import torch
from transformers import AutoTokenizer, AutoModel
import faiss
import numpy as np
from typing import List, Dict, Any, Optional

class LogicRAG:
    """LogicRAG系统主类"""
    
    def __init__(self, config: Dict[str, Any]):
        """
        初始化LogicRAG系统
        
        Args:
            config: 系统配置参数
        """
        # 初始化检索模块
        self.retriever = Retriever(config.get("retriever", {}))
        
        # 初始化逻辑推理模块
        self.reasoner = Reasoner(config.get("reasoner", {}))
        
        # 初始化生成模块
        self.generator = Generator(config.get("generator", {}))
        
        # 初始化知识库
        self.knowledge_base = KnowledgeBase(config.get("knowledge_base", {}))
        
        # 初始化规则库
        self.rule_base = RuleBase(config.get("rule_base", {}))
    
    def process_query(self, query: str, top_k: int = 5) -> Dict[str, Any]:
        """
        处理用户查询
        
        Args:
            query: 用户查询文本
            top_k: 检索的文档数量
            
        Returns:
            包含回答和推理结果的字典
        """
        # 步骤1：检索相关知识
        retrieved_docs = self.retriever.search(query, self.knowledge_base, top_k)
        
        # 步骤2：逻辑推理分析
        reasoning_results = self.reasoner.reason(
            query=query,
            documents=retrieved_docs,
            rule_base=self.rule_base
        )
        
        # 步骤3：生成回答
        response = self.generator.generate(
            query=query,
            context=retrieved_docs,
            reasoning=reasoning_results
        )
        
        return {
            "query": query,
            "response": response,
            "retrieved_docs": retrieved_docs,
            "reasoning_results": reasoning_results
        }

class Retriever:
    """检索模块"""
    
    def __init__(self, config: Dict[str, Any]):
        """初始化检索模块"""
        self.embedding_model = AutoModel.from_pretrained(config.get("embedding_model", "bert-base-uncased"))
        self.tokenizer = AutoTokenizer.from_pretrained(config.get("embedding_model", "bert-base-uncased"))
        self.index = None
        self.documents = []
        
    def build_index(self, documents: List[str]):
        """构建检索索引"""
        self.documents = documents
        embeddings = self._encode_documents(documents)
        
        # 使用FAISS构建索引
        dimension = embeddings.shape[1]
        self.index = faiss.IndexFlatL2(dimension)
        self.index.add(embeddings.astype(np.float32))
        
    def search(self, query: str, knowledge_base: 'KnowledgeBase', top_k: int = 5) -> List[Dict[str, Any]]:
        """检索相关文档"""
        if self.index is None:
            self.build_index(knowledge_base.get_documents())
            
        # 编码查询
        query_embedding = self._encode_query(query)
        
        # 检索最相似的文档
        distances, indices = self.index.search(query_embedding.astype(np.float32), top_k)
        
        # 返回检索结果
        results = []
        for i, idx in enumerate(indices[0]):
            if idx < len(self.documents):
                results.append({
                    "document": self.documents[idx],
                    "score": float(distances[0][i]),
                    "index": int(idx)
                })
                
        return results
    
    def _encode_documents(self, documents: List[str]) -> np.ndarray:
        """编码文档"""
        # 实现文档编码逻辑
        pass
    
    def _encode_query(self, query: str) -> np.ndarray:
        """编码查询"""
        # 实现查询编码逻辑
        pass

class Reasoner:
    """逻辑推理模块"""
    
    def __init__(self, config: Dict[str, Any]):
        """初始化逻辑推理模块"""
        self.knowledge_graph_builder = KnowledgeGraphBuilder(config.get("kg_builder", {}))
        self.inference_engine = InferenceEngine(config.get("inference_engine", {}))
        self.consistency_checker = ConsistencyChecker(config.get("consistency_checker", {}))
        
    def reason(self, query: str, documents: List[Dict[str, Any]], rule_base: 'RuleBase') -> Dict[str, Any]:
        """
        执行逻辑推理
        
        Args:
            query: 用户查询
            documents: 检索到的文档
            rule_base: 规则库
            
