# 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

  
  # 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: 2.5
    tags: 2.0
    category_name_text: 2.0
    category_path: 2.0

    brief: 1.5

    description: 1.5

    vendor: 1.5

    option1_values: 1.5
    option2_values: 1.5
    option3_values: 1.5

  
  # 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: 200  # 80
    translation_embedding_wait_budget_ms_source_not_in_index: 300  # 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
      - qanchors
      - 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
    - 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: 700
    output_window: 240
    fusion:
      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: 1.0
      knn_tie_breaker: 0.1
      knn_bias: 0.6
      knn_exponent: 0.0

  
  # 精排配置（轻量 reranker）

  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）

  rerank:

    enabled: true

    rerank_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（rerank / text / knn 三项）
    # 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:

      rerank_bias: 1.0e-05
      rerank_exponent: 1.15
      fine_bias: 1.0e-05

      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: 1.0
      knn_tie_breaker: 0.1

      knn_bias: 0.6

      knn_exponent: 0.0

  
  # 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: qwen3_vllm_score
          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