busya
3214d8c659
Root cause: _classify() immediately skipped (continue) when detected language matched ANY target language, preventing cache lookup and LLM translation for remaining targets. E.g., ru source + targets [ru, en] → only ru row created, en never processed (8978 ru vs 18 en in DB). Fix: 1. _classify(): Only short-circuit when ALL targets match detected language. Partial match → mark _same_language but continue to cache check and LLM for non-matching targets. 2. _persist_pre(): Unify two code paths into single loop over all target languages. Same-language targets use source_text as-is; cached targets use cache value; fallback to approved_translation. 3. SIM102: Merge nested 'if cl: if all(...)' → 'if cl and all(...)' to keep cyclomatic complexity ≤ 10 (INV_7). QA: All 69 translate tests pass. 7 scenario traces (A-G) verified. No regressions in approved_translation, preview edits, or single- target same-language flows.
232 lines
11 KiB
Python
232 lines
11 KiB
Python
# #region BatchProcessingService [C:4] [TYPE Module] [SEMANTICS translate, batch, process, classify, cache]
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# @BRIEF Batch processing for translation: classify rows (same-language/cache/preview/LLM),
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# call LLM service, persist TranslationRecord/TranslationLanguage rows.
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# Local language detection (lingua) replaces LLM-based detection.
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# @LAYER Domain
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# @RELATION DEPENDS_ON -> [TranslationBatch], [TranslationRecord], [TranslationLanguage]
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# @RELATION DEPENDS_ON -> [DictionaryManager], [LLMTranslationService], [LanguageDetectService]
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# @RELATION DEPENDS_ON -> [estimate_token_budget], [ConfigManager]
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# @PRE DB session is available. Job configuration is valid.
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# @POST TranslationBatch, TranslationRecord, TranslationLanguage rows created and committed.
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# @SIDE_EFFECT LLM API calls via LLMTranslationService; DB writes.
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# @RATIONALE Extracted from TranslationExecutor. Batch insert delegated to _batch_insert.py.
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# @REJECTED Keeping batch processing inside TranslationExecutor — caused class to exceed INV_7.
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from datetime import UTC, datetime
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import time
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from typing import Any
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import uuid
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from sqlalchemy.orm import Session
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from ...core.config_manager import ConfigManager
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from ...core.logger import belief_scope, logger
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from ...models.translate import TranslationBatch, TranslationJob, TranslationLanguage, TranslationRecord
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from ...services.llm_provider import LLMProviderService
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from ._batch_insert import insert_batch_to_target
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from ._lang_detect import batch_detect
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from ._llm_call import LLMTranslationService
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from ._token_budget import estimate_token_budget
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from ._utils import _check_translation_cache, _compute_key_hash, _compute_source_hash
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from .dictionary import DictionaryManager
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# #region BatchProcessingService [C:4] [TYPE Class]
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# @BRIEF Create batch records, classify rows, process LLM calls, persist results.
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class BatchProcessingService:
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"""Process a batch: classify (cache/preview/LLM), persist, and insert to target."""
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def __init__(self, db: Session, config_manager: ConfigManager) -> None:
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self.db = db
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self.config_manager = config_manager
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self._llm_service = LLMTranslationService(db)
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# #region process_batch [C:3] [TYPE Function]
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# @BRIEF Process a single batch: create record, classify rows, call LLM, persist.
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# @PRE job and batch_rows are valid.
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# @POST TranslationBatch and TranslationRecord rows are created.
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# @SIDE_EFFECT LLM API call; DB writes.
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def process_batch(
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self, job: TranslationJob, run_id: str, batch_index: int,
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batch_rows: list[dict[str, Any]],
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dict_snapshot_hash: str | None = None, config_hash: str | None = None,
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preview_edits_cache: dict[str, dict[str, str]] | None = None,
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) -> dict[str, int]:
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"""Process a single batch: classify rows, call LLM (if needed), persist records."""
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with belief_scope("BatchProcessingService.process_batch"):
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batch_start = time.monotonic()
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batch = self._create_batch(run_id, batch_index, batch_rows)
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bid = batch.id
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result = {"successful": 0, "failed": 0, "skipped": 0, "retries": 0}
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tls = job.target_languages or [job.target_dialect or "en"]
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tls = [str(tls)] if not isinstance(tls, list) else tls
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# ★ Run local language detection on all rows (heuristic, no LLM)
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self._detect_languages(batch_rows, tls)
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source_texts = [r.get("source_text", "") for r in batch_rows if r.get("source_text")]
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rc = batch_rows[0].get("source_data") if batch_rows else None
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dict_matches = DictionaryManager.filter_for_batch(self.db, source_texts, job.id, row_context=rc)
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self._check_cache(job, batch_rows, dict_snapshot_hash, config_hash)
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llm_rows, pre_rows = self._classify(batch_rows, preview_edits_cache, tls)
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result["successful"] += self._persist_pre(pre_rows, bid, run_id, tls)
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if llm_rows:
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llm_res = self._process_llm(job, run_id, llm_rows, dict_matches, bid, tls)
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for k in ("successful", "failed", "skipped", "retries"):
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result[k] += llm_res.get(k, 0)
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batch.successful_records = result["successful"]
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batch.failed_records = result["failed"]
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batch.completed_at = datetime.now(UTC)
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batch.status = "COMPLETED" if result["failed"] == 0 else "COMPLETED_WITH_ERRORS"
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self.db.flush()
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latency = int((time.monotonic() - batch_start) * 1000)
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logger.reason(f"Batch {batch_index} complete", {"batch_id": bid, "latency_ms": latency, **result})
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return {**result, "batch_id": bid}
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# #endregion process_batch
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def _create_batch(self, run_id, batch_index, batch_rows):
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b = TranslationBatch(id=str(uuid.uuid4()), run_id=run_id, batch_index=batch_index,
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status="RUNNING", total_records=len(batch_rows), started_at=datetime.now(UTC))
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self.db.add(b)
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self.db.flush()
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return b
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def _check_cache(self, job, batch_rows, dict_snapshot_hash, config_hash):
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for row in batch_rows:
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if row.get("approved_translation"):
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continue
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st = row.get("source_text", "")
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if not st:
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continue
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ctx = list(job.context_columns or [])
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h = _compute_source_hash(st, row.get("source_data"), dict_snapshot_hash, config_hash, ctx)
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row["_source_hash"] = h
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cached = _check_translation_cache(self.db, h)
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if cached:
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row["_cached_lang_values"] = cached
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logger.reason("Translation cache hit", {"source_hash": h[:12], "langs": list(cached.keys())})
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# ★ Local language detection — replaces LLM-based detection
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def _detect_languages(self, batch_rows: list[dict], target_languages: list[str]) -> None:
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"""Run local language detection on all batch rows (no LLM).
