Address review feedback: fix tiebreaker map, error handling, regex patterns
- Add BlueskyItem/TruthSocialItem to _ITEM_SOURCE_MAP (wrong tiebreaker) - Add bluesky/truthsocial to _DEFAULT_TIEBREAKER - Log HTTPError in select_openai_model instead of silent fallback - Remove overly broad 'or.*for' from comparison regex (false positives) - Remove bare 'will' from prediction regex (misclassifies feature queries) - Narrow brave_search except clauses to ValueError/TypeError - Fix stale comments: pricing table, docstrings, penalty descriptions
This commit is contained in:
Jeffrey Sperling
@@ -1,7 +1,7 @@
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"""Brave Search web search for last30days skill.
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Uses the Brave Search API as a web search backend.
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Requires a paid Brave Search subscription (free tier eliminated Feb 2026).
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Requires a paid Brave Search subscription.
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Two modes:
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- Standard: /res/v1/web/search — returns URLs + snippets (default)
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@@ -191,7 +191,7 @@ def _normalize_results(
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continue
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if domain.startswith("www."):
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domain = domain[4:]
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except Exception:
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except (ValueError, TypeError):
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domain = ""
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title = _clean_html(str(result.get("title", "")).strip())
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@@ -247,7 +247,7 @@ def _normalize_llm_context(response: Dict[str, Any]) -> List[Dict[str, Any]]:
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continue
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if domain.startswith("www."):
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domain = domain[4:]
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except Exception:
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except (ValueError, TypeError):
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domain = ""
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title = str(result.get("title", "")).strip()
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+8
-11
@@ -6,19 +6,12 @@ reasoning-heavy or creative work — mini models handle it equally well
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at ~3-5x lower cost. We prefer the newest-generation mini model, falling
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back to mainline only when mini isn't available.
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OpenAI cost per Reddit search call (web_search tool + JSON output):
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gpt-4.1-mini: ~$0.014 (fixed 8K search token block)
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gpt-5-mini: ~$0.015
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gpt-4.1: ~$0.044
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gpt-5.2: ~$0.043
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gpt-4o: ~$0.053
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xAI: grok-4-1-fast reasoning vs non-reasoning have identical token
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pricing ($0.20/1M in, $0.50/1M out). Non-reasoning skips the thinking
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phase, saving latency and reasoning token output costs.
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xAI non-reasoning variant preferred: same pricing as reasoning, but
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faster (skips thinking phase, saves reasoning token output costs).
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"""
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import re
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import sys
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from typing import Dict, List, Optional, Tuple
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from . import cache, http, env
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@@ -119,7 +112,11 @@ def select_openai_model(
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headers = {"Authorization": f"Bearer {api_key}"}
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response = http.get(OPENAI_MODELS_URL, headers=headers)
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models = response.get("data", [])
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except http.HTTPError:
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except http.HTTPError as e:
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sys.stderr.write(f"[Models] Failed to fetch OpenAI models: {e}")
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if hasattr(e, 'status_code') and e.status_code in (401, 403):
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sys.stderr.write(" — API key may be invalid or lack permissions")
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sys.stderr.write(f", using fallback {OPENAI_FALLBACK_MODELS[0]}\n")
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return OPENAI_FALLBACK_MODELS[0]
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candidates = [m for m in models if is_search_capable_model(m.get("id", ""))]
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@@ -19,21 +19,21 @@ _HOWTO_PATTERNS = re.compile(
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r"\b(how to|tutorial|step by step|setup|install|configure|deploy|migrate|implement|build a|create a)\b", re.I
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)
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_COMPARISON_PATTERNS = re.compile(
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r"\b(vs\.?|versus|compared to|comparison|better than|or\b.*\bfor\b|difference between|switch from)\b", re.I
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r"\b(vs\.?|versus|compared to|comparison|better than|difference between|switch from)\b", re.I
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)
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_BREAKING_PATTERNS = re.compile(
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r"\b(latest|breaking|just announced|launched|released|new|update|news|happened|today|this week)\b", re.I
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)
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_PREDICTION_PATTERNS = re.compile(
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r"\b(will|predict|forecast|odds|chance|probability|election|outcome|bet on|market for)\b", re.I
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r"\b(predict|forecast|odds|chance|probability|election|outcome|bet on|market for)\b", re.I
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)
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def detect_query_type(topic: str) -> QueryType:
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"""Classify a query into a type using pattern matching.
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Returns the most specific match. When multiple patterns match,
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priority order is: comparison > how_to > product > opinion > prediction > concept > breaking_news.
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Returns the first match in priority order:
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comparison > how_to > product > opinion > prediction > concept > breaking_news.
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"""
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# Most specific first
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if _COMPARISON_PATTERNS.search(topic):
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@@ -69,6 +69,7 @@ SOURCE_TIERS = {
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}
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# WebSearch penalty adjustment by query type.
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# Points subtracted from websearch score (0-100 scale). 0 = no penalty, 15 = full penalty.
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# Concept/how_to queries benefit from authoritative web sources.
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WEBSEARCH_PENALTY_BY_TYPE = {
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"product": 15, # default: social discussion > blog posts
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+11
-6
@@ -11,10 +11,12 @@ WEIGHT_RELEVANCE = 0.45
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WEIGHT_RECENCY = 0.25
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WEIGHT_ENGAGEMENT = 0.30
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# WebSearch weights (no engagement, reweighted to 100%)
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# WebSearch weights (no engagement data available)
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WEBSEARCH_WEIGHT_RELEVANCE = 0.55
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WEBSEARCH_WEIGHT_RECENCY = 0.45
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WEBSEARCH_SOURCE_PENALTY = 15 # Points deducted for lacking engagement
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# Default web search penalty (fallback when query_type is not provided).
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# Per-type penalties in query_type.WEBSEARCH_PENALTY_BY_TYPE.
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WEBSEARCH_SOURCE_PENALTY = 15
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# WebSearch date confidence adjustments
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WEBSEARCH_VERIFIED_BONUS = 10 # Bonus for URL-verified recent date (high confidence)
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@@ -706,16 +708,19 @@ _ITEM_SOURCE_MAP = {
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schema.TikTokItem: "tiktok",
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schema.InstagramItem: "instagram",
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schema.HackerNewsItem: "hn",
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schema.BlueskyItem: "bluesky",
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schema.TruthSocialItem: "truthsocial",
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schema.PolymarketItem: "polymarket",
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}
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_DEFAULT_TIEBREAKER = {"reddit": 0, "x": 1, "youtube": 2, "tiktok": 3, "instagram": 4, "hn": 5, "polymarket": 6, "web": 7}
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_DEFAULT_TIEBREAKER = {"reddit": 0, "x": 1, "youtube": 2, "tiktok": 3, "instagram": 4, "hn": 5, "bluesky": 6, "truthsocial": 7, "polymarket": 8, "web": 9}
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def sort_items(items: List[Union[schema.RedditItem, schema.XItem, schema.WebSearchItem, schema.YouTubeItem, schema.TikTokItem, schema.InstagramItem, schema.HackerNewsItem, schema.PolymarketItem]], query_type: QueryType = None) -> List:
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"""Sort items by score (descending), then date, then source priority.
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"""Sort items by score (descending), then date, then source tiebreaker.
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Source priority varies by query type: YouTube ranks first for how_to,
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X ranks first for breaking_news, Polymarket ranks first for prediction.
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Tiebreaker (tertiary sort key, after score and date): source priority
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varies by query type. YouTube ranks first for how_to, X ranks first
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for breaking_news, Polymarket ranks first for prediction.
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Args:
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items: List of items to sort
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