"""
  Unified application configuration loader.
  
  This module is the single source of truth for loading, merging, normalizing,
  and validating application configuration.
  """
  
  from __future__ import annotations
  
  import hashlib
  import json
  import os

  import csv

  from copy import deepcopy
  from dataclasses import asdict
  from functools import lru_cache
  from pathlib import Path
  from typing import Any, Dict, Iterable, List, Optional, Tuple
  
  import yaml
  
  try:
      from dotenv import load_dotenv as _load_dotenv  # type: ignore
  except Exception:  # pragma: no cover
      _load_dotenv = None
  
  from config.schema import (
      AppConfig,
      AssetsConfig,

      CoarseRankConfig,
      CoarseRankFusionConfig,

      ConfigMetadata,
      DatabaseSettings,
      ElasticsearchSettings,
      EmbeddingServiceConfig,

      FineRankConfig,

      FunctionScoreConfig,
      IndexConfig,
      InfrastructureConfig,
      QueryConfig,

      ProductEnrichConfig,

      RedisSettings,
      RerankConfig,

      RerankFusionConfig,

      RerankServiceConfig,

      RerankServiceInstanceConfig,

      RuntimeConfig,
      SearchConfig,

      SearchEvaluationConfig,

      SearchEvaluationDatasetConfig,

      SecretsConfig,
      ServicesConfig,

      SuggestionConfig,

      SPUConfig,
      TenantCatalogConfig,
      TranslationServiceConfig,
  )
  from translation.settings import build_translation_config
  
  
  class ConfigurationError(Exception):
      """Raised when configuration validation fails."""
  
  
  def _deep_merge(base: Dict[str, Any], override: Dict[str, Any]) -> Dict[str, Any]:
      result = deepcopy(base)
      for key, value in (override or {}).items():
          if (
              key in result
              and isinstance(result[key], dict)
              and isinstance(value, dict)
          ):
              result[key] = _deep_merge(result[key], value)
          else:
              result[key] = deepcopy(value)
      return result
  
  
  def _load_yaml(path: Path) -> Dict[str, Any]:
      with open(path, "r", encoding="utf-8") as handle:
          data = yaml.safe_load(handle) or {}
      if not isinstance(data, dict):
          raise ConfigurationError(f"Configuration file root must be a mapping: {path}")
      return data
  
  
  def _read_rewrite_dictionary(path: Path) -> Dict[str, str]:
      rewrite_dict: Dict[str, str] = {}
      if not path.exists():
          return rewrite_dict
  
      with open(path, "r", encoding="utf-8") as handle:
          for raw_line in handle:
              line = raw_line.strip()
              if not line or line.startswith("#"):
                  continue
              parts = line.split("\t")
              if len(parts) < 2:
                  continue
              original = parts[0].strip()
              replacement = parts[1].strip()
              if original and replacement:
                  rewrite_dict[original] = replacement
      return rewrite_dict
  
  

  def _read_synonym_csv_dictionary(path: Path) -> List[Dict[str, List[str]]]:
      rows: List[Dict[str, List[str]]] = []

      if not path.exists():
          return rows
  

      def _split_terms(cell: str) -> List[str]:
          return [item.strip() for item in str(cell or "").split(",") if item.strip()]
  

      with open(path, "r", encoding="utf-8") as handle:

          reader = csv.reader(handle)
          for parts in reader:
              if not parts:

                  continue

              if parts[0].strip().startswith("#"):
                  continue
  
              normalized = [segment.strip() for segment in parts]
              if len(normalized) < 3:
                  continue
  
              row = {
                  "en_terms": _split_terms(normalized[0]),
                  "zh_terms": _split_terms(normalized[1]),
                  "attribute_terms": _split_terms(normalized[2]),
              }
              if any(row.values()):
                  rows.append(row)

      return rows
  
  

  def _read_product_title_exclusion_dictionary(path: Path) -> List[Dict[str, List[str]]]:
      rules: List[Dict[str, List[str]]] = []
      if not path.exists():
          return rules
  
      with open(path, "r", encoding="utf-8") as handle:
          for raw_line in handle:
              line = raw_line.strip()
              if not line or line.startswith("#"):
                  continue
              parts = [segment.strip() for segment in line.split("\t")]
              if len(parts) != 4:
                  continue
  
              def _split_cell(cell: str) -> List[str]:
                  return [item.strip() for item in cell.split(",") if item.strip()]
  
              rules.append(
                  {
                      "zh_trigger_terms": _split_cell(parts[0]),
                      "en_trigger_terms": _split_cell(parts[1]),
                      "zh_title_exclusions": _split_cell(parts[2]),
                      "en_title_exclusions": _split_cell(parts[3]),
                  }
              )
      return rules
  
