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Replace hardcoded 0.7 relevance with computed token-overlap scores

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- bird_x: parse_bird_response now accepts query param and computes
  token_overlap_relevance against tweet text
- reddit: _normalize_post computes relevance from query vs title+selftext
- hackernews: blends 60% Algolia rank + 40% token overlap + engagement

This makes the 45%-weight relevance factor in score.py actually
differentiate results instead of being a constant.
This commit is contained in:
Jeffrey Sperling
2026-03-11 15:21:52 -07:00
parent 38caae3288
commit c5be117701
4 changed files with 36 additions and 18 deletions
Download Patch File Download Diff File Expand all files Collapse all files
scripts/last30days.py
+2 -2
View File
@@ -353,7 +353,7 @@ def _search_x(
raw_response = {"error": str(e)}
x_error = f"{type(e).__name__}: {e}"
x_items = bird_x.parse_bird_response(raw_response or {})
x_items = bird_x.parse_bird_response(raw_response or {}, query=topic)
# Check for error in response (Bird returns list on success, dict on error)
if raw_response and isinstance(raw_response, dict) and raw_response.get("error") and not x_error:
@@ -508,7 +508,7 @@ def _search_hackernews(
except Exception as e:
return [], f"{type(e).__name__}: {e}"
hn_items = hackernews.parse_hackernews_response(response)
hn_items = hackernews.parse_hackernews_response(response, query=topic)
if response.get("error"):
hn_error = response["error"]
scripts/lib/bird_x.py
+8 -5
View File
@@ -14,6 +14,8 @@ from pathlib import Path
from datetime import datetime
from typing import Any, Dict, List, Optional, Tuple
from .relevance import token_overlap_relevance as _compute_relevance
# Path to the vendored bird-search wrapper
_BIRD_SEARCH_MJS = Path(__file__).parent / "vendor" / "bird-search" / "bird-search.mjs"
@@ -233,7 +235,7 @@ def search_x(
response = _run_bird_search(query, count, timeout)
# Check if we got results
items = parse_bird_response(response)
items = parse_bird_response(response, query=core_topic)
# Retry with fewer keywords if 0 results and query has 3+ words
core_words = core_topic.split()
@@ -242,7 +244,7 @@ def search_x(
_log(f"0 results for '{core_topic}', retrying with '{shorter}'")
query = f"{shorter} since:{from_date}"
response = _run_bird_search(query, count, timeout)
items = parse_bird_response(response)
items = parse_bird_response(response, query=core_topic)
# Last-chance retry: use strongest remaining token (often the product name)
if not items and core_words:
@@ -329,7 +331,7 @@ def search_handles(
continue
response = json.loads(output)
items = parse_bird_response(response)
items = parse_bird_response(response, query=core_topic)
all_items.extend(items)
except json.JSONDecodeError:
@@ -340,11 +342,12 @@ def search_handles(
return all_items
def parse_bird_response(response: Dict[str, Any]) -> List[Dict[str, Any]]:
def parse_bird_response(response: Dict[str, Any], query: str = "") -> List[Dict[str, Any]]:
"""Parse Bird response to match xai_x output format.
Args:
response: Raw Bird JSON response
query: Original search query for relevance scoring
Returns:
List of normalized item dicts matching xai_x.parse_x_response() format.
@@ -422,7 +425,7 @@ def parse_bird_response(response: Dict[str, Any]) -> List[Dict[str, Any]]:
"date": date,
"engagement": engagement if any(v is not None for v in engagement.values()) else None,
"why_relevant": "", # Bird doesn't provide relevance explanations
"relevance": 0.7, # Default relevance, let score.py re-rank
"relevance": _compute_relevance(query, str(tweet.get("text", ""))) if query else 0.7,
}
items.append(item)
scripts/lib/hackernews.py
+12 -4
View File
@@ -12,6 +12,7 @@ from concurrent.futures import ThreadPoolExecutor, as_completed
from typing import Any, Dict, List, Optional
from . import http
from .relevance import token_overlap_relevance
ALGOLIA_SEARCH_URL = "https://hn.algolia.com/api/v1/search"
ALGOLIA_SEARCH_BY_DATE_URL = "https://hn.algolia.com/api/v1/search_by_date"
@@ -111,9 +112,13 @@ def search_hackernews(
return response
def parse_hackernews_response(response: Dict[str, Any]) -> List[Dict[str, Any]]:
def parse_hackernews_response(response: Dict[str, Any], query: str = "") -> List[Dict[str, Any]]:
"""Parse Algolia response into normalized item dicts.
