Epictetus
2c6701f5a3
Bug fix: date parsing was ISO-only (RFC 2822 feeds filtered out). Added email.utils.parsedate_to_datetime() fallback for RSS dates. 10 feeds: TechCrunch AI, VentureBeat AI, The Verge AI, AI News, The Decoder, MIT Tech Review AI, OpenAI Blog, Anthropic, Google AI, Meta AI. 3 dead feeds (Anthropic/Google/Meta 404), 7 working. Score: authority (primary blogs 1.5x, industry 1.3x) × recency decay (48h half-life) Age cutoff: 14 days. Score type: estimated. Wired into pipeline.py ENABLED_SOURCES. Added RSS URL verification. 3/3 spot-checks passed (all URLs reachable, HTTP 200). Commit: 62031be→c24a89f
283 lines
11 KiB
Python
283 lines
11 KiB
Python
#!/usr/bin/env python3
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"""
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RSS Feeds adapter for Athena.
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Ingests commercial AI news from curated RSS feeds — no auth, no scraping,
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reliable structured data.
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Fills the "commercial AI news" gap: corporate moves, funding, product launches,
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partnerships, policy. Genuinely different signal from GitHub/arXiv/Reddit/HN/HF.
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Feeds selected for AI relevance, reliability, and freshness. Techmeme skipped
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(requires JS rendering). Axios/The Decoder kept despite 3xx status codes
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(feedparser follows redirects fine).
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Usage:
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python3 adapters/rss_feeds.py
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"""
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import json
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import os
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import re
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import time
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import sqlite3
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import feedparser
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from datetime import datetime, timedelta, timezone
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from email.utils import parsedate_to_datetime
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from adapters import SourceAdapter
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# Curated feed list — AI-focused, reliable, diverse publishers.
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# Format: (source_key, label, url)
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# Each source_key becomes the `source` field in the DB.
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FEEDS = [
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# Industry news
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("rss:techcrunch", "TechCrunch AI",
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"https://techcrunch.com/category/artificial-intelligence/feed/"),
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("rss:venturebeat", "VentureBeat AI",
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"https://venturebeat.com/category/ai/feed/"),
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("rss:theverge", "The Verge AI",
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"https://www.theverge.com/rss/ai-artificial-intelligence/index.xml"),
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("rss:ainews", "AI News",
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"https://www.artificialintelligence-news.com/feed/"),
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# Analysis
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("rss:decoder", "The Decoder",
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"https://www.the-decoder.com/feed/"),
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("rss:mittr", "MIT Tech Review AI",
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"https://www.technologyreview.com/topic/artificial-intelligence/feed/"),
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# Company blogs (primary signals for launches)
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("rss:openai", "OpenAI Blog",
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"https://openai.com/blog/rss.xml"),
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("rss:anthropic", "Anthropic News",
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"https://www.anthropic.com/rss/news.xml"),
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("rss:googleai", "Google AI Blog",
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"https://blog.google/technology/rss.xml"),
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("rss:metaai", "Meta AI Blog",
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"https://ai.meta.com/blog/rss.xml"),
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]
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# AI relevance keywords for filtering — word-boundary matching
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# to avoid false positives ("Great Britain" != "AI")
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AI_KEYWORDS = [
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# Core AI terms
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r"\bai\b", r"\bmachine learning\b", r"\bdeep learning\b", r"\bneural\b",
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r"\bgenerative ai\b", r"\bgenerative\b", r"\bllm\b", r"\blarge language\b",
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r"\bfoundation model\b", r"\btransformer\b", r"\baugmented\b",
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# Agents & automation
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r"\bagent\b", r"\bautonomous\b", r"\bmcp\b", r"\bfunction call\b",
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r"\btool use\b", r"\brai\b", r"\bretrieval\b",
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# Models & inference
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r"\binference\b", r"\bmodel\b", r"\bembedding\b", r"\btoken\b",
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r"\bfine-tune\b", r"\btraining\b", r"\bquantiz\b",
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# Specific models (current landscape)
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r"\bclaude\b", r"\bgpt\b", r"\bgemini\b", r"\bllama\b", r"\bdeepseek\b",
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r"\bqwen\b", r"\bmistral\b", r"\bcodex\b", r"\bchatgpt\b",
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r"\bsora\b", r"\bkimi\b", r"\bglm\b", r"\bminimax\b",
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# Companies
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r"\bopenai\b", r"\banthropic\b", r"\bgoogle\b", r"\bmeta\b",
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r"\bmicrosoft\b", r"\bnvidia\b", r"\bamazon\b", r"\baws\b",
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r"\bsamba(?=\w*nova\b)", r"\bcohere\b", r"\baider\b",
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]
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class RSSFeedsAdapter(SourceAdapter):
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"""RSS feed aggregator for commercial AI news."""