        Returns:
            推理结果
        """
        # 步骤1：构建知识图谱
        knowledge_graph = self.knowledge_graph_builder.build(documents)
        
        # 步骤2：执行推理
        inferred_facts = self.inference_engine.infer(
            query=query,
            knowledge_graph=knowledge_graph,
            rules=rule_base.get_rules()
        )
        
        # 步骤3：检查一致性
        consistency_score = self.consistency_checker.check(
            query=query,
            knowledge_graph=knowledge_graph,
            inferred_facts=inferred_facts
        )
        
        # 步骤4：生成推理路径
        reasoning_path = self._generate_reasoning_path(
            query=query,
            knowledge_graph=knowledge_graph,
            inferred_facts=inferred_facts
        )
        
        return {
            "knowledge_graph": knowledge_graph,
            "inferred_facts": inferred_facts,
            "consistency_score": consistency_score,
            "reasoning_path": reasoning_path
        }
    
    def _generate_reasoning_path(self, query: str, knowledge_graph: Any, inferred_facts: List[Any]) -> List[Dict[str, Any]]:
        """生成推理路径"""
        # 实现推理路径生成逻辑
        pass

class KnowledgeGraphBuilder:
    """知识图谱构建器"""
    
    def __init__(self, config: Dict[str, Any]):
        """初始化知识图谱构建器"""
        self.entity_extractor = EntityExtractor(config.get("entity_extractor", {}))
        self.relation_extractor = RelationExtractor(config.get("relation_extractor", {}))
        
    def build(self, documents: List[Dict[str, Any]]) -> Any:
        """构建知识图谱"""
        # 提取实体和关系
        entities = []
        relations = []
        
        for doc in documents:
            doc_entities = self.entity_extractor.extract(doc["document"])
            doc_relations = self.relation_extractor.extract(doc["document"], doc_entities)
            
            entities.extend(doc_entities)
            relations.extend(doc_relations)
        
        # 构建知识图谱
        knowledge_graph = self._construct_graph(entities, relations)
        
        return knowledge_graph
    
    def _construct_graph(self, entities: List[Any], relations: List[Any]) -> Any:
        """构建图结构"""
        # 实现图构建逻辑
        pass

class InferenceEngine:
    """推理引擎"""
    
    def __init__(self, config: Dict[str, Any]):
        """初始化推理引擎"""
        self.reasoning_type = config.get("reasoning_type", "symbolic")
        
    def infer(self, query: str, knowledge_graph: Any, rules: List[Any]) -> List[Any]:
        """执行推理"""
        if self.reasoning_type == "symbolic":
            return self._symbolic_reasoning(query, knowledge_graph, rules)
        elif self.reasoning_type == "neural":
            return self._neural_reasoning(query, knowledge_graph, rules)
        else:
            return self._hybrid_reasoning(query, knowledge_graph, rules)
    
    def _symbolic_reasoning(self, query: str, knowledge_graph: Any, rules: List[Any]) -> List[Any]:
        """符号逻辑推理"""
        # 实现符号逻辑推理
        pass
    
    def _neural_reasoning(self, query: str, knowledge_graph: Any, rules: List[Any]) -> List[Any]:
        """神经网络推理"""
        # 实现神经网络推理
        pass
    
    def _hybrid_reasoning(self, query: str, knowledge_graph: Any, rules: List[Any]) -> List[Any]:
        """混合推理"""
        # 实现混合推理
        pass

class Generator:
    """生成模块"""
    
    def __init__(self, config: Dict[str, Any]):
        """初始化生成模块"""
        self.model_name = config.get("model_name", "gpt-3.5-turbo")
        self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
        self.model = AutoModelForCausalLM.from_pretrained(self.model_name)
        self.context_builder = ContextBuilder(config.get("context_builder", {}))
        self.fact_checker = FactChecker(config.get("fact_checker", {}))
        
    def generate(self, query: str, context: List[Dict[str, Any]], reasoning: Dict[str, Any]) -> str:
        """
        生成回答
        
        Args:
            query: 用户查询
            context: 检索到的上下文
            reasoning: 推理结果
            
        Returns:
            生成的回答
        """
        # 步骤1：构建上下文
        prompt = self.context_builder.build(query, context, reasoning)
        
