search/es_query_builder.py

"""
Elasticsearch query builder.
Converts parsed queries and search parameters into ES DSL queries.
Simplified architecture:
- filters and (text_recall or embedding_recall)
- function_score wrapper for boosting fields
"""
from typing import Dict, Any, List, Optional, Union, Tuple
import numpy as np
from .boolean_parser import QueryNode
from config import FunctionScoreConfig
class ESQueryBuilder:
    """Builds Elasticsearch DSL queries."""
    def __init__(
        self,
        index_name: str,
        match_fields: List[str],
        text_embedding_field: Optional[str] = None,
        image_embedding_field: Optional[str] = None,
        source_fields: Optional[List[str]] = None,
        function_score_config: Optional[FunctionScoreConfig] = None,
        enable_multilang_search: bool = True,
        default_language: str = "zh",
        knn_boost: float = 0.25
    ):
        """
        Initialize query builder.
        Args:
            index_name: ES index name
            match_fields: Fields to search for text matching
            text_embedding_field: Field name for text embeddings
            image_embedding_field: Field name for image embeddings
            source_fields: Fields to return in search results (_source includes)
            function_score_config: Function score configuration
            enable_multilang_search: Enable multi-language search using translations
            default_language: Default language to use when detection fails or returns "unknown"
            knn_boost: Boost value for KNN (embedding recall)
        """
        self.index_name = index_name
        self.match_fields = match_fields
        self.text_embedding_field = text_embedding_field
        self.image_embedding_field = image_embedding_field
        self.source_fields = source_fields
        self.function_score_config = function_score_config
        self.enable_multilang_search = enable_multilang_search
        self.default_language = default_language
        self.knn_boost = knn_boost
    def _split_filters_for_faceting(
        self,
        filters: Optional[Dict[str, Any]],
        facet_configs: Optional[List[Any]]
    ) -> tuple:
        """
        Split filters into conjunctive (query) and disjunctive (post_filter) based on facet configs.
        
        Disjunctive filters (multi-select facets):
        - Applied via post_filter (affects results but not aggregations)
        - Allows showing other options in the same facet even when filtered
        
        Conjunctive filters (standard facets):
        - Applied in query.bool.filter (affects both results and aggregations)
        - Standard drill-down behavior
        
        Args:
            filters: All filters from request
            facet_configs: Facet configurations with disjunctive flags
            
        Returns:
            (conjunctive_filters, disjunctive_filters)
        """
        if not filters or not facet_configs:
            return filters or {}, {}
        
        # Get fields that support multi-select
        multi_select_fields = set()
        for fc in facet_configs:
            if getattr(fc, 'disjunctive', False):
                # Handle specifications.xxx format
                if fc.field.startswith('specifications.'):
                    multi_select_fields.add('specifications')
                else:
                    multi_select_fields.add(fc.field)
        
        # Split filters
        conjunctive = {}
        disjunctive = {}
        
        for field, value in filters.items():
            if field in multi_select_fields:
                disjunctive[field] = value
            else:
                conjunctive[field] = value
        
        return conjunctive, disjunctive
    def build_query(
        self,
        query_text: str,
        query_vector: Optional[np.ndarray] = None,
        query_node: Optional[QueryNode] = None,
        filters: Optional[Dict[str, Any]] = None,
        range_filters: Optional[Dict[str, Any]] = None,
        facet_configs: Optional[List[Any]] = None,
        size: int = 10,
        from_: int = 0,
        enable_knn: bool = True,
        knn_k: int = 50,
        knn_num_candidates: int = 200,
        min_score: Optional[float] = None,
        parsed_query: Optional[Any] = None
    ) -> Dict[str, Any]:
        """
        Build complete ES query with post_filter support for multi-select faceting.
        结构：filters and (text_recall or embedding_recall) + post_filter
        - conjunctive_filters: 应用在 query.bool.filter（影响结果和聚合）
        - disjunctive_filters: 应用在 post_filter（只影响结果，不影响聚合）
        - text_recall: 文本相关性召回（中英文字段都用）
        - embedding_recall: 向量召回（KNN）
        - function_score: 包装召回部分，支持提权字段
        Args:
            query_text: Query text for BM25 matching
            query_vector: Query embedding for KNN search
            query_node: Parsed boolean expression tree
            filters: Exact match filters
            range_filters: Range filters for numeric fields (always applied in query)
            facet_configs: Facet configurations (used to identify multi-select facets)
            size: Number of results
            from_: Offset for pagination
            enable_knn: Whether to use KNN search
            knn_k: K value for KNN
            knn_num_candidates: Number of candidates for KNN
            min_score: Minimum score threshold
        Returns:
            ES query DSL dictionary
        """
        es_query = {
            "size": size,
            "from": from_
        }
        # Add _source filtering if source_fields are configured
        if self.source_fields:
            es_query["_source"] = {
                "includes": self.source_fields
            }
        # 1. Build recall queries (text or embedding)
        recall_clauses = []
        