  

  _DEFAULT_STYLE_INTENT_DIMENSION_ALIASES: Dict[str, List[str]] = {
      "color": ["color", "colors", "colour", "colours", "颜色", "色", "色系"],
      "size": ["size", "sizes", "sizing", "尺码", "尺寸", "码数", "号码", "码"],
  }
  
  

  class AppConfigLoader:
      """Load the unified application configuration."""
  
      def __init__(
          self,
          *,
          config_dir: Optional[Path] = None,
          config_file: Optional[Path] = None,
          env_file: Optional[Path] = None,
      ) -> None:
          self.config_dir = Path(config_dir or Path(__file__).parent)
          self.config_file = Path(config_file) if config_file is not None else None
          self.project_root = self.config_dir.parent
          self.env_file = Path(env_file) if env_file is not None else self.project_root / ".env"
  
      def load(self, validate: bool = True) -> AppConfig:
          self._load_env()
          raw_config, loaded_files = self._load_raw_config()
          app_config = self._build_app_config(raw_config, loaded_files)
          if validate:
              self._validate(app_config)
          return app_config
  
      def _load_env(self) -> None:
          if _load_dotenv is not None:
              _load_dotenv(self.env_file, override=False)
              return
          _load_env_file_fallback(self.env_file)
  
      def _load_raw_config(self) -> Tuple[Dict[str, Any], List[str]]:
          env_name = (os.getenv("APP_ENV") or os.getenv("RUNTIME_ENV") or "prod").strip().lower() or "prod"
          loaded_files: List[str] = []
          raw: Dict[str, Any] = {}
  
          if self.config_file is not None:
              config_path = self.config_file
              if not config_path.exists():
                  raise ConfigurationError(f"Configuration file not found: {config_path}")
              raw = _deep_merge(raw, _load_yaml(config_path))
              loaded_files.append(str(config_path))
          else:
              base_path = self.config_dir / "base.yaml"
              legacy_path = self.config_dir / "config.yaml"
              primary_path = base_path if base_path.exists() else legacy_path
              if not primary_path.exists():
                  raise ConfigurationError(f"Configuration file not found: {primary_path}")
              raw = _deep_merge(raw, _load_yaml(primary_path))
              loaded_files.append(str(primary_path))
  
              env_path = self.config_dir / "environments" / f"{env_name}.yaml"
              if env_path.exists():
                  raw = _deep_merge(raw, _load_yaml(env_path))
                  loaded_files.append(str(env_path))
  
          tenant_dir = self.config_dir / "tenants"
          if tenant_dir.is_dir():
              tenant_files = sorted(tenant_dir.glob("*.yaml"))
              if tenant_files:
                  tenant_config = {"default": {}, "tenants": {}}
                  default_path = tenant_dir / "_default.yaml"
                  if default_path.exists():
                      tenant_config["default"] = _load_yaml(default_path)
                      loaded_files.append(str(default_path))
                  for tenant_path in tenant_files:
                      if tenant_path.name == "_default.yaml":
                          continue
                      tenant_config["tenants"][tenant_path.stem] = _load_yaml(tenant_path)
                      loaded_files.append(str(tenant_path))
                  raw["tenant_config"] = tenant_config
  
          return raw, loaded_files
  
      def _build_app_config(self, raw: Dict[str, Any], loaded_files: List[str]) -> AppConfig:
          assets_cfg = raw.get("assets") if isinstance(raw.get("assets"), dict) else {}
          rewrite_path = (
              assets_cfg.get("query_rewrite_dictionary_path")
              or assets_cfg.get("rewrite_dictionary_path")
              or self.config_dir / "dictionaries" / "query_rewrite.dict"
          )
          rewrite_path = Path(rewrite_path)
          if not rewrite_path.is_absolute():
              rewrite_path = (self.project_root / rewrite_path).resolve()
          if not rewrite_path.exists():
              legacy_rewrite_path = (self.config_dir / "query_rewrite.dict").resolve()
              if legacy_rewrite_path.exists():
                  rewrite_path = legacy_rewrite_path
  
          rewrite_dictionary = _read_rewrite_dictionary(rewrite_path)
          search_config = self._build_search_config(raw, rewrite_dictionary)

          suggestion_config = self._build_suggestion_config(
              raw.get("suggestion") if isinstance(raw.get("suggestion"), dict) else {}
          )

          services_config = self._build_services_config(raw.get("services") or {})
          tenants_config = self._build_tenants_config(raw.get("tenant_config") or {})
          runtime_config = self._build_runtime_config()
          infrastructure_config = self._build_infrastructure_config(runtime_config.environment)
  

