feat: tiered PDF processor with pypdfium2 fast path (deprecate pymupdf4llm)

Replaces single-engine pymupdf4llm extraction with a tiered pipeline (Deck #205,
follows the tier-0 classifier #855). pypdfium2 becomes the default and only
hot-path PDF extractor; pymupdf4llm is deprecated to a rollback toggle.

Why: pymupdf4llm's O(n^2) find_tables drove the OOM (#852) and the form-PDF
parse timeouts (#856), carries AGPL/commercial licensing liability, and -- per
the benchmarks -- recovers near-zero usable tables on the real corpus. pypdfium2
(Apache/BSD) extracts the same text far faster (Student 1a.pdf: 120s timeout ->
0.2s) with no table-detection bomb.

- document_processors/pypdfium2_fast.py: tier-1 "fast" processor emitting text +
  exact page_boundaries (the pdf_highlighter contract). pymupdf processor is now
  tier "structured" (the rollback engine), registered but not default.
- registry: tiered routing in ProcessorRegistry. tier-1 fast extracts, then
  classification is DERIVED from that text (classifier.classify_from_text -- no
  PDF re-open), records the classification metrics, and escalates scanned /
  no-text-layer docs to the "ocr" tier when document_ocr_enabled (default off;
  no provider yet, so fast is terminal). Wires record_document_escalation + the
  real "escalated" span attribute (was hardcoded False).
- Removes the separate _shadow_classify pass from vector/processor.py -- it
  re-opened every PDF and re-extracted text (~0.5-1.3s/doc of pure duplicated
  CPU that lowered throughput); classification now rides the tier-1 extraction.
- Settings: document_tier1_engine ("pypdfium2" default | "pymupdf" rollback,
  enum-validated), document_ocr_enabled (default false).

Tests: pypdfium2 extractor, registry tiering (fast routing, rollback, classify
recording, OCR escalation on/off), classify_from_text. Full unit suite green.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

tests/unit/test_doc_classifier.py

@@ -156,3 +156,23 @@ def test_image_heavy_flag_without_ocr_routing():
     assert "image_heavy" in c.flags
     assert c.recommended_tier == "fast"
     assert c.ocr_page_fraction < clf.OCR_PAGE_FRACTION
+
+
+# --- classify_from_text (hot-path, derived from tier-1 extraction) -----------
+
+
+def test_classify_from_text_clean_routes_fast():
+    txt = "the quick brown fox jumps over the lazy dog " * 3
+    c = clf.classify_from_text(
+        txt, [{"page": 1, "start_offset": 0, "end_offset": len(txt)}]
+    )
+    assert c.recommended_tier == "fast"
+    assert c.mean_text_quality > 0.8
+    assert c.flags == set()
+
+
+def test_classify_from_text_empty_routes_ocr():
+    c = clf.classify_from_text("", [{"page": 1, "start_offset": 0, "end_offset": 0}])
+    assert c.recommended_tier == "ocr"
+    assert "no_text_layer" in c.flags
+    assert c.total_chars == 0