runtime:
    environment: prod
    index_namespace: ''
    api_host: 0.0.0.0
    api_port: 6002
    indexer_host: 0.0.0.0
    indexer_port: 6004
    embedding_host: 0.0.0.0
    embedding_port: 6005
    embedding_text_port: 6005
    embedding_image_port: 6008
    translator_host: 0.0.0.0
    translator_port: 6006
    reranker_host: 0.0.0.0
    reranker_port: 6007
  infrastructure:
    elasticsearch:
      host: http://localhost:9200
      username: null
      password: null
    redis:
      host: localhost
      port: 6479
      snapshot_db: 0
      password: null
      socket_timeout: 1
      socket_connect_timeout: 1
      retry_on_timeout: false
      cache_expire_days: 720
      embedding_cache_prefix: embedding
      anchor_cache_prefix: product_anchors
      anchor_cache_expire_days: 30
    database:
      host: null
      port: 3306
      database: null
      username: null
      password: null
    secrets:
      dashscope_api_key: null
      deepl_auth_key: null
  es_index_name: search_products
  indexes: []
  assets:
    query_rewrite_dictionary_path: config/dictionaries/query_rewrite.dict
  product_enrich:
    max_workers: 40
  suggestion:
    sat_recall_min: 40
    sat_recall_cap: 100
  search_evaluation:
    artifact_root: artifacts/search_evaluation
    queries_file: scripts/evaluation/queries/queries.txt
    default_dataset_id: core_queries
    datasets:
    - dataset_id: core_queries
      display_name: Core Queries
      description: Legacy baseline evaluation set from queries.txt
      query_file: scripts/evaluation/queries/queries.txt
      tenant_id: '163'
      language: en
      enabled: true
    - dataset_id: clothing_top771
      display_name: Clothing Filtered 771
      description: 771 clothing / shoes / accessories queries filtered from top1k
      query_file: scripts/evaluation/queries/all_keywords.txt.top1w.shuf.top1k.clothing_filtered
      tenant_id: '163'
      language: en
      enabled: true
    eval_log_dir: logs
    default_tenant_id: '163'
    search_base_url: ''
    web_host: 0.0.0.0
    web_port: 6010
    judge_model: qwen3.6-plus
    judge_enable_thinking: false
    judge_dashscope_batch: false
    intent_model: qwen3.6-plus
    intent_enable_thinking: true
    judge_batch_completion_window: 24h
    judge_batch_poll_interval_sec: 10.0
    build_search_depth: 1000
    build_rerank_depth: 10000
    annotate_search_top_k: 120
    annotate_rerank_top_k: 200
    batch_top_k: 100
    audit_top_k: 100
    audit_limit_suspicious: 5
    default_language: en
    search_recall_top_k: 200
    rerank_high_threshold: 0.5
    rerank_high_skip_count: 1000
    rebuild_llm_batch_size: 50
    rebuild_min_llm_batches: 10
    rebuild_max_llm_batches: 40
    rebuild_irrelevant_stop_ratio: 0.799
    rebuild_irrel_low_combined_stop_ratio: 0.959
    rebuild_irrelevant_stop_streak: 3
  es_settings:
    number_of_shards: 1
    number_of_replicas: 0
    refresh_interval: 30s
  
  # 统一按“字段基名”配置；查询时按实际检索语言动态拼接 .{lang}
  field_boosts:
    title: 3.0
    # qanchors enriched_tags 在 enriched_attributes.value中也存在，所以其实他的权重为自身权重+enriched_attributes.value的权重
    qanchors: 1.0
    enriched_tags: 1.0
    enriched_attributes.value: 1.5
    category_name_text: 2.0
    category_path: 2.0
    keywords: 2.0
    tags: 2.0
    option1_values: 1.7
    option2_values: 1.7
    option3_values: 1.7
    brief: 1.0
    description: 1.0
    vendor: 1.0
  
  query_config:
    supported_languages:
    - zh
    - en
    default_language: en
    enable_text_embedding: true
    enable_query_rewrite: true
  
    zh_to_en_model: nllb-200-distilled-600m # nllb-200-distilled-600m deepl opus-mt-zh-en / opus-mt-en-zh
    en_to_zh_model: nllb-200-distilled-600m
    default_translation_model: nllb-200-distilled-600m
    # 源语种不在 index_languages时翻译质量比较重要，因此单独配置
    zh_to_en_model__source_not_in_index: deepl
    en_to_zh_model__source_not_in_index: deepl
    default_translation_model__source_not_in_index: deepl
  
