72 lines
2.2 KiB
Python
72 lines
2.2 KiB
Python
"""fetch_hackernews_search — busca en Hacker News via la API Algolia publica.
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Funcion impura: hace peticiones HTTP a hn.algolia.com (sin auth ni anti-bot).
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Normaliza cada hit a un shape comun de market intelligence.
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"""
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import requests
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_TIMEOUT = 15
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def _parse_hits(hits: list, query: str) -> list[dict]:
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"""Normaliza la lista hits de la respuesta de Algolia al shape comun."""
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rows = []
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for hit in hits:
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if not isinstance(hit, dict):
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continue
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object_id = str(hit.get("objectID", ""))
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external_url = hit.get("url")
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url = external_url if external_url else (
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f"https://news.ycombinator.com/item?id={object_id}"
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)
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body = hit.get("story_text") or hit.get("comment_text") or ""
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rows.append({
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"source": "hackernews",
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"platform_id": object_id,
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"title": hit.get("title", "") or "",
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"body": body,
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"url": url,
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"author": hit.get("author", "") or "",
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"channel": "hn",
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"created_utc": float(hit.get("created_at_i") or 0.0),
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"platform_score": int(hit.get("points") or 0),
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"query": query,
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})
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return rows
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def fetch_hackernews_search(
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query: str,
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limit: int = 50,
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tags: str = "story",
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) -> list[dict]:
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"""Busca en Hacker News usando la API Algolia publica (sin autenticacion).
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Args:
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query: Termino de busqueda.
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limit: Maximo de resultados (hitsPerPage de Algolia).
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tags: Filtro de tipo de item: "story" (default), "comment",
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"story,comment", "show_hn", "ask_hn", etc.
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Returns:
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Lista de dicts normalizados (puede ser []). Cada dict tiene las claves:
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source, platform_id, title, body, url, author, channel, created_utc,
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platform_score, query.
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"""
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url = "https://hn.algolia.com/api/v1/search"
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params = {
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"query": query,
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"tags": tags,
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"hitsPerPage": limit,
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}
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try:
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resp = requests.get(url, params=params, timeout=_TIMEOUT)
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resp.raise_for_status()
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payload = resp.json()
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hits = payload.get("hits", []) if isinstance(payload, dict) else []
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return _parse_hits(hits, query)
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except Exception:
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return []
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