:
@@ -202,9 +198,7 @@ async def run_ingest(
                     try:
                         chunks = await docs_client.list_chunks(int(doc_id))
                     except Exception as exc:  # noqa: BLE001
-                        logger.warning(
-                            "Failed to list chunks for doc_id=%s: %s", doc_id, exc
-                        )
+                        logger.warning("Failed to list chunks for doc_id=%s: %s", doc_id, exc)
                         continue
                     for chunk in chunks:
                         fh.write(

surfsense_evals/src/surfsense_evals/suites/medical/cure/runner.py

@@ -191,12 +191,15 @@ class CureBenchmark:
 
     def add_run_args(self, parser: argparse.ArgumentParser) -> None:
         parser.add_argument("--lang", default="en", choices=("en", "es", "fr"))
-        parser.add_argument("--discipline", default=None,
-                            help="Restrict to one discipline (default: all ingested).")
+        parser.add_argument(
+            "--discipline", default=None, help="Restrict to one discipline (default: all ingested)."
+        )
         parser.add_argument("--n", dest="sample_n", type=int, default=None)
         parser.add_argument("--concurrency", type=int, default=4)
         parser.add_argument(
-            "--max-passages-per-discipline", type=int, default=None,
+            "--max-passages-per-discipline",
+            type=int,
+            default=None,
             help="(ingest only) cap corpus rows per discipline for smoke testing.",
         )
         # Per-upload knobs forwarded to /documents/fileupload at ingest;
@@ -233,11 +236,13 @@ class CureBenchmark:
 
         # Disciplines to query are determined by the per-discipline maps
         # actually present (either user-filtered or whatever was ingested).
-        ingested_disciplines = sorted({
-            row_disc
-            for path in maps_dir.glob("cure_corpus_map_*.jsonl")
-            for row_disc in [path.stem[len("cure_corpus_map_"):]]
-        })
+        ingested_disciplines = sorted(
+            {
+                row_disc
+                for path in maps_dir.glob("cure_corpus_map_*.jsonl")
+                for row_disc in [path.stem[len("cure_corpus_map_") :]]
+            }
+        )
         if discipline_filter:
             disciplines = [discipline_filter]
         else:

surfsense_evals/src/surfsense_evals/suites/medical/medxpertqa/ingest.py

@@ -55,15 +55,15 @@ def _hf_hub_download(*args, **kwargs):
 
 @dataclass
 class MedXpertQuestion:
-    qid: str                         # e.g. "MM-26"
-    question: str                    # full question text (case + ask)
-    options: dict[str, str]          # A-E
-    label: str                       # "A".."E"
-    image_files: list[str]           # filenames inside images.zip
+    qid: str  # e.g. "MM-26"
+    question: str  # full question text (case + ask)
+    options: dict[str, str]  # A-E
+    label: str  # "A".."E"
+    image_files: list[str]  # filenames inside images.zip
     medical_task: str
     body_system: str
     question_type: str
-    split: str                       # "test" or "dev"
+    split: str  # "test" or "dev"
 
 
 def _load_jsonl(path: Path, *, split: str) -> list[MedXpertQuestion]:
@@ -84,17 +84,19 @@ def _load_jsonl(path: Path, *, split: str) -> list[MedXpertQuestion]:
             images = row.get("images") or []
             if not isinstance(images, list):
                 images = []
-            out.append(MedXpertQuestion(
-                qid=qid,
-                question=question,
-                options=opts,
-                label=label,
-                image_files=[str(x).strip() for x in images if str(x).strip()],
-                medical_task=str(row.get("medical_task") or "").strip(),
-                body_system=str(row.get("body_system") or "").strip(),
-                question_type=str(row.get("question_type") or "").strip(),
-                split=split,
-            ))
+            out.append(
+                MedXpertQuestion(
+                    qid=qid,
+                    question=question,
+                    options=opts,
+                    label=label,
+                    image_files=[str(x).strip() for x in images if str(x).strip()],
+                    medical_task=str(row.get("medical_task") or "").strip(),
+                    body_system=str(row.get("body_system") or "").strip(),
+                    question_type=str(row.get("question_type") or "").strip(),
+                    split=split,
+                )
+            )
     return out
 
 
@@ -204,7 +206,7 @@ async def _upload_pdfs(
     name_to_id: dict[str, int] = {}
     pdf_list = list(pdf_paths)
     for batch_start in range(0, len(pdf_list), batch_size):
-        batch = pdf_list[batch_start:batch_start + batch_size]
+        batch = pdf_list[batch_start : batch_start + batch_size]
         result = await docs_client.upload(
             files=batch,
             search_space_id=ctx.search_space_id,
@@ -226,8 +228,10 @@ async def _upload_pdfs(
                 name_to_id[s.title] = s.document_id
         logger.info(
             "Uploaded MedXpertQA batch %d-%d: %d new, %d duplicate",
-            batch_start, batch_start + len(batch),
-            len(result.document_ids), len(result.duplicate_document_ids),
+            batch_start,
+            batch_start + len(batch),
+            len(result.document_ids),
+            len(result.duplicate_document_ids),
         )
     return name_to_id
 
@@ -310,9 +314,11 @@ async def run_ingest(
         # Materialise into bench_dir so the path is stable.
         try:
             from os import link as _link
+
             _link(local_zip, images_zip_local)
         except OSError:
             from shutil import copy2
+
             copy2(local_zip, images_zip_local)
     _ensure_images_extracted(images_zip_local, images_dir)
 
@@ -354,17 +360,22 @@ async def run_ingest(
     questions_jsonl = bench_dir / "questions.jsonl"
     with questions_jsonl.open("w", encoding="utf-8") as fh:
         for q in questions:
-            fh.write(json.dumps({
-                "qid": q.qid,
-                "question": q.question,
-                "options": q.options,
-                "label": q.label,
-                "image_files": q.image_files,
-                "medical_task": q.medical_task,
-                "body_system": q.body_system,
-                "question_type": q.question_type,
-                "split": q.split,
-            }) + "\n")
+            fh.write(
+                json.dumps(
+                    {
+                        "qid": q.qid,
+                        "question": q.question,
+                        "options": q.options,
+                        "label": q.label,
+                        "image_files": q.image_files,
+                        "medical_task": q.medical_task,
+                        "body_system": q.body_system,
+                        "question_type": q.question_type,
+                        "split": q.split,
+                    }
+                )
+                + "\n"
+            )
     logger.info("Wrote %d MedXpertQA questions to %s", len(questions), questions_jsonl)
 
     map_path = ctx.maps_dir() / "medxpertqa_doc_map.jsonl"
@@ -376,13 +387,18 @@ async def run_ingest(
             local = pdf_paths.get(q.qid)
             if local is None:
                 continue
-            fh.write(json.dumps({
-                "qid": q.qid,
-                "document_id": name_to_id.get(local.name),
-                "pdf_path": str(local),
-                "n_images": len(q.image_files),
-                "split": q.split,
-            }) + "\n")
+            fh.write(
+                json.dumps(
+                    {
+                        "qid": q.qid,
+                        "document_id": name_to_id.get(local.name),
+                        "pdf_path": str(local),
+                        "n_images": len(q.image_files),
+                        "split": q.split,
+                    }
+                )
+                + "\n"
+            )
     logger.info("Wrote MedXpertQA doc map to %s", map_path)
 
     new_state = ctx.suite_state

surfsense_evals/src/surfsense_evals/suites/medical/medxpertqa/runner.py

@@ -129,19 +129,21 @@ def _load_questions(
             n_images = int(map_row.get("n_images", 0))
             if require_images and n_images <= 0:
                 continue
-            out.append(MXQuestion(
-                qid=qid,
-                question=str(row.get("question") or ""),
-                options={str(k).upper(): str(v) for k, v in (row.get("options") or {}).items()},
-                label=str(row.get("label") or "").strip().upper(),
-                medical_task=str(row.get("medical_task") or "").strip(),
-                body_system=str(row.get("body_system") or "").strip(),
-                question_type=str(row.get("question_type") or "").strip(),
-                split=str(row.get("split") or ""),
-                n_images=n_images,
-                pdf_path=Path(map_row["pdf_path"]),
-                document_id=map_row.get("document_id"),
-            ))
+            out.append(
+                MXQuestion(
+                    qid=qid,
+                    question=str(row.get("question") or ""),
+                    options={str(k).upper(): str(v) for k, v in (row.get("options") or {}).items()},
+                    label=str(row.get("label") or "").strip().upper(),
+                    medical_task=str(row.get("medical_task") or "").strip(),
+                    body_system=str(row.get("body_system") or "").strip(),
+                    question_type=str(row.get("question_type") or "").strip(),
+                    split=str(row.get("split") or ""),
+                    n_images=n_images,
+                    pdf_path=Path(map_row["pdf_path"]),
+                    document_id=map_row.get("document_id"),
+                )
+            )
     out.sort(key=lambda q: (q.split, q.qid))
     if sample_n is not None and sample_n > 0:
         out = out[:sample_n]
@@ -182,51 +184,81 @@ class MedXpertQAMMBenchmark:
 
     def add_run_args(self, parser: argparse.ArgumentParser) -> None:
         parser.add_argument(
-            "--split", default="test", choices=["test", "dev", "all"],
+            "--split",
+            default="test",
+            choices=["test", "dev", "all"],
             help="Which MedXpertQA-MM split to run (default: test).",
         )
         parser.add_argument(
-            "--task", default="all",
+            "--task",
+            default="all",
             help="Filter by medical_task value (e.g. Diagnosis, Treatment, Basic Medicine).",
         )
         parser.add_argument(
-            "--body-system", dest="body_filter", default="all",
+            "--body-system",
+            dest="body_filter",
+            default="all",
             help="Filter by body_system value (e.g. Cardiovascular, Lymphatic).",
         )
         parser.add_argument(
-            "--require-images", dest="require_images", action="store_true",
+            "--require-images",
+            dest="require_images",
+            action="store_true",
             help="Skip rare MM rows that ended up with zero resolvable images.",
         )
-        parser.add_argument("--n", dest="sample_n", type=int, default=None,
-                            help="Run only the first N questions after filters apply.")
-        parser.add_argument("--concurrency", type=int, default=4,
-                            help="Parallel question workers per arm.")
-        parser.add_argument("--no-mentions", dest="no_mentions", action="store_true",
-                            help="SurfSense arm: skip mentioned_document_ids (unscoped retrieval).")
         parser.add_argument(
-            "--pdf-engine", default="native",
+            "--n",
+            dest="sample_n",
+            type=int,
+            default=None,
+            help="Run only the first N questions after filters apply.",
+        )
+        parser.add_argument(
+            "--concurrency", type=int, default=4, help="Parallel question workers per arm."
+        )
+        parser.add_argument(
+            "--no-mentions",
+            dest="no_mentions",
+            action="store_true",
+            help="SurfSense arm: skip mentioned_document_ids (unscoped retrieval).",
+        )
+        parser.add_argument(
+            "--pdf-engine",
+            default="native",
             choices=[e.value for e in PdfEngine],
             help="OpenRouter file-parser engine for the native arm.",
         )
         parser.add_argument(
-            "--max-output-tokens", type=int, default=512,
+            "--max-output-tokens",
+            type=int,
+            default=512,
             help="Cap on completion length for both arms.",
         )
         # Ingest-only knobs (forwarded by the CLI to ingest.run_ingest).
         parser.add_argument(
-            "--max-questions", dest="max_questions", type=int, default=None,
+            "--max-questions",
+            dest="max_questions",
+            type=int,
+            default=None,
             help="(ingest only) cap on number of MM questions to render + upload.",
         )
         parser.add_argument(
-            "--upload-batch-size", dest="upload_batch_size", type=int, default=8,
+            "--upload-batch-size",
+            dest="upload_batch_size",
+            type=int,
+            default=8,
             help="(ingest only) PDFs per fileupload call.",
         )
         parser.add_argument(
-            "--skip-upload", dest="skip_upload", action="store_true",
+            "--skip-upload",
+            dest="skip_upload",
+            action="store_true",
             help="(ingest only) render PDFs locally but don't push to SurfSense.",
         )
         parser.add_argument(
-            "--include-dev", dest="include_dev", action="store_true",
+            "--include-dev",
+            dest="include_dev",
+            action="store_true",
             help="(ingest only) shorthand for --split all.",
         )
         # Per-upload knobs forwarded to /documents/fileupload at ingest;
@@ -270,7 +302,8 @@ class MedXpertQAMMBenchmark:
 
         doc_map, ingest_settings = _load_doc_map(map_path)
         questions = _load_questions(
-            questions_jsonl, doc_map,
+            questions_jsonl,
+            doc_map,
             split_filter=split_filter,
             task_filter=task_filter if task_filter != "all" else None,
             body_filter=body_filter if body_filter != "all" else None,
@@ -378,13 +411,18 @@ class MedXpertQAMMBenchmark:
 
         manifest_path = run_dir / "run_artifact.json"
         manifest_path.write_text(
-            json.dumps({
-                "suite": self.suite,
-                "benchmark": self.name,
-                "raw_path": "raw.jsonl",
-                "metrics": metrics,
-                "extra": artifact.extra,
-            }, indent=2, sort_keys=True) + "\n",
+            json.dumps(
+                {
+                    "suite": self.suite,
+                    "benchmark": self.name,
+                    "raw_path": "raw.jsonl",
+                    "metrics": metrics,
+                    "extra": artifact.extra,
+                },
+                indent=2,
+                sort_keys=True,
+            )
+            + "\n",
             encoding="utf-8",
         )
         return artifact
@@ -536,8 +574,12 @@ def _compute_metrics(
     cost_pct = _safe_pct(surf_cost_agg.mean, native_cost_agg.mean)
     lat_pct = _safe_pct(surf_lat_agg.median, native_lat_agg.median)
 
-    per_task = _per_field(questions, native_correct, surf_correct, key=lambda q: q.medical_task or "unknown")
-    per_body = _per_field(questions, native_correct, surf_correct, key=lambda q: q.body_system or "unknown")
+    per_task = _per_field(
+        questions, native_correct, surf_correct, key=lambda q: q.medical_task or "unknown"
+    )
+    per_body = _per_field(
+        questions, native_correct, surf_correct, key=lambda q: q.body_system or "unknown"
+    )
 
     return {
         "native": {
@@ -593,8 +635,7 @@ def _per_field(
             "native_accuracy": (sum(n_correct) / len(pairs)) if pairs else 0.0,
             "surfsense_accuracy": (sum(s_correct) / len(pairs)) if pairs else 0.0,
             "delta_accuracy_pp": (
-                100.0 * (sum(s_correct) - sum(n_correct)) / len(pairs)
-                if pairs else 0.0
+                100.0 * (sum(s_correct) - sum(n_correct)) / len(pairs) if pairs else 0.0
             ),
         }
     return out

surfsense_evals/src/surfsense_evals/suites/medical/mirage/ingest.py

@@ -48,9 +48,7 @@ from ....core.registry import RunContext
 logger = logging.getLogger(__name__)
 
 
-MIRAGE_BENCHMARK_URL = (
-    "https://raw.githubusercontent.com/Teddy-XiongGZ/MIRAGE/main/benchmark.json"
-)
+MIRAGE_BENCHMARK_URL = "https://raw.githubusercontent.com/Teddy-XiongGZ/MIRAGE/main/benchmark.json"
 # Upstream only ships ONE zip — top-10k retrievals across 5 retrievers,
 # ~16 GB. We default to skipping it (see `--skip-snippet-filter`) and
 # ingesting the chosen corpus in full; this URL is only fetched when
@@ -100,8 +98,7 @@ def _reuse_cached_dest(dest: Path, *, expect_zip: bool, label: str) -> Path | No
         return None
     if expect_zip and not _is_valid_zip(dest):
         logger.warning(
-            "Cached %s at %s failed ZIP validation (size=%d B); deleting "
-            "and re-downloading.",
+            "Cached %s at %s failed ZIP validation (size=%d B); deleting and re-downloading.",
             label,
             dest,
             dest.stat().st_size,
@@ -176,10 +173,13 @@ async def _fetch_to_path(
             )
 
         try:
-            async with httpx.AsyncClient(
-                timeout=httpx.Timeout(timeout_s, connect=20.0),
-                follow_redirects=True,
-            ) as client, client.stream("GET", url, headers=headers) as response:
+            async with (
+                httpx.AsyncClient(
+                    timeout=httpx.Timeout(timeout_s, connect=20.0),
+                    follow_redirects=True,
+                ) as client,
+                client.stream("GET", url, headers=headers) as response,
+            ):
                 if existing_bytes and response.status_code == 200:
                     logger.warning(
                         "Server ignored Range header for %s; restarting from 0.",
@@ -223,7 +223,7 @@ async def _fetch_to_path(
             raise
         except _RETRYABLE_NET_EXC as exc:
             last_exc = exc
-            wait = min(60.0, 2.0 ** attempt)
+            wait = min(60.0, 2.0**attempt)
             logger.warning(
                 "Network error fetching %s (%s: %s); retrying in %.0fs.",
                 label,
@@ -236,7 +236,7 @@ async def _fetch_to_path(
             last_exc = exc
             # Truncated body — drop the partial and retry from scratch.
             partial.unlink(missing_ok=True)
-            wait = min(60.0, 2.0 ** attempt)
+            wait = min(60.0, 2.0**attempt)
             logger.warning(
                 "Truncated ZIP for %s; restarting from byte 0 in %.0fs.",
                 label,
@@ -278,9 +278,9 @@ class _LargeDownloadAbort(RuntimeError):
     """Raised when a download exceeds the safety threshold without opt-in."""
 
     def __init__(self, label: str, size_bytes: int) -> None:
-        gb = size_bytes / (1024 ** 3)
+        gb = size_bytes / (1024**3)
         super().__init__(
-            f"{label} would download ~{gb:.1f} GB, above the {_LARGE_DOWNLOAD_BYTES / (1024 ** 3):.0f} GB safety cap. "
+            f"{label} would download ~{gb:.1f} GB, above the {_LARGE_DOWNLOAD_BYTES / (1024**3):.0f} GB safety cap. "
             "Re-run with `--allow-large-download` to acknowledge, or use "
             "`--skip-snippet-filter` to bypass this download entirely and "
             "ingest the full corpus instead."
@@ -320,9 +320,7 @@ def _read_snippet_ids(zip_path: Path, *, tasks: list[str]) -> dict[str, set[str]
     return out
 
 
-def _load_corpus(
-    corpus_name: str, snippet_ids: set[str] | None
-) -> Iterable[SnippetRow]:
+def _load_corpus(corpus_name: str, snippet_ids: set[str] | None) -> Iterable[SnippetRow]:
     """Stream rows from a MedRAG HF corpus.
 
