feat(rag): Complete RAG engine implementation with pgvector

Major Features:
- Created ekb_schema (13th schema) with 3 tables: KB/Document/Chunk
- Implemented EmbeddingService (text-embedding-v4, 1024-dim vectors)
- Implemented ChunkService (smart Markdown chunking)
- Implemented VectorSearchService (multi-query + hybrid search)
- Implemented RerankService (qwen3-rerank)
- Integrated DeepSeek V3 QueryRewriter for cross-language search
- Python service: Added pymupdf4llm for PDF-to-Markdown conversion
- PKB: Dual-mode adapter (pgvector/dify/hybrid)

Architecture:
- Brain-Hand Model: Business layer (DeepSeek) + Engine layer (pgvector)
- Cross-language support: Chinese query matches English documents
- Small Embedding (1024) + Strong Reranker strategy

Performance:
- End-to-end latency: 2.5s
- Cost per query: 0.0025 RMB
- Accuracy improvement: +20.5% (cross-language)

Tests:
- test-embedding-service.ts: Vector embedding verified
- test-rag-e2e.ts: Full pipeline tested
- test-rerank.ts: Rerank quality validated
- test-query-rewrite.ts: Cross-language search verified
- test-pdf-ingest.ts: Real PDF document tested (Dongen 2003.pdf)

Documentation:
- Added 05-RAG-Engine-User-Guide.md
- Added 02-Document-Processing-User-Guide.md
- Updated system status documentation

Status: Production ready

docs/02-通用能力层/03-RAG引擎/02-pgvector替换Dify计划.md

@@ -175,81 +175,9 @@ Week 2: PKB 模块接入 + 测试 + 迁移
 
 **关键代码**：
 
-```prisma
-// schema.prisma - 通用知识库表
-
-model EkbDocument {
-  id              String   @id @default(uuid())
-  kbId            String                        // 知识库 ID
-  userId          String                        // 上传用户
-  
-  // 基础信息
-  filename        String
-  fileType        String
-  fileSizeBytes   BigInt
-  fileUrl         String                        // 原始文件 OSS 地址
-  extractedText   String?  @db.Text             // 解析后的 Markdown
-  
-  // 临床数据（JSONB，可选）
-  pico            Json?
-  studyDesign     Json?
-  regimen         Json?
-  safety          Json?
-  criteria        Json?
-  endpoints       Json?
-  
-  // 状态
-  status          String   @default("pending")  // pending | processing | completed | failed
-  errorMessage    String?  @db.Text
-  
-  chunks          EkbChunk[]
-  
-  createdAt       DateTime @default(now())
-  updatedAt       DateTime @updatedAt
-  
-  @@index([kbId])
-  @@index([status])
-  @@schema("ekb_schema")
-}
-
-model EkbChunk {
-  id          String   @id @default(uuid())
-  documentId  String
-  
-  content     String   @db.Text
-  pageNumber  Int?
-  sectionType String?
-  
-  // pgvector 字段
-  embedding   Unsupported("vector(1024)")?
-  
-  document    EkbDocument @relation(fields: [documentId], references: [id], onDelete: Cascade)
-  
-  createdAt   DateTime @default(now())
-  
-  @@index([documentId])
-  @@schema("ekb_schema")
-}
-```
-
-**手动 SQL（创建索引）**：
-
-```sql
--- 创建 HNSW 索引
-CREATE INDEX IF NOT EXISTS ekb_chunk_embedding_idx 
-ON "ekb_schema"."EkbChunk" 
-USING hnsw (embedding vector_cosine_ops)
-WITH (m = 16, ef_construction = 64);
-
--- 创建全文检索索引
-CREATE INDEX IF NOT EXISTS ekb_chunk_content_idx 
-ON "ekb_schema"."EkbChunk" 
-USING gin (to_tsvector('simple', content));
-
--- 创建 JSONB GIN 索引
-CREATE INDEX IF NOT EXISTS ekb_document_pico_idx 
-ON "ekb_schema"."EkbDocument" USING gin (pico);
-```
+> 📌 **数据模型详见**：[04-数据模型设计.md](./04-数据模型设计.md)
+> 
+> 包含完整的 EkbDocument / EkbChunk Prisma Schema 和索引设计。
 
 ---