config/config_loader.py

"""
Configuration loader and validator for search engine configurations.
This module handles loading, parsing, and validating YAML configuration files
that define how search should be executed (NOT how data should be indexed).
索引结构由 mappings/search_products.json 定义。
此配置只定义搜索行为：字段权重、搜索域、查询策略等。
"""
import yaml
from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from pathlib import Path
@dataclass
class IndexConfig:
    """Configuration for an index domain (e.g., default, title, brand)."""
    name: str
    label: str
    fields: List[str]  # List of field names to include in this search domain
    boost: float = 1.0
    example: Optional[str] = None
@dataclass
class QueryConfig:
    """Configuration for query processing."""
    supported_languages: List[str] = field(default_factory=lambda: ["zh", "en"])
    default_language: str = "en"
    
    # Feature flags
    enable_text_embedding: bool = True
    enable_query_rewrite: bool = True
    enable_multilang_search: bool = True  # Enable multi-language search using translations
    
    # Query rewrite dictionary (loaded from external file)
    rewrite_dictionary: Dict[str, str] = field(default_factory=dict)
    
    # Translation settings (provider/URL in services.translation)
    translation_service: str = "deepl"
    translation_api_key: Optional[str] = None
    translation_glossary_id: Optional[str] = None
    translation_context: str = "e-commerce product search"
    translation_prompts: Dict[str, str] = field(default_factory=dict)
    
    # Embedding field names
    text_embedding_field: Optional[str] = "title_embedding"
    image_embedding_field: Optional[str] = None
    
    # Embedding disable thresholds (disable vector search for short queries)
    embedding_disable_chinese_char_limit: int = 4
    embedding_disable_english_word_limit: int = 3
    
    # Source fields configuration
    source_fields: Optional[List[str]] = None
    
    # KNN boost configuration
    knn_boost: float = 0.25  # Boost value for KNN (embedding recall)
@dataclass
class SPUConfig:
    """Configuration for SPU aggregation."""
    enabled: bool = False
    spu_field: Optional[str] = None
    inner_hits_size: int = 3
    # 配置哪些option维度参与检索（进索引、以及在线搜索）
    searchable_option_dimensions: List[str] = field(default_factory=lambda: ['option1', 'option2', 'option3'])
@dataclass
class FunctionScoreConfig:
    """Function Score配置（ES层打分规则）"""
    score_mode: str = "sum"
    boost_mode: str = "multiply"
    functions: List[Dict[str, Any]] = field(default_factory=list)
@dataclass
class RankingConfig:
    """Configuration for ranking expressions."""
    expression: str = "bm25()"
    description: str = "Default BM25 ranking"
@dataclass
class RerankConfig:
    """重排配置（provider/URL 在 services.rerank）"""
    rerank_window: int = 1000
    timeout_sec: float = 15.0
    weight_es: float = 0.4
    weight_ai: float = 0.6
    rerank_query_template: str = "{query}"
    rerank_doc_template: str = "{title}"
@dataclass
class SearchConfig:
    """Complete configuration for search engine (multi-tenant)."""
    
    # 字段权重配置（用于搜索）
    field_boosts: Dict[str, float]
    
    # Index structure (query domains)
    indexes: List[IndexConfig]
    
    # Query processing
    query_config: QueryConfig
    
    # Ranking configuration
    ranking: RankingConfig
    
    # Function Score configuration (ES层打分)
    function_score: FunctionScoreConfig
    
    # Rerank configuration (本地重排)
    rerank: RerankConfig
    
    # SPU configuration
    spu_config: SPUConfig
    
    # ES index settings
    es_index_name: str
    
    # Tenant configuration
    tenant_config: Dict[str, Any] = field(default_factory=dict)
    
    # ES settings
    es_settings: Dict[str, Any] = field(default_factory=dict)
    # Extensible service/provider registry (translation/embedding/rerank/...)
    services: Dict[str, Any] = field(default_factory=dict)
class ConfigurationError(Exception):
    """Raised when configuration validation fails."""
    pass
class ConfigLoader:
    """Loads and validates unified search engine configuration from YAML file."""
    
    def __init__(self, config_file: Optional[Path] = None):
        """
        Initialize config loader.
        