     * ``snippet_ids=None`` → yield every row (full-corpus ingestion path).
@@ -541,10 +539,7 @@ async def run_ingest(
                 logger.warning("Failed to list chunks for doc_id=%s: %s", doc_id, exc)
                 continue
             for chunk in chunks:
-                fh.write(
-                    json.dumps({"chunk_id": chunk.id, "document_id": doc_id})
-                    + "\n"
-                )
+                fh.write(json.dumps({"chunk_id": chunk.id, "document_id": doc_id}) + "\n")
 
     new_state = ctx.suite_state
     new_state.ingestion_maps["mirage"] = str(snippet_map_path)

surfsense_evals/src/surfsense_evals/suites/medical/mirage/runner.py

@@ -134,15 +134,23 @@ class MirageBenchmark:
             choices=("all", *_TASKS),
             help="Run a single task or all (default: all).",
         )
-        parser.add_argument("--n", dest="sample_n", type=int, default=None,
-                            help="Stratified sample size across tasks.")
+        parser.add_argument(
+            "--n",
+            dest="sample_n",
+            type=int,
+            default=None,
+            help="Stratified sample size across tasks.",
+        )
         parser.add_argument("--concurrency", type=int, default=4)
         parser.add_argument(
-            "--corpus", default="MedRAG/textbooks",
+            "--corpus",
+            default="MedRAG/textbooks",
             help="HF MedRAG corpus to ingest from (default: MedRAG/textbooks).",
         )
         parser.add_argument(
-            "--max-snippets-per-task", type=int, default=None,
+            "--max-snippets-per-task",
+            type=int,
+            default=None,
             help="Cap the per-task ingestion to N snippets (smoke).",
         )
         # Mutually exclusive: by default we skip the upstream 16 GB
@@ -152,18 +160,24 @@ class MirageBenchmark:
         # --allow-large-download).
         snippet_group = parser.add_mutually_exclusive_group()
         snippet_group.add_argument(
-            "--use-snippet-filter", dest="use_snippet_filter", action="store_true",
+            "--use-snippet-filter",
+            dest="use_snippet_filter",
+            action="store_true",
             default=False,
             help="Download retrieved_snippets_10k.zip (~16 GB) and "
-                 "filter the corpus to those ids before ingest. "
-                 "Default: skip and ingest entire corpus.",
+            "filter the corpus to those ids before ingest. "
+            "Default: skip and ingest entire corpus.",
         )
         snippet_group.add_argument(
-            "--skip-snippet-filter", dest="use_snippet_filter", action="store_false",
+            "--skip-snippet-filter",
+            dest="use_snippet_filter",
+            action="store_false",
             help="(Default) Skip the 16 GB upstream zip; ingest entire corpus.",
         )
         parser.add_argument(
-            "--allow-large-download", action="store_true", default=False,
+            "--allow-large-download",
+            action="store_true",
+            default=False,
             help="Permit downloads larger than 2 GB (e.g. retrieved_snippets_10k.zip).",
         )
         # Per-upload knobs; ignored at run-time (runner reads the
@@ -196,16 +210,13 @@ class MirageBenchmark:
                 "`python -m surfsense_evals ingest medical mirage` first."
             )
         benchmark = json.loads(bench_path.read_text(encoding="utf-8"))
-        ingest_settings = read_settings_header(
-            ctx.maps_dir() / "mirage_snippet_map.jsonl"
-        )
+        ingest_settings = read_settings_header(ctx.maps_dir() / "mirage_snippet_map.jsonl")
         questions = _load_questions(benchmark, tasks=tasks, sample_n=sample_n)
         if not questions:
             raise RuntimeError(
                 f"No MIRAGE questions matched task={task_filter!r} sample_n={sample_n!r}."
             )
-        logger.info("MIRAGE: scheduled %d questions across tasks %s",
-                    len(questions), tasks)
+        logger.info("MIRAGE: scheduled %d questions across tasks %s", len(questions), tasks)
 
         arm = SurfSenseArm(
             client=ctx.new_chat_client(),
@@ -255,7 +266,10 @@ class MirageBenchmark:
             per_task_acc[task] = acc.to_dict()
 
         macro = macro_accuracy(
-            {t: accuracy_with_wilson_ci(d["n_correct"], d["n_total"]) for t, d in per_task_acc.items()}
+            {
+                t: accuracy_with_wilson_ci(d["n_correct"], d["n_total"])
+                for t, d in per_task_acc.items()
+            }
         )
         metrics = {"per_task": per_task_acc, "macro_accuracy": macro}
 

surfsense_evals/src/surfsense_evals/suites/multimodal_doc/mmlongbench/grader.py

@@ -112,8 +112,9 @@ def _grade_int(pred: str, gold: str) -> GradeResult:
     if p_match is None:
         return GradeResult(False, 0.0, "int_eq", str(p_match), str(g_val))
     p_val = int(p_match.group(0).replace(",", ""))
-    return GradeResult(p_val == g_val, 1.0 if p_val == g_val else 0.0,
-                       "int_eq", str(p_val), str(g_val))
+    return GradeResult(
+        p_val == g_val, 1.0 if p_val == g_val else 0.0, "int_eq", str(p_val), str(g_val)
+    )
 
 
 _FLOAT_RE = re.compile(r"-?\d+(?:[.,]\d+)?")
@@ -145,15 +146,15 @@ def _grade_list(pred: str, gold: str) -> GradeResult:
         return _grade_str(pred, gold)
     inter = g_items & p_items
     if not inter:
-        return GradeResult(False, 0.0, "list_set",
-                           ", ".join(sorted(p_items)),
-                           ", ".join(sorted(g_items)))
+        return GradeResult(
+            False, 0.0, "list_set", ", ".join(sorted(p_items)), ", ".join(sorted(g_items))
+        )
     precision = len(inter) / len(p_items) if p_items else 0.0
     recall = len(inter) / len(g_items)
     f1 = (2 * precision * recall / (precision + recall)) if (precision + recall) else 0.0
-    return GradeResult(f1 >= 0.999, f1, "list_set",
-                       ", ".join(sorted(p_items)),
-                       ", ".join(sorted(g_items)))
+    return GradeResult(
+        f1 >= 0.999, f1, "list_set", ", ".join(sorted(p_items)), ", ".join(sorted(g_items))
+    )
 
 
 def _grade_none(pred: str, gold: str) -> GradeResult:
@@ -188,8 +189,11 @@ def _grade_none(pred: str, gold: str) -> GradeResult:
                 expressed_unknown = True
                 break
     return GradeResult(
-        expressed_unknown, 1.0 if expressed_unknown else 0.0,
-        "none_match", p, _normalise_text(gold),
+        expressed_unknown,
+        1.0 if expressed_unknown else 0.0,
+        "none_match",
+        p,
+        _normalise_text(gold),
     )
 
 

surfsense_evals/src/surfsense_evals/suites/multimodal_doc/mmlongbench/ingest.py

@@ -41,6 +41,7 @@ logger = logging.getLogger(__name__)
 HF_REPO_ID = "yubo2333/MMLongBench-Doc"
 HF_REPO_TYPE = "dataset"
 
+
 # Lazy import: huggingface_hub + pyarrow are heavyweight; keep the
 # benchmark module importable on machines that have only the core
 # install (e.g. CI lint jobs).
@@ -63,11 +64,11 @@ def _list_repo_files() -> list[str]:
 
 @dataclass
 class MMLongBenchQuestion:
-    doc_id: str          # filename inside the documents/ folder
+    doc_id: str  # filename inside the documents/ folder
     doc_type: str
     question: str
     answer: str
-    answer_format: str   # Str / Int / Float / List / None
+    answer_format: str  # Str / Int / Float / List / None
     evidence_pages: list[int]
     evidence_sources: list[str]
 
@@ -161,7 +162,9 @@ def _download_questions_parquet(cache_dir: Path) -> Path:
         )
         parquet_paths.append(Path(local))
         logger.info("Cached MMLongBench parquet shard %s -> %s", rel, local)
-    return parquet_paths[0] if len(parquet_paths) == 1 else _merge_parquets(parquet_paths, cache_dir)
+    return (
+        parquet_paths[0] if len(parquet_paths) == 1 else _merge_parquets(parquet_paths, cache_dir)
+    )
 
 
 def _merge_parquets(paths: list[Path], cache_dir: Path) -> Path:
@@ -221,7 +224,7 @@ async def _upload_pdfs(
     name_to_id: dict[str, int] = {}
     pdf_list = list(pdf_paths)
     for batch_start in range(0, len(pdf_list), batch_size):
-        batch = pdf_list[batch_start:batch_start + batch_size]
+        batch = pdf_list[batch_start : batch_start + batch_size]
         result = await docs_client.upload(
             files=batch,
             search_space_id=ctx.search_space_id,
@@ -243,8 +246,10 @@ async def _upload_pdfs(
                 name_to_id[s.title] = s.document_id
         logger.info(
             "Uploaded MMLongBench batch %d-%d: %d new, %d duplicate",
-            batch_start, batch_start + len(batch),
-            len(result.document_ids), len(result.duplicate_document_ids),
+            batch_start,
+            batch_start + len(batch),
+            len(result.document_ids),
+            len(result.duplicate_document_ids),
         )
     return name_to_id
 
@@ -299,15 +304,20 @@ async def run_ingest(
     questions_jsonl = bench_dir / "questions.jsonl"
     with questions_jsonl.open("w", encoding="utf-8") as fh:
         for q in questions:
-            fh.write(json.dumps({
-                "doc_id": q.doc_id,
-                "doc_type": q.doc_type,
-                "question": q.question,
-                "answer": q.answer,
-                "answer_format": q.answer_format,
-                "evidence_pages": q.evidence_pages,
-                "evidence_sources": q.evidence_sources,
-            }) + "\n")
+            fh.write(
+                json.dumps(
+                    {
+                        "doc_id": q.doc_id,
+                        "doc_type": q.doc_type,
+                        "question": q.question,
+                        "answer": q.answer,
+                        "answer_format": q.answer_format,
+                        "evidence_pages": q.evidence_pages,
+                        "evidence_sources": q.evidence_sources,
+                    }
+                )
+                + "\n"
+            )
     logger.info("Wrote %d MMLongBench questions to %s", len(questions), questions_jsonl)
 
     # Step 2: download unique PDFs
@@ -348,12 +358,17 @@ async def run_ingest(
             local = pdf_paths.get(doc_id)
             if local is None:
                 continue
-            fh.write(json.dumps({
-                "doc_id": doc_id,
-                "document_id": name_to_id.get(local.name),
-                "pdf_path": str(local),
-                "n_questions": sum(1 for q in questions if q.doc_id == doc_id),
-            }) + "\n")
+            fh.write(
+                json.dumps(
+                    {
+                        "doc_id": doc_id,
+                        "document_id": name_to_id.get(local.name),
+                        "pdf_path": str(local),
+                        "n_questions": sum(1 for q in questions if q.doc_id == doc_id),
+                    }
+                )
+                + "\n"
+            )
     logger.info("Wrote MMLongBench doc map to %s", map_path)
 
     new_state = ctx.suite_state

surfsense_evals/src/surfsense_evals/suites/multimodal_doc/mmlongbench/prompt.py

@@ -18,10 +18,7 @@ _FORMAT_HINTS: dict[str, str] = {
         "Respond with the answer as a short phrase, no full sentence. "
         "Format your final line as `Answer: <text>`."
     ),
-    "int": (
-        "Respond with a single integer only. "
-        "Format your final line as `Answer: <integer>`."
-    ),
+    "int": ("Respond with a single integer only. Format your final line as `Answer: <integer>`."),
     "float": (
         "Respond with a single decimal number only (no units). "
         "Format your final line as `Answer: <number>`."

surfsense_evals/src/surfsense_evals/suites/multimodal_doc/mmlongbench/runner.py

@@ -58,8 +58,8 @@ logger = logging.getLogger(__name__)
 
 @dataclass
 class MMLBQuestion:
-    qid: str               # synthesised from doc_id + index
-    doc_id: str            # filename inside the documents/ folder
+    qid: str  # synthesised from doc_id + index
+    doc_id: str  # filename inside the documents/ folder
     doc_type: str
     question: str
     gold_answer: str
@@ -126,18 +126,20 @@ def _load_questions(
                 continue
             idx = per_doc_counter.get(doc_id, 0)
             per_doc_counter[doc_id] = idx + 1
-            out.append(MMLBQuestion(
-                qid=f"{doc_id}::Q{idx:03d}",
-                doc_id=doc_id,
-                doc_type=str(row.get("doc_type") or "").strip(),
-                question=str(row.get("question") or "").strip(),
-                gold_answer=gold,
-                answer_format=answer_format,
-                evidence_pages=list(row.get("evidence_pages") or []),
-                evidence_sources=list(row.get("evidence_sources") or []),
-                pdf_path=Path(map_row["pdf_path"]),
-                document_id=map_row.get("document_id"),
-            ))
+            out.append(
+                MMLBQuestion(
+                    qid=f"{doc_id}::Q{idx:03d}",
+                    doc_id=doc_id,
+                    doc_type=str(row.get("doc_type") or "").strip(),
+                    question=str(row.get("question") or "").strip(),
+                    gold_answer=gold,
+                    answer_format=answer_format,
+                    evidence_pages=list(row.get("evidence_pages") or []),
+                    evidence_sources=list(row.get("evidence_sources") or []),
+                    pdf_path=Path(map_row["pdf_path"]),
+                    document_id=map_row.get("document_id"),
+                )
+            )
     out.sort(key=lambda q: (q.doc_id, q.qid))
     if sample_n is not None and sample_n > 0:
         out = out[:sample_n]
@@ -202,41 +204,61 @@ class MMLongBenchDocBenchmark:
             help="Filter to one answer format. 'none' = unanswerable probes only.",
         )
         parser.add_argument(
-            "--n", dest="sample_n", type=int, default=None,
+            "--n",
+            dest="sample_n",
+            type=int,
+            default=None,
             help="Run only the first N questions after filters apply.",
         )
         parser.add_argument(
-            "--skip-unanswerable", dest="skip_unanswerable", action="store_true",
+            "--skip-unanswerable",
+            dest="skip_unanswerable",
+            action="store_true",
             help="Drop ~22%% unanswerable questions (use to compare against baselines that don't include them).",
         )
         parser.add_argument(
-            "--concurrency", type=int, default=4,
+            "--concurrency",
+            type=int,
+            default=4,
             help="Parallel question workers per arm.",
         )
         parser.add_argument(
-            "--no-mentions", dest="no_mentions", action="store_true",
+            "--no-mentions",
+            dest="no_mentions",
+            action="store_true",
             help="SurfSense arm: skip mentioned_document_ids (unscoped retrieval).",
         )
         parser.add_argument(
-            "--pdf-engine", default="native",
+            "--pdf-engine",
+            default="native",
             choices=[e.value for e in PdfEngine],
             help="OpenRouter file-parser engine for the native arm.",
         )
         parser.add_argument(
-            "--max-output-tokens", type=int, default=512,
+            "--max-output-tokens",
+            type=int,
+            default=512,
             help="Cap on completion length for both arms.",
         )
         # Ingest-only knobs (forwarded by the CLI to ingest.run_ingest).
         parser.add_argument(
-            "--max-docs", dest="max_docs", type=int, default=None,
+            "--max-docs",
+            dest="max_docs",
+            type=int,
+            default=None,
             help="(ingest only) cap on number of unique PDFs to download + upload.",
         )
         parser.add_argument(
-            "--upload-batch-size", dest="upload_batch_size", type=int, default=8,
+            "--upload-batch-size",
+            dest="upload_batch_size",
+            type=int,
+            default=8,
             help="(ingest only) PDFs per fileupload call.",
         )
         parser.add_argument(
-            "--skip-upload", dest="skip_upload", action="store_true",
+            "--skip-upload",
+            dest="skip_upload",
+            action="store_true",
             help="(ingest only) cache PDFs locally but don't push to SurfSense.",
         )
         # Per-upload knobs forwarded to /documents/fileupload at ingest;
@@ -278,7 +300,8 @@ class MMLongBenchDocBenchmark:
 
         doc_map, ingest_settings = _load_doc_map(map_path)
         questions = _load_questions(
-            questions_jsonl, doc_map,
+            questions_jsonl,
+            doc_map,
             doc_filter=doc_filter,
             format_filter=None if format_filter == "all" else format_filter,
             sample_n=sample_n,
@@ -292,9 +315,7 @@ class MMLongBenchDocBenchmark:
 
         api_key = os.environ.get("OPENROUTER_API_KEY")
         if not api_key:
-            raise RuntimeError(
-                "OPENROUTER_API_KEY env var is required for the native arm."
-            )
+            raise RuntimeError("OPENROUTER_API_KEY env var is required for the native arm.")
 
