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.

config.py

@@ -77,6 +77,21 @@ class Config:
     QUALITY_L0_SAMPLE_SIZE = int(os.getenv("QUALITY_L0_SAMPLE_SIZE", "20"))
 
 
+    # ============== Quality Layer (L1) ==============
+    # Track A3 of the dev plan — API-based LLM judge.
+    # Observability first; the verdict is logged but never used to retry
+    # or block a job (QUALITY_L1_LOG_ONLY=true). After 2 weeks of
+    # monitoring, set QUALITY_L1_LOG_ONLY=false to enable auto-retry.
+    QUALITY_L1_ENABLED = os.getenv("QUALITY_L1_ENABLED", "false").lower() == "true"
+    QUALITY_L1_LOG_ONLY = os.getenv("QUALITY_L1_LOG_ONLY", "true").lower() == "true"
+    # Number of chunks sampled per job. 5 is the sweet spot (cost vs coverage).
+    QUALITY_L1_SAMPLE_SIZE = int(os.getenv("QUALITY_L1_SAMPLE_SIZE", "5"))
+    # Skip the check if the document has fewer than this many chunks.
+    QUALITY_L1_MIN_CHUNKS = int(os.getenv("QUALITY_L1_MIN_CHUNKS", "10"))
+    # Hard ceiling on the L1 call (seconds). Anything longer is a skip.
+    QUALITY_L1_TIMEOUT_SEC = float(os.getenv("QUALITY_L1_TIMEOUT_SEC", "8.0"))
+
+
     # ============== API Configuration ==============
     API_TITLE = "Document Translation API"
     API_VERSION = "1.0.0"