feat: RSS embeddings, semantic news in chat, article-to-chat, richer briefings

- Embed RSS items at fetch time (nomic-embed-text); backfill at startup
- Semantic news search injected into chat system prompt ("Recent News You've Seen")
  when items match query above 0.55 cosine threshold (independent of note RAG)
- "Discuss in chat" button on news cards — creates a seeded conversation with
  the article title + full content, navigates directly to the new chat
- Briefing compilation now passes 500-char article excerpts (not just headlines)
  to the LLM and uses 8192 num_ctx to accommodate the larger prompt

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

alembic/versions/0035_add_rss_item_embeddings.py

@@ -0,0 +1,28 @@
+"""Add rss_item_embeddings table for semantic news search."""
+
+import sqlalchemy as sa
+from alembic import op
+from sqlalchemy.dialects.postgresql import JSONB
+
+revision = "0035"
+down_revision = "0034"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    op.create_table(
+        "rss_item_embeddings",
+        sa.Column("rss_item_id", sa.Integer(), sa.ForeignKey("rss_items.id", ondelete="CASCADE"), primary_key=True),
+        sa.Column("user_id", sa.Integer(), nullable=False),
+        sa.Column("embedding", JSONB(), nullable=False),
+        sa.Column("updated_at", sa.DateTime(timezone=True), nullable=False, server_default=sa.func.now()),
+    )
+    op.create_index("ix_rss_item_embeddings_user_id", "rss_item_embeddings", ["user_id"])
+    op.create_index("ix_rss_item_embeddings_rss_item_id", "rss_item_embeddings", ["rss_item_id"])
+
+
+def downgrade() -> None:
+    op.drop_index("ix_rss_item_embeddings_rss_item_id", table_name="rss_item_embeddings")
+    op.drop_index("ix_rss_item_embeddings_user_id", table_name="rss_item_embeddings")
+    op.drop_table("rss_item_embeddings")

frontend/src/api/client.ts

@@ -426,6 +426,10 @@ export async function deleteRssReaction(rssItemId: number): Promise<void> {
   return apiDelete(`/api/briefing/rss-reactions/${rssItemId}`);
 }
 
+export async function openArticleInChat(itemId: number): Promise<{ conversation_id: number }> {
+  return apiPost(`/api/chat/from-article/${itemId}`, {});
+}
+
 export async function geocodeAddress(address: string): Promise<{ lat: number; lon: number; display_name: string } | null> {
   try {
     const r = await apiPost<{ lat: number; lon: number; label: string }>('/api/briefing/weather/geocode', { query: address });

frontend/src/views/NewsView.vue

@@ -1,14 +1,18 @@
 <script setup lang="ts">
 import { ref, onMounted } from 'vue'
+import { useRouter } from 'vue-router'
 import {
   getBriefingFeeds,
   postRssReaction,
   deleteRssReaction,
   getNewsItems,
+  openArticleInChat,
   type BriefingFeed,
 } from '@/api/client'
 import type { NewsItem } from '@/types/news'
 
+const router = useRouter()
+
 const LIMIT = 40
 
 const items = ref<NewsItem[]>([])
@@ -20,6 +24,8 @@ const selectedFeedId = ref<number | null>(null)
 
 // Reactions map: item id → current reaction
 const reactions = ref<Record<number, 'up' | 'down' | null>>({})
+// Track which items are currently being opened in chat
+const openingChat = ref<Set<number>>(new Set())
 
 async function loadMore() {
   if (loading.value || !hasMore.value) return
@@ -79,6 +85,19 @@ function formatRelativeDate(iso: string | null): string {
   return d.toLocaleDateString(undefined, { month: 'short', day: 'numeric' })
 }
 