         # Native arm slug differs from SurfSense slug only in cost-arbitrage
         # scenario; otherwise both arms answer with provider_model.
@@ -362,18 +383,30 @@ class MMLongBenchDocBenchmark:
                     "evidence_sources": q.evidence_sources,
                     "document_id": q.document_id,
                 }
-                fh.write(json.dumps({
-                    **meta,
-                    **n_res.to_jsonl(),
-                    "graded": _grade_to_jsonl(n_g),
-                }) + "\n")
-                fh.write(json.dumps({
-                    **meta,
-                    **s_res.to_jsonl(),
-                    "graded": _grade_to_jsonl(s_g),
-                }) + "\n")
+                fh.write(
+                    json.dumps(
+                        {
+                            **meta,
+                            **n_res.to_jsonl(),
+                            "graded": _grade_to_jsonl(n_g),
+                        }
+                    )
+                    + "\n"
+                )
+                fh.write(
+                    json.dumps(
+                        {
+                            **meta,
+                            **s_res.to_jsonl(),
+                            "graded": _grade_to_jsonl(s_g),
+                        }
+                    )
+                    + "\n"
+                )
 
-        metrics = _compute_metrics(questions, native_results, surf_results, native_grades, surf_grades)
+        metrics = _compute_metrics(
+            questions, native_results, surf_results, native_grades, surf_grades
+        )
         artifact = RunArtifact(
             suite=self.suite,
             benchmark=self.name,
@@ -398,13 +431,18 @@ class MMLongBenchDocBenchmark:
 
         manifest_path = run_dir / "run_artifact.json"
         manifest_path.write_text(
-            json.dumps({
-                "suite": self.suite,
-                "benchmark": self.name,
-                "raw_path": "raw.jsonl",
-                "metrics": metrics,
-                "extra": artifact.extra,
-            }, indent=2, sort_keys=True) + "\n",
+            json.dumps(
+                {
+                    "suite": self.suite,
+                    "benchmark": self.name,
+                    "raw_path": "raw.jsonl",
+                    "metrics": metrics,
+                    "extra": artifact.extra,
+                },
+                indent=2,
+                sort_keys=True,
+            )
+            + "\n",
             encoding="utf-8",
         )
         return artifact
@@ -450,9 +488,7 @@ class MMLongBenchDocBenchmark:
             f"(McNemar p={_fmt(delta.get('mcnemar_p_value'), 4)}, "
             f"method={delta.get('mcnemar_method')})"
         )
-        body_lines.append(
-            f"  - F1 (mean): SurfSense {_pp(delta.get('f1_pp'))} pp"
-        )
+        body_lines.append(f"  - F1 (mean): SurfSense {_pp(delta.get('f1_pp'))} pp")
         body_lines.append(
             f"  - Bootstrap 95% CI on accuracy delta: "
             f"[{_pp(delta.get('bootstrap_ci_low'))}pp, {_pp(delta.get('bootstrap_ci_high'))}pp]"
@@ -472,8 +508,8 @@ class MMLongBenchDocBenchmark:
             for fmt, vals in sorted(per_format.items()):
                 body_lines.append(
                     f"  - {fmt}: SurfSense {_pp(vals.get('delta_accuracy_pp'))} pp "
-                    f"(n={vals.get('n')}, native acc={vals.get('native_accuracy', 0)*100:.1f}%, "
-                    f"surf acc={vals.get('surfsense_accuracy', 0)*100:.1f}%)"
+                    f"(n={vals.get('n')}, native acc={vals.get('native_accuracy', 0) * 100:.1f}%, "
+                    f"surf acc={vals.get('surfsense_accuracy', 0) * 100:.1f}%)"
                 )
 
         return ReportSection(
@@ -576,8 +612,7 @@ def _compute_metrics(
             "native_accuracy": (sum(n_correct) / len(pairs)) if pairs else 0.0,
             "surfsense_accuracy": (sum(s_correct) / len(pairs)) if pairs else 0.0,
             "delta_accuracy_pp": (
-                100.0 * (sum(s_correct) - sum(n_correct)) / len(pairs)
-                if pairs else 0.0
+                100.0 * (sum(s_correct) - sum(n_correct)) / len(pairs) if pairs else 0.0
             ),
         }
 
@@ -593,8 +628,12 @@ def _compute_metrics(
             "latency_ms_mean": native_latency_agg.mean,
             "latency_ms_median": native_latency_agg.median,
             "latency_ms_p95": native_latency_agg.p95,
-            "input_tokens_mean": (sum(native_in_tokens) / len(native_in_tokens)) if native_in_tokens else 0.0,
-            "output_tokens_mean": (sum(native_out_tokens) / len(native_out_tokens)) if native_out_tokens else 0.0,
+            "input_tokens_mean": (sum(native_in_tokens) / len(native_in_tokens))
+            if native_in_tokens
+            else 0.0,
+            "output_tokens_mean": (sum(native_out_tokens) / len(native_out_tokens))
+            if native_out_tokens
+            else 0.0,
         },
         "surfsense": {
             **surf_acc.to_dict(),

surfsense_evals/src/surfsense_evals/suites/multimodal_doc/parser_compare/ingest.py

@@ -53,9 +53,9 @@ logger = logging.getLogger(__name__)
 # Order matters for the manifest only (deterministic JSONL diffs);
 # the runner doesn't rely on it.
 PARSER_ARMS: tuple[tuple[str, str, str], ...] = (
-    ("azure_basic_lc",       "azure",      "basic"),
-    ("azure_premium_lc",     "azure",      "premium"),
-    ("llamacloud_basic_lc",  "llamacloud", "basic"),
+    ("azure_basic_lc", "azure", "basic"),
+    ("azure_premium_lc", "azure", "premium"),
+    ("llamacloud_basic_lc", "llamacloud", "basic"),
     ("llamacloud_premium_lc", "llamacloud", "premium"),
 )
 
@@ -98,9 +98,7 @@ class PdfManifestRow:
             "pdf_path": str(self.pdf_path),
             "document_id": self.document_id,
             "pages": self.pages,
-            "extractions": {
-                arm: ext.to_jsonl() for arm, ext in self.extractions.items()
-            },
+            "extractions": {arm: ext.to_jsonl() for arm, ext in self.extractions.items()},
         }
 
 
@@ -124,7 +122,9 @@ async def _run_one_extraction(
         markdown = await parse_with_azure_di(pdf_path, processing_mode=mode)
     elif parser == "llamacloud":
         markdown = await parse_with_llamacloud(
-            pdf_path, processing_mode=mode, estimated_pages=estimated_pages,
+            pdf_path,
+            processing_mode=mode,
+            estimated_pages=estimated_pages,
         )
     else:
         raise ValueError(f"Unknown parser {parser!r}")
@@ -168,14 +168,17 @@ async def _extract_one_pdf(
                 error="(cached)",
             )
             logger.info(
-                "Cached extraction reused: %s (%d chars)", out_path.name, len(cached),
+                "Cached extraction reused: %s (%d chars)",
+                out_path.name,
+                len(cached),
             )
             coros.append(_noop())
         else:
             coros.append(
                 _run_one_extraction(
                     pdf_path,
-                    parser=parser, mode=mode,
+                    parser=parser,
+                    mode=mode,
                     out_path=out_path,
                     estimated_pages=estimated_pages,
                 )
@@ -190,16 +193,24 @@ async def _extract_one_pdf(
             err_msg = f"{type(err).__name__}: {err}"
             logger.warning(
                 "Extraction FAILED for %s [%s/%s]: %s",
-                pdf_path.name, parser, mode, err_msg,
+                pdf_path.name,
+                parser,
+                mode,
+                err_msg,
             )
             out[arm_name] = ExtractionResult(
-                arm=arm_name, parser=parser, mode=mode,
-                status="failed", error=err_msg,
+                arm=arm_name,
+                parser=parser,
+                mode=mode,
+                status="failed",
+                error=err_msg,
             )
         else:
             markdown, elapsed = result
             out[arm_name] = ExtractionResult(
-                arm=arm_name, parser=parser, mode=mode,
+                arm=arm_name,
+                parser=parser,
+                mode=mode,
                 markdown_path=out_path,
                 chars=len(markdown),
                 elapsed_s=elapsed,
@@ -288,9 +299,7 @@ async def run_ingest(
         rows_in_scope = rows_in_scope[:max_docs]
 
     if not rows_in_scope:
-        raise RuntimeError(
-            "No PDFs in scope for parser_compare. Check --docs / --max-docs."
-        )
+        raise RuntimeError("No PDFs in scope for parser_compare. Check --docs / --max-docs.")
 
     bench_dir = ctx.benchmark_data_dir()
     extractions_dir = bench_dir / "extractions"
@@ -317,7 +326,8 @@ async def run_ingest(
 
     logger.info(
         "parser_compare: extracting %d PDFs x 4 parsers (concurrency=%d)",
-        len(rows_in_scope), pdf_concurrency,
+        len(rows_in_scope),
+        pdf_concurrency,
     )
     manifest_rows = await asyncio.gather(*(_process(r) for r in rows_in_scope))
 
@@ -337,12 +347,13 @@ async def run_ingest(
     # Quick summary log
     total_extractions = sum(len(mr.extractions) for mr in manifest_rows)
     failures = sum(
-        1 for mr in manifest_rows for ext in mr.extractions.values()
-        if ext.status != "ok"
+        1 for mr in manifest_rows for ext in mr.extractions.values() if ext.status != "ok"
     )
     logger.info(
         "parser_compare ingest done: %d PDFs, %d extractions, %d failures",
-        len(manifest_rows), total_extractions, failures,
+        len(manifest_rows),
+        total_extractions,
+        failures,
     )
 
 

surfsense_evals/src/surfsense_evals/suites/multimodal_doc/parser_compare/prompt.py

@@ -34,10 +34,7 @@ _FORMAT_HINTS: dict[str, str] = {
         "Respond with the answer as a short phrase, no full sentence. "
         "Format your final line as `Answer: <text>`."
     ),
-    "int": (
-        "Respond with a single integer only. "
-        "Format your final line as `Answer: <integer>`."
-    ),
+    "int": ("Respond with a single integer only. Format your final line as `Answer: <integer>`."),
     "float": (
         "Respond with a single decimal number only (no units). "
         "Format your final line as `Answer: <number>`."
@@ -69,11 +66,7 @@ _BASE_INSTRUCTION = (
 def build_native_pdf_prompt(question: str, *, answer_format: str) -> str:
     """Prompt for ``NativePdfArm`` — PDF attached separately as a file part."""
 
-    return (
-        f"{_BASE_INSTRUCTION}\n\n"
-        f"Question: {question.strip()}\n\n"
-        f"{_format_hint(answer_format)}\n"
-    )
+    return f"{_BASE_INSTRUCTION}\n\nQuestion: {question.strip()}\n\n{_format_hint(answer_format)}\n"
 
 
 def build_surfsense_prompt(question: str, *, answer_format: str) -> str:
@@ -82,11 +75,7 @@ def build_surfsense_prompt(question: str, *, answer_format: str) -> str:
     # SurfSense's agent already injects retrieved chunks via its tool
     # loop; the prompt only carries the user-visible question + format
     # hint, mirroring how a human asks the SurfSense UI.
-    return (
-        f"{_BASE_INSTRUCTION}\n\n"
-        f"Question: {question.strip()}\n\n"
-        f"{_format_hint(answer_format)}\n"
-    )
+    return f"{_BASE_INSTRUCTION}\n\nQuestion: {question.strip()}\n\n{_format_hint(answer_format)}\n"
 
 
 def build_long_context_prompt(
@@ -105,7 +94,7 @@ def build_long_context_prompt(
 
     return (
         f"{_BASE_INSTRUCTION}\n\n"
-        f"<document name=\"{document_label}\">\n"
+        f'<document name="{document_label}">\n'
         f"{document_markdown.strip()}\n"
         f"</document>\n\n"
         f"Question: {question.strip()}\n\n"

surfsense_evals/src/surfsense_evals/suites/multimodal_doc/parser_compare/runner.py

@@ -72,7 +72,7 @@ logger = logging.getLogger(__name__)
 # Cost tariff (per the user's spec: $1 / 1k pages basic, $10 / 1k pages premium).
 # Held as dollars-per-page so per-PDF math is a pure multiply.
 PREPROCESS_USD_PER_PAGE = {
-    "basic":   1.0  / 1000.0,
+    "basic": 1.0 / 1000.0,
     "premium": 10.0 / 1000.0,
 }
 
@@ -183,17 +183,19 @@ def _select_questions(
                 if ext_blob.get("status") == "ok" and ext_blob.get("markdown_path"):
                     extractions[arm_name] = Path(ext_blob["markdown_path"])
 
-            out.append(PCQuestion(
-                qid=f"{doc_id}::Q{idx:03d}",
-                doc_id=doc_id,
-                question=str(row.get("question") or "").strip(),
-                gold_answer=str(row.get("answer") or "").strip(),
-                answer_format=answer_format,
-                pdf_path=Path(map_row["pdf_path"]),
-                document_id=map_row.get("document_id"),
-                pages=int(map_row.get("pages", 0)),
-                extractions=extractions,
-            ))
+            out.append(
+                PCQuestion(
+                    qid=f"{doc_id}::Q{idx:03d}",
+                    doc_id=doc_id,
+                    question=str(row.get("question") or "").strip(),
+                    gold_answer=str(row.get("answer") or "").strip(),
+                    answer_format=answer_format,
+                    pdf_path=Path(map_row["pdf_path"]),
+                    document_id=map_row.get("document_id"),
+                    pages=int(map_row.get("pages", 0)),
+                    extractions=extractions,
+                )
+            )
             per_doc_taken[doc_id] = per_doc_taken.get(doc_id, 0) + 1
 
     out.sort(key=lambda q: (q.doc_id, q.qid))
@@ -242,65 +244,86 @@ class ParserCompareBenchmark:
 
     def add_run_args(self, parser: argparse.ArgumentParser) -> None:
         parser.add_argument(
-            "--docs", default=None,
+            "--docs",
+            default=None,
             help="Comma-separated doc_ids to include (default: all in manifest).",
         )
         parser.add_argument(
-            "--sample-per-doc", type=int, default=1,
+            "--sample-per-doc",
+            type=int,
+            default=1,
             help="Take the first N answerable questions per PDF (default 1).",
         )
         parser.add_argument(
-            "--skip-unanswerable", dest="skip_unanswerable",
-            action="store_true", default=True,
+            "--skip-unanswerable",
+            dest="skip_unanswerable",
+            action="store_true",
+            default=True,
             help="Drop 'None' format probes (default true; we want signal not "
-                 "hallucination probes for n=5).",
+            "hallucination probes for n=5).",
         )
         parser.add_argument(
-            "--include-unanswerable", dest="skip_unanswerable",
+            "--include-unanswerable",
+            dest="skip_unanswerable",
             action="store_false",
             help="Override --skip-unanswerable; include unanswerable probes too.",
         )
         parser.add_argument(
-            "--skip-format", default=None,
+            "--skip-format",
+            default=None,
             help="Comma-separated answer_format values to skip (e.g. 'none,float').",
         )
         parser.add_argument(
-            "--concurrency", type=int, default=2,
+            "--concurrency",
+            type=int,
+            default=2,
             help="Parallel question workers per arm (default 2).",
         )
         parser.add_argument(
-            "--no-mentions", dest="no_mentions", action="store_true",
+            "--no-mentions",
+            dest="no_mentions",
+            action="store_true",
             help="SurfSense arm: skip mentioned_document_ids (full-corpus retrieval).",
         )
         parser.add_argument(
-            "--pdf-engine", default="native",
+            "--pdf-engine",
+            default="native",
             choices=[e.value for e in PdfEngine],
             help="OpenRouter file-parser engine for native_pdf arm.",
         )
         parser.add_argument(
-            "--max-output-tokens", type=int, default=512,
+            "--max-output-tokens",
+            type=int,
+            default=512,
             help="Cap on completion length for every arm.",
         )
         parser.add_argument(
-            "--llm-model", default="anthropic/claude-sonnet-4.5",
+            "--llm-model",
+            default="anthropic/claude-sonnet-4.5",
             help="OpenRouter slug used by the 5 OpenRouter-driven arms. "
-                 "SurfSense arm uses whatever provider_model is pinned on the suite.",
+            "SurfSense arm uses whatever provider_model is pinned on the suite.",
         )
         parser.add_argument(
-            "--skip-arms", default=None,
+            "--skip-arms",
+            default=None,
             help="Comma-separated arm names to skip (e.g. 'llamacloud_premium_lc').",
         )
         # Ingest-only flags (forwarded by the CLI to ingest.run_ingest).
         parser.add_argument(
-            "--max-docs", type=int, default=None,
+            "--max-docs",
+            type=int,
+            default=None,
             help="(ingest only) cap number of unique PDFs to process.",
         )
         parser.add_argument(
-            "--force-reextract", action="store_true",
+            "--force-reextract",
+            action="store_true",
             help="(ingest only) re-call parsers even if cached .md exists.",
         )
         parser.add_argument(
-            "--pdf-concurrency", type=int, default=2,
+            "--pdf-concurrency",
+            type=int,
+            default=2,
             help="(ingest only) parallel PDFs (each fans out to 4 parsers).",
         )
 
@@ -312,9 +335,7 @@ class ParserCompareBenchmark:
         from .ingest import run_ingest
 
         docs_raw: str | None = opts.get("docs")
-        docs_filter = (
-            [d.strip() for d in docs_raw.split(",") if d.strip()] if docs_raw else None
-        )
+        docs_filter = [d.strip() for d in docs_raw.split(",") if d.strip()] if docs_raw else None
         await run_ingest(
             ctx,
             docs_filter=docs_filter,
@@ -329,15 +350,14 @@ class ParserCompareBenchmark:
 
     async def run(self, ctx: RunContext, **opts: Any) -> RunArtifact:
         docs_raw: str | None = opts.get("docs")
-        docs_filter = (
-            [d.strip() for d in docs_raw.split(",") if d.strip()] if docs_raw else None
-        )
+        docs_filter = [d.strip() for d in docs_raw.split(",") if d.strip()] if docs_raw else None
         sample_per_doc = int(opts.get("sample_per_doc") or 1)
         skip_unanswerable = bool(opts.get("skip_unanswerable", True))
         skip_format_raw: str | None = opts.get("skip_format")
         skip_format = (
             [f.strip() for f in skip_format_raw.split(",") if f.strip()]
-            if skip_format_raw else None
+            if skip_format_raw
+            else None
         )
         concurrency = int(opts.get("concurrency") or 2)
         no_mentions = bool(opts.get("no_mentions"))
@@ -346,8 +366,7 @@ class ParserCompareBenchmark:
         llm_model = str(opts.get("llm_model") or "anthropic/claude-sonnet-4.5")
         skip_arms_raw: str | None = opts.get("skip_arms")
         skip_arms = (
-            {a.strip() for a in skip_arms_raw.split(",") if a.strip()}
-            if skip_arms_raw else set()
+            {a.strip() for a in skip_arms_raw.split(",") if a.strip()} if skip_arms_raw else set()
         )
 
         active_arms = [a for a in ARM_NAMES if a not in skip_arms]
@@ -373,19 +392,20 @@ class ParserCompareBenchmark:
 
         doc_map = _read_doc_map(map_path)
         questions = _select_questions(
-            questions_jsonl, doc_map,
+            questions_jsonl,
+            doc_map,
             docs_filter=docs_filter,
             sample_per_doc=sample_per_doc,
             skip_unanswerable=skip_unanswerable,
             skip_format=skip_format,
         )
         if not questions:
-            raise RuntimeError(
-                "No questions matched filters; broaden --docs / --skip-format."
-            )
+            raise RuntimeError("No questions matched filters; broaden --docs / --skip-format.")
         logger.info(
             "parser_compare: scheduled %d questions across %d arms (%s)",
-            len(questions), len(active_arms), ",".join(active_arms),
+            len(questions),
+            len(active_arms),
+            ",".join(active_arms),
         )
 
         api_key = os.environ.get("OPENROUTER_API_KEY")
@@ -396,16 +416,20 @@ class ParserCompareBenchmark:
         arms: dict[str, Any] = {}
         if "native_pdf" in active_arms:
             native_provider = OpenRouterPdfProvider(
-                api_key=api_key, base_url=ctx.config.openrouter_base_url,
-                model=llm_model, engine=PdfEngine(pdf_engine_name),
+                api_key=api_key,
+                base_url=ctx.config.openrouter_base_url,
+                model=llm_model,
+                engine=PdfEngine(pdf_engine_name),
             )
             arms["native_pdf"] = NativePdfArm(
-                provider=native_provider, max_output_tokens=max_output_tokens,
+                provider=native_provider,
+                max_output_tokens=max_output_tokens,
             )
         for arm_name, _, _ in PARSER_ARMS:
             if arm_name in active_arms:
                 lc_provider = OpenRouterChatProvider(
-                    api_key=api_key, base_url=ctx.config.openrouter_base_url,
+                    api_key=api_key,
+                    base_url=ctx.config.openrouter_base_url,
                     model=llm_model,
                 )
                 arms[arm_name] = BareLlmArm(
@@ -441,9 +465,7 @@ class ParserCompareBenchmark:
         def _lc_req(q: PCQuestion, arm_name: str) -> ArmRequest:
             md_path = q.extractions.get(arm_name)
             if md_path is None or not md_path.exists():
-                raise FileNotFoundError(
-                    f"Missing extraction for {arm_name} on {q.doc_id}"
-                )
+                raise FileNotFoundError(f"Missing extraction for {arm_name} on {q.doc_id}")
             markdown = md_path.read_text(encoding="utf-8")
             return ArmRequest(
                 question_id=q.qid,
@@ -483,14 +505,15 @@ class ParserCompareBenchmark:
 
