Claire Novotny
378 lines
12 KiB
Python
378 lines
12 KiB
Python
"""Standalone Reddit public JSON search module.
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Searches Reddit using the free public JSON endpoints (no API key required).
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Promoted from last-resort fallback to robust primary free path.
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Endpoints:
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- Global: https://www.reddit.com/search.json?q={query}&sort=relevance&t=month&limit={limit}
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- Subreddit: https://www.reddit.com/r/{sub}/search.json?q={query}&restrict_sr=on&sort=relevance&t=month
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Handles 429 rate limits with exponential backoff, HTML anti-bot responses,
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network timeouts, and missing subreddits.
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"""
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import json
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import sys
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import time
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import urllib.error
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import urllib.parse
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import urllib.request
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from concurrent.futures import ThreadPoolExecutor, TimeoutError as FuturesTimeoutError
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from typing import Any, Dict, List, Optional
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USER_AGENT = "last30days/3.0 (research tool)"
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# Depth-aware limits for thread counts
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DEPTH_LIMITS = {
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"quick": 10,
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"default": 25,
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"deep": 50,
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}
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# How many top posts to enrich with comments, by depth
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ENRICH_LIMITS = {
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"quick": 3,
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"default": 5,
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"deep": 8,
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}
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MAX_RETRIES = 3
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BASE_BACKOFF = 2.0 # seconds
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def _log(msg: str):
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"""Log to stderr."""
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sys.stderr.write(f"[RedditPublic] {msg}\n")
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sys.stderr.flush()
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def _url_encode(text: str) -> str:
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"""URL-encode a query string."""
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return urllib.parse.quote_plus(text)
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def _fetch_json(url: str, timeout: int = 15) -> Optional[Dict[str, Any]]:
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"""Fetch JSON from a URL with retry on 429 and error handling.
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Returns parsed JSON dict, or None on unrecoverable failure.
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"""
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headers = {
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"User-Agent": USER_AGENT,
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"Accept": "application/json",
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}
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req = urllib.request.Request(url, headers=headers)
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for attempt in range(MAX_RETRIES):
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try:
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with urllib.request.urlopen(req, timeout=timeout) as resp:
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content_type = resp.headers.get("Content-Type", "")
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if "json" not in content_type and "text/html" in content_type:
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_log(f"Anti-bot HTML response (Content-Type: {content_type})")
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return None
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body = resp.read().decode("utf-8")
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return json.loads(body)
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except urllib.error.HTTPError as e:
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if e.code == 429:
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delay = BASE_BACKOFF * (2 ** attempt)
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retry_after = None
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if hasattr(e, "headers"):
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retry_after = e.headers.get("Retry-After")
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if retry_after:
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try:
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delay = float(retry_after)
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except ValueError:
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pass
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_log(f"429 rate limited, retry {attempt + 1}/{MAX_RETRIES} after {delay:.1f}s")
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if attempt < MAX_RETRIES - 1:
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time.sleep(delay)
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continue
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# Last attempt exhausted
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_log("429 retries exhausted")
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return None
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elif e.code == 404:
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_log(f"404 not found: {url}")
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return None
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elif e.code == 403:
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_log(f"403 forbidden: {url}")
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return None
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else:
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_log(f"HTTP {e.code}: {e.reason}")
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return None
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except (urllib.error.URLError, OSError, TimeoutError) as e:
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_log(f"Network error: {e}")
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return None
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except json.JSONDecodeError as e:
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_log(f"JSON decode error: {e}")
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return None
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return None
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def _parse_posts(data: Optional[Dict[str, Any]]) -> List[Dict[str, Any]]:
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"""Parse Reddit listing JSON into normalized post dicts."""