        Args:
            config_file: Path to config YAML file (defaults to config/config.yaml)
        """
        if config_file is None:
            config_file = Path(__file__).parent / "config.yaml"
        self.config_file = Path(config_file)
    
    def _load_rewrite_dictionary(self) -> Dict[str, str]:
        """Load query rewrite dictionary from external file."""
        rewrite_file = Path(__file__).parent / "rewrite_dictionary.txt"
        rewrite_dict = {}
        
        if not rewrite_file.exists():
            return rewrite_dict
        
        try:
            with open(rewrite_file, 'r', encoding='utf-8') as f:
                for line in f:
                    line = line.strip()
                    if not line or line.startswith('#'):
                        continue
                    
                    parts = line.split('\t')
                    if len(parts) >= 2:
                        original = parts[0].strip()
                        replacement = parts[1].strip()
                        if original and replacement:
                            rewrite_dict[original] = replacement
        except Exception as e:
            print(f"Warning: Failed to load rewrite dictionary: {e}")
        
        return rewrite_dict
    
    def load_config(self, validate: bool = True) -> SearchConfig:
        """
        Load unified configuration from YAML file.
        
        Args:
            validate: Whether to validate configuration after loading
        
        Returns:
            SearchConfig object
        
        Raises:
            ConfigurationError: If config file not found, invalid, or validation fails
        """
        if not self.config_file.exists():
            raise ConfigurationError(f"Configuration file not found: {self.config_file}")
        
        try:
            with open(self.config_file, 'r', encoding='utf-8') as f:
                config_data = yaml.safe_load(f)
        except yaml.YAMLError as e:
            raise ConfigurationError(f"Invalid YAML in {self.config_file}: {e}")
        
        config = self._parse_config(config_data)
        
        # Auto-validate configuration
        if validate:
            errors = self.validate_config(config)
            if errors:
                error_msg = "Configuration validation failed:\n" + "\n".join(f"  - {err}" for err in errors)
                raise ConfigurationError(error_msg)
        
        return config
    
    def _parse_config(self, config_data: Dict[str, Any]) -> SearchConfig:
        """Parse configuration dictionary into SearchConfig object."""
        
        # Parse field_boosts
        field_boosts = config_data.get("field_boosts", {})
        if not isinstance(field_boosts, dict):
            raise ConfigurationError("field_boosts must be a dictionary")
        
        # Parse indexes
        indexes = []
        for index_data in config_data.get("indexes", []):
            indexes.append(self._parse_index_config(index_data))
        
        # Parse query config
        query_config_data = config_data.get("query_config", {})
        services_data = config_data.get("services", {}) if isinstance(config_data.get("services", {}), dict) else {}
        rewrite_dictionary = self._load_rewrite_dictionary()
        embedding_thresholds = query_config_data.get("embedding_disable_thresholds", {})
        query_config = QueryConfig(
            supported_languages=query_config_data.get("supported_languages") or ["zh", "en"],
            default_language=query_config_data.get("default_language") or "en",
            enable_text_embedding=query_config_data.get("enable_text_embedding", True),
            enable_query_rewrite=query_config_data.get("enable_query_rewrite", True),
            rewrite_dictionary=rewrite_dictionary,
            translation_api_key=query_config_data.get("translation_api_key"),
            translation_service=query_config_data.get("translation_service") or "deepl",
            translation_glossary_id=query_config_data.get("translation_glossary_id"),
            translation_context=query_config_data.get("translation_context") or "e-commerce product search",
            translation_prompts=query_config_data.get("translation_prompts", {}),
            text_embedding_field=query_config_data.get("text_embedding_field"),
            image_embedding_field=query_config_data.get("image_embedding_field"),
            embedding_disable_chinese_char_limit=embedding_thresholds.get("chinese_char_limit", 4),
            embedding_disable_english_word_limit=embedding_thresholds.get("english_word_limit", 3),
            source_fields=query_config_data.get("source_fields"),
            knn_boost=query_config_data.get("knn_boost", 0.25)
        )
        