    # 查询解析阶段：翻译与 query 向量并发执行，共用同一等待预算（毫秒）
    translation_embedding_wait_budget_ms_source_in_index: 300
    translation_embedding_wait_budget_ms_source_not_in_index: 400
    style_intent:
      enabled: true
      selected_sku_boost: 1.2
      color_dictionary_path: config/dictionaries/style_intent_color.csv
      size_dictionary_path: config/dictionaries/style_intent_size.csv
      dimension_aliases:
        color:
        - color
        - colors
        - colour
        - colours
        - 颜色
        - 色
        - 色系
        size:
        - size
        - sizes
        - sizing
        - 尺码
        - 尺寸
        - 码数
        - 号码
        - 码
    product_title_exclusion:
      enabled: true
      dictionary_path: config/dictionaries/product_title_exclusion.tsv
    search_fields:
      # 统一按“字段基名”配置；查询时按实际检索语言动态拼接 .{lang}
      multilingual_fields:
      - title
      - keywords
      - qanchors
      - enriched_tags
      - enriched_attributes.value
      # - enriched_taxonomy_attributes.value
      - option1_values
      - option2_values
      - option3_values
      - category_path
      - category_name_text
      # - brief
      # - description
      # - vendor
      # shared_fields: 无语言后缀字段；示例: tags, option1_values, option2_values, option3_values
  
      shared_fields: null
      core_multilingual_fields:
      - title
      - qanchors
      - category_name_text
  
    # 文本召回（主查询 + 翻译查询）
    text_query_strategy:
      base_minimum_should_match: 60%
      translation_minimum_should_match: 60%
      translation_boost: 0.75
      tie_breaker_base_query: 0.5
      best_fields_boost: 2.0
      best_fields:
        title: 4.0
        qanchors: 3.0
        category_name_text: 2.0
      phrase_fields:
        title: 5.0
        qanchors: 4.0
      phrase_match_boost: 3.0
    text_embedding_field: title_embedding
    image_embedding_field: image_embedding.vector
  
    # null表示返回所有字段，[]表示不返回任何字段
    source_fields:
    - spu_id
    - handle
    - title
    - brief
    - description
    - vendor
    - category_name
    - category_name_text
    - category_path
    - category_id
    - category_level
    - category1_name
    - category2_name
    - category3_name
    # - tags
    # - keywords
    # - qanchors
    # - enriched_tags
    - enriched_attributes
    - enriched_taxonomy_attributes
  
    - min_price
    - compare_at_price
    - image_url
    - sku_prices
    - sku_weights
    - sku_weight_units
    - total_inventory
    - option1_name
    - option1_values
    - option2_name
    - option2_values
    - option3_name
    - option3_values
    - specifications
    - skus
  
    # KNN：文本向量与多模态（图片）向量各自 boost 与召回（k / num_candidates）
    knn_text_boost: 4
    knn_image_boost: 4
    knn_text_k: 160
    knn_text_num_candidates: 560 #  k * 3.4
    knn_text_k_long: 400
    knn_text_num_candidates_long: 1200
    knn_image_k: 400
    knn_image_num_candidates: 1200
  
  function_score:
    score_mode: sum
    boost_mode: multiply
    functions: []

  coarse_rank:
    enabled: true
    input_window: 480
    output_window: 160
    fusion:
      es_bias: 10.0
      es_exponent: 0.05
      text_bias: 0.1
      text_exponent: 0.35
      # base_query_trans_* 相对 base_query 的权重（见 search/rerank_client 中文本 dismax 融合）
      # 因为es的打分已经给了trans进行了折扣，所以这里不再继续折扣

