search/searcher.py

"""
Main Searcher module - executes search queries against Elasticsearch.
Handles query parsing, boolean expressions, ranking, and result formatting.
"""
from typing import Dict, Any, List, Optional, Union
import time
import logging
from utils.es_client import ESClient
from query import QueryParser, ParsedQuery
from embeddings import CLIPImageEncoder
from .boolean_parser import BooleanParser, QueryNode
from .es_query_builder import ESQueryBuilder
from .rerank_engine import RerankEngine
from .query_config import (
    DEFAULT_INDEX_NAME,
    DEFAULT_MATCH_FIELDS,
    TEXT_EMBEDDING_FIELD,
    IMAGE_EMBEDDING_FIELD,
    SOURCE_FIELDS,
    ENABLE_TRANSLATION,
    ENABLE_TEXT_EMBEDDING,
    RANKING_EXPRESSION
)
from context.request_context import RequestContext, RequestContextStage, create_request_context
from api.models import FacetResult, FacetValue
from api.result_formatter import ResultFormatter
logger = logging.getLogger(__name__)
class SearchResult:
    """Container for search results (外部友好格式)."""
    def __init__(
        self,
        results: List[Any],  # List[SpuResult]
        total: int,
        max_score: float,
        took_ms: int,
        facets: Optional[List[FacetResult]] = None,
        query_info: Optional[Dict[str, Any]] = None,
        suggestions: Optional[List[str]] = None,
        related_searches: Optional[List[str]] = None,
        debug_info: Optional[Dict[str, Any]] = None
    ):
        self.results = results
        self.total = total
        self.max_score = max_score
        self.took_ms = took_ms
        self.facets = facets
        self.query_info = query_info or {}
        self.suggestions = suggestions or []
        self.related_searches = related_searches or []
        self.debug_info = debug_info
    def to_dict(self) -> Dict[str, Any]:
        """Convert to dictionary representation."""
        result = {
            "results": [r.model_dump() if hasattr(r, 'model_dump') else r for r in self.results],
            "total": self.total,
            "max_score": self.max_score,
            "took_ms": self.took_ms,
            "facets": [f.model_dump() for f in self.facets] if self.facets else None,
            "query_info": self.query_info,
            "suggestions": self.suggestions,
            "related_searches": self.related_searches
        }
        if self.debug_info is not None:
            result["debug_info"] = self.debug_info
        return result
class Searcher:
    """
    Main search engine class.
    Handles:
    - Query parsing and translation
    - Boolean expression parsing
    - ES query building
    - Result ranking and formatting
    """
    def __init__(
        self,
        es_client: ESClient,
        query_parser: Optional[QueryParser] = None,
        index_name: str = DEFAULT_INDEX_NAME
    ):
        """
        Initialize searcher.
        Args:
            es_client: Elasticsearch client
            query_parser: Query parser (created if not provided)
            index_name: ES index name (default: search_products)
        """
        self.es_client = es_client
        self.index_name = index_name
        self.query_parser = query_parser or QueryParser()
        # Initialize components
        self.boolean_parser = BooleanParser()
        self.rerank_engine = RerankEngine(RANKING_EXPRESSION, enabled=False)
        # Use constants from query_config
        self.match_fields = DEFAULT_MATCH_FIELDS
        self.text_embedding_field = TEXT_EMBEDDING_FIELD
        self.image_embedding_field = IMAGE_EMBEDDING_FIELD
        # Query builder - simplified single-layer architecture
        self.query_builder = ESQueryBuilder(
            index_name=index_name,
            match_fields=self.match_fields,
            text_embedding_field=self.text_embedding_field,
            image_embedding_field=self.image_embedding_field,
            source_fields=SOURCE_FIELDS
        )
    def search(
        self,
        query: str,
        tenant_id: str,
        size: int = 10,
        from_: int = 0,
        filters: Optional[Dict[str, Any]] = None,
        range_filters: Optional[Dict[str, Any]] = None,
        facets: Optional[List[Any]] = None,
        min_score: Optional[float] = None,
        context: Optional[RequestContext] = None,
        sort_by: Optional[str] = None,
        sort_order: Optional[str] = "desc",
        debug: bool = False,
        language: str = "zh",
    ) -> SearchResult:
        """
        Execute search query (外部友好格式).
        Args:
            query: Search query string
            tenant_id: Tenant ID (required for filtering)
            size: Number of results to return
            from_: Offset for pagination
            filters: Exact match filters
            range_filters: Range filters for numeric fields
            facets: Facet configurations for faceted search
            min_score: Minimum score threshold
            context: Request context for tracking (created if not provided)
            sort_by: Field name for sorting
            sort_order: Sort order: 'asc' or 'desc'
            debug: Enable debug information output
        Returns:
            SearchResult object with formatted results
        """
        # Create context if not provided (backward compatibility)
        if context is None:
            context = create_request_context()
        # Always use config defaults (these are backend configuration, not user parameters)
