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
This commit is contained in:
@@ -155,6 +155,9 @@ https://iit.xunzhengyixue.com/api/v1/iit/health
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@@ -316,6 +316,9 @@ npx tsx src/modules/iit-manager/test-patient-wechat-url-verify.ts
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@@ -178,6 +178,9 @@ npm run dev
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@@ -59,3 +59,6 @@ main()
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@@ -53,3 +53,6 @@ main()
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@@ -80,3 +80,6 @@ main()
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@@ -43,3 +43,6 @@ main()
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@@ -84,3 +84,6 @@ main()
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@@ -31,3 +31,6 @@ main()
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@@ -119,3 +119,6 @@ main()
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@@ -90,3 +90,6 @@ main()
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@@ -76,3 +76,6 @@ main()
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@@ -118,3 +118,6 @@ main()
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@@ -29,3 +29,6 @@ ON CONFLICT (id) DO NOTHING;
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@@ -61,3 +61,6 @@ ON CONFLICT (id) DO NOTHING;
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@@ -75,6 +75,9 @@ WHERE table_schema = 'dc_schema'
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@@ -113,6 +113,9 @@ ORDER BY ordinal_position;
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@@ -126,6 +126,9 @@ runMigration()
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@@ -60,6 +60,9 @@ COMMENT ON COLUMN "dc_schema"."dc_tool_c_sessions"."column_mapping" IS '列名
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@@ -87,6 +87,9 @@ COMMENT ON COLUMN dc_schema.dc_tool_c_sessions.expires_at IS '过期时间（创
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64
backend/prisma/migrations/manual/ekb_create_indexes.sql
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64
backend/prisma/migrations/manual/ekb_create_indexes.sql
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@@ -0,0 +1,64 @@
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-- ============================================================
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-- EKB Schema 索引创建脚本
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-- 执行时机：prisma migrate 之后手动执行
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-- 参考文档：docs/02-通用能力层/03-RAG引擎/04-数据模型设计.md
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-- ============================================================
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-- 1. 确保 pgvector 扩展已启用
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CREATE EXTENSION IF NOT EXISTS vector;
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-- 2. 确保 pg_bigm 扩展已启用（中文关键词检索）
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CREATE EXTENSION IF NOT EXISTS pg_bigm;
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-- ===== MVP 阶段必须创建 =====
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-- 3. HNSW 向量索引（语义检索核心）
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-- 参数说明：m=16 每层最大连接数，ef_construction=64 构建时搜索范围
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CREATE INDEX IF NOT EXISTS idx_ekb_chunk_embedding
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ON "ekb_schema"."ekb_chunk"
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USING hnsw (embedding vector_cosine_ops)
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WITH (m = 16, ef_construction = 64);
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-- ===== Phase 2 阶段使用（可预创建）=====
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-- 4. pg_bigm 中文关键词索引
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CREATE INDEX IF NOT EXISTS idx_ekb_chunk_content_bigm
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ON "ekb_schema"."ekb_chunk"
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USING gin (content gin_bigm_ops);
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-- 5. 文档摘要关键词索引
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CREATE INDEX IF NOT EXISTS idx_ekb_doc_summary_bigm
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ON "ekb_schema"."ekb_document"
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USING gin (summary gin_bigm_ops);
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-- 6. 全文内容关键词索引
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CREATE INDEX IF NOT EXISTS idx_ekb_doc_text_bigm
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ON "ekb_schema"."ekb_document"
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USING gin (extracted_text gin_bigm_ops);
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-- ===== Phase 3 阶段使用（可预创建）=====
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-- 7. JSONB GIN 索引（metadata 查询加速）
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CREATE INDEX IF NOT EXISTS idx_ekb_doc_metadata_gin
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ON "ekb_schema"."ekb_document"
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USING gin (metadata jsonb_path_ops);
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-- 8. JSONB GIN 索引（structuredData 查询加速）
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CREATE INDEX IF NOT EXISTS idx_ekb_doc_structured_gin
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ON "ekb_schema"."ekb_document"
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USING gin (structured_data jsonb_path_ops);
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-- 9. 标签数组索引
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CREATE INDEX IF NOT EXISTS idx_ekb_doc_tags_gin
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ON "ekb_schema"."ekb_document"
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USING gin (tags);
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-- 10. 切片元数据索引
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CREATE INDEX IF NOT EXISTS idx_ekb_chunk_metadata_gin
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ON "ekb_schema"."ekb_chunk"
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USING gin (metadata jsonb_path_ops);
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-- ===== 验证索引创建 =====
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-- SELECT indexname, indexdef FROM pg_indexes WHERE schemaname = 'ekb_schema';
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31
backend/prisma/migrations/manual/ekb_create_indexes_mvp.sql
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31
backend/prisma/migrations/manual/ekb_create_indexes_mvp.sql
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@@ -0,0 +1,31 @@
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-- ============================================================
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-- EKB Schema MVP 索引创建脚本
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-- 执行时机：prisma db push 之后手动执行
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-- 说明：MVP 阶段只创建 HNSW 向量索引，pg_bigm 索引在 Phase 2 创建
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-- ============================================================
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-- 1. 确保 pgvector 扩展已启用
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CREATE EXTENSION IF NOT EXISTS vector;
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-- 2. HNSW 向量索引（语义检索核心）
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-- 参数说明：m=16 每层最大连接数，ef_construction=64 构建时搜索范围
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CREATE INDEX IF NOT EXISTS idx_ekb_chunk_embedding
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ON "ekb_schema"."ekb_chunk"
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USING hnsw (embedding vector_cosine_ops)
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WITH (m = 16, ef_construction = 64);
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-- 3. JSONB GIN 索引（可选，提升查询性能）
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CREATE INDEX IF NOT EXISTS idx_ekb_doc_metadata_gin
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ON "ekb_schema"."ekb_document"
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USING gin (metadata jsonb_path_ops);
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CREATE INDEX IF NOT EXISTS idx_ekb_doc_structured_gin
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ON "ekb_schema"."ekb_document"
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USING gin (structured_data jsonb_path_ops);
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-- 4. 标签数组索引
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CREATE INDEX IF NOT EXISTS idx_ekb_doc_tags_gin
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ON "ekb_schema"."ekb_document"
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USING gin (tags);
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datasource db {
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provider = "postgresql"
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url = env("DATABASE_URL")
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schemas = ["admin_schema", "aia_schema", "asl_schema", "capability_schema", "common_schema", "dc_schema", "iit_schema", "pkb_schema", "platform_schema", "public", "rvw_schema", "ssa_schema", "st_schema"]
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schemas = ["admin_schema", "aia_schema", "asl_schema", "capability_schema", "common_schema", "dc_schema", "ekb_schema", "iit_schema", "pkb_schema", "platform_schema", "public", "rvw_schema", "ssa_schema", "st_schema"]
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}
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/// 应用缓存表 - Postgres-Only架构
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@@schema("capability_schema")
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}
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// ============================================================
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// EKB Schema - 知识库引擎 (Enterprise Knowledge Base)
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// 参考文档: docs/02-通用能力层/03-RAG引擎/04-数据模型设计.md
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// ============================================================
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/// 知识库容器表 - 管理知识库的归属和策略配置
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model EkbKnowledgeBase {
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id String @id @default(uuid())
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name String /// 知识库名称
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description String? /// 描述
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/// 核心隔离字段
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/// USER: 用户私有，ownerId = userId
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/// SYSTEM: 系统公共，ownerId = moduleId (如 "ASL", "AIA")
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type String @default("USER") /// USER | SYSTEM
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ownerId String @map("owner_id") /// userId 或 moduleId
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/// 策略配置 (JSONB)
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/// { chunkSize, topK, enableRerank, embeddingModel }
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config Json? @db.JsonB
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documents EkbDocument[]
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createdAt DateTime @default(now()) @map("created_at")
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updatedAt DateTime @updatedAt @map("updated_at")
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@@index([ownerId], map: "idx_ekb_kb_owner")
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@@index([type], map: "idx_ekb_kb_type")
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@@map("ekb_knowledge_base")
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@@schema("ekb_schema")
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}
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/// 文档表 - 存储上传的文档及其元数据
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model EkbDocument {
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id String @id @default(uuid())
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kbId String @map("kb_id") /// 所属知识库
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userId String @map("user_id") /// 上传者（冗余存储）
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// ===== Layer 1: 基础信息（必须）=====
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filename String /// 文件名
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fileType String @map("file_type") /// pdf, docx, pptx, xlsx, md, txt
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fileSizeBytes BigInt @map("file_size_bytes") /// 文件大小（字节）
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fileUrl String @map("file_url") /// OSS 存储路径
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fileHash String? @map("file_hash") /// SHA256 哈希（秒传去重）
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status String @default("pending") /// pending, processing, completed, failed
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errorMessage String? @map("error_message") @db.Text
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// ===== Layer 0: RAG 核心（必须）=====
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extractedText String? @map("extracted_text") @db.Text /// Markdown 全文
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// ===== Layer 2: 内容增强（可选）=====
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summary String? @db.Text /// AI 摘要
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tokenCount Int? @map("token_count") /// Token 数量
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pageCount Int? @map("page_count") /// 页数
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// ===== Layer 3: 分类标签（可选）=====
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contentType String? @map("content_type") /// 内容类型
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tags String[] /// 用户标签
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category String? /// 分类目录
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// ===== Layer 4: 结构化数据（可选）=====
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metadata Json? @db.JsonB /// 文献属性 JSONB
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structuredData Json? @map("structured_data") @db.JsonB /// 类型特定数据 JSONB
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// ===== 关联 =====
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knowledgeBase EkbKnowledgeBase @relation(fields: [kbId], references: [id], onDelete: Cascade)
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chunks EkbChunk[]
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createdAt DateTime @default(now()) @map("created_at")
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updatedAt DateTime @updatedAt @map("updated_at")
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@@index([kbId], map: "idx_ekb_doc_kb")
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@@index([userId], map: "idx_ekb_doc_user")
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@@index([status], map: "idx_ekb_doc_status")
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@@index([contentType], map: "idx_ekb_doc_content_type")
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@@index([fileHash], map: "idx_ekb_doc_file_hash")
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@@map("ekb_document")
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@@schema("ekb_schema")
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}
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/// 切片表 - 存储文档切片和向量嵌入
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model EkbChunk {
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id String @id @default(uuid())
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documentId String @map("document_id") /// 所属文档
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// ===== 核心内容 =====
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content String @db.Text /// 切片文本（Markdown）
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chunkIndex Int @map("chunk_index") /// 切片序号（从 0 开始）
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// ===== 向量 =====
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/// pgvector 1024 维向量
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/// 注意：需要手动创建 HNSW 索引
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embedding Unsupported("vector(1024)")?
