# 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: "opus-mt-zh-en"
  # en_to_zh_model: "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: "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: 500 # 80
  translation_embedding_wait_budget_ms_source_not_in_index: 700 #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:
      # - "tags"
      # - "option1_values"
      # - "option2_values"
      # - "option3_values"
    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: "75%"
    translation_minimum_should_match: "75%"
    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: null

  # 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 boost配置（向量召回的boost值）
  knn_boost: 2.0  # Lower boost for embedding recall

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

# 重排配置（provider/URL 在 services.rerank）
rerank:
  enabled: true
  rerank_window: 400
  timeout_sec: 15.0
  weight_es: 0.4
  weight_ai: 0.6
  rerank_query_template: "{query}"
  rerank_doc_template: "{title}"
  # 乘法融合：fused = Π (max(score,0) + bias) ** exponent（rerank / text / knn 三项）
  fusion:
    rerank_bias: 0.00001
    rerank_exponent: 1.0
    text_bias: 0.1
    text_exponent: 0.35
    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_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: true
        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: true
        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）
    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"
    base_url: "http://127.0.0.1:6007"
    providers:
      http:
        base_url: "http://127.0.0.1:6007"
        service_url: "http://127.0.0.1:6007/rerank"
    request:
      max_docs: 1000
      normalize: true
    # 服务内后端（reranker 进程启动时读取）
    backend: "qwen3_vllm"  # bge | qwen3_vllm | qwen3_vllm_score | qwen3_transformers | qwen3_transformers_packed | qwen3_gguf | qwen3_gguf_06b | dashscope_rerank
    backends:
      bge:
        model_name: "BAAI/bge-reranker-v2-m3"
        device: null
        use_fp16: true
        batch_size: 64
        max_length: 160
        cache_dir: "./model_cache"
        enable_warmup: true
      qwen3_vllm:
        model_name: "Qwen/Qwen3-Reranker-0.6B"
        engine: "vllm"
        max_model_len: 160
        tensor_parallel_size: 1
        gpu_memory_utilization: 0.20
        dtype: "float16"
        enable_prefix_caching: true
        enforce_eager: false
        infer_batch_size: 100
        sort_by_doc_length: true
        # 与 reranker/backends/qwen3_vllm.py 一致：standard=_format_instruction__standard（固定 yes/no system）；compact=_format_instruction（instruction 作 system 且 user 内重复 Instruct）
        # instruction_format: compact
        instruction_format: compact
        # 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"
      # 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 0.18：算力 < 8（如 T4）默认自动用 TRITON_ATTN；Ampere+ 可省略或设 auto。也可设环境变量 RERANK_VLLM_ATTENTION_BACKEND
        # vllm_attention_backend: "auto"
        # 可选：与 vLLM 对齐；一般保持 auto
        # 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: 160
        tensor_parallel_size: 1
        gpu_memory_utilization: 0.20
        dtype: "float16"
        enable_prefix_caching: true
        enforce_eager: false
        infer_batch_size: 100
        sort_by_doc_length: true
        # 与 qwen3_vllm 同名项语义一致；默认 standard 与 vLLM 官方 Qwen3 reranker 前缀一致
        # instruction_format: compact
        instruction_format: standard
        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: 4096
        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"]