        enable_translation = ENABLE_TRANSLATION
        enable_embedding = ENABLE_TEXT_EMBEDDING
        enable_rerank = False  # Temporarily disabled
        # Start timing
        context.start_stage(RequestContextStage.TOTAL)
        context.logger.info(
            f"开始搜索请求 | 查询: '{query}' | 参数: size={size}, from_={from_}, "
            f"enable_translation={enable_translation}, enable_embedding={enable_embedding}, "
            f"enable_rerank={enable_rerank}, min_score={min_score}",
            extra={'reqid': context.reqid, 'uid': context.uid}
        )
        # Store search parameters in context
        context.metadata['search_params'] = {
            'size': size,
            'from_': from_,
            'filters': filters,
            'range_filters': range_filters,
            'facets': facets,
            'enable_translation': enable_translation,
            'enable_embedding': enable_embedding,
            'enable_rerank': enable_rerank,
            'min_score': min_score,
            'sort_by': sort_by,
            'sort_order': sort_order
        }
        context.metadata['feature_flags'] = {
            'translation_enabled': enable_translation,
            'embedding_enabled': enable_embedding,
            'rerank_enabled': enable_rerank
        }
        # Step 1: Parse query
        context.start_stage(RequestContextStage.QUERY_PARSING)
        try:
            parsed_query = self.query_parser.parse(
                query,
                generate_vector=enable_embedding,
                context=context
            )
            # Store query analysis results in context
            context.store_query_analysis(
                original_query=parsed_query.original_query,
                normalized_query=parsed_query.normalized_query,
                rewritten_query=parsed_query.rewritten_query,
                detected_language=parsed_query.detected_language,
                translations=parsed_query.translations,
                query_vector=parsed_query.query_vector.tolist() if parsed_query.query_vector is not None else None,
                domain=parsed_query.domain,
                is_simple_query=self.boolean_parser.is_simple_query(parsed_query.rewritten_query)
            )
            context.logger.info(
                f"查询解析完成 | 原查询: '{parsed_query.original_query}' | "
                f"重写后: '{parsed_query.rewritten_query}' | "
                f"语言: {parsed_query.detected_language} | "
                f"域: {parsed_query.domain} | "
                f"向量: {'是' if parsed_query.query_vector is not None else '否'}",
                extra={'reqid': context.reqid, 'uid': context.uid}
            )
        except Exception as e:
            context.set_error(e)
            context.logger.error(
                f"查询解析失败 | 错误: {str(e)}",
                extra={'reqid': context.reqid, 'uid': context.uid}
            )
            raise
        finally:
            context.end_stage(RequestContextStage.QUERY_PARSING)
        # Step 2: Boolean parsing
        context.start_stage(RequestContextStage.BOOLEAN_PARSING)
        try:
            query_node = None
            if self.boolean_parser.is_simple_query(parsed_query.rewritten_query):
                # Simple query
                query_text = parsed_query.rewritten_query
                context.logger.debug(
                    f"简单查询 | 无布尔表达式",
                    extra={'reqid': context.reqid, 'uid': context.uid}
                )
            else:
                # Complex boolean query
                query_node = self.boolean_parser.parse(parsed_query.rewritten_query)
                query_text = parsed_query.rewritten_query
                context.store_intermediate_result('query_node', query_node)
                context.store_intermediate_result('boolean_ast', str(query_node))
                context.logger.info(
                    f"布尔表达式解析 | AST: {query_node}",
                    extra={'reqid': context.reqid, 'uid': context.uid}
                )
        except Exception as e:
            context.set_error(e)
            context.logger.error(
                f"布尔表达式解析失败 | 错误: {str(e)}",
                extra={'reqid': context.reqid, 'uid': context.uid}
            )
            raise
        finally:
            context.end_stage(RequestContextStage.BOOLEAN_PARSING)
        # Step 3: Query building
        context.start_stage(RequestContextStage.QUERY_BUILDING)
        try:
            # Add tenant_id to filters (required)
            if filters is None:
                filters = {}
            filters['tenant_id'] = tenant_id
            es_query = self.query_builder.build_query(
                query_text=parsed_query.rewritten_query or parsed_query.normalized_query,
                query_vector=parsed_query.query_vector if enable_embedding else None,
                query_node=query_node,
                filters=filters,
                range_filters=range_filters,
                size=size,
                from_=from_,
                enable_knn=enable_embedding and parsed_query.query_vector is not None,
                min_score=min_score
            )
            # Add facets for faceted search
            if facets:
                facet_aggs = self.query_builder.build_facets(facets)
                if facet_aggs:
                    if "aggs" not in es_query:
                        es_query["aggs"] = {}