+async function openInChat(itemId: number) {
+  if (openingChat.value.has(itemId)) return
+  openingChat.value.add(itemId)
+  try {
+    const result = await openArticleInChat(itemId)
+    router.push(`/chat/${result.conversation_id}`)
+  } catch {
+    // silently fail — button returns to enabled state
+  } finally {
+    openingChat.value.delete(itemId)
+  }
+}
+
 onMounted(async () => {
   feeds.value = await getBriefingFeeds().catch(() => [])
   await loadMore()
@@ -145,6 +164,13 @@ onMounted(async () => {
             @click="handleReaction(item.id, 'down')"
             title="Not interested"
           >👎</button>
+          <button
+            class="reaction-btn open-chat-btn"
+            :class="{ busy: openingChat.has(item.id) }"
+            :disabled="openingChat.has(item.id)"
+            @click="openInChat(item.id)"
+            title="Discuss in chat"
+          >{{ openingChat.has(item.id) ? '…' : '💬' }}</button>
         </div>
       </div>
 
@@ -335,6 +361,15 @@ a.news-card-title:hover {
   background: color-mix(in srgb, var(--color-primary) 12%, transparent);
 }
 
+.open-chat-btn {
+  margin-left: auto;
+}
+
+.open-chat-btn.busy {
+  opacity: 0.4;
+  cursor: wait;
+}
+
 .news-footer {
   display: flex;
   justify-content: center;

src/fabledassistant/app.py

@@ -261,6 +261,11 @@ def create_app() -> Quart:
                 await backfill_project_summaries()
             except Exception:
                 logger.warning("Project summary backfill failed", exc_info=True)
+            try:
+                from fabledassistant.services.embeddings import backfill_rss_item_embeddings
+                await backfill_rss_item_embeddings()
+            except Exception:
+                logger.warning("RSS embedding backfill failed", exc_info=True)
 
         asyncio.create_task(_delayed_backfill())
 

src/fabledassistant/models/__init__.py

@@ -42,3 +42,4 @@ from fabledassistant.models.rss_feed import RssFeed, RssItem  # noqa: E402, F401
 from fabledassistant.models.weather_cache import WeatherCache  # noqa: E402, F401
 from fabledassistant.models.api_key import ApiKey  # noqa: E402, F401
 from fabledassistant.models.user_profile import UserProfile  # noqa: E402, F401
+from fabledassistant.models.rss_item_embedding import RssItemEmbedding  # noqa: E402, F401

src/fabledassistant/models/rss_item_embedding.py

@@ -0,0 +1,25 @@
+from datetime import datetime, timezone
+
+from sqlalchemy import DateTime, ForeignKey, Integer
+from sqlalchemy.dialects.postgresql import JSONB
+from sqlalchemy.orm import Mapped, mapped_column
+
+from fabledassistant.models import Base
+
+
+class RssItemEmbedding(Base):
+    """Stores the embedding vector for an RSS item, used for semantic news search."""
+
+    __tablename__ = "rss_item_embeddings"
+
+    rss_item_id: Mapped[int] = mapped_column(
+        Integer,
+        ForeignKey("rss_items.id", ondelete="CASCADE"),
+        primary_key=True,
+    )
+    user_id: Mapped[int] = mapped_column(Integer, nullable=False, index=True)
+    embedding: Mapped[list] = mapped_column(JSONB, nullable=False)
+    updated_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True),
+        default=lambda: datetime.now(timezone.utc),
+    )

src/fabledassistant/routes/chat.py

@@ -502,3 +502,52 @@ async def delete_model_route():
     except Exception as e:
         logger.warning("Failed to delete model %s: %s", model_name, e)
         return jsonify({"error": str(e)}), 500
+
+
+@chat_bp.route("/from-article/<int:item_id>", methods=["POST"])
+@login_required
+async def create_conversation_from_article(item_id: int):
+    """Create a chat conversation seeded with an RSS article's content."""
+    from sqlalchemy import select as _select
+    from fabledassistant.models import async_session as _async_session
+    from fabledassistant.models.rss_feed import RssItem, RssFeed
+
+    uid = get_current_user_id()
+
+    async with _async_session() as session:
+        result = await session.execute(
+            _select(RssItem, RssFeed.title.label("feed_title"))
+            .join(RssFeed, RssItem.feed_id == RssFeed.id)
+            .where(RssItem.id == item_id, RssFeed.user_id == uid)
+        )
+        row = result.first()
+
+    if row is None:
+        return jsonify({"error": "Article not found"}), 404
+
+    item, feed_title = row
+
+    conv_title = (item.title or "Article discussion")[:80]
+    conv = await create_conversation(uid, title=conv_title, conversation_type="chat")
+
+    source = feed_title or "News"
+    content_body = (item.content or "").strip()
+    seeded_text = f"**{source}**\n\n**{item.title}**"
+    if content_body:
+        seeded_text += f"\n\n{content_body}"
+    if item.url:
+        seeded_text += f"\n\nSource: {item.url}"
+
+    from fabledassistant.models.conversation import Message
+    from fabledassistant.models import async_session as _session2
+    async with _session2() as session:
+        msg = Message(
+            conversation_id=conv.id,
+            role="assistant",
+            content=seeded_text,
+            msg_metadata={"rss_item_ids": [item_id]},
+        )
+        session.add(msg)
+        await session.commit()
+
+    return jsonify({"conversation_id": conv.id}), 201