        # Text recall (always include if query_text exists)
        if query_text:
            if query_node and query_node.operator != 'TERM':
                # Complex boolean query
                text_query = self._build_boolean_query(query_node)
            else:
                # Simple text query - use advanced should-based multi-query strategy
                text_query = self._build_advanced_text_query(query_text, parsed_query)
            recall_clauses.append(text_query)
        
        # Embedding recall (KNN - separate from query, handled below)
        has_embedding = enable_knn and query_vector is not None and self.text_embedding_field
        
        # 2. Split filters for multi-select faceting
        conjunctive_filters, disjunctive_filters = self._split_filters_for_faceting(
            filters, facet_configs
        )
        
        # Build filter clauses for query (conjunctive filters + range filters)
        filter_clauses = self._build_filters(conjunctive_filters, range_filters)
        
        # 3. Build main query structure: filters and recall
        if recall_clauses:
            # Combine text recalls with OR logic (if multiple)
            if len(recall_clauses) == 1:
                recall_query = recall_clauses[0]
            else:
                recall_query = {
                    "bool": {
                        "should": recall_clauses,
                        "minimum_should_match": 1
                    }
                }
            
            # Wrap recall with function_score for boosting
            recall_query = self._wrap_with_function_score(recall_query)
            
            # Combine filters and recall
            if filter_clauses:
                es_query["query"] = {
                    "bool": {
                        "must": [recall_query],
                        "filter": filter_clauses
                    }
                }
            else:
                es_query["query"] = recall_query
        else:
            # No recall queries, only filters (match_all filtered)
            if filter_clauses:
                es_query["query"] = {
                    "bool": {
                        "must": [{"match_all": {}}],
                        "filter": filter_clauses
                    }
                }
            else:
                es_query["query"] = {"match_all": {}}
        # 4. Add KNN search if enabled (separate from query, ES will combine)
        if has_embedding:
            knn_clause = {
                "field": self.text_embedding_field,
                "query_vector": query_vector.tolist(),
                "k": knn_k,
                "num_candidates": knn_num_candidates,
                "boost": self.knn_boost  # Lower boost for embedding recall
            }
            es_query["knn"] = knn_clause
        # 5. Add post_filter for disjunctive (multi-select) filters
        if disjunctive_filters:
            post_filter_clauses = self._build_filters(disjunctive_filters, None)
            if post_filter_clauses:
                if len(post_filter_clauses) == 1:
                    es_query["post_filter"] = post_filter_clauses[0]
                else:
                    es_query["post_filter"] = {
                        "bool": {"filter": post_filter_clauses}
                    }
        # 6. Add minimum score filter
        if min_score is not None:
            es_query["min_score"] = min_score
        return es_query
    
    def _wrap_with_function_score(self, query: Dict[str, Any]) -> Dict[str, Any]:
        """
        Wrap query with function_score for boosting fields.
        
        Args:
            query: Base query to wrap
            
        Returns:
            Function score query or original query if no functions configured
        """
        functions = self._build_score_functions()
        
        # If no functions configured, return original query
        if not functions:
            return query
        
        # Build function_score query
        score_mode = self.function_score_config.score_mode if self.function_score_config else "sum"
        boost_mode = self.function_score_config.boost_mode if self.function_score_config else "multiply"
        
        function_score_query = {
            "function_score": {
                "query": query,
                "functions": functions,
                "score_mode": score_mode,
                "boost_mode": boost_mode
            }
        }
        
        return function_score_query
    
    def _build_score_functions(self) -> List[Dict[str, Any]]:
        """
        Build function_score functions from config.
        