          product_enrich_raw = raw.get("product_enrich") if isinstance(raw.get("product_enrich"), dict) else {}
          product_enrich_config = ProductEnrichConfig(
              max_workers=int(product_enrich_raw.get("max_workers", 40)),
          )

          search_evaluation_config = self._build_search_evaluation_config(raw, runtime_config)

          metadata = ConfigMetadata(
              loaded_files=tuple(loaded_files),
              config_hash="",
              deprecated_keys=tuple(self._detect_deprecated_keys(raw)),
          )
  
          app_config = AppConfig(
              runtime=runtime_config,
              infrastructure=infrastructure_config,

              product_enrich=product_enrich_config,

              search=search_config,

              suggestion=suggestion_config,

              services=services_config,
              tenants=tenants_config,
              assets=AssetsConfig(query_rewrite_dictionary_path=rewrite_path),

              search_evaluation=search_evaluation_config,

              metadata=metadata,
          )
  
          config_hash = self._compute_hash(app_config)
          return AppConfig(
              runtime=app_config.runtime,
              infrastructure=app_config.infrastructure,

              product_enrich=app_config.product_enrich,

              search=app_config.search,

              suggestion=app_config.suggestion,

              services=app_config.services,
              tenants=app_config.tenants,
              assets=app_config.assets,

              search_evaluation=app_config.search_evaluation,

              metadata=ConfigMetadata(
                  loaded_files=app_config.metadata.loaded_files,
                  config_hash=config_hash,
                  deprecated_keys=app_config.metadata.deprecated_keys,
              ),
          )
  

      def _build_search_evaluation_config(self, raw: Dict[str, Any], runtime: RuntimeConfig) -> SearchEvaluationConfig:
          se = raw.get("search_evaluation") if isinstance(raw.get("search_evaluation"), dict) else {}
          default_artifact = (self.project_root / "artifacts" / "search_evaluation").resolve()
          default_queries = (self.project_root / "scripts" / "evaluation" / "queries" / "queries.txt").resolve()
          default_log_dir = (self.project_root / "logs").resolve()
          default_search_base = f"http://127.0.0.1:{int(runtime.api_port)}"
  
          def _project_path(value: Any, default: Path) -> Path:
              if value in (None, ""):
                  return default
              candidate = Path(str(value))
              if candidate.is_absolute():
                  return candidate.resolve()
              return (self.project_root / candidate).resolve()
  
          def _str(key: str, default: str) -> str:
              v = se.get(key)
              if v is None or (isinstance(v, str) and not v.strip()):
                  return default
              return str(v).strip()
  
          def _int(key: str, default: int) -> int:
              v = se.get(key)
              if v is None:
                  return default
              return int(v)
  
          def _float(key: str, default: float) -> float:
              v = se.get(key)
              if v is None:
                  return default
              return float(v)
  
          def _bool(key: str, default: bool) -> bool:
              v = se.get(key)
              if v is None:
                  return default
              if isinstance(v, bool):
                  return v
              if isinstance(v, str):
                  return v.strip().lower() in {"1", "true", "yes", "on"}
              return bool(v)
  
          raw_search_url = se.get("search_base_url")
          if raw_search_url is None or (isinstance(raw_search_url, str) and not str(raw_search_url).strip()):
              search_base_url = default_search_base
          else:
              search_base_url = str(raw_search_url).strip()
  

          default_tenant_id = _str("default_tenant_id", "163")
          default_language = _str("default_language", "en")
          datasets_raw = se.get("datasets")
          datasets: List[SearchEvaluationDatasetConfig] = []
          if isinstance(datasets_raw, list):
              for idx, item in enumerate(datasets_raw):
                  if not isinstance(item, dict):
                      raise ConfigurationError(
                          f"search_evaluation.datasets[{idx}] must be a mapping, got {type(item).__name__}"
                      )
                  dataset_id = str(item.get("dataset_id") or "").strip()
                  if not dataset_id:
                      raise ConfigurationError(f"search_evaluation.datasets[{idx}].dataset_id is required")
                  display_name = str(item.get("display_name") or dataset_id).strip() or dataset_id
                  description = str(item.get("description") or "").strip()
                  query_file = _project_path(item.get("query_file"), default_queries)
                  tenant_id = str(item.get("tenant_id") or default_tenant_id).strip() or default_tenant_id
                  language = str(item.get("language") or default_language).strip() or default_language
                  enabled = bool(item.get("enabled", True))
                  datasets.append(
                      SearchEvaluationDatasetConfig(
                          dataset_id=dataset_id,
                          display_name=display_name,
                          description=description,
                          query_file=query_file,
                          tenant_id=tenant_id,
                          language=language,
                          enabled=enabled,
                      )
                  )
          if not datasets:
              datasets = [
                  SearchEvaluationDatasetConfig(
                      dataset_id="core_queries",
                      display_name="Core Queries",
                      description="Legacy evaluation query set",
                      query_file=_project_path(se.get("queries_file"), default_queries),
                      tenant_id=default_tenant_id,
                      language=default_language,
                      enabled=True,
                  )
              ]
          default_dataset_id = str(se.get("default_dataset_id") or "").strip() or datasets[0].dataset_id
          dataset_ids = {item.dataset_id for item in datasets}
          if default_dataset_id not in dataset_ids:
              raise ConfigurationError(
                  f"search_evaluation.default_dataset_id={default_dataset_id!r} is not present in search_evaluation.datasets"
              )
          legacy_queries_file = next(
              (item.query_file for item in datasets if item.dataset_id == default_dataset_id),
              datasets[0].query_file,
          )
  