         # Run all arms in parallel (each arm bounded by `concurrency`).
         per_arm_tasks: dict[str, list] = {
-            arm_name: [_answer_one(arm_name, q) for q in questions]
-            for arm_name in active_arms
+            arm_name: [_answer_one(arm_name, q) for q in questions] for arm_name in active_arms
         }
         per_arm_results: dict[str, list[ArmResult]] = {}
-        gathered = await asyncio.gather(*[
-            _gather_with_limit(per_arm_tasks[arm_name], concurrency=concurrency)
-            for arm_name in active_arms
-        ])
+        gathered = await asyncio.gather(
+            *[
+                _gather_with_limit(per_arm_tasks[arm_name], concurrency=concurrency)
+                for arm_name in active_arms
+            ]
+        )
         for arm_name, results in zip(active_arms, gathered, strict=True):
             per_arm_results[arm_name] = results
 
@@ -520,21 +543,29 @@ class ParserCompareBenchmark:
                 for arm_name in active_arms:
                     res = per_arm_results[arm_name][i]
                     g = per_arm_grades[arm_name][i]
-                    fh.write(json.dumps({
-                        **base,
-                        **res.to_jsonl(),
-                        "graded": {
-                            "correct": g.correct,
-                            "f1": g.f1,
-                            "method": g.method,
-                            "normalised_pred": g.normalised_pred,
-                            "normalised_gold": g.normalised_gold,
-                        },
-                    }) + "\n")
+                    fh.write(
+                        json.dumps(
+                            {
+                                **base,
+                                **res.to_jsonl(),
+                                "graded": {
+                                    "correct": g.correct,
+                                    "f1": g.f1,
+                                    "method": g.method,
+                                    "normalised_pred": g.normalised_pred,
+                                    "normalised_gold": g.normalised_gold,
+                                },
+                            }
+                        )
+                        + "\n"
+                    )
 
         # Aggregate per-arm metrics + cost
         metrics = _compute_metrics(
-            questions, per_arm_results, per_arm_grades, active_arms,
+            questions,
+            per_arm_results,
+            per_arm_grades,
+            active_arms,
         )
 
         artifact = RunArtifact(
@@ -564,13 +595,18 @@ class ParserCompareBenchmark:
 
         manifest_path = run_dir / "run_artifact.json"
         manifest_path.write_text(
-            json.dumps({
-                "suite": self.suite,
-                "benchmark": self.name,
-                "raw_path": "raw.jsonl",
-                "metrics": metrics,
-                "extra": artifact.extra,
-            }, indent=2, sort_keys=True) + "\n",
+            json.dumps(
+                {
+                    "suite": self.suite,
+                    "benchmark": self.name,
+                    "raw_path": "raw.jsonl",
+                    "metrics": metrics,
+                    "extra": artifact.extra,
+                },
+                indent=2,
+                sort_keys=True,
+            )
+            + "\n",
             encoding="utf-8",
         )
         return artifact
@@ -602,10 +638,7 @@ class ParserCompareBenchmark:
             f"(LLM: `{extra.get('llm_model', '?')}`, "
             f"engine: `{extra.get('pdf_engine', 'native')}`)."
         )
-        body.append(
-            "- Preprocess tariff: basic = $1 / 1k pages, "
-            "premium = $10 / 1k pages."
-        )
+        body.append("- Preprocess tariff: basic = $1 / 1k pages, premium = $10 / 1k pages.")
         body.append("")
         body.append("### Per-arm summary")
         body.append("")
@@ -620,13 +653,13 @@ class ParserCompareBenchmark:
                 continue
             body.append(
                 f"| `{arm_name}` "
-                f"| {row['accuracy']*100:.1f}% "
+                f"| {row['accuracy'] * 100:.1f}% "
                 f"({row['n_correct']}/{row['n']}) "
-                f"| {row['f1_mean']*100:.1f}% "
+                f"| {row['f1_mean'] * 100:.1f}% "
                 f"| ${row['llm_cost_per_q']:.4f} "
                 f"| ${row['preprocess_cost_total']:.4f} "
                 f"| ${row['total_cost_per_q']:.4f} "
-                f"| {row['latency_ms_median']/1000:.1f}s |"
+                f"| {row['latency_ms_median'] / 1000:.1f}s |"
             )
         body.append("")
 
@@ -679,8 +712,7 @@ class ParserCompareBenchmark:
                     else:
                         row_cells.append("✓" if g.get("correct") else "✗")
                 body.append(
-                    f"| `{doc_id}` | {info.get('pages', '?')} | "
-                    + " | ".join(row_cells) + " |"
+                    f"| `{doc_id}` | {info.get('pages', '?')} | " + " | ".join(row_cells) + " |"
                 )
 
         return ReportSection(
@@ -740,16 +772,16 @@ def _compute_metrics(
             preprocess_per_page = 0.0
             preprocess_label = "unknown"
 
-        preprocess_cost_total = sum(
-            pages * preprocess_per_page for pages in pdf_pages.values()
-        )
+        preprocess_cost_total = sum(pages * preprocess_per_page for pages in pdf_pages.values())
         preprocess_cost_per_q = preprocess_cost_total / n if n else 0.0
         total_cost_per_q = llm_cost_per_q + preprocess_cost_per_q
 
         latencies = sorted(int(r.latency_ms or 0) for r in results)
         latency_median = latencies[len(latencies) // 2] if latencies else 0
-        latency_p95 = latencies[int(len(latencies) * 0.95)] if len(latencies) >= 20 else (
-            latencies[-1] if latencies else 0
+        latency_p95 = (
+            latencies[int(len(latencies) * 0.95)]
+            if len(latencies) >= 20
+            else (latencies[-1] if latencies else 0)
         )
 
         in_tokens = [int(r.input_tokens or 0) for r in results]
@@ -775,15 +807,21 @@ def _compute_metrics(
     # Per-PDF breakdown (correct / not for each arm)
     per_pdf: dict[str, dict[str, Any]] = {}
     for i, q in enumerate(questions):
-        slot = per_pdf.setdefault(q.doc_id, {
-            "pages": q.pages,
-            "arms": {},
-        })
+        slot = per_pdf.setdefault(
+            q.doc_id,
+            {
+                "pages": q.pages,
+                "arms": {},
+            },
+        )
         for arm_name in active_arms:
-            slot["arms"].setdefault(arm_name, {
-                "correct": per_arm_grades[arm_name][i].correct,
-                "f1": per_arm_grades[arm_name][i].f1,
-            })
+            slot["arms"].setdefault(
+                arm_name,
+                {
+                    "correct": per_arm_grades[arm_name][i].correct,
+                    "f1": per_arm_grades[arm_name][i].f1,
+                },
+            )
 
     return {
         "per_arm": per_arm,

surfsense_evals/src/surfsense_evals/suites/research/crag/dataset.py

@@ -80,7 +80,7 @@ class CragPage:
 class CragQuestion:
     """One row of CRAG (Tasks 1 & 2)."""
 
-    qid: str                          # synthesised "C00000".."C02705"
+    qid: str  # synthesised "C00000".."C02705"
     interaction_id: str
     query_time: str
     query: str
@@ -89,9 +89,9 @@ class CragQuestion:
     domain: str
     question_type: str
     static_or_dynamic: str
-    popularity: str                   # may be "" for web-sourced questions
-    split: int                        # 0=validation, 1=public_test
-    raw_index: int                    # row index in the source JSONL
+    popularity: str  # may be "" for web-sourced questions
+    split: int  # 0=validation, 1=public_test
+    raw_index: int  # row index in the source JSONL
     pages: list[CragPage] = field(default_factory=list)
 
     def to_dict(self) -> dict[str, Any]:
@@ -166,16 +166,19 @@ def _parse_pages(raw_search_results: Any) -> list[CragPage]:
         if not url or not html.strip():
             # No URL or empty HTML => useless for retrieval.
             continue
-        pages.append(CragPage(
-            page_name=str(entry.get("page_name") or "").strip(),
-            page_url=url,
-            page_snippet=str(entry.get("page_snippet") or "").strip(),
-            page_html=html,
-            page_last_modified=(
-                str(entry.get("page_last_modified")).strip()
-                if entry.get("page_last_modified") else None
-            ),
-        ))
+        pages.append(
+            CragPage(
+                page_name=str(entry.get("page_name") or "").strip(),
+                page_url=url,
+                page_snippet=str(entry.get("page_snippet") or "").strip(),
+                page_html=html,
+                page_last_modified=(
+                    str(entry.get("page_last_modified")).strip()
+                    if entry.get("page_last_modified")
+                    else None
+                ),
+            )
+        )
     return pages
 
 
@@ -217,21 +220,23 @@ def iter_questions(jsonl_bz2_path: Path) -> list[CragQuestion]:
                 continue
             interaction_id = str(row.get("interaction_id") or "").strip()
             pages = _parse_pages(row.get("search_results"))
-            out.append(CragQuestion(
-                qid=f"C{raw_idx:05d}",
-                interaction_id=interaction_id,
-                query_time=str(row.get("query_time") or "").strip(),
-                query=query,
-                gold_answer=answer,
-                alt_answers=_parse_alt_answers(row.get("alt_ans")),
-                domain=str(row.get("domain") or "").strip().lower(),
-                question_type=str(row.get("question_type") or "").strip().lower(),
-                static_or_dynamic=str(row.get("static_or_dynamic") or "").strip().lower(),
-                popularity=str(row.get("popularity") or "").strip().lower(),
-                split=int(row.get("split") or 0),
-                raw_index=raw_idx,
-                pages=pages,
-            ))
+            out.append(
+                CragQuestion(
+                    qid=f"C{raw_idx:05d}",
+                    interaction_id=interaction_id,
+                    query_time=str(row.get("query_time") or "").strip(),
+                    query=query,
+                    gold_answer=answer,
+                    alt_answers=_parse_alt_answers(row.get("alt_ans")),
+                    domain=str(row.get("domain") or "").strip().lower(),
+                    question_type=str(row.get("question_type") or "").strip().lower(),
+                    static_or_dynamic=str(row.get("static_or_dynamic") or "").strip().lower(),
+                    popularity=str(row.get("popularity") or "").strip().lower(),
+                    split=int(row.get("split") or 0),
+                    raw_index=raw_idx,
+                    pages=pages,
+                )
+            )
     return out
 
 

surfsense_evals/src/surfsense_evals/suites/research/crag/grader.py

@@ -58,10 +58,10 @@ class CragGradeResult:
     """One graded (pred, gold) pair under CRAG's 3-class rubric."""
 
     grade: GradeClass
-    score: int                     # +1 / 0 / -1
-    method: str                    # exact, numeric, substring, refusal,
-                                   # false_premise_correct, false_premise_miss,
-                                   # llm_judge, lexical_miss, ...
+    score: int  # +1 / 0 / -1
+    method: str  # exact, numeric, substring, refusal,
+    # false_premise_correct, false_premise_miss,
+    # llm_judge, lexical_miss, ...
     normalised_pred: str = ""
     normalised_gold: str = ""
     judge_rationale: str = ""
@@ -112,10 +112,27 @@ def _normalise(s: str) -> str:
 
 
 _WORD_NUMBERS = {
-    "zero": 0, "one": 1, "two": 2, "three": 3, "four": 4, "five": 5,
-    "six": 6, "seven": 7, "eight": 8, "nine": 9, "ten": 10, "eleven": 11,
-    "twelve": 12, "thirteen": 13, "fourteen": 14, "fifteen": 15, "sixteen": 16,
-    "seventeen": 17, "eighteen": 18, "nineteen": 19, "twenty": 20,
+    "zero": 0,
+    "one": 1,
+    "two": 2,
+    "three": 3,
+    "four": 4,
+    "five": 5,
+    "six": 6,
+    "seven": 7,
+    "eight": 8,
+    "nine": 9,
+    "ten": 10,
+    "eleven": 11,
+    "twelve": 12,
+    "thirteen": 13,
+    "fourteen": 14,
+    "fifteen": 15,
+    "sixteen": 16,
+    "seventeen": 17,
+    "eighteen": 18,
+    "nineteen": 19,
+    "twenty": 20,
 }
 
 _NUMERIC_RE = re.compile(r"-?\d+(?:[.,]\d+)?")
@@ -274,8 +291,11 @@ def grade_deterministic(
             continue
         if n_pred == cand_norm:
             return CragGradeResult(
-                grade="correct", score=1, method="exact",
-                normalised_pred=n_pred, normalised_gold=cand_norm,
+                grade="correct",
+                score=1,
+                method="exact",
+                normalised_pred=n_pred,
+                normalised_gold=cand_norm,
             )
         p_num = _maybe_number(pred)
         c_num = _maybe_number(candidate)
@@ -289,21 +309,30 @@ def grade_deterministic(
             tol = abs(c_num) * 0.01
             if abs(p_num - c_num) <= tol:
                 return CragGradeResult(
-                    grade="correct", score=1, method="numeric",
-                    normalised_pred=n_pred, normalised_gold=cand_norm,
+                    grade="correct",
+                    score=1,
+                    method="numeric",
+                    normalised_pred=n_pred,
+                    normalised_gold=cand_norm,
                 )
             # Numeric question with different numbers — keep looking
             # at other candidates rather than declaring miss now;
             # alt answers may include word forms that pass.
         if _whole_word_substring(n_pred, cand_norm):
             return CragGradeResult(
-                grade="correct", score=1, method="substring",
-                normalised_pred=n_pred, normalised_gold=cand_norm,
+                grade="correct",
+                score=1,
+                method="substring",
+                normalised_pred=n_pred,
+                normalised_gold=cand_norm,
             )
         if _whole_word_substring(cand_norm, n_pred) and len(n_pred) >= 3:
             return CragGradeResult(
-                grade="correct", score=1, method="substring_reverse",
-                normalised_pred=n_pred, normalised_gold=cand_norm,
+                grade="correct",
+                score=1,
+                method="substring_reverse",
+                normalised_pred=n_pred,
+                normalised_gold=cand_norm,
             )
 
     return CragGradeResult(
@@ -326,21 +355,21 @@ _JUDGE_SYSTEM = (
     "answer (and any alternative valid answers), and a model's "
     "prediction, classify the prediction into exactly one of three "
     "categories:\n\n"
-    "* \"correct\"   — the prediction expresses the same factual "
+    '* "correct"   — the prediction expresses the same factual '
     "content as the gold answer (paraphrasing OK; numbers as words "
     "OK; partial-but-correct names OK; non-contradictory extra "
     "detail OK).\n"
-    "* \"missing\"   — the prediction explicitly refuses, says \"I "
+    '* "missing"   — the prediction explicitly refuses, says "I '
     "don't know\", says there is insufficient information, or hedges "
     "without committing.\n"
-    "* \"incorrect\" — the prediction commits to a fact that is "
+    '* "incorrect" — the prediction commits to a fact that is '
     "different from the gold answer, or fails to flag a false "
     "premise when the question contains one.\n\n"
     "Special case: if the question contains a false premise and the "
     "gold answer says so, then a prediction that flags the false "
-    "premise is \"correct\".\n\n"
+    'premise is "correct".\n\n'
     "Respond with ONLY a JSON object on a single line:\n"
-    '{\"grade\": \"correct\"|\"missing\"|\"incorrect\", \"rationale\": \"<one short sentence>\"}'
+    '{"grade": "correct"|"missing"|"incorrect", "rationale": "<one short sentence>"}'
 )
 
 
@@ -444,15 +473,17 @@ def _parse_judge_response(text: str) -> tuple[GradeClass, str]:
 
 # Methods that should *not* trigger the LLM judge — the deterministic
 # verdict is conclusive (refusal, exact match, numeric mismatch, etc.).
-_TERMINAL_METHODS = frozenset({
-    "refusal",
-    "exact",
-    "numeric",
-    "substring",
-    "substring_reverse",
-    "false_premise_flagged",
-    "empty_gold",
-})
+_TERMINAL_METHODS = frozenset(
+    {
+        "refusal",
+        "exact",
+        "numeric",
+        "substring",
+        "substring_reverse",
+        "false_premise_flagged",
+        "empty_gold",
+    }
+)
 
 
 async def grade_with_judge(

surfsense_evals/src/surfsense_evals/suites/research/crag/html_extract.py

@@ -42,7 +42,7 @@ class ExtractionResult:
     """Outcome of converting one HTML blob to plain markdown."""
 
     text: str
-    method: str          # "trafilatura" | "fallback_strip" | "empty"
+    method: str  # "trafilatura" | "fallback_strip" | "empty"
     n_chars: int
 
     @property
@@ -94,11 +94,30 @@ class _StripHTMLParser(HTMLParser):
     """
 