        Returns:
            List of function score functions
        """
        functions = []
        if not self.function_score_config:
            return functions
        
        config_functions = self.function_score_config.functions or []
        
        for func_config in config_functions:
            func_type = func_config.get("type")
            
            if func_type == "filter_weight":
                # Filter + Weight
                functions.append({
                    "filter": func_config["filter"],
                    "weight": func_config.get("weight", 1.0)
                })
            
            elif func_type == "field_value_factor":
                # Field Value Factor
                functions.append({
                    "field_value_factor": {
                        "field": func_config["field"],
                        "factor": func_config.get("factor", 1.0),
                        "modifier": func_config.get("modifier", "none"),
                        "missing": func_config.get("missing", 1.0)
                    }
                })
            
            elif func_type == "decay":
                # Decay Function (gauss/exp/linear)
                decay_func = func_config.get("function", "gauss")
                field = func_config["field"]
                
                decay_params = {
                    "origin": func_config.get("origin", "now"),
                    "scale": func_config["scale"]
                }
                
                if "offset" in func_config:
                    decay_params["offset"] = func_config["offset"]
                if "decay" in func_config:
                    decay_params["decay"] = func_config["decay"]
                
                functions.append({
                    decay_func: {
                        field: decay_params
                    }
                })
        
        return functions
    def _build_text_query(self, query_text: str) -> Dict[str, Any]:
        """
        Build simple text matching query (BM25).
        Legacy method - kept for backward compatibility.
        Args:
            query_text: Query text
        Returns:
            ES query clause
        """
        return {
            "multi_match": {
                "query": query_text,
                "fields": self.match_fields,
                "minimum_should_match": "67%",
                "tie_breaker": 0.9,
                "boost": 1.0,
                "_name": "base_query"
            }
        }
    
    def _get_match_fields(self, language: str) -> Tuple[List[str], List[str]]:
        """
        Get match fields for a specific language.
        
        Args:
            language: Language code ('zh' or 'en')
            
        Returns:
            (all_fields, core_fields) - core_fields are for phrase/keyword queries
        """
        if language == 'zh':
            all_fields = [
                "title.zh^3.0",
                "brief.zh^1.5",
                "description.zh",
                "vendor.zh^1.5",
                "tags",
                "category_path.zh^1.5",
                "category_name_text.zh^1.5",
                "option1_values^0.5"
            ]
            core_fields = [
                "title.zh^3.0",
                "brief.zh^1.5",
                "vendor.zh^1.5",
                "category_name_text.zh^1.5"
            ]
        else:  # en
            all_fields = [
                "title.en^3.0",
                "brief.en^1.5",
                "description.en",
                "vendor.en^1.5",
                "tags",
                "category_path.en^1.5",
                "category_name_text.en^1.5",
                "option1_values^0.5"
            ]
            core_fields = [
                "title.en^3.0",
                "brief.en^1.5",
                "vendor.en^1.5",
                "category_name_text.en^1.5"
            ]
        return all_fields, core_fields
    
    def _get_embedding_field(self, language: str) -> str:
        """Get embedding field name for a language."""
        # Currently using unified embedding field
        return self.text_embedding_field or "title_embedding"
    
    def _build_advanced_text_query(self, query_text: str, parsed_query: Optional[Any] = None) -> Dict[str, Any]:
        """
        Build advanced text query using should clauses with multiple query strategies.
        
        Reference implementation:
        - base_query: main query with AND operator and 75% minimum_should_match
        - translation queries: lower boost (0.4) for other languages
        - phrase query: for short queries (2+ tokens)
        - keywords query: extracted nouns from query
        - KNN query: added separately in build_query
        
        Args:
            query_text: Query text
            parsed_query: ParsedQuery object with analysis results
            
        Returns:
            ES bool query with should clauses
        """
        should_clauses = []
        
        # Get query analysis from parsed_query
        translations = {}
        language = self.default_language
        keywords = ""
        token_count = 0
        is_short_query = False
        is_long_query = False
        
        if parsed_query:
            translations = parsed_query.translations or {}
            # Use default language if detected_language is None or "unknown"
            detected_lang = parsed_query.detected_language
            if not detected_lang or detected_lang == "unknown":
                language = self.default_language
            else:
                language = detected_lang
            keywords = getattr(parsed_query, 'keywords', '') or ""
            token_count = getattr(parsed_query, 'token_count', 0) or 0
            is_short_query = getattr(parsed_query, 'is_short_query', False)
            is_long_query = getattr(parsed_query, 'is_long_query', False)
        
        # Get match fields for the detected language
        match_fields, core_fields = self._get_match_fields(language)
        
        # Tie breaker values
        tie_breaker_base_query = 0.9
        tie_breaker_long_query = 0.9
        tie_breaker_keywords = 0.9
        