src/fabledassistant/services/briefing_pipeline.py

@@ -233,7 +233,7 @@ async def _gather_external(user_id: int) -> dict:
 
 # ── LLM synthesis ─────────────────────────────────────────────────────────────
 
-async def _llm_synthesise(system_prompt: str, user_prompt: str, model: str) -> str:
+async def _llm_synthesise(system_prompt: str, user_prompt: str, model: str, num_ctx: int = 4096) -> str:
     """Single non-streaming LLM call. Returns the assistant's response text."""
     payload = {
         "model": model,
@@ -242,7 +242,7 @@ async def _llm_synthesise(system_prompt: str, user_prompt: str, model: str) -> s
             {"role": "user", "content": user_prompt},
         ],
         "stream": False,
-        "options": {"num_ctx": 4096, "temperature": 0.4},
+        "options": {"num_ctx": num_ctx, "temperature": 0.4},
     }
     try:
         async with httpx.AsyncClient(timeout=120.0) as client:
@@ -309,13 +309,17 @@ def _unified_user_prompt(internal_data: dict, external_data: dict, slot: str, te
             lines.append(f"  (and {len(overdue) - 3} more)")
         lines.append("")
 
-    # News highlights (top 3 — right panel shows full list)
+    # News highlights (top 3 with excerpts — right panel shows full list)
     rss = external_data.get("rss_items") or []
     if rss:
-        lines.append("NEWS HIGHLIGHTS (mention 1-2 briefly, the full list is shown separately):")
+        lines.append("NEWS HIGHLIGHTS (weave 1-2 into your briefing naturally; the full list is shown separately):")
         for item in rss[:3]:
             source = item.get("feed_title") or item.get("source") or "News"
-            lines.append(f"  [{source}] {item.get('title', '')}")
+            title = item.get("title", "")
+            excerpt = (item.get("content") or item.get("snippet") or "")[:500].strip()
+            lines.append(f"  [{source}] {title}")
+            if excerpt:
+                lines.append(f"    {excerpt}")
         lines.append("")
 
     return "\n".join(lines)
@@ -427,6 +431,7 @@ async def run_compilation(
         _unified_system_prompt(profile_context),
         _unified_user_prompt(internal_data_filtered, external_data_filtered, slot, temp_unit),
         model,
+        num_ctx=8192,
     )
 
     # ── Post-processing ─────────────────────────────────────────────────────────

src/fabledassistant/services/embeddings.py

@@ -1,6 +1,7 @@
 """Semantic note search via Ollama embedding model (nomic-embed-text).
 
 Embeddings are stored in the note_embeddings table (one row per note).
+RSS item embeddings are stored in rss_item_embeddings (one row per item).
 All search operations degrade gracefully — if the embedding model is
 unavailable the callers fall back to keyword search.
 """
@@ -8,6 +9,7 @@ unavailable the callers fall back to keyword search.
 import asyncio
 import logging
 import math
+from datetime import datetime, timedelta, timezone
 
 import httpx
 from sqlalchemy import delete, select
@@ -16,6 +18,8 @@ from fabledassistant.config import Config
 from fabledassistant.models import async_session
 from fabledassistant.models.embedding import NoteEmbedding
 from fabledassistant.models.note import Note
+from fabledassistant.models.rss_feed import RssItem
+from fabledassistant.models.rss_item_embedding import RssItemEmbedding
 
 logger = logging.getLogger(__name__)
 
