feat(article-discuss): unify /news + briefing entry points, persist summaries to RAG
Both the /news discuss button and the briefing discuss button now call a shared seed_article_discussion() helper that stages the synthetic read_article tool exchange and the conversational seed prompt — behavior stays byte-identical across entry points. /news also auto-starts generation so the chat screen lands on an in-flight stream. First assistant reply in a seeded article conversation is persisted as a Note (tags: article-summary + article topics) and backlinked via rss_items.discussion_note_id, so the knowledge base stops being amnesiac about articles the user has engaged with. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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@@ -532,54 +532,15 @@ async def discuss_article(item_id: int):
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if get_buffer(conv_id) is not None:
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return jsonify({"error": "Generation already in progress"}), 409
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# Three-layer cache: context_prepared (post-map-reduce) → content_full
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# (raw trafilatura) → fresh fetch. Only the first miss pays the fetch
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# cost; only a large uncached article pays the map-reduce cost. Repeat
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# clicks on the same article skip straight to the chat turn.
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from fabledassistant.services.article_context import prepare_article_context
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from fabledassistant.services.rss import get_or_fetch_full_article
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# Shared helper handles the three-layer cache (context_prepared →
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# content_full → fresh fetch), writes the synthetic read_article tool
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# exchange and the conversational seed user prompt into the conversation.
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# The /news from-article route calls the same helper so behavior stays
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# byte-identical across entry points.
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from fabledassistant.services.article_context import seed_article_discussion
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model = await get_setting(uid, "default_model", "") or ""
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if item.context_prepared:
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article_content = item.context_prepared
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else:
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raw_body = await get_or_fetch_full_article(item) or item.content or ""
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article_content = await prepare_article_context(
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item.title or "", item.url, raw_body, model,
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)
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if article_content:
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async with async_session() as session:
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fresh = await session.get(RssItem, item.id)
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if fresh is not None:
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fresh.context_prepared = article_content
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await session.commit()
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# Store synthetic assistant message with read_article tool result
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synthetic_tool_calls = [{
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"function": "read_article",
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"arguments": {"url": item.url},
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"result": {
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"success": True,
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"type": "article_content",
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"url": item.url,
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"content": article_content,
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"truncated": False,
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},
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}]
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await add_message(conv_id, "assistant", "", status="complete", tool_calls=synthetic_tool_calls)
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# Conversational seed — invites a real discussion rather than asking for
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# a one-shot summary. The model sees the article context in the tool
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# result above and responds to this user turn as the start of an ongoing
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# conversation the user will steer with follow-ups.
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discuss_prompt = (
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"I want to talk about this article. Start with a substantive summary "
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"of what it's arguing and the key evidence it uses, then tell me what "
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"stood out to you or seems worth pushing back on. I'll ask follow-ups "
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"from there."
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)
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await add_message(conv_id, "user", discuss_prompt)
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discuss_prompt = await seed_article_discussion(conv_id, item, model)
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# Reload conversation with fresh messages to build history
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conv = await get_conversation(uid, conv_id)
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@@ -510,47 +510,66 @@ async def delete_model_route():
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@chat_bp.route("/from-article/<int:item_id>", methods=["POST"])
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@login_required
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async def create_conversation_from_article(item_id: int):
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"""Create a chat conversation seeded with an RSS article's content."""
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"""Create a chat conversation seeded for article discussion and auto-run.
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Mirrors the briefing ``discuss_article`` route: creates a fresh
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conversation, stages the shared synthetic read_article exchange + seed
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prompt, then kicks off generation so the client lands on an in-flight
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stream. The Flutter and web chat screens reconnect to the running buffer
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on mount.
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"""
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from sqlalchemy import select as _select
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from fabledassistant.models import async_session as _async_session
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from fabledassistant.models.conversation import Message
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from fabledassistant.models.rss_feed import RssItem, RssFeed
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from fabledassistant.services.article_context import seed_article_discussion
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uid = get_current_user_id()
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async with _async_session() as session:
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result = await session.execute(
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_select(RssItem, RssFeed.title.label("feed_title"))
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_select(RssItem)
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.join(RssFeed, RssItem.feed_id == RssFeed.id)
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.where(RssItem.id == item_id, RssFeed.user_id == uid)
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)
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row = result.first()
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item = result.scalars().first()
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if row is None:
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if item is None:
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return jsonify({"error": "Article not found"}), 404
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item, feed_title = row
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conv_title = (item.title or "Article discussion")[:80]
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conv = await create_conversation(uid, title=conv_title, conversation_type="chat")
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from fabledassistant.services.rss import _fetch_full_article
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source = feed_title or "News"
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content_body = (await _fetch_full_article(item.url) if item.url else None) or (item.content or "").strip()
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seeded_text = f"**{source}**\n\n**{item.title}**"
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if content_body:
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seeded_text += f"\n\n{content_body}"
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if item.url:
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seeded_text += f"\n\nSource: {item.url}"
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model = await get_setting(uid, "default_model", "") or Config.OLLAMA_MODEL
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discuss_prompt = await seed_article_discussion(conv.id, item, model)
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async with _async_session() as session:
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msg = Message(
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conversation_id=conv.id,
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role="assistant",
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content=seeded_text,
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msg_metadata={"rss_item_ids": [item_id]},
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)
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session.add(msg)
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await session.commit()
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# Reload conversation so we see the two messages the helper just added.
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conv = await get_conversation(uid, conv.id)
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assert conv is not None
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return jsonify({"conversation_id": conv.id}), 201
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history: list[dict] = []
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for msg in conv.messages:
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if msg.role == "system":
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continue
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msg_dict: dict = {"role": msg.role, "content": msg.content or ""}
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if msg.tool_calls:
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msg_dict["tool_calls"] = [
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{"function": {"name": tc["function"], "arguments": tc["arguments"]}}
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for tc in msg.tool_calls
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]
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history.append(msg_dict)
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for tc in msg.tool_calls:
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history.append({"role": "tool", "content": json.dumps(tc.get("result", {}))})
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else:
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history.append(msg_dict)
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assistant_msg = await add_message(conv.id, "assistant", "", status="generating")
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buf = create_buffer(conv.id, assistant_msg.id)
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asyncio.create_task(run_generation(
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buf, history, model, uid, conv.id, conv.title or "", discuss_prompt,
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))
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return jsonify({
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"conversation_id": conv.id,
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"assistant_message_id": assistant_msg.id,
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"status": "generating",
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}), 202
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