        # 1. Base query - main query with AND operator
        should_clauses.append({
            "multi_match": {
                "_name": "base_query",
                "fields": match_fields,
                "minimum_should_match": "75%",
                # "operator": "AND",
                "query": query_text,
                "tie_breaker": tie_breaker_base_query
            }
        })
        
        # 2. Translation queries - lower boost (0.4) for other languages
        if self.enable_multilang_search:
            if language != 'zh' and translations.get('zh'):
                zh_fields, _ = self._get_match_fields('zh')
                should_clauses.append({
                    "multi_match": {
                        "query": translations['zh'],
                        "fields": zh_fields,
                        # "operator": "AND",
                        "minimum_should_match": "75%",
                        "tie_breaker": tie_breaker_base_query,
                        "boost": 0.4,
                        "_name": "base_query_trans_zh"
                    }
                })
            
            if language != 'en' and translations.get('en'):
                en_fields, _ = self._get_match_fields('en')
                should_clauses.append({
                    "multi_match": {
                        "query": translations['en'],
                        "fields": en_fields,
                        # "operator": "AND",
                        "minimum_should_match": "75%",
                        "tie_breaker": tie_breaker_base_query,
                        "boost": 0.4,
                        "_name": "base_query_trans_en"
                    }
                })
        
        # 3. Long query - add a query with lower minimum_should_match
        # Currently disabled (False condition in reference)
        if False and is_long_query:
            boost = 0.5 * pow(min(1.0, token_count / 10.0), 0.9)
            minimum_should_match = "70%"
            should_clauses.append({
                "multi_match": {
                    "query": query_text,
                    "fields": match_fields,
                    "minimum_should_match": minimum_should_match,
                    "boost": boost,
                    "tie_breaker": tie_breaker_long_query,
                    "_name": "long_query"
                }
            })
        
        # 4. Short query - add phrase query
        ENABLE_PHRASE_QUERY = True
        if ENABLE_PHRASE_QUERY and token_count >= 2 and is_short_query:
            query_length = len(query_text)
            slop = 0 if query_length < 3 else 1 if query_length < 5 else 2
            should_clauses.append({
                "multi_match": {
                    "query": query_text,
                    "fields": core_fields,
                    "type": "phrase",
                    "slop": slop,
                    "boost": 1.0,
                    "_name": "phrase_query"
                }
            })
        
        # 5. Keywords query - extracted nouns from query
        elif keywords and len(keywords.split()) <= 2 and 2 * len(keywords.replace(' ', '')) <= len(query_text):
            should_clauses.append({
                "multi_match": {
                    "query": keywords,
                    "fields": core_fields,
                    # "operator": "AND",
                    "tie_breaker": tie_breaker_keywords,
                    "boost": 0.1,
                    "_name": "keywords_query"
                }
            })
        
        # Return bool query with should clauses
        if len(should_clauses) == 1:
            return should_clauses[0]
        
        return {
            "bool": {
                "should": should_clauses,
                "minimum_should_match": 1
            }
        }
    def _build_boolean_query(self, node: QueryNode) -> Dict[str, Any]:
        """
        Build query from boolean expression tree.
        Args:
            node: Query tree node
        Returns:
            ES query clause
        """
        if node.operator == 'TERM':
            # Leaf node - simple text query
            return self._build_text_query(node.value)
        elif node.operator == 'AND':
            # All terms must match
            return {
                "bool": {
                    "must": [
                        self._build_boolean_query(term)
                        for term in node.terms
                    ]
                }
            }
        elif node.operator == 'OR':
            # Any term must match
            return {
                "bool": {
                    "should": [
                        self._build_boolean_query(term)
                        for term in node.terms
                    ],
                    "minimum_should_match": 1
                }
            }
        elif node.operator == 'ANDNOT':
            # First term must match, second must not
            if len(node.terms) >= 2:
                return {
                    "bool": {
                        "must": [self._build_boolean_query(node.terms[0])],
                        "must_not": [self._build_boolean_query(node.terms[1])]
                    }
                }
            else:
                return self._build_boolean_query(node.terms[0])
        elif node.operator == 'RANK':
            # Like OR but for ranking (all terms contribute to score)
            return {
                "bool": {
                    "should": [
                        self._build_boolean_query(term)
                        for term in node.terms
                    ]
                }
            }
        else:
            # Unknown operator
            return {"match_all": {}}
    def _build_filters(
        self, 
        filters: Optional[Dict[str, Any]] = None,
        range_filters: Optional[Dict[str, 'RangeFilter']] = None
    ) -> List[Dict[str, Any]]:
        """
        构建过滤子句。
        