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if not data:
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return []
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children = data.get("data", {}).get("children", [])
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posts = []
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for child in children:
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if child.get("kind") != "t3":
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continue
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post = child.get("data", {})
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permalink = str(post.get("permalink", "")).strip()
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if not permalink or "/comments/" not in permalink:
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continue
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score = int(post.get("score", 0) or 0)
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num_comments = int(post.get("num_comments", 0) or 0)
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selftext = str(post.get("selftext", ""))
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author = str(post.get("author", "[deleted]"))
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created_utc = post.get("created_utc")
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# Parse date
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date_str = None
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if created_utc:
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try:
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from datetime import datetime, timezone
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dt = datetime.fromtimestamp(float(created_utc), tz=timezone.utc)
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date_str = dt.strftime("%Y-%m-%d")
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except (ValueError, TypeError, OSError):
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pass
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posts.append({
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"id": "", # Will be assigned after dedup
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"title": str(post.get("title", "")).strip(),
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"url": f"https://www.reddit.com{permalink}",
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"score": score,
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"num_comments": num_comments,
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"subreddit": str(post.get("subreddit", "")).strip(),
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"created_utc": float(created_utc) if created_utc else None,
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"author": author if author not in ("[deleted]", "[removed]") else "[deleted]",
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"selftext": selftext[:500] if selftext else "",
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# Normalized fields matching ScrapeCreators output
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"date": date_str,
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"engagement": {
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"score": score,
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"num_comments": num_comments,
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"upvote_ratio": post.get("upvote_ratio"),
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},
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"relevance": _compute_relevance(score, num_comments),
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"why_relevant": "Reddit public search",
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"metadata": {},
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})
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return posts
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def _compute_relevance(score: int, num_comments: int) -> float:
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"""Estimate relevance from engagement signals."""
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score_component = min(1.0, max(0.0, score / 500.0))
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comments_component = min(1.0, max(0.0, num_comments / 200.0))
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return round((score_component * 0.6) + (comments_component * 0.4), 3)
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def search(
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query: str,
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depth: str = "default",
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subreddit: Optional[str] = None,
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timeout: int = 15,
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) -> List[Dict[str, Any]]:
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"""Search Reddit via the public JSON endpoint.
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Args:
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query: Search query string
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depth: 'quick', 'default', or 'deep' — controls result limit
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subreddit: Optional subreddit name (without r/) for scoped search
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timeout: HTTP timeout in seconds
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Returns:
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List of normalized post dicts. Empty list on any failure.
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"""
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limit = DEPTH_LIMITS.get(depth, DEPTH_LIMITS["default"])
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encoded_query = _url_encode(query)
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if subreddit:
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sub = subreddit.lstrip("r/").strip()
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url = (
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f"https://www.reddit.com/r/{sub}/search.json"
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f"?q={encoded_query}&restrict_sr=on&sort=relevance&t=month&limit={limit}&raw_json=1"
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)
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else:
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url = (
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f"https://www.reddit.com/search.json"
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f"?q={encoded_query}&sort=relevance&t=month&limit={limit}&raw_json=1"
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)
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data = _fetch_json(url, timeout=timeout)
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posts = _parse_posts(data)
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# Dedupe by URL and assign IDs
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seen_urls = set()
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unique = []
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for post in posts:
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if post["url"] not in seen_urls:
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seen_urls.add(post["url"])
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unique.append(post)
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for i, post in enumerate(unique):
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post["id"] = f"R{i + 1}"
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return unique[:limit]
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def _enrich_post(item: Dict[str, Any], timeout: int = 10) -> Dict[str, Any]:
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"""Enrich a single post with top comments. Never raises."""
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try:
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from . import reddit_enrich
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thread_data = reddit_enrich.fetch_thread_data(item["url"], timeout=timeout)
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if not thread_data:
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return item
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parsed = reddit_enrich.parse_thread_data(thread_data)
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comments = parsed.get("comments", [])
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top = reddit_enrich.get_top_comments(comments)
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item["top_comments"] = [
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{
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"score": c.get("score", 0),
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"excerpt": (c.get("body") or "")[:200],
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"author": c.get("author", ""),
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}
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for c in top[:10]
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]
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except Exception:
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# Never discard — keep post with empty metadata
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pass
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return item
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def _enrich_posts(posts: List[Dict[str, Any]], depth: str = "default") -> List[Dict[str, Any]]:
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"""Enrich top N posts with comment data using threads. Total budget 45s."""