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Attaches '_detected_lang' (BCP-47 code or 'und') to each row dict.
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Uses batch_detect() for efficient multi-text processing.
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"""
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texts = [row.get("source_text", "") for row in batch_rows]
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results = batch_detect(texts, target_languages)
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for row, lang in zip(batch_rows, results):
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row["_detected_lang"] = lang
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def _classify(self, batch_rows, preview_edits_cache, tls):
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llm_rows, pre_rows = [], []
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tls_lower = [str(t).lower() for t in tls]
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for row in batch_rows:
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# ★ Same-language pre-filter: only short-circuit when ALL targets
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# match the detected language. If only SOME match, still process
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# other targets via cache or LLM.
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dl = row.get("_detected_lang")
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if dl and str(dl).lower() not in ("und", "") and str(dl).lower() in tls_lower:
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non_matching = [t for t in tls if str(t).lower() != str(dl).lower()]
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if not non_matching:
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# All targets are the same as detected language — no translation needed
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row["_same_language"] = True
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pre_rows.append(row)
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continue
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# Partial match: mark same-language for later use, but don't skip
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row["_same_language"] = True
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if row.get("approved_translation"):
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pre_rows.append(row)
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continue
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cl = row.get("_cached_lang_values")
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if cl and all(lc in cl for lc in tls):
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pre_rows.append(row)
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continue
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if preview_edits_cache:
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sd = row.get("source_data") or {}
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if sd:
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kh = _compute_key_hash(sd)
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pe = preview_edits_cache.get(kh)
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if pe:
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fe = next(iter(pe.values()), None)
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if fe:
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row["approved_translation"] = fe
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pre_rows.append(row)
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continue
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llm_rows.append(row)
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return llm_rows, pre_rows
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def _persist_pre(self, pre_rows, bid, run_id, tls):
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count = 0
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for row in pre_rows:
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cl = row.get("_cached_lang_values")
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detected_lang = row.get("_detected_lang", "und") or "und"
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source_text = row.get("source_text", "")
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is_same = row.get("_same_language")
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# target_sql: prefer approved_translation, then first cached value, then source
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primary_cached = next(iter(cl.values()), "") if cl else ""
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target_sql = row.get("approved_translation") or primary_cached or source_text
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rec = TranslationRecord(
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id=str(uuid.uuid4()), batch_id=bid, run_id=run_id,
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source_sql=source_text, target_sql=target_sql,
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source_object_type="table_row", source_object_id=row.get("row_index"),
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source_object_name=row.get("source_object_name", ""),
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source_data=row.get("source_data"), source_hash=row.get("_source_hash"),
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status="SUCCESS",
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)
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self.db.add(rec)
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for lc in tls:
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if is_same and str(lc).lower() == str(detected_lang).lower():
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# Same language: use source text as-is (no translation needed)
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fv = source_text
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elif cl and lc in cl:
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fv = cl[lc]
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else:
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fv = row.get("approved_translation", "")
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self.db.add(TranslationLanguage(
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id=str(uuid.uuid4()), record_id=rec.id, language_code=lc,
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source_language_detected=detected_lang, translated_value=fv,
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final_value=fv, status="translated", needs_review=False,
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))
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count += 1
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return count
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def _process_llm(self, job, run_id, rows_for_llm, dict_matches, bid, tls):
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provider_model = None
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if job.provider_id:
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try:
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p = LLMProviderService(self.db).get_provider(job.provider_id)
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if p:
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provider_model = p.default_model or "gpt-4o-mini"
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except Exception:
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provider_model = None
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tb = estimate_token_budget(
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source_rows=rows_for_llm, target_languages=tls,
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source_column="source_text", context_columns=None,
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dictionary_entries=dict_matches, batch_size=len(rows_for_llm),
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provider_info=provider_model,
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)
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if tb["warning"]:
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logger.explore("Token budget warning", {"batch_id": bid, "warning": tb["warning"]})
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return self._llm_service.call_llm_for_batch(
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job=job, run_id=run_id, batch_rows=rows_for_llm,
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dict_matches=dict_matches, batch_id=bid,
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max_tokens=tb["max_output_needed"],
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)
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# -- Batch insert (delegation) --
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def insert_batch_to_target(self, job: TranslationJob, batch_id: str, run_id: str) -> None:
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insert_batch_to_target(self.db, self.config_manager, job, batch_id, run_id)
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# #endregion BatchProcessingService
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# #endregion BatchProcessingService
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