          return SearchEvaluationConfig(
              artifact_root=_project_path(se.get("artifact_root"), default_artifact),

              queries_file=legacy_queries_file,
              default_dataset_id=default_dataset_id,
              datasets=tuple(datasets),

              eval_log_dir=_project_path(se.get("eval_log_dir"), default_log_dir),

              default_tenant_id=default_tenant_id,

              search_base_url=search_base_url,
              web_host=_str("web_host", "0.0.0.0"),
              web_port=_int("web_port", 6010),
              judge_model=_str("judge_model", "qwen3.5-plus"),
              judge_enable_thinking=_bool("judge_enable_thinking", False),
              judge_dashscope_batch=_bool("judge_dashscope_batch", False),
              intent_model=_str("intent_model", "qwen3-max"),
              intent_enable_thinking=_bool("intent_enable_thinking", True),
              judge_batch_completion_window=_str("judge_batch_completion_window", "24h"),
              judge_batch_poll_interval_sec=_float("judge_batch_poll_interval_sec", 10.0),
              build_search_depth=_int("build_search_depth", 1000),
              build_rerank_depth=_int("build_rerank_depth", 10000),
              annotate_search_top_k=_int("annotate_search_top_k", 120),
              annotate_rerank_top_k=_int("annotate_rerank_top_k", 200),
              batch_top_k=_int("batch_top_k", 100),
              audit_top_k=_int("audit_top_k", 100),
              audit_limit_suspicious=_int("audit_limit_suspicious", 5),

              default_language=default_language,

              search_recall_top_k=_int("search_recall_top_k", 200),
              rerank_high_threshold=_float("rerank_high_threshold", 0.5),
              rerank_high_skip_count=_int("rerank_high_skip_count", 1000),
              rebuild_llm_batch_size=_int("rebuild_llm_batch_size", 50),
              rebuild_min_llm_batches=_int("rebuild_min_llm_batches", 10),
              rebuild_max_llm_batches=_int("rebuild_max_llm_batches", 40),
              rebuild_irrelevant_stop_ratio=_float("rebuild_irrelevant_stop_ratio", 0.799),
              rebuild_irrel_low_combined_stop_ratio=_float("rebuild_irrel_low_combined_stop_ratio", 0.959),
              rebuild_irrelevant_stop_streak=_int("rebuild_irrelevant_stop_streak", 3),
          )
  

      def _build_search_config(self, raw: Dict[str, Any], rewrite_dictionary: Dict[str, str]) -> SearchConfig:
          field_boosts = raw.get("field_boosts") or {}
          if not isinstance(field_boosts, dict):
              raise ConfigurationError("field_boosts must be a mapping")
  
          indexes: List[IndexConfig] = []
          for item in raw.get("indexes") or []:
              if not isinstance(item, dict):
                  raise ConfigurationError("indexes items must be mappings")
              indexes.append(
                  IndexConfig(
                      name=str(item["name"]),
                      label=str(item.get("label") or item["name"]),
                      fields=list(item.get("fields") or []),
                      boost=float(item.get("boost", 1.0)),
                      example=item.get("example"),
                  )
              )
  
          query_cfg = raw.get("query_config") if isinstance(raw.get("query_config"), dict) else {}
          search_fields = query_cfg.get("search_fields") if isinstance(query_cfg.get("search_fields"), dict) else {}
          text_strategy = (
              query_cfg.get("text_query_strategy")
              if isinstance(query_cfg.get("text_query_strategy"), dict)
              else {}
          )

          style_intent_cfg = (
              query_cfg.get("style_intent")
              if isinstance(query_cfg.get("style_intent"), dict)
              else {}
          )

          product_title_exclusion_cfg = (
              query_cfg.get("product_title_exclusion")
              if isinstance(query_cfg.get("product_title_exclusion"), dict)
              else {}
          )

