| 1 | """Normalization of source-specific payloads into the v3 generic item model.""" |
| 2 | |
| 3 | from __future__ import annotations |
| 4 | |
| 5 | from typing import Any |
| 6 | from urllib.parse import urlparse |
| 7 | |
| 8 | from . import dates, schema |
| 9 | |
| 10 | |
| 11 | def filter_by_date_range( |
| 12 | items: list[schema.SourceItem], |
| 13 | from_date: str, |
| 14 | to_date: str, |
| 15 | require_date: bool = False, |
| 16 | ) -> list[schema.SourceItem]: |
| 17 | """Keep only items within the requested window.""" |
| 18 | filtered: list[schema.SourceItem] = [] |
| 19 | for item in items: |
| 20 | if not item.published_at: |
| 21 | if not require_date: |
| 22 | filtered.append(item) |
| 23 | continue |
| 24 | if item.published_at < from_date or item.published_at > to_date: |
| 25 | continue |
| 26 | filtered.append(item) |
| 27 | return filtered |
| 28 | |
| 29 | |
| 30 | def normalize_source_items( |
| 31 | source: str, |
| 32 | items: list[dict[str, Any]], |
| 33 | from_date: str, |
| 34 | to_date: str, |
| 35 | freshness_mode: str = "balanced_recent", |
| 36 | ) -> list[schema.SourceItem]: |
| 37 | """Normalize raw source items, filter by date range, with evergreen fallback for how_to queries.""" |
| 38 | source = source.lower() |
| 39 | normalizers = { |
| 40 | "reddit": _normalize_reddit, |
| 41 | "x": _normalize_x, |
| 42 | "youtube": _normalize_youtube, |
| 43 | "tiktok": lambda s, i, idx, fd, td: _normalize_shortform_video( |
| 44 | s, i, idx, fd, td, "TK", "TikTok post" |
| 45 | ), |
| 46 | "instagram": lambda s, i, idx, fd, td: _normalize_shortform_video( |
| 47 | s, i, idx, fd, td, "IG", "Instagram reel" |
| 48 | ), |
| 49 | "hackernews": _normalize_hackernews, |
| 50 | "stocktwits": _normalize_stocktwits, |
| 51 | "dripstack": _normalize_dripstack, |
| 52 | "bluesky": lambda s, i, idx, fd, td: _normalize_microblog( |
| 53 | s, i, idx, fd, td, "BS", "Bluesky post" |
| 54 | ), |
| 55 | "truthsocial": lambda s, i, idx, fd, td: _normalize_microblog( |
| 56 | s, i, idx, fd, td, "TS", "Truth Social post" |
| 57 | ), |
| 58 | "threads": lambda s, i, idx, fd, td: _normalize_microblog( |
| 59 | s, i, idx, fd, td, "TH", "Threads post" |
| 60 | ), |
| 61 | "telegram": lambda s, i, idx, fd, td: _normalize_microblog( |
| 62 | s, i, idx, fd, td, "TG", "Telegram post" |
| 63 | ), |
| 64 | "xquik": _normalize_x, |
| 65 | "pinterest": _normalize_pinterest, |
| 66 | "polymarket": _normalize_polymarket, |
| 67 | "digg": _normalize_digg, |
| 68 | "arxiv": _normalize_arxiv, |
| 69 | "techmeme": _normalize_techmeme, |
| 70 | "trustpilot": _normalize_trustpilot, |
| 71 | "amazon": _normalize_amazon, |
| 72 | "meta_ads": _normalize_meta_ads, |
| 73 | "grounding": _normalize_grounding, |
| 74 | "xiaohongshu": _normalize_grounding, |
| 75 | "github": _normalize_github, |
| 76 | "perplexity": _normalize_grounding, |
| 77 | "jobs": _normalize_jobs, |
| 78 | "linkedin": _normalize_linkedin, |
| 79 | } |
| 80 | normalizer = normalizers.get(source) |
| 81 | if normalizer is None: |
| 82 | raise ValueError(f"Unsupported source: {source}") |
| 83 | normalized = [ |
| 84 | normalizer(source, item, index, from_date, to_date) |
| 85 | for index, item in enumerate(items) |
| 86 | ] |
| 87 | if source == "arxiv": |
| 88 | # The adapter owns arXiv's 365-day recency contract. Applying the |
| 89 | # report window again here drops relevant papers the adapter accepted. |
| 90 | return normalized |
| 91 | if source == "jobs": |
| 92 | # A careers board is a snapshot of CURRENTLY OPEN roles. An open posting |
| 93 | # is current evidence regardless of when it was posted, so date-windowing |
| 94 | # it drops still-open roles (the "Founding Research Scientist, Human |
| 95 | # Simulation" miss: 26 open roles filtered to 3 by a 30-day window). |
| 96 | # Keep the full board; recency is annotated, not used to drop. |
| 97 | return normalized |
| 98 | require_date = source == "grounding" |
| 99 | filtered = filter_by_date_range( |
| 100 | normalized, from_date, to_date, require_date=require_date |
| 101 | ) |
| 102 | if filtered: |
| 103 | return filtered |
| 104 | # YouTube search already keeps out-of-window videos when fewer than 3 |
| 105 | # are recent, then pays for transcripts. A second hard date filter here |
| 106 | # dropped those transcribed items to zero (#1043). Keep transcript-backed |
| 107 | # retrieved items instead of paying for transcripts that never appear in |
| 108 | # the brief. Metadata-only / caption-failed videos are not that rescue. |
| 109 | if source == "youtube" and normalized: |
| 110 | transcribed = [ |
| 111 | item for item in normalized if str(item.snippet or "").strip() |
| 112 | ] |
| 113 | if transcribed: |
| 114 | if require_date: |
| 115 | dated = [item for item in transcribed if item.published_at] |
| 116 | return dated or transcribed |
| 117 | return transcribed |
| 118 | if freshness_mode == "evergreen_ok" and source == "youtube": |
| 119 | if require_date: |
| 120 | return [item for item in normalized if item.published_at] |
| 121 | return normalized |
| 122 | return filtered |
| 123 | |
| 124 | |
| 125 | def _remap_comments( |
| 126 | raw: list[Any], |
| 127 | score_keys: tuple[str, ...], |
| 128 | excerpt_keys: tuple[str, ...], |
| 129 | *, |
| 130 | preserve_absent_score: bool = False, |
| 131 | ) -> list[dict[str, Any]]: |
| 132 | """Normalize comments from any source into the shared Reddit-compatible shape. |
| 133 | |