      text_translation_weight: 1.0

      knn_text_weight: 1.0
      knn_image_weight: 2.0
      knn_tie_breaker: 0.3
      knn_bias: 0.6
      knn_exponent: 0.4
      knn_text_bias: 0.2
      knn_text_exponent: 0.0
      knn_image_bias: 0.2
      knn_image_exponent: 0.0
  fine_rank:
    enabled: false # false 时保序透传
    input_window: 160
    output_window: 80
    timeout_sec: 10.0
    rerank_query_template: '{query}'
    rerank_doc_template: '{title}'
    service_profile: fine
  rerank:
    enabled: true # false 时保序透传
    rerank_window: 160
    exact_knn_rescore_enabled: true
    exact_knn_rescore_window: 160
    timeout_sec: 15.0
    weight_es: 0.4
    weight_ai: 0.6
    rerank_query_template: '{query}'
    rerank_doc_template: '{title}'
    service_profile: default
    # 乘法融合：fused = Π (max(score,0) + bias) ** exponent（es / rerank / fine / text / knn）
    # 其中 knn_score 先做一层 dis_max：
    #   max(knn_text_weight * text_knn, knn_image_weight * image_knn)
    #   + knn_tie_breaker * 另一侧较弱信号
    fusion:
      es_bias: 10.0
      es_exponent: 0.05
      rerank_bias: 0.1
      rerank_exponent: 1.15
      fine_bias: 0.1
      fine_exponent: 1.0
      text_bias: 0.1
      # base_query_trans_* 相对 base_query 的权重（见 search/rerank_client 中文本 dismax 融合）
      text_exponent: 0.25
      text_translation_weight: 0.8
      knn_text_weight: 1.0
      knn_image_weight: 2.0
      knn_tie_breaker: 0.3
      knn_bias: 0.6
      knn_exponent: 0.4
  
  services:
    translation:
      service_url: http://127.0.0.1:6006
      default_model: nllb-200-distilled-600m
      default_scene: general
      timeout_sec: 10.0
      cache:
        ttl_seconds: 62208000
        sliding_expiration: true
        # When false, cache keys are exact-match per request model only (ignores model_quality_tiers for lookups)
        # Higher tier = better quality. Multiple models may share one tier (同级).
        # A request may reuse Redis keys from models with tier > A or tier == A (not from lower tiers).
        enable_model_quality_tier_cache: true
        model_quality_tiers:
          deepl: 30
          qwen-mt: 30
          llm: 30
          nllb-200-distilled-600m: 20
          opus-mt-zh-en: 10
          opus-mt-en-zh: 10
      capabilities:
        qwen-mt:
          enabled: true
          backend: qwen_mt
          model: qwen-mt-flash
          base_url: https://dashscope-us.aliyuncs.com/compatible-mode/v1
          timeout_sec: 10.0
          use_cache: true
        llm:
          enabled: true
          backend: llm
          model: qwen-flash
          base_url: https://dashscope-us.aliyuncs.com/compatible-mode/v1
          timeout_sec: 30.0
          use_cache: true
        deepl:
          enabled: true
          backend: deepl
          api_url: https://api.deepl.com/v2/translate
          timeout_sec: 10.0
          glossary_id: ''
          use_cache: true
        nllb-200-distilled-600m:
          enabled: true
          backend: local_nllb
          model_id: facebook/nllb-200-distilled-600M
          model_dir: ./models/translation/facebook/nllb-200-distilled-600M
          ct2_model_dir: ./models/translation/facebook/nllb-200-distilled-600M/ctranslate2-float16
          ct2_compute_type: float16
          ct2_conversion_quantization: float16
          ct2_auto_convert: true
          ct2_inter_threads: 4
          ct2_intra_threads: 0
          ct2_max_queued_batches: 32
          ct2_batch_type: examples
          ct2_decoding_length_mode: source
          ct2_decoding_length_extra: 16
          ct2_decoding_length_min: 32
          device: cuda
          torch_dtype: float16
          batch_size: 64
          max_input_length: 256
          max_new_tokens: 96
          num_beams: 4
          use_cache: true
        opus-mt-zh-en:
          enabled: false
          backend: local_marian
          model_id: Helsinki-NLP/opus-mt-zh-en
          model_dir: ./models/translation/Helsinki-NLP/opus-mt-zh-en
