feat: replace OpenAI Reddit search with ScrapeCreators API
- New scripts/lib/reddit.py: multi-query expansion, global search, subreddit discovery, targeted subreddit search, comment enrichment - 68 results in 17s vs ~15 results in 60-90s (OpenAI) - Cost: ~$0.02/search vs $0.03-0.10 (15-50x cheaper) - Real engagement data (score, comments, dates) from API - No more 429 rate limits on comment enrichment - Falls back to OpenAI if SCRAPECREATORS_API_KEY missing - Registered as last30daysbeta for parallel local testing Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Matt Van Horn
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"""Reddit search via ScrapeCreators API for /last30days.
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Uses ScrapeCreators REST API to search Reddit globally, discover relevant
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subreddits, run targeted subreddit searches, and fetch comment trees.
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Replaces openai_reddit.py as the primary Reddit search backend.
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Falls back to openai_reddit.py if SCRAPECREATORS_API_KEY is missing but
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OPENAI_API_KEY is present.
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Requires SCRAPECREATORS_API_KEY in config (same key as TikTok + Instagram).
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API docs: https://scrapecreators.com/docs
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"""
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import re
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import sys
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from collections import Counter
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from datetime import datetime, timezone
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from typing import Any, Dict, List, Optional, Set
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try:
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import requests as _requests
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except ImportError:
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_requests = None
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from . import http
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SCRAPECREATORS_BASE = "https://api.scrapecreators.com/v1/reddit"
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# Depth configurations: how many API calls per phase
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DEPTH_CONFIG = {
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"quick": {
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"global_searches": 1,
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"subreddit_searches": 2,
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"comment_enrichments": 3,
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"timeframe": "week",
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},
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"default": {
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"global_searches": 2,
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"subreddit_searches": 3,
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"comment_enrichments": 5,
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"timeframe": "month",
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},
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"deep": {
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"global_searches": 3,
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"subreddit_searches": 5,
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"comment_enrichments": 8,
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"timeframe": "month",
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},
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}
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# Stopwords for query extraction
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NOISE_WORDS = frozenset({
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'best', 'top', 'good', 'great', 'awesome', 'killer',
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'latest', 'new', 'news', 'update', 'updates',
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'trending', 'hottest', 'popular',
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'practices', 'features', 'tips',
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'recommendations', 'advice',
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'prompt', 'prompts', 'prompting',
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'methods', 'strategies', 'approaches',
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'how', 'to', 'the', 'a', 'an', 'for', 'with',
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'of', 'in', 'on', 'is', 'are', 'what', 'which',
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'guide', 'tutorial', 'using',
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})
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def _log(msg: str):
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"""Log to stderr."""
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sys.stderr.write(f"[Reddit/SC] {msg}\n")
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sys.stderr.flush()
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def _sc_headers(token: str) -> Dict[str, str]:
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"""Build ScrapeCreators request headers."""
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return {
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"x-api-key": token,
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"Content-Type": "application/json",
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}
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def _extract_core_subject(topic: str) -> str:
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"""Extract core subject from verbose query.
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Strips meta/research words to keep only the core product/concept name.
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"""
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text = topic.lower().strip()
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# Strip multi-word prefixes
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prefixes = [
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'what are the best', 'what is the best', 'what are the latest',
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'what are people saying about', 'what do people think about',
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'how do i use', 'how to use', 'how to',
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'what are', 'what is', 'tips for', 'best practices for',
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]
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for p in prefixes:
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if text.startswith(p + ' '):
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text = text[len(p):].strip()
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words = text.split()
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filtered = [w for w in words if w not in NOISE_WORDS]
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result = ' '.join(filtered) if filtered else text
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return result.rstrip('?!.')
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def expand_reddit_queries(topic: str, depth: str) -> List[str]:
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"""Generate multiple Reddit search queries from a topic.
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Uses local logic (no LLM call needed):
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1. Extract core subject (strip noise words)
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2. Include original topic if different from core
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3. For default/deep: add casual/review variant
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4. For deep: add problem/issues variant
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Returns 1-4 query strings depending on depth.
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"""
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core = _extract_core_subject(topic)
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queries = [core]
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# Broader variant: include more context from original topic
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original_clean = topic.strip().rstrip('?!.')
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if core.lower() != original_clean.lower() and len(original_clean.split()) <= 8:
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queries.append(original_clean)
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if depth in ("default", "deep"):
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queries.append(f"{core} worth it OR thoughts OR review")
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if depth == "deep":
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queries.append(f"{core} issues OR problems OR bug OR broken")
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return queries
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def discover_subreddits(results: List[Dict[str, Any]], max_subs: int = 5) -> List[str]:
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"""Extract top subreddits from global search results by frequency.