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// ===== 溯源信息（可选）=====
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pageNumber Int? @map("page_number") /// 页码（PDF 溯源）
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sectionType String? @map("section_type") /// 章节类型
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|
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// ===== 扩展元数据（可选）=====
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metadata Json? @db.JsonB /// 切片级元数据 JSONB
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document EkbDocument @relation(fields: [documentId], references: [id], onDelete: Cascade)
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createdAt DateTime @default(now()) @map("created_at")
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@@index([documentId], map: "idx_ekb_chunk_doc")
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@@map("ekb_chunk")
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@@schema("ekb_schema")
|
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}
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|
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@@ -127,6 +127,9 @@ Write-Host ""
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@@ -237,6 +237,9 @@ function extractCodeBlocks(obj, blocks = []) {
|
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|
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@@ -38,3 +38,6 @@ CREATE TABLE IF NOT EXISTS platform_schema.job_common (
|
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|
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|
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|
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|
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@@ -112,3 +112,6 @@ CREATE OR REPLACE FUNCTION platform_schema.delete_queue(queue_name text) RETURNS
|
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|
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|
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|
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|
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|
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|
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|
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@@ -256,6 +256,9 @@ checkDCTables();
|
||||
|
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|
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|
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|
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@@ -13,3 +13,6 @@ CREATE SCHEMA IF NOT EXISTS capability_schema;
|
||||
|
||||
|
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|
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|
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|
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|
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|
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@@ -208,6 +208,9 @@ createAiHistoryTable()
|
||||
|
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|
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|
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@@ -195,6 +195,9 @@ createToolCTable()
|
||||
|
||||
|
||||
|
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|
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|
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|
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|
||||
@@ -192,6 +192,9 @@ createToolCTable()
|
||||
|
||||
|
||||
|
||||
|
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|
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|
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|
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|
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|
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|
||||
@@ -316,3 +316,6 @@ main()
|
||||
.finally(() => prisma.$disconnect());
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -123,3 +123,6 @@ main()
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -340,6 +340,9 @@ runTests().catch(error => {
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -89,3 +89,6 @@ testAPI().catch(console.error);
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -119,3 +119,6 @@ async function testDeepSearch() {
|
||||
testDeepSearch().catch(console.error);
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -305,6 +305,9 @@ verifySchemas()
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -196,3 +196,6 @@ export const jwtService = new JWTService();
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -324,6 +324,9 @@ export function getBatchItems<T>(
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -79,3 +79,6 @@ export interface VariableValidation {
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
354
backend/src/common/rag/ChunkService.ts
Normal file
354
backend/src/common/rag/ChunkService.ts
Normal file
@@ -0,0 +1,354 @@
|
||||
/**
|
||||
* ChunkService - 文本分块服务
|
||||
*
|
||||
* 将长文本按语义边界分割为适合向量化的小块
|
||||
* 支持 Markdown 格式的智能分块
|
||||
*
|
||||
* 分块策略：
|
||||
* 1. 按标题层级分割（# ## ###）
|
||||
* 2. 按段落分割
|
||||
* 3. 按字符数限制分割（带重叠）
|
||||
*/
|
||||
|
||||
import { logger } from '../logging/index.js';
|
||||
|
||||
// ==================== 类型定义 ====================
|
||||
|
||||
export interface ChunkConfig {
|
||||
maxChunkSize?: number; // 单块最大字符数，默认 1000
|
||||
chunkOverlap?: number; // 块间重叠字符数，默认 200
|
||||
separators?: string[]; // 分隔符优先级列表
|
||||
preserveMarkdown?: boolean; // 保留 Markdown 格式，默认 true
|
||||
}
|
||||
|
||||
export interface TextChunk {
|
||||
content: string; // 分块内容
|
||||
index: number; // 分块索引（从 0 开始）
|
||||
startChar: number; // 在原文中的起始位置
|
||||
endChar: number; // 在原文中的结束位置
|
||||
metadata?: Record<string, unknown>; // 可选元数据（如标题层级）
|
||||
}
|
||||
|
||||
export interface ChunkResult {
|
||||
chunks: TextChunk[];
|
||||
totalChunks: number;
|
||||
originalLength: number;
|
||||
}
|
||||
|
||||
// ==================== 默认配置 ====================
|
||||
|
||||
const DEFAULT_CONFIG: Required<ChunkConfig> = {
|
||||
maxChunkSize: 1000,
|
||||
chunkOverlap: 200,
|
||||
separators: [
|
||||
'\n## ', // H2 标题
|
||||
'\n### ', // H3 标题
|
||||
'\n#### ', // H4 标题
|
||||
'\n\n', // 段落
|
||||
'\n', // 换行
|
||||
'。', // 中文句号
|
||||
'. ', // 英文句号
|
||||
'；', // 中文分号
|
||||
'; ', // 英文分号
|
||||
' ', // 空格
|
||||
],
|
||||
preserveMarkdown: true,
|
||||
};
|
||||
|
||||
// ==================== ChunkService ====================
|
||||
|
||||
export class ChunkService {
|
||||
private config: Required<ChunkConfig>;
|
||||
|
||||
constructor(config: ChunkConfig = {}) {
|
||||
this.config = { ...DEFAULT_CONFIG, ...config };
|
||||
logger.debug(`ChunkService 初始化: maxChunkSize=${this.config.maxChunkSize}, overlap=${this.config.chunkOverlap}`);
|
||||
}
|
||||
|
||||
/**
|
||||
* 将文本分割为多个块
|
||||
*/
|
||||
chunk(text: string): ChunkResult {
|
||||
if (!text || text.trim().length === 0) {
|
||||
return { chunks: [], totalChunks: 0, originalLength: 0 };
|
||||
}
|
||||
|
||||
const originalLength = text.length;
|
||||
const chunks: TextChunk[] = [];
|
||||
|
||||
// 使用递归分割策略
|
||||
const rawChunks = this.recursiveSplit(text, this.config.separators);
|
||||
|
||||
// 合并过小的块，分割过大的块
|
||||
const normalizedChunks = this.normalizeChunks(rawChunks);
|
||||
|
||||
// 添加重叠
|
||||
const overlappedChunks = this.addOverlap(normalizedChunks, text);
|
||||
|
||||
// 构建结果
|
||||
let charPosition = 0;
|
||||
for (let i = 0; i < overlappedChunks.length; i++) {
|
||||
const content = overlappedChunks[i];
|
||||
const startChar = text.indexOf(content.trim(), charPosition);
|
||||
const endChar = startChar + content.trim().length;
|
||||
|
||||
chunks.push({
|
||||
content: content.trim(),
|
||||
index: i,
|
||||
startChar: startChar >= 0 ? startChar : charPosition,
|
||||
endChar: endChar >= 0 ? endChar : charPosition + content.length,
|
||||
});
|
||||
|
||||
if (startChar >= 0) {
|
||||
charPosition = startChar + 1;
|
||||
}
|
||||
}
|
||||
|
||||
logger.info(`文本分块完成: ${originalLength} 字符 -> ${chunks.length} 块`);
|
||||
|
||||
return {
|
||||
chunks,
|
||||
totalChunks: chunks.length,
|
||||
originalLength,
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* 递归分割文本
|
||||
*/
|
||||
private recursiveSplit(text: string, separators: string[]): string[] {
|
||||
if (text.length <= this.config.maxChunkSize) {
|
||||
return [text];
|
||||
}
|
||||
|
||||
if (separators.length === 0) {
|
||||
// 没有更多分隔符，强制按字符数分割
|
||||
return this.forceSplit(text);
|
||||
}
|
||||
|
||||
const [separator, ...restSeparators] = separators;
|
||||
const parts = text.split(separator);
|
||||
|
||||
if (parts.length === 1) {
|
||||
// 当前分隔符无效，尝试下一个
|
||||
return this.recursiveSplit(text, restSeparators);
|
||||
}
|
||||
|
||||
const result: string[] = [];
|
||||
let currentChunk = '';
|
||||
|
||||
for (const part of parts) {
|
||||
const potentialChunk = currentChunk
|
||||
? currentChunk + separator + part
|
||||
: part;
|
||||
|
||||
if (potentialChunk.length <= this.config.maxChunkSize) {
|
||||
currentChunk = potentialChunk;
|
||||
} else {
|
||||
if (currentChunk) {
|
||||
result.push(currentChunk);
|
||||
}
|
||||
// 如果单个 part 仍然过大，递归处理
|
||||
if (part.length > this.config.maxChunkSize) {
|
||||
result.push(...this.recursiveSplit(part, restSeparators));
|
||||
currentChunk = '';
|
||||
} else {
|
||||
currentChunk = part;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (currentChunk) {
|
||||
result.push(currentChunk);
|
||||
}
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
/**
|
||||
* 强制按字符数分割（最后手段）
|
||||
*/
|
||||
private forceSplit(text: string): string[] {
|
||||
const chunks: string[] = [];
|
||||
const { maxChunkSize } = this.config;
|
||||
|
||||
for (let i = 0; i < text.length; i += maxChunkSize) {
|
||||
chunks.push(text.slice(i, i + maxChunkSize));
|
||||
}
|
||||
|
||||
return chunks;
|
||||
}
|
||||
|
||||
/**
|
||||
* 规范化块大小
|
||||
*/
|
||||
private normalizeChunks(chunks: string[]): string[] {
|
||||
const { maxChunkSize } = this.config;
|
||||
const minChunkSize = Math.floor(maxChunkSize * 0.3); // 最小块为最大块的 30%
|
||||
const result: string[] = [];
|
||||
let buffer = '';
|
||||
|
||||
for (const chunk of chunks) {
|
||||
const trimmed = chunk.trim();
|
||||
if (!trimmed) continue;
|
||||
|
||||
if (buffer) {
|
||||
const combined = buffer + '\n' + trimmed;
|
||||
if (combined.length <= maxChunkSize) {
|
||||
buffer = combined;
|
||||
} else {
|
||||
result.push(buffer);
|
||||
buffer = trimmed;
|
||||
}
|
||||
} else {
|
||||
buffer = trimmed;
|
||||
}
|
||||
|
||||
// 如果 buffer 足够大，输出
|
||||
if (buffer.length >= minChunkSize && buffer.length <= maxChunkSize) {
|
||||
result.push(buffer);
|
||||
buffer = '';
|
||||
}
|
||||
}
|
||||
|
||||
if (buffer) {
|
||||
// 尝试合并到最后一个块
|
||||
if (result.length > 0 && (result[result.length - 1].length + buffer.length) <= maxChunkSize) {
|
||||
result[result.length - 1] += '\n' + buffer;
|
||||
} else {
|
||||
result.push(buffer);
|
||||
}
|
||||
}
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
/**
|
||||
* 添加块间重叠（提高检索连贯性）
|
||||
*/
|
||||
private addOverlap(chunks: string[], originalText: string): string[] {
|
||||
if (this.config.chunkOverlap <= 0 || chunks.length <= 1) {
|
||||
return chunks;
|
||||
}
|
||||
|
||||
const result: string[] = [];
|
||||
const { chunkOverlap } = this.config;
|
||||
|
||||
for (let i = 0; i < chunks.length; i++) {
|
||||
let chunk = chunks[i];
|
||||
|
||||
// 添加前一块的结尾作为上下文
|
||||
if (i > 0) {
|
||||
const prevChunk = chunks[i - 1];
|
||||
const overlap = prevChunk.slice(-chunkOverlap);
|
||||
// 尝试从句子边界开始
|
||||
const sentenceStart = this.findSentenceStart(overlap);
