"""Standalone Reddit public JSON search module. Searches Reddit using the free public JSON endpoints (no API key required). Promoted from last-resort fallback to robust primary free path. Endpoints: - Global: https://www.reddit.com/search.json?q={query}&sort=relevance&t=month&limit={limit} - Subreddit: https://www.reddit.com/r/{sub}/search.json?q={query}&restrict_sr=on&sort=relevance&t=month Handles 429 rate limits with exponential backoff, HTML anti-bot responses, network timeouts, and missing subreddits. """ import json import sys import time import urllib.error import urllib.parse import urllib.request from typing import Any, Dict, List, Optional USER_AGENT = "last30days/3.0 (research tool)" # Depth-aware limits for thread counts DEPTH_LIMITS = { "quick": 10, "default": 25, "deep": 50, } MAX_RETRIES = 3 BASE_BACKOFF = 2.0 # seconds def _log(msg: str): """Log to stderr.""" sys.stderr.write(f"[RedditPublic] {msg}\n") sys.stderr.flush() def _url_encode(text: str) -> str: """URL-encode a query string.""" return urllib.parse.quote_plus(text) def _fetch_json(url: str, timeout: int = 15) -> Optional[Dict[str, Any]]: """Fetch JSON from a URL with retry on 429 and error handling. Returns parsed JSON dict, or None on unrecoverable failure. """ headers = { "User-Agent": USER_AGENT, "Accept": "application/json", } req = urllib.request.Request(url, headers=headers) for attempt in range(MAX_RETRIES): try: with urllib.request.urlopen(req, timeout=timeout) as resp: content_type = resp.headers.get("Content-Type", "") if "json" not in content_type and "text/html" in content_type: _log(f"Anti-bot HTML response (Content-Type: {content_type})") return None body = resp.read().decode("utf-8") return json.loads(body) except urllib.error.HTTPError as e: if e.code == 429: delay = BASE_BACKOFF * (2 ** attempt) retry_after = None if hasattr(e, "headers"): retry_after = e.headers.get("Retry-After") if retry_after: try: delay = float(retry_after) except ValueError: pass _log(f"429 rate limited, retry {attempt + 1}/{MAX_RETRIES} after {delay:.1f}s") if attempt < MAX_RETRIES - 1: time.sleep(delay) continue # Last attempt exhausted _log("429 retries exhausted") return None elif e.code == 404: _log(f"404 not found: {url}") return None elif e.code == 403: _log(f"403 forbidden: {url}") return None else: _log(f"HTTP {e.code}: {e.reason}") return None except (urllib.error.URLError, OSError, TimeoutError) as e: _log(f"Network error: {e}") return None except json.JSONDecodeError as e: _log(f"JSON decode error: {e}") return None return None def _parse_posts(data: Optional[Dict[str, Any]]) -> List[Dict[str, Any]]: """Parse Reddit listing JSON into normalized post dicts.""" if not data: return [] children = data.get("data", {}).get("children", []) posts = [] for child in children: if child.get("kind") != "t3": continue post = child.get("data", {}) permalink = str(post.get("permalink", "")).strip() if not permalink or "/comments/" not in permalink: continue score = int(post.get("score", 0) or 0) num_comments = int(post.get("num_comments", 0) or 0) selftext = str(post.get("selftext", "")) author = str(post.get("author", "[deleted]")) created_utc = post.get("created_utc") # Parse date date_str = None if created_utc: try: from datetime import datetime, timezone dt = datetime.fromtimestamp(float(created_utc), tz=timezone.utc) date_str = dt.strftime("%Y-%m-%d") except (ValueError, TypeError, OSError): pass posts.append({ "id": "", # Will be assigned after dedup "title": str(post.get("title", "")).strip(), "url": f"https://www.reddit.com{permalink}", "score": score, "num_comments": num_comments, "subreddit": str(post.get("subreddit", "")).strip(), "created_utc": float(created_utc) if created_utc else None, "author": author if author not in ("[deleted]", "[removed]") else "[deleted]", "selftext": selftext[:500] if selftext else "", # Normalized fields matching ScrapeCreators output "date": date_str, "engagement": { "score": score, "num_comments": num_comments, "upvote_ratio": post.get("upvote_ratio"), }, "relevance": _compute_relevance(score, num_comments), "why_relevant": "Reddit public search", }) return posts def _compute_relevance(score: int, num_comments: int) -> float: """Estimate relevance from engagement signals.""" score_component = min(1.0, max(0.0, score / 500.0)) comments_component = min(1.0, max(0.0, num_comments / 200.0)) return round((score_component * 0.6) + (comments_component * 0.4), 3) def search( query: str, depth: str = "default", subreddit: Optional[str] = None, timeout: int = 15, ) -> List[Dict[str, Any]]: """Search Reddit via the public JSON endpoint. Args: query: Search query string depth: 'quick', 'default', or 'deep' — controls result limit subreddit: Optional subreddit name (without r/) for scoped search timeout: HTTP timeout in seconds Returns: List of normalized post dicts. Empty list on any failure. """ limit = DEPTH_LIMITS.get(depth, DEPTH_LIMITS["default"]) encoded_query = _url_encode(query) if subreddit: sub = subreddit.lstrip("r/").strip() url = ( f"https://www.reddit.com/r/{sub}/search.json" f"?q={encoded_query}&restrict_sr=on&sort=relevance&t=month&limit={limit}&raw_json=1" ) else: url = ( f"https://www.reddit.com/search.json" f"?q={encoded_query}&sort=relevance&t=month&limit={limit}&raw_json=1" ) data = _fetch_json(url, timeout=timeout) posts = _parse_posts(data) # Dedupe by URL and assign IDs seen_urls = set() unique = [] for post in posts: if post["url"] not in seen_urls: seen_urls.add(post["url"]) unique.append(post) for i, post in enumerate(unique): post["id"] = f"R{i + 1}" return unique[:limit] def search_reddit_public( topic: str, from_date: str, to_date: str, depth: str = "default", ) -> List[Dict[str, Any]]: """High-level Reddit public search matching the openai_reddit interface. Runs global search, deduplicates, filters by date range, and sorts by engagement. Compatible as a drop-in replacement in the fallback chain. Args: topic: Search topic from_date: Start date (YYYY-MM-DD) to_date: End date (YYYY-MM-DD) depth: 'quick', 'default', or 'deep' Returns: List of normalized item dicts matching ScrapeCreators output format. """ results = search(topic, depth=depth) # Date filter: keep posts in range or with unknown dates filtered = [] for item in results: d = item.get("date") if d is None or (from_date <= d <= to_date): filtered.append(item) # Sort by engagement (score desc) filtered.sort( key=lambda x: x.get("engagement", {}).get("score", 0), reverse=True, ) # Re-index IDs for i, item in enumerate(filtered): item["id"] = f"R{i + 1}" return filtered