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Args:
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results: List of post dicts from global search
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max_subs: Maximum subreddits to return
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Returns:
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Top subreddit names sorted by post count
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"""
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counts = Counter()
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for post in results:
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sub = post.get("subreddit", "")
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if sub:
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counts[sub] += 1
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return [sub for sub, _ in counts.most_common(max_subs)]
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def _parse_date(created_utc) -> Optional[str]:
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"""Convert Unix timestamp to YYYY-MM-DD."""
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if not created_utc:
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return None
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try:
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dt = datetime.fromtimestamp(float(created_utc), tz=timezone.utc)
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return dt.strftime("%Y-%m-%d")
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except (ValueError, TypeError, OSError):
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return None
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def _normalize_post(post: Dict[str, Any], idx: int, source_label: str = "global") -> Dict[str, Any]:
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"""Normalize a ScrapeCreators Reddit post to our internal format."""
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permalink = post.get("permalink", "")
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url = f"https://www.reddit.com{permalink}" if permalink else post.get("url", "")
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# Ensure URL looks like a Reddit thread
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if url and "reddit.com" not in url:
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url = ""
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return {
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"id": f"R{idx}",
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"reddit_id": post.get("id", ""),
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"title": str(post.get("title", "")).strip(),
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"url": url,
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"subreddit": str(post.get("subreddit", "")).strip(),
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"date": _parse_date(post.get("created_utc")),
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"engagement": {
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"score": post.get("ups") or post.get("score", 0),
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"num_comments": post.get("num_comments", 0),
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"upvote_ratio": post.get("upvote_ratio"),
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},
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"relevance": 0.7,
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"why_relevant": f"Reddit {source_label} search",
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"selftext": str(post.get("selftext", ""))[:500],
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}
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def _global_search(
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query: str,
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token: str,
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sort: str = "relevance",
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timeframe: str = "month",
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) -> List[Dict[str, Any]]:
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"""Search across all of Reddit via ScrapeCreators global search.
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Args:
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query: Search query
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token: ScrapeCreators API key
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sort: Sort order (relevance, hot, top, new)
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timeframe: Time filter (hour, day, week, month, year, all)
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Returns:
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List of post dicts
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"""
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if not _requests:
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_log("requests library not installed, falling back to urllib")
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# Use stdlib http module as fallback
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try:
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from urllib.parse import urlencode
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params = urlencode({"query": query, "sort": sort, "timeframe": timeframe})
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url = f"{SCRAPECREATORS_BASE}/search?{params}"
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headers = _sc_headers(token)
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headers["User-Agent"] = http.USER_AGENT
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data = http.get(url, headers=headers, timeout=30, retries=2)
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return data.get("posts", data.get("data", []))
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except Exception as e:
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_log(f"Global search error (urllib): {e}")
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return []
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try:
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resp = _requests.get(
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f"{SCRAPECREATORS_BASE}/search",
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params={"query": query, "sort": sort, "timeframe": timeframe},
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headers=_sc_headers(token),
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timeout=30,
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)
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resp.raise_for_status()
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data = resp.json()
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return data.get("posts", data.get("data", []))
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except Exception as e:
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_log(f"Global search error: {e}")
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return []
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def _subreddit_search(
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subreddit: str,
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query: str,
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token: str,
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sort: str = "relevance",
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timeframe: str = "month",
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) -> List[Dict[str, Any]]:
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"""Search within a specific subreddit via ScrapeCreators.
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Args:
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subreddit: Subreddit name (without r/)
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query: Search query
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token: ScrapeCreators API key
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sort: Sort order
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timeframe: Time filter
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Returns:
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List of post dicts
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"""
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if not _requests:
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try:
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from urllib.parse import urlencode
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params = urlencode({
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"subreddit": subreddit, "query": query,
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"sort": sort, "timeframe": timeframe,
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})
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url = f"{SCRAPECREATORS_BASE}/subreddit/search?{params}"
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headers = _sc_headers(token)
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headers["User-Agent"] = http.USER_AGENT
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data = http.get(url, headers=headers, timeout=30, retries=2)
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return data.get("posts", data.get("data", []))
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except Exception as e:
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_log(f"Subreddit search error (urllib) for r/{subreddit}: {e}")
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return []
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try:
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resp = _requests.get(
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f"{SCRAPECREATORS_BASE}/subreddit/search",
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params={
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"subreddit": subreddit,
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"query": query,
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"sort": sort,
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"timeframe": timeframe,
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},
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headers=_sc_headers(token),
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timeout=30,
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)
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resp.raise_for_status()
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data = resp.json()
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return data.get("posts", data.get("data", []))
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except Exception as e:
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_log(f"Subreddit search error for r/{subreddit}: {e}")
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return []
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def fetch_post_comments(
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url: str,
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token: str,
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) -> List[Dict[str, Any]]:
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"""Fetch comments for a Reddit post via ScrapeCreators.
