news-mcp/news_mcp/jobs/poller.py

from __future__ import annotations

import asyncio
import hashlib
import logging
import sys
from collections import defaultdict
from dataclasses import dataclass, field
from datetime import datetime, timezone, timedelta
from typing import Any, Dict

from news_mcp.config import (
    DEFAULT_TOPICS,
    DB_PATH,
    ENRICH_OTHER_TOPICS_ONLY,
    NEWS_EXTRACT_PROVIDER,
    NEWS_FEED_URL,
    NEWS_FEED_URLS,
    NEWS_PRUNE_INTERVAL_HOURS,
    NEWS_PRUNING_ENABLED,
    NEWS_RETENTION_DAYS,
    NEWS_CLUSTER_MAX_AGE_HOURS,
    llm_concurrency,
    llm_rate_limit,
)
from news_mcp.dedup.cluster import dedup_and_cluster_articles, _cluster_is_within_age_window, _parse_ts
from news_mcp.enrichment.enrich import enrich_cluster
from news_mcp.enrichment.llm_enrich import classify_cluster_llm
from news_mcp.sources.news_feeds import fetch_news_articles
from news_mcp.storage.sqlite_store import SQLiteClusterStore


# --------------------------------------------------------------------------- #
#  Per-feed + per-cycle statistics
# --------------------------------------------------------------------------- #

@dataclass
class FeedStats:
    """Per-feed statistics for one polling cycle."""
    feed_url: str
    fetched: int = 0         # total items fetched from the feed
    duplicate: int = 0       # unchanged hash → skipped entirely
    stale: int = 0           # older than retention window (dropped)
    seen: int = 0            # already in seen_articles table → skipped
    ingested: int = 0        # passed dedup + retention + seen, entered clustering
    enriched: int = 0        # newly LLM-enriched this cycle
    already_enriched: int = 0  # cache hit — already had entities+keywords
    failed: int = 0          # LLM enrichment failed after retries


@dataclass
class PollStats:
    """Aggregated statistics for one polling cycle."""
    started_at: str = ""
    feeds: list[FeedStats] = field(default_factory=list)
    total_clusters: int = 0
    total_newly_enriched: int = 0
    total_already_enriched: int = 0
    total_failed: int = 0

    def summary(self) -> dict:
        return {
            "started_at": self.started_at,
            "feeds": [
                {
                    "feed_url": f.feed_url,
                    "fetched": f.fetched,
                    "duplicate": f.duplicate,
                    "stale": f.stale,
                    "seen": f.seen,
                    "ingested": f.ingested,
                }
                for f in self.feeds
            ],
            "total_clusters": self.total_clusters,
            "total_newly_enriched": self.total_newly_enriched,
            "total_already_enriched": self.total_already_enriched,
            "total_failed": self.total_failed,
        }


# --------------------------------------------------------------------------- #
#  Poller
# --------------------------------------------------------------------------- #

class ClusterPoller:
    """One polling cycle: fetch → dedup → cluster → enrich-once → store."""

    MAX_ENRICHMENT_RETRIES = 3

    def __init__(
        self,
        store: SQLiteClusterStore,
        logger: logging.Logger | None = None,
    ):
        self.store = store
        self.logger = logger or logging.getLogger("news_mcp.refresh")
        self.stats = PollStats()

    # ------------------------------------------------------------------ #
    #  Public entry point
    # ------------------------------------------------------------------ #

    async def poll(self, topic_filter: str | None = None) -> PollStats:
        """Run one full polling cycle. Returns statistics."""
        self.stats = PollStats(started_at=datetime.now(timezone.utc).isoformat())

        # 1. Load configured + enabled feed URLs
        configured_urls = self._load_feed_urls()
        enabled_urls = self.store.get_enabled_feed_urls(configured_urls)
        self.logger.info("poll start: enabled_feeds=%d configured=%d", len(enabled_urls), len(configured_urls))

        # 2. Fetch articles from enabled feeds only, per-feed dedup
        feed_map, feed_stats = await self._fetch_feeds(enabled_urls)

        # Flatten all fresh articles (stats already tracked per-feed in feed_stats)
        all_fresh = [a for articles in feed_map.values() for a in articles]

        if not all_fresh:
            self.logger.info("poll: no fresh articles from any feed")
            self.stats.feeds = feed_stats
            self._save_feed_stats(feed_stats)
            self._prune_and_finalize(enabled_urls, feed_map)
            return self.stats