        Args:
            filters: 精确匹配过滤器字典
            range_filters: 范围过滤器（Dict[str, RangeFilter]，RangeFilter 是 Pydantic 模型）
        
        Returns:
            ES filter 子句列表
        """
        filter_clauses = []
        
        # 1. 处理精确匹配过滤
        if filters:
            for field, value in filters.items():
                # 特殊处理：specifications 嵌套过滤
                if field == "specifications":
                    if isinstance(value, dict):
                        # 单个规格过滤：{"name": "color", "value": "green"}
                        name = value.get("name")
                        spec_value = value.get("value")
                        if name and spec_value:
                            filter_clauses.append({
                                "nested": {
                                    "path": "specifications",
                                    "query": {
                                        "bool": {
                                            "must": [
                                                {"term": {"specifications.name": name}},
                                                {"term": {"specifications.value": spec_value}}
                                            ]
                                        }
                                    }
                                }
                            })
                    elif isinstance(value, list):
                        # 多个规格过滤：按 name 分组，相同维度 OR，不同维度 AND
                        # 例如：[{"name": "size", "value": "3"}, {"name": "size", "value": "4"}, {"name": "color", "value": "green"}]
                        # 应该生成：(size=3 OR size=4) AND color=green
                        from collections import defaultdict
                        specs_by_name = defaultdict(list)
                        for spec in value:
                            if isinstance(spec, dict):
                                name = spec.get("name")
                                spec_value = spec.get("value")
                                if name and spec_value:
                                    specs_by_name[name].append(spec_value)
                        
                        # 为每个 name 维度生成一个过滤子句
                        for name, values in specs_by_name.items():
                            if len(values) == 1:
                                # 单个值，直接生成 term 查询
                                filter_clauses.append({
                                    "nested": {
                                        "path": "specifications",
                                        "query": {
                                            "bool": {
                                                "must": [
                                                    {"term": {"specifications.name": name}},
                                                    {"term": {"specifications.value": values[0]}}
                                                ]
                                            }
                                        }
                                    }
                                })
                            else:
                                # 多个值，使用 should (OR) 连接
                                should_clauses = []
                                for spec_value in values:
                                    should_clauses.append({
                                        "bool": {
                                            "must": [
                                                {"term": {"specifications.name": name}},
                                                {"term": {"specifications.value": spec_value}}
                                            ]
                                        }
                                    })
                                filter_clauses.append({
                                    "nested": {
                                        "path": "specifications",
                                        "query": {
                                            "bool": {
                                                "should": should_clauses,
                                                "minimum_should_match": 1
                                            }
                                        }
                                    }
                                })
                    continue
                
                # 普通字段过滤
                if isinstance(value, list):
                    # 多值匹配（OR）
                    filter_clauses.append({
                        "terms": {field: value}
                    })
                else:
                    # 单值精确匹配
                    filter_clauses.append({
                        "term": {field: value}
                    })
        
        # 2. 处理范围过滤（支持 RangeFilter Pydantic 模型或字典）
        if range_filters:
            for field, range_filter in range_filters.items():
                # 支持 Pydantic 模型或字典格式
                if hasattr(range_filter, 'model_dump'):
                    # Pydantic 模型
                    range_dict = range_filter.model_dump(exclude_none=True)
                elif isinstance(range_filter, dict):
                    # 已经是字典格式
                    range_dict = {k: v for k, v in range_filter.items() if v is not None}
                else:
                    # 其他格式，跳过
                    continue
                
                if range_dict:
                    filter_clauses.append({
                        "range": {field: range_dict}
                    })
        