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Args:
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url: Reddit post URL or permalink
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token: ScrapeCreators API key
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Returns:
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List of comment dicts with score, author, body, etc.
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"""
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if not _requests:
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try:
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from urllib.parse import urlencode
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params = urlencode({"url": url})
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api_url = f"{SCRAPECREATORS_BASE}/post/comments?{params}"
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headers = _sc_headers(token)
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headers["User-Agent"] = http.USER_AGENT
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data = http.get(api_url, headers=headers, timeout=30, retries=2)
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return data.get("comments", data.get("data", []))
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except Exception as e:
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_log(f"Comment fetch error (urllib): {e}")
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return []
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try:
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resp = _requests.get(
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f"{SCRAPECREATORS_BASE}/post/comments",
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params={"url": url},
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headers=_sc_headers(token),
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timeout=30,
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)
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resp.raise_for_status()
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data = resp.json()
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return data.get("comments", data.get("data", []))
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except Exception as e:
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_log(f"Comment fetch error: {e}")
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return []
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def _dedupe_posts(posts: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
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"""Deduplicate posts by reddit_id, keeping first occurrence."""
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seen_ids = set()
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seen_urls = set()
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unique = []
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for post in posts:
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rid = post.get("reddit_id", "")
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url = post.get("url", "")
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if rid and rid in seen_ids:
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continue
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if url and url in seen_urls:
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continue
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if rid:
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seen_ids.add(rid)
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if url:
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seen_urls.add(url)
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unique.append(post)
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return unique
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def search_reddit(
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topic: str,
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from_date: str,
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to_date: str,
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depth: str = "default",
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token: str = None,
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) -> Dict[str, Any]:
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"""Full Reddit search: multi-query global discovery + subreddit drill-down.
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This is the main entry point. Replaces openai_reddit.search_reddit().
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Args:
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topic: Search topic
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from_date: Start date (YYYY-MM-DD)
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to_date: End date (YYYY-MM-DD)
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depth: 'quick', 'default', or 'deep'
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token: ScrapeCreators API key
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Returns:
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Dict with 'items' list and optional 'error'.
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"""
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if not token:
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return {"items": [], "error": "No SCRAPECREATORS_API_KEY configured"}
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config = DEPTH_CONFIG.get(depth, DEPTH_CONFIG["default"])
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timeframe = config["timeframe"]
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# === Phase 1: Query Expansion ===
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queries = expand_reddit_queries(topic, depth)
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_log(f"Expanded '{topic}' into {len(queries)} queries: {queries}")
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# === Phase 2: Global Discovery ===
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all_raw_posts = []
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max_global = config["global_searches"]
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for i, query in enumerate(queries[:max_global]):
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sort = "relevance" if i == 0 else "top"
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_log(f"Global search {i+1}/{max_global}: '{query}' (sort={sort})")
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posts = _global_search(query, token, sort=sort, timeframe=timeframe)
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_log(f" -> {len(posts)} results")
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all_raw_posts.extend(posts)
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# Normalize all posts
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all_items = []
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for i, post in enumerate(all_raw_posts):
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item = _normalize_post(post, i + 1, "global")
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all_items.append(item)
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# === Phase 3: Subreddit Discovery + Targeted Search ===
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discovered_subs = discover_subreddits(all_raw_posts, max_subs=config["subreddit_searches"])
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_log(f"Discovered subreddits: {discovered_subs}")
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core = _extract_core_subject(topic)
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for sub in discovered_subs[:config["subreddit_searches"]]:
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_log(f"Subreddit search: r/{sub} for '{core}'")
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sub_posts = _subreddit_search(sub, core, token, sort="relevance", timeframe=timeframe)
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_log(f" -> {len(sub_posts)} results from r/{sub}")
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for j, post in enumerate(sub_posts):
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item = _normalize_post(post, len(all_items) + j + 1, f"r/{sub}")
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all_items.append(item)
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# === Phase 4: Deduplicate ===
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all_items = _dedupe_posts(all_items)
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_log(f"After dedup: {len(all_items)} unique posts")
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# === Phase 5: Date filter ===
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in_range = []
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out_of_range = 0
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for item in all_items:
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if item["date"] and from_date <= item["date"] <= to_date:
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in_range.append(item)
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elif item["date"] is None:
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in_range.append(item) # Keep unknown dates
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else:
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out_of_range += 1
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if in_range:
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all_items = in_range
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if out_of_range:
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_log(f"Filtered {out_of_range} posts outside date range")
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else:
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_log(f"No posts within date range, keeping all {len(all_items)}")
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# === Phase 6: Sort by engagement ===
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all_items.sort(
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key=lambda x: (x.get("engagement", {}).get("score", 0) or 0),
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reverse=True,
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)
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# Re-index IDs
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for i, item in enumerate(all_items):
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item["id"] = f"R{i+1}"
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_log(f"Final: {len(all_items)} Reddit posts")
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return {"items": all_items}
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def enrich_with_comments(
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items: List[Dict[str, Any]],
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token: str,
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depth: str = "default",
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) -> List[Dict[str, Any]]:
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"""Enrich top items with comment data from ScrapeCreators.