          ct2_model_dir: ./models/translation/Helsinki-NLP/opus-mt-zh-en/ctranslate2-float16
          ct2_compute_type: float16
          ct2_conversion_quantization: float16
          ct2_auto_convert: true
          ct2_inter_threads: 1
          ct2_intra_threads: 0
          ct2_max_queued_batches: 0
          ct2_batch_type: examples
          device: cuda
          torch_dtype: float16
          batch_size: 16
          max_input_length: 256
          max_new_tokens: 256
          num_beams: 1
          use_cache: true
        opus-mt-en-zh:
          enabled: false
          backend: local_marian
          model_id: Helsinki-NLP/opus-mt-en-zh
          model_dir: ./models/translation/Helsinki-NLP/opus-mt-en-zh
          ct2_model_dir: ./models/translation/Helsinki-NLP/opus-mt-en-zh/ctranslate2-float16
          ct2_compute_type: float16
          ct2_conversion_quantization: float16
          ct2_auto_convert: true
          ct2_inter_threads: 1
          ct2_intra_threads: 0
          ct2_max_queued_batches: 0
          ct2_batch_type: examples
          device: cuda
          torch_dtype: float16
          batch_size: 16
          max_input_length: 256
          max_new_tokens: 256
          num_beams: 1
          use_cache: true
    embedding:
      provider: http
      providers:
        http:
          text_base_url: http://127.0.0.1:6005
          image_base_url: http://127.0.0.1:6008
      backend: tei
      backends:
        tei:
          base_url: http://127.0.0.1:8080
          timeout_sec: 20
          model_id: Qwen/Qwen3-Embedding-0.6B
        local_st:
          model_id: Qwen/Qwen3-Embedding-0.6B
          device: cuda
          batch_size: 32
          normalize_embeddings: true
      # 服务内图片后端（embedding 进程启动时读取；cnclip gRPC 与 6008 须同一 model_name）
      # Chinese-CLIP：ViT-H-14 → 1024 维，ViT-L-14 → 768 维。须与 mappings/search_products.json 中
      # image_embedding.vector.dims 一致（当前索引为 1024 → 默认 ViT-H-14）。
      image_backend: clip_as_service  # clip_as_service | local_cnclip
      image_backends:
        clip_as_service:
          server: grpc://127.0.0.1:51000
          model_name: CN-CLIP/ViT-L-14
          batch_size: 8
          normalize_embeddings: true
        local_cnclip:
          model_name: ViT-L-14
          device: null
          batch_size: 8
          normalize_embeddings: true
    rerank:
      provider: http
      providers:
        http:
          instances:
            default:
              base_url: http://127.0.0.1:6007
              service_url: http://127.0.0.1:6007/rerank
            fine:
              base_url: http://127.0.0.1:6009
              service_url: http://127.0.0.1:6009/rerank
      request:
        max_docs: 1000
        normalize: true
      # 命名实例：同一套 reranker 代码按实例名读取不同端口 / 后端 / runtime 目录。
      default_instance: default
      instances:
        default:
          host: 0.0.0.0
          port: 6007
          backend: bge
          runtime_dir: ./.runtime/reranker/default
        fine:
          host: 0.0.0.0
          port: 6009
          backend: bge
          runtime_dir: ./.runtime/reranker/fine
      backends:
        bge:
          model_name: BAAI/bge-reranker-v2-m3
          device: null
          use_fp16: true
          batch_size: 80
          max_length: 160
          cache_dir: ./model_cache
          enable_warmup: true
        jina_reranker_v3:
          model_name: jinaai/jina-reranker-v3
          device: null
          dtype: float16
          batch_size: 64
          max_doc_length: 160
          max_query_length: 64
          sort_by_doc_length: true
          cache_dir: ./model_cache
          trust_remote_code: true
        qwen3_vllm:
          model_name: Qwen/Qwen3-Reranker-0.6B
          engine: vllm
          max_model_len: 256
          tensor_parallel_size: 1
          gpu_memory_utilization: 0.2
          dtype: float16
          enable_prefix_caching: true
          enforce_eager: false
          infer_batch_size: 100
          sort_by_doc_length: true
  