|
||||
chunk = sentenceStart + chunk;
|
||||
}
|
||||
|
||||
result.push(chunk);
|
||||
}
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
/**
|
||||
* 查找句子起始位置
|
||||
*/
|
||||
private findSentenceStart(text: string): string {
|
||||
const sentenceEnders = ['。', '.', '！', '!', '？', '?', '\n'];
|
||||
|
||||
for (let i = 0; i < text.length; i++) {
|
||||
if (sentenceEnders.includes(text[i])) {
|
||||
return text.slice(i + 1).trimStart();
|
||||
}
|
||||
}
|
||||
|
||||
return text;
|
||||
}
|
||||
|
||||
/**
|
||||
* 为 Markdown 文档智能分块（保留标题层级）
|
||||
*/
|
||||
chunkMarkdown(markdown: string): ChunkResult {
|
||||
const chunks: TextChunk[] = [];
|
||||
|
||||
// 按一级/二级标题分割
|
||||
const sections = markdown.split(/(?=^#{1,2}\s)/m);
|
||||
let globalIndex = 0;
|
||||
let charPosition = 0;
|
||||
|
||||
for (const section of sections) {
|
||||
if (!section.trim()) continue;
|
||||
|
||||
// 提取标题
|
||||
const titleMatch = section.match(/^(#{1,6})\s+(.+?)$/m);
|
||||
const title = titleMatch ? titleMatch[2] : undefined;
|
||||
const level = titleMatch ? titleMatch[1].length : 0;
|
||||
|
||||
// 分块该 section
|
||||
const sectionResult = this.chunk(section);
|
||||
|
||||
for (const chunk of sectionResult.chunks) {
|
||||
chunks.push({
|
||||
...chunk,
|
||||
index: globalIndex++,
|
||||
startChar: charPosition + chunk.startChar,
|
||||
endChar: charPosition + chunk.endChar,
|
||||
metadata: title ? { title, level } : undefined,
|
||||
});
|
||||
}
|
||||
|
||||
charPosition += section.length;
|
||||
}
|
||||
|
||||
logger.info(`Markdown 分块完成: ${markdown.length} 字符 -> ${chunks.length} 块`);
|
||||
|
||||
return {
|
||||
chunks,
|
||||
totalChunks: chunks.length,
|
||||
originalLength: markdown.length,
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* 获取当前配置
|
||||
*/
|
||||
getConfig(): Required<ChunkConfig> {
|
||||
return { ...this.config };
|
||||
}
|
||||
}
|
||||
|
||||
// ==================== 单例和快捷方法 ====================
|
||||
|
||||
let _chunkService: ChunkService | null = null;
|
||||
|
||||
/**
|
||||
* 获取 ChunkService 单例
|
||||
*/
|
||||
export function getChunkService(config?: ChunkConfig): ChunkService {
|
||||
if (!_chunkService) {
|
||||
_chunkService = new ChunkService(config);
|
||||
}
|
||||
return _chunkService;
|
||||
}
|
||||
|
||||
/**
|
||||
* 快捷方法：分块普通文本
|
||||
*/
|
||||
export function chunkText(text: string, config?: ChunkConfig): TextChunk[] {
|
||||
const service = config ? new ChunkService(config) : getChunkService();
|
||||
return service.chunk(text).chunks;
|
||||
}
|
||||
|
||||
/**
|
||||
* 快捷方法：分块 Markdown 文本
|
||||
*/
|
||||
export function chunkMarkdown(markdown: string, config?: ChunkConfig): TextChunk[] {
|
||||
const service = config ? new ChunkService(config) : getChunkService();
|
||||
return service.chunkMarkdown(markdown).chunks;
|
||||
}
|
||||
|
||||
export default ChunkService;
|
||||
|
||||
|
||||
337
backend/src/common/rag/DocumentIngestService.ts
Normal file
337
backend/src/common/rag/DocumentIngestService.ts
Normal file
@@ -0,0 +1,337 @@
|
||||
/**
|
||||
* DocumentIngestService - 文档入库服务
|
||||
*
|
||||
* 负责文档的完整入库流程：
|
||||
* 1. 调用 Python 微服务转换为 Markdown
|
||||
* 2. 文本分块
|
||||
* 3. 向量化
|
||||
* 4. 存入数据库
|
||||
*
|
||||
* 支持异步任务模式（通过 PgBoss）
|
||||
*/
|
||||
|
||||
import { PrismaClient, Prisma } from '@prisma/client';
|
||||
import { logger } from '../logging/index.js';
|
||||
import { getEmbeddingService } from './EmbeddingService.js';
|
||||
import { getChunkService, TextChunk } from './ChunkService.js';
|
||||
import crypto from 'crypto';
|
||||
|
||||
// ==================== 类型定义 ====================
|
||||
|
||||
export interface IngestOptions {
|
||||
kbId: string; // 知识库 ID
|
||||
generateSummary?: boolean; // 是否生成摘要（消耗 LLM）
|
||||
extractClinicalData?: boolean; // 是否提取临床数据（消耗 LLM）
|
||||
contentType?: string; // 内容类型
|
||||
tags?: string[]; // 标签
|
||||
metadata?: Record<string, unknown>; // 额外元数据
|
||||
}
|
||||
|
||||
export interface IngestResult {
|
||||
success: boolean;
|
||||
documentId?: string;
|
||||
chunkCount?: number;
|
||||
tokenCount?: number;
|
||||
error?: string;
|
||||
duration?: number; // 处理耗时（毫秒）
|
||||
}
|
||||
|
||||
export interface DocumentInput {
|
||||
filename: string;
|
||||
fileUrl?: string; // OSS/本地文件路径
|
||||
fileBuffer?: Buffer; // 文件内容（二选一）
|
||||
mimeType?: string;
|
||||
}
|
||||
|
||||
// ==================== 配置 ====================
|
||||
|
||||
const PYTHON_SERVICE_URL = process.env.EXTRACTION_SERVICE_URL || 'http://localhost:8000';
|
||||
|
||||
// ==================== DocumentIngestService ====================
|
||||
|
||||
export class DocumentIngestService {
|
||||
private prisma: PrismaClient;
|
||||
|
||||
constructor(prisma: PrismaClient) {
|
||||
this.prisma = prisma;
|
||||
logger.info('DocumentIngestService 初始化完成');
|
||||
}
|
||||
|
||||
/**
|
||||
* 入库单个文档（完整流程）
|
||||
*/
|
||||
async ingestDocument(
|
||||
input: DocumentInput,
|
||||
options: IngestOptions
|
||||
): Promise<IngestResult> {
|
||||
const startTime = Date.now();
|
||||
const { filename, fileUrl, fileBuffer } = input;
|
||||
const { kbId, contentType, tags, metadata } = options;
|
||||
|
||||
logger.info(`开始入库文档: ${filename}, kbId=${kbId}`);
|
||||
|
||||
try {
|
||||
// Step 1: 计算文件哈希（用于去重和秒传）
|
||||
let fileHash: string | undefined;
|
||||
if (fileBuffer) {
|
||||
fileHash = crypto.createHash('sha256').update(fileBuffer).digest('hex');
|
||||
|
||||
// 检查是否已存在
|
||||
const existing = await this.prisma.ekbDocument.findFirst({
|
||||
where: { kbId, fileHash },
|
||||
});
|
||||
|
||||
if (existing) {
|
||||
logger.info(`文档已存在（秒传）: ${filename}, docId=${existing.id}`);
|
||||
return {
|
||||
success: true,
|
||||
documentId: existing.id,
|
||||
chunkCount: await this.prisma.ekbChunk.count({ where: { documentId: existing.id } }),
|
||||
duration: Date.now() - startTime,
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
// Step 2: 调用 Python 微服务转换为 Markdown
|
||||
const markdown = await this.convertToMarkdown(input);
|
||||
|
||||
if (!markdown || markdown.trim().length === 0) {
|
||||
throw new Error('文档转换失败：内容为空');
|
||||
}
|
||||
|
||||
// Step 3: 文本分块
|
||||
const chunkService = getChunkService();
|
||||
const { chunks } = chunkService.chunkMarkdown(markdown);
|
||||
|
||||
if (chunks.length === 0) {
|
||||
throw new Error('文档分块失败：无有效内容');
|
||||
}
|
||||
|
||||
// Step 4: 批量向量化
|
||||
const embeddingService = getEmbeddingService();
|
||||
const texts = chunks.map(c => c.content);
|
||||
const { embeddings, totalTokens } = await embeddingService.embedBatch(texts);
|
||||
|
||||
// Step 5: 创建文档记录
|
||||
const document = await this.prisma.ekbDocument.create({
|
||||
data: {
|
||||
kbId,
|
||||
userId: 'system', // TODO: 从上下文获取用户 ID
|
||||
filename,
|
||||
fileType: this.getFileType(filename),
|
||||
fileSizeBytes: fileBuffer?.length || 0,
|
||||
fileUrl: fileUrl || '',
|
||||
fileHash: fileHash || null,
|
||||
extractedText: markdown,
|
||||
contentType: contentType || this.detectContentType(filename),
|
||||
tags: tags || [],
|
||||
metadata: (metadata || {}) as Prisma.InputJsonValue,
|
||||
tokenCount: totalTokens,
|
||||
pageCount: this.estimatePageCount(markdown),
|
||||
status: 'completed',
|
||||
},
|
||||
});
|
||||
|
||||
// Step 6: 批量创建分块记录
|
||||
const chunkData = chunks.map((chunk, index) => ({
|
||||
documentId: document.id,
|
||||
content: chunk.content,
|
||||
chunkIndex: index,
|
||||
embedding: embeddings[index],
|
||||
tokenCount: Math.round(totalTokens / chunks.length), // 估算
|
||||
metadata: chunk.metadata || {},
|
||||
}));
|
||||
|
||||
// 使用 createMany 批量插入（性能优化）
|
||||
// 注意：pgvector 的 embedding 需要特殊处理
|
||||
// 实际列名: id, document_id, content, chunk_index, embedding, page_number, section_type, metadata, created_at
|
||||
for (const data of chunkData) {
|
||||
await this.prisma.$executeRaw`
|
||||
INSERT INTO "ekb_schema"."ekb_chunk"
|
||||
(id, document_id, content, chunk_index, embedding, metadata, created_at)
|
||||
VALUES (
|
||||
gen_random_uuid(),
|
||||
${data.documentId},
|
||||
${data.content},
|
||||
${data.chunkIndex},
|
||||
${`[${data.embedding.join(',')}]`}::vector,
|
||||
${JSON.stringify(data.metadata)}::jsonb,
|
||||
NOW()
|
||||
)
|
||||
`;
|
||||
}
|
||||
|
||||
const duration = Date.now() - startTime;
|
||||
logger.info(`文档入库完成: ${filename}, chunks=${chunks.length}, tokens=${totalTokens}, 耗时=${duration}ms`);
|
||||
|
||||
return {
|
||||
success: true,
|
||||
documentId: document.id,
|
||||
chunkCount: chunks.length,
|
||||
tokenCount: totalTokens,
|
||||
duration,
|
||||
};
|
||||
|
||||
} catch (error) {
|
||||
const duration = Date.now() - startTime;
|
||||
const errorMessage = error instanceof Error ? error.message : String(error);
|
||||
|
||||
logger.error(`文档入库失败: ${filename}`, { error: errorMessage, duration });
|
||||
|
||||
return {
|
||||
success: false,
|
||||
error: errorMessage,
|
||||
duration,
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 调用 Python 微服务转换文档为 Markdown
|
||||
*/
|
||||
private async convertToMarkdown(input: DocumentInput): Promise<string> {
|
||||
const { filename, fileUrl, fileBuffer } = input;
|
||||
|
||||
try {
|
||||
let response: Response;
|
||||
|
||||
if (fileBuffer) {
|
||||
// 上传文件
|
||||
const formData = new FormData();
|
||||
const blob = new Blob([fileBuffer]);
|
||||
formData.append('file', blob, filename);
|
||||
|
||||
response = await fetch(`${PYTHON_SERVICE_URL}/api/document/to-markdown`, {
|
||||
method: 'POST',
|
||||
body: formData,
|
||||
});
|
||||
} else if (fileUrl) {
|
||||
// TODO: 支持 URL 方式
|
||||
throw new Error('URL 方式暂不支持，请使用 fileBuffer');
|
||||
} else {
|
||||
throw new Error('必须提供 fileBuffer 或 fileUrl');
|
||||
}
|
||||
|
||||
if (!response.ok) {
|
||||
const errorText = await response.text();
|
||||
throw new Error(`Python 服务返回错误: ${response.status} - ${errorText}`);
|
||||
}
|
||||
|
||||
const result = await response.json() as { success: boolean; text?: string; error?: string };
|
||||
|
||||
if (!result.success) {
|
||||
throw new Error(result.error || '转换失败');
|
||||
}
|
||||
|
||||
return result.text || '';
|
||||
|
||||
} catch (error) {
|
||||
logger.error('调用 Python 微服务失败', { error, filename });
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 获取文件扩展名类型
|
||||
*/
|
||||
private getFileType(filename: string): string {
|
||||
const ext = filename.toLowerCase().split('.').pop();
|
||||
return ext || 'unknown';
|
||||
}
|
||||
|
||||
/**
|
||||
* 根据文件名检测内容类型
|
||||
*/
|
||||
private detectContentType(filename: string): string {
|
||||
const ext = filename.toLowerCase().split('.').pop();
|
||||
|
||||
const typeMap: Record<string, string> = {
|
||||
pdf: 'LITERATURE',
|
||||
docx: 'DOCUMENT',
|
||||
doc: 'DOCUMENT',
|
||||
txt: 'NOTE',
|
||||
md: 'NOTE',
|
||||
xlsx: 'DATA',
|
||||
xls: 'DATA',
|
||||
csv: 'DATA',
|
||||
pptx: 'PRESENTATION',
|
||||
ppt: 'PRESENTATION',
|
||||
};
|
||||
|
||||
return typeMap[ext || ''] || 'OTHER';
|
||||
}
|
||||
|
||||
/**
|
||||
* 估算页数
|
||||
*/
|
||||
private estimatePageCount(content: string): number {
|
||||
// 假设每页约 2000 字符
|
||||
return Math.max(1, Math.ceil(content.length / 2000));
|
||||
}
|
||||
|
||||
/**
|
||||
* 删除文档及其分块
|
||||
*/
|
||||
async deleteDocument(documentId: string): Promise<boolean> {
|
||||
try {
|
||||
// Cascade 删除会自动删除关联的 chunks
|
||||
await this.prisma.ekbDocument.delete({
|
||||
where: { id: documentId },
|
||||
});
|
||||
|
||||
logger.info(`文档删除成功: ${documentId}`);
|
||||
return true;
|
||||
} catch (error) {
|
||||
logger.error('文档删除失败', { error, documentId });
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 获取文档处理状态
|
||||
*/
|
||||
async getDocumentStatus(documentId: string): Promise<{
|
||||
