"""
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
import numpy as np
from .boolean_parser import QueryNode
from .query_config import FUNCTION_SCORE_CONFIG


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
    ):
        """
        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)
        """
        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

    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,
        size: int = 10,
        from_: int = 0,
        enable_knn: bool = True,
        knn_k: int = 50,
        knn_num_candidates: int = 200,
        min_score: Optional[float] = None
    ) -> Dict[str, Any]:
        """
        Build complete ES query (简化版).

        结构：filters and (text_recall or embedding_recall)
        - filters: 前端传递的过滤条件永远起作用
        - 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 (always applied)
            range_filters: Range filters for numeric fields (always applied)
            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
                text_query = self._build_text_query(query_text)
            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. Build filter clauses (always applied)
        filter_clauses = self._build_filters(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": 0.2  # Lower boost for embedding recall
            }
            es_query["knn"] = knn_clause

        # 5. 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
        function_score_query = {
            "function_score": {
                "query": query,
                "functions": functions,
                "score_mode": FUNCTION_SCORE_CONFIG.get("score_mode", "sum"),
                "boost_mode": FUNCTION_SCORE_CONFIG.get("boost_mode", "multiply")
            }
        }
        
        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 = []
        config_functions = FUNCTION_SCORE_CONFIG.get("functions", [])
        
        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).

        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 _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():
                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
            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"

        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[Union[str, 'FacetConfig']]] = None
    ) -> Dict[str, Any]:
        """
        构建分面聚合。
        
        支持：
        1. 分类分面：category1_name, category2_name, category3_name, category_name
        2. specifications分面：嵌套聚合，按name聚合，然后按value聚合
        
        Args:
            facet_configs: 分面配置列表（标准格式）：
                - str: 字段名，使用默认 terms 配置
                - FacetConfig: 详细的分面配置对象
                - 特殊值 "specifications": 构建specifications嵌套分面
        
        Returns:
            ES aggregations 字典
        """
        if not facet_configs:
            return {}
        
        aggs = {}
        
        for config in facet_configs:
            # 特殊处理：specifications嵌套分面
            if isinstance(config, str) and config == "specifications":
                # 构建specifications嵌套分面（按name聚合，然后按value聚合）
                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": 10,
                                        "order": {"_count": "desc"}
                                    }
                                }
                            }
                        }
                    }
                }
                continue
            
            # 简单模式：只有字段名（字符串）
            if isinstance(config, str):
                field = config
                agg_name = f"{field}_facet"
                aggs[agg_name] = {
                    "terms": {
                        "field": field,
                        "size": 10,
                        "order": {"_count": "desc"}
                    }
                }
            
            # 高级模式：FacetConfig 对象
            else:
                # 此时 config 应该是 FacetConfig 对象
                field = config.field
                facet_type = config.type
                size = config.size
                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