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limit = ENRICH_LIMITS.get(depth, ENRICH_LIMITS["default"])
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to_enrich = posts[:limit]
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rest = posts[limit:]
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if not to_enrich:
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return posts
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enriched = []
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try:
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with ThreadPoolExecutor(max_workers=min(limit, 4)) as executor:
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futures = {
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executor.submit(_enrich_post, post, 10): i
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for i, post in enumerate(to_enrich)
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}
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# Collect results with 45s total budget
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import concurrent.futures
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done, not_done = concurrent.futures.wait(futures, timeout=45)
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# Build result list preserving order
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result_map: Dict[int, Dict[str, Any]] = {}
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for future in done:
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idx = futures[future]
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try:
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result_map[idx] = future.result(timeout=0)
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except Exception:
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result_map[idx] = to_enrich[idx]
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# Any not-done futures: keep original post
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for future in not_done:
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idx = futures[future]
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result_map[idx] = to_enrich[idx]
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future.cancel()
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enriched = [result_map[i] for i in range(len(to_enrich))]
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except Exception:
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enriched = to_enrich
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return enriched + rest
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def _search_subreddit(sub: str, topic: str, depth: str, timeout: int = 15) -> List[Dict[str, Any]]:
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"""Search a single subreddit. Never raises."""
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try:
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return search(topic, depth=depth, subreddit=sub, timeout=timeout)
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except Exception as e:
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_log(f"Subreddit search failed for r/{sub}: {e}")
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return []
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def search_reddit_public(
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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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subreddits: Optional[List[str]] = None,
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) -> List[Dict[str, Any]]:
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"""High-level Reddit public search matching the openai_reddit interface.
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When subreddits are provided (from agent planning), searches each targeted
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sub first, then does global search, and deduplicates across both. This
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mirrors the SC search_and_enrich() flow where pre-resolved subreddits get
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priority.
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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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subreddits: Optional list of subreddit names (without r/) for targeted search
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Returns:
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List of normalized item dicts matching ScrapeCreators output format.
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"""
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all_posts: List[Dict[str, Any]] = []
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# Phase 1: Search targeted subreddits in parallel (if provided)
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if subreddits:
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_log(f"Searching {len(subreddits)} targeted subreddits: {subreddits}")
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workers = min(4, len(subreddits))
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with ThreadPoolExecutor(max_workers=workers) as executor:
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futures = {
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executor.submit(_search_subreddit, sub, topic, depth): sub
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for sub in subreddits
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}
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for future in futures:
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sub = futures[future]
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try:
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sub_posts = future.result(timeout=30)
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_log(f" -> {len(sub_posts)} results from r/{sub}")
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all_posts.extend(sub_posts)
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except (Exception, FuturesTimeoutError) as e:
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_log(f" -> r/{sub} failed: {e}")
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# Phase 2: Global search
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global_posts = search(topic, depth=depth)
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all_posts.extend(global_posts)
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# Deduplicate by URL (targeted results keep priority since they come first)
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seen_urls: set = set()
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results: List[Dict[str, Any]] = []
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for post in all_posts:
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if post["url"] not in seen_urls:
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seen_urls.add(post["url"])
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results.append(post)
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# Date filter: keep posts in range or with unknown dates
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filtered = []
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for item in results:
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d = item.get("date")
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if d is None or (from_date <= d <= to_date):
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filtered.append(item)
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# Sort by engagement (score desc)
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filtered.sort(
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key=lambda x: x.get("engagement", {}).get("score", 0),
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reverse=True,
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)
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# Enrich top posts with comments
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filtered = _enrich_posts(filtered, depth=depth)
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# Re-index IDs
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for i, item in enumerate(filtered):
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item["id"] = f"R{i + 1}"
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return filtered
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