                    es_query["aggs"].update(facet_aggs)
            # Add sorting if specified
            if sort_by:
                es_query = self.query_builder.add_sorting(es_query, sort_by, sort_order)
            # Extract size and from from body for ES client parameters
            body_for_es = {k: v for k, v in es_query.items() if k not in ['size', 'from']}
            # Store ES query in context
            context.store_intermediate_result('es_query', es_query)
            context.store_intermediate_result('es_body_for_search', body_for_es)
            context.logger.info(
                f"ES查询构建完成 | 大小: {len(str(es_query))}字符 | "
                f"KNN: {'是' if enable_embedding and parsed_query.query_vector is not None else '否'} | "
                f"分面: {'是' if facets else '否'}",
                extra={'reqid': context.reqid, 'uid': context.uid}
            )
            context.logger.debug(
                f"ES查询详情: {es_query}",
                extra={'reqid': context.reqid, 'uid': context.uid}
            )
        except Exception as e:
            context.set_error(e)
            context.logger.error(
                f"ES查询构建失败 | 错误: {str(e)}",
                extra={'reqid': context.reqid, 'uid': context.uid}
            )
            raise
        finally:
            context.end_stage(RequestContextStage.QUERY_BUILDING)
        # Step 4: Elasticsearch search
        context.start_stage(RequestContextStage.ELASTICSEARCH_SEARCH)
        try:
            es_response = self.es_client.search(
                index_name=self.index_name,
                body=body_for_es,
                size=size,
                from_=from_
            )
            # Store ES response in context
            context.store_intermediate_result('es_response', es_response)
            # Extract timing from ES response
            es_took = es_response.get('took', 0)
            context.logger.info(
                f"ES搜索完成 | 耗时: {es_took}ms | "
                f"命中数: {es_response.get('hits', {}).get('total', {}).get('value', 0)} | "
                f"最高分: {(es_response.get('hits', {}).get('max_score') or 0):.3f}",
                extra={'reqid': context.reqid, 'uid': context.uid}
            )
        except Exception as e:
            context.set_error(e)
            context.logger.error(
                f"ES搜索执行失败 | 错误: {str(e)}",
                extra={'reqid': context.reqid, 'uid': context.uid}
            )
            raise
        finally:
            context.end_stage(RequestContextStage.ELASTICSEARCH_SEARCH)
        # Step 5: Result processing
        context.start_stage(RequestContextStage.RESULT_PROCESSING)
        try:
            # Extract ES hits
            es_hits = []
            if 'hits' in es_response and 'hits' in es_response['hits']:
                es_hits = es_response['hits']['hits']
            # Extract total and max_score
            total = es_response.get('hits', {}).get('total', {})
            if isinstance(total, dict):
                total_value = total.get('value', 0)
            else:
                total_value = total
            max_score = es_response.get('hits', {}).get('max_score') or 0.0
            # Format results using ResultFormatter
            formatted_results = ResultFormatter.format_search_results(
                es_hits,
                max_score,
                language=language
            )
            # Format facets
            standardized_facets = None
            if facets:
                standardized_facets = ResultFormatter.format_facets(
                    es_response.get('aggregations', {}),
                    facets
                )
            # Generate suggestions and related searches
            query_text = parsed_query.original_query if parsed_query else query
            suggestions = ResultFormatter.generate_suggestions(query_text, formatted_results)
            related_searches = ResultFormatter.generate_related_searches(query_text, formatted_results)
            context.logger.info(
                f"结果处理完成 | 返回: {len(formatted_results)}条 | 总计: {total_value}条",
                extra={'reqid': context.reqid, 'uid': context.uid}
            )
        except Exception as e:
            context.set_error(e)
            context.logger.error(
                f"结果处理失败 | 错误: {str(e)}",
                extra={'reqid': context.reqid, 'uid': context.uid}
            )
            raise
        finally:
            context.end_stage(RequestContextStage.RESULT_PROCESSING)
        # End total timing and build result
        total_duration = context.end_stage(RequestContextStage.TOTAL)
        context.performance_metrics.total_duration = total_duration
        # Collect debug information if requested
        debug_info = None
        if debug:
            debug_info = {
                "query_analysis": {
                    "original_query": context.query_analysis.original_query,
                    "normalized_query": context.query_analysis.normalized_query,
                    "rewritten_query": context.query_analysis.rewritten_query,
                    "detected_language": context.query_analysis.detected_language,
                    "translations": context.query_analysis.translations,
                    "has_vector": context.query_analysis.query_vector is not None,
                    "is_simple_query": context.query_analysis.is_simple_query,
                    "boolean_ast": context.get_intermediate_result('boolean_ast'),