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Args:
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items: Reddit items from search_reddit()
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token: ScrapeCreators API key
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depth: Depth for comment limit
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Returns:
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Items with top_comments and comment_insights added.
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"""
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config = DEPTH_CONFIG.get(depth, DEPTH_CONFIG["default"])
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max_comments = config["comment_enrichments"]
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if not items or not token:
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return items
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top_items = items[:max_comments]
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_log(f"Enriching comments for {len(top_items)} posts")
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for item in top_items:
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url = item.get("url", "")
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if not url:
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||||
continue
|
||||
|
||||
raw_comments = fetch_post_comments(url, token)
|
||||
if not raw_comments:
|
||||
continue
|
||||
|
||||
# Parse comments into our format
|
||||
top_comments = []
|
||||
insights = []
|
||||
|
||||
for c in raw_comments[:10]: # Take top 10 comments
|
||||
body = c.get("body", "")
|
||||
if not body or body in ("[deleted]", "[removed]"):
|
||||
continue
|
||||
|
||||
score = c.get("ups") or c.get("score", 0)
|
||||
author = c.get("author", "[deleted]")
|
||||
permalink = c.get("permalink", "")
|
||||
comment_url = f"https://reddit.com{permalink}" if permalink else ""
|
||||
|
||||
top_comments.append({
|
||||
"score": score,
|
||||
"date": _parse_date(c.get("created_utc")),
|
||||
"author": author,
|
||||
"excerpt": body[:300],
|
||||
"url": comment_url,
|
||||
})
|
||||
|
||||
# Extract insights from substantive comments
|
||||
if len(body) >= 30 and author not in ("[deleted]", "[removed]", "AutoModerator"):
|
||||
insight = body[:150]
|
||||
if len(body) > 150:
|
||||
for i, char in enumerate(insight):
|
||||
if char in '.!?' and i > 50:
|
||||
insight = insight[:i+1]
|
||||
break
|
||||
else:
|
||||
insight = insight.rstrip() + "..."
|
||||
insights.append(insight)
|
||||
|
||||
# Sort comments by score
|
||||
top_comments.sort(key=lambda c: c.get("score", 0), reverse=True)
|
||||
|
||||
item["top_comments"] = top_comments[:10]
|
||||
item["comment_insights"] = insights[:7]
|
||||
|
||||
return items
|
||||
|
||||
|
||||
def search_and_enrich(
|
||||
topic: str,
|
||||
from_date: str,
|
||||
to_date: str,
|
||||
depth: str = "default",
|
||||
token: str = None,
|
||||
) -> Dict[str, Any]:
|
||||
"""Full Reddit pipeline: search + comment enrichment.
|
||||
|
||||
This is the convenience function that does everything.
|
||||
|
||||
Args:
|
||||
topic: Search topic
|
||||
from_date: Start date (YYYY-MM-DD)
|
||||
to_date: End date (YYYY-MM-DD)
|
||||
depth: 'quick', 'default', or 'deep'
|
||||
token: ScrapeCreators API key
|
||||
|
||||
Returns:
|
||||
Dict with 'items' list. Items include top_comments and comment_insights.
|
||||
"""
|
||||
result = search_reddit(topic, from_date, to_date, depth, token)
|
||||
items = result.get("items", [])
|
||||
|
||||
if items and token:
|
||||
items = enrich_with_comments(items, token, depth)
|
||||
result["items"] = items
|
||||
|
||||
return result
|
||||
|
||||
|
||||
def parse_reddit_response(response: Dict[str, Any]) -> List[Dict[str, Any]]:
|
||||
"""Parse ScrapeCreators response to item list.
|
||||
|
||||
Compatibility shim matching openai_reddit.parse_reddit_response() signature.
|
||||
"""
|
||||
return response.get("items", [])
|
||||
Reference in New Issue
Block a user