          # standard=_format_instruction__standard（固定 yes/no system）；compact=_format_instruction（instruction 作 system 且 user 内重复 Instruct）
          instruction_format: standard  # compact standard
          # instruction: "Given a query, score the product for relevance"
          # "rank products by given query" 比 “Given a query, score the product for relevance” 更好点
          # instruction: "rank products by given query, category match first"
          # instruction: "Rank products by query relevance, prioritizing category match"
          # instruction: "Rank products by query relevance, prioritizing category and style match"
          # instruction: "Rank by query relevance, prioritize category & style"
          # instruction: "Relevance ranking: category & style match first"
          # instruction: "Score product relevance by query with category & style match prioritized"
          # instruction: "Rank products by query with category & style match prioritized"
          # instruction: "Given a fashion shopping query, retrieve relevant products that answer the query"
          instruction: rank products by given query
  
        # vLLM LLM.score()（跨编码打分）。独立高性能环境 .venv-reranker-score（vllm 0.18 固定版）：./scripts/setup_reranker_venv.sh qwen3_vllm_score
        # 与 qwen3_vllm 可共用同一 model_name / HF 缓存；venv 分离以便升级 vLLM 而不影响 generate 后端。
        qwen3_vllm_score:
          model_name: Qwen/Qwen3-Reranker-0.6B
          # 官方 Hub 原版需 true；若改用已转换的 seq-cls 权重（如 tomaarsen/...-seq-cls）则设为 false
          use_original_qwen3_hf_overrides: true
          # vllm_runner: "auto"
          # vllm_convert: "auto"
          # 可选：在 use_original_qwen3_hf_overrides 为 true 时与内置 overrides 合并
          # hf_overrides: {}
          engine: vllm
          max_model_len: 172
          tensor_parallel_size: 1
          gpu_memory_utilization: 0.15
          dtype: float16
          enable_prefix_caching: true
          enforce_eager: false
          infer_batch_size: 80
          sort_by_doc_length: true
          # 默认 standard 与 vLLM 官方 Qwen3 reranker 前缀一致
          instruction_format: standard  # compact standard
          # instruction: "Rank products by query with category & style match prioritized"
          # instruction: "Given a shopping query, rank products by relevance"
          instruction: Rank products by query with category & style match prioritized
        qwen3_transformers:
          model_name: Qwen/Qwen3-Reranker-0.6B
          instruction: rank products by given query
          max_length: 8192
          batch_size: 64
          use_fp16: true
          attn_implementation: sdpa
        qwen3_transformers_packed:
          model_name: Qwen/Qwen3-Reranker-0.6B
          instruction: Rank products by query with category & style match prioritized
          max_model_len: 256
          max_doc_len: 160
          max_docs_per_pack: 0
          use_fp16: true
          sort_by_doc_length: true
          attn_implementation: eager
        qwen3_gguf:
          repo_id: DevQuasar/Qwen.Qwen3-Reranker-4B-GGUF
          filename: '*Q8_0.gguf'
          cache_dir: ./model_cache
          local_dir: ./models/reranker/qwen3-reranker-4b-gguf
          instruction: Rank products by query with category & style match prioritized
          n_ctx: 512
          n_batch: 512
          n_ubatch: 512
          n_gpu_layers: 999
          main_gpu: 0
          n_threads: 2
          n_threads_batch: 4
          flash_attn: true
          offload_kqv: true
          use_mmap: true
          use_mlock: false
          infer_batch_size: 8
          sort_by_doc_length: true
          length_sort_mode: char
          enable_warmup: true
          verbose: false
        qwen3_gguf_06b:
          repo_id: ggml-org/Qwen3-Reranker-0.6B-Q8_0-GGUF
          filename: qwen3-reranker-0.6b-q8_0.gguf
          cache_dir: ./model_cache
          local_dir: ./models/reranker/qwen3-reranker-0.6b-q8_0-gguf
          instruction: Rank products by query with category & style match prioritized
          n_ctx: 256
          n_batch: 256
          n_ubatch: 256
          n_gpu_layers: 999
          main_gpu: 0
          n_threads: 2
          n_threads_batch: 4
          flash_attn: true
          offload_kqv: true
          use_mmap: true
          use_mlock: false
          infer_batch_size: 32
          sort_by_doc_length: true
          length_sort_mode: char
          reuse_query_state: false
          enable_warmup: true
          verbose: false
        dashscope_rerank:
          model_name: qwen3-rerank
          endpoint: https://dashscope.aliyuncs.com/compatible-api/v1/reranks
          api_key_env: RERANK_DASHSCOPE_API_KEY_CN
          timeout_sec: 10.0
          top_n_cap: 0 # 0 表示 top_n=当前请求文档数
          batchsize: 64 # 0 关闭；>0 启用并发小包调度（top_n/top_n_cap 仍生效，分包后全局截断）
          instruct: Given a shopping query, rank product titles by relevance
          max_retries: 2
          retry_backoff_sec: 0.2
  
  spu_config:
    enabled: true
    spu_field: spu_id
    inner_hits_size: 10
    # 配置哪些option维度参与检索（进索引、以及在线搜索）
    # 格式为list，选择option1/option2/option3中的一个或多个
    searchable_option_dimensions:
    - option1
    - option2
    - option3
  
  # 每个租户可配置主语言 primary_language 与索引语言 index_languages（主市场语言，商家可勾选）
  # 默认 index_languages: [en, zh]，可配置为任意 SOURCE_LANG_CODE_MAP.keys() 的子集
  tenant_config:
    default:
      primary_language: en
      index_languages:
      - en
      - zh
    tenants:
      '1':
        primary_language: zh
        index_languages:
        - zh
        - en
      '2':
        primary_language: en
        index_languages:
        - en
        - zh
      '3':
        primary_language: zh
        index_languages:
        - zh
        - en
      '162':
        primary_language: zh
        index_languages:
        - zh
        - en
      '170':
        primary_language: en
        index_languages:
        - en
        - zh