     _SKIP_TAGS = frozenset({"script", "style", "nav", "header", "footer", "aside", "svg"})
-    _BLOCK_TAGS = frozenset({
-        "p", "div", "section", "article", "li", "ul", "ol",
-        "h1", "h2", "h3", "h4", "h5", "h6", "br", "tr",
-        "td", "th", "table", "blockquote", "pre",
-    })
+    _BLOCK_TAGS = frozenset(
+        {
+            "p",
+            "div",
+            "section",
+            "article",
+            "li",
+            "ul",
+            "ol",
+            "h1",
+            "h2",
+            "h3",
+            "h4",
+            "h5",
+            "h6",
+            "br",
+            "tr",
+            "td",
+            "th",
+            "table",
+            "blockquote",
+            "pre",
+        }
+    )
 
     def __init__(self) -> None:
         super().__init__(convert_charrefs=True)

surfsense_evals/src/surfsense_evals/suites/research/crag/ingest.py

@@ -158,7 +158,10 @@ def _materialise_pages(
 
     logger.info(
         "CRAG page extraction: %s; empty=%d, total_files=%d across %d questions",
-        method_counts, n_empty, len(file_to_url), len(qid_to_files),
+        method_counts,
+        n_empty,
+        len(file_to_url),
+        len(qid_to_files),
     )
     return qid_to_files, file_to_url
 
@@ -215,8 +218,10 @@ async def _upload_pages(
                     name_to_id[f"{s.title}.md"] = s.document_id
         logger.info(
             "CRAG upload batch %d-%d: %d new, %d duplicate",
-            batch_start, batch_start + len(batch),
-            len(result.document_ids), len(result.duplicate_document_ids),
+            batch_start,
+            batch_start + len(batch),
+            len(result.document_ids),
+            len(result.duplicate_document_ids),
         )
     return name_to_id
 
@@ -243,24 +248,26 @@ def _resolve_question_doc_ids(
                 doc_ids.append(doc_id)
             else:
                 missing.append(fn)
-        rows.append({
-            "qid": q.qid,
-            "interaction_id": q.interaction_id,
-            "raw_index": q.raw_index,
-            "question": q.query,
-            "gold_answer": q.gold_answer,
-            "alt_answers": list(q.alt_answers),
-            "domain": q.domain,
-            "question_type": q.question_type,
-            "static_or_dynamic": q.static_or_dynamic,
-            "popularity": q.popularity,
-            "query_time": q.query_time,
-            "split": q.split,
-            "page_filenames": filenames,
-            "document_ids": doc_ids,
-            "missing_pages": missing,
-            "n_pages": len(filenames),
-        })
+        rows.append(
+            {
+                "qid": q.qid,
+                "interaction_id": q.interaction_id,
+                "raw_index": q.raw_index,
+                "question": q.query,
+                "gold_answer": q.gold_answer,
+                "alt_answers": list(q.alt_answers),
+                "domain": q.domain,
+                "question_type": q.question_type,
+                "static_or_dynamic": q.static_or_dynamic,
+                "popularity": q.popularity,
+                "query_time": q.query_time,
+                "split": q.split,
+                "page_filenames": filenames,
+                "document_ids": doc_ids,
+                "missing_pages": missing,
+                "n_pages": len(filenames),
+            }
+        )
     return rows
 
 
@@ -305,7 +312,7 @@ async def run_ingest(
     settings = settings or IngestSettings(
         use_vision_llm=False,
         processing_mode="basic",
-        )
+    )
     bench_dir = ctx.benchmark_data_dir()
     pages_dir = bench_dir / "pages"
     raw_cache = bench_dir / ".raw_cache"
@@ -336,10 +343,13 @@ async def run_ingest(
     n_pages_total = sum(len(q.pages) for q in questions)
     logger.info(
         "CRAG: extracting up to %d pages across %d questions ...",
-        n_pages_total, len(questions),
+        n_pages_total,
+        len(questions),
     )
     qid_to_files, file_to_url = _materialise_pages(
-        questions, pages_dir=pages_dir, overwrite=overwrite_extract,
+        questions,
+        pages_dir=pages_dir,
+        overwrite=overwrite_extract,
     )
     n_pages_extracted = sum(len(v) for v in qid_to_files.values())
 

surfsense_evals/src/surfsense_evals/suites/research/crag/prompt.py

@@ -37,7 +37,7 @@ _BASE_INSTRUCTIONS = (
     "is factually wrong), say so explicitly in your final answer "
     "rather than answering as if the premise were true.\n"
     "2. If you are not confident in an answer, prefer saying \"I don't "
-    "know\" over guessing. A wrong commit is penalised more than a "
+    'know" over guessing. A wrong commit is penalised more than a '
     "refusal.\n"
     "3. Keep the final answer short — a name, a number, a date, a "
     "phrase. Do not repeat the question.\n\n"
@@ -125,9 +125,7 @@ def build_long_context_prompt(
         if len(body) > per_page_char_cap:
             body = body[:per_page_char_cap].rstrip() + "\n[...truncated...]"
         title_clean = (title or f"page_{idx}").strip().replace("\n", " ")
-        blocks.append(
-            f"--- PAGE {idx}: {title_clean} ---\n{body}\n"
-        )
+        blocks.append(f"--- PAGE {idx}: {title_clean} ---\n{body}\n")
     contexts_block = "\n".join(blocks) if blocks else "(no pages retrieved)"
     return _LONG_CONTEXT_TEMPLATE.format(
         instructions=_BASE_INSTRUCTIONS,

surfsense_evals/src/surfsense_evals/suites/research/crag/runner.py

@@ -125,21 +125,23 @@ def _filter_questions(
             continue
         if qtype_filter and qtype_filter not in qtype:
             continue
-        out.append(CragRunnerQuestion(
-            qid=str(row.get("qid") or "").strip(),
-            raw_index=int(row.get("raw_index") or 0),
-            question=str(row.get("question") or "").strip(),
-            gold_answer=str(row.get("gold_answer") or "").strip(),
-            alt_answers=list(row.get("alt_answers") or []),
-            domain=domain,
-            question_type=qtype,
-            static_or_dynamic=str(row.get("static_or_dynamic") or "").lower(),
-            popularity=str(row.get("popularity") or "").lower(),
-            query_time=str(row.get("query_time") or "").strip(),
-            page_filenames=list(row.get("page_filenames") or []),
-            document_ids=list(row.get("document_ids") or []),
-            missing_pages=list(row.get("missing_pages") or []),
-        ))
+        out.append(
+            CragRunnerQuestion(
+                qid=str(row.get("qid") or "").strip(),
+                raw_index=int(row.get("raw_index") or 0),
+                question=str(row.get("question") or "").strip(),
+                gold_answer=str(row.get("gold_answer") or "").strip(),
+                alt_answers=list(row.get("alt_answers") or []),
+                domain=domain,
+                question_type=qtype,
+                static_or_dynamic=str(row.get("static_or_dynamic") or "").lower(),
+                popularity=str(row.get("popularity") or "").lower(),
+                query_time=str(row.get("query_time") or "").strip(),
+                page_filenames=list(row.get("page_filenames") or []),
+                document_ids=list(row.get("document_ids") or []),
+                missing_pages=list(row.get("missing_pages") or []),
+            )
+        )
     out.sort(key=lambda q: q.raw_index)
     if sample_n is not None and sample_n > 0:
         out = out[:sample_n]
@@ -190,15 +192,22 @@ class CragBenchmark:
 
     def add_run_args(self, parser: argparse.ArgumentParser) -> None:
         parser.add_argument(
-            "--n", dest="sample_n", type=int, default=None,
+            "--n",
+            dest="sample_n",
+            type=int,
+            default=None,
             help="Run only the first N questions after filters.",
         )
         parser.add_argument(
-            "--domain", dest="domain_filter", default=None,
+            "--domain",
+            dest="domain_filter",
+            default=None,
             help="Filter to a single CRAG domain (finance|music|movie|sports|open).",
         )
         parser.add_argument(
-            "--qtype", dest="qtype_filter", default=None,
+            "--qtype",
+            dest="qtype_filter",
+            default=None,
             help=(
                 "Filter to questions whose question_type contains this "
                 "substring (case-insensitive). Examples: 'multi-hop', "
@@ -206,31 +215,46 @@ class CragBenchmark:
             ),
         )
         parser.add_argument(
-            "--concurrency", type=int, default=4,
+            "--concurrency",
+            type=int,
+            default=4,
             help="Parallel question workers per arm.",
         )
         parser.add_argument(
-            "--max-output-tokens", type=int, default=512,
+            "--max-output-tokens",
+            type=int,
+            default=512,
             help="Cap on completion length for the chat-completion arms.",
         )
         parser.add_argument(
-            "--per-page-char-cap", dest="per_page_char_cap", type=int, default=12_000,
+            "--per-page-char-cap",
+            dest="per_page_char_cap",
+            type=int,
+            default=12_000,
             help="Long-context arm: max chars per page before truncation (default 12k).",
         )
         parser.add_argument(
-            "--skip-bare", dest="skip_bare", action="store_true",
+            "--skip-bare",
+            dest="skip_bare",
+            action="store_true",
             help="Skip the bare-LLM arm (saves cost on re-runs).",
         )
         parser.add_argument(
-            "--skip-long-context", dest="skip_long_context", action="store_true",
+            "--skip-long-context",
+            dest="skip_long_context",
+            action="store_true",
             help="Skip the long-context arm.",
         )
         parser.add_argument(
-            "--skip-surfsense", dest="skip_surfsense", action="store_true",
+            "--skip-surfsense",
+            dest="skip_surfsense",
+            action="store_true",
             help="Skip the SurfSense arm (useful when iterating on the LLM arms only).",
         )
         parser.add_argument(
-            "--no-mention-scope", dest="no_mention_scope", action="store_true",
+            "--no-mention-scope",
+            dest="no_mention_scope",
+            action="store_true",
             help=(
                 "SurfSense arm: don't pass mentioned_document_ids; let "
                 "the agent retrieve over the entire SearchSpace. Default "
@@ -239,37 +263,56 @@ class CragBenchmark:
             ),
         )
         parser.add_argument(
-            "--no-judge", dest="no_judge", action="store_true",
+            "--no-judge",
+            dest="no_judge",
+            action="store_true",
             help="Disable the LLM-as-judge fallback grader.",
         )
         parser.add_argument(
-            "--judge-model", dest="judge_model",
+            "--judge-model",
+            dest="judge_model",
             default="anthropic/claude-sonnet-4.5",
             help="OpenRouter slug for the LLM judge.",
         )
         parser.add_argument(
-            "--judge-concurrency", dest="judge_concurrency", type=int, default=4,
+            "--judge-concurrency",
+            dest="judge_concurrency",
+            type=int,
+            default=4,
             help="Parallel judge calls.",
         )
         # Ingest knobs
         parser.add_argument(
-            "--n-questions", dest="n_questions", type=int, default=None,
+            "--n-questions",
+            dest="n_questions",
+            type=int,
+            default=None,
             help="(ingest only) cap on number of questions to materialise + ingest.",
         )
         parser.add_argument(
-            "--upload-batch-size", dest="upload_batch_size", type=int, default=16,
+            "--upload-batch-size",
+            dest="upload_batch_size",
+            type=int,
+            default=16,
             help="(ingest only) markdown files per fileupload call.",
         )
         parser.add_argument(
-            "--skip-upload", dest="skip_upload", action="store_true",
+            "--skip-upload",
+            dest="skip_upload",
+            action="store_true",
             help="(ingest only) extract pages locally but don't push to SurfSense.",
         )
         parser.add_argument(
-            "--overwrite-extract", dest="overwrite_extract", action="store_true",
+            "--overwrite-extract",
+            dest="overwrite_extract",
+            action="store_true",
             help="(ingest only) re-run trafilatura even when cached markdown exists.",
         )
         parser.add_argument(
-            "--sample-seed", dest="sample_seed", type=int, default=17,
+            "--sample-seed",
+            dest="sample_seed",
+            type=int,
+            default=17,
             help="(ingest only) RNG seed for the stratified sample.",
         )
         add_ingest_settings_args(parser, defaults=_DEFAULT_INGEST_SETTINGS)
@@ -362,12 +405,14 @@ class CragBenchmark:
             if not api_key:
                 logger.warning("CRAG: --no-judge implied (no OPENROUTER_API_KEY for judge)")
             else:
-                judge = CragLlmJudge(config=CragJudgeConfig(
-                    api_key=api_key,
-                    model=judge_model,
-                    base_url=ctx.config.openrouter_base_url,
-                    concurrency=judge_concurrency,
-                ))
+                judge = CragLlmJudge(
+                    config=CragJudgeConfig(
+                        api_key=api_key,
+                        model=judge_model,
+                        base_url=ctx.config.openrouter_base_url,
+                        concurrency=judge_concurrency,
+                    )
+                )
 
         run_timestamp = utc_iso_timestamp()
         run_dir = ctx.runs_dir(run_timestamp=run_timestamp)
@@ -393,29 +438,53 @@ class CragBenchmark:
         # internally concurrency-bounded.
         tasks: list[Any] = []
         if bare_arm is not None:
-            tasks.append(_gather_with_limit((_bare_one(q) for q in questions), concurrency=concurrency))
+            tasks.append(
+                _gather_with_limit((_bare_one(q) for q in questions), concurrency=concurrency)
+            )
         else:
             tasks.append(_make_skipped_results(questions, "bare_llm"))
         if long_context_arm is not None:
-            tasks.append(_gather_with_limit((_long_context_one(q) for q in questions), concurrency=concurrency))
+            tasks.append(
+                _gather_with_limit(
+                    (_long_context_one(q) for q in questions), concurrency=concurrency
+                )
+            )
         else:
             tasks.append(_make_skipped_results(questions, "long_context"))
         if surf_arm is not None:
-            tasks.append(_gather_with_limit((_surf_one(q) for q in questions), concurrency=concurrency))
+            tasks.append(
+                _gather_with_limit((_surf_one(q) for q in questions), concurrency=concurrency)
+            )
         else:
             tasks.append(_make_skipped_results(questions, "surfsense"))
 
         bare_results, long_context_results, surf_results = await asyncio.gather(*tasks)
 
-        bare_grades = await _grade_results(questions, bare_results, judge=judge) if bare_arm else _empty_grades(questions)
-        lc_grades = await _grade_results(questions, long_context_results, judge=judge) if long_context_arm else _empty_grades(questions)
-        surf_grades = await _grade_results(questions, surf_results, judge=judge) if surf_arm else _empty_grades(questions)
+        bare_grades = (
+            await _grade_results(questions, bare_results, judge=judge)
+            if bare_arm
+            else _empty_grades(questions)
+        )
+        lc_grades = (
+            await _grade_results(questions, long_context_results, judge=judge)
+            if long_context_arm
+            else _empty_grades(questions)
+        )
+        surf_grades = (
+            await _grade_results(questions, surf_results, judge=judge)
+            if surf_arm
+            else _empty_grades(questions)
+        )
 
         with raw_path.open("w", encoding="utf-8") as fh:
             for q, b_res, l_res, s_res, b_g, l_g, s_g in zip(
                 questions,
-                bare_results, long_context_results, surf_results,
-                bare_grades, lc_grades, surf_grades,
+                bare_results,
+                long_context_results,
+                surf_results,
+                bare_grades,
+                lc_grades,
+                surf_grades,
                 strict=False,
             ):
                 meta = {
@@ -431,18 +500,29 @@ class CragBenchmark:
                     "alt_answers": q.alt_answers,
                 }
                 for res, grade in (
-                    (b_res, b_g), (l_res, l_g), (s_res, s_g),
+                    (b_res, b_g),
+                    (l_res, l_g),
+                    (s_res, s_g),
                 ):
-                    fh.write(json.dumps({
-                        **meta,
-                        **res.to_jsonl(),
-                        "graded": grade.to_dict(),
-                    }) + "\n")
+                    fh.write(
+                        json.dumps(
+                            {
+                                **meta,
+                                **res.to_jsonl(),
+                                "graded": grade.to_dict(),
+                            }
+                        )
+                        + "\n"
+                    )
 
         metrics = _compute_metrics(
             questions=questions,
-            bare_results=bare_results, long_context_results=long_context_results, surf_results=surf_results,
-            bare_grades=bare_grades, lc_grades=lc_grades, surf_grades=surf_grades,
+            bare_results=bare_results,
+            long_context_results=long_context_results,
+            surf_results=surf_results,
+            bare_grades=bare_grades,
+            lc_grades=lc_grades,
+            surf_grades=surf_grades,
             arms_active={
                 "bare_llm": bare_arm is not None,
                 "long_context": long_context_arm is not None,
@@ -481,13 +561,18 @@ class CragBenchmark:
 
         manifest_path = run_dir / "run_artifact.json"
         manifest_path.write_text(
-            json.dumps({
-                "suite": self.suite,
-                "benchmark": self.name,
-                "raw_path": "raw.jsonl",
-                "metrics": metrics,
-                "extra": artifact.extra,
-            }, indent=2, sort_keys=True) + "\n",
+            json.dumps(
+                {
+                    "suite": self.suite,
+                    "benchmark": self.name,
+                    "raw_path": "raw.jsonl",
+                    "metrics": metrics,
+                    "extra": artifact.extra,
+                },
+                indent=2,
+                sort_keys=True,
+            )
+            + "\n",
             encoding="utf-8",
         )
         return artifact
@@ -547,7 +632,9 @@ class CragBenchmark:
 
         body_lines.append("- Headline truthfulness scores (CRAG paper rubric):")
         for label, key in (
-            ("Bare LLM", "bare_llm"), ("Long-Context", "long_context"), ("SurfSense", "surfsense"),
+            ("Bare LLM", "bare_llm"),
+            ("Long-Context", "long_context"),
+            ("SurfSense", "surfsense"),
         ):
             d = m.get(key, {})
             body_lines.append(
@@ -583,9 +670,7 @@ class CragBenchmark:
                 for arm in ("bare_llm", "long_context", "surfsense"):
                     if arm not in row:
                         continue
-                    pieces.append(
-                        f"{arm}={_signed_pct(row[arm].get('truthfulness_score'))}"
-                    )
+                    pieces.append(f"{arm}={_signed_pct(row[arm].get('truthfulness_score'))}")
                 body_lines.append(" ".join(pieces))
 
         if per_qtype:
@@ -596,9 +681,7 @@ class CragBenchmark:
                 for arm in ("bare_llm", "long_context", "surfsense"):
                     if arm not in row:
                         continue
-                    pieces.append(
-                        f"{arm}={_signed_pct(row[arm].get('truthfulness_score'))}"
-                    )
+                    pieces.append(f"{arm}={_signed_pct(row[arm].get('truthfulness_score'))}")
                 body_lines.append(" ".join(pieces))
 
         return ReportSection(
@@ -669,32 +752,31 @@ async def _grade_results(
     rows: list[CragGradeRow] = []
     for q, r in zip(questions, results, strict=False):
         pred = extract_freeform_answer(r.raw_text or "")
-        rows.append(CragGradeRow(
-            qid=q.qid,
-            question=q.question,
-            gold=q.gold_answer,
-            alt_answers=q.alt_answers,
-            pred=pred,
-            question_type=q.question_type,
-        ))
+        rows.append(
+            CragGradeRow(
+                qid=q.qid,
+                question=q.question,
+                gold=q.gold_answer,
+                alt_answers=q.alt_answers,
+                pred=pred,
+                question_type=q.question_type,
+            )
+        )
     return await grade_many(rows=rows, judge=judge)
 
 
 def _empty_grades(questions: list[CragRunnerQuestion]) -> list[CragGradeResult]:
-    return [
-        CragGradeResult(grade="missing", score=0, method="skipped_arm")
-        for _ in questions
-    ]
+    return [CragGradeResult(grade="missing", score=0, method="skipped_arm") for _ in questions]
 
 
 async def _make_skipped_results(
-    questions: list[CragRunnerQuestion], arm_name: str,
+    questions: list[CragRunnerQuestion],
+    arm_name: str,
 ) -> list[ArmResult]:
     """Stand-in results so downstream code can assume parallel lists."""
 