        # 3. Retention filter
        articles = self._apply_retention(all_fresh, feed_map)

        if not articles:
            self.logger.info("poll: all %d fresh articles dropped by retention", len(all_fresh))
            self.stats.feeds = feed_stats
            self._save_feed_stats(feed_stats)
            self._prune_and_finalize(enabled_urls, feed_map)
            return self.stats

        # 3b. Seen-articles filter: drop articles whose URL was already
        # processed with the same content.  Three outcomes:
        #   new → never seen, full clustering + enrichment
        #   seen_unchanged → same URL, same content hash → skip entirely
        #   seen_changed → same URL, different content → re-cluster to update
        #                  the existing cluster (triggers re-enrichment)
        new_articles, seen_unchanged, seen_changed = self.store.filter_already_seen(articles)
        changed_cluster_ids: set[str] = set()
        if seen_unchanged or seen_changed:
            for a in seen_unchanged:
                fu = a.get("feed_url", "")
                for fs in feed_stats:
                    if fs.feed_url == fu:
                        fs.seen += 1
                        break
            for a in seen_changed:
                fu = a.get("feed_url", "")
                for fs in feed_stats:
                    if fs.feed_url == fu:
                        fs.seen += 1  # still "seen" (by URL), but content changed
                        break
            self.logger.info(
                "seen_articles: total=%d new=%d unchanged=%d changed=%d",
                len(articles), len(new_articles), len(seen_unchanged), len(seen_changed),
            )
        # Merge changed articles with new ones for clustering
        articles = new_articles + seen_changed

        if not articles:
            self.logger.info("poll: all articles already seen (nothing new to cluster)")
            self.stats.feeds = feed_stats
            self._save_feed_stats(feed_stats)
            self._prune_and_finalize(enabled_urls, feed_map)
            return self.stats

        # 3c. For changed-content articles, clear enriched_at in the cluster
        # payload JSON so the next enrichment cycle re-processes them with
        # the updated article data.  (enriched_at is stored inside the JSON
        # payload, not as a separate SQL column.)
        if seen_changed:
            from news_mcp.article_identity import article_key as _ak
            import json as _json
            changed_keys = {_ak(a) for a in seen_changed}
            with self.store._conn() as conn:
                for ak in changed_keys:
                    row = conn.execute(
                        "SELECT sa.cluster_id, c.payload FROM seen_articles sa "
                        "JOIN clusters c ON c.cluster_id = sa.cluster_id "
                        "WHERE sa.article_key=?",
                        (ak,),
                    ).fetchone()
                    if row:
                        changed_cluster_ids.add(row[0])
                        payload = _json.loads(row[1])
                        payload.pop("enriched_at", None)
                        conn.execute(
                            "UPDATE clusters SET payload=? WHERE cluster_id=?",
                            (_json.dumps(payload, ensure_ascii=False), row[0]),
                        )
            if changed_cluster_ids:
                self.logger.info("content_changed: clusters=%d will re-enrich", len(changed_cluster_ids))

        # 4. Pre-seed existing clusters for cross-cycle merging
        existing_clusters = self._preseed_clusters()

        # 5. Cluster (sync, may do concurrent embeddings internally)
        clustered_by_topic = await self._cluster(articles, existing_clusters)

        # 6. Enrich every cluster that needs it, store immediately
        await self._enrich_all(clustered_by_topic)

        # 7. Retry previously failed enrichments
        await self._retry_failed()

        # 8. Persist feed stats + prune
        self.stats.feeds = feed_stats
        self._save_feed_stats(feed_stats)
        self._prune_and_finalize(enabled_urls, feed_map)

        self.logger.info(
            "poll complete: clusters=%d newly_enriched=%d already_enriched=%d failed=%d",
            self.stats.total_clusters,
            self.stats.total_newly_enriched,
            self.stats.total_already_enriched,
            self.stats.total_failed,
        )
        return self.stats

    # ------------------------------------------------------------------ #
    #  Phase 1: Load feed URLs
    # ------------------------------------------------------------------ #

    @staticmethod
    def _load_feed_urls() -> list[str]:
        urls = [u.strip() for u in NEWS_FEED_URLS.split(",") if u.strip()]
        if not urls:
            urls = [NEWS_FEED_URL]
        return urls

    # ------------------------------------------------------------------ #
    #  Phase 2: Fetch + per-feed dedup
    # ------------------------------------------------------------------ #

    async def _fetch_feeds(
        self, feed_urls: list[str],
    ) -> tuple[dict[str, list[dict]], list[FeedStats]]:
        """Fetch all feeds concurrently. Returns {feed_url: fresh_articles}
        and per-feed stats. Unchanged feeds (same content hash) are dropped."""
        articles = await fetch_news_articles(limit=9999, url_list=feed_urls)
        # limit=9999 effectively means no per-feed cap — fetches everything
        # the feed gives us.  fetch_news_articles applies max(1, limit).

