"""
Hybrid Knowledge Manager - 混合知识库管理器
支持向量数据库、关系型数据库和图数据库的统一管理
"""

import asyncio
import logging
from typing import Dict, Any, List, Optional, Union
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum

logger = logging.getLogger(__name__)


class KnowledgeType(Enum):
    """知识类型"""
    DOCUMENT = "document"      # 文档类知识（适合向量数据库）
    STRUCTURED = "structured"  # 结构化知识（适合关系型数据库）
    RELATIONAL = "relational"  # 关系类知识（适合图数据库）
    MIXED = "mixed"           # 混合类型


class DatabaseType(Enum):
    """数据库类型"""
    VECTOR = "vector"         # 向量数据库（如Chroma、Pinecone）
    RELATIONAL = "relational" # 关系型数据库（如PostgreSQL、MySQL）
    GRAPH = "graph"          # 图数据库（如Neo4j、ArangoDB）


@dataclass
class KnowledgeItem:
    """知识项"""
    id: str
    type: KnowledgeType
    content: Any
    metadata: Dict[str, Any] = field(default_factory=dict)
    created_at: datetime = field(default_factory=datetime.now)
    updated_at: datetime = field(default_factory=datetime.now)
    tags: List[str] = field(default_factory=list)
    source: str = ""
    confidence: float = 1.0


@dataclass
class QueryResult:
    """查询结果"""
    items: List[KnowledgeItem]
    total_count: int
    relevance_scores: List[float]
    source_databases: List[DatabaseType]
    query_time: float


class HybridKnowledgeManager:
    """混合知识库管理器"""
    
    def __init__(self):
        self.logger = logging.getLogger(f"{self.__class__.__name__}")
        
        # 初始化各种数据库连接
        self.vector_db = None  # 向量数据库连接
        self.relational_db = None  # 关系型数据库连接
        self.graph_db = None  # 图数据库连接
        
        # 知识类型到数据库类型的映射
        self.type_mapping = {
            KnowledgeType.DOCUMENT: DatabaseType.VECTOR,
            KnowledgeType.STRUCTURED: DatabaseType.RELATIONAL,
            KnowledgeType.RELATIONAL: DatabaseType.GRAPH,
            KnowledgeType.MIXED: [DatabaseType.VECTOR, DatabaseType.RELATIONAL, DatabaseType.GRAPH]
        }
        
    async def initialize_databases(self, config: Dict[str, Any]):
        """初始化数据库连接"""
        try:
            # 初始化向量数据库
            if 'vector_db' in config:
                self.vector_db = await self._init_vector_db(config['vector_db'])
            
            # 初始化关系型数据库
            if 'relational_db' in config:
                self.relational_db = await self._init_relational_db(config['relational_db'])
            
            # 初始化图数据库
            if 'graph_db' in config:
                self.graph_db = await self._init_graph_db(config['graph_db'])
            
            self.logger.info("All databases initialized successfully")
            
        except Exception as e:
            self.logger.error(f"Error initializing databases: {str(e)}")
            raise
    
    async def _init_vector_db(self, config: Dict[str, Any]):
        """初始化向量数据库"""
        # 这里应该根据配置初始化具体的向量数据库
        # 例如：Chroma、Pinecone、Weaviate等
        self.logger.info("Initializing vector database...")
        return {"type": "vector", "config": config}
    
    async def _init_relational_db(self, config: Dict[str, Any]):
        """初始化关系型数据库"""
        # 这里应该根据配置初始化具体的关系型数据库
        # 例如：PostgreSQL、MySQL等
        self.logger.info("Initializing relational database...")
        return {"type": "relational", "config": config}
    
    async def _init_graph_db(self, config: Dict[str, Any]):
        """初始化图数据库"""
        # 这里应该根据配置初始化具体的图数据库
        # 例如：Neo4j、ArangoDB等
        self.logger.info("Initializing graph database...")
        return {"type": "graph", "config": config}
    
    async def add_knowledge(self, knowledge_item: KnowledgeItem) -> bool:
        """添加知识"""
        try:
            self.logger.info(f"Adding knowledge item: {knowledge_item.id}")
            
            # 确定存储位置
            target_dbs = self._get_target_databases(knowledge_item.type)
            
            # 并行存储到多个数据库
            tasks = []
            for db_type in target_dbs:
                task = self._store_to_database(knowledge_item, db_type)
                tasks.append(task)
            
            results = await asyncio.gather(*tasks, return_exceptions=True)
            