| 134 | Downstream code (signals._top_comment_score, render._top_comments_list, |
| 135 | entity_extract, rerank) all expect `score` and `excerpt`. This helper maps |
| 136 | per-source field names (YT: likes/text, TikTok: digg_count/text) onto that |
| 137 | shape while preserving author/date/url passthrough. |
| 138 | |
| 139 | Sources that distinguish an absent vote from a measured zero can opt into |
| 140 | preserving the absent value as ``None``. |
| 141 | """ |
| 142 | out: list[dict[str, Any]] = [] |
| 143 | for raw_c in raw: |
| 144 | if not isinstance(raw_c, dict): |
| 145 | continue |
| 146 | score = _first_present( |
| 147 | raw_c, |
| 148 | score_keys, |
| 149 | default=None if preserve_absent_score else 0, |
| 150 | ) |
| 151 | excerpt = _first_present(raw_c, excerpt_keys, default="") |
| 152 | if score is None and preserve_absent_score: |
| 153 | normalized_score = None |
| 154 | else: |
| 155 | try: |
| 156 | normalized_score = int(score or 0) |
| 157 | except (TypeError, ValueError): |
| 158 | normalized_score = 0 |
| 159 | entry: dict[str, Any] = { |
| 160 | "score": normalized_score, |
| 161 | "excerpt": str(excerpt or "")[:400], |
| 162 | "author": str(raw_c.get("author") or ""), |
| 163 | "date": str(raw_c.get("date") or ""), |
| 164 | } |
| 165 | if raw_c.get("url"): |
| 166 | entry["url"] = str(raw_c["url"]) |
| 167 | out.append(entry) |
| 168 | return out |
| 169 | |
| 170 | |
| 171 | def _first_present(d: dict[str, Any], keys: tuple[str, ...], default: Any) -> Any: |
| 172 | for key in keys: |
| 173 | if key in d and d[key] not in (None, ""): |
| 174 | return d[key] |
| 175 | return default |
| 176 | |
| 177 | |
| 178 | def _join_comment_excerpts( |
| 179 | top_comments: list[Any], |
| 180 | key: str, |
| 181 | limit: int = 3, |
| 182 | ) -> str: |
| 183 | """Space-join the `key` field from the first `limit` dict-shaped comments.""" |
| 184 | return " ".join( |
| 185 | str(comment.get(key) or "").strip() |
| 186 | for comment in top_comments[:limit] |
| 187 | if isinstance(comment, dict) |
| 188 | ) |
| 189 | |
| 190 | |
| 191 | def _domain_from_url(url: str) -> str | None: |
| 192 | if not url: |
| 193 | return None |
| 194 | domain = urlparse(url).netloc.strip().lower() |
| 195 | return domain or None |
| 196 | |
| 197 | |
| 198 | def _date_confidence( |
| 199 | item: dict[str, Any], from_date: str, to_date: str, default: str = "low" |
| 200 | ) -> str: |
| 201 | if item.get("date_confidence"): |
| 202 | return str(item["date_confidence"]) |
| 203 | date_value = item.get("date") |
| 204 | if not date_value: |
| 205 | return default |
| 206 | return dates.get_date_confidence(str(date_value), from_date, to_date) |
| 207 | |
| 208 | |
| 209 | def _source_item( |
| 210 | *, |
| 211 | item_id: str, |
| 212 | source: str, |
| 213 | title: str, |
| 214 | body: str, |
| 215 | url: str, |
| 216 | published_at: str | None, |
| 217 | date_confidence: str, |
| 218 | relevance_hint: float, |
| 219 | why_relevant: str, |
| 220 | author: str | None = None, |
| 221 | container: str | None = None, |
| 222 | engagement: dict[str, float | int] | None = None, |
| 223 | snippet: str = "", |
| 224 | metadata: dict[str, Any] | None = None, |
| 225 | ) -> schema.SourceItem: |
| 226 | return schema.SourceItem( |
| 227 | item_id=item_id, |
| 228 | source=source, |
| 229 | title=title.strip() or body.strip()[:160] or item_id, |
| 230 | body=body.strip(), |
| 231 | url=url.strip(), |
| 232 | author=(author or "").strip() or None, |
| 233 | container=(container or "").strip() or None, |
| 234 | published_at=published_at, |
| 235 | date_confidence=date_confidence, |
| 236 | engagement=engagement or {}, |
| 237 | relevance_hint=max(0.0, min(1.0, float(relevance_hint or 0.0))), |
| 238 | why_relevant=why_relevant.strip(), |
| 239 | snippet=snippet.strip(), |
| 240 | metadata=metadata or {}, |
| 241 | ) |
| 242 | |
| 243 | |
| 244 | def _normalize_stocktwits( |
| 245 | source: str, |
| 246 | item: dict[str, Any], |
| 247 | index: int, |
| 248 | from_date: str, |
| 249 | to_date: str, |
| 250 | ) -> schema.SourceItem: |
| 251 | meta = item.get("metadata") or {} |
| 252 | return _source_item( |
| 253 | item_id=str(item.get("id") or f"ST{index + 1}"), |
| 254 | source=source, |
| 255 | title=str(item.get("title") or ""), |
| 256 | body=str(item.get("snippet") or ""), |
| 257 | url=str(item.get("url") or ""), |
| 258 | author=str(item.get("author") or "") or None, |
| 259 | container=str(meta.get("symbol") or "") or None, |
| 260 | published_at=item.get("date"), |
| 261 | date_confidence=_date_confidence(item, from_date, to_date, default="high"), |
| 262 | engagement=item.get("engagement") or {}, |
| 263 | relevance_hint=item.get("relevance", 0.7), |
| 264 | why_relevant=str(item.get("why_relevant") or ""), |
| 265 | snippet=str(item.get("snippet") or "")[:400], |
| 266 | metadata=meta, # carries sentiment + symbol-level bull/bear aggregate |
| 267 | ) |
| 268 | |
| 269 | |
| 270 | def _normalize_dripstack( |
| 271 | source: str, |
| 272 | item: dict[str, Any], |
| 273 | index: int, |
| 274 | from_date: str, |
| 275 | to_date: str, |
| 276 | ) -> schema.SourceItem: |
| 277 | """Normalizer for DripStack newsletter search results. |
| 278 | |
| 279 | DripStack returns article metadata from paid financial newsletters. |
| 280 | No engagement signal — ranking relies on DripStack's own relevanceScore |
| 281 | (0-100, normalized to 0-1) plus recency. The publication name serves as |
| 282 | author/attribution (e.g. "SemiAnalysis", "Bloomberg"). |