        # Parse ranking config
        ranking_data = config_data.get("ranking", {})
        ranking = RankingConfig(
            expression=ranking_data.get("expression") or "bm25() + 0.2*text_embedding_relevance()",
            description=ranking_data.get("description") or "Default BM25 + text embedding ranking"
        )
        
        # Parse Function Score configuration
        fs_data = config_data.get("function_score", {})
        function_score = FunctionScoreConfig(
            score_mode=fs_data.get("score_mode") or "sum",
            boost_mode=fs_data.get("boost_mode") or "multiply",
            functions=fs_data.get("functions") or []
        )
        
        # Parse Rerank (provider/URL in services.rerank)
        rerank_data = config_data.get("rerank", {})
        rerank = RerankConfig(
            rerank_window=int(rerank_data.get("rerank_window", 1000)),
            timeout_sec=float(rerank_data.get("timeout_sec", 15.0)),
            weight_es=float(rerank_data.get("weight_es", 0.4)),
            weight_ai=float(rerank_data.get("weight_ai", 0.6)),
            rerank_query_template=str(rerank_data.get("rerank_query_template") or "{query}"),
            rerank_doc_template=str(rerank_data.get("rerank_doc_template") or "{title}"),
        )
        
        # Parse SPU config
        spu_data = config_data.get("spu_config", {})
        spu_config = SPUConfig(
            enabled=spu_data.get("enabled", False),
            spu_field=spu_data.get("spu_field"),
            inner_hits_size=spu_data.get("inner_hits_size", 3),
            searchable_option_dimensions=spu_data.get("searchable_option_dimensions", ['option1', 'option2', 'option3'])
        )
        
        # Parse tenant config
        tenant_config_data = config_data.get("tenant_config", {})
        
        return SearchConfig(
            field_boosts=field_boosts,
            indexes=indexes,
            query_config=query_config,
            ranking=ranking,
            function_score=function_score,
            rerank=rerank,
            spu_config=spu_config,
            tenant_config=tenant_config_data,
            es_index_name=config_data.get("es_index_name", "search_products"),
            es_settings=config_data.get("es_settings", {}),
            services=services_data
        )
    
    def _parse_index_config(self, index_data: Dict[str, Any]) -> IndexConfig:
        """Parse index configuration from dictionary."""
        return IndexConfig(
            name=index_data["name"],
            label=index_data.get("label", index_data["name"]),
            fields=index_data.get("fields", []),
            boost=index_data.get("boost", 1.0),
            example=index_data.get("example")
        )
    
    def validate_config(self, config: SearchConfig) -> List[str]:
        """
        Validate configuration for common errors.
        
        Args:
            config: SearchConfig to validate
        
        Returns:
            List of error messages (empty if valid)
        """
        errors = []
        
        # Validate es_index_name
        if not config.es_index_name:
            errors.append("es_index_name is required")
        
        # Validate field_boosts
        if not config.field_boosts:
            errors.append("field_boosts is empty")
        
        for field_name, boost in config.field_boosts.items():
            if not isinstance(boost, (int, float)):
                errors.append(f"field_boosts['{field_name}']: boost must be a number, got {type(boost).__name__}")
            elif boost < 0:
                errors.append(f"field_boosts['{field_name}']: boost must be non-negative")
        
        # Validate indexes
        if not config.indexes:
            errors.append("At least one index domain must be defined")
        
        index_names = set()
        for index in config.indexes:
            # Check for duplicate index names
            if index.name in index_names:
                errors.append(f"Duplicate index name: {index.name}")
            index_names.add(index.name)
            