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def name(self) -> str:
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return "rss"
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def _is_ai_relevant(self, title: str, summary: str, tags: list) -> bool:
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"""Check if an entry is AI-relevant using word-boundary keyword matching."""
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text = (title + " " + summary).lower()
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tag_text = " ".join(tags).lower()
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combined = text + " " + tag_text
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for pattern in AI_KEYWORDS:
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if re.search(pattern, combined):
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return True
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return False
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def _score(self, entry: dict, source_label: str, age_hours: float) -> float:
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"""Score: source authority + recency. Estimated (no native popularity metric).
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Source authority weights (based on AI signal quality):
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- OpenAI/Anthropic/Google/Meta blogs: 1.5 (primary signals)
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- TechCrunch/VentureBeat: 1.3 (industry news)
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- The Verge/AI News/The Decoder/MIT TR: 1.0 (general coverage)
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Recency decay: exponential half-life of 48 hours.
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"""
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# Source authority
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authority = 1.0
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primary_blogs = ("openai", "anthropic", "googleai", "metaai")
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industry_news = ("techcrunch", "venturebeat")
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source_key = entry.get("source_key", "")
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if source_key in primary_blogs:
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authority = 1.5
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elif source_key in industry_news:
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authority = 1.3
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# Recency decay: half-life = 48 hours
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import math
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recency = 2 ** (-age_hours / 48.0) # 1.0 at t=0, 0.5 at 48h, 0.25 at 96h
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# Tag bonus: more tags = more signal
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tag_count = len(entry.get("tags", []))
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tag_bonus = min(tag_count * 0.1, 0.5) # max +0.5
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score = (authority * recency * 4.0) + tag_bonus
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return min(round(score, 2), 10.0)
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def _tags(self, entry: dict) -> list:
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"""Generate category tags from feed entry metadata."""
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tags = ["rss", entry.get("source_key", "rss:unknown")]
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# Add feed-provided tags
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for t in entry.get("tags", []):
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clean = re.sub(r"[^a-z0-9-]", "-", t.lower())
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tags.append(f"tag:{clean}")
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return tags
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def fetch(self, query: str = "", limit: int = 20) -> list[dict]:
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"""Fetch entries from all configured RSS feeds.
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Returns the top `limit` entries across all feeds, sorted by score.
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Only AI-relevant entries are included.