        return filter_clauses
    def add_spu_collapse(
        self,
        es_query: Dict[str, Any],
        spu_field: str,
        inner_hits_size: int = 3
    ) -> Dict[str, Any]:
        """
        Add SPU aggregation/collapse to query.
        Args:
            es_query: Existing ES query
            spu_field: Field containing SPU ID
            inner_hits_size: Number of SKUs to return per SPU
        Returns:
            Modified ES query
        """
        # Add collapse
        es_query["collapse"] = {
            "field": spu_field,
            "inner_hits": {
                "_source": False,
                "name": "top_docs",
                "size": inner_hits_size
            }
        }
        # Add cardinality aggregation to count unique SPUs
        if "aggs" not in es_query:
            es_query["aggs"] = {}
        es_query["aggs"]["unique_count"] = {
            "cardinality": {
                "field": spu_field
            }
        }
        return es_query
    def add_sorting(
        self,
        es_query: Dict[str, Any],
        sort_by: str,
        sort_order: str = "desc"
    ) -> Dict[str, Any]:
        """
        Add sorting to ES query.
        Args:
            es_query: Existing ES query
            sort_by: Field name for sorting (支持 'price' 自动映射)
            sort_order: Sort order: 'asc' or 'desc'
        Returns:
            Modified ES query
        """
        if not sort_by:
            return es_query
        if not sort_order:
            sort_order = "desc"
        # Auto-map 'price' to 'min_price' or 'max_price' based on sort_order
        if sort_by == "price":
            if sort_order.lower() == "asc":
                sort_by = "min_price"  # 价格从低到高
            else:
                sort_by = "max_price"  # 价格从高到低
        if "sort" not in es_query:
            es_query["sort"] = []
        # Add the specified sort
        sort_field = {
            sort_by: {
                "order": sort_order.lower()
            }
        }
        es_query["sort"].append(sort_field)
        return es_query
    def build_facets(
        self,
        facet_configs: Optional[List['FacetConfig']] = None,
        use_reverse_nested: bool = True
    ) -> Dict[str, Any]:
        """
        构建分面聚合。
        
        Args:
            facet_configs: 分面配置对象列表
            use_reverse_nested: 是否使用 reverse_nested 统计产品数量（默认 True）
                               如果为 False，将统计嵌套文档数量（性能更好但计数可能不准确）
            
            支持的字段类型：
                - 普通字段: 如 "category1_name"（terms 或 range 类型）
                - specifications: "specifications"（返回所有规格名称及其值）
                - specifications.{name}: 如 "specifications.color"（返回指定规格名称的值）
        
        Returns:
            ES aggregations 字典
        
        性能说明：
            - use_reverse_nested=True: 统计产品数量，准确性高但性能略差（通常影响 < 20%）
            - use_reverse_nested=False: 统计嵌套文档数量，性能更好但计数可能不准确
        """
        if not facet_configs:
            return {}
        
        aggs = {}
        
        for config in facet_configs:
            field = config.field
            size = config.size
            facet_type = config.type
            
            # 处理 specifications（所有规格名称）
            if field == "specifications":
                aggs["specifications_facet"] = {
                    "nested": {"path": "specifications"},
                    "aggs": {
                        "by_name": {
                            "terms": {
                                "field": "specifications.name",
                                "size": 20,
                                "order": {"_count": "desc"}
                            },
                            "aggs": {
                                "value_counts": {
                                    "terms": {
                                        "field": "specifications.value",
                                        "size": size,
                                        "order": {"_count": "desc"}
                                    }
                                }
                            }
                        }
                    }
                }
                continue
            
            # 处理 specifications.{name}（指定规格名称）
            if field.startswith("specifications."):
                name = field[len("specifications."):]
                agg_name = f"specifications_{name}_facet"
                # 使用 reverse_nested 统计产品（父文档）数量，而不是规格条目（嵌套文档）数量
                # 这样可以确保分面计数反映实际的产品数量，与搜索结果数量一致
                base_value_counts = {
                    "terms": {
                        "field": "specifications.value",
                        "size": size,
                        "order": {"_count": "desc"}
                    }
                }
                
                # 如果启用 reverse_nested，添加子聚合统计产品数量
                if use_reverse_nested:
                    base_value_counts["aggs"] = {
                        "product_count": {
                            "reverse_nested": {}
                        }
                    }
                
                aggs[agg_name] = {
                    "nested": {"path": "specifications"},
                    "aggs": {
                        "filter_by_name": {
                            "filter": {"term": {"specifications.name": name}},
                            "aggs": {
                                "value_counts": base_value_counts
                            }
                        }
                    }
                }
                continue
            
            # 处理普通字段
            agg_name = f"{field}_facet"
            
            if facet_type == 'terms':
                aggs[agg_name] = {
                    "terms": {
                        "field": field,
                        "size": size,
                        "order": {"_count": "desc"}
                    }
                }
            elif facet_type == 'range':
                if config.ranges:
                    aggs[agg_name] = {
                        "range": {
                            "field": field,
                            "ranges": config.ranges
                        }
                    }
        
        return aggs