The Discuss button on news cards was producing one-shot replies because
the model got the whole trafilatura blob dropped into history with a
canned "summarize and discuss this article" prompt — no length guard, no
prep, no invitation to converse. Large articles got silently truncated by
Ollama; small articles got a tepid reply.
This reworks discuss_article around a three-layer cache:
context_prepared → content_full → fresh trafilatura fetch
First click on a small article fetches once, writes through to both
caches, and passes the body straight into the synthetic read_article
tool-result. First click on a large article additionally runs a parallel
map step (services/article_context.py) that chunks the body on paragraph
boundaries, summarizes each ~8k chunk to ~300 words of dense factual
prose via the background model, and concatenates the summaries under
section headers — all pinned to num_ctx=16384 so the map step doesn't
itself fall victim to silent truncation. Repeat clicks on either path
skip straight to the chat turn.
The canned summary prompt is replaced with a conversational seed that
invites the user into an actual discussion rather than a one-shot
synopsis, matching the goal of "have a conversation about an article,
not just read it."
discuss_topic is intentionally left untouched — it's the multi-article
aggregation path and needs a separate rework. Follow-up task will decide
whether to retire it or rework it on the cached-context approach.
Closes task #106.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Trafilatura extracts only article body text (typically 2K–15K chars),
so storing the full content is safe without an artificial ceiling.
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>
feedparser returns HTML in content/summary fields for many feeds.
Raw tags were being stored in the DB and passed to the LLM/embeddings.
Added a stdlib HTMLParser-based stripper in extract_item() — block elements
become newlines, script/style content is dropped, plain text passes through.
No new dependencies required.
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>
bvandeusen