  
          def _resolve_project_path(value: Any, default_path: Path) -> Path:
              if value in (None, ""):
                  return default_path
              candidate = Path(str(value))
              if candidate.is_absolute():
                  return candidate
              return self.project_root / candidate
  
          style_color_path = _resolve_project_path(
              style_intent_cfg.get("color_dictionary_path"),
              self.config_dir / "dictionaries" / "style_intent_color.csv",
          )
          style_size_path = _resolve_project_path(
              style_intent_cfg.get("size_dictionary_path"),
              self.config_dir / "dictionaries" / "style_intent_size.csv",
          )
          configured_dimension_aliases = (
              style_intent_cfg.get("dimension_aliases")
              if isinstance(style_intent_cfg.get("dimension_aliases"), dict)
              else {}
          )
          style_dimension_aliases: Dict[str, List[str]] = {}
          for intent_type, default_aliases in _DEFAULT_STYLE_INTENT_DIMENSION_ALIASES.items():
              aliases = configured_dimension_aliases.get(intent_type)
              if isinstance(aliases, list) and aliases:
                  style_dimension_aliases[intent_type] = [str(alias) for alias in aliases if str(alias).strip()]
              else:
                  style_dimension_aliases[intent_type] = list(default_aliases)
  
          style_intent_terms = {
              "color": _read_synonym_csv_dictionary(style_color_path),
              "size": _read_synonym_csv_dictionary(style_size_path),
          }

          product_title_exclusion_path = _resolve_project_path(
              product_title_exclusion_cfg.get("dictionary_path"),
              self.config_dir / "dictionaries" / "product_title_exclusion.tsv",
          )

          query_config = QueryConfig(
              supported_languages=list(query_cfg.get("supported_languages") or ["zh", "en"]),
              default_language=str(query_cfg.get("default_language") or "en"),
              enable_text_embedding=bool(query_cfg.get("enable_text_embedding", True)),
              enable_query_rewrite=bool(query_cfg.get("enable_query_rewrite", True)),
              rewrite_dictionary=rewrite_dictionary,
              text_embedding_field=query_cfg.get("text_embedding_field"),
              image_embedding_field=query_cfg.get("image_embedding_field"),
              source_fields=query_cfg.get("source_fields"),

              knn_text_boost=float(
                  query_cfg.get("knn_text_boost", query_cfg.get("knn_boost", 0.25))
              ),
              knn_image_boost=float(
                  query_cfg.get("knn_image_boost", query_cfg.get("knn_boost", 0.25))
              ),
              knn_text_k=int(query_cfg.get("knn_text_k", 120)),
              knn_text_num_candidates=int(query_cfg.get("knn_text_num_candidates", 400)),
              knn_text_k_long=int(query_cfg.get("knn_text_k_long", 160)),
              knn_text_num_candidates_long=int(
                  query_cfg.get("knn_text_num_candidates_long", 500)
              ),
              knn_image_k=int(query_cfg.get("knn_image_k", 120)),
              knn_image_num_candidates=int(query_cfg.get("knn_image_num_candidates", 400)),

              multilingual_fields=list(
                  search_fields.get(
                      "multilingual_fields",

                      [],

                  )
              ),
              shared_fields=list(
                  search_fields.get(
                      "shared_fields",

                      [],
                  ) or []

              ),
              core_multilingual_fields=list(
                  search_fields.get(
                      "core_multilingual_fields",

                      [],

                  )
              ),

              base_minimum_should_match=str(text_strategy.get("base_minimum_should_match", "70%")),
              translation_minimum_should_match=str(text_strategy.get("translation_minimum_should_match", "70%")),

              translation_boost=float(text_strategy.get("translation_boost", 0.4)),

              tie_breaker_base_query=float(text_strategy.get("tie_breaker_base_query", 0.9)),

              best_fields={
                  str(field): float(boost)
                  for field, boost in dict(text_strategy.get("best_fields") or {}).items()
              },
              best_fields_boost=float(text_strategy.get("best_fields_boost", 2.0)),
              phrase_fields={
                  str(field): float(boost)
                  for field, boost in dict(text_strategy.get("phrase_fields") or {}).items()
              },
              phrase_match_boost=float(text_strategy.get("phrase_match_boost", 3.0)),

              zh_to_en_model=str(query_cfg.get("zh_to_en_model") or "opus-mt-zh-en"),
              en_to_zh_model=str(query_cfg.get("en_to_zh_model") or "opus-mt-en-zh"),
              default_translation_model=str(
                  query_cfg.get("default_translation_model") or "nllb-200-distilled-600m"
              ),