        # Group by feed URL
        per_feed: dict[str, list[dict]] = defaultdict(list)
        for a in articles:
            fu = str(a.get("feed_url") or NEWS_FEED_URL).strip() or NEWS_FEED_URL
            per_feed[fu].append(a)

        # Per-feed content hash dedup
        feed_map: dict[str, list[dict]] = {}
        feed_stats_list: list[FeedStats] = []

        for feed_url in feed_urls:
            feed_articles = per_feed.get(feed_url, [])
            stats = FeedStats(feed_url=feed_url, fetched=len(feed_articles))

            if not feed_articles:
                self.logger.info("feed empty: feed_url=%s", feed_url)
                feed_stats_list.append(stats)
                continue

            material = "\n".join(
                f"{a.get('title','')}|{a.get('url','')}"
                for a in feed_articles
            )
            content_hash = hashlib.sha1(material.encode("utf-8")).hexdigest()
            prev_hash = self.store.get_feed_hash(feed_url)

            if prev_hash == content_hash:
                stats.duplicate = len(feed_articles)
                self.logger.info("feed unchanged: feed_url=%s items=%d", feed_url, len(feed_articles))
                feed_stats_list.append(stats)
                continue

            feed_map[feed_url] = feed_articles
            self.logger.info(
                "feed changed: feed_url=%s items=%d hash_prev=%s hash_now=%s",
                feed_url, len(feed_articles),
                (prev_hash or "-")[:12], content_hash[:12],
            )
            feed_stats_list.append(stats)

        return feed_map, feed_stats_list

    # ------------------------------------------------------------------ #
    #  Phase 3: Retention filter
    # ------------------------------------------------------------------ #

    def _apply_retention(
        self, articles: list[dict], feed_map: dict[str, list[dict]],
    ) -> list[dict]:
        """Drop articles older than NEWS_RETENTION_DAYS. Updates FeedStats."""
        if NEWS_RETENTION_DAYS <= 0:
            return articles
        cutoff = datetime.now(timezone.utc) - timedelta(days=NEWS_RETENTION_DAYS)

        # Build a lookup: article_url → feed_url for stats
        article_feed: dict[str, str] = {}
        for fu, arts in feed_map.items():
            for a in arts:
                article_feed[a.get("url", "")] = fu

        fresh = []
        dropped = 0
        for a in articles:
            ts_str = a.get("timestamp", "")
            if not ts_str:
                fresh.append(a)
                continue
            dt = _parse_ts(ts_str)
            if dt is None or dt >= cutoff:
                fresh.append(a)
            else:
                dropped += 1
                fu = article_feed.get(a.get("url", ""), "")
                if fu:
                    # Find matching FeedStats and increment stale
                    for fs in self.stats.feeds:
                        if fs.feed_url == fu:
                            fs.stale += 1
                            break
        if dropped:
            self.logger.info("retention: dropped=%d remaining=%d retention_days=%.0f", dropped, len(fresh), NEWS_RETENTION_DAYS)
        return fresh

    # ------------------------------------------------------------------ #
    #  Phase 4: Pre-seed existing clusters
    # ------------------------------------------------------------------ #

    def _preseed_clusters(self) -> list[dict]:
        """Load recent clusters from DB for cross-cycle article merging."""
        max_age = NEWS_CLUSTER_MAX_AGE_HOURS
        if max_age == 0:
            return []
        lookback = max_age if max_age > 0 else 72
        all_recent = self.store.get_latest_clusters_all_topics(ttl_hours=lookback, limit=500)
        recent = [c for c in all_recent if _cluster_is_within_age_window(c, max_age_hours=max_age)]
        self.logger.info("pre-seeded: existing_clusters=%d max_age_h=%.1f", len(recent), max_age)
        return recent