            # Validate fields in index
            if not index.fields:
                errors.append(f"Index '{index.name}': fields list is empty")
        
        # Validate SPU config
        if config.spu_config.enabled:
            if not config.spu_config.spu_field:
                errors.append("SPU aggregation enabled but no spu_field specified")
        
        # Validate query config
        if not config.query_config.supported_languages:
            errors.append("At least one supported language must be specified")
        
        if config.query_config.default_language not in config.query_config.supported_languages:
            errors.append(
                f"Default language '{config.query_config.default_language}' "
                f"not in supported languages: {config.query_config.supported_languages}"
            )
        # Validate source_fields tri-state semantics
        source_fields = config.query_config.source_fields
        if source_fields is not None:
            if not isinstance(source_fields, list):
                errors.append("query_config.source_fields must be null or list[str]")
            else:
                for idx, field_name in enumerate(source_fields):
                    if not isinstance(field_name, str) or not field_name.strip():
                        errors.append(
                            f"query_config.source_fields[{idx}] must be a non-empty string"
                        )
        # Validate tenant config shape (default must exist in config)
        tenant_cfg = config.tenant_config
        if not isinstance(tenant_cfg, dict):
            errors.append("tenant_config must be an object")
        else:
            default_cfg = tenant_cfg.get("default")
            if not isinstance(default_cfg, dict):
                errors.append("tenant_config.default must be configured")
            else:
                index_languages = default_cfg.get("index_languages")
                if not isinstance(index_languages, list) or len(index_languages) == 0:
                    errors.append("tenant_config.default.index_languages must be a non-empty list")
        return errors
    
    def to_dict(self, config: SearchConfig) -> Dict[str, Any]:
        """Convert SearchConfig to dictionary representation."""
        
        # Build query_config dict
        query_config_dict = {
            "supported_languages": config.query_config.supported_languages,
            "default_language": config.query_config.default_language,
            "enable_text_embedding": config.query_config.enable_text_embedding,
            "enable_query_rewrite": config.query_config.enable_query_rewrite,
            "translation_service": config.query_config.translation_service,
            "text_embedding_field": config.query_config.text_embedding_field,
            "image_embedding_field": config.query_config.image_embedding_field,
            "embedding_disable_thresholds": {
                "chinese_char_limit": config.query_config.embedding_disable_chinese_char_limit,
                "english_word_limit": config.query_config.embedding_disable_english_word_limit
            },
            "source_fields": config.query_config.source_fields
        }
        
        return {
            "es_index_name": config.es_index_name,
            "es_settings": config.es_settings,
            "field_boosts": config.field_boosts,
            "indexes": [self._index_to_dict(index) for index in config.indexes],
            "query_config": query_config_dict,
            "ranking": {
                "expression": config.ranking.expression,
                "description": config.ranking.description
            },
            "function_score": {
                "score_mode": config.function_score.score_mode,
                "boost_mode": config.function_score.boost_mode,
                "functions": config.function_score.functions
            },
            "rerank": {
                "rerank_window": config.rerank.rerank_window,
                "timeout_sec": config.rerank.timeout_sec,
                "weight_es": config.rerank.weight_es,
                "weight_ai": config.rerank.weight_ai,
                "rerank_query_template": config.rerank.rerank_query_template,
                "rerank_doc_template": config.rerank.rerank_doc_template,
            },
            "spu_config": {
                "enabled": config.spu_config.enabled,
                "spu_field": config.spu_config.spu_field,
                "inner_hits_size": config.spu_config.inner_hits_size,
                "searchable_option_dimensions": config.spu_config.searchable_option_dimensions
            },
            "services": config.services,
        }
    
    def _index_to_dict(self, index: IndexConfig) -> Dict[str, Any]:
        """Convert IndexConfig to dictionary."""
        result = {
            "name": index.name,
            "label": index.label,
            "fields": index.fields,
            "boost": index.boost
        }
        
        if index.example:
            result["example"] = index.example
        
        return result