              zh_to_en_model_source_not_in_index=(
                  str(v)
                  if (v := query_cfg.get("zh_to_en_model__source_not_in_index"))
                  not in (None, "")
                  else None
              ),
              en_to_zh_model_source_not_in_index=(
                  str(v)
                  if (v := query_cfg.get("en_to_zh_model__source_not_in_index"))
                  not in (None, "")
                  else None
              ),
              default_translation_model_source_not_in_index=(
                  str(v)
                  if (v := query_cfg.get("default_translation_model__source_not_in_index"))
                  not in (None, "")
                  else None
              ),

              translation_embedding_wait_budget_ms_source_in_index=int(
                  query_cfg.get("translation_embedding_wait_budget_ms_source_in_index", 80)
              ),
              translation_embedding_wait_budget_ms_source_not_in_index=int(
                  query_cfg.get("translation_embedding_wait_budget_ms_source_not_in_index", 200)
              ),

              style_intent_enabled=bool(style_intent_cfg.get("enabled", True)),

              style_intent_selected_sku_boost=float(
                  style_intent_cfg.get("selected_sku_boost", 1.2)
              ),

              style_intent_terms=style_intent_terms,
              style_intent_dimension_aliases=style_dimension_aliases,

              product_title_exclusion_enabled=bool(product_title_exclusion_cfg.get("enabled", True)),
              product_title_exclusion_rules=_read_product_title_exclusion_dictionary(
                  product_title_exclusion_path
              ),

          )
  
          function_score_cfg = raw.get("function_score") if isinstance(raw.get("function_score"), dict) else {}

          coarse_rank_cfg = raw.get("coarse_rank") if isinstance(raw.get("coarse_rank"), dict) else {}
          coarse_fusion_raw = (
              coarse_rank_cfg.get("fusion") if isinstance(coarse_rank_cfg.get("fusion"), dict) else {}
          )
          fine_rank_cfg = raw.get("fine_rank") if isinstance(raw.get("fine_rank"), dict) else {}

          rerank_cfg = raw.get("rerank") if isinstance(raw.get("rerank"), dict) else {}

          fusion_raw = rerank_cfg.get("fusion") if isinstance(rerank_cfg.get("fusion"), dict) else {}

          spu_cfg = raw.get("spu_config") if isinstance(raw.get("spu_config"), dict) else {}
  
          return SearchConfig(
              field_boosts={str(key): float(value) for key, value in field_boosts.items()},
              indexes=indexes,
              query_config=query_config,
              function_score=FunctionScoreConfig(
                  score_mode=str(function_score_cfg.get("score_mode") or "sum"),
                  boost_mode=str(function_score_cfg.get("boost_mode") or "multiply"),
                  functions=list(function_score_cfg.get("functions") or []),
              ),

              coarse_rank=CoarseRankConfig(
                  enabled=bool(coarse_rank_cfg.get("enabled", True)),
                  input_window=int(coarse_rank_cfg.get("input_window", 700)),
                  output_window=int(coarse_rank_cfg.get("output_window", 240)),
                  fusion=CoarseRankFusionConfig(

                      es_bias=float(coarse_fusion_raw.get("es_bias", 0.1)),
                      es_exponent=float(coarse_fusion_raw.get("es_exponent", 0.0)),

                      text_bias=float(coarse_fusion_raw.get("text_bias", 0.1)),
                      text_exponent=float(coarse_fusion_raw.get("text_exponent", 0.35)),
                      knn_text_weight=float(coarse_fusion_raw.get("knn_text_weight", 1.0)),
                      knn_image_weight=float(coarse_fusion_raw.get("knn_image_weight", 1.0)),
                      knn_tie_breaker=float(coarse_fusion_raw.get("knn_tie_breaker", 0.0)),
                      knn_bias=float(coarse_fusion_raw.get("knn_bias", 0.6)),
                      knn_exponent=float(coarse_fusion_raw.get("knn_exponent", 0.2)),

                      knn_text_bias=float(
                          coarse_fusion_raw.get("knn_text_bias", coarse_fusion_raw.get("knn_bias", 0.6))
                      ),
                      knn_text_exponent=float(coarse_fusion_raw.get("knn_text_exponent", 0.0)),
                      knn_image_bias=float(
                          coarse_fusion_raw.get("knn_image_bias", coarse_fusion_raw.get("knn_bias", 0.6))
                      ),
                      knn_image_exponent=float(coarse_fusion_raw.get("knn_image_exponent", 0.0)),

                      text_translation_weight=float(
                          coarse_fusion_raw.get("text_translation_weight", 0.8)
                      ),