    # ------------------------------------------------------------------ #
    #  Phase 5: Clustering
    # ------------------------------------------------------------------ #

    async def _cluster(
        self, articles: list[dict], existing_clusters: list[dict],
    ) -> dict[str, list[dict]]:
        """Run dedup_and_cluster_articles. Returns {topic: [clusters]}."""
        self.logger.info("clustering: articles=%d existing_clusters=%d", len(articles), len(existing_clusters))
        clustered = await asyncio.to_thread(
            dedup_and_cluster_articles,
            articles,
            None,  # default similarity_threshold
            existing_clusters=existing_clusters if existing_clusters else None,
            max_age_hours=NEWS_CLUSTER_MAX_AGE_HOURS,
        )
        self.logger.info("clustered: topics=%s", list(clustered.keys()))
        return clustered

    # ------------------------------------------------------------------ #
    #  Phase 6: Enrich + store
    # ------------------------------------------------------------------ #

    async def _enrich_all(self, clustered_by_topic: dict[str, list[dict]]) -> None:
        """Enrich every cluster that needs it and store immediately."""
        semaphore = asyncio.Semaphore(llm_concurrency(NEWS_EXTRACT_PROVIDER))
        rate = llm_rate_limit(NEWS_EXTRACT_PROVIDER)

        self.logger.info(
            "enrich: semaphore_limit=%d rate_limit=%s/s provider=%s",
            llm_concurrency(NEWS_EXTRACT_PROVIDER), rate, NEWS_EXTRACT_PROVIDER,
        )

        # Flatten all clusters into one list with their topics
        all_targets: list[tuple[str, dict]] = []
        for topic, clusters in clustered_by_topic.items():
            for c in clusters:
                all_targets.append((topic, c))

        if not all_targets:
            return

        # Enrich concurrently
        tasks = [
            self._enrich_one(c, topic, semaphore, rate)
            for topic, c in all_targets
        ]
        results = await asyncio.gather(*tasks, return_exceptions=False)

        # Store each cluster individually, grouped by final topic
        by_final_topic: dict[str, list[dict]] = defaultdict(list)
        for c2, was_new in results:
            final_topic = str(c2.get("topic") or "other").strip().lower()
            if final_topic not in {t.lower() for t in DEFAULT_TOPICS}:
                final_topic = "other"
            by_final_topic[final_topic].append(c2)
            self.stats.total_clusters += 1
            if was_new:
                self.stats.total_newly_enriched += 1
            else:
                self.stats.total_already_enriched += 1

        for final_topic, group in by_final_topic.items():
            self.store.upsert_clusters(group, topic=final_topic)
            self.logger.info("stored: topic=%s clusters=%d", final_topic, len(group))

    async def _enrich_one(
        self,
        cluster: dict,
        topic: str,
        semaphore: asyncio.Semaphore,
        rate: float,
    ) -> tuple[dict, bool]:
        """Enrich a single cluster. Returns (cluster, was_newly_enriched).

        If the cluster already has entities AND keywords, skip LLM entirely.
        The data on the dict IS the cache — no timestamp or DB lookup needed.
        """
        c2 = enrich_cluster(cluster)
        c2.setdefault("topic", topic)

        llm_enabled = (not ENRICH_OTHER_TOPICS_ONLY) or (topic == "other")
        cluster_id = c2.get("cluster_id")

        if not llm_enabled or not cluster_id:
            return c2, False

        # Cache check: entities + keywords already present → skip
        # Exception: enriched_at missing means content changed → force re-enrich
        if c2.get("enriched_at") and (c2.get("entities") or []) and (c2.get("keywords") or []):
            self.logger.debug("enrich skip (cached): cluster=%s topic=%s", cluster_id, topic)
            return c2, False

        # Actually call the LLM
        last_err = ""
        for attempt in range(1 + self.MAX_ENRICHMENT_RETRIES):
            if attempt > 0:
                backoff = 2 ** attempt
                self.logger.info("retry: cluster=%s attempt=%d backoff=%.0fs", cluster_id, attempt, backoff)
                await asyncio.sleep(backoff)
            try:
                async with semaphore:
                    c2 = await classify_cluster_llm(dict(c2))
                c2["enriched_at"] = datetime.now(timezone.utc).isoformat()
                return c2, True
            except Exception:
                last_err = str(sys.exc_info()[1])[:200] if sys.exc_info()[1] else "unknown"
                self.logger.warning(
                    "enrich failed: cluster=%s attempt=%d err=%s",
                    cluster_id, attempt, last_err,
                )