status: string;
|
||||
chunkCount: number;
|
||||
tokenCount: number;
|
||||
} | null> {
|
||||
try {
|
||||
const document = await this.prisma.ekbDocument.findUnique({
|
||||
where: { id: documentId },
|
||||
select: { status: true, tokenCount: true },
|
||||
});
|
||||
|
||||
if (!document) return null;
|
||||
|
||||
const chunkCount = await this.prisma.ekbChunk.count({
|
||||
where: { documentId },
|
||||
});
|
||||
|
||||
return {
|
||||
status: document.status,
|
||||
chunkCount,
|
||||
tokenCount: document.tokenCount || 0,
|
||||
};
|
||||
} catch (error) {
|
||||
logger.error('获取文档状态失败', { error, documentId });
|
||||
return null;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// ==================== 单例导出 ====================
|
||||
|
||||
let _documentIngestService: DocumentIngestService | null = null;
|
||||
|
||||
/**
|
||||
* 获取 DocumentIngestService 单例
|
||||
*/
|
||||
export function getDocumentIngestService(prisma: PrismaClient): DocumentIngestService {
|
||||
if (!_documentIngestService) {
|
||||
_documentIngestService = new DocumentIngestService(prisma);
|
||||
}
|
||||
return _documentIngestService;
|
||||
}
|
||||
|
||||
export default DocumentIngestService;
|
||||
|
||||
239
backend/src/common/rag/EmbeddingService.ts
Normal file
239
backend/src/common/rag/EmbeddingService.ts
Normal file
@@ -0,0 +1,239 @@
|
||||
/**
|
||||
* EmbeddingService - 文本向量化服务
|
||||
*
|
||||
* 使用阿里云 DashScope text-embedding-v4 模型
|
||||
* 通过 OpenAI 兼容接口调用
|
||||
*
|
||||
* @see https://help.aliyun.com/zh/model-studio/developer-reference/text-embedding-api
|
||||
*/
|
||||
|
||||
import OpenAI from 'openai';
|
||||
import { logger } from '../logging/index.js';
|
||||
|
||||
// ==================== 类型定义 ====================
|
||||
|
||||
export interface EmbeddingResult {
|
||||
embedding: number[];
|
||||
tokenCount: number;
|
||||
}
|
||||
|
||||
export interface BatchEmbeddingResult {
|
||||
embeddings: number[][];
|
||||
totalTokens: number;
|
||||
}
|
||||
|
||||
export interface EmbeddingConfig {
|
||||
apiKey?: string;
|
||||
baseUrl?: string;
|
||||
model?: string;
|
||||
dimensions?: number; // text-embedding-v4 支持 512/1024/2048，不传则使用模型默认值
|
||||
}
|
||||
|
||||
// ==================== 默认配置 ====================
|
||||
|
||||
/**
|
||||
* 环境变量说明（文本向量模型专用）：
|
||||
*
|
||||
* - DASHSCOPE_API_KEY: 阿里云百炼 API Key（必填，可与其他模型共用）
|
||||
*
|
||||
* - TEXT_EMBEDDING_BASE_URL: 文本向量 API 地址（可选）
|
||||
* - 北京地域（默认）: https://dashscope.aliyuncs.com/compatible-mode/v1
|
||||
* - 新加坡地域: https://dashscope-intl.aliyuncs.com/compatible-mode/v1
|
||||
*
|
||||
* - TEXT_EMBEDDING_MODEL: 向量模型名称（可选，默认 text-embedding-v4）
|
||||
* - text-embedding-v4: 最新版，推荐
|
||||
* - text-embedding-v3: 旧版
|
||||
*
|
||||
* - TEXT_EMBEDDING_DIMENSIONS: 向量维度（可选，默认 1024）
|
||||
* - text-embedding-v4 支持: 512, 1024, 2048
|
||||
*/
|
||||
|
||||
// 使用函数延迟读取环境变量，确保 dotenv 已加载
|
||||
function getDefaultConfig() {
|
||||
return {
|
||||
apiKey: process.env.DASHSCOPE_API_KEY || '',
|
||||
baseUrl: process.env.TEXT_EMBEDDING_BASE_URL || 'https://dashscope.aliyuncs.com/compatible-mode/v1',
|
||||
model: process.env.TEXT_EMBEDDING_MODEL || 'text-embedding-v4',
|
||||
dimensions: process.env.TEXT_EMBEDDING_DIMENSIONS
|
||||
? parseInt(process.env.TEXT_EMBEDDING_DIMENSIONS, 10)
|
||||
: 1024,
|
||||
};
|
||||
}
|
||||
|
||||
// ==================== EmbeddingService ====================
|
||||
|
||||
export class EmbeddingService {
|
||||
private client: OpenAI;
|
||||
private model: string;
|
||||
private dimensions?: number;
|
||||
|
||||
constructor(config: EmbeddingConfig = {}) {
|
||||
const finalConfig = { ...getDefaultConfig(), ...config };
|
||||
|
||||
if (!finalConfig.apiKey) {
|
||||
throw new Error('DASHSCOPE_API_KEY 未配置，请在环境变量中设置');
|
||||
}
|
||||
|
||||
this.client = new OpenAI({
|
||||
apiKey: finalConfig.apiKey,
|
||||
baseURL: finalConfig.baseUrl,
|
||||
});
|
||||
|
||||
this.model = finalConfig.model;
|
||||
this.dimensions = finalConfig.dimensions;
|
||||
|
||||
logger.info(`EmbeddingService 初始化完成: model=${this.model}, dimensions=${this.dimensions}`);
|
||||
}
|
||||
|
||||
/**
|
||||
* 单文本向量化
|
||||
*/
|
||||
async embed(text: string): Promise<EmbeddingResult> {
|
||||
try {
|
||||
// 构建请求参数（与官方示例一致）
|
||||
const params: OpenAI.EmbeddingCreateParams = {
|
||||
model: this.model,
|
||||
input: text,
|
||||
};
|
||||
|
||||
// dimensions 为可选参数，仅在配置时传递
|
||||
if (this.dimensions) {
|
||||
params.dimensions = this.dimensions;
|
||||
}
|
||||
|
||||
const response = await this.client.embeddings.create(params);
|
||||
|
||||
const embedding = response.data[0].embedding;
|
||||
const tokenCount = response.usage?.total_tokens || 0;
|
||||
|
||||
logger.debug(`文本向量化完成: ${text.substring(0, 50)}... tokens=${tokenCount}`);
|
||||
|
||||
return {
|
||||
embedding,
|
||||
tokenCount,
|
||||
};
|
||||
} catch (error) {
|
||||
logger.error('文本向量化失败', { error, text: text.substring(0, 100) });
|
||||
throw new Error(`向量化失败: ${error instanceof Error ? error.message : String(error)}`);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 批量文本向量化
|
||||
*
|
||||
* 注意：DashScope 单次请求最多支持 25 条文本
|
||||
*/
|
||||
async embedBatch(texts: string[]): Promise<BatchEmbeddingResult> {
|
||||
if (texts.length === 0) {
|
||||
return { embeddings: [], totalTokens: 0 };
|
||||
}
|
||||
|
||||
// DashScope 限制：单次最多 10 条
|
||||
const BATCH_SIZE = 10;
|
||||
const allEmbeddings: number[][] = [];
|
||||
let totalTokens = 0;
|
||||
|
||||
for (let i = 0; i < texts.length; i += BATCH_SIZE) {
|
||||
const batch = texts.slice(i, i + BATCH_SIZE);
|
||||
|
||||
try {
|
||||
// 构建请求参数（与官方示例一致）
|
||||
const params: OpenAI.EmbeddingCreateParams = {
|
||||
model: this.model,
|
||||
input: batch,
|
||||
};
|
||||
|
||||
if (this.dimensions) {
|
||||
params.dimensions = this.dimensions;
|
||||
}
|
||||
|
||||
const response = await this.client.embeddings.create(params);
|
||||
|
||||
// 按原始顺序排列
|
||||
const sortedData = response.data.sort((a, b) => a.index - b.index);
|
||||
allEmbeddings.push(...sortedData.map(d => d.embedding));
|
||||
totalTokens += response.usage?.total_tokens || 0;
|
||||
|
||||
logger.debug(`批量向量化进度: ${Math.min(i + BATCH_SIZE, texts.length)}/${texts.length}`);
|
||||
} catch (error) {
|
||||
logger.error(`批量向量化失败 (batch ${i}-${i + batch.length})`, { error });
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
|
||||
logger.info(`批量向量化完成: ${texts.length} 条文本, ${totalTokens} tokens`);
|
||||
|
||||
return {
|
||||
embeddings: allEmbeddings,
|
||||
totalTokens,
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* 计算两个向量的余弦相似度
|
||||
*/
|
||||
static cosineSimilarity(a: number[], b: number[]): number {
|
||||
if (a.length !== b.length) {
|
||||
throw new Error('向量维度不匹配');
|
||||
}
|
||||
|
||||
let dotProduct = 0;
|
||||
let normA = 0;
|
||||
let normB = 0;
|
||||
|
||||
for (let i = 0; i < a.length; i++) {
|
||||
dotProduct += a[i] * b[i];
|
||||
normA += a[i] * a[i];
|
||||
normB += b[i] * b[i];
|
||||
}
|
||||
|
||||
return dotProduct / (Math.sqrt(normA) * Math.sqrt(normB));
|
||||
}
|
||||
|
||||
/**
|
||||
* 获取当前配置信息
|
||||
*/
|
||||
getConfig(): { model: string; dimensions?: number } {
|
||||
return {
|
||||
model: this.model,
|
||||
dimensions: this.dimensions,
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
// ==================== 单例导出 ====================
|
||||
|
||||
let _embeddingService: EmbeddingService | null = null;
|
||||
|
||||
/**
|
||||
* 获取 EmbeddingService 单例
|
||||
*
|
||||
* 首次调用时初始化，后续调用返回同一实例
|
||||
*/
|
||||
export function getEmbeddingService(config?: EmbeddingConfig): EmbeddingService {
|
||||
if (!_embeddingService) {
|
||||
_embeddingService = new EmbeddingService(config);
|
||||
}
|
||||
return _embeddingService;
|
||||
}
|
||||
|
||||
/**
|
||||
* 快捷方法：单文本向量化
|
||||
*/
|
||||
export async function embed(text: string): Promise<number[]> {
|
||||
const service = getEmbeddingService();
|
||||
const result = await service.embed(text);
|
||||
return result.embedding;
|
||||
}
|
||||
|
||||
/**
|
||||
* 快捷方法：批量文本向量化
|
||||
*/
|
||||
export async function embedBatch(texts: string[]): Promise<number[][]> {
|
||||
const service = getEmbeddingService();
|
||||
const result = await service.embedBatch(texts);
|
||||
return result.embeddings;
|
||||
}
|
||||
|
||||
export default EmbeddingService;
|
||||
|
||||
155
backend/src/common/rag/QueryRewriter.ts
Normal file
155
backend/src/common/rag/QueryRewriter.ts
Normal file
@@ -0,0 +1,155 @@
|
||||
/**
|
||||
* QueryRewriter - 查询重写服务
|
||||
*
|
||||
* 功能：
|
||||
* - 检测中文查询
|
||||
* - 调用 DeepSeek V3 翻译为英文医学术语
|
||||
* - 生成同义扩展查询
|
||||
*
|
||||
* 用于跨语言检索优化
|
||||
*/
|
||||
|
||||
import { logger } from '../logging/index.js';
|
||||
import { LLMFactory } from '../llm/adapters/LLMFactory.js';
|
||||
import type { ILLMAdapter } from '../llm/adapters/types.js';
|
||||
|
||||
// ==================== 类型定义 ====================
|
||||
|
||||
export interface RewriteResult {
|
||||
original: string; // 原始查询
|
||||
rewritten: string[]; // 重写后的查询列表
|
||||
isChinese: boolean; // 是否为中文查询
|
||||
cost: number; // 成本（元）
|
||||
duration: number; // 耗时（毫秒）
|
||||
}
|
||||
|
||||
// ==================== QueryRewriter ====================
|
||||
|
||||
export class QueryRewriter {
|
||||
private llmAdapter: ILLMAdapter;
|
||||
|
||||
constructor(llmAdapter?: ILLMAdapter) {
|
||||
// 如果未传入，使用默认的 DeepSeek V3
|
||||
this.llmAdapter = llmAdapter || LLMFactory.getAdapter('deepseek-v3');
|
||||
logger.info('QueryRewriter 初始化完成 (使用 DeepSeek V3)');
|
||||
}
|
||||
|
||||
/**
|
||||
* 重写查询（如果是中文）
|
||||
*/
|
||||
async rewrite(query: string): Promise<RewriteResult> {
|
||||
const startTime = Date.now();
|
||||
|
||||
// 1. 检测是否包含中文
|
||||
const isChinese = this.containsChinese(query);
|
||||
|
||||
if (!isChinese) {
|
||||
// 非中文直接返回
|
||||
return {
|
||||
original: query,
|
||||
rewritten: [query],
|
||||
isChinese: false,
|
||||
cost: 0,
|
||||
duration: Date.now() - startTime,
|
||||
};
|
||||
}
|
||||
|
||||
// 2. 调用 LLM 重写查询
|
||||
try {
|
||||
const prompt = `你是医学检索专家。将以下中文查询翻译为精准的英文医学术语，并提供1-2个同义扩展查询。
|
||||
只返回JSON数组格式，不要其他内容。
|
||||
|
||||
示例输入：帕博利珠单抗治疗肺癌的效果
|
||||
示例输出：["Pembrolizumab efficacy in lung cancer", "Keytruda treatment for NSCLC"]
|
||||
|
||||
现在请处理：${query}`;
|
||||
|
||||
const response = await this.llmAdapter.chat(
|
||||
[{ role: 'user', content: prompt }],
|
||||
{
|
||||
temperature: 0.3, // 低温度，更确定性
|
||||
maxTokens: 100, // 短输出
|
||||
}
|
||||
);
|
||||
|
||||
const content = response.content.trim();
|
||||
|
||||
// 3. 解析 JSON 数组
|
||||
const rewritten = this.parseRewrittenQueries(content, query);
|
||||
|
||||
// 4. 计算成本（DeepSeek V3: 输入 ¥0.5/百万，输出 ¥2/百万）
|
||||
const inputTokens = response.usage?.promptTokens || 50;
|
||||
const outputTokens = response.usage?.completionTokens || 30;
|
||||
const cost = (inputTokens * 0.5 + outputTokens * 2) / 1_000_000;
|
||||
|
||||
const duration = Date.now() - startTime;
|
||||
|
||||
logger.info(`查询重写完成: "${query}" → ${rewritten.length}条`, {
|
||||
original: query,
|
||||
rewritten,
|
||||
cost: `¥${cost.toFixed(6)}`,
|
||||
duration: `${duration}ms`,
|
||||
});
|
||||
|
||||
return {
|
||||
original: query,
|
||||
rewritten,
|
||||
isChinese: true,
|
||||
cost,
|
||||
duration,
|
||||
};
|
||||
|
||||
} catch (error) {
|
||||
logger.error('查询重写失败，返回原查询', { error, query });
|
||||
|
||||
// 降级：返回原查询
|
||||
return {
|
||||
original: query,
|
||||
rewritten: [query],
|
||||
isChinese: true,
|
||||
cost: 0,
|
||||
duration: Date.now() - startTime,
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 检测是否包含中文
|
||||
*/
|
||||
private containsChinese(text: string): boolean {
|
||||
return /[\u4e00-\u9fa5]/.test(text);
|
||||
}
|
||||
|