Args:
response: Algolia search response
query: Original search query for token-overlap relevance scoring
Returns:
List of item dicts ready for normalization.
"""
@@ -134,11 +139,14 @@ def parse_hackernews_response(response: Dict[str, Any]) -> List[Dict[str, Any]]:
article_url = hit.get("url") or ""
hn_url = f"https://news.ycombinator.com/item?id={object_id}"
# Relevance: Algolia rank position gives a base, engagement boosts it
# Position 0 = most relevant from Algolia
# Relevance: blend Algolia rank with token-overlap content matching
rank_score = max(0.3, 1.0 - (i * 0.02)) # 1.0 -> 0.3 over 35 items
engagement_boost = min(0.2, math.log1p(points) / 40)
relevance = min(1.0, rank_score * 0.7 + engagement_boost + 0.1)
if query:
content_score = token_overlap_relevance(query, hit.get("title", ""))
relevance = min(1.0, 0.6 * rank_score + 0.4 * content_score + engagement_boost)
else:
relevance = min(1.0, rank_score * 0.7 + engagement_boost + 0.1)
items.append({
"object_id": object_id,
scripts/lib/reddit.py
+14 -7
View File
@@ -49,6 +49,7 @@ DEPTH_CONFIG = {
}
from .query import extract_core_subject as _query_extract
from .relevance import token_overlap_relevance
# Reddit-specific noise words (preserves original smaller set)
NOISE_WORDS = frozenset({
@@ -184,7 +185,7 @@ def _parse_date(created_utc) -> Optional[str]:
return None
def _normalize_post(post: Dict[str, Any], idx: int, source_label: str = "global") -> Dict[str, Any]:
def _normalize_post(post: Dict[str, Any], idx: int, source_label: str = "global", query: str = "") -> Dict[str, Any]:
"""Normalize a ScrapeCreators Reddit post to our internal format."""
permalink = post.get("permalink", "")
url = f"https://www.reddit.com{permalink}" if permalink else post.get("url", "")
@@ -193,10 +194,16 @@ def _normalize_post(post: Dict[str, Any], idx: int, source_label: str = "global"
if url and "reddit.com" not in url:
url = ""
title = str(post.get("title", "")).strip()
selftext = str(post.get("selftext", ""))
# Compute relevance from query-to-content overlap (or default 0.7)
relevance = token_overlap_relevance(query, title + " " + selftext) if query else 0.7
return {
"id": f"R{idx}",
"reddit_id": post.get("id", ""),
"title": str(post.get("title", "")).strip(),
"title": title,
"url": url,
"subreddit": str(post.get("subreddit", "")).strip(),
"date": _parse_date(post.get("created_utc")),
@@ -205,7 +212,7 @@ def _normalize_post(post: Dict[str, Any], idx: int, source_label: str = "global"
"num_comments": post.get("num_comments", 0),
"upvote_ratio": post.get("upvote_ratio"),
},
"relevance": 0.7,
"relevance": relevance,
"why_relevant": f"Reddit {source_label} search",
"selftext": str(post.get("selftext", ""))[:500],
}
@@ -416,23 +423,23 @@ def search_reddit(
_log(f" -> {len(posts)} results")
all_raw_posts.extend(posts)
# Normalize all posts
# Normalize all posts (with query for relevance scoring)
core = _extract_core_subject(topic)
all_items = []
for i, post in enumerate(all_raw_posts):
item = _normalize_post(post, i + 1, "global")
item = _normalize_post(post, i + 1, "global", query=core)
all_items.append(item)
# === Phase 3: Subreddit Discovery + Targeted Search ===
discovered_subs = discover_subreddits(all_raw_posts, topic=topic, max_subs=config["subreddit_searches"])
_log(f"Discovered subreddits: {discovered_subs}")
core = _extract_core_subject(topic)
for sub in discovered_subs[:config["subreddit_searches"]]:
_log(f"Subreddit search: r/{sub} for '{core}'")
sub_posts = _subreddit_search(sub, core, token, sort="relevance", timeframe=timeframe)
_log(f" -> {len(sub_posts)} results from r/{sub}")
for j, post in enumerate(sub_posts):
item = _normalize_post(post, len(all_items) + j + 1, f"r/{sub}")
item = _normalize_post(post, len(all_items) + j + 1, f"r/{sub}", query=core)
all_items.append(item)
# === Phase 4: Deduplicate ===