                    "domain": context.query_analysis.domain
                },
                "es_query": context.get_intermediate_result('es_query', {}),
                "es_response": {
                    "took_ms": es_response.get('took', 0),
                    "total_hits": total_value,
                    "max_score": max_score,
                    "shards": es_response.get('_shards', {})
                },
                "feature_flags": context.metadata.get('feature_flags', {}),
                "stage_timings": {
                    k: round(v, 2) for k, v in context.performance_metrics.stage_timings.items()
                },
                "search_params": context.metadata.get('search_params', {})
            }
        # Build result
        result = SearchResult(
            results=formatted_results,
            total=total_value,
            max_score=max_score,
            took_ms=int(total_duration),
            facets=standardized_facets,
            query_info=parsed_query.to_dict(),
            suggestions=suggestions,
            related_searches=related_searches,
            debug_info=debug_info
        )
        # Log complete performance summary
        context.log_performance_summary()
        return result
    def search_by_image(
        self,
        image_url: str,
        tenant_id: str,
        size: int = 10,
        filters: Optional[Dict[str, Any]] = None,
        range_filters: Optional[Dict[str, Any]] = None
    ) -> SearchResult:
        """
        Search by image similarity (外部友好格式).
        Args:
            image_url: URL of query image
            tenant_id: Tenant ID (required for filtering)
            size: Number of results
            filters: Exact match filters
            range_filters: Range filters for numeric fields
        Returns:
            SearchResult object with formatted results
        """
        if not self.image_embedding_field:
            raise ValueError("Image embedding field not configured")
        # Generate image embedding
        image_encoder = CLIPImageEncoder()
        image_vector = image_encoder.encode_image_from_url(image_url)
        if image_vector is None:
            raise ValueError(f"Failed to encode image: {image_url}")
        # Add tenant_id to filters (required)
        if filters is None:
            filters = {}
        filters['tenant_id'] = tenant_id
        # Build KNN query
        es_query = {
            "size": size,
            "knn": {
                "field": self.image_embedding_field,
                "query_vector": image_vector.tolist(),
                "k": size,
                "num_candidates": size * 10
            }
        }
        # Add _source filtering if source_fields are configured
        if SOURCE_FIELDS:
            es_query["_source"] = {
                "includes": SOURCE_FIELDS
            }
        if filters or range_filters:
            filter_clauses = self.query_builder._build_filters(filters, range_filters)
            if filter_clauses:
                es_query["query"] = {
                    "bool": {
                        "filter": filter_clauses
                    }
                }
        # Execute search
        es_response = self.es_client.search(
            index_name=self.index_name,
            body=es_query,
            size=size
        )
        # Extract ES hits
        es_hits = []
        if 'hits' in es_response and 'hits' in es_response['hits']:
            es_hits = es_response['hits']['hits']
        # Extract total and max_score
        total = es_response.get('hits', {}).get('total', {})
        if isinstance(total, dict):
            total_value = total.get('value', 0)
        else:
            total_value = total
        max_score = es_response.get('hits', {}).get('max_score') or 0.0
        # Format results using ResultFormatter
        formatted_results = ResultFormatter.format_search_results(es_hits, max_score)
        return SearchResult(
            results=formatted_results,
            total=total_value,
            max_score=max_score,
            took_ms=es_response.get('took', 0),
            facets=None,
            query_info={'image_url': image_url, 'search_type': 'image_similarity'},
            suggestions=[],
            related_searches=[]
        )
    def get_domain_summary(self) -> Dict[str, Any]:
        """
        Get summary of all configured domains.
        Returns:
            Dictionary with domain information
        """
        return self.query_builder.get_domain_summary()
    def get_document(self, doc_id: str) -> Optional[Dict[str, Any]]:
        """
        Get single document by ID.
        Args:
            doc_id: Document ID
        Returns:
            Document or None if not found
        """
        try:
            response = self.es_client.client.get(
                index=self.index_name,
                id=doc_id
            )
            return response.get('_source')
        except Exception as e:
            logger.error(f"Failed to get document {doc_id}: {e}", exc_info=True)
            return None
    def _standardize_facets(
        self,
        es_aggregations: Dict[str, Any],
        facet_configs: Optional[List[Union[str, Any]]],
        current_filters: Optional[Dict[str, Any]]
    ) -> Optional[List[FacetResult]]:
        """
        将 ES 聚合结果转换为标准化的分面格式（返回 Pydantic 模型）。
        