            # 检查结果
            success_count = sum(1 for result in results if result is True)
            total_count = len(results)
            
            if success_count == total_count:
                self.logger.info(f"Knowledge item {knowledge_item.id} stored successfully")
                return True
            else:
                self.logger.warning(f"Partial storage success: {success_count}/{total_count}")
                return success_count > 0
                
        except Exception as e:
            self.logger.error(f"Error adding knowledge: {str(e)}")
            return False
    
    def _get_target_databases(self, knowledge_type: KnowledgeType) -> List[DatabaseType]:
        """获取目标数据库类型"""
        mapping = self.type_mapping.get(knowledge_type, [])
        
        if isinstance(mapping, list):
            return mapping
        else:
            return [mapping]
    
    async def _store_to_database(self, knowledge_item: KnowledgeItem, db_type: DatabaseType) -> bool:
        """存储到指定数据库"""
        try:
            if db_type == DatabaseType.VECTOR and self.vector_db:
                return await self._store_to_vector_db(knowledge_item)
            elif db_type == DatabaseType.RELATIONAL and self.relational_db:
                return await self._store_to_relational_db(knowledge_item)
            elif db_type == DatabaseType.GRAPH and self.graph_db:
                return await self._store_to_graph_db(knowledge_item)
            else:
                self.logger.warning(f"Database {db_type} not available")
                return False
                
        except Exception as e:
            self.logger.error(f"Error storing to {db_type}: {str(e)}")
            return False
    
    async def _store_to_vector_db(self, knowledge_item: KnowledgeItem) -> bool:
        """存储到向量数据库"""
        # 实现向量数据库存储逻辑
        # 包括文档分块、向量化、存储等
        self.logger.info(f"Storing to vector database: {knowledge_item.id}")
        return True
    
    async def _store_to_relational_db(self, knowledge_item: KnowledgeItem) -> bool:
        """存储到关系型数据库"""
        # 实现关系型数据库存储逻辑
        # 包括表结构设计、数据插入等
        self.logger.info(f"Storing to relational database: {knowledge_item.id}")
        return True
    
    async def _store_to_graph_db(self, knowledge_item: KnowledgeItem) -> bool:
        """存储到图数据库"""
        # 实现图数据库存储逻辑
        # 包括节点、边的创建等
        self.logger.info(f"Storing to graph database: {knowledge_item.id}")
        return True
    
    async def search_knowledge(self, 
                             query: str,
                             knowledge_types: List[KnowledgeType] = None,
                             limit: int = 10,
                             threshold: float = 0.7) -> QueryResult:
        """搜索知识"""
        try:
            self.logger.info(f"Searching knowledge: {query}")
            
            # 确定搜索范围
            if knowledge_types is None:
                knowledge_types = list(KnowledgeType)
            
            # 并行搜索多个数据库
            search_tasks = []
            for knowledge_type in knowledge_types:
                target_dbs = self._get_target_databases(knowledge_type)
                for db_type in target_dbs:
                    task = self._search_database(query, db_type, limit, threshold)
                    search_tasks.append(task)
            
            # 执行搜索
            search_results = await asyncio.gather(*search_tasks, return_exceptions=True)
            
            # 合并结果
            all_items = []
            all_scores = []
            all_sources = []
            
            for i, result in enumerate(search_results):
                if isinstance(result, dict) and 'items' in result:
                    all_items.extend(result['items'])
                    all_scores.extend(result.get('scores', []))
                    all_sources.extend([result.get('source', 'unknown')] * len(result['items']))
            
            # 去重和排序
            unique_items, unique_scores, unique_sources = self._deduplicate_results(
                all_items, all_scores, all_sources
            )
            
            # 按相关性排序
            sorted_results = sorted(
                zip(unique_items, unique_scores, unique_sources),
                key=lambda x: x[1],
                reverse=True
            )
            
            # 限制结果数量
            final_items = [item for item, _, _ in sorted_results[:limit]]
            final_scores = [score for _, score, _ in sorted_results[:limit]]
            final_sources = [source for _, _, source in sorted_results[:limit]]
            
            return QueryResult(
                items=final_items,
                total_count=len(final_items),
                relevance_scores=final_scores,
                source_databases=[DatabaseType(source) for source in final_sources],
                query_time=0.0  # 实际应该计算查询时间
            )
            
        except Exception as e:
            self.logger.error(f"Error searching knowledge: {str(e)}")
            return QueryResult([], 0, [], [], 0.0)
    
    async def _search_database(self, 
                             query: str, 
                             db_type: DatabaseType, 
                             limit: int, 
                             threshold: float) -> Dict[str, Any]:
        """在指定数据库中搜索"""
        try:
            if db_type == DatabaseType.VECTOR and self.vector_db:
                return await self._search_vector_db(query, limit, threshold)
            elif db_type == DatabaseType.RELATIONAL and self.relational_db:
                return await self._search_relational_db(query, limit, threshold)
            elif db_type == DatabaseType.GRAPH and self.graph_db:
                return await self._search_graph_db(query, limit, threshold)
            else:
                return {"items": [], "scores": [], "source": db_type.value}
                
        except Exception as e:
            self.logger.error(f"Error searching {db_type}: {str(e)}")
            return {"items": [], "scores": [], "source": db_type.value}
    
    async def _search_vector_db(self, query: str, limit: int, threshold: float) -> Dict[str, Any]:
        """在向量数据库中搜索"""
        # 实现向量数据库搜索逻辑
        # 包括查询向量化、相似度计算、结果排序等
        self.logger.info(f"Searching vector database: {query}")
        return {"items": [], "scores": [], "source": "vector"}
    
    async def _search_relational_db(self, query: str, limit: int, threshold: float) -> Dict[str, Any]:
        """在关系型数据库中搜索"""
        # 实现关系型数据库搜索逻辑
        # 包括SQL查询、关键词匹配等
        self.logger.info(f"Searching relational database: {query}")
        return {"items": [], "scores": [], "source": "relational"}
    
    async def _search_graph_db(self, query: str, limit: int, threshold: float) -> Dict[str, Any]:
        """在图数据库中搜索"""
        # 实现图数据库搜索逻辑
        # 包括图遍历、路径查询等
        self.logger.info(f"Searching graph database: {query}")
        return {"items": [], "scores": [], "source": "graph"}
    
    def _deduplicate_results(self, items: List[KnowledgeItem], 
                           scores: List[float], 
                           sources: List[str]) -> tuple[List, List, List]:
        """去重结果"""
        seen_ids = set()
        unique_items = []
        unique_scores = []
        unique_sources = []
        
        for item, score, source in zip(items, scores, sources):
            if item.id not in seen_ids:
                seen_ids.add(item.id)
                unique_items.append(item)
                unique_scores.append(score)
                unique_sources.append(source)
        
        return unique_items, unique_scores, unique_sources
    
    async def create_rag_context(self, query: str, retrieved_items: List[KnowledgeItem]) -> str:
        """创建RAG上下文"""
        context_parts = []
        
        for i, item in enumerate(retrieved_items, 1):
            if item.type == KnowledgeType.DOCUMENT:
                context_parts.append(f"文档{i}: {item.content}")
            elif item.type == KnowledgeType.STRUCTURED:
                context_parts.append(f"结构化数据{i}: {item.content}")
            elif item.type == KnowledgeType.RELATIONAL:
                context_parts.append(f"关系数据{i}: {item.content}")
        
        return "\n\n".join(context_parts)
    
    async def get_knowledge_statistics(self) -> Dict[str, Any]:
        """获取知识库统计信息"""
        stats = {
            "total_items": 0,
            "by_type": {},
            "by_database": {},
            "last_updated": None
        }
        
        # 这里应该从各个数据库获取统计信息
        # 暂时返回模拟数据
        return stats
    
    async def backup_knowledge_base(self, backup_path: str) -> bool:
        """备份知识库"""
        try:
            self.logger.info(f"Backing up knowledge base to: {backup_path}")
            
            # 实现备份逻辑
            # 包括各个数据库的备份
            return True
            
        except Exception as e:
            self.logger.error(f"Error backing up knowledge base: {str(e)}")
            return False
    
    async def restore_knowledge_base(self, backup_path: str) -> bool:
        """恢复知识库"""
        try:
            self.logger.info(f"Restoring knowledge base from: {backup_path}")
            
            # 实现恢复逻辑
            # 包括各个数据库的恢复
            return True
            
        except Exception as e:
            self.logger.error(f"Error restoring knowledge base: {str(e)}")
            return False 