                  ),
              ),
              fine_rank=FineRankConfig(
                  enabled=bool(fine_rank_cfg.get("enabled", True)),
                  input_window=int(fine_rank_cfg.get("input_window", 240)),
                  output_window=int(fine_rank_cfg.get("output_window", 80)),
                  timeout_sec=float(fine_rank_cfg.get("timeout_sec", 10.0)),
                  rerank_query_template=str(fine_rank_cfg.get("rerank_query_template") or "{query}"),
                  rerank_doc_template=str(fine_rank_cfg.get("rerank_doc_template") or "{title}"),
                  service_profile=(
                      str(v)
                      if (v := fine_rank_cfg.get("service_profile")) not in (None, "")
                      else "fine"
                  ),
              ),

              rerank=RerankConfig(
                  enabled=bool(rerank_cfg.get("enabled", True)),
                  rerank_window=int(rerank_cfg.get("rerank_window", 384)),

                  exact_knn_rescore_enabled=bool(
                      rerank_cfg.get("exact_knn_rescore_enabled", False)
                  ),
                  exact_knn_rescore_window=int(
                      rerank_cfg.get("exact_knn_rescore_window", 0)
                  ),

                  timeout_sec=float(rerank_cfg.get("timeout_sec", 15.0)),
                  weight_es=float(rerank_cfg.get("weight_es", 0.4)),
                  weight_ai=float(rerank_cfg.get("weight_ai", 0.6)),
                  rerank_query_template=str(rerank_cfg.get("rerank_query_template") or "{query}"),
                  rerank_doc_template=str(rerank_cfg.get("rerank_doc_template") or "{title}"),

                  service_profile=(
                      str(v)
                      if (v := rerank_cfg.get("service_profile")) not in (None, "")
                      else None
                  ),

                  fusion=RerankFusionConfig(

                      es_bias=float(fusion_raw.get("es_bias", 0.1)),
                      es_exponent=float(fusion_raw.get("es_exponent", 0.0)),

                      rerank_bias=float(fusion_raw.get("rerank_bias", 0.00001)),
                      rerank_exponent=float(fusion_raw.get("rerank_exponent", 1.0)),
                      text_bias=float(fusion_raw.get("text_bias", 0.1)),
                      text_exponent=float(fusion_raw.get("text_exponent", 0.35)),

                      knn_text_weight=float(fusion_raw.get("knn_text_weight", 1.0)),
                      knn_image_weight=float(fusion_raw.get("knn_image_weight", 1.0)),
                      knn_tie_breaker=float(fusion_raw.get("knn_tie_breaker", 0.0)),

                      knn_bias=float(fusion_raw.get("knn_bias", 0.6)),
                      knn_exponent=float(fusion_raw.get("knn_exponent", 0.2)),

                      knn_text_bias=float(
                          fusion_raw.get("knn_text_bias", fusion_raw.get("knn_bias", 0.6))
                      ),
                      knn_text_exponent=float(fusion_raw.get("knn_text_exponent", 0.0)),
                      knn_image_bias=float(
                          fusion_raw.get("knn_image_bias", fusion_raw.get("knn_bias", 0.6))
                      ),
                      knn_image_exponent=float(fusion_raw.get("knn_image_exponent", 0.0)),

                      fine_bias=float(fusion_raw.get("fine_bias", 0.00001)),
                      fine_exponent=float(fusion_raw.get("fine_exponent", 1.0)),

                      text_translation_weight=float(
                          fusion_raw.get("text_translation_weight", 0.8)
                      ),

                  ),

              ),
              spu_config=SPUConfig(
                  enabled=bool(spu_cfg.get("enabled", False)),
                  spu_field=spu_cfg.get("spu_field"),
                  inner_hits_size=int(spu_cfg.get("inner_hits_size", 3)),
                  searchable_option_dimensions=list(
                      spu_cfg.get("searchable_option_dimensions") or ["option1", "option2", "option3"]
                  ),
              ),
              es_index_name=str(raw.get("es_index_name") or "search_products"),
              es_settings=dict(raw.get("es_settings") or {}),
          )
  

      def _build_suggestion_config(self, raw: Dict[str, Any]) -> SuggestionConfig:
          if not isinstance(raw, dict):
              raw = {}
          mn = int(raw.get("sat_recall_min", 40))
          cap = int(raw.get("sat_recall_cap", 100))
          if mn < 1:
              mn = 1
          if cap < mn:
              cap = mn
          return SuggestionConfig(sat_recall_min=mn, sat_recall_cap=cap)
  

      def _build_services_config(self, raw: Dict[str, Any]) -> ServicesConfig:
          if not isinstance(raw, dict):
              raise ConfigurationError("services must be a mapping")
  
          translation_raw = raw.get("translation") if isinstance(raw.get("translation"), dict) else {}
          normalized_translation = build_translation_config(translation_raw)