||||
/**
|
||||
* 解析 LLM 返回的查询列表
|
||||
*/
|
||||
private parseRewrittenQueries(content: string, fallback: string): string[] {
|
||||
try {
|
||||
// 尝试直接解析 JSON
|
||||
const parsed = JSON.parse(content);
|
||||
if (Array.isArray(parsed) && parsed.length > 0) {
|
||||
return parsed.filter(q => typeof q === 'string' && q.length > 0);
|
||||
}
|
||||
} catch {
|
||||
// JSON 解析失败，尝试提取
|
||||
const match = content.match(/\[([^\]]+)\]/);
|
||||
if (match) {
|
||||
try {
|
||||
const parsed = JSON.parse(match[0]);
|
||||
if (Array.isArray(parsed)) {
|
||||
return parsed.filter(q => typeof q === 'string' && q.length > 0);
|
||||
}
|
||||
} catch {}
|
||||
}
|
||||
}
|
||||
|
||||
// 都失败了，返回原查询
|
||||
logger.warn('LLM 返回格式异常，使用原查询', { content, fallback });
|
||||
return [fallback];
|
||||
}
|
||||
}
|
||||
|
||||
// ==================== 导出 ====================
|
||||
|
||||
export default QueryRewriter;
|
||||
|
||||
210
backend/src/common/rag/RerankService.ts
Normal file
210
backend/src/common/rag/RerankService.ts
Normal file
@@ -0,0 +1,210 @@
|
||||
/**
|
||||
* RerankService - 重排序服务
|
||||
*
|
||||
* 使用阿里云 qwen3-rerank 模型
|
||||
* 通过 OpenAI 兼容接口调用
|
||||
*
|
||||
* @see https://help.aliyun.com/zh/model-studio/text-rerank-api
|
||||
*/
|
||||
|
||||
import { logger } from '../logging/index.js';
|
||||
|
||||
// ==================== 类型定义 ====================
|
||||
|
||||
export interface RerankDocument {
|
||||
text: string;
|
||||
index?: number; // 可选：原始索引
|
||||
metadata?: Record<string, unknown>;
|
||||
}
|
||||
|
||||
export interface RerankResult {
|
||||
text: string;
|
||||
index: number; // 原始索引
|
||||
relevanceScore: number; // 相关性分数 (0-1)
|
||||
metadata?: Record<string, unknown>;
|
||||
}
|
||||
|
||||
export interface RerankOptions {
|
||||
topN?: number; // 返回数量，默认 10
|
||||
instruct?: string; // 任务指令（可选）
|
||||
}
|
||||
|
||||
export interface RerankConfig {
|
||||
apiKey?: string;
|
||||
baseUrl?: string;
|
||||
model?: string;
|
||||
}
|
||||
|
||||
// ==================== 默认配置 ====================
|
||||
|
||||
/**
|
||||
* 环境变量说明（Rerank 模型专用）：
|
||||
*
|
||||
* - DASHSCOPE_API_KEY: 阿里云百炼 API Key（必填，可与其他模型共用）
|
||||
*
|
||||
* - RERANK_BASE_URL: Rerank API 地址（可选）
|
||||
* - 默认: https://dashscope.aliyuncs.com/compatible-api/v1
|
||||
*
|
||||
* - RERANK_MODEL: Rerank 模型名称（可选，默认 qwen3-rerank）
|
||||
*/
|
||||
function getDefaultConfig() {
|
||||
return {
|
||||
apiKey: process.env.DASHSCOPE_API_KEY || '',
|
||||
baseUrl: process.env.RERANK_BASE_URL || 'https://dashscope.aliyuncs.com/compatible-api/v1',
|
||||
model: process.env.RERANK_MODEL || 'qwen3-rerank',
|
||||
};
|
||||
}
|
||||
|
||||
// ==================== RerankService ====================
|
||||
|
||||
export class RerankService {
|
||||
private apiKey: string;
|
||||
private baseUrl: string;
|
||||
private model: string;
|
||||
|
||||
constructor(config: RerankConfig = {}) {
|
||||
const finalConfig = { ...getDefaultConfig(), ...config };
|
||||
|
||||
if (!finalConfig.apiKey) {
|
||||
throw new Error('DASHSCOPE_API_KEY 未配置，请在环境变量中设置');
|
||||
}
|
||||
|
||||
this.apiKey = finalConfig.apiKey;
|
||||
this.baseUrl = finalConfig.baseUrl;
|
||||
this.model = finalConfig.model;
|
||||
|
||||
logger.info(`RerankService 初始化完成: model=${this.model}`);
|
||||
}
|
||||
|
||||
/**
|
||||
* 重排序文档
|
||||
*
|
||||
* 限制：
|
||||
* - 单个 Query/Document 最大 4000 tokens
|
||||
* - 最多 500 个 documents
|
||||
* - 总 tokens 不超过 30000
|
||||
*/
|
||||
async rerank(
|
||||
query: string,
|
||||
documents: RerankDocument[],
|
||||
options: RerankOptions = {}
|
||||
): Promise<RerankResult[]> {
|
||||
if (documents.length === 0) {
|
||||
return [];
|
||||
}
|
||||
|
||||
const { topN = 10, instruct } = options;
|
||||
|
||||
// 限制 documents 数量
|
||||
const maxDocs = Math.min(documents.length, 500);
|
||||
const limitedDocs = documents.slice(0, maxDocs);
|
||||
|
||||
try {
|
||||
const requestBody = {
|
||||
model: this.model,
|
||||
query,
|
||||
documents: limitedDocs.map(doc => doc.text),
|
||||
top_n: Math.min(topN, limitedDocs.length),
|
||||
...(instruct && { instruct }),
|
||||
};
|
||||
|
||||
logger.debug(`Rerank 请求: query="${query.substring(0, 30)}...", docs=${limitedDocs.length}, topN=${topN}`);
|
||||
|
||||
// 调试日志
|
||||
logger.debug(`Rerank API URL: ${this.baseUrl}/reranks`);
|
||||
logger.debug(`Rerank 请求体: ${JSON.stringify(requestBody).substring(0, 200)}...`);
|
||||
|
||||
const response = await fetch(`${this.baseUrl}/reranks`, {
|
||||
method: 'POST',
|
||||
headers: {
|
||||
'Authorization': `Bearer ${this.apiKey}`,
|
||||
'Content-Type': 'application/json',
|
||||
},
|
||||
body: JSON.stringify(requestBody),
|
||||
});
|
||||
|
||||
const responseText = await response.text();
|
||||
logger.debug(`Rerank 响应状态: ${response.status}`);
|
||||
logger.debug(`Rerank 响应内容: ${responseText.substring(0, 500)}...`);
|
||||
|
||||
if (!response.ok) {
|
||||
throw new Error(`Rerank API 返回错误: ${response.status} - ${responseText}`);
|
||||
}
|
||||
|
||||
const result = JSON.parse(responseText) as {
|
||||
object: string;
|
||||
results: Array<{
|
||||
index: number;
|
||||
relevance_score: number;
|
||||
}>;
|
||||
model: string;
|
||||
usage: { total_tokens: number };
|
||||
id: string;
|
||||
};
|
||||
|
||||
const totalTokens = result.usage?.total_tokens || 0;
|
||||
const cost = (totalTokens * 0.8) / 1_000_000; // ¥0.8/百万token
|
||||
|
||||
logger.info(`Rerank 完成: 返回 ${result.results.length} 条, tokens=${totalTokens}, cost=¥${cost.toFixed(6)}`);
|
||||
|
||||
// 映射回原始 metadata
|
||||
return result.results.map(r => ({
|
||||
text: limitedDocs[r.index].text,
|
||||
index: r.index,
|
||||
relevanceScore: r.relevance_score,
|
||||
metadata: limitedDocs[r.index]?.metadata,
|
||||
}));
|
||||
|
||||
} catch (error) {
|
||||
const errorMessage = error instanceof Error ? error.message : String(error);
|
||||
const errorDetails = error instanceof Error ? error.stack : JSON.stringify(error);
|
||||
|
||||
logger.error('Rerank 失败', {
|
||||
error: errorMessage,
|
||||
details: errorDetails,
|
||||
query: query.substring(0, 100),
|
||||
docCount: limitedDocs.length,
|
||||
});
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 获取当前配置
|
||||
*/
|
||||
getConfig(): { model: string; baseUrl: string } {
|
||||
return {
|
||||
model: this.model,
|
||||
baseUrl: this.baseUrl,
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
// ==================== 单例导出 ====================
|
||||
|
||||
let _rerankService: RerankService | null = null;
|
||||
|
||||
/**
|
||||
* 获取 RerankService 单例
|
||||
*/
|
||||
export function getRerankService(config?: RerankConfig): RerankService {
|
||||
if (!_rerankService) {
|
||||
_rerankService = new RerankService(config);
|
||||
}
|
||||
return _rerankService;
|
||||
}
|
||||
|
||||
/**
|
||||
* 快捷方法：重排序
|
||||
*/
|
||||
export async function rerank(
|
||||
query: string,
|
||||
documents: RerankDocument[],
|
||||
options?: RerankOptions
|
||||
): Promise<RerankResult[]> {
|
||||
const service = getRerankService();
|
||||
return service.rerank(query, documents, options);
|
||||
}
|
||||
|
||||
export default RerankService;
|
||||
|
||||
448
backend/src/common/rag/VectorSearchService.ts
Normal file
448
backend/src/common/rag/VectorSearchService.ts
Normal file
@@ -0,0 +1,448 @@
|
||||
/**
|
||||
* VectorSearchService - 向量检索服务
|
||||
*
|
||||
* 基于 pgvector 实现语义检索
|
||||
* 支持：
|
||||
* - 纯向量检索（余弦相似度）
|
||||
* - 混合检索（向量 + 关键词，RRF 融合）
|
||||
* - Rerank 重排序
|
||||
*/
|
||||
|
||||
import { PrismaClient, Prisma } from '@prisma/client';
|
||||
import { logger } from '../logging/index.js';
|
||||
import { getEmbeddingService } from './EmbeddingService.js';
|
||||
import { getRerankService } from './RerankService.js';
|
||||
|
||||
// ==================== 类型定义 ====================
|
||||
|
||||
export interface SearchResult {
|
||||
chunkId: string;
|
||||
documentId: string;
|
||||
content: string;
|
||||
score: number; // 相似度分数 (0-1)
|
||||
metadata?: Record<string, unknown>;
|
||||
}
|
||||
|
||||
export interface SearchOptions {
|
||||
topK?: number; // 返回数量，默认 10
|
||||
minScore?: number; // 最低分数阈值，默认 0.5
|
||||
filter?: SearchFilter; // 过滤条件
|
||||
}
|
||||
|
||||
export interface SearchFilter {
|
||||
kbId?: string; // 知识库 ID
|
||||
documentIds?: string[]; // 文档 ID 列表
|
||||
contentType?: string; // 内容类型
|
||||
tags?: string[]; // 标签（任一匹配）
|
||||
}
|
||||
|
||||
export interface HybridSearchOptions extends SearchOptions {
|
||||
vectorWeight?: number; // 向量检索权重，默认 0.7
|
||||
keywordWeight?: number; // 关键词检索权重，默认 0.3
|
||||
}
|
||||
|
||||
export interface RerankOptions {
|
||||
model?: string; // Rerank 模型
|
||||
topK?: number; // 重排后返回数量
|
||||
}
|
||||
|
||||
// ==================== VectorSearchService ====================
|
||||
|
||||
export class VectorSearchService {
|
||||
private prisma: PrismaClient;
|
||||
|
||||
constructor(prisma: PrismaClient) {
|
||||
this.prisma = prisma;
|
||||
logger.info('VectorSearchService 初始化完成');
|
||||
}
|
||||
|
||||
/**
|
||||
* 向量语义检索（单查询）
|
||||
*/
|
||||
async vectorSearch(
|
||||
query: string,
|
||||
options: SearchOptions = {}
|
||||
): Promise<SearchResult[]> {
|
||||
return this.searchWithQueries([query], options);
|
||||
}
|
||||
|
||||
/**
|
||||
* 多查询向量检索（引擎核心方法）
|
||||
*
|
||||
* 接收业务层生成的多个查询词，并行检索后 RRF 融合
|
||||
*
|
||||
* @param queries 查询词列表（由业务层 DeepSeek 生成）
|
||||
* @param options 检索选项
|
||||
*/
|
||||
async searchWithQueries(
|
||||
queries: string[],
|
||||
options: SearchOptions = {}
|
||||
): Promise<SearchResult[]> {
|
||||
const { topK = 10, minScore = 0.5, filter } = options;
|
||||
|
||||
if (queries.length === 0) {
|
||||
return [];
|
||||
}
|
||||
|
||||
try {
|
||||
// 单查询：直接检索
|
||||
if (queries.length === 1) {
|
||||
return this.vectorSearchSingle(queries[0], { topK, minScore, filter });
|
||||
}
|
||||
|
||||
// 多查询：并行检索 + RRF 融合
|
||||
const allResults = await Promise.all(
|
||||
queries.map(q => this.vectorSearchSingle(q, { topK: topK * 2, minScore, filter }))
|
||||
);
|
||||
|
||||
const fused = this.fuseMultiQueryResults(allResults, topK);
|
||||
|
||||
logger.info(`多查询检索完成: ${queries.length}条查询 → ${fused.length}条结果`);
|
||||
|
||||
return fused;
|
||||
|
||||
} catch (error) {
|
||||
logger.error('向量检索失败', { error, queries });
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 单查询向量检索（内部方法）
|
||||
*/
|
||||
private async vectorSearchSingle(
|
||||
query: string,
|
||||
options: { topK: number; minScore: number; filter?: SearchFilter }
|
||||
): Promise<SearchResult[]> {
|
||||
const { topK, minScore, filter } = options;
|
||||
|
||||
try {
|
||||
// 1. 将查询文本向量化
|
||||
const embeddingService = getEmbeddingService();
|
||||
const { embedding } = await embeddingService.embed(query);
|
||||
|
||||
// 2. 构建 SQL 查询（使用 pgvector 的余弦距离）
|
||||
const vectorStr = `[${embedding.join(',')}]`;
|
||||
|
||||
// 构建过滤条件（直接嵌入值，用于 $queryRawUnsafe）
|
||||
const whereConditions: string[] = [];
|
||||
|
||||
if (filter?.kbId) {
|
||||
// 转义单引号防止 SQL 注入
|
||||
const safeKbId = filter.kbId.replace(/'/g, "''");
|
||||
whereConditions.push(`d."kb_id" = '${safeKbId}'`);
|
||||
}
|
||||
|
||||
if (filter?.documentIds && filter.documentIds.length > 0) {
|
||||
const safeIds = filter.documentIds.map(id => `'${id.replace(/'/g, "''")}'`).join(',');
|
||||
whereConditions.push(`c."document_id" IN (${safeIds})`);
|
||||
}
|
||||
|
||||
if (filter?.contentType) {
|
||||
const safeContentType = filter.contentType.replace(/'/g, "''");
|
||||
whereConditions.push(`d."content_type" = '${safeContentType}'`);
|
||||
}
|
||||
|
||||
const whereClause = whereConditions.length > 0
|
||||
? `WHERE ${whereConditions.join(' AND ')}`
|
||||
: '';
|
||||
|
||||
// 3. 执行向量检索
|
||||