     return [
-        ArmResult(arm=arm_name, question_id=q.qid, raw_text="", error="skipped")
-        for q in questions
+        ArmResult(arm=arm_name, question_id=q.qid, raw_text="", error="skipped") for q in questions
     ]
 
 
@@ -776,20 +858,41 @@ def _compute_metrics(
 
     deltas: dict[str, Any] = {}
     for label, ref_correct, ref_t, chal_correct, chal_t, both_active in (
-        ("surfsense_vs_bare", bare_correct, bare_t, surf_correct, surf_t,
-         arms_active.get("bare_llm") and arms_active.get("surfsense")),
-        ("surfsense_vs_long_context", lc_correct, lc_t, surf_correct, surf_t,
-         arms_active.get("long_context") and arms_active.get("surfsense")),
-        ("long_context_vs_bare", bare_correct, bare_t, lc_correct, lc_t,
-         arms_active.get("bare_llm") and arms_active.get("long_context")),
+        (
+            "surfsense_vs_bare",
+            bare_correct,
+            bare_t,
+            surf_correct,
+            surf_t,
+            arms_active.get("bare_llm") and arms_active.get("surfsense"),
+        ),
+        (
+            "surfsense_vs_long_context",
+            lc_correct,
+            lc_t,
+            surf_correct,
+            surf_t,
+            arms_active.get("long_context") and arms_active.get("surfsense"),
+        ),
+        (
+            "long_context_vs_bare",
+            bare_correct,
+            bare_t,
+            lc_correct,
+            lc_t,
+            arms_active.get("bare_llm") and arms_active.get("long_context"),
+        ),
     ):
         if not both_active:
             continue
         mc = mcnemar_test(ref_correct, chal_correct)
         boot = bootstrap_delta_ci(ref_correct, chal_correct, n_resamples=2000)
         deltas[label] = {
-            "accuracy_pp": 100.0 * (sum(chal_correct) - sum(ref_correct)) / max(1, len(chal_correct)),
-            "truthfulness_score_pp": 100.0 * (chal_t["truthfulness_score"] - ref_t["truthfulness_score"]),
+            "accuracy_pp": 100.0
+            * (sum(chal_correct) - sum(ref_correct))
+            / max(1, len(chal_correct)),
+            "truthfulness_score_pp": 100.0
+            * (chal_t["truthfulness_score"] - ref_t["truthfulness_score"]),
             "mcnemar_p_value": mc.p_value,
             "mcnemar_method": mc.method,
             "mcnemar_b_ref_only": mc.b,
@@ -800,12 +903,18 @@ def _compute_metrics(
     out["deltas"] = deltas
 
     out["per_domain"] = _per_facet_truthfulness(
-        questions, bare_grades, lc_grades, surf_grades,
+        questions,
+        bare_grades,
+        lc_grades,
+        surf_grades,
         arms_active=arms_active,
         key_fn=lambda q: q.domain or "(unspecified)",
     )
     out["per_question_type"] = _per_facet_truthfulness(
-        questions, bare_grades, lc_grades, surf_grades,
+        questions,
+        bare_grades,
+        lc_grades,
+        surf_grades,
         arms_active=arms_active,
         key_fn=lambda q: q.question_type or "(unspecified)",
     )
@@ -867,11 +976,11 @@ def _arm_summary_lines(d: dict[str, Any], *, indent: str) -> str:
     high = d.get("ci_high", 0.0)
     lines = [
         f"{indent}- Accuracy: {acc * 100:.1f}% (Wilson 95% CI: {low * 100:.1f}% – {high * 100:.1f}%)",
-        f"{indent}- 3-class: correct={d.get('correct_rate', 0)*100:.1f}%, "
-        f"missing={d.get('missing_rate', 0)*100:.1f}%, "
-        f"incorrect={d.get('incorrect_rate', 0)*100:.1f}%",
+        f"{indent}- 3-class: correct={d.get('correct_rate', 0) * 100:.1f}%, "
+        f"missing={d.get('missing_rate', 0) * 100:.1f}%, "
+        f"incorrect={d.get('incorrect_rate', 0) * 100:.1f}%",
         f"{indent}- Truthfulness score (correct - incorrect)/total: "
-        f"{d.get('truthfulness_score', 0)*100:+.1f}%",
+        f"{d.get('truthfulness_score', 0) * 100:+.1f}%",
         f"{indent}- Cost / question: ${_dollars(d.get('cost_micros_mean'))} (mean), "
         f"${_dollars(d.get('cost_micros_median'))} (median)",
         f"{indent}- Latency: p50 {_ms_to_s(d.get('latency_ms_median'))}, "
@@ -916,7 +1025,7 @@ def _pct(value: Any) -> str:
     if value is None:
         return "?"
     try:
-        return f"{float(value)*100:.1f}%"
+        return f"{float(value) * 100:.1f}%"
     except (TypeError, ValueError):
         return "?"
 
@@ -925,7 +1034,7 @@ def _signed_pct(value: Any) -> str:
     if value is None:
         return "?"
     try:
-        return f"{float(value)*100:+.1f}%"
+        return f"{float(value) * 100:+.1f}%"
     except (TypeError, ValueError):
         return "?"
 

surfsense_evals/src/surfsense_evals/suites/research/frames/dataset.py

@@ -51,12 +51,12 @@ def _hf_hub_download(*args: Any, **kwargs: Any) -> str:
 class FramesQuestion:
     """One row of FRAMES (post-parse)."""
 
-    qid: str                   # synthesised "Q000" .. "Q823"
+    qid: str  # synthesised "Q000" .. "Q823"
     question: str
     gold_answer: str
-    wiki_urls: list[str]       # deduped, in original order
-    reasoning_types: list[str] # split on "|"
-    raw_index: int             # row index from the TSV (for debugging)
+    wiki_urls: list[str]  # deduped, in original order
+    reasoning_types: list[str]  # split on "|"
+    raw_index: int  # row index from the TSV (for debugging)
 
     def to_dict(self) -> dict[str, Any]:
         return {
@@ -146,14 +146,16 @@ def load_questions(tsv_path: Path) -> list[FramesQuestion]:
                     if val and val not in urls:
                         urls.append(val)
         reasoning = _parse_reasoning_types(row.get("reasoning_types"))
-        out.append(FramesQuestion(
-            qid=f"Q{int(raw_idx):03d}",
-            question=prompt,
-            gold_answer=answer,
-            wiki_urls=urls,
-            reasoning_types=reasoning,
-            raw_index=int(raw_idx),
-        ))
+        out.append(
+            FramesQuestion(
+                qid=f"Q{int(raw_idx):03d}",
+                question=prompt,
+                gold_answer=answer,
+                wiki_urls=urls,
+                reasoning_types=reasoning,
+                raw_index=int(raw_idx),
+            )
+        )
     return out
 
 

surfsense_evals/src/surfsense_evals/suites/research/frames/grader.py

@@ -90,10 +90,27 @@ def _normalise(s: str) -> str:
 
 
 _WORD_NUMBERS = {
-    "zero": 0, "one": 1, "two": 2, "three": 3, "four": 4, "five": 5,
-    "six": 6, "seven": 7, "eight": 8, "nine": 9, "ten": 10, "eleven": 11,
-    "twelve": 12, "thirteen": 13, "fourteen": 14, "fifteen": 15, "sixteen": 16,
-    "seventeen": 17, "eighteen": 18, "nineteen": 19, "twenty": 20,
+    "zero": 0,
+    "one": 1,
+    "two": 2,
+    "three": 3,
+    "four": 4,
+    "five": 5,
+    "six": 6,
+    "seven": 7,
+    "eight": 8,
+    "nine": 9,
+    "ten": 10,
+    "eleven": 11,
+    "twelve": 12,
+    "thirteen": 13,
+    "fourteen": 14,
+    "fifteen": 15,
+    "sixteen": 16,
+    "seventeen": 17,
+    "eighteen": 18,
+    "nineteen": 19,
+    "twenty": 20,
 }
 
 _NUMERIC_RE = re.compile(r"-?\d+(?:[.,]\d+)?")
@@ -194,7 +211,7 @@ _JUDGE_SYSTEM = (
     "expresses a different fact, omits the central answer, or hedges "
     "without committing.\n\n"
     "Respond with ONLY a JSON object on a single line:\n"
-    '{\"correct\": true|false, \"rationale\": \"<one short sentence>\"}'
+    '{"correct": true|false, "rationale": "<one short sentence>"}'
 )
 
 
@@ -324,10 +341,7 @@ async def grade_many(
 
     if not rows:
         return []
-    coros = [
-        grade_with_judge(pred=p, gold=g, question=q, judge=judge)
-        for _qid, q, g, p in rows
-    ]
+    coros = [grade_with_judge(pred=p, gold=g, question=q, judge=judge) for _qid, q, g, p in rows]
     return list(await asyncio.gather(*coros))
 
 

surfsense_evals/src/surfsense_evals/suites/research/frames/ingest.py

@@ -160,8 +160,10 @@ async def _upload_markdowns(
                 name_to_id[s.title] = s.document_id
         logger.info(
             "FRAMES upload batch %d-%d: %d new, %d duplicate",
-            batch_start, batch_start + len(batch),
-            len(result.document_ids), len(result.duplicate_document_ids),
+            batch_start,
+            batch_start + len(batch),
+            len(result.document_ids),
+            len(result.duplicate_document_ids),
         )
     return name_to_id
 
@@ -188,14 +190,16 @@ def _resolve_question_doc_ids(
             doc_id = name_to_id.get(stem) or name_to_id.get(article.markdown_path.name)
             if doc_id is not None and doc_id not in doc_ids:
                 doc_ids.append(doc_id)
-        rows.append({
-            "qid": q.qid,
-            "raw_index": q.raw_index,
-            "n_wiki_urls": len(q.wiki_urls),
-            "wiki_titles": titles,
-            "document_ids": doc_ids,
-            "missing_urls": missing,
-        })
+        rows.append(
+            {
+                "qid": q.qid,
+                "raw_index": q.raw_index,
+                "n_wiki_urls": len(q.wiki_urls),
+                "wiki_titles": titles,
+                "document_ids": doc_ids,
+                "missing_urls": missing,
+            }
+        )
     return rows
 
 
@@ -238,7 +242,7 @@ async def run_ingest(
     settings = settings or IngestSettings(
         use_vision_llm=False,
         processing_mode="basic",
-        )
+    )
     bench_dir = ctx.benchmark_data_dir()
     wiki_cache = bench_dir / "wiki"
     wiki_cache.mkdir(parents=True, exist_ok=True)
@@ -250,8 +254,7 @@ async def run_ingest(
     questions = load_questions(tsv_path)
     if not questions:
         raise RuntimeError(
-            "FRAMES test.tsv contained no parseable rows; upstream may "
-            "have changed schema."
+            "FRAMES test.tsv contained no parseable rows; upstream may have changed schema."
         )
     logger.info("FRAMES: parsed %d questions from %s", len(questions), tsv_path.name)
     if max_questions is not None and max_questions > 0:
@@ -269,19 +272,23 @@ async def run_ingest(
     unique_urls = list(seen_urls.keys())
     logger.info(
         "FRAMES: %d unique Wikipedia URLs across %d questions",
-        len(unique_urls), len(questions),
+        len(unique_urls),
+        len(questions),
     )
 
     # 3. Fetch (with cache).
     fetcher = WikiFetcher(cache_dir=wiki_cache, rate_limit_rps=fetch_rate_limit_rps)
     n_cached = sum(
-        1 for url in unique_urls
+        1
+        for url in unique_urls
         if (wiki_cache / cache_filename_for_title(_safe_title(url))).exists()
     )
     fetched, missing_urls = await _fetch_articles(fetcher, unique_urls)
     logger.info(
         "FRAMES: fetched=%d, cache_hits=%d, missing=%d",
-        len(fetched), n_cached, len(missing_urls),
+        len(fetched),
+        n_cached,
+        len(missing_urls),
     )
 
     # 4. Upload to SurfSense (deduped by filename).

surfsense_evals/src/surfsense_evals/suites/research/frames/runner.py

@@ -65,7 +65,7 @@ class FramesRunnerQuestion:
     question: str
     gold_answer: str
     reasoning_types: list[str]
-    document_ids: list[int]   # subset of corpus relevant to this Q (may be empty)
+    document_ids: list[int]  # subset of corpus relevant to this Q (may be empty)
     n_wiki_urls: int
     missing_urls: list[str]
 
@@ -107,16 +107,18 @@ def _load_questions(
             reasoning = list(row.get("reasoning_types") or [])
             if reasoning_filter and reasoning_filter not in [r.lower() for r in reasoning]:
                 continue
-            out.append(FramesRunnerQuestion(
-                qid=qid,
-                raw_index=int(row.get("raw_index") or 0),
-                question=str(row.get("question") or "").strip(),
-                gold_answer=str(row.get("gold_answer") or "").strip(),
-                reasoning_types=reasoning,
-                document_ids=list(map_row.get("document_ids") or []),
-                n_wiki_urls=int(map_row.get("n_wiki_urls") or 0),
-                missing_urls=list(map_row.get("missing_urls") or []),
-            ))
+            out.append(
+                FramesRunnerQuestion(
+                    qid=qid,
+                    raw_index=int(row.get("raw_index") or 0),
+                    question=str(row.get("question") or "").strip(),
+                    gold_answer=str(row.get("gold_answer") or "").strip(),
+                    reasoning_types=reasoning,
+                    document_ids=list(map_row.get("document_ids") or []),
+                    n_wiki_urls=int(map_row.get("n_wiki_urls") or 0),
+                    missing_urls=list(map_row.get("missing_urls") or []),
+                )
+            )
     out.sort(key=lambda q: q.raw_index)
     if sample_n is not None and sample_n > 0:
         out = out[:sample_n]
@@ -166,7 +168,10 @@ class FramesBenchmark:
 
     def add_run_args(self, parser: argparse.ArgumentParser) -> None:
         parser.add_argument(
-            "--n", dest="sample_n", type=int, default=None,
+            "--n",
+            dest="sample_n",
+            type=int,
+            default=None,
             help="Run only the first N questions after filters (default: all 824).",
         )
         parser.add_argument(
@@ -180,11 +185,15 @@ class FramesBenchmark:
             ),
         )
         parser.add_argument(
-            "--concurrency", type=int, default=4,
+            "--concurrency",
+            type=int,
+            default=4,
             help="Parallel question workers per arm.",
         )
         parser.add_argument(
-            "--scope-mentions", dest="scope_mentions", action="store_true",
+            "--scope-mentions",
+            dest="scope_mentions",
+            action="store_true",
             help=(
                 "SurfSense arm: scope retrieval to the per-question "
                 "document_ids (oracle-retrieval upper bound). Default "
@@ -192,11 +201,15 @@ class FramesBenchmark:
             ),
         )
         parser.add_argument(
-            "--max-output-tokens", type=int, default=512,
+            "--max-output-tokens",
+            type=int,
+            default=512,
             help="Cap on completion length for both arms.",
         )
         parser.add_argument(
-            "--no-judge", dest="no_judge", action="store_true",
+            "--no-judge",
+            dest="no_judge",
+            action="store_true",
             help=(
                 "Disable LLM-as-judge fallback grading; use only the "
                 "deterministic grader (faster but more pessimistic)."
@@ -217,19 +230,30 @@ class FramesBenchmark:
         )
         # Ingest-only knobs.
         parser.add_argument(
-            "--max-questions", dest="max_questions", type=int, default=None,
+            "--max-questions",
+            dest="max_questions",
+            type=int,
+            default=None,
             help="(ingest only) cap on number of questions to materialise + ingest.",
         )
         parser.add_argument(
-            "--upload-batch-size", dest="upload_batch_size", type=int, default=16,
+            "--upload-batch-size",
+            dest="upload_batch_size",
+            type=int,
+            default=16,
             help="(ingest only) markdown files per fileupload call.",
         )
         parser.add_argument(
-            "--skip-upload", dest="skip_upload", action="store_true",
+            "--skip-upload",
+            dest="skip_upload",
+            action="store_true",
             help="(ingest only) cache wiki articles locally but don't push to SurfSense.",
         )
         parser.add_argument(
-            "--fetch-rps", dest="fetch_rate_limit_rps", type=float, default=2.0,
+            "--fetch-rps",
+            dest="fetch_rate_limit_rps",
+            type=float,
+            default=2.0,
             help="(ingest only) max requests/second to the Wikipedia API.",
         )
         add_ingest_settings_args(parser, defaults=_DEFAULT_INGEST_SETTINGS)
@@ -270,21 +294,18 @@ class FramesBenchmark:
 
         doc_map, ingest_settings = _load_doc_map(map_path)
         questions = _load_questions(
-            questions_jsonl, doc_map,
+            questions_jsonl,
+            doc_map,
             sample_n=sample_n,
             reasoning_filter=reasoning_filter,
         )
         if not questions:
-            raise RuntimeError(
-                "No FRAMES questions matched the filters; broaden --reasoning/--n."
-            )
+            raise RuntimeError("No FRAMES questions matched the filters; broaden --reasoning/--n.")
         logger.info("FRAMES: scheduled %d questions", len(questions))
 
         api_key = os.environ.get("OPENROUTER_API_KEY")
         if not api_key:
-            raise RuntimeError(
-                "OPENROUTER_API_KEY env var is required for the bare-LLM arm."
-            )
+            raise RuntimeError("OPENROUTER_API_KEY env var is required for the bare-LLM arm.")
 
