# Unified Configuration for Multi-Tenant Search Engine
# 统一配置文件，所有租户共用一套配置
# 注意：索引结构由 mappings/search_products.json 定义，此文件只配置搜索行为
#
# 约定：下列键为必填；进程环境变量可覆盖 infrastructure / runtime 中同名语义项
#（如 ES_HOST、API_PORT 等），未设置环境变量时使用本文件中的值。

# Process / bind addresses (环境变量 APP_ENV、RUNTIME_ENV、ES_INDEX_NAMESPACE 可覆盖前两者的语义)
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

# 基础设施连接（敏感项优先读环境变量：ES_*、REDIS_*、DB_*、DASHSCOPE_API_KEY、DEEPL_AUTH_KEY）
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

# Elasticsearch Index
es_index_name: search_products

# 检索域 / 索引列表（可为空列表；每项字段均需显式给出）
indexes: []

# Config assets
assets:
  query_rewrite_dictionary_path: config/dictionaries/query_rewrite.dict

# Product content understanding (LLM enrich-content) configuration
product_enrich:
  max_workers: 40

# 离线 / Web 相关性评估（scripts/evaluation、eval-web）
# CLI 未显式传参时使用此处默认值；search_base_url 未配置时自动为 http://127.0.0.1:{runtime.api_port}
search_evaluation:
  artifact_root: artifacts/search_evaluation
  queries_file: scripts/evaluation/queries/queries.txt
  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 Index Settings (基础设置)
es_settings:
  number_of_shards: 1
  number_of_replicas: 0
  refresh_interval: 30s

# 字段权重配置（用于搜索时的字段boost）
# 统一按“字段基名”配置；查询时按实际检索语言动态拼接 .{lang}。
# 若需要按某个语言单独调权，也可以加显式 key（例如 title.de: 3.2）。
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
  # enriched_taxonomy_attributes.value: 0.3
  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 Configuration（查询配置）
query_config:
  # 支持的语言
  supported_languages:
  - zh
  - en
  default_language: en

  # 功能开关（翻译开关由tenant_config控制）
  enable_text_embedding: true
  enable_query_rewrite: true

  # 查询翻译模型（须与 services.translation.capabilities 中某项一致）
  # 源语种在租户 index_languages 内：主召回可打在源语种字段，用下面三项。
  zh_to_en_model: nllb-200-distilled-600m  # "opus-mt-zh-en"
  en_to_zh_model: nllb-200-distilled-600m  # "opus-mt-en-zh"
  default_translation_model: nllb-200-distilled-600m
  # zh_to_en_model: deepl
  # en_to_zh_model: deepl
  # default_translation_model: deepl
  # 源语种不在 index_languages：翻译对可检索文本更关键，可单独指定（缺省则与上一组相同）
  zh_to_en_model__source_not_in_index: nllb-200-distilled-600m
  en_to_zh_model__source_not_in_index: nllb-200-distilled-600m
  default_translation_model__source_not_in_index: nllb-200-distilled-600m
  # 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）。
  # 检测语言已在租户 index_languages 内：较短；不在索引语言内：较长（翻译对召回更关键）。
  translation_embedding_wait_budget_ms_source_in_index: 300  # 80
  translation_embedding_wait_budget_ms_source_not_in_index: 400  # 200
  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

  # 动态多语言检索字段配置
  # multilingual_fields 会被拼成 title.{lang}/brief.{lang}/... 形式；
  # shared_fields 为无语言后缀字段。
  search_fields:
    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

  # 统一文本召回策略（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

  # Embedding字段名称
  text_embedding_field: title_embedding
  image_embedding_field: image_embedding.vector

  # 返回字段配置（_source includes）
  # null表示返回所有字段，[]表示不返回任何字段，列表表示只返回指定字段
  # 下列字段与 api/result_formatter.py（SpuResult 填充）及 search/searcher.py（SKU 排序/主图替换）一致
  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.value
  - 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_num_candidates = k * 3.4
  knn_text_k: 160
  knn_text_num_candidates: 560
  knn_text_k_long: 400
  knn_text_num_candidates_long: 1200
  knn_image_k: 400
  knn_image_num_candidates: 1200

# Function Score配置（ES层打分规则）
function_score:
  score_mode: sum
  boost_mode: multiply
  functions: []

# 粗排配置（仅融合 ES 文本/向量信号，不调用模型）
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

# 精排配置（轻量 reranker）
# enabled=false 时仍进入 fine 阶段，但保序透传，不调用 fine 模型服务
fine_rank:
  enabled: false
  input_window: 160
  output_window: 80
  timeout_sec: 10.0
  rerank_query_template: '{query}'
  rerank_doc_template: '{title}'
  service_profile: fine

# 重排配置（provider/URL 在 services.rerank）
# enabled=false 时仍进入 rerank 阶段，但保序透传，不调用最终 rerank 服务
rerank:
  enabled: true
  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
    text_exponent: 0.25
    # base_query_trans_* 相对 base_query 的权重（见 search/rerank_client 中文本 dismax 融合）
    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

# 可扩展服务/provider 注册表（单一配置源）
services:
  translation:
    service_url: http://127.0.0.1:6006
    # default_model: nllb-200-distilled-600m
    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).
      enable_model_quality_tier_cache: true
      # 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).
      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: 8
        ct2_decoding_length_min: 32
        device: cuda
        torch_dtype: float16
        batch_size: 64
        max_input_length: 256
        max_new_tokens: 64
        num_beams: 1
        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  # http
    providers:
      http:
        text_base_url: http://127.0.0.1:6005
        image_base_url: http://127.0.0.1:6008
    # 服务内文本后端（embedding 进程启动时读取）
    backend: tei  # tei | local_st
    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
    default_instance: default
    # 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。
    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
        # instruction: "Score the product’s relevance to the given query"
        max_length: 8192
        batch_size: 64
        use_fp16: true
        # sdpa：默认无需 flash-attn；若已安装 flash_attn 可改为 flash_attention_2
        attn_implementation: sdpa
      # Packed Transformers backend: shared query prefix + custom position_ids/attention_mask.
      # For 1 query + many short docs (for example 400 product titles), this usually reduces
      # repeated prefix work and padding waste compared with pairwise batching.
      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
        # Packed mode relies on a custom 4D attention mask. "eager" is the safest default.
        # If your torch/transformers stack validates it, you can benchmark "sdpa".
        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
        # T4 16GB / 性能优先配置：全量层 offload，实测比保守配置明显更快
        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
        # 0.6B GGUF / online rerank baseline:
        # 实测 400 titles 单请求约 265s，因此它更适合作为低显存功能后备，不适合在线低延迟主路由。
        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
        # 按地域选择 endpoint:
        # 中国:   https://dashscope.aliyuncs.com/compatible-api/v1/reranks
        # 新加坡: https://dashscope-intl.aliyuncs.com/compatible-api/v1/reranks
        # 美国:   https://dashscope-us.aliyuncs.com/compatible-api/v1/reranks
        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；>0 则限制 top_n 上限
        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配置（已启用，使用嵌套skus）
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

# 租户配置（Tenant Configuration）
# 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