// 注意：Prisma 将表名转换为小写下划线格式
|
||||
// 使用 $queryRawUnsafe 避免参数类型推断问题
|
||||
const sql = `
|
||||
SELECT
|
||||
c.id as "chunkId",
|
||||
c.document_id as "documentId",
|
||||
c.content,
|
||||
1 - (c.embedding <=> '${vectorStr}'::vector) as score,
|
||||
c.metadata
|
||||
FROM "ekb_schema"."ekb_chunk" c
|
||||
JOIN "ekb_schema"."ekb_document" d ON c.document_id = d.id
|
||||
${whereClause}
|
||||
ORDER BY c.embedding <=> '${vectorStr}'::vector
|
||||
LIMIT ${topK}
|
||||
`;
|
||||
|
||||
const results = await this.prisma.$queryRawUnsafe<SearchResult[]>(sql);
|
||||
|
||||
// 4. 过滤低分结果
|
||||
const filtered = results.filter(r => r.score >= minScore);
|
||||
|
||||
logger.info(`向量检索完成: query="${query.substring(0, 30)}...", 返回 ${filtered.length} 条`);
|
||||
|
||||
return filtered;
|
||||
} catch (error) {
|
||||
logger.error('向量检索失败', { error, query: query.substring(0, 100) });
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 关键词检索（基于 PostgreSQL 全文搜索）
|
||||
*
|
||||
* 注意：完整的 pg_bigm 支持需要安装扩展
|
||||
* MVP 阶段使用 ILIKE 模糊匹配
|
||||
*/
|
||||
async keywordSearch(
|
||||
query: string,
|
||||
options: SearchOptions = {}
|
||||
): Promise<SearchResult[]> {
|
||||
const { topK = 10, filter } = options;
|
||||
|
||||
try {
|
||||
// 构建过滤条件
|
||||
const whereConditions: Prisma.EkbChunkWhereInput[] = [
|
||||
{ content: { contains: query, mode: 'insensitive' } }
|
||||
];
|
||||
|
||||
if (filter?.kbId) {
|
||||
whereConditions.push({ document: { kbId: filter.kbId } });
|
||||
}
|
||||
|
||||
if (filter?.documentIds && filter.documentIds.length > 0) {
|
||||
whereConditions.push({ documentId: { in: filter.documentIds } });
|
||||
}
|
||||
|
||||
const chunks = await this.prisma.ekbChunk.findMany({
|
||||
where: { AND: whereConditions },
|
||||
take: topK,
|
||||
select: {
|
||||
id: true,
|
||||
documentId: true,
|
||||
content: true,
|
||||
metadata: true,
|
||||
},
|
||||
});
|
||||
|
||||
// 简单的关键词匹配分数（基于出现次数）
|
||||
const results: SearchResult[] = chunks.map(chunk => {
|
||||
const occurrences = (chunk.content.match(new RegExp(query, 'gi')) || []).length;
|
||||
const score = Math.min(1, occurrences * 0.2 + 0.5); // 简单评分
|
||||
return {
|
||||
chunkId: chunk.id,
|
||||
documentId: chunk.documentId,
|
||||
content: chunk.content,
|
||||
score,
|
||||
metadata: chunk.metadata as Record<string, unknown> | undefined,
|
||||
};
|
||||
});
|
||||
|
||||
logger.info(`关键词检索完成: query="${query}", 返回 ${results.length} 条`);
|
||||
|
||||
return results.sort((a, b) => b.score - a.score);
|
||||
} catch (error) {
|
||||
logger.error('关键词检索失败', { error, query });
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 混合检索（向量 + 关键词，RRF 融合）
|
||||
*
|
||||
* 注意：如果 query 为中文但文档为英文，业务层应先调用 DeepSeek 翻译
|
||||
*/
|
||||
async hybridSearch(
|
||||
query: string,
|
||||
options: HybridSearchOptions = {}
|
||||
): Promise<SearchResult[]> {
|
||||
const {
|
||||
topK = 10,
|
||||
vectorWeight = 0.7,
|
||||
keywordWeight = 0.3,
|
||||
...baseOptions
|
||||
} = options;
|
||||
|
||||
try {
|
||||
// 并行执行两种检索
|
||||
const [vectorResults, keywordResults] = await Promise.all([
|
||||
this.vectorSearch(query, { ...baseOptions, topK: topK * 2 }),
|
||||
this.keywordSearch(query, { ...baseOptions, topK: topK * 2 }),
|
||||
]);
|
||||
|
||||
// RRF (Reciprocal Rank Fusion) 融合
|
||||
const rrfScores = new Map<string, { result: SearchResult; score: number }>();
|
||||
const k = 60; // RRF 常数
|
||||
|
||||
// 处理向量检索结果
|
||||
vectorResults.forEach((result, rank) => {
|
||||
const rrfScore = vectorWeight / (k + rank + 1);
|
||||
const existing = rrfScores.get(result.chunkId);
|
||||
if (existing) {
|
||||
existing.score += rrfScore;
|
||||
} else {
|
||||
rrfScores.set(result.chunkId, { result, score: rrfScore });
|
||||
}
|
||||
});
|
||||
|
||||
// 处理关键词检索结果
|
||||
keywordResults.forEach((result, rank) => {
|
||||
const rrfScore = keywordWeight / (k + rank + 1);
|
||||
const existing = rrfScores.get(result.chunkId);
|
||||
if (existing) {
|
||||
existing.score += rrfScore;
|
||||
} else {
|
||||
rrfScores.set(result.chunkId, { result, score: rrfScore });
|
||||
}
|
||||
});
|
||||
|
||||
// 排序并返回
|
||||
const merged = Array.from(rrfScores.values())
|
||||
.sort((a, b) => b.score - a.score)
|
||||
.slice(0, topK)
|
||||
.map(({ result, score }) => ({
|
||||
...result,
|
||||
score: Math.min(1, score * 100), // 归一化
|
||||
}));
|
||||
|
||||
logger.info(`混合检索完成: query="${query.substring(0, 30)}...", 返回 ${merged.length} 条`);
|
||||
|
||||
return merged;
|
||||
} catch (error) {
|
||||
logger.error('混合检索失败', { error, query: query.substring(0, 100) });
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Rerank 重排序
|
||||
*
|
||||
* 使用阿里云 qwen3-rerank 模型
|
||||
*/
|
||||
async rerank(
|
||||
query: string,
|
||||
results: SearchResult[],
|
||||
options: RerankOptions = {}
|
||||
): Promise<SearchResult[]> {
|
||||
const { topK = results.length } = options;
|
||||
|
||||
if (results.length === 0) {
|
||||
return [];
|
||||
}
|
||||
|
||||
try {
|
||||
const rerankService = getRerankService();
|
||||
|
||||
// 转换为 Rerank 输入格式
|
||||
const documents = results.map((r, index) => ({
|
||||
text: r.content,
|
||||
index,
|
||||
metadata: r.metadata,
|
||||
}));
|
||||
|
||||
// 调用 Rerank API
|
||||
const reranked = await rerankService.rerank(query, documents, {
|
||||
topN: topK,
|
||||
instruct: 'Given a medical query, retrieve relevant passages that answer the query.',
|
||||
});
|
||||
|
||||
// 映射回 SearchResult 格式
|
||||
return reranked.map(r => {
|
||||
const original = results[r.index];
|
||||
return {
|
||||
...original,
|
||||
score: r.relevanceScore, // 用 Rerank 分数替换原分数
|
||||
};
|
||||
});
|
||||
|
||||
} catch (error) {
|
||||
logger.error('Rerank 失败，返回原始排序', { error });
|
||||
return results.slice(0, topK);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 获取文档完整内容（用于小文档全文检索策略）
|
||||
*/
|
||||
async getDocumentFullText(documentId: string): Promise<string | null> {
|
||||
try {
|
||||
const document = await this.prisma.ekbDocument.findUnique({
|
||||
where: { id: documentId },
|
||||
select: { extractedText: true },
|
||||
});
|
||||
|
||||
return document?.extractedText || null;
|
||||
} catch (error) {
|
||||
logger.error('获取文档全文失败', { error, documentId });
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 融合多个查询的检索结果（RRF）
|
||||
*/
|
||||
private fuseMultiQueryResults(
|
||||
allResults: SearchResult[][],
|
||||
topK: number
|
||||
): SearchResult[] {
|
||||
const k = 60; // RRF 常数
|
||||
const fusedScores = new Map<string, { result: SearchResult; score: number }>();
|
||||
|
||||
// 对每个查询的结果应用 RRF
|
||||
allResults.forEach((results, queryIndex) => {
|
||||
results.forEach((result, rank) => {
|
||||
const rrfScore = 1 / (k + rank + 1);
|
||||
const existing = fusedScores.get(result.chunkId);
|
||||
|
||||
if (existing) {
|
||||
existing.score += rrfScore;
|
||||
} else {
|
||||
fusedScores.set(result.chunkId, { result, score: rrfScore });
|
||||
}
|
||||
});
|
||||
});
|
||||
|
||||
// 排序并返回
|
||||
return Array.from(fusedScores.values())
|
||||
.sort((a, b) => b.score - a.score)
|
||||
.slice(0, topK)
|
||||
.map(({ result, score }) => ({
|
||||
...result,
|
||||
score: Math.min(1, score * 100), // 归一化
|
||||
}));
|
||||
}
|
||||
|
||||
/**
|
||||
* 获取知识库所有文档（用于判断检索策略）
|
||||
*/
|
||||
async getKnowledgeBaseStats(kbId: string): Promise<{
|
||||
documentCount: number;
|
||||
totalTokens: number;
|
||||
avgDocumentSize: number;
|
||||
}> {
|
||||
try {
|
||||
const stats = await this.prisma.ekbDocument.aggregate({
|
||||
where: { kbId },
|
||||
_count: { id: true },
|
||||
_sum: { tokenCount: true },
|
||||
_avg: { tokenCount: true },
|
||||
});
|
||||
|
||||
return {
|
||||
documentCount: stats._count.id,
|
||||
totalTokens: stats._sum.tokenCount || 0,
|
||||
avgDocumentSize: Math.round(stats._avg.tokenCount || 0),
|
||||
};
|
||||
} catch (error) {
|
||||
logger.error('获取知识库统计失败', { error, kbId });
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// ==================== 单例导出 ====================
|
||||
|
||||
let _vectorSearchService: VectorSearchService | null = null;
|
||||
|
||||
/**
|
||||
* 获取 VectorSearchService 单例
|
||||
*/
|
||||
export function getVectorSearchService(prisma: PrismaClient): VectorSearchService {
|
||||
if (!_vectorSearchService) {
|
||||
_vectorSearchService = new VectorSearchService(prisma);
|
||||
}
|
||||
return _vectorSearchService;
|
||||
}
|
||||
|
||||
export default VectorSearchService;
|
||||
|
||||
66
backend/src/common/rag/index.ts
Normal file
66
backend/src/common/rag/index.ts
Normal file
@@ -0,0 +1,66 @@
|
||||
/**
|
||||
* RAG 引擎 - 统一导出
|
||||
*
|
||||
* 基于 PostgreSQL + pgvector 的 RAG 实现
|
||||
* 替代原 Dify 外部服务
|
||||
*/
|
||||
|
||||
// ==================== 服务导出 ====================
|
||||
|
||||
export {
|
||||
EmbeddingService,
|
||||
getEmbeddingService,
|
||||
embed,
|
||||
embedBatch,
|
||||
type EmbeddingResult,
|
||||
type BatchEmbeddingResult,
|
||||
type EmbeddingConfig,
|
||||
} from './EmbeddingService.js';
|
||||
|
||||
export {
|
||||
ChunkService,
|
||||
getChunkService,
|
||||
chunkText,
|
||||
chunkMarkdown,
|
||||
type ChunkConfig,
|
||||
type TextChunk,
|
||||
type ChunkResult,
|
||||
} from './ChunkService.js';
|
||||
|
||||
export {
|
||||
VectorSearchService,
|
||||
getVectorSearchService,
|
||||
type SearchResult,
|
||||
type SearchOptions,
|
||||
type SearchFilter,
|
||||
type HybridSearchOptions,
|
||||
type RerankOptions,
|
||||
} from './VectorSearchService.js';
|
||||
|
||||
// QueryRewriter 独立导出（供业务层使用）
|
||||
export { default as QueryRewriter, type RewriteResult } from './QueryRewriter.js';
|
||||
|
||||
|
||||
export {
|
||||
RerankService,
|
||||
getRerankService,
|
||||
rerank,
|
||||
type RerankDocument,
|
||||
type RerankResult,
|
||||
type RerankOptions as RerankServiceOptions,
|
||||
type RerankConfig,
|
||||
} from './RerankService.js';
|
||||
|
||||
export {
|
||||
DocumentIngestService,
|
||||
getDocumentIngestService,
|
||||
type IngestOptions,
|
||||
type IngestResult,
|
||||
type DocumentInput,
|
||||
} from './DocumentIngestService.js';
|
||||
|
||||
// ==================== 旧版兼容（Dify）====================
|
||||
|
||||
export { DifyClient } from './DifyClient.js';
|
||||
export * from './types.js';
|
||||
|
||||
@@ -200,3 +200,6 @@ export function createOpenAIStreamAdapter(
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -206,3 +206,6 @@ export async function streamChat(
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -24,3 +24,6 @@ export { THINKING_TAGS } from './types';
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -99,3 +99,6 @@ export type SSEEventType =
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -85,3 +85,6 @@ export async function moduleRoutes(fastify: FastifyInstance) {
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -115,3 +115,6 @@ export interface PaginatedResponse<T> {
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -162,3 +162,6 @@ export const ROLE_DISPLAY_NAMES: Record<UserRole, string> = {
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -237,3 +237,6 @@ async function matchIntent(query: string): Promise<{
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -91,3 +91,6 @@ export async function uploadAttachment(
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -20,3 +20,6 @@ export { aiaRoutes };
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -360,6 +360,9 @@ runTests().catch((error) => {
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -301,6 +301,9 @@ runTest()
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -339,6 +339,9 @@ Content-Type: application/json
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -275,6 +275,9 @@ export const conflictDetectionService = new ConflictDetectionService();