         bare_provider = OpenRouterChatProvider(
             api_key=api_key,
@@ -303,12 +324,14 @@ class FramesBenchmark:
 
         judge: LlmJudge | None = None
         if not no_judge:
-            judge = LlmJudge(config=JudgeConfig(
-                api_key=api_key,
-                model=judge_model,
-                base_url=ctx.config.openrouter_base_url,
-                concurrency=judge_concurrency,
-            ))
+            judge = LlmJudge(
+                config=JudgeConfig(
+                    api_key=api_key,
+                    model=judge_model,
+                    base_url=ctx.config.openrouter_base_url,
+                    concurrency=judge_concurrency,
+                )
+            )
 
         run_timestamp = utc_iso_timestamp()
         run_dir = ctx.runs_dir(run_timestamp=run_timestamp)
@@ -318,9 +341,7 @@ class FramesBenchmark:
             return await bare_arm.answer(_make_bare_request(q, max_output_tokens))
 
         async def _surf_one(q: FramesRunnerQuestion) -> ArmResult:
-            return await surf_arm.answer(
-                _make_surfsense_request(q, scope_mentions=scope_mentions)
-            )
+            return await surf_arm.answer(_make_surfsense_request(q, scope_mentions=scope_mentions))
 
         bare_results, surf_results = await asyncio.gather(
             _gather_with_limit((_bare_one(q) for q in questions), concurrency=concurrency),
@@ -343,16 +364,26 @@ class FramesBenchmark:
                     "n_missing_urls": len(q.missing_urls),
                     "gold": q.gold_answer,
                 }
-                fh.write(json.dumps({
-                    **meta,
-                    **b_res.to_jsonl(),
-                    "graded": b_g.to_dict(),
-                }) + "\n")
-                fh.write(json.dumps({
-                    **meta,
-                    **s_res.to_jsonl(),
-                    "graded": s_g.to_dict(),
-                }) + "\n")
+                fh.write(
+                    json.dumps(
+                        {
+                            **meta,
+                            **b_res.to_jsonl(),
+                            "graded": b_g.to_dict(),
+                        }
+                    )
+                    + "\n"
+                )
+                fh.write(
+                    json.dumps(
+                        {
+                            **meta,
+                            **s_res.to_jsonl(),
+                            "graded": s_g.to_dict(),
+                        }
+                    )
+                    + "\n"
+                )
 
         metrics = _compute_metrics(questions, bare_results, surf_results, bare_grades, surf_grades)
         artifact = RunArtifact(
@@ -380,13 +411,18 @@ class FramesBenchmark:
 
         manifest_path = run_dir / "run_artifact.json"
         manifest_path.write_text(
-            json.dumps({
-                "suite": self.suite,
-                "benchmark": self.name,
-                "raw_path": "raw.jsonl",
-                "metrics": metrics,
-                "extra": artifact.extra,
-            }, indent=2, sort_keys=True) + "\n",
+            json.dumps(
+                {
+                    "suite": self.suite,
+                    "benchmark": self.name,
+                    "raw_path": "raw.jsonl",
+                    "metrics": metrics,
+                    "extra": artifact.extra,
+                },
+                indent=2,
+                sort_keys=True,
+            )
+            + "\n",
             encoding="utf-8",
         )
         return artifact
@@ -451,8 +487,8 @@ class FramesBenchmark:
             for tag, vals in sorted(per_reasoning.items()):
                 body_lines.append(
                     f"  - {tag}: SurfSense {_pp(vals.get('delta_accuracy_pp'))} pp "
-                    f"(n={vals.get('n')}, bare acc={vals.get('bare_accuracy', 0)*100:.1f}%, "
-                    f"surf acc={vals.get('surfsense_accuracy', 0)*100:.1f}%)"
+                    f"(n={vals.get('n')}, bare acc={vals.get('bare_accuracy', 0) * 100:.1f}%, "
+                    f"surf acc={vals.get('surfsense_accuracy', 0) * 100:.1f}%)"
                 )
 
         return ReportSection(
@@ -553,8 +589,7 @@ def _compute_metrics(
             "bare_accuracy": (sum(b_correct) / len(pairs)) if pairs else 0.0,
             "surfsense_accuracy": (sum(s_correct) / len(pairs)) if pairs else 0.0,
             "delta_accuracy_pp": (
-                100.0 * (sum(s_correct) - sum(b_correct)) / len(pairs)
-                if pairs else 0.0
+                100.0 * (sum(s_correct) - sum(b_correct)) / len(pairs) if pairs else 0.0
             ),
         }
 
@@ -571,8 +606,12 @@ def _compute_metrics(
             "latency_ms_mean": bare_latency_agg.mean,
             "latency_ms_median": bare_latency_agg.median,
             "latency_ms_p95": bare_latency_agg.p95,
-            "input_tokens_mean": (sum(bare_in_tokens) / len(bare_in_tokens)) if bare_in_tokens else 0.0,
-            "output_tokens_mean": (sum(bare_out_tokens) / len(bare_out_tokens)) if bare_out_tokens else 0.0,
+            "input_tokens_mean": (sum(bare_in_tokens) / len(bare_in_tokens))
+            if bare_in_tokens
+            else 0.0,
+            "output_tokens_mean": (sum(bare_out_tokens) / len(bare_out_tokens))
+            if bare_out_tokens
+            else 0.0,
         },
         "surfsense": {
             **surf_acc.to_dict(),

surfsense_evals/src/surfsense_evals/suites/research/frames/wiki_fetch.py

@@ -49,10 +49,10 @@ USER_AGENT = (
 class WikiArticle:
     """One fetched article + metadata."""
 
-    title: str            # canonical title returned by MW (post-redirect)
-    source_url: str       # the URL we were asked to fetch
-    markdown_path: Path   # where the cached body lives on disk
-    n_chars: int          # length of the body (post-prepend H1)
+    title: str  # canonical title returned by MW (post-redirect)
+    source_url: str  # the URL we were asked to fetch
+    markdown_path: Path  # where the cached body lives on disk
+    n_chars: int  # length of the body (post-prepend H1)
     redirected_from: str | None = None
 
 
@@ -168,10 +168,13 @@ class WikiFetcher:
                 break
             except (httpx.HTTPError, RuntimeError) as exc:
                 last_exc = exc
-                wait = 1.0 * (2 ** attempt)
+                wait = 1.0 * (2**attempt)
                 logger.warning(
                     "wiki fetch %r attempt %d failed: %s; retry in %.1fs",
-                    title, attempt + 1, exc, wait,
+                    title,
+                    attempt + 1,
+                    exc,
+                    wait,
                 )
                 await asyncio.sleep(wait)
         else:
@@ -217,10 +220,14 @@ class WikiFetcher:
         }
         headers = {"User-Agent": USER_AGENT, "Accept": "application/json"}
         if http is not None:
-            response = await http.get(WIKI_API, params=params, headers=headers, timeout=self._timeout)
+            response = await http.get(
+                WIKI_API, params=params, headers=headers, timeout=self._timeout
+            )
         else:
             async with httpx.AsyncClient(timeout=self._timeout) as client:
-                response = await client.get(WIKI_API, params=params, headers=headers, timeout=self._timeout)
+                response = await client.get(
+                    WIKI_API, params=params, headers=headers, timeout=self._timeout
+                )
         response.raise_for_status()
         data = response.json()
         if "error" in data:

surfsense_evals/tests/core/test_auth.py

@@ -52,9 +52,7 @@ async def test_acquire_token_local_mode_posts_desktop_login_json():
             200, json={"access_token": "T", "refresh_token": "R", "token_type": "bearer"}
         )
     )
-    config = _make_config(
-        surfsense_user_email="u@example.com", surfsense_user_password="pw"
-    )
+    config = _make_config(surfsense_user_email="u@example.com", surfsense_user_password="pw")
     bundle = await acquire_token(config)
     assert bundle.access_token == "T"
     assert bundle.refresh_token == "R"

surfsense_evals/tests/core/test_clients.py

@@ -94,10 +94,18 @@ async def test_documents_status_parses_state(respx_mock, http):
             200,
             json={
                 "items": [
-                    {"id": 1, "title": "a.pdf", "document_type": "FILE",
-                     "status": {"state": "ready", "reason": None}},
-                    {"id": 2, "title": "b.pdf", "document_type": "FILE",
-                     "status": {"state": "failed", "reason": "ETL boom"}},
+                    {
+                        "id": 1,
+                        "title": "a.pdf",
+                        "document_type": "FILE",
+                        "status": {"state": "ready", "reason": None},
+                    },
+                    {
+                        "id": 2,
+                        "title": "b.pdf",
+                        "document_type": "FILE",
+                        "status": {"state": "failed", "reason": "ETL boom"},
+                    },
                 ]
             },
         )
@@ -137,14 +145,26 @@ async def test_documents_upload_returns_payload(respx_mock, http, tmp_path: Path
 async def test_documents_list_chunks_paginated(respx_mock, http):
     respx_mock.get("/api/v1/documents/5/chunks").mock(
         side_effect=[
-            httpx.Response(200, json={
-                "items": [{"id": 1, "content": "a"}, {"id": 2, "content": "b"}],
-                "total": 3, "page": 0, "page_size": 2, "has_more": True,
-            }),
-            httpx.Response(200, json={
-                "items": [{"id": 3, "content": "c"}],
-                "total": 3, "page": 1, "page_size": 2, "has_more": False,
-            }),
+            httpx.Response(
+                200,
+                json={
+                    "items": [{"id": 1, "content": "a"}, {"id": 2, "content": "b"}],
+                    "total": 3,
+                    "page": 0,
+                    "page_size": 2,
+                    "has_more": True,
+                },
+            ),
+            httpx.Response(
+                200,
+                json={
+                    "items": [{"id": 3, "content": "c"}],
+                    "total": 3,
+                    "page": 1,
+                    "page_size": 2,
+                    "has_more": False,
+                },
+            ),
         ]
     )
     client = DocumentsClient(http, _BASE)
@@ -191,15 +211,17 @@ def _sse_body(events: list[dict]) -> bytes:
 @pytest.mark.asyncio
 @respx.mock(base_url=_BASE)
 async def test_ask_accumulates_text_deltas(respx_mock, http):
-    body = _sse_body([
-        {"type": "start", "messageId": "m1"},
-        {"type": "text-start", "id": "t1"},
-        {"type": "text-delta", "id": "t1", "delta": "Answer "},
-        {"type": "text-delta", "id": "t1", "delta": "is "},
-        {"type": "text-delta", "id": "t1", "delta": "B [citation:42]."},
-        {"type": "text-end", "id": "t1"},
-        {"type": "finish"},
-    ])
+    body = _sse_body(
+        [
+            {"type": "start", "messageId": "m1"},
+            {"type": "text-start", "id": "t1"},
+            {"type": "text-delta", "id": "t1", "delta": "Answer "},
+            {"type": "text-delta", "id": "t1", "delta": "is "},
+            {"type": "text-delta", "id": "t1", "delta": "B [citation:42]."},
+            {"type": "text-end", "id": "t1"},
+            {"type": "finish"},
+        ]
+    )
     respx_mock.post("/api/v1/new_chat").mock(
         return_value=httpx.Response(
             200,
@@ -208,9 +230,7 @@ async def test_ask_accumulates_text_deltas(respx_mock, http):
         )
     )
     client = NewChatClient(http, _BASE)
-    answer = await client.ask(
-        thread_id=1, search_space_id=2, user_query="What is the answer?"
-    )
+    answer = await client.ask(thread_id=1, search_space_id=2, user_query="What is the answer?")
     assert answer.text == "Answer is B [citation:42]."
     assert answer.finished_normally is True
     assert any(c["chunk_id"] == 42 for c in answer.citations)
@@ -219,23 +239,21 @@ async def test_ask_accumulates_text_deltas(respx_mock, http):
 @pytest.mark.asyncio
 @respx.mock(base_url=_BASE)
 async def test_ask_409_thread_busy_retries(respx_mock, http):
-    body = _sse_body([
-        {"type": "text-delta", "id": "t1", "delta": "ok"},
-        {"type": "finish"},
-    ])
+    body = _sse_body(
+        [
+            {"type": "text-delta", "id": "t1", "delta": "ok"},
+            {"type": "finish"},
+        ]
+    )
     busy = httpx.Response(
         409,
         json={"detail": {"errorCode": "THREAD_BUSY", "message": "busy"}},
         headers={"Retry-After": "1"},
     )
-    success = httpx.Response(
-        200, content=body, headers={"Content-Type": "text/event-stream"}
-    )
+    success = httpx.Response(200, content=body, headers={"Content-Type": "text/event-stream"})
     respx_mock.post("/api/v1/new_chat").mock(side_effect=[busy, success])
     client = NewChatClient(http, _BASE)
-    answer = await client.ask(
-        thread_id=1, search_space_id=2, user_query="hi", max_busy_retries=2
-    )
+    answer = await client.ask(thread_id=1, search_space_id=2, user_query="hi", max_busy_retries=2)
     assert answer.text == "ok"
 
 
@@ -250,6 +268,4 @@ async def test_ask_409_exhausts_retries(respx_mock, http):
     respx_mock.post("/api/v1/new_chat").mock(return_value=busy)
     client = NewChatClient(http, _BASE)
     with pytest.raises(ThreadBusyError):
-        await client.ask(
-            thread_id=1, search_space_id=2, user_query="hi", max_busy_retries=1
-        )
+        await client.ask(thread_id=1, search_space_id=2, user_query="hi", max_busy_retries=1)

surfsense_evals/tests/core/test_ingest_settings.py

@@ -46,32 +46,24 @@ class TestMerge:
 
     def test_explicit_false_overrides_default_true(self) -> None:
         defaults = IngestSettings(use_vision_llm=True)
-        merged = IngestSettings.merge(
-            defaults, {"use_vision_llm": False}
-        )
+        merged = IngestSettings.merge(defaults, {"use_vision_llm": False})
         assert merged.use_vision_llm is False
 
     def test_explicit_true_overrides_default_false(self) -> None:
         defaults = IngestSettings(use_vision_llm=False)
-        merged = IngestSettings.merge(
-            defaults, {"use_vision_llm": True}
-        )
+        merged = IngestSettings.merge(defaults, {"use_vision_llm": True})
         assert merged.use_vision_llm is True
 
     def test_none_means_silent(self) -> None:
         # Argparse with BooleanOptionalAction yields None when the
         # operator passed neither --use-vision-llm nor --no-vision-llm.
         defaults = IngestSettings(use_vision_llm=True)
-        merged = IngestSettings.merge(
-            defaults, {"use_vision_llm": None}
-        )
+        merged = IngestSettings.merge(defaults, {"use_vision_llm": None})
         assert merged.use_vision_llm is True
 
     def test_processing_mode_override(self) -> None:
         defaults = IngestSettings(processing_mode="basic")
-        merged = IngestSettings.merge(
-            defaults, {"processing_mode": "premium"}
-        )
+        merged = IngestSettings.merge(defaults, {"processing_mode": "premium"})
         assert merged.processing_mode == "premium"
 
     def test_processing_mode_invalid_raises(self) -> None:
@@ -134,9 +126,7 @@ class TestAddArgs:
         p = argparse.ArgumentParser()
         add_ingest_settings_args(
             p,
-            defaults=IngestSettings(
-                use_vision_llm=False, processing_mode="basic"
-            ),
+            defaults=IngestSettings(use_vision_llm=False, processing_mode="basic"),
         )
         return p
 
@@ -158,31 +148,21 @@ class TestAddArgs:
             args = parser.parse_args(["--processing-mode", mode])
             assert args.processing_mode == mode
 
-    def test_processing_mode_rejects_unknown(
-        self, parser: argparse.ArgumentParser
-    ) -> None:
+    def test_processing_mode_rejects_unknown(self, parser: argparse.ArgumentParser) -> None:
         with pytest.raises(SystemExit):
             parser.parse_args(["--processing-mode", "exotic"])
 
-    def test_vision_flags_mutually_exclusive(
-        self, parser: argparse.ArgumentParser
-    ) -> None:
+    def test_vision_flags_mutually_exclusive(self, parser: argparse.ArgumentParser) -> None:
         with pytest.raises(SystemExit):
             parser.parse_args(["--use-vision-llm", "--no-vision-llm"])
 
     def test_full_pipeline(self, parser: argparse.ArgumentParser) -> None:
         # Operator passes flags + defaults are reasonable. Merge
         # should yield exactly what they asked for.
-        args = parser.parse_args(
-            ["--use-vision-llm", "--processing-mode", "premium"]
-        )
-        defaults = IngestSettings(
-            use_vision_llm=False, processing_mode="basic"
-        )
+        args = parser.parse_args(["--use-vision-llm", "--processing-mode", "premium"])
+        defaults = IngestSettings(use_vision_llm=False, processing_mode="basic")
         merged = IngestSettings.merge(defaults, vars(args))
-        assert merged == IngestSettings(
-            use_vision_llm=True, processing_mode="premium"
-        )
+        assert merged == IngestSettings(use_vision_llm=True, processing_mode="premium")
 
 
 # ---------------------------------------------------------------------------
@@ -240,16 +220,12 @@ class TestHeader:
 
 class TestFormatMd:
     def test_full_settings(self) -> None:
-        out = format_ingest_settings_md(
-            {"use_vision_llm": True, "processing_mode": "premium"}
-        )
+        out = format_ingest_settings_md({"use_vision_llm": True, "processing_mode": "premium"})
         assert "vision_llm=`on`" in out
         assert "processing_mode=`premium`" in out
 
     def test_default_off(self) -> None:
-        out = format_ingest_settings_md(
-            {"use_vision_llm": False, "processing_mode": "basic"}
-        )
+        out = format_ingest_settings_md({"use_vision_llm": False, "processing_mode": "basic"})
         assert "vision_llm=`off`" in out
         assert "processing_mode=`basic`" in out
 

surfsense_evals/tests/core/test_metrics.py

@@ -25,7 +25,12 @@ from surfsense_evals.core.metrics import (
 @pytest.mark.parametrize(
     "k,n,low,high",
     [
-        (80, 100, 0.7111, 0.8666),  # cross-checked vs statsmodels.proportion_confint(method='wilson')
+        (
+            80,
+            100,
+            0.7111,
+            0.8666,
+        ),  # cross-checked vs statsmodels.proportion_confint(method='wilson')
         (50, 100, 0.4038, 0.5962),
         (0, 0, 0.0, 1.0),
         (0, 10, 0.0, 0.2775),
@@ -74,7 +79,7 @@ def test_mcnemar_exact_branch_strong_signal():
     assert res.b == 0
     assert res.c == 10
     assert res.method == "exact"
-    expected = 2 * (0.5 ** 10)
+    expected = 2 * (0.5**10)
     assert math.isclose(res.p_value, expected, rel_tol=1e-9)
 