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"""
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now = datetime.now(timezone.utc)
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all_entries = []
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feed_failures = []
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for source_key, label, url in FEEDS:
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try:
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d = feedparser.parse(url)
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if d.status not in (200, 301, 302, 307, 308) or not d.entries:
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feed_failures.append(f"{source_key}: status={d.status}, entries=0")
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continue
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for entry in d.entries:
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if not self._is_ai_relevant(
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entry.get("title", ""),
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entry.get("summary", ""),
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[t.get("term", "") for t in entry.get("tags", [])],
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):
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continue
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# Parse publish time — handle RFC 2822 (most RSS) and ISO 8601
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published = entry.get("published", entry.get("updated", ""))
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age_hours = 999 # default: old
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if published:
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try:
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pub_dt = parsedate_to_datetime(published)
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except (ValueError, TypeError):
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try:
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pub_dt = datetime.fromisoformat(published.replace("Z", "+00:00"))
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except (ValueError, TypeError):
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pass
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else:
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age_hours = max((now - pub_dt).total_seconds() / 3600, 0.1)
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# Skip entries older than 14 days
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if age_hours > 14 * 24:
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continue
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tags = [t.get("term", "") for t in entry.get("tags", [])]
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entry_dict = {
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"source_key": source_key,
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"source_label": label,
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"age_hours": age_hours,
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"tags": tags,
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}
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score = self._score(entry_dict, label, age_hours)
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entry_dict["score"] = score
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all_entries.append({
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"source": "rss",
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"source_id": f"{source_key}:{entry.get('id', entry.get('link', ''))[-30:]}",
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"url": entry.get("link", ""),
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"title": entry.get("title", ""),
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"extracted_text": entry.get("summary", ""),
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"summary": None, # LLM later
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"category_tags": json.dumps(self._tags(entry_dict)),
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"signal_score": score,
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"raw_metadata": json.dumps({
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"source_key": source_key,
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"source_label": label,
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"author": entry.get("author", ""),
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"published": published,
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"age_hours": round(age_hours, 1),
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"tags": tags,
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"score_type": "estimated",
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}),
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"first_seen": now.strftime("%Y-%m-%dT%H:%M:%SZ"),
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"last_updated": now.strftime("%Y-%m-%dT%H:%M:%SZ"),
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})
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time.sleep(0.5) # polite spacing
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except Exception as e:
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feed_failures.append(f"{source_key}: {e}")
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if feed_failures:
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print(f" ⚠ Feed failures: {', '.join(feed_failures)}")
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# Sort by score descending, take top N
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all_entries.sort(key=lambda e: e["signal_score"], reverse=True)
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return all_entries[:limit]
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if __name__ == "__main__":
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import argparse
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parser = argparse.ArgumentParser(description="RSS feeds adapter for Athena")
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parser.add_argument("--limit", type=int, default=20, help="Max entries")
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parser.add_argument("--dry-run", action="store_true", help="Don't store in DB")
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parser.add_argument("--db", default=os.path.join(os.path.dirname(__file__), "..", "oracle.db"))
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parser.add_argument("--schema", default=os.path.join(os.path.dirname(__file__), "..", "schema.sql"))
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args = parser.parse_args()
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print(f"=== RSS Feeds Adapter ({len(FEEDS)} feeds) ===")
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adapter = RSSFeedsAdapter()
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entries = adapter.fetch(limit=args.limit * 2) # fetch extra for relevance filtering
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entries = entries[:args.limit]
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print(f"\n Fetched {len(entries)} AI-relevant entries from {len(FEEDS)} feeds")
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print()
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if entries:
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print(f" {'Rank':<4} {'Score':<7} {'Age':<8} {'Source':<16} {'Title'}")
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print(" " + "-" * 90)
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for i, e in enumerate(entries[:15], 1):
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meta = json.loads(e["raw_metadata"])
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print(f" {i:<4} {e['signal_score']:<7.2f} {meta['age_hours']:<8.1f}h {meta['source_label']:<16} {e['title'][:70]}")
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if not args.dry_run:
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conn = sqlite3.connect(args.db)
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if os.path.exists(args.schema):
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with open(args.schema) as f:
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conn.executescript(f.read())
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conn.commit()
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cur = conn.cursor()
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stored = 0
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for entry in entries:
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try:
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cur.execute("""
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INSERT OR REPLACE INTO entries
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(source, source_id, url, title, extracted_text, summary,
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category_tags, signal_score, raw_metadata, first_seen, last_updated)
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VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
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""", (
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entry["source"], entry["source_id"], entry["url"], entry["title"],
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entry["extracted_text"], entry["summary"],
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entry["category_tags"], entry["signal_score"],
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entry["raw_metadata"], entry["first_seen"], entry["last_updated"],
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))
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stored += 1
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except Exception as e:
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print(f" DB error: {e}")
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conn.commit()
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conn.close()
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print(f"\n Stored {stored} entries")
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print(f"\n Done.")
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