feat(quality): A3 — L1 LLM judge via API (5 chunks, 0.0003 USD/job)

L1 quality layer — uses a cheap LLM via the OpenAI-compatible API to
validate translation quality. Designed to be the SECOND line of defense
after L0 (script detection, length, pattern).

Architecture:
  - sampler.py — picks 5 representative chunks per job (longest first,
    skips L0-failed indices, skips too-short or identical pairs)
  - llm_judge.py — OpenAI-compatible client, binary verdict per chunk
    (accurate / fluent / correct_language / no_leaks), JSON output,
    hard timeout, defensive (never raises), cost estimation built in
  - pipeline.py — defensive wrapper that integrates both, never breaks
    a translation job, always logs a structured event

Integration:
  - 5 feature flags in config.py (QUALITY_L1_ENABLED, _LOG_ONLY, etc.)
  - QUALITY_L1_LOG_ONLY=true by default: log-only mode, verdict NEVER
    blocks or retries a job
  - Reuses the chunks extracted by L0 (no double work)
  - Passes the set of L0-failed indices so L1 doesn't re-judge them
  - Wrapped in try/except so a misconfigured L1 NEVER breaks a job

Default config: deepseek-chat via DeepSeek API
  - Cost: ~0.0003 USD per job (5 chunks)
  - Speed: typically 1-2s per call, hard ceiling at 8s
  - Easy to swap: just set L1_JUDGE_BASE_URL and L1_JUDGE_MODEL

LLM judge is intentionally a SEPARATE model from the translator
(self-evaluation bias mitigation — Meta/Stanford papers 2024-2025).

Tests:
  test_sampler.py — 9 tests covering the sampling strategy
  test_llm_judge.py — 22 tests covering init, parsing, mocked API,
    cost estimation, env factory
  test_l1_pipeline.py — 6 tests covering the wrapper
  Total new: 37 tests, all pass
  Grand total quality+format: 264 tests passing (0 regression)

  All 36 new tests + 111 L0 tests + 117 existing translator tests = 264

Phase 1 (observation) for 2 weeks. Then QUALITY_L1_LOG_ONLY=false
to enable auto-retry via the fallback chain.

services/quality/sampler.py

@@ -0,0 +1,79 @@
+"""
+Stratified sampler for the L1 quality layer.
+
+The L1 layer sends a small number of (source, translation) pairs to a
+cheap LLM for a quality verdict. We don't want to send ALL chunks
+(cost, latency) and we don't want to send random chunks (might miss
+the problematic ones). We use a simple but effective strategy:
+
+  - Prefer the LONGEST chunks (they contain the most diagnostic
+    information per call).
+  - Skip chunks that the L0 already flagged (we already know they're
+    bad; we don't need the LLM to confirm).
+  - Never sample more than `max_samples` chunks.
+  - If `min_chunks` chunks aren't available, skip the L1 entirely
+    (small documents don't need it).
+"""
+
+from __future__ import annotations
+
+from typing import List, Tuple
+
+
+def sample_chunks_for_l1(
+    source_chunks: List[str],
+    translated_chunks: List[str],
+    failed_indices: set,
+    max_samples: int = 5,
+    min_chunks: int = 10,
+) -> List[Tuple[str, str]]:
+    """
+    Select a sample of (source, translation) pairs to send to the L1 judge.
+
+    Args:
+        source_chunks: Original texts.
+        translated_chunks: Translated texts (same length as source_chunks).
+        failed_indices: Set of indices that L0 already flagged as bad.
+                        These are SKIPPED — we want fresh signal, not
+                        confirmation of an already-detected failure.
+        max_samples: Maximum number of pairs to return.
+        min_chunks: If the document has fewer than this many chunks,
+                    return an empty list (not enough signal to bother
+                    calling the LLM).
+
+    Returns:
+        A list of (source, translated) tuples, ready to send to the LLM
+        judge. Order: longest chunks first.
+    """
+    n = min(len(source_chunks), len(translated_chunks))
+
+    if n < min_chunks:
+        return []
+
+    # Build candidate list, excluding L0 failures
+    candidates: List[Tuple[int, int, str, str]] = []
+    for i in range(n):
+        if i in failed_indices:
+            continue
+        src = (source_chunks[i] or "").strip()
+        trans = (translated_chunks[i] or "").strip()
+        # Skip very short pairs (not enough context for the LLM to judge)
+        if len(src) < 5 and len(trans) < 5:
+            continue
+        # Skip pairs where source and translation are identical
+        # (probably a non-translated cell like a number, code, or brand)
+        if src == trans:
+            continue
+        # Rank by length of the translation (longer = more diagnostic)
+        rank = len(trans) + len(src) // 2
+        candidates.append((rank, i, src, trans))
+
+    # Sort by length descending and take top N
+    candidates.sort(key=lambda c: c[0], reverse=True)
+    selected = candidates[:max_samples]
+
+    # Return in original document order (helps the LLM judge maintain
+    # context, and makes the verdict easier to interpret)
+    selected.sort(key=lambda c: c[1])
+
+    return [(src, trans) for _rank, _i, src, trans in selected]