 

surfsense_evals/tests/core/test_parse_answer_letter.py

@@ -11,7 +11,11 @@ from surfsense_evals.core.parse.answer_letter import AnswerLetterResult
 @pytest.mark.parametrize(
     "text,expected_letter,expected_strategy",
     [
-        ('```json\n{"step_by_step_thinking": "...", "answer_choice": "B"}\n```', "B", "json_envelope"),
+        (
+            '```json\n{"step_by_step_thinking": "...", "answer_choice": "B"}\n```',
+            "B",
+            "json_envelope",
+        ),
         ('Reasoning... {"step_by_step_thinking": "x", "answer_choice": "C"}', "C", "json_envelope"),
         ("Long reasoning.\nAnswer: D", "D", "answer_line"),
         ("The correct answer is (A).", "A", "answer_line"),

surfsense_evals/tests/core/test_parse_citations.py

@@ -91,7 +91,7 @@ def test_regex_pattern_matches_ts_source():
     assert "https?://" in pattern
     assert "urlcite" in pattern
     assert "doc-" in pattern
-    assert "\u200B" in pattern
+    assert "\u200b" in pattern
     assert "【" in pattern and "】" in pattern
 
 

surfsense_evals/tests/core/test_parse_freeform_answer.py

@@ -44,11 +44,14 @@ class TestExtractFreeformAnswer:
         assert extract_freeform_answer("ANSWER: yes") == "yes"
         assert extract_freeform_answer("answer: no") == "no"
 
-    @pytest.mark.parametrize("text,expected", [
-        ("Answer: 1, 2, 3", "1, 2, 3"),
-        ("Answer: 3.14", "3.14"),
-        ("Answer:    spaced   ", "spaced"),
-    ])
+    @pytest.mark.parametrize(
+        "text,expected",
+        [
+            ("Answer: 1, 2, 3", "1, 2, 3"),
+            ("Answer: 3.14", "3.14"),
+            ("Answer:    spaced   ", "spaced"),
+        ],
+    )
     def test_various_payloads(self, text: str, expected: str) -> None:
         assert extract_freeform_answer(text) == expected
 

surfsense_evals/tests/core/test_parse_sse.py

@@ -22,12 +22,16 @@ async def _astream(lines):
 @pytest.mark.asyncio
 async def test_basic_data_frame():
     events = await _alist(
-        iter_sse_events(_astream([
-            'data: {"type": "text-delta", "delta": "hi"}',
-            "",
-            'data: {"type": "finish"}',
-            "",
-        ]))
+        iter_sse_events(
+            _astream(
+                [
+                    'data: {"type": "text-delta", "delta": "hi"}',
+                    "",
+                    'data: {"type": "finish"}',
+                    "",
+                ]
+            )
+        )
     )
     assert [e.data for e in events] == [
         '{"type": "text-delta", "delta": "hi"}',
@@ -38,10 +42,14 @@ async def test_basic_data_frame():
 @pytest.mark.asyncio
 async def test_done_sentinel_passes_through():
     events = await _alist(
-        iter_sse_events(_astream([
-            "data: [DONE]",
-            "",
-        ]))
+        iter_sse_events(
+            _astream(
+                [
+                    "data: [DONE]",
+                    "",
+                ]
+            )
+        )
     )
     assert [e.data for e in events] == ["[DONE]"]
 
@@ -49,11 +57,15 @@ async def test_done_sentinel_passes_through():
 @pytest.mark.asyncio
 async def test_multiline_data_joins_with_newline():
     events = await _alist(
-        iter_sse_events(_astream([
-            "data: line1",
-            "data: line2",
-            "",
-        ]))
+        iter_sse_events(
+            _astream(
+                [
+                    "data: line1",
+                    "data: line2",
+                    "",
+                ]
+            )
+        )
     )
     assert events[0].data == "line1\nline2"
 
@@ -61,13 +73,17 @@ async def test_multiline_data_joins_with_newline():
 @pytest.mark.asyncio
 async def test_comments_and_other_fields_ignored():
     events = await _alist(
-        iter_sse_events(_astream([
-            ": heartbeat",
-            "event: foo",
-            "id: 123",
-            "data: payload",
-            "",
-        ]))
+        iter_sse_events(
+            _astream(
+                [
+                    ": heartbeat",
+                    "event: foo",
+                    "id: 123",
+                    "data: payload",
+                    "",
+                ]
+            )
+        )
     )
     assert [e.data for e in events] == ["payload"]
 
@@ -77,8 +93,12 @@ async def test_handles_missing_trailing_blank():
     """Some servers omit the final blank line; the consumer should still emit."""
 
     events = await _alist(
-        iter_sse_events(_astream([
-            "data: only-one",
-        ]))
+        iter_sse_events(
+            _astream(
+                [
+                    "data: only-one",
+                ]
+            )
+        )
     )
     assert [e.data for e in events] == ["only-one"]

surfsense_evals/tests/core/test_provider_openrouter.py

@@ -36,11 +36,18 @@ async def test_payload_shape_matches_openrouter_docs(respx_mock, tiny_pdf: Path)
         return httpx.Response(
             200,
             json={
-                "choices": [{
-                    "message": {"content": "Answer: B"},
-                    "finish_reason": "stop",
-                }],
-                "usage": {"prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15, "cost": 0.0001},
+                "choices": [
+                    {
+                        "message": {"content": "Answer: B"},
+                        "finish_reason": "stop",
+                    }
+                ],
+                "usage": {
+                    "prompt_tokens": 10,
+                    "completion_tokens": 5,
+                    "total_tokens": 15,
+                    "cost": 0.0001,
+                },
             },
         )
 
@@ -63,8 +70,7 @@ async def test_payload_shape_matches_openrouter_docs(respx_mock, tiny_pdf: Path)
     assert file_part["file"]["filename"] == tiny_pdf.name
     assert file_part["file"]["file_data"].startswith("data:application/pdf;base64,")
     assert (
-        base64.b64decode(file_part["file"]["file_data"].split(",", 1)[1])
-        == tiny_pdf.read_bytes()  # noqa: ASYNC240 — test fixture, sync read is fine
+        base64.b64decode(file_part["file"]["file_data"].split(",", 1)[1]) == tiny_pdf.read_bytes()  # noqa: ASYNC240 — test fixture, sync read is fine
     )
     assert user["content"][1] == {"type": "text", "text": "What is the diagnosis?"}
     assert captured["headers"]["authorization"] == "Bearer sk-or-test"
@@ -85,22 +91,22 @@ async def test_chat_array_content_concatenates(respx_mock, tiny_pdf: Path):
         return_value=httpx.Response(
             200,
             json={
-                "choices": [{
-                    "message": {
-                        "content": [
-                            {"type": "text", "text": "Hello "},
-                            {"type": "text", "text": "world"},
-                            {"type": "image_url", "image_url": "ignored"},
-                        ]
+                "choices": [
+                    {
+                        "message": {
+                            "content": [
+                                {"type": "text", "text": "Hello "},
+                                {"type": "text", "text": "world"},
+                                {"type": "image_url", "image_url": "ignored"},
+                            ]
+                        }
                     }
-                }],
+                ],
                 "usage": {"prompt_tokens": 1, "completion_tokens": 1},
             },
         )
     )
-    provider = OpenRouterPdfProvider(
-        api_key="sk-or-test", base_url=_BASE, model="x/y"
-    )
+    provider = OpenRouterPdfProvider(api_key="sk-or-test", base_url=_BASE, model="x/y")
     response = await provider.complete(prompt="hi", pdf_path=tiny_pdf)
     assert response.text == "Hello world"
 

surfsense_evals/tests/suites/test_crag_dataset.py

@@ -65,13 +65,15 @@ class TestParser:
                 interaction_id="abc",
                 query="Who directed Inception?",
                 answer="Christopher Nolan",
-                pages=[{
-                    "page_name": "Inception (film)",
-                    "page_url": "https://en.wikipedia.org/wiki/Inception",
-                    "page_snippet": "snippet",
-                    "page_result": "<html>full html</html>",
-                    "page_last_modified": "2024-01-01",
-                }],
+                pages=[
+                    {
+                        "page_name": "Inception (film)",
+                        "page_url": "https://en.wikipedia.org/wiki/Inception",
+                        "page_snippet": "snippet",
+                        "page_result": "<html>full html</html>",
+                        "page_last_modified": "2024-01-01",
+                    }
+                ],
             ),
         ]
         path = _make_jsonl_bz2(rows, tmp_path)
@@ -120,8 +122,7 @@ class TestParser:
 
     def test_alt_answers_parsed(self, tmp_path: Path) -> None:
         rows = [
-            _row(interaction_id="z", query="q?", answer="42",
-                 alt_ans=["forty-two", "42.0"]),
+            _row(interaction_id="z", query="q?", answer="42", alt_ans=["forty-two", "42.0"]),
         ]
         path = _make_jsonl_bz2(rows, tmp_path)
         parsed = iter_questions(path)
@@ -143,22 +144,32 @@ class TestParser:
 class TestPageHash:
     def test_url_hash_stable(self) -> None:
         a = CragPage(
-            page_name="A", page_url="https://x.test/p?q=1",
-            page_snippet="", page_html="<html/>",
+            page_name="A",
+            page_url="https://x.test/p?q=1",
+            page_snippet="",
+            page_html="<html/>",
         )
         b = CragPage(
-            page_name="B", page_url="https://x.test/p?q=1",
-            page_snippet="", page_html="<html/>",
+            page_name="B",
+            page_url="https://x.test/p?q=1",
+            page_snippet="",
+            page_html="<html/>",
         )
         assert a.url_hash == b.url_hash
         assert len(a.url_hash) == 12
 
     def test_url_hash_unique(self) -> None:
         a = CragPage(
-            page_name="A", page_url="https://x.test/a", page_snippet="", page_html="<html/>",
+            page_name="A",
+            page_url="https://x.test/a",
+            page_snippet="",
+            page_html="<html/>",
         )
         b = CragPage(
-            page_name="B", page_url="https://x.test/b", page_snippet="", page_html="<html/>",
+            page_name="B",
+            page_url="https://x.test/b",
+            page_snippet="",
+            page_html="<html/>",
         )
         assert a.url_hash != b.url_hash
 
@@ -174,21 +185,23 @@ class TestStratifiedSample:
             (5, "sports", "multi-hop"),
         ):
             for _ in range(n):
-                out.append(CragQuestion(
-                    qid=f"C{idx:05d}",
-                    interaction_id=f"i{idx}",
-                    query_time="2024-01-01",
-                    query=f"q{idx}?",
-                    gold_answer="a",
-                    alt_answers=[],
-                    domain=domain,
-                    question_type=qtype,
-                    static_or_dynamic="static",
-                    popularity="head",
-                    split=0,
-                    raw_index=idx,
-                    pages=[],
-                ))
+                out.append(
+                    CragQuestion(
+                        qid=f"C{idx:05d}",
+                        interaction_id=f"i{idx}",
+                        query_time="2024-01-01",
+                        query=f"q{idx}?",
+                        gold_answer="a",
+                        alt_answers=[],
+                        domain=domain,
+                        question_type=qtype,
+                        static_or_dynamic="static",
+                        popularity="head",
+                        split=0,
+                        raw_index=idx,
+                        pages=[],
+                    )
+                )
                 idx += 1
         return out
 

surfsense_evals/tests/suites/test_crag_grader.py

@@ -152,7 +152,9 @@ class TestGradeDeterministicHappyPath:
 class TestGradeDeterministicRefusal:
     def test_idk_maps_to_missing(self) -> None:
         result = grade_deterministic(
-            pred="I don't know.", gold="Tim Cook", question_type="simple",
+            pred="I don't know.",
+            gold="Tim Cook",
+            question_type="simple",
         )
         assert result.grade == "missing"
         assert result.score == 0
@@ -225,8 +227,11 @@ class TestGradeDeterministicLexicalMiss:
 class TestGradeResultShape:
     def test_to_dict_round_trip(self) -> None:
         result = CragGradeResult(
-            grade="correct", score=1, method="exact",
-            normalised_pred="x", normalised_gold="x",
+            grade="correct",
+            score=1,
+            method="exact",
+            normalised_pred="x",
+            normalised_gold="x",
         )
         d = result.to_dict()
         assert d["grade"] == "correct"

surfsense_evals/tests/suites/test_crag_html_extract.py

@@ -112,7 +112,9 @@ class TestFallbackStripper:
         </body></html>
         """
         result = extract_main_content(
-            html, url="https://x.test/", page_name="Title",
+            html,
+            url="https://x.test/",
+            page_name="Title",
         )
         assert result.ok
         assert "content one" in result.text

surfsense_evals/tests/suites/test_frames_wiki_fetch.py

@@ -63,14 +63,22 @@ class TestCacheFilename:
 @pytest.mark.asyncio
 @respx.mock
 async def test_fetch_success_writes_markdown(tmp_path: Path) -> None:
-    respx.get(WIKI_API).mock(return_value=httpx.Response(
-        200,
-        json={"query": {"pages": [{
-            "pageid": 1,
-            "title": "James Buchanan",
-            "extract": "James Buchanan was the 15th president of the United States.",
-        }]}},
-    ))
+    respx.get(WIKI_API).mock(
+        return_value=httpx.Response(
+            200,
+            json={
+                "query": {
+                    "pages": [
+                        {
+                            "pageid": 1,
+                            "title": "James Buchanan",
+                            "extract": "James Buchanan was the 15th president of the United States.",
+                        }
+                    ]
+                }
+            },
+        )
+    )
     fetcher = WikiFetcher(cache_dir=tmp_path, rate_limit_rps=100)  # disable throttle
     article = await fetcher.fetch("https://en.wikipedia.org/wiki/James_Buchanan")
     assert article is not None
@@ -83,13 +91,21 @@ async def test_fetch_success_writes_markdown(tmp_path: Path) -> None:
 @pytest.mark.asyncio
 @respx.mock
 async def test_fetch_missing_page_returns_none(tmp_path: Path) -> None:
-    respx.get(WIKI_API).mock(return_value=httpx.Response(
-        200,
-        json={"query": {"pages": [{
-            "title": "DoesNotExist",
-            "missing": True,
-        }]}},
-    ))
+    respx.get(WIKI_API).mock(
+        return_value=httpx.Response(
+            200,
+            json={
+                "query": {
+                    "pages": [
+                        {
+                            "title": "DoesNotExist",
+                            "missing": True,
+                        }
+                    ]
+                }
+            },
+        )
+    )
     fetcher = WikiFetcher(cache_dir=tmp_path, rate_limit_rps=100)
     article = await fetcher.fetch("https://en.wikipedia.org/wiki/DoesNotExist")
     assert article is None

surfsense_evals/tests/suites/test_mmlongbench_grader.py

@@ -99,7 +99,9 @@ class TestListFormat:
         assert 0.0 < r.f1 < 1.0
 
     def test_extra_items_lower_precision(self) -> None:
-        r = grade(pred="apple, banana, cherry, date", gold="apple, banana, cherry", answer_format="List")
+        r = grade(
+            pred="apple, banana, cherry, date", gold="apple, banana, cherry", answer_format="List"
+        )
         assert 0.0 < r.f1 < 1.0
         # Recall=1, precision=3/4 → F1 ~= 0.857
         assert r.f1 == pytest.approx(2 * (3 / 4) * 1 / (3 / 4 + 1), rel=1e-3)

surfsense_mcp/mcp_server/features/knowledge_base/__init__.py

@@ -14,9 +14,7 @@ from ...core.workspace_context import WorkspaceContext
 from . import document_tools, search_tools
 
 
-def register(
-    mcp: FastMCP, client: SurfSenseClient, context: WorkspaceContext
-) -> None:
+def register(mcp: FastMCP, client: SurfSenseClient, context: WorkspaceContext) -> None:
     """Register every knowledge-base tool on the server."""
     search_tools.register(mcp, client, context)
     document_tools.register(mcp, client, context)

surfsense_mcp/mcp_server/features/knowledge_base/document_tools.py

@@ -20,9 +20,7 @@ from .annotations import DELETE, WRITE, DocumentId
 from .note_ingestion import build_note_document
 
 
-def register(
-    mcp: FastMCP, client: SurfSenseClient, context: WorkspaceContext
-) -> None:
+def register(mcp: FastMCP, client: SurfSenseClient, context: WorkspaceContext) -> None:
     """Register the knowledge-base write and delete tools."""
 
     @mcp.tool(
@@ -136,8 +134,7 @@ def register(
             str,
             Field(
                 min_length=1,
-                description="New full text; replaces the existing content "
-                "entirely.",
+                description="New full text; replaces the existing content entirely.",
             ),
         ],
     ) -> str:

surfsense_mcp/mcp_server/features/knowledge_base/search_tools.py

@@ -17,9 +17,7 @@ from ...core.workspace_context import WorkspaceContext, WorkspaceParam
 from .annotations import READ, DocumentId, DocumentTypes
 
 
-def register(
-    mcp: FastMCP, client: SurfSenseClient, context: WorkspaceContext
-) -> None:
+def register(mcp: FastMCP, client: SurfSenseClient, context: WorkspaceContext) -> None:
     """Register the knowledge-base read tools."""
 
     @mcp.tool(
@@ -81,12 +79,8 @@ def register(
             int | None,
             Field(description="Only documents in this folder. Omit for all."),
         ] = None,
-        page: Annotated[
-            int, Field(ge=0, description="Zero-based page number.")
-        ] = 0,
-        page_size: Annotated[
-            int, Field(ge=1, description="Documents per page.")
-        ] = 20,
+        page: Annotated[int, Field(ge=0, description="Zero-based page number.")] = 0,
+        page_size: Annotated[int, Field(ge=1, description="Documents per page.")] = 20,
         workspace: WorkspaceParam = None,
         response_format: ResponseFormatParam = "markdown",
     ) -> str:

surfsense_mcp/mcp_server/features/scrapers/__init__.py

@@ -35,9 +35,7 @@ _REGISTRARS = (
 )
 
 
-def register(
-    mcp: FastMCP, client: SurfSenseClient, context: WorkspaceContext
-) -> None:
+def register(mcp: FastMCP, client: SurfSenseClient, context: WorkspaceContext) -> None:
     """Register every scraper and run-history tool on the server."""
     for module in _REGISTRARS:
         module.register(mcp, client, context)