diff --git a/AITrain/pdf_to_rag_processor.py b/AITrain/pdf_to_rag_processor.py
new file mode 100644
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+++ b/AITrain/pdf_to_rag_processor.py
@@ -0,0 +1,431 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+'''
+PDF文档转RAG知识库处理模块
+支持将世界观文档(如COC.pdf)转换为RAG检索格式
+'''
+
+import json
+import os
+import re
+from typing import List, Dict, Tuple
+import hashlib
+from datetime import datetime
+
+try:
+    import PyPDF2
+    import fitz  # pymupdf
+    PYMUPDF_AVAILABLE = True
+except ImportError:
+    PYMUPDF_AVAILABLE = False
+    print("Warning: pymupdf not available, using PyPDF2 only")
+
+try:
+    from sentence_transformers import SentenceTransformer
+    import faiss
+    import numpy as np
+    EMBEDDING_AVAILABLE = True
+except ImportError:
+    EMBEDDING_AVAILABLE = False
+    print("Warning: sentence-transformers or faiss not available, using simple text matching")
+
+class PDFToRAGProcessor:
+    def __init__(self, embedding_model: str = "./sentence-transformers/all-MiniLM-L6-v2"):
+        """
+        初始化PDF处理器
+        
+        Args:
+            embedding_model: 向量化模型名称
+        """
+        global EMBEDDING_AVAILABLE
+        self.chunks = []
+        self.embeddings = None
+        self.index = None
+        
+        if EMBEDDING_AVAILABLE:
+            try:
+                self.embedding_model = SentenceTransformer(embedding_model)
+                print(f"✓ 向量模型加载成功: {embedding_model}")
+            except Exception as e:
+                print(f"✗ 向量模型加载失败: {e}")
+                self.embedding_model = None
+                EMBEDDING_AVAILABLE = False
+        else:
+            self.embedding_model = None
+    
+    def extract_text_from_pdf(self, pdf_path: str) -> str:
+        """从PDF提取文本"""
+        if not os.path.exists(pdf_path):
+            raise FileNotFoundError(f"PDF文件不存在: {pdf_path}")
+        
+        text = ""
+        
+        # 优先使用pymupdf，效果更好
+        if PYMUPDF_AVAILABLE:
+            try:
+                doc = fitz.open(pdf_path)
+                for page_num in range(len(doc)):
+                    page = doc.load_page(page_num)
+                    text += page.get_text()
+                doc.close()
+                print(f"✓ 使用pymupdf提取文本成功")
+                return text
+            except Exception as e:
+                print(f"✗ pymupdf提取失败: {e}, 尝试PyPDF2")
+        
+        # 备用PyPDF2
+        try:
+            with open(pdf_path, 'rb') as file:
+                pdf_reader = PyPDF2.PdfReader(file)
+                for page in pdf_reader.pages:
+                    text += page.extract_text()
+            print(f"✓ 使用PyPDF2提取文本成功")
+        except Exception as e:
+            raise Exception(f"PDF文本提取失败: {e}")
+        
+        return text
+    
+    def clean_text(self, text: str) -> str:
+        """清理文本"""
+        # 移除多余空行
+        text = re.sub(r'\n\s*\n', '\n\n', text)
+        # 移除页码等
+        text = re.sub(r'第\s*\d+\s*页', '', text)
+        text = re.sub(r'Page\s*\d+', '', text)
+        # 统一标点符号
+        text = text.replace('　', ' ')  # 全角空格转半角
+        text = re.sub(r'\s+', ' ', text)  # 多个空格合并
+        
+        return text.strip()
+    
+    def chunk_text_by_semantic(self, text: str, max_chunk_size: int = 500) -> List[Dict]:
+        """按语义分块文本"""
+        # 按段落分割
+        paragraphs = [p.strip() for p in text.split('\n\n') if p.strip()]
+        
+        chunks = []
+        current_chunk = ""
+        current_size = 0
+        
+        for para in paragraphs:
+            para_size = len(para)
+            
+            # 如果当前段落太长，需要进一步分割
+            if para_size > max_chunk_size:
+                # 保存当前chunk
+                if current_chunk:
+                    chunks.append({
+                        "content": current_chunk.strip(),
+                        "size": current_size,
+                        "type": "paragraph"
+                    })
+                    current_chunk = ""
+                    current_size = 0
+                
+                # 按句子分割长段落
+                sentences = re.split(r'[。！？；]\s*', para)
+                for sentence in sentences:
+                    if sentence.strip():
+                        sentence += '。'  # 添加标点
+                        if current_size + len(sentence) > max_chunk_size and current_chunk:
+                            chunks.append({
+                                "content": current_chunk.strip(),
+                                "size": current_size,
+                                "type": "sentence"
+                            })
+                            current_chunk = sentence
+                            current_size = len(sentence)
+                        else:
+                            current_chunk += sentence
+                            current_size += len(sentence)
+            else:
+                # 正常段落处理
+                if current_size + para_size > max_chunk_size and current_chunk:
+                    chunks.append({