        Args:
            es_aggregations: ES 原始聚合结果
            facet_configs: 分面配置列表（str 或 FacetConfig）
            current_filters: 当前应用的过滤器
        
        Returns:
            标准化的分面结果列表（FacetResult 对象）
        """
        if not es_aggregations or not facet_configs:
            return None
        
        standardized_facets: List[FacetResult] = []
        
        for config in facet_configs:
            # 解析配置
            if isinstance(config, str):
                field = config
                facet_type = "terms"
            else:
                # FacetConfig 对象
                field = config.field
                facet_type = config.type
            
            agg_name = f"{field}_facet"
            
            if agg_name not in es_aggregations:
                continue
            
            agg_result = es_aggregations[agg_name]
            
            # 获取当前字段的选中值
            selected_values = set()
            if current_filters and field in current_filters:
                filter_value = current_filters[field]
                if isinstance(filter_value, list):
                    selected_values = set(filter_value)
                else:
                    selected_values = {filter_value}
            
            # 转换 buckets 为 FacetValue 对象
            facet_values: List[FacetValue] = []
            if 'buckets' in agg_result:
                for bucket in agg_result['buckets']:
                    value = bucket.get('key')
                    count = bucket.get('doc_count', 0)
                    
                    facet_values.append(FacetValue(
                        value=value,
                        label=str(value),
                        count=count,
                        selected=value in selected_values
                    ))
            
            # 构建 FacetResult 对象
            facet_result = FacetResult(
                field=field,
                label=self._get_field_label(field),
                type=facet_type,
                values=facet_values
            )
            
            standardized_facets.append(facet_result)
        
        return standardized_facets if standardized_facets else None
    def _get_field_label(self, field: str) -> str:
        """获取字段的显示标签"""
        # 字段标签映射（简化版，不再从配置读取）
        field_labels = {
            "category1_name": "一级分类",
            "category2_name": "二级分类",
            "category3_name": "三级分类",
            "specifications": "规格"
        }
        return field_labels.get(field, field)