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -225,6 +225,9 @@ curl -X POST http://localhost:3000/api/v1/dc/tool-c/test/execute \
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -279,6 +279,9 @@ export const streamAIController = new StreamAIController();
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -46,26 +46,69 @@ export class DataProcessService {
|
||||
* @param buffer - 文件Buffer
|
||||
* @returns 解析后的数据
|
||||
*/
|
||||
parseExcel(buffer: Buffer): ParsedExcelData {
|
||||
parseExcel(buffer: Buffer, fileName?: string): ParsedExcelData {
|
||||
try {
|
||||
logger.info('[DataProcessService] 开始解析Excel文件');
|
||||
logger.info('[DataProcessService] 开始解析文件');
|
||||
|
||||
// 1. 读取Excel文件（内存操作）
|
||||
const workbook = xlsx.read(buffer, { type: 'buffer' });
|
||||
// 1. 读取文件（内存操作）
|
||||
// ✅ 修复乱码问题：添加 codepage 支持（.xls 和 .csv 文件）
|
||||
const fileNameLower = fileName?.toLowerCase() ?? '';
|
||||
const isXls = fileNameLower.endsWith('.xls') && !fileNameLower.endsWith('.xlsx');
|
||||
const isCsv = fileNameLower.endsWith('.csv');
|
||||
const needCodepage = isXls || isCsv;
|
||||
|
||||
// 对于 CSV，移除 UTF-8 BOM
|
||||
let processedBuffer = buffer;
|
||||
if (isCsv && buffer[0] === 0xEF && buffer[1] === 0xBB && buffer[2] === 0xBF) {
|
||||
logger.info('[DataProcessService] 检测到 UTF-8 BOM，移除中...');
|
||||
processedBuffer = buffer.slice(3);
|
||||
}
|
||||
|
||||
const workbook = xlsx.read(processedBuffer, {
|
||||
type: 'buffer',
|
||||
codepage: needCodepage ? 936 : undefined, // .xls/.csv 文件使用 GBK 编码
|
||||
cellDates: true,
|
||||
});
|
||||
|
||||
// 2. 获取第一个工作表
|
||||
const sheetName = workbook.SheetNames[0];
|
||||
if (!sheetName) {
|
||||
throw new Error('Excel文件中没有工作表');
|
||||
throw new Error('文件中没有工作表');
|
||||
}
|
||||
|
||||
const sheet = workbook.Sheets[sheetName];
|
||||
|
||||
// 3. 转换为JSON格式
|
||||
const data = xlsx.utils.sheet_to_json(sheet);
|
||||
let data = xlsx.utils.sheet_to_json(sheet) as any[];
|
||||
|
||||
// 4. 清理列名中的特殊字符（BOM残留、空白字符）
|
||||
if (data.length > 0) {
|
||||
const originalColumns = Object.keys(data[0] || {});
|
||||
const columnMapping: Record<string, string> = {};
|
||||
let hasCleanedColumns = false;
|
||||
|
||||
originalColumns.forEach(col => {
|
||||
const cleanedCol = col.replace(/^\uFEFF/, '').trim();
|
||||
if (cleanedCol !== col) {
|
||||
columnMapping[col] = cleanedCol;
|
||||
hasCleanedColumns = true;
|
||||
}
|
||||
});
|
||||
|
||||
if (hasCleanedColumns) {
|
||||
data = data.map((row: any) => {
|
||||
const newRow: any = {};
|
||||
Object.keys(row).forEach(key => {
|
||||
const newKey = columnMapping[key] || key;
|
||||
newRow[newKey] = row[key];
|
||||
});
|
||||
return newRow;
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
if (data.length === 0) {
|
||||
throw new Error('Excel文件没有数据');
|
||||
throw new Error('文件没有数据');
|
||||
}
|
||||
|
||||
// 4. 提取元数据
|
||||
|
||||
@@ -208,20 +208,33 @@ export class SessionService {
|
||||
|
||||
// 3. ⚠️ Fallback：从原始文件重新解析（兼容旧数据或 clean data 不存在）
|
||||
logger.info(`[SessionService] 从原始文件解析（clean data不存在）: ${session.fileKey}`);
|
||||
const buffer = await storage.download(session.fileKey);
|
||||
let buffer = await storage.download(session.fileKey);
|
||||
|
||||
// ✅ 修复乱码问题：添加 codepage 支持（.xls 和 .csv 文件）
|
||||
const fileNameLower = session.fileName?.toLowerCase() ?? '';
|
||||
const isXls = fileNameLower.endsWith('.xls') && !fileNameLower.endsWith('.xlsx');
|
||||
const isCsv = fileNameLower.endsWith('.csv');
|
||||
const needCodepage = isXls || isCsv;
|
||||
|
||||
// 对于 CSV，移除 UTF-8 BOM
|
||||
if (isCsv && buffer[0] === 0xEF && buffer[1] === 0xBB && buffer[2] === 0xBF) {
|
||||
buffer = buffer.slice(3);
|
||||
}
|
||||
|
||||
const workbook = xlsx.read(buffer, {
|
||||
type: 'buffer',
|
||||
raw: true,
|
||||
cellText: false,
|
||||
cellDates: false,
|
||||
codepage: needCodepage ? 936 : undefined, // .xls/.csv 文件使用 GBK 编码
|
||||
cellDates: true,
|
||||
});
|
||||
const sheetName = workbook.SheetNames[0];
|
||||
const sheet = workbook.Sheets[sheetName];
|
||||
const rawData = xlsx.utils.sheet_to_json(sheet, {
|
||||
let rawData = xlsx.utils.sheet_to_json(sheet, {
|
||||
raw: false,
|
||||
defval: null,
|
||||
});
|
||||
|
||||
// 清理列名中的特殊字符
|
||||
rawData = this.cleanColumnNames(rawData);
|
||||
|
||||
// 智能清洗
|
||||
const data = this.intelligentCleanData(rawData);
|
||||
@@ -270,20 +283,33 @@ export class SessionService {
|
||||
|
||||
// 3. ⚠️ Fallback：从原始文件重新解析（兼容旧数据或 clean data 不存在）
|
||||
logger.info(`[SessionService] 从原始文件解析（clean data不存在）: ${session.fileKey}`);
|
||||
const buffer = await storage.download(session.fileKey);
|
||||
let bufferFull = await storage.download(session.fileKey);
|
||||
|
||||
const workbook = xlsx.read(buffer, {
|
||||
// ✅ 修复乱码问题：添加 codepage 支持（.xls 和 .csv 文件）
|
||||
const fileNameLowerFull = session.fileName?.toLowerCase() ?? '';
|
||||
const isXlsFull = fileNameLowerFull.endsWith('.xls') && !fileNameLowerFull.endsWith('.xlsx');
|
||||
const isCsvFull = fileNameLowerFull.endsWith('.csv');
|
||||
const needCodepageFull = isXlsFull || isCsvFull;
|
||||
|
||||
// 对于 CSV，移除 UTF-8 BOM
|
||||
if (isCsvFull && bufferFull[0] === 0xEF && bufferFull[1] === 0xBB && bufferFull[2] === 0xBF) {
|
||||
bufferFull = bufferFull.slice(3);
|
||||
}
|
||||
|
||||
const workbook = xlsx.read(bufferFull, {
|
||||
type: 'buffer',
|
||||
raw: true,
|
||||
cellText: false,
|
||||
cellDates: false,
|
||||
codepage: needCodepageFull ? 936 : undefined, // .xls/.csv 文件使用 GBK 编码
|
||||
cellDates: true,
|
||||
});
|
||||
const sheetName = workbook.SheetNames[0];
|
||||
const sheet = workbook.Sheets[sheetName];
|
||||
const rawData = xlsx.utils.sheet_to_json(sheet, {
|
||||
let rawData = xlsx.utils.sheet_to_json(sheet, {
|
||||
raw: false,
|
||||
defval: null,
|
||||
});
|
||||
|
||||
// 清理列名中的特殊字符
|
||||
rawData = this.cleanColumnNames(rawData);
|
||||
|
||||
// 智能清洗
|
||||
const data = this.intelligentCleanData(rawData);
|
||||
@@ -818,6 +844,46 @@ export class SessionService {
|
||||
});
|
||||
}
|
||||
|
||||
/**
|
||||
* 清理列名中的特殊字符（BOM、空白字符等）
|
||||
*
|
||||
* @param data - 原始数据数组
|
||||
* @returns 清理后的数据数组
|
||||
*/
|
||||
private cleanColumnNames(data: any[]): any[] {
|
||||
if (data.length === 0) {
|
||||
return data;
|
||||
}
|
||||
|
||||
const originalColumns = Object.keys(data[0] || {});
|
||||
const columnMapping: Record<string, string> = {};
|
||||
let hasCleanedColumns = false;
|
||||
|
||||
originalColumns.forEach(col => {
|
||||
// 清理 BOM 字符 (\uFEFF) 和首尾空白
|
||||
const cleanedCol = col.replace(/^\uFEFF/, '').trim();
|
||||
if (cleanedCol !== col) {
|
||||
columnMapping[col] = cleanedCol;
|
||||
hasCleanedColumns = true;
|
||||
logger.info(`[SessionService] 清理列名: "${col}" → "${cleanedCol}"`);
|
||||
}
|
||||
});
|
||||
|
||||
// 如果有列名需要清理，重新映射数据
|
||||
if (hasCleanedColumns) {
|
||||
return data.map((row: any) => {
|
||||
const newRow: any = {};
|
||||
Object.keys(row).forEach(key => {
|
||||
const newKey = columnMapping[key] || key;
|
||||
newRow[newKey] = row[key];
|
||||
});
|
||||
return newRow;
|
||||
});
|
||||
}
|
||||
|
||||
return data;
|
||||
}
|
||||
|
||||
/**
|
||||
* 检测列的数据类型
|
||||
*
|
||||
|
||||
@@ -68,31 +68,80 @@ export function registerParseExcelWorker() {
|
||||
});
|
||||
|
||||
// ========================================
|
||||
// 2. 解析 Excel
|
||||
// 2. 解析 Excel/CSV（修复中文编码问题）
|
||||
// ========================================
|
||||
logger.info('[parseExcelWorker] Parsing Excel...');
|
||||
logger.info('[parseExcelWorker] Parsing file...');
|
||||
let workbook: xlsx.WorkBook;
|
||||
const fileNameLower = fileName.toLowerCase();
|
||||
const isXls = fileNameLower.endsWith('.xls') && !fileNameLower.endsWith('.xlsx');
|
||||
const isCsv = fileNameLower.endsWith('.csv');
|
||||
|
||||
try {
|
||||
workbook = xlsx.read(buffer, {
|
||||
// ✅ 修复乱码问题：
|
||||
// - .xls 和 .csv 文件：添加 codepage: 936（支持 GBK/GB2312 编码）
|
||||
// - 中文 Windows 导出的 CSV 通常是 GBK 编码，不是 UTF-8
|
||||
// - .xlsx 文件：内部使用 UTF-8，不需要指定 codepage
|
||||
const needCodepage = isXls || isCsv;
|
||||
|
||||
// 对于 CSV 文件，先尝试检测是否是 UTF-8 BOM
|
||||
let processedBuffer = buffer;
|
||||
if (isCsv) {
|
||||
// 检测并移除 UTF-8 BOM (0xEF 0xBB 0xBF)
|
||||
if (buffer[0] === 0xEF && buffer[1] === 0xBB && buffer[2] === 0xBF) {
|
||||
logger.info('[parseExcelWorker] 检测到 UTF-8 BOM，移除中...');
|
||||
processedBuffer = buffer.slice(3);
|
||||
}
|
||||
}
|
||||
|
||||
workbook = xlsx.read(processedBuffer, {
|
||||
type: 'buffer',
|
||||
raw: true,
|
||||
cellText: false,
|
||||
cellDates: false,
|
||||
codepage: needCodepage ? 936 : undefined, // .xls/.csv 文件使用 GBK 编码
|
||||
cellDates: true, // 正确处理日期
|
||||
});
|
||||
} catch (error: any) {
|
||||
throw new Error(`Excel文件解析失败: ${error.message}`);
|
||||
throw new Error(`文件解析失败: ${error.message}`);
|
||||
}
|
||||
|
||||
const sheetName = workbook.SheetNames[0];
|
||||
if (!sheetName) {
|
||||
throw new Error('Excel文件中没有工作表');
|
||||
throw new Error('文件中没有工作表');
|
||||
}
|
||||
|
||||
const sheet = workbook.Sheets[sheetName];
|
||||
const rawData = xlsx.utils.sheet_to_json(sheet, {
|
||||
let rawData = xlsx.utils.sheet_to_json(sheet, {
|
||||
raw: false,
|
||||
defval: null,
|
||||
});
|
||||
|
||||
// ✅ 清理列名中的特殊字符（BOM残留、空白字符等）
|
||||
if (rawData.length > 0) {
|
||||
const originalColumns = Object.keys(rawData[0] || {});
|
||||
const columnMapping: Record<string, string> = {};
|
||||
let hasCleanedColumns = false;
|
||||
|
||||
originalColumns.forEach(col => {
|
||||
// 清理 BOM 字符 (\uFEFF) 和首尾空白
|
||||
const cleanedCol = col.replace(/^\uFEFF/, '').trim();
|
||||
if (cleanedCol !== col) {
|
||||
columnMapping[col] = cleanedCol;
|
||||
hasCleanedColumns = true;
|
||||
logger.info(`[parseExcelWorker] 清理列名: "${col}" → "${cleanedCol}"`);
|
||||
}
|
||||
});
|
||||
|
||||
// 如果有列名需要清理，重新映射数据
|
||||
if (hasCleanedColumns) {
|
||||
rawData = rawData.map((row: any) => {
|
||||
const newRow: any = {};
|
||||
Object.keys(row).forEach(key => {
|
||||
const newKey = columnMapping[key] || key;
|
||||
newRow[newKey] = row[key];
|
||||
});
|
||||
return newRow;
|
||||
});
|
||||
logger.info(`[parseExcelWorker] 已清理 ${Object.keys(columnMapping).length} 个列名`);
|
||||
}
|
||||
}
|
||||
|
||||
logger.info('[parseExcelWorker] Excel parsed', {
|
||||
rows: rawData.length,
|
||||
|
||||
@@ -188,6 +188,9 @@ logger.info('[SessionMemory] 会话记忆管理器已启动', {
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -122,6 +122,9 @@ checkTableStructure();
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -109,6 +109,9 @@ checkProjectConfig().catch(console.error);
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -91,6 +91,9 @@ main();
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -548,6 +548,9 @@ URL: https://iit.xunzhengyixue.com/api/v1/iit/patient-wechat/callback
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -183,6 +183,9 @@ console.log('');
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -500,6 +500,9 @@ export const patientWechatService = new PatientWechatService();
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -145,6 +145,9 @@ testDifyIntegration().catch(error => {
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -174,6 +174,9 @@ testIitDatabase()
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -160,6 +160,9 @@ if (hasError) {
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -186,6 +186,9 @@ async function testUrlVerification() {
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -267,6 +267,9 @@ main().catch((error) => {
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -151,6 +151,9 @@ Write-Host ""
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -244,6 +244,9 @@ export interface CachedProtocolRules {