+                        "content": current_chunk.strip(),
+                        "size": current_size,
+                        "type": "paragraph"
+                    })
+                    current_chunk = para
+                    current_size = para_size
+                else:
+                    current_chunk += "\n" + para if current_chunk else para
+                    current_size += para_size
+        
+        # 保存最后的chunk
+        if current_chunk:
+            chunks.append({
+                "content": current_chunk.strip(),
+                "size": current_size,
+                "type": "paragraph"
+            })
+        
+        # 为每个chunk添加唯一ID和元数据
+        for i, chunk in enumerate(chunks):
+            chunk["id"] = f"chunk_{i:04d}"
+            chunk["hash"] = hashlib.md5(chunk["content"].encode()).hexdigest()[:8]
+        
+        return chunks
+    
+    def extract_key_concepts(self, chunks: List[Dict]) -> List[Dict]:
+        """提取关键概念和术语"""
+        concepts = []
+        
+        # 简单的关键词提取规则
+        concept_patterns = [
+            r'(?:技能|属性|规则)[:：]\s*([^。\n]+)',
+            r'([A-Z][a-z]+(?:\s+[A-Z][a-z]+)*)',  # 英文专有名词
+            r'【([^】]+)】',  # 中文术语标记
+            r'"([^"]+)"',    # 引号内容
+        ]
+        
+        for chunk in chunks:
+            content = chunk["content"]
+            chunk_concepts = []
+            
+            for pattern in concept_patterns:
+                matches = re.findall(pattern, content)
+                chunk_concepts.extend(matches)
+            
+            if chunk_concepts:
+                concepts.append({
+                    "chunk_id": chunk["id"],
+                    "concepts": list(set(chunk_concepts)),  # 去重
+                    "content_preview": content[:100] + "..." if len(content) > 100 else content
+                })
+        
+        return concepts
+    
+    def build_vector_index(self, chunks: List[Dict]) -> bool:
+        """构建向量索引"""
+        if not EMBEDDING_AVAILABLE or not self.embedding_model:
+            print("✗ 向量化功能不可用，将使用文本匹配")
+            return False
+        
+        try:
+            # 提取文本内容
+            texts = [chunk["content"] for chunk in chunks]
+            
+            # 生成向量
+            print("生成文档向量...")
+            embeddings = self.embedding_model.encode(texts, show_progress_bar=True)
+            
+            # 构建FAISS索引
+            dimension = embeddings.shape[1]
+            self.index = faiss.IndexFlatL2(dimension)
+            self.index.add(embeddings.astype(np.float32))
+            
+            self.embeddings = embeddings
+            print(f"✓ 向量索引构建完成，维度: {dimension}, 文档数: {len(chunks)}")
+            return True
+            
+        except Exception as e:
+            print(f"✗ 向量索引构建失败: {e}")
+            return False
+    
+    def process_pdf_to_rag(self, pdf_path: str, output_dir: str = "./rag_knowledge") -> Dict:
+        """
+        完整处理PDF到RAG知识库
+        
+        Args:
+            pdf_path: PDF文件路径
+            output_dir: 输出目录
+            
+        Returns:
+            处理结果统计
+        """
+        print(f"开始处理PDF: {pdf_path}")
+        
+        # 创建输出目录
+        os.makedirs(output_dir, exist_ok=True)
+        
+        # 1. 提取文本
+        raw_text = self.extract_text_from_pdf(pdf_path)
+        print(f"✓ 提取文本长度: {len(raw_text)} 字符")
+        
+        # 2. 清理文本
+        clean_text = self.clean_text(raw_text)
+        
+        # 保存清理后的文本
+        text_output_path = os.path.join(output_dir, "extracted_text.txt")
+        with open(text_output_path, 'w', encoding='utf-8') as f:
+            f.write(clean_text)
+        print(f"✓ 清理后文本保存至: {text_output_path}")
+        
+        # 3. 语义分块
+        chunks = self.chunk_text_by_semantic(clean_text)
+        self.chunks = chunks
+        print(f"✓ 文档分块完成: {len(chunks)} 个块")
+        
+        # 4. 提取关键概念
+        concepts = self.extract_key_concepts(chunks)
+        print(f"✓ 提取关键概念: {len(concepts)} 个")
+        
+        # 5. 构建向量索引
+        vector_success = self.build_vector_index(chunks)
+        
+        # 6. 保存知识库文件
+        knowledge_base = {
+            "metadata": {
+                "source_file": os.path.basename(pdf_path),
+                "processed_time": datetime.now().isoformat(),
+                "total_chunks": len(chunks),
+                "total_concepts": len(concepts),
+                "vector_enabled": vector_success
+            },
+            "chunks": chunks,
+            "concepts": concepts
+        }
+        
+        # 保存JSON知识库
+        kb_output_path = os.path.join(output_dir, "knowledge_base.json")
+        with open(kb_output_path, 'w', encoding='utf-8') as f:
+            json.dump(knowledge_base, f, ensure_ascii=False, indent=2)
+        print(f"✓ 知识库保存至: {kb_output_path}")
+        
+        # 保存向量索引
+        if vector_success:
+            index_path = os.path.join(output_dir, "vector_index.faiss")
+            faiss.write_index(self.index, index_path)
+            
+            embeddings_path = os.path.join(output_dir, "embeddings.npy")