@@ -24,6 +28,10 @@ logger = logging.getLogger(__name__)
 # 0.45 keeps only genuinely relevant notes; lower values like 0.30 let in
 # loosely-related results that pad the sidebar without adding real value.
 _SIMILARITY_THRESHOLD = 0.45
+_RSS_SIMILARITY_THRESHOLD = 0.55
+_RSS_SEARCH_LIMIT = 3
+_RSS_SEARCH_DAYS = 30
+_RSS_SNIPPET_CHARS = 500
 
 
 async def get_embedding(text: str, model: str | None = None) -> list[float]:
@@ -172,3 +180,120 @@ async def backfill_note_embeddings() -> None:
         await asyncio.sleep(0.05)  # gentle pacing
 
     logger.info("Embedding backfill complete: %d/%d notes embedded", success, len(notes_to_embed))
+
+
+# ── RSS item embeddings ───────────────────────────────────────────────────────
+
+async def upsert_rss_item_embedding(item_id: int, user_id: int, title: str, content: str) -> None:
+    """Generate and persist an embedding for an RSS item. Safe to fire-and-forget."""
+    text = f"{title}\n{content}".strip()
+    if not text:
+        return
+    try:
+        embedding = await get_embedding(text)
+    except Exception:
+        logger.debug("Skipping embedding for RSS item %d — model unavailable", item_id)
+        return
+
+    try:
+        async with async_session() as session:
+            await session.execute(
+                delete(RssItemEmbedding).where(RssItemEmbedding.rss_item_id == item_id)
+            )
+            session.add(RssItemEmbedding(rss_item_id=item_id, user_id=user_id, embedding=embedding))
+            await session.commit()
+        logger.debug("Upserted embedding for RSS item %d", item_id)
+    except Exception:
+        logger.warning("Failed to persist embedding for RSS item %d", item_id, exc_info=True)
+
+
+async def semantic_search_rss_items(
+    user_id: int,
+    query_vector: list[float],
+    limit: int = _RSS_SEARCH_LIMIT,
+    days: int = _RSS_SEARCH_DAYS,
+) -> list[tuple[float, RssItem]]:
+    """Return up to *limit* (score, RssItem) pairs most relevant to *query_vector*.
+
+    Only considers items fetched within the last *days* days.
+    Returns an empty list on any error.
+    """
+    since = datetime.now(timezone.utc) - timedelta(days=days)
+    try:
+        async with async_session() as session:
+            stmt = (
+                select(RssItemEmbedding, RssItem)
+                .join(RssItem, RssItemEmbedding.rss_item_id == RssItem.id)
+                .where(
+                    RssItemEmbedding.user_id == user_id,
+                    RssItem.fetched_at >= since,
+                )
+            )
+            rows = list((await session.execute(stmt)).all())
+    except Exception:
+        logger.warning("Failed to query RSS item embeddings", exc_info=True)
+        return []
+
+    if not rows:
+        return []
+
+    scored: list[tuple[float, RssItem]] = []
+    for rie, item in rows:
+        try:
+            sim = _cosine_similarity(query_vector, rie.embedding)
+        except Exception:
+            continue
+        if sim >= _RSS_SIMILARITY_THRESHOLD:
+            scored.append((sim, item))
+
+    scored.sort(key=lambda x: x[0], reverse=True)
+    return scored[:limit]
+
+
+async def backfill_rss_item_embeddings() -> None:
+    """Generate embeddings for all RSS items that don't have one yet.
+
+    Runs as a background task at startup. Adds a small sleep between items
+    to avoid overwhelming Ollama.
+    """
+    try:
+        async with async_session() as session:
+            existing = {
+                row[0]
+                for row in (
+                    await session.execute(select(RssItemEmbedding.rss_item_id))
+                ).fetchall()
+            }
+            result = await session.execute(
+                select(RssItem.id, RssItem.feed_id, RssItem.title, RssItem.content)
+            )
+            items_to_embed = [row for row in result.fetchall() if row[0] not in existing]
+    except Exception:
+        logger.warning("RSS embedding backfill: failed to query items", exc_info=True)
+        return
+
+    if not items_to_embed:
+        logger.info("RSS embedding backfill: all items already have embeddings")
+        return
+
+    # Resolve user_id per feed_id
+    try:
+        from fabledassistant.models.rss_feed import RssFeed
+        async with async_session() as session:
+            result = await session.execute(select(RssFeed.id, RssFeed.user_id))
+            feed_user_map = {fid: uid for fid, uid in result.fetchall()}
+    except Exception:
+        logger.warning("RSS embedding backfill: failed to load feed user map", exc_info=True)
+        return
+
+    logger.info("RSS embedding backfill: generating embeddings for %d items", len(items_to_embed))
+    success = 0
+    for item_id, feed_id, title, content in items_to_embed:
+        user_id = feed_user_map.get(feed_id)
+        if user_id is None:
+            continue
+        await upsert_rss_item_embedding(item_id, user_id, title or "", content or "")
+        success += 1
+        await asyncio.sleep(0.05)
+
+    logger.info("RSS embedding backfill complete: %d/%d items embedded", success, len(items_to_embed))