          local_translation_backends = {"local_nllb", "local_marian"}
          for capability_name, capability_cfg in normalized_translation["capabilities"].items():
              backend_name = str(capability_cfg.get("backend") or "").strip().lower()
              if backend_name not in local_translation_backends:
                  continue
              for path_key in ("model_dir", "ct2_model_dir"):
                  if capability_cfg.get(path_key) not in (None, ""):
                      capability_cfg[path_key] = str(self._resolve_project_path_value(capability_cfg[path_key]).resolve())

          translation_config = TranslationServiceConfig(
              endpoint=str(normalized_translation["service_url"]).rstrip("/"),
              timeout_sec=float(normalized_translation["timeout_sec"]),
              default_model=str(normalized_translation["default_model"]),
              default_scene=str(normalized_translation["default_scene"]),
              cache=dict(normalized_translation["cache"]),
              capabilities={str(key): dict(value) for key, value in normalized_translation["capabilities"].items()},
          )
  
          embedding_raw = raw.get("embedding") if isinstance(raw.get("embedding"), dict) else {}
          embedding_provider = str(embedding_raw.get("provider") or "http").strip().lower()
          embedding_providers = dict(embedding_raw.get("providers") or {})
          if embedding_provider not in embedding_providers:
              raise ConfigurationError(f"services.embedding.providers.{embedding_provider} must be configured")
          embedding_backend = str(embedding_raw.get("backend") or "").strip().lower()
          embedding_backends = {
              str(key).strip().lower(): dict(value)
              for key, value in dict(embedding_raw.get("backends") or {}).items()
          }
          if embedding_backend not in embedding_backends:
              raise ConfigurationError(f"services.embedding.backends.{embedding_backend} must be configured")
          image_backend = str(embedding_raw.get("image_backend") or "clip_as_service").strip().lower()
          image_backends = {
              str(key).strip().lower(): dict(value)
              for key, value in dict(embedding_raw.get("image_backends") or {}).items()
          }
          if not image_backends:
              image_backends = {
                  "clip_as_service": {
                      "server": "grpc://127.0.0.1:51000",

                      "model_name": "CN-CLIP/ViT-H-14",

                      "batch_size": 8,
                      "normalize_embeddings": True,
                  },
                  "local_cnclip": {

                      "model_name": "ViT-H-14",

                      "device": None,
                      "batch_size": 8,
                      "normalize_embeddings": True,
                  },
              }
          if image_backend not in image_backends:
              raise ConfigurationError(f"services.embedding.image_backends.{image_backend} must be configured")
  
          embedding_config = EmbeddingServiceConfig(
              provider=embedding_provider,
              providers=embedding_providers,
              backend=embedding_backend,
              backends=embedding_backends,
              image_backend=image_backend,
              image_backends=image_backends,
          )
  
          rerank_raw = raw.get("rerank") if isinstance(raw.get("rerank"), dict) else {}
          rerank_provider = str(rerank_raw.get("provider") or "http").strip().lower()
          rerank_providers = dict(rerank_raw.get("providers") or {})
          if rerank_provider not in rerank_providers:
              raise ConfigurationError(f"services.rerank.providers.{rerank_provider} must be configured")

          rerank_backends = {
              str(key).strip().lower(): dict(value)
              for key, value in dict(rerank_raw.get("backends") or {}).items()
          }

          default_instance = str(rerank_raw.get("default_instance") or "default").strip() or "default"
          raw_instances = rerank_raw.get("instances") if isinstance(rerank_raw.get("instances"), dict) else {}
          if not raw_instances:
              legacy_backend = str(rerank_raw.get("backend") or "").strip().lower()
              if legacy_backend not in rerank_backends:
                  raise ConfigurationError(f"services.rerank.backends.{legacy_backend} must be configured")
              provider_cfg = dict(rerank_providers.get(rerank_provider) or {})
              raw_instances = {
                  default_instance: {
                      "host": "0.0.0.0",
                      "port": 6007,
                      "backend": legacy_backend,
                      "base_url": provider_cfg.get("base_url"),
                      "service_url": provider_cfg.get("service_url"),
                  }
              }
          rerank_instances = {}
          for instance_name, instance_raw in raw_instances.items():
              if not isinstance(instance_raw, dict):
                  raise ConfigurationError(f"services.rerank.instances.{instance_name} must be a mapping")
              normalized_instance_name = str(instance_name).strip()
              backend_name = str(instance_raw.get("backend") or "").strip().lower()
              if backend_name not in rerank_backends:
                  raise ConfigurationError(
                      f"services.rerank.instances.{normalized_instance_name}.backend must reference configured services.rerank.backends"
                  )
              port = int(instance_raw.get("port", 6007))
              rerank_instances[normalized_instance_name] = RerankServiceInstanceConfig(
                  host=str(instance_raw.get("host") or "0.0.0.0"),
                  port=port,
                  backend=backend_name,
                  runtime_dir=(