| 283 | """ |
| 284 | meta = item.get("metadata") or {} |
| 285 | return _source_item( |
| 286 | item_id=str(item.get("id") or f"DS{index + 1}"), |
| 287 | source=source, |
| 288 | title=str(item.get("title") or ""), |
| 289 | body=str(item.get("body") or "") |
| 290 | or str(item.get("snippet") or "") |
| 291 | or str(item.get("title") or ""), |
| 292 | url=str(item.get("url") or ""), |
| 293 | author=str(item.get("author") or "") or None, |
| 294 | container=str(meta.get("publication_slug") or "") or None, |
| 295 | published_at=item.get("date"), |
| 296 | date_confidence=_date_confidence(item, from_date, to_date, default="high"), |
| 297 | engagement={}, |
| 298 | relevance_hint=item.get("relevance", 0.5), |
| 299 | why_relevant=str(item.get("why_relevant") or ""), |
| 300 | snippet=str(item.get("snippet") or "")[:400], |
| 301 | metadata={ |
| 302 | **meta, |
| 303 | "publication_slug": meta.get("publication_slug"), |
| 304 | }, |
| 305 | ) |
| 306 | |
| 307 | |
| 308 | def _normalize_reddit( |
| 309 | source: str, |
| 310 | item: dict[str, Any], |
| 311 | index: int, |
| 312 | from_date: str, |
| 313 | to_date: str, |
| 314 | ) -> schema.SourceItem: |
| 315 | top_comments = item.get("top_comments") or [] |
| 316 | comment_text = _join_comment_excerpts(top_comments, "excerpt") |
| 317 | body = "\n".join( |
| 318 | part |
| 319 | for part in [ |
| 320 | str(item.get("title") or "").strip(), |
| 321 | str(item.get("selftext") or "").strip(), |
| 322 | comment_text, |
| 323 | ] |
| 324 | if part |
| 325 | ) |
| 326 | return _source_item( |
| 327 | item_id=str(item.get("id") or f"R{index + 1}"), |
| 328 | source=source, |
| 329 | title=str(item.get("title") or ""), |
| 330 | body=body, |
| 331 | url=str(item.get("url") or ""), |
| 332 | author=None, |
| 333 | container=str(item.get("subreddit") or ""), |
| 334 | published_at=item.get("date"), |
| 335 | date_confidence=_date_confidence(item, from_date, to_date), |
| 336 | engagement=item.get("engagement") or {}, |
| 337 | relevance_hint=item.get("relevance", 0.5), |
| 338 | why_relevant=str(item.get("why_relevant") or ""), |
| 339 | snippet=comment_text or str(item.get("selftext") or "")[:400], |
| 340 | metadata={ |
| 341 | "top_comments": top_comments, |
| 342 | "comment_insights": item.get("comment_insights") or [], |
| 343 | }, |
| 344 | ) |
| 345 | |
| 346 | |
| 347 | def _normalize_x( |
| 348 | source: str, |
| 349 | item: dict[str, Any], |
| 350 | index: int, |
| 351 | from_date: str, |
| 352 | to_date: str, |
| 353 | ) -> schema.SourceItem: |
| 354 | text = str(item.get("text") or "").strip() |
| 355 | mentioned = item.get("mentioned_handles") or [] |
| 356 | return _source_item( |
| 357 | item_id=str(item.get("id") or f"X{index + 1}"), |
| 358 | source=source, |
| 359 | title=text[:140] or f"X post {index + 1}", |
| 360 | body=text, |
| 361 | url=str(item.get("url") or ""), |
| 362 | author=str(item.get("author_handle") or "").lstrip("@"), |
| 363 | published_at=item.get("date"), |
| 364 | date_confidence=_date_confidence(item, from_date, to_date), |
| 365 | engagement=item.get("engagement") or {}, |
| 366 | relevance_hint=item.get("relevance", 0.5), |
| 367 | why_relevant=str(item.get("why_relevant") or ""), |
| 368 | metadata={"mentioned_handles": list(mentioned)} if mentioned else {}, |
| 369 | ) |
| 370 | |
| 371 | |
| 372 | def _normalize_jobs( |
| 373 | source: str, |
| 374 | item: dict[str, Any], |
| 375 | index: int, |
| 376 | from_date: str, |
| 377 | to_date: str, |
| 378 | ) -> schema.SourceItem: |
| 379 | description = str(item.get("description") or item.get("snippet") or "").strip() |
| 380 | title = str(item.get("title") or "").strip() |
| 381 | department = str(item.get("department") or "").strip() |
| 382 | location = str(item.get("location") or "").strip() |
| 383 | body = "\n".join( |
| 384 | part for part in [title, department, location, description] if part |
| 385 | ) |
| 386 | provider = str(item.get("provider") or "").strip() |
| 387 | return _source_item( |
| 388 | item_id=str(item.get("id") or f"J{index + 1}"), |
| 389 | source=source, |
| 390 | title=title or f"Job posting {index + 1}", |
| 391 | body=body, |
| 392 | url=str(item.get("url") or ""), |
| 393 | author=provider or None, |
| 394 | container=department or None, |
| 395 | published_at=item.get("date"), |
| 396 | date_confidence=_date_confidence(item, from_date, to_date), |
| 397 | engagement={"open_roles": 1}, |
| 398 | relevance_hint=item.get("relevance", 0.65), |
| 399 | why_relevant=str(item.get("why_relevant") or "Public job posting"), |
| 400 | snippet=description[:500], |
| 401 | metadata={ |
| 402 | "provider": provider, |
| 403 | "department": department, |
| 404 | "departments": item.get("departments") |
| 405 | or ([department] if department else []), |
| 406 | "location": location, |
| 407 | "offices": item.get("offices") or [], |
| 408 | "board_token": item.get("board_token") or "", |
| 409 | "source_url": item.get("source_url") or "", |
| 410 | "source_domain": item.get("source_domain") |
| 411 | or _domain_from_url(str(item.get("url") or "")) |
| 412 | or "", |
| 413 | }, |
| 414 | ) |
| 415 | |
| 416 | |
| 417 | def _normalize_youtube( |
| 418 | source: str, |
| 419 | item: dict[str, Any], |
| 420 | index: int, |
| 421 | from_date: str, |
| 422 | to_date: str, |
| 423 | ) -> schema.SourceItem: |
| 424 | transcript = str(item.get("transcript_snippet") or "").strip() |
| 425 | description = str(item.get("description") or "").strip() |
| 426 | title = str(item.get("title") or "").strip() |
| 427 | highlights = item.get("transcript_highlights") or [] |
| 428 | metadata: dict[str, Any] = {} |
| 429 | if highlights: |
| 430 | metadata["transcript_highlights"] = highlights |
| 431 | if item.get("captions_disabled"): |
| 432 | # Surfaced for quality_nudge: uploader disabled captions, so this |
| 433 | # video should be subtracted from the degraded-transcript-ratio |
| 434 | # denominator (it was never going to produce a transcript). |
| 435 | metadata["captions_disabled"] = True |
| 436 | metadata["top_comments"] = _remap_comments( |
| 437 | item.get("top_comments") or [], |
| 438 | score_keys=("score", "likes"), |
| 439 | excerpt_keys=("excerpt", "text"), |
| 440 | ) |
| 441 | return _source_item( |
| 442 | item_id=str(item.get("video_id") or item.get("id") or f"YT{index + 1}"), |
| 443 | source=source, |
| 444 | title=title, |
| 445 | body="\n".join(part for part in [title, description, transcript] if part), |
| 446 | url=str(item.get("url") or ""), |
| 447 | author=str(item.get("channel_name") or ""), |
| 448 | published_at=item.get("date"), |
| 449 | date_confidence=_date_confidence(item, from_date, to_date, default="high"), |
| 450 | engagement=item.get("engagement") or {}, |
| 451 | relevance_hint=item.get("relevance", 0.5), |
| 452 | why_relevant=str(item.get("why_relevant") or ""), |
| 453 | snippet=transcript, |
| 454 | metadata=metadata, |
| 455 | ) |
| 456 | |
| 457 | |
| 458 | def _normalize_shortform_video( |
| 459 | source: str, |
| 460 | item: dict[str, Any], |
| 461 | index: int, |
| 462 | from_date: str, |
| 463 | to_date: str, |
| 464 | id_prefix: str, |
| 465 | default_title: str, |
| 466 | ) -> schema.SourceItem: |
| 467 | """Shared normalizer for TikTok and Instagram (identical structure).""" |
| 468 | caption = str(item.get("caption_snippet") or "").strip() |
| 469 | text = str(item.get("text") or "").strip() |
| 470 | return _source_item( |
| 471 | item_id=str(item.get("id") or f"{id_prefix}{index + 1}"), |
| 472 | source=source, |
| 473 | title=text[:140] or caption[:140] or f"{default_title} {index + 1}", |
| 474 | body="\n".join(part for part in [text, caption] if part), |
| 475 | url=str(item.get("url") or ""), |
| 476 | author=str(item.get("author_name") or ""), |
| 477 | published_at=item.get("date"), |
| 478 | date_confidence=_date_confidence(item, from_date, to_date, default="high"), |
| 479 | engagement=item.get("engagement") or {}, |
| 480 | relevance_hint=item.get("relevance", 0.5), |
| 481 | why_relevant=str(item.get("why_relevant") or ""), |
| 482 | snippet=caption, |
| 483 | metadata={ |
| 484 | "hashtags": item.get("hashtags") or [], |
| 485 | "top_comments": _remap_comments( |
| 486 | item.get("top_comments") or [], |
| 487 | # Instagram comments use comment_like_count as the vote field |
| 488 | # (ScrapeCreators /v2/instagram/post/comments); digg_count/likes |
| 489 | # kept for shape compatibility. |
| 490 | score_keys=("score", "comment_like_count", "digg_count", "likes"), |
| 491 | excerpt_keys=("excerpt", "text"), |
| 492 | ), |
| 493 | }, |
| 494 | ) |
| 495 | |
| 496 | |
| 497 | def _normalize_pinterest( |
| 498 | source: str, |
| 499 | item: dict[str, Any], |
| 500 | index: int, |
| 501 | from_date: str, |
| 502 | to_date: str, |
| 503 | ) -> schema.SourceItem: |
| 504 | """Normalizer for Pinterest pins (visual content with descriptions). |
| 505 | |
| 506 | Saves are the primary engagement signal, analogous to likes/upvotes. |
| 507 | """ |
| 508 | description = str(item.get("description") or "").strip() |
| 509 | return _source_item( |
| 510 | item_id=str(item.get("pin_id") or item.get("id") or f"PI{index + 1}"), |
| 511 | source=source, |
| 512 | title=description[:140] or f"Pinterest pin {index + 1}", |
| 513 | body=description, |
| 514 | url=str(item.get("url") or ""), |
| 515 | author=str(item.get("author") or ""), |
| 516 | container=str(item.get("board") or ""), |
| 517 | published_at=item.get("date"), |
| 518 | date_confidence=_date_confidence(item, from_date, to_date, default="low"), |
| 519 | engagement=item.get("engagement") or {}, |
| 520 | relevance_hint=item.get("relevance", 0.5), |
| 521 | why_relevant=str(item.get("why_relevant") or ""), |
| 522 | snippet=description[:400], |
| 523 | ) |
| 524 | |
| 525 | |
| 526 | def _normalize_hackernews( |
| 527 | source: str, |
| 528 | item: dict[str, Any], |
| 529 | index: int, |
| 530 | from_date: str, |
| 531 | to_date: str, |
| 532 | ) -> schema.SourceItem: |
| 533 | # HN comments arrive as {author, text, points}; downstream code keys on |
| 534 | # score/excerpt, so remap here exactly as the YouTube and TikTok normalisers |
| 535 | # do. Without this the per-source floor in render._top_comments_list reads a |
| 536 | # `score` that is never present and rejects every HN comment. |
| 537 | top_comments = _remap_comments( |
| 538 | item.get("top_comments") or [], |
| 539 | score_keys=("points", "score"), |
| 540 | excerpt_keys=("text", "excerpt"), |
| 541 | preserve_absent_score=True, |
| 542 | ) |
| 543 | comment_text = _join_comment_excerpts(top_comments, "excerpt") |
| 544 | title = str(item.get("title") or "").strip() |
| 545 | body = "\n".join( |
| 546 | part |
| 547 | for part in [title, str(item.get("text") or "").strip(), comment_text] |
| 548 | if part |
| 549 | ) |
| 550 | return _source_item( |
| 551 | item_id=str(item.get("id") or f"HN{index + 1}"), |
| 552 | source=source, |
| 553 | title=title or f"HN story {index + 1}", |
| 554 | body=body, |
| 555 | url=str(item.get("url") or item.get("hn_url") or ""), |
| 556 | author=str(item.get("author") or ""), |
| 557 | container="Hacker News", |
| 558 | published_at=item.get("date"), |
| 559 | date_confidence=_date_confidence(item, from_date, to_date, default="high"), |
| 560 | engagement=item.get("engagement") or {}, |
| 561 | relevance_hint=item.get("relevance", 0.5), |
| 562 | why_relevant=str(item.get("why_relevant") or ""), |
| 563 | snippet=comment_text, |
| 564 | metadata={ |
| 565 | "hn_url": item.get("hn_url"), |
| 566 | "top_comments": top_comments, |
| 567 | "comment_insights": item.get("comment_insights") or [], |
| 568 | }, |
| 569 | ) |
| 570 | |
| 571 | |
| 572 | def _normalize_microblog( |
| 573 | source: str, |
| 574 | item: dict[str, Any], |
| 575 | index: int, |
| 576 | from_date: str, |
| 577 | to_date: str, |
| 578 | id_prefix: str, |
| 579 | default_title: str, |
| 580 | ) -> schema.SourceItem: |
| 581 | """Shared normalizer for Bluesky and Truth Social (identical structure).""" |
| 582 | text = str(item.get("text") or "").strip() |
| 583 | return _source_item( |
| 584 | item_id=str(item.get("id") or f"{id_prefix}{index + 1}"), |
| 585 | source=source, |
| 586 | title=text[:140] or f"{default_title} {index + 1}", |
| 587 | body=text, |
| 588 | url=str(item.get("url") or ""), |
| 589 | author=str(item.get("handle") or item.get("author_handle") or "").lstrip("@"), |
| 590 | published_at=item.get("date"), |
| 591 | date_confidence=_date_confidence(item, from_date, to_date, default="high"), |
| 592 | engagement=item.get("engagement") or {}, |
| 593 | relevance_hint=item.get("relevance", 0.5), |
| 594 | why_relevant=str(item.get("why_relevant") or ""), |
| 595 | metadata={"display_name": item.get("display_name")}, |
| 596 | ) |
| 597 | |
| 598 | |
| 599 | def _normalize_digg( |
| 600 | source: str, |
| 601 | item: dict[str, Any], |
| 602 | index: int, |
| 603 | from_date: str, |
| 604 | to_date: str, |
| 605 | ) -> schema.SourceItem: |
| 606 | """Normalizer for Digg AI 1000 clusters. |
| 607 | |
| 608 | Each cluster is one item. The TLDR carries the most useful body for |
| 609 | rerank and synthesis. Top-ranked X posts attached at search time are |
| 610 | passed through under metadata['posts'] so render can emit them as |
| 611 | inline 'via Digg' quotes. |
| 612 | """ |
| 613 | title = str(item.get("title") or "").strip() |
| 614 | tldr = str(item.get("tldr") or "").strip() |
| 615 | body = "\n\n".join(part for part in [title, tldr] if part) |
| 616 | posts = item.get("posts") or [] |
| 617 | if not isinstance(posts, list): |
| 618 | posts = [] |
| 619 | cluster_url_id = str(item.get("id") or f"DG{index + 1}") |
| 620 | return _source_item( |
| 621 | item_id=cluster_url_id, |
| 622 | source=source, |
| 623 | title=title or f"Digg cluster {index + 1}", |
| 624 | body=body, |
| 625 | url=str(item.get("url") or f"https://di.gg/ai/{cluster_url_id}"), |
| 626 | author="", |
| 627 | container="Digg", |
| 628 | published_at=item.get("date"), |
| 629 | date_confidence=_date_confidence(item, from_date, to_date, default="high"), |
| 630 | engagement=item.get("engagement") or {}, |
| 631 | relevance_hint=item.get("relevance", 0.5), |
| 632 | why_relevant=str(item.get("why_relevant") or ""), |
| 633 | snippet=tldr[:400], |
| 634 | metadata={ |
| 635 | "clusterUrlId": cluster_url_id, |
| 636 | "tldr": tldr, |
| 637 | "rank": (item.get("engagement") or {}).get("rank"), |
| 638 | "uniqueAuthors": (item.get("engagement") or {}).get("uniqueAuthors"), |
| 639 | "postCount": (item.get("engagement") or {}).get("postCount"), |
| 640 | "firstPostAge": item.get("first_post_age"), |
| 641 | "posts": posts, |
| 642 | }, |
| 643 | ) |
| 644 | |
| 645 | |
| 646 | def _normalize_arxiv( |
| 647 | source: str, |
| 648 | item: dict[str, Any], |
| 649 | index: int, |
| 650 | from_date: str, |
| 651 | to_date: str, |
| 652 | ) -> schema.SourceItem: |
| 653 | """Normalizer for arXiv papers. |
| 654 | |
| 655 | The abstract (summary) is the body that feeds rerank and synthesis. arXiv |
| 656 | has no engagement signal, so engagement is empty and ranking leans on |
| 657 | relevance and recency. |
| 658 | """ |
| 659 | title = str(item.get("title") or "").strip() |
| 660 | summary = str(item.get("summary") or "").strip() |
| 661 | body = "\n\n".join(part for part in [title, summary] if part) |
| 662 | authors = item.get("authors") or [] |
| 663 | if not isinstance(authors, list): |
| 664 | authors = [] |
| 665 | paper_id = str(item.get("id") or f"AX{index + 1}") |
| 666 | return _source_item( |
| 667 | item_id=paper_id, |
| 668 | source=source, |
| 669 | title=title or f"arXiv paper {index + 1}", |
| 670 | body=body, |
| 671 | url=str(item.get("url") or ""), |
| 672 | author=str(item.get("author") or "") or None, |
| 673 | container="arXiv", |
| 674 | published_at=item.get("date"), |
| 675 | date_confidence=_date_confidence(item, from_date, to_date, default="high"), |
| 676 | engagement={}, |
| 677 | relevance_hint=item.get("relevance", 0.5), |
| 678 | why_relevant=str(item.get("why_relevant") or ""), |
| 679 | snippet=summary[:400], |
| 680 | metadata={ |
| 681 | "authors": authors, |
| 682 | "summary": summary, |
| 683 | }, |
| 684 | ) |
| 685 | |
| 686 | |
| 687 | def _normalize_techmeme( |
| 688 | source: str, |
| 689 | item: dict[str, Any], |
| 690 | index: int, |
| 691 | from_date: str, |
| 692 | to_date: str, |
| 693 | ) -> schema.SourceItem: |
| 694 | """Normalizer for Techmeme headlines. |
| 695 | |
| 696 | The headline is both title and body (Techmeme carries no abstract). The |
| 697 | publication is the container/author. No engagement signal in the search |
| 698 | shape, so ranking leans on relevance and recency. |
| 699 | """ |
| 700 | title = str(item.get("title") or "").strip() |
| 701 | source_name = str(item.get("source_name") or "").strip() |
| 702 | return _source_item( |
| 703 | item_id=str(item.get("id") or f"TM{index + 1}"), |
| 704 | source=source, |
| 705 | title=title or f"Techmeme headline {index + 1}", |
| 706 | body=title, |
| 707 | url=str(item.get("url") or ""), |
| 708 | author=source_name or None, |
| 709 | container=source_name or "Techmeme", |
| 710 | published_at=item.get("date"), |
| 711 | date_confidence=_date_confidence(item, from_date, to_date, default="low"), |
| 712 | engagement={}, |
| 713 | relevance_hint=item.get("relevance", 0.5), |
| 714 | why_relevant=str(item.get("why_relevant") or ""), |
| 715 | snippet=title[:400], |
| 716 | metadata={ |
| 717 | "publication": source_name, |
| 718 | }, |
| 719 | ) |
| 720 | |
| 721 | |
| 722 | def _normalize_meta_ads( |
| 723 | source: str, |
| 724 | item: dict[str, Any], |
| 725 | index: int, |
| 726 | from_date: str, |
| 727 | to_date: str, |
| 728 | ) -> schema.SourceItem: |
| 729 | """Normalizer for Meta Ad Library creatives. |
| 730 | |
| 731 | One item per distinct creative launched inside the window. Identity is the |
| 732 | Ad Library permalink, never the landing URL: many creatives for one product |
| 733 | share a landing page, and fusion merges by normalized URL, so keying on the |
| 734 | landing page would collapse a whole campaign into one candidate and lose |
| 735 | every transcript but the first. |
| 736 | |
| 737 | Grounding-exempt on the Amazon precedent. Ad copy is written to sell, not |
| 738 | to name the brand -- "Stop making boring drinks" never repeats the topic -- |
| 739 | and the advertiser page was already resolved by name before any of these |
| 740 | were created, so they are on-entity by construction. |
| 741 | """ |
| 742 | body = str(item.get("text") or "").strip() |
| 743 | transcript = str(item.get("transcript") or "").strip() |
| 744 | advertiser = str(item.get("advertiser") or "").strip() |
| 745 | cta = str(item.get("cta") or "").strip() |
| 746 | landing = str(item.get("landing_url") or "").strip() |
| 747 | placements = [str(p) for p in (item.get("placements") or [])] |
| 748 | promo = str(item.get("promo_code") or "").strip() |
| 749 | |
| 750 | # The spoken script is usually the sharper version of the pitch, so it |
| 751 | # leads the evidence snippet when present. |
| 752 | snippet_parts = [part for part in [body, transcript] if part] |
| 753 | # The adapter deliberately keeps creatives that launched inside the window |
| 754 | # and have since ended -- a one-week promo push is exactly the signal this |
| 755 | # source exists for -- so the wording has to follow the stored state rather |
| 756 | # than calling every creative active. |
| 757 | running = bool(item.get("is_active")) |
| 758 | ended_on = str(item.get("ended_on") or "").strip() |
| 759 | if running: |
| 760 | state_word = "Running paid creative" |
| 761 | elif ended_on: |
| 762 | state_word = f"Paid creative that ran until {ended_on}" |
| 763 | else: |
| 764 | state_word = "Paid creative that has since ended" |
| 765 | default_why = f"{state_word} from {advertiser}" if advertiser else state_word |
| 766 | context = " │ ".join( |
| 767 | part |
| 768 | for part in [ |
| 769 | cta or "", |
| 770 | f"code {promo}" if promo else "", |
| 771 | landing, |
| 772 | ] |
| 773 | if part |
| 774 | ) |
| 775 | return _source_item( |
| 776 | item_id=str(item.get("id") or f"MA{index + 1}"), |
| 777 | source=source, |
| 778 | title=str(item.get("title") or "").strip() or body[:140] or f"Meta ad {index + 1}", |
| 779 | body=body, |
| 780 | url=str(item.get("url") or "").strip(), |
| 781 | author=advertiser, |
| 782 | container="Meta Ad Library", |
| 783 | published_at=item.get("date"), |
| 784 | date_confidence=_date_confidence(item, from_date, to_date, default="high"), |
| 785 | engagement={"variants": int(item.get("variants") or 1)}, |
| 786 | relevance_hint=0.8, |
| 787 | why_relevant=str(item.get("why_relevant") or "") or default_why, |
| 788 | snippet=" ".join(snippet_parts)[:400], |
| 789 | metadata={ |
| 790 | "grounding_exempt": True, |
| 791 | "advertiser": advertiser, |
| 792 | "page_id": str(item.get("page_id") or ""), |
| 793 | "is_active": bool(item.get("is_active")), |
| 794 | "ended_on": item.get("ended_on"), |
| 795 | "display_format": str(item.get("display_format") or ""), |
| 796 | "placements": placements, |
| 797 | "cta": cta, |
| 798 | "landing_url": landing, |
| 799 | "promo_code": promo, |
| 800 | "variants": int(item.get("variants") or 1), |
| 801 | "has_video": bool(item.get("has_video")), |
| 802 | "transcript_snippet": transcript[:400], |
| 803 | "ad_context": context, |
| 804 | }, |
| 805 | ) |
| 806 | |
| 807 | |
| 808 | def _normalize_trustpilot( |
| 809 | source: str, |
| 810 | item: dict[str, Any], |
| 811 | index: int, |
| 812 | from_date: str, |
| 813 | to_date: str, |
| 814 | ) -> schema.SourceItem: |
| 815 | """Normalizer for Trustpilot company sentiment. |
| 816 | |
| 817 | One item per company. The AI summary (already balanced positive/negative) |
| 818 | is the body. TrustScore and review count are engagement and metadata. |
| 819 | """ |
| 820 | title = str(item.get("title") or "").strip() |
| 821 | name = str(item.get("name") or "").strip() |
| 822 | summary = str(item.get("summary") or "").strip() |
| 823 | body = "\n\n".join(part for part in [title, summary] if part) |
| 824 | return _source_item( |
| 825 | item_id=str(item.get("id") or f"TP{index + 1}"), |
| 826 | source=source, |
| 827 | title=title |
| 828 | or (f"{name} on Trustpilot" if name else f"Trustpilot reviews {index + 1}"), |
| 829 | body=body, |
| 830 | url=str(item.get("url") or ""), |
| 831 | author=name or None, |
| 832 | container="Trustpilot", |
| 833 | published_at=item.get("date"), |
| 834 | date_confidence=_date_confidence(item, from_date, to_date, default="low"), |
| 835 | engagement=item.get("engagement") or {}, |
| 836 | relevance_hint=item.get("relevance", 0.6), |
| 837 | why_relevant=str(item.get("why_relevant") or ""), |
| 838 | snippet=summary[:400], |
| 839 | metadata={ |
| 840 | "name": name, |
| 841 | "trustScore": item.get("trustScore"), |
| 842 | "reviewCount": item.get("reviewCount"), |
| 843 | "aiSummary": summary, |
| 844 | }, |
| 845 | ) |
| 846 | |
| 847 | |
| 848 | def _normalize_amazon( |
| 849 | source: str, |
| 850 | item: dict[str, Any], |
| 851 | index: int, |
| 852 | from_date: str, |
| 853 | to_date: str, |
| 854 | ) -> schema.SourceItem: |
| 855 | """Normalizer for Amazon product-and-review signals. |
| 856 | |
| 857 | One item per product. The aggregate rating is current-state evidence, so |
| 858 | the item is stamped with today's date on the Trustpilot precedent -- a |
| 859 | live 4.4-star average is a fact about now, not about whenever the |
| 860 | product launched. |
| 861 | |
| 862 | Reviews arrive already in the shared score/excerpt comment shape (built |
| 863 | in the amazon adapter, deliberately not routed through _remap_comments, |
| 864 | which would strip the rating/date/verified keys this source needs), so |
| 865 | they pass straight through to metadata. |
| 866 | """ |
| 867 | name = str(item.get("name") or "").strip() |
| 868 | brand = str(item.get("brand") or "").strip() |
| 869 | top_comments = item.get("top_comments") or [] |
| 870 | comment_text = _join_comment_excerpts(top_comments, "excerpt") |
| 871 | rating = item.get("product_rating") if item.get("product_rating") is not None else item.get("rating") |
| 872 | ratings_total = item.get("product_rating_count") or item.get("num_ratings") or 0 |
| 873 | |
| 874 | headline = " ".join( |
| 875 | part for part in [ |
| 876 | f"{rating}/5" if rating is not None else "", |
| 877 | f"({ratings_total:,} ratings)" if ratings_total else "", |
| 878 | ] if part |
| 879 | ) |
| 880 | # The brand rides in its own field and is usually absent from the name, |
| 881 | # so prepend it -- unless the name already leads with it, which would |
| 882 | # otherwise read "Weber Weber Spirit E-325". |
| 883 | if brand and not name.lower().startswith(brand.lower()): |
| 884 | product_label = f"{brand} {name}".strip() |
| 885 | else: |
| 886 | product_label = name or brand |
| 887 | title = " - ".join(part for part in [product_label, headline] if part) |
| 888 | body = "\n".join(part for part in [title, comment_text] if part) |
| 889 | |
| 890 | return _source_item( |
| 891 | item_id=str(item.get("asin") or f"AMZ{index + 1}"), |
| 892 | source=source, |
| 893 | title=title or f"Amazon product {index + 1}", |
| 894 | body=body, |
| 895 | url=str(item.get("url") or ""), |
| 896 | author=brand or None, |
| 897 | container="Amazon", |
| 898 | published_at=item.get("date"), |
| 899 | date_confidence=_date_confidence(item, from_date, to_date, default="low"), |
| 900 | engagement=item.get("engagement") or {"ratings": ratings_total}, |
| 901 | relevance_hint=item.get("relevance", 0.6), |
| 902 | why_relevant=str(item.get("why_relevant") or ""), |
| 903 | snippet=comment_text[:400], |
| 904 | metadata={ |
| 905 | "asin": str(item.get("asin") or ""), |
| 906 | "name": name, |
| 907 | "short_name": item.get("short_name") or "", |
| 908 | "brand": brand, |
| 909 | "rating": item.get("rating"), |
| 910 | "num_ratings": item.get("num_ratings") or 0, |
| 911 | "price": item.get("price"), |
| 912 | "currency": item.get("currency") or "", |
| 913 | "badge": item.get("badge") or "", |
| 914 | # Recorded, never used as a filter: the flag's distribution |
| 915 | # swings with keyword phrasing, so filtering can blank the lane. |
| 916 | "sponsored": bool(item.get("sponsored")), |
| 917 | "top_comments": top_comments, |
| 918 | "product_rating": item.get("product_rating"), |
| 919 | "product_rating_count": item.get("product_rating_count") or 0, |
| 920 | "star_distribution": item.get("star_distribution") or {}, |
| 921 | # Relevant by construction: the adapter already gated products |
| 922 | # against the model-supplied keyword, and review text rarely |
| 923 | # names the product (KTD8). |
| 924 | "grounding_exempt": True, |
| 925 | }, |
| 926 | ) |
| 927 | |
| 928 | |
| 929 | def _normalize_polymarket( |
| 930 | source: str, |
| 931 | item: dict[str, Any], |
| 932 | index: int, |
| 933 | from_date: str, |
| 934 | to_date: str, |
| 935 | ) -> schema.SourceItem: |
| 936 | title = str(item.get("title") or "").strip() |
| 937 | question = str(item.get("question") or "").strip() |
| 938 | engagement = { |
| 939 | "volume": item.get("volume1mo") or item.get("volume24hr") or 0, |
| 940 | "liquidity": item.get("liquidity") or 0, |
| 941 | } |
| 942 | return _source_item( |