        # All retries exhausted
        prev_count = c2.get("enrichment_retry_count", 0)
        c2["enrichment_failed_at"] = datetime.now(timezone.utc).isoformat()
        c2["enrichment_retry_count"] = prev_count + 1
        self.logger.error("enrich exhausted: cluster=%s after %d retries", cluster_id, self.MAX_ENRICHMENT_RETRIES)
        self.stats.total_failed += 1
        return c2, True  # was "newly" enriched (attempted), but failed

    # ------------------------------------------------------------------ #
    #  Phase 7: Retry failed enrichments
    # ------------------------------------------------------------------ #

    async def _retry_failed(self) -> None:
        """Retry clusters whose previous enrichment failed."""
        failed = self.store.get_failed_enrichment_clusters(max_retries=3)
        if not failed:
            return

        self.logger.info("retry: failed_clusters=%d", len(failed))
        semaphore = asyncio.Semaphore(llm_concurrency(NEWS_EXTRACT_PROVIDER))
        rate = llm_rate_limit(NEWS_EXTRACT_PROVIDER)

        tasks = [
            self._enrich_one(c, str(c.get("topic") or "other"), semaphore, rate)
            for c in failed
        ]
        results = await asyncio.gather(*tasks, return_exceptions=False)

        by_topic: dict[str, list[dict]] = defaultdict(list)
        attempted = 0
        now_success = 0
        still_failed = 0
        for c2, was_new in results:
            if not was_new:
                continue
            attempted += 1
            # Clear failure marker on success
            if c2.get("enriched_at") and not c2.get("enrichment_failed_at"):
                c2.pop("enrichment_failed_at", None)
                c2.pop("enrichment_retry_count", None)
                now_success += 1
            else:
                still_failed += 1
            t = str(c2.get("topic") or "other").strip().lower()
            if t not in {x.lower() for x in DEFAULT_TOPICS}:
                t = "other"
            by_topic[t].append(c2)

        for t, group in by_topic.items():
            self.store.upsert_clusters(group, topic=t)
            self.logger.info("retry stored: topic=%s clusters=%d", t, len(group))

        if attempted:
            self.logger.info("retry done: attempted=%d recovered=%d still_failed=%d", attempted, now_success, still_failed)

    # ------------------------------------------------------------------ #
    #  Phase 8: Feed stats + prune
    # ------------------------------------------------------------------ #

    def _save_feed_stats(self, feed_stats: list[FeedStats]) -> None:
        """Log per-feed statistics. ingested = fetched - duplicate - stale - seen."""
        for fs in feed_stats:
            fs.ingested = max(0, fs.fetched - fs.duplicate - fs.stale - fs.seen)
            self.logger.info(
                "feed stats: feed_url=%s fetched=%d duplicate=%d stale=%d seen=%d ingested=%d",
                fs.feed_url, fs.fetched, fs.duplicate, fs.stale, fs.seen, fs.ingested,
            )

    def _prune_and_finalize(
        self,
        enabled_urls: list[str],
        feed_map: dict[str, list[dict]],
    ) -> None:
        """Run pruning and update feed_state hashes + timestamps."""
        prune_result = self.store.prune_if_due(
            pruning_enabled=NEWS_PRUNING_ENABLED,
            retention_days=NEWS_RETENTION_DAYS,
            interval_hours=NEWS_PRUNE_INTERVAL_HOURS,
        )

        # Update feed_state: hash + item_count for feeds that had changes
        for feed_url, feed_articles in feed_map.items():
            material = "\n".join(
                f"{a.get('title','')}|{a.get('url','')}"
                for a in feed_articles
            )
            content_hash = hashlib.sha1(material.encode("utf-8")).hexdigest()
            self.store.set_feed_state(feed_url, content_hash, len(feed_articles))

        # Drop legacy aggregate feed-state rows
        with self.store._conn() as conn:
            conn.execute("DELETE FROM feed_state WHERE feed_key LIKE 'newsfeeds:%'")

        self.store.set_meta("last_refresh_at", datetime.now(timezone.utc).isoformat())
        self.logger.info("prune: %s", prune_result)


# --------------------------------------------------------------------------- #
#  Compatibility wrapper (used by background loop + tests)
# --------------------------------------------------------------------------- #

async def refresh_clusters(topic: str | None = None, limit: int = 80) -> None:
    """Backward-compatible entry point. Delegates to ClusterPoller."""
    store = SQLiteClusterStore(DB_PATH)
    poller = ClusterPoller(store)
    await poller.poll(topic_filter=topic)