        # 步骤2：生成回答
        response = self._generate_response(prompt)
        
        # 步骤3：事实核查
        fact_check_result = self.fact_checker.check(response, context, reasoning)
        
        # 步骤4：如果事实核查不通过，重新生成
        if not fact_check_result["passed"]:
            response = self._regenerate_response(query, context, reasoning, fact_check_result)
        
        return response
    
    def _generate_response(self, prompt: str) -> str:
        """生成回答"""
        inputs = self.tokenizer(prompt, return_tensors="pt")
        
        with torch.no_grad():
            outputs = self.model.generate(
                inputs["input_ids"],
                max_length=1024,
                num_return_sequences=1,
                temperature=0.7,
                do_sample=True
            )
        
        response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
        
        # 提取生成的回答部分
        response = self._extract_response(response, prompt)
        
        return response
    
    def _regenerate_response(self, query: str, context: List[Dict[str, Any]], reasoning: Dict[str, Any], 
                           fact_check_result: Dict[str, Any]) -> str:
        """重新生成回答"""
        # 根据事实核查结果调整提示
        adjusted_prompt = self.context_builder.adjust_prompt(
            query, context, reasoning, fact_check_result
        )
        
        # 重新生成回答
        return self._generate_response(adjusted_prompt)
    
    def _extract_response(self, full_text: str, prompt: str) -> str:
        """从生成的文本中提取回答部分"""
        # 实现回答提取逻辑
        pass

class ContextBuilder:
    """上下文构建器"""
    
    def __init__(self, config: Dict[str, Any]):
        """初始化上下文构建器"""
        self.template = config.get("template", "default")
        self.max_context_length = config.get("max_context_length", 4096)
        
    def build(self, query: str, context: List[Dict[str, Any]], reasoning: Dict[str, Any]) -> str:
        """构建上下文"""
        # 格式化检索到的文档
        formatted_docs = self._format_documents(context)
        
        # 格式化推理结果
        formatted_reasoning = self._format_reasoning(reasoning)
        
        # 根据模板构建提示
        if self.template == "default":
            prompt = f"""
请根据以下提供的信息回答问题。你的回答应该基于提供的信息，并且逻辑一致。

问题: {query}

相关信息:
{formatted_docs}

推理结果:
{formatted_reasoning}

请提供逻辑一致且基于上述信息的回答:
"""
        elif self.template == "detailed":
            prompt = f"""
你是一个逻辑推理专家。请仔细分析以下提供的信息，并基于这些信息回答问题。

问题: {query}

相关信息:
{formatted_docs}

推理过程:
{formatted_reasoning}

要求:
1. 你的回答必须基于提供的信息，不要添加未经证实的内容
2. 确保回答逻辑一致，不包含矛盾
3. 如果信息不足以回答问题，请明确指出
4. 提供推理依据和引用来源

请提供详细、逻辑一致的回答:
"""
        
        # 确保上下文长度不超过限制
        if len(prompt) > self.max_context_length:
            prompt = self._truncate_prompt(prompt, self.max_context_length)
        
        return prompt
    
    def adjust_prompt(self, query: str, context: List[Dict[str, Any]], reasoning: Dict[str, Any], 
                     fact_check_result: Dict[str, Any]) -> str:
        """根据事实核查结果调整提示"""
        # 构建调整后的提示
        adjusted_prompt = f"""
请重新回答以下问题。之前的回答存在以下问题: {fact_check_result["issues"]}

问题: {query}

相关信息:
{self._format_documents(context)}

推理结果:
{self._format_reasoning(reasoning)}

请确保回答逻辑一致，并解决上述问题:
"""
        
        return adjusted_prompt
    
    def _format_documents(self, documents: List[Dict[str, Any]]) -> str:
        """格式化文档"""
        # 实现文档格式化逻辑
        pass
    
    def _format_reasoning(self, reasoning: Dict[str, Any]) -> str:
        """格式化推理结果"""
        # 实现推理结果格式化逻辑
        pass
    
    def _truncate_prompt(self, prompt: str, max_length: int) -> str:
        """截断提示"""
        # 实现提示截断逻辑
        pass