feat(briefing): cache + map-reduce article context for rich discuss chats

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>

tests/test_article_reading.py

@@ -134,3 +134,83 @@ def test_history_builder_no_tool_calls_unchanged():
     assert len(history) == 2
     assert history[0] == {"role": "user", "content": "Hello"}
     assert history[1] == {"role": "assistant", "content": "Hi there!"}
+
+
+# ---------------------------------------------------------------------------
+# prepare_article_context tests
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.asyncio
+async def test_prepare_article_context_small_passthrough():
+    """Articles under CHAR_BUDGET pass through unchanged with zero LLM calls."""
+    from fabledassistant.services import article_context
+
+    body = "A short article.\n\nWith two paragraphs."
+    with patch(
+        "fabledassistant.services.article_context.generate_completion",
+        new_callable=AsyncMock,
+    ) as mock_gen:
+        out = await article_context.prepare_article_context(
+            "Title", "https://example.com", body, "test-model",
+        )
+
+    assert out == body
+    mock_gen.assert_not_called()
+
+
+@pytest.mark.asyncio
+async def test_prepare_article_context_large_runs_map_reduce():
+    """Articles over CHAR_BUDGET are chunked and map-reduced via the background model."""
+    from fabledassistant.services import article_context
+
+    # CHAR_BUDGET is 48_000 — build a body well over that with paragraph breaks
+    # so the chunker has natural splits to work with.
+    paragraph = ("Lorem ipsum dolor sit amet, consectetur adipiscing elit. " * 40).strip()
+    body = "\n\n".join([paragraph] * 30)  # ~70k+ chars across 30 paragraphs
+    assert len(body) > article_context.CHAR_BUDGET
+
+    with patch(
+        "fabledassistant.services.article_context.generate_completion",
+        new_callable=AsyncMock,
+        return_value="Summary of this section with specific claims preserved.",
+    ) as mock_gen:
+        out = await article_context.prepare_article_context(
+            "Long Article", "https://example.com/long", body, "test-model",
+        )
+
+    # At least one LLM call fired (the map step ran)
+    assert mock_gen.await_count >= 1
+    # Output carries the oversized-article header and section markers
+    assert "longer than the chat window" in out
+    assert "## Section 1" in out
+    # Map output is much smaller than the raw body
+    assert len(out) < len(body)
+
+
+def test_chunk_by_paragraph_respects_boundaries():
+    """Chunker splits on paragraph breaks, not mid-sentence."""
+    from fabledassistant.services.article_context import _chunk_by_paragraph, CHUNK_CHARS
+
+    paragraphs = [f"Paragraph {i}. " + ("x" * 1000) for i in range(20)]
+    body = "\n\n".join(paragraphs)
+
+    chunks = _chunk_by_paragraph(body)
+
+    # Each chunk stays under the budget
+    for chunk in chunks:
+        assert len(chunk) <= CHUNK_CHARS
+    # Total content is preserved (modulo overlap duplication, so ≥ original)
+    assert sum(len(c) for c in chunks) >= len(body) * 0.9
+
+
+def test_chunk_by_paragraph_handles_oversized_paragraph():
+    """A single paragraph larger than CHUNK_CHARS gets split mid-paragraph."""
+    from fabledassistant.services.article_context import _chunk_by_paragraph, CHUNK_CHARS
+
+    body = "x" * (CHUNK_CHARS * 3)  # one huge paragraph, no breaks
+    chunks = _chunk_by_paragraph(body)
+
+    assert len(chunks) >= 3
+    for chunk in chunks:
+        assert len(chunk) <= CHUNK_CHARS