| 943 | item_id=str(item.get("event_id") or item.get("id") or f"PM{index + 1}"), |
| 944 | source=source, |
| 945 | title=title or question or f"Polymarket event {index + 1}", |
| 946 | body="\n".join( |
| 947 | part |
| 948 | for part in [title, question, str(item.get("price_movement") or "")] |
| 949 | if part |
| 950 | ), |
| 951 | url=str(item.get("url") or ""), |
| 952 | author=None, |
| 953 | container="Polymarket", |
| 954 | published_at=item.get("date"), |
| 955 | date_confidence=_date_confidence(item, from_date, to_date, default="high"), |
| 956 | engagement=engagement, |
| 957 | relevance_hint=item.get("relevance", 0.5), |
| 958 | why_relevant=str(item.get("why_relevant") or ""), |
| 959 | snippet=str(item.get("price_movement") or ""), |
| 960 | metadata={ |
| 961 | "event_id": item.get("event_id"), |
| 962 | "question": question, |
| 963 | "end_date": item.get("end_date"), |
| 964 | "outcome_prices": item.get("outcome_prices") or [], |
| 965 | "outcomes_remaining": item.get("outcomes_remaining"), |
| 966 | }, |
| 967 | ) |
| 968 | |
| 969 | |
| 970 | def _normalize_github( |
| 971 | source: str, |
| 972 | item: dict[str, Any], |
| 973 | index: int, |
| 974 | from_date: str, |
| 975 | to_date: str, |
| 976 | ) -> schema.SourceItem: |
| 977 | title = str(item.get("title") or "").strip() |
| 978 | snippet_text = str(item.get("snippet") or "").strip() |
| 979 | top_comments = item.get("metadata", {}).get("top_comments") or [] |
| 980 | comment_text = _join_comment_excerpts(top_comments, "excerpt") |
| 981 | body = "\n".join(part for part in [title, snippet_text, comment_text] if part) |
| 982 | metadata = item.get("metadata") or {} |
| 983 | return _source_item( |
| 984 | item_id=str(item.get("id") or f"GH{index + 1}"), |
| 985 | source=source, |
| 986 | title=title or f"GitHub item {index + 1}", |
| 987 | body=body, |
| 988 | url=str(item.get("url") or ""), |
| 989 | author=str(item.get("author") or ""), |
| 990 | container=str(item.get("container") or ""), |
| 991 | published_at=item.get("date"), |
| 992 | date_confidence=_date_confidence(item, from_date, to_date, default="high"), |
| 993 | engagement=item.get("engagement") or {}, |
| 994 | relevance_hint=item.get("relevance", 0.5), |
| 995 | why_relevant=str(item.get("why_relevant") or ""), |
| 996 | snippet=comment_text or snippet_text[:400], |
| 997 | metadata={ |
| 998 | "top_comments": top_comments, |
| 999 | "labels": metadata.get("labels") or [], |
| 1000 | "state": metadata.get("state", ""), |
| 1001 | "is_pr": metadata.get("is_pr", False), |
| 1002 | }, |
| 1003 | ) |
| 1004 | |
| 1005 | |
| 1006 | def _normalize_grounding( |
| 1007 | source: str, |
| 1008 | item: dict[str, Any], |
| 1009 | index: int, |
| 1010 | from_date: str, |
| 1011 | to_date: str, |
| 1012 | ) -> schema.SourceItem: |
| 1013 | title = str(item.get("title") or "").strip() |
| 1014 | snippet = str(item.get("snippet") or "").strip() |
| 1015 | url = str(item.get("url") or "").strip() |
| 1016 | return _source_item( |
| 1017 | item_id=str(item.get("id") or f"W{index + 1}"), |
| 1018 | source=source, |
| 1019 | title=title or _domain_from_url(url) or f"Web result {index + 1}", |
| 1020 | body="\n".join(part for part in [title, snippet] if part), |
| 1021 | url=url, |
| 1022 | author=None, |
| 1023 | container=str(item.get("source_domain") or _domain_from_url(url) or ""), |
| 1024 | published_at=item.get("date"), |
| 1025 | date_confidence=_date_confidence(item, from_date, to_date), |
| 1026 | engagement=item.get("engagement") or {}, |
| 1027 | relevance_hint=item.get("relevance", 0.5), |
| 1028 | why_relevant=str(item.get("why_relevant") or ""), |
| 1029 | snippet=snippet, |
| 1030 | metadata=item.get("metadata") or {}, |
| 1031 | ) |
| 1032 | |
| 1033 | |
| 1034 | def _normalize_linkedin( |
| 1035 | source: str, |
| 1036 | item: dict[str, Any], |
| 1037 | index: int, |
| 1038 | from_date: str, |
| 1039 | to_date: str, |
| 1040 | ) -> schema.SourceItem: |
| 1041 | """Normalizer for LinkedIn posts and articles via ScrapeCreators. |
| 1042 | |
| 1043 | A LinkedIn article (Pulse long-form, under a /pulse/ URL) is treated as |
| 1044 | high signal: it ranks above ordinary posts. Detection is belt-and-suspenders |
| 1045 | — honor the parser's `is_article` flag, and re-derive from the URL so an |
| 1046 | article still ranks high even if the flag wasn't set upstream. |
| 1047 | """ |
| 1048 | text = str(item.get("text") or "").strip() |
| 1049 | author = str(item.get("author") or "").strip() |
| 1050 | url = str(item.get("url") or "").strip() |
| 1051 | is_article = bool(item.get("is_article")) or "/pulse/" in url.lower() |
| 1052 | kind = "article" if is_article else "post" |
| 1053 | default_relevance = 0.9 if is_article else 0.5 |
| 1054 | return _source_item( |
| 1055 | item_id=str(item.get("id") or f"LI{index + 1}"), |
| 1056 | source=source, |
| 1057 | title=text[:140] or f"LinkedIn {kind} {index + 1}", |
| 1058 | body=text, |
| 1059 | url=url, |
| 1060 | author=author, |
| 1061 | container="LinkedIn Article" if is_article else "LinkedIn", |
| 1062 | published_at=item.get("date"), |
| 1063 | date_confidence=_date_confidence(item, from_date, to_date, default="medium"), |
| 1064 | engagement=item.get("engagement") or {}, |
| 1065 | relevance_hint=item.get("relevance", default_relevance), |
| 1066 | why_relevant=str(item.get("why_relevant") or ""), |
| 1067 | snippet=text[:200], |
| 1068 | metadata={"author_display": author, "is_article": is_article}, |
| 1069 | ) |
| 1070 |