Merge pull request #388 from bradferguson/fix/sc-youtube-and-hn-tokenization

fix(sources): unblock SC YouTube + multi-token HN searches
This commit is contained in:
Trevin Chow
2026-05-16 19:41:12 -07:00
committed by GitHub
3 changed files with 94 additions and 22 deletions
+54 -17
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@@ -88,17 +88,26 @@ def search_hackernews(
# Use extracted core subject instead of raw topic for cleaner Algolia matching
core = extract_core_subject(topic)
_log(f"Searching for '{core}' (raw: '{topic}', since {from_date}, count={count})")
# Hyphens and commas tokenize awkwardly in Algolia; flatten them so themed
# queries like "ts-bun-node" or "claude, personal agents" become plain words.
core_flat = _flatten_query_for_algolia(core)
_log(f"Searching for '{core_flat}' (raw: '{topic}', since {from_date}, count={count})")
# Use relevance-sorted search with minimum engagement filter.
# NOTE: restrictSearchableAttributes=title omitted intentionally — it would
# miss Ask HN/Show HN threads where the topic appears in the body.
params = {
"query": core,
"query": core_flat,
"tags": "story",
"numericFilters": f"created_at_i>{from_ts},created_at_i<{to_ts},points>2",
"hitsPerPage": str(count),
}
# Algolia defaults to AND across query tokens, so a 4-5 word theme query
# matches no stories. Mark all-but-the-first token as optional so Algolia
# ranks by how many tokens match instead of requiring every one.
tokens = core_flat.split()
if len(tokens) > 1:
params["optionalWords"] = " ".join(tokens[1:])
from urllib.parse import urlencode
url = f"{ALGOLIA_SEARCH_URL}?{urlencode(params)}"
@@ -117,28 +126,56 @@ def search_hackernews(
return response
def _title_matches_query(title: str, query: str, author: str = "") -> bool:
"""Check if the query term appears in the title content, not just an HN prefix or author.
_WORD_BOUNDARY_RE_CACHE: Dict[str, "re.Pattern[str]"] = {}
Returns True if the query (or any multi-word token) appears in the title
after stripping "Tell HN:", "Show HN:", "Ask HN:", "Launch HN:" prefixes
and ignoring the author name. Returns True when query is empty (no filter).
def _flatten_query_for_algolia(text: str) -> str:
"""Normalise query for Algolia + post-filter comparison.
Multi-keyword theme queries frequently contain commas (delimiters) or
hyphens (compound terms like ``ts-bun-node``); both tokenize awkwardly.
Flatten them to spaces and collapse runs of whitespace so the search
parameter and the post-filter operate on the same shape.
"""
return " ".join(text.replace(",", " ").replace("-", " ").split())
def _title_matches_query(title: str, query: str, author: str = "") -> bool:
"""Check if any query token appears as a whole word in the title.
Returns True when the query is empty (no filter), or when at least one
query token matches as a whole word in the title after stripping
"Tell HN:", "Show HN:", "Ask HN:", "Launch HN:" prefixes.
We previously required *every* token to appear (all-words), which killed
every Algolia hit on multi-keyword themes like "claude, personal agents,
agentic infra" because real HN titles never contain all five tokens
verbatim. Relaxing to any-word matches Algolia's `optionalWords` behaviour
in `search_hackernews`. Token-overlap relevance scoring at parse time
demotes hits where only one weak token matched, so the loosened gate
won't surface noise to the top of the ranking.
Word-boundary matching (rather than naive substring) prevents short
tokens like ``ai`` or ``ts`` from matching unrelated words like
``email`` or ``artists``.
"""
if not query:
return True
stripped = _HN_PREFIXES.sub("", title).strip()
# Also check that the match isn't solely in the author's username
check_text = stripped.lower()
query_lower = query.lower()
# Check each word of the query independently; all must appear somewhere
# in the stripped title (not just the prefix).
query_words = query_lower.split()
for word in query_words:
if word in check_text:
continue
# Word not found in stripped title — reject
return False
# Normalise the query the same way search_hackernews does so post-filter
# tokens line up with what Algolia actually saw.
query_words = [w for w in _flatten_query_for_algolia(query.lower()).split() if w]
if not query_words:
return True
for word in query_words:
pattern = _WORD_BOUNDARY_RE_CACHE.get(word)
if pattern is None:
pattern = re.compile(rf"\b{re.escape(word)}\b")
_WORD_BOUNDARY_RE_CACHE[word] = pattern
if pattern.search(check_text):
return True
return False
def parse_hackernews_response(response: Dict[str, Any], query: str = "") -> List[Dict[str, Any]]:
+4 -1
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@@ -866,9 +866,12 @@ def _sc_youtube_search(keyword: str, token: str) -> List[Dict[str, Any]]:
List of raw video dicts from the API.
"""
try:
# SC's /v1/youtube/search rejects ?keyword= with HTTP 400; the canonical
# parameter for that endpoint is `query`. Other SC endpoints use their
# own per-endpoint param names so this was the lone outlier.
data = http.get(
f"{SCRAPECREATORS_YT_BASE}/search",
params={"keyword": keyword},
params={"query": keyword},
headers=http.scrapecreators_headers(token),
timeout=30,
retries=2,
+35 -3
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@@ -160,12 +160,44 @@ def test_title_matches_query_empty_query():
def test_title_matches_query_partial_match():
"""Test that all query words must match."""
"""Any-word matching: at least one query token in title is enough.
Previously required *all* tokens, which killed every hit on multi-keyword
theme queries like 'claude, personal agents, agentic infra' since no real
HN title contains all 5 tokens verbatim. Token-overlap relevance at parse
time still demotes weak matches, so the loosened gate is safe.
"""
title = "New AI framework"
query = "AI blockchain"
# "blockchain" is not in title, so should fail
assert hackernews._title_matches_query(title, query) is False
# "AI" matches as a whole word, even though "blockchain" doesn't appear
assert hackernews._title_matches_query(title, query) is True
def test_title_matches_query_no_token_in_title():
"""If no query token appears in the title at all, reject."""
assert hackernews._title_matches_query("New rust compiler", "AI blockchain") is False
def test_title_matches_query_word_boundary_not_substring():
"""Short tokens must match on word boundaries, not as substrings.
Without word-boundary matching, 'ai' would falsely match 'email',
'rail', 'artists', etc.
"""
# 'ai' as a substring of 'email' must not match
assert hackernews._title_matches_query("New email service", "ai blockchain") is False
# 'ai' as a whole word does match
assert hackernews._title_matches_query("Cool AI tool launched", "ai blockchain") is True
def test_title_matches_query_flattens_hyphens_and_commas():
"""Query tokens split on hyphens/commas the same way search_hackernews
flattens them, so the post-filter stays aligned with what Algolia saw."""
# query 'ts-bun-node' flattens to ['ts', 'bun', 'node']; title contains 'bun'
assert hackernews._title_matches_query("Bun 1.2 released", "ts-bun-node") is True
# query 'rust, go, zig' flattens; title contains 'go'
assert hackernews._title_matches_query("Go 1.24 generics update", "rust, go, zig") is True
# === Tests for search_hackernews() ===