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -58,6 +58,9 @@ export default async function healthRoutes(fastify: FastifyInstance) {
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
440
backend/src/modules/pkb/services/ragService.ts
Normal file
440
backend/src/modules/pkb/services/ragService.ts
Normal file
@@ -0,0 +1,440 @@
|
||||
/**
|
||||
* PKB RAG 服务 - 双轨模式
|
||||
*
|
||||
* 支持两种后端：
|
||||
* 1. pgvector（新）- 基于 PostgreSQL + pgvector 的本地 RAG
|
||||
* 2. Dify（旧）- 基于 Dify 外部服务
|
||||
*
|
||||
* 通过环境变量 PKB_RAG_BACKEND 控制：
|
||||
* - 'pgvector'（默认）：使用新的 pgvector 方案
|
||||
* - 'dify'：使用旧的 Dify 方案
|
||||
* - 'hybrid'：同时使用，结果合并
|
||||
*/
|
||||
|
||||
import { prisma } from '../../../config/database.js';
|
||||
import { logger } from '../../../common/logging/index.js';
|
||||
import { difyClient } from '../../../common/rag/DifyClient.js';
|
||||
import {
|
||||
getVectorSearchService,
|
||||
getDocumentIngestService,
|
||||
QueryRewriter,
|
||||
type SearchResult,
|
||||
type IngestResult,
|
||||
} from '../../../common/rag/index.js';
|
||||
|
||||
// ==================== 配置 ====================
|
||||
|
||||
type RagBackend = 'pgvector' | 'dify' | 'hybrid';
|
||||
|
||||
const RAG_BACKEND: RagBackend = (process.env.PKB_RAG_BACKEND as RagBackend) || 'pgvector';
|
||||
|
||||
logger.info(`PKB RAG 后端: ${RAG_BACKEND}`);
|
||||
|
||||
// ==================== 类型定义 ====================
|
||||
|
||||
export interface RagSearchOptions {
|
||||
topK?: number;
|
||||
minScore?: number;
|
||||
mode?: 'vector' | 'keyword' | 'hybrid';
|
||||
}
|
||||
|
||||
export interface RagSearchResult {
|
||||
content: string;
|
||||
score: number;
|
||||
documentId?: string;
|
||||
chunkId?: string;
|
||||
metadata?: Record<string, unknown>;
|
||||
source: 'pgvector' | 'dify';
|
||||
}
|
||||
|
||||
export interface RagIngestOptions {
|
||||
contentType?: string;
|
||||
tags?: string[];
|
||||
metadata?: Record<string, unknown>;
|
||||
generateSummary?: boolean;
|
||||
}
|
||||
|
||||
// ==================== 检索服务 ====================
|
||||
|
||||
/**
|
||||
* 检索知识库
|
||||
*/
|
||||
export async function searchKnowledgeBase(
|
||||
userId: string,
|
||||
kbId: string,
|
||||
query: string,
|
||||
options: RagSearchOptions = {}
|
||||
): Promise<RagSearchResult[]> {
|
||||
const { topK = 10, minScore = 0.5, mode = 'hybrid' } = options;
|
||||
|
||||
logger.info(`[RAG] 检索知识库: kbId=${kbId}, query="${query.substring(0, 30)}...", backend=${RAG_BACKEND}`);
|
||||
|
||||
// 验证权限
|
||||
const knowledgeBase = await prisma.knowledgeBase.findFirst({
|
||||
where: { id: kbId, userId },
|
||||
});
|
||||
|
||||
if (!knowledgeBase) {
|
||||
throw new Error('Knowledge base not found or access denied');
|
||||
}
|
||||
|
||||
// 根据后端选择执行检索
|
||||
if (RAG_BACKEND === 'pgvector') {
|
||||
return searchWithPgvector(kbId, query, { topK, minScore, mode });
|
||||
} else if (RAG_BACKEND === 'dify') {
|
||||
return searchWithDify(knowledgeBase.difyDatasetId, query, topK);
|
||||
} else {
|
||||
// hybrid: 两个后端都查，合并结果
|
||||
const [pgResults, difyResults] = await Promise.all([
|
||||
searchWithPgvector(kbId, query, { topK, minScore, mode }).catch(() => []),
|
||||
searchWithDify(knowledgeBase.difyDatasetId, query, topK).catch(() => []),
|
||||
]);
|
||||
return mergeSearchResults(pgResults, difyResults, topK);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 使用 pgvector 检索（业务层：负责查询理解）
|
||||
*/
|
||||
async function searchWithPgvector(
|
||||
kbId: string,
|
||||
query: string,
|
||||
options: RagSearchOptions
|
||||
): Promise<RagSearchResult[]> {
|
||||
const { topK = 10, minScore = 0.5, mode = 'hybrid' } = options;
|
||||
|
||||
// 查找对应的 EKB 知识库
|
||||
const searchService = getVectorSearchService(prisma);
|
||||
|
||||
// ==================== 业务层：查询理解（DeepSeek V3）====================
|
||||
|
||||
// 1. 生成检索查询词（中英双语）
|
||||
const queryRewriter = new QueryRewriter();
|
||||
const rewriteResult = await queryRewriter.rewrite(query);
|
||||
|
||||
let searchQueries: string[];
|
||||
if (rewriteResult.isChinese && rewriteResult.rewritten.length > 0) {
|
||||
// 中文查询：生成中英双语查询词
|
||||
searchQueries = [
|
||||
query, // 保留原中文（匹配中文文档）
|
||||
...rewriteResult.rewritten, // 添加英文（匹配英文文档）
|
||||
];
|
||||
|
||||
logger.info(`PKB 查询策略: 中英双语检索`, {
|
||||
original: query,
|
||||
queries: searchQueries,
|
||||
cost: `¥${rewriteResult.cost.toFixed(6)}`,
|
||||
});
|
||||
} else {
|
||||
// 英文查询：直接使用
|
||||
searchQueries = [query];
|
||||
}
|
||||
|
||||
// ==================== 引擎层：执行检索 ====================
|
||||
|
||||
let results: SearchResult[];
|
||||
if (mode === 'vector') {
|
||||
// 纯向量检索（支持多查询）
|
||||
results = await searchService.searchWithQueries(searchQueries, {
|
||||
topK,
|
||||
minScore,
|
||||
filter: { kbId }
|
||||
});
|
||||
} else if (mode === 'keyword') {
|
||||
// 纯关键词检索（使用第一个翻译结果）
|
||||
const keywordQuery = searchQueries[searchQueries.length - 1]; // 优先用英文
|
||||
results = await searchService.keywordSearch(keywordQuery, { topK, filter: { kbId } });
|
||||
} else {
|
||||
// 混合检索：向量 + 关键词
|
||||
// 对每个查询词都执行混合检索，然后融合
|
||||
const allResults = await Promise.all(
|
||||
searchQueries.map(q => searchService.hybridSearch(q, { topK: topK * 2, filter: { kbId } }))
|
||||
);
|
||||
|
||||
// RRF 融合多个查询的结果
|
||||
results = fuseMultiQueryResults(allResults, topK);
|
||||
}
|
||||
|
||||
return results.map(r => ({
|
||||
content: r.content,
|
||||
score: r.score,
|
||||
documentId: r.documentId,
|
||||
chunkId: r.chunkId,
|
||||
metadata: r.metadata,
|
||||
source: 'pgvector' as const,
|
||||
}));
|
||||
}
|
||||
|
||||
/**
|
||||
* 融合多个查询的结果（RRF）
|
||||
*/
|
||||
function fuseMultiQueryResults(
|
||||
allResults: SearchResult[][],
|
||||
topK: number
|
||||
): SearchResult[] {
|
||||
const k = 60;
|
||||
const fusedScores = new Map<string, { result: SearchResult; score: number }>();
|
||||
|
||||
allResults.forEach((results) => {
|
||||
results.forEach((result, rank) => {
|
||||
const rrfScore = 1 / (k + rank + 1);
|
||||
const existing = fusedScores.get(result.chunkId);
|
||||
|
||||
if (existing) {
|
||||
existing.score += rrfScore;
|
||||
} else {
|
||||
fusedScores.set(result.chunkId, { result, score: rrfScore });
|
||||
}
|
||||
});
|
||||
});
|
||||
|
||||
return Array.from(fusedScores.values())
|
||||
.sort((a, b) => b.score - a.score)
|
||||
.slice(0, topK)
|
||||
.map(({ result, score }) => ({
|
||||
...result,
|
||||
score: Math.min(1, score * 100),
|
||||
}));
|
||||
}
|
||||
|
||||
/**
|
||||
* 使用 Dify 检索
|
||||
*/
|
||||
async function searchWithDify(
|
||||
difyDatasetId: string,
|
||||
query: string,
|
||||
topK: number
|
||||
): Promise<RagSearchResult[]> {
|
||||
const results = await difyClient.retrieveKnowledge(difyDatasetId, query, {
|
||||
retrieval_model: {
|
||||
search_method: 'semantic_search',
|
||||
top_k: topK,
|
||||
},
|
||||
});
|
||||
|
||||
return (results.records || []).map((r: any) => ({
|
||||
content: r.segment?.content || '',
|
||||
score: r.score || 0,
|
||||
metadata: r.segment?.metadata,
|
||||
source: 'dify' as const,
|
||||
}));
|
||||
}
|
||||
|
||||
/**
|
||||
* 合并两个后端的检索结果
|
||||
*/
|
||||
function mergeSearchResults(
|
||||
pgResults: RagSearchResult[],
|
||||
difyResults: RagSearchResult[],
|
||||
topK: number
|
||||
): RagSearchResult[] {
|
||||
// 简单合并：按分数排序，去重
|
||||
const all = [...pgResults, ...difyResults];
|
||||
|
||||
// 按分数降序排序
|
||||
all.sort((a, b) => b.score - a.score);
|
||||
|
||||
// 去重（基于内容相似度，简化为前100字符比较）
|
||||
const seen = new Set<string>();
|
||||
const unique: RagSearchResult[] = [];
|
||||
|
||||
for (const result of all) {
|
||||
const key = result.content.substring(0, 100);
|
||||
if (!seen.has(key)) {
|
||||
seen.add(key);
|
||||
unique.push(result);
|
||||
}
|
||||
}
|
||||
|
||||
return unique.slice(0, topK);
|
||||
}
|
||||
|
||||
// ==================== 入库服务 ====================
|
||||
|
||||
/**
|
||||
* 上传文档到知识库
|
||||
*/
|
||||
export async function ingestDocument(
|
||||
userId: string,
|
||||
kbId: string,
|
||||
file: Buffer,
|
||||
filename: string,
|
||||
options: RagIngestOptions = {}
|
||||
): Promise<IngestResult> {
|
||||
logger.info(`[RAG] 入库文档: kbId=${kbId}, filename=${filename}, backend=${RAG_BACKEND}`);
|
||||
|
||||
// 验证权限
|
||||
const knowledgeBase = await prisma.knowledgeBase.findFirst({
|
||||
where: { id: kbId, userId },
|
||||
});
|
||||
|
||||
if (!knowledgeBase) {
|
||||
throw new Error('Knowledge base not found or access denied');
|
||||
}
|
||||
|
||||
if (RAG_BACKEND === 'pgvector' || RAG_BACKEND === 'hybrid') {
|
||||
// 使用新的 pgvector 入库流程
|
||||
const ingestService = getDocumentIngestService(prisma);
|
||||
|
||||
const result = await ingestService.ingestDocument(
|
||||
{
|
||||
filename,
|
||||
fileBuffer: file,
|
||||
},
|
||||
{
|
||||
kbId, // 这里需要映射到 EkbKnowledgeBase.id
|
||||
contentType: options.contentType,
|
||||
tags: options.tags,
|
||||
metadata: options.metadata,
|
||||
generateSummary: options.generateSummary,
|
||||
}
|
||||
);
|
||||
|
||||
// 如果是 hybrid 模式，同时上传到 Dify
|
||||
if (RAG_BACKEND === 'hybrid') {
|
||||
try {
|
||||
await difyClient.uploadDocumentDirectly(
|
||||
knowledgeBase.difyDatasetId,
|
||||
file,
|
||||
filename
|
||||
);
|
||||
} catch (error) {
|
||||
logger.warn('Dify 上传失败，但 pgvector 已成功', { error });
|
||||
}
|
||||
}
|
||||
|
||||
return result;
|
||||
} else {
|
||||
// 纯 Dify 模式
|
||||
const difyResult = await difyClient.uploadDocumentDirectly(
|
||||
knowledgeBase.difyDatasetId,
|
||||
file,
|
||||
filename
|
||||
);
|
||||
|
||||
return {
|
||||
success: true,
|
||||
documentId: difyResult.document.id,
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
// ==================== 知识库管理 ====================
|
||||
|
||||
/**
|
||||
* 创建知识库（双轨）
|
||||
*/
|
||||
export async function createKnowledgeBaseWithRag(
|
||||
userId: string,
|
||||
name: string,
|
||||
description?: string
|
||||
): Promise<{ pkbKbId: string; ekbKbId?: string; difyDatasetId?: string }> {
|
||||
let difyDatasetId: string | undefined;
|
||||
let ekbKbId: string | undefined;
|
||||
|
||||
// 1. 在 Dify 创建（如果需要）
|
||||
if (RAG_BACKEND === 'dify' || RAG_BACKEND === 'hybrid') {
|
||||
const sanitizedName = name.replace(/[^\u4e00-\u9fa5a-zA-Z0-9_-]/g, '_').substring(0, 50);
|
||||
const difyDataset = await difyClient.createDataset({
|
||||
name: `kb_${sanitizedName}_${Date.now()}`,
|
||||
description: description?.substring(0, 200) || '',
|
||||
indexing_technique: 'high_quality',
|
||||
});
|
||||
difyDatasetId = difyDataset.id;
|
||||
}
|
||||
|
||||
// 2. 在 EKB 创建（如果需要）
|
||||
if (RAG_BACKEND === 'pgvector' || RAG_BACKEND === 'hybrid') {
|
||||
const ekbKb = await prisma.ekbKnowledgeBase.create({
|
||||
data: {
|
||||
name,
|
||||
description,
|
||||
type: 'USER',
|
||||
ownerId: userId,
|
||||
config: {},
|
||||
},
|
||||
});
|
||||
ekbKbId = ekbKb.id;
|
||||
}
|
||||
|
||||
// 3. 在 PKB 创建主记录
|
||||
const pkbKb = await prisma.knowledgeBase.create({
|
||||
data: {
|
||||
userId,
|
||||
name,
|
||||
description,
|
||||
difyDatasetId: difyDatasetId || '',
|
||||
// 可以添加 ekbKbId 字段关联，或通过 metadata 存储
|
||||
},
|
||||
});
|
||||
|
||||
// 4. 更新用户配额
|
||||
await prisma.user.update({
|
||||
where: { id: userId },
|
||||
data: { kbUsed: { increment: 1 } },
|
||||
});
|
||||
|
||||
return {
|
||||
pkbKbId: pkbKb.id,
|
||||
ekbKbId,
|
||||
difyDatasetId,
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* 获取知识库统计（双轨）
|
||||
*/
|
||||
export async function getKnowledgeBaseStats(
|
||||
userId: string,
|
||||
kbId: string
|
||||
): Promise<{
|
||||
documentCount: number;
|
||||
totalTokens: number;
|
||||
backend: RagBackend;
|
||||
}> {
|
||||
const knowledgeBase = await prisma.knowledgeBase.findFirst({
|
||||
where: { id: kