Drift-audit Group 9 (param-cliff / unbounded search work):
- semantic_search_notes: the O(rows) cosine-similarity scoring loop ran
synchronously on the event loop, so every RAG injection / search stalled
other requests proportional to the user's embedding count. Move the scoring
into asyncio.to_thread (results unchanged). The deeper fix — bounding the
candidate set via pgvector ORDER BY/LIMIT — is noted as separate infra work.
- _semantic_knowledge_search: documented the best-effort top-N semantics —
is the capped candidate-window size (not the true match count),
matches beyond the cap aren't page-reachable, and each page recomputes the
full merge. Prevents the silent-truncation trap; cached ranked-id paging /
pgvector is the fix if exhaustive pagination is ever required.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Replaces the Ollama HTTP get_embedding with a fastembed.TextEmbedding
singleton loaded lazily on first call. Model: BAAI/bge-small-en-v1.5
(384-dim), cached to /data/fastembed-cache.
Public API unchanged:
- get_embedding(text, model=None) — `model` now silently ignored
- upsert_note_embedding
- semantic_search_notes
- backfill_note_embeddings
_cosine_similarity gains a defensive length-mismatch check so any
stale 768-dim row that survived the migration is treated as 0.0
similarity rather than crashing zip().
The Ollama client dep stays in pyproject for now (other services still
use it); Phase 7 removes it once chat/journal/curator are gone.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Removes the entire RSS feature surface — feeds, items, embeddings, reactions,
discussion-note flow, briefing news context, settings, env-vars, and DB
tables. Keeps the URL-generic article-reader (the read_article LLM tool)
under a clean module so the LLM can still fetch arbitrary article content
from URLs the user provides.
Backend:
- New services/article_fetcher.py — single source of trafilatura URL→text
- New services/tools/article.py — read_article tool (was nested under tools/rss)
- Delete services/rss.py, rss_classifier.py, rss_filtering.py, article_context.py
- Delete services/tools/rss.py
- Delete models/rss_feed.py (RssFeed, RssItem), models/rss_item_embedding.py
- services/embeddings.py: drop upsert/semantic_search/backfill RSS helpers
- services/llm.py: remove _build_briefing_article_context, briefing-conv branch,
ARTICLE_DISCUSS_SEED skip-RAG branch; drop get_rss_items / add_rss_feed from
the actions list
- services/generation_task.py: drop _maybe_save_article_discussion_note + caller
- routes/chat.py: drop /api/chat/from-article/<id> endpoint
- routes/journal.py: re-import via web.py refactor (article_fetcher path)
- services/tools/__init__.py: register `article`, drop `rss`
- services/tools/_registry.py: drop the requires=='rss' check
- app.py: drop backfill_rss_item_embeddings + backfill_rss_article_content tasks
- config.py: prose-only edit (no env var change — RSS env vars were never first-class)
Frontend:
- stores/settings.ts: drop rssEnabled
- SettingsView.vue: drop the RSS-classification mention
- api/client.ts: drop openArticleInChat (the from-article endpoint is gone)
Tests:
- Delete tests/test_rss_service.py, test_news_api.py, test_article_reading.py
Migration:
- 0042_drop_rss: DROP TABLE rss_item_embeddings, rss_item_reactions, rss_items,
rss_feeds; DELETE settings rows for rss_enabled / briefing_*_topics
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
CONTENT_MAX_CHARS was removed from rss.py when the article content cap
was lifted. backfill_rss_article_content still referenced it, causing an
ImportError on startup.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Add trafilatura + html2text to dependencies
- Replace custom HTMLStripper with html2text for RSS feed content
- Fetch full article text via httpx + trafilatura after each new item is stored;
falls back to RSS-provided content if fetch/extraction fails
- Raise CONTENT_MAX_CHARS from 2000 to 50000 (TEXT column, no migration needed)
- Re-embed items with full article content once enrichment completes
- Startup backfill enriches existing items with short content (<1000 chars)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- 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>
create_note now only checks against existing notes (is_task=False);
create_task only checks against existing tasks (is_task=True). All three
checks (exact title, fuzzy title, semantic similarity) pass the type
filter through to list_notes and semantic_search_notes.
Adds is_task param to semantic_search_notes() so callers can restrict
results to notes or tasks independently.
Prevents a note titled "Design enemy AI" from blocking a task with the
same title, and vice versa.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Raise similarity threshold 0.30 → 0.45: only genuinely relevant notes
shown; loosely-related notes no longer pad the sidebar
- Increase max suggested notes 3 → 8 (zero added compute — threshold is
the real gate; the embedding call is fixed regardless of limit)
- semantic_search_notes now returns list[tuple[float, Note]] instead of
list[Note] so scores propagate through context_meta to the frontend
- Keyword fallback notes carry score=null (no cosine similarity available)
- ChatView sidebar shows % badge on each suggested note:
green ≥75%, amber 60–74%, muted <60%
Hovering reveals the raw score in a tooltip
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- New NoteEmbedding model + migration 0014 stores float embeddings (JSONB)
- services/embeddings.py: get_embedding, upsert_note_embedding,
semantic_search_notes (cosine similarity), backfill_note_embeddings
- build_context() now tries semantic search first, falls back to keyword search;
accepts cached_note_ids to reuse last-turn notes and stabilise the system
prompt prefix for Ollama's KV cache
- generation_buffer.py: per-conversation note ID cache (get/set/clear)
- generation_task.py: passes cached IDs into build_context, updates cache
after each turn, and invalidates it after create_note/update_note/create_task
- app.py: pulls nomic-embed-text at startup and launches a background backfill
to embed all existing notes (30 s delay so Ollama has time to load the model)
- routes/notes.py + services/tools.py: fire-and-forget embedding update on
every note create or update via the API or LLM tool calls
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
bvandeusen