+            np.save(embeddings_path, self.embeddings)
+            print(f"✓ 向量索引保存至: {index_path}")
+        
+        return {
+            "status": "success",
+            "chunks_count": len(chunks),
+            "concepts_count": len(concepts),
+            "vector_enabled": vector_success,
+            "output_dir": output_dir
+        }
+    
+    def load_knowledge_base(self, knowledge_dir: str) -> bool:
+        """加载已有知识库"""
+        try:
+            # 加载JSON知识库
+            kb_path = os.path.join(knowledge_dir, "knowledge_base.json")
+            with open(kb_path, 'r', encoding='utf-8') as f:
+                knowledge_base = json.load(f)
+            
+            self.chunks = knowledge_base["chunks"]
+            
+            # 加载向量索引
+            if EMBEDDING_AVAILABLE:
+                index_path = os.path.join(knowledge_dir, "vector_index.faiss")
+                embeddings_path = os.path.join(knowledge_dir, "embeddings.npy")
+                
+                if os.path.exists(index_path) and os.path.exists(embeddings_path):
+                    self.index = faiss.read_index(index_path)
+                    self.embeddings = np.load(embeddings_path)
+                    print(f"✓ 向量索引加载成功")
+            
+            print(f"✓ 知识库加载成功: {len(self.chunks)} 个文档块")
+            return True
+            
+        except Exception as e:
+            print(f"✗ 知识库加载失败: {e}")
+            return False
+    
+    def search_relevant_content(self, query: str, top_k: int = 3) -> List[Dict]:
+        """搜索相关内容"""
+        if not self.chunks:
+            return []
+        
+        # 向量搜索
+        if EMBEDDING_AVAILABLE and self.embedding_model and self.index:
+            try:
+                query_vector = self.embedding_model.encode([query])
+                distances, indices = self.index.search(query_vector.astype(np.float32), top_k)
+                
+                results = []
+                for i, (distance, idx) in enumerate(zip(distances[0], indices[0])):
+                    if idx < len(self.chunks):
+                        result = self.chunks[idx].copy()
+                        result["relevance_score"] = float(1 / (1 + distance))  # 转换为相似度分数
+                        result["rank"] = i + 1
+                        results.append(result)
+                
+                return results
+                
+            except Exception as e:
+                print(f"向量搜索失败: {e}, 使用文本匹配")
+        
+        # 文本匹配搜索
+        query_lower = query.lower()
+        scored_chunks = []
+        
+        for chunk in self.chunks:
+            content_lower = chunk["content"].lower()
+            
+            # 简单的相关性评分
+            score = 0
+            query_words = query_lower.split()
+            
+            for word in query_words:
+                if word in content_lower:
+                    score += content_lower.count(word)
+            
+            if score > 0:
+                result = chunk.copy()
+                result["relevance_score"] = score
+                scored_chunks.append(result)
+        
+        # 按分数排序
+        scored_chunks.sort(key=lambda x: x["relevance_score"], reverse=True)
+        
+        # 添加排名
+        for i, chunk in enumerate(scored_chunks[:top_k]):
+            chunk["rank"] = i + 1
+        
+        return scored_chunks[:top_k]
+
+def main():
+    """测试PDF处理功能"""
+    processor = PDFToRAGProcessor()
+    
+    # 示例：处理COC规则书
+    pdf_path = input("请输入PDF文件路径 (如: ./coc.pdf): ").strip()
+    
+    if not os.path.exists(pdf_path):
+        print(f"文件不存在: {pdf_path}")
+        return
+    
+    try:
+        result = processor.process_pdf_to_rag(pdf_path)
+        
+        print(f"\n{'='*50}")
+        print(f"PDF处理完成!")
+        print(f"状态: {result['status']}")
+        print(f"文档块数量: {result['chunks_count']}")
+        print(f"关键概念数量: {result['concepts_count']}")
+        print(f"向量索引: {'启用' if result['vector_enabled'] else '未启用'}")
+        print(f"输出目录: {result['output_dir']}")
+        
+        # 测试搜索
+        print(f"\n{'='*50}")
+        print("测试知识库搜索:")
+        
+        while True:
+            query = input("\n请输入搜索关键词 (输入'quit'退出): ").strip()
+            if query.lower() == 'quit':
+                break
+            
+            results = processor.search_relevant_content(query, top_k=3)
+            
+            if results:
+                print(f"\n找到 {len(results)} 个相关结果:")
+                for result in results:
+                    print(f"\n排名 {result['rank']} (相关度: {result['relevance_score']:.3f}):")
+                    content = result['content']
+                    preview = content[:200] + "..." if len(content) > 200 else content
+                    print(f"{preview}")
+                    print("-" * 40)
+            else:
+                print("未找到相关内容")
+    
+    except Exception as e:
+        print(f"处理失败: {e}")
+
+if __name__ == '__main__':
+    main()
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