src/fabledassistant/services/llm.py

@@ -668,6 +668,33 @@ async def build_context(
                 + "\n--- End Included Notes ---"
             )
 
+    # Search for semantically relevant recent news items
+    try:
+        from fabledassistant.services.embeddings import get_embedding, semantic_search_rss_items
+        news_query_vec = await get_embedding(user_message)
+        news_hits = await semantic_search_rss_items(user_id, news_query_vec)
+        if news_hits:
+            news_snippets = []
+            for score, rss_item in news_hits:
+                feed_title = getattr(rss_item, "feed_title", "") or ""
+                excerpt = (rss_item.content or "")[:500].strip()
+                news_snippets.append(
+                    f"[{feed_title or 'News'}] {rss_item.title} (relevance: {round(score * 100)}%)\n"
+                    + (f"{excerpt}\n" if excerpt else "")
+                    + f"URL: {rss_item.url}"
+                )
+            system_parts.append(
+                "\n\n--- Recent News You've Seen ---\n"
+                + "\n\n".join(news_snippets)
+                + "\n--- End Recent News ---"
+            )
+            context_meta["rss_news"] = [
+                {"id": item.id, "title": item.title, "score": round(score, 2)}
+                for score, item in news_hits
+            ]
+    except Exception:
+        logger.debug("RSS semantic search skipped", exc_info=True)
+
     # Fetch URL content from user message
     urls = _find_urls(user_message)
     for url in urls[:2]:  # Limit to 2 URLs

src/fabledassistant/services/rss.py

@@ -112,14 +112,18 @@ async def fetch_and_cache_feed(feed_id: int, url: str) -> int:
         # only writes to items it successfully classifies, so already-classified items
         # are not re-processed (they have classified_at set).
         unclassified_ids: list[int] = []
+        new_item_data: list[tuple[int, str, str]] = []  # (id, title, content) for embedding
         if new_count > 0:
             result = await session.execute(
-                select(RssItem.id).where(
+                select(RssItem.id, RssItem.title, RssItem.content, RssItem.classified_at).where(
                     RssItem.feed_id == feed_id,
-                    RssItem.classified_at.is_(None),
                 )
             )
-            unclassified_ids = list(result.scalars().all())
+            for row in result.fetchall():
+                item_id, title, content, classified_at = row
+                if classified_at is None:
+                    unclassified_ids.append(item_id)
+                new_item_data.append((item_id, title or "", content or ""))
 
     # Prune old items to keep DB tidy
     await _prune_old_items(feed_id)
@@ -129,6 +133,17 @@ async def fetch_and_cache_feed(feed_id: int, url: str) -> int:
         from fabledassistant.services.rss_classifier import classify_and_store
         asyncio.create_task(classify_and_store(unclassified_ids, feed_user_id))
 
+    # Fire-and-forget embedding for new items
+    if new_item_data and feed_user_id is not None:
+        from fabledassistant.services.embeddings import upsert_rss_item_embedding
+
+        async def _embed_new_items() -> None:
+            for item_id, title, content in new_item_data:
+                await upsert_rss_item_embedding(item_id, feed_user_id, title, content)
+                await asyncio.sleep(0.05)
+
+        asyncio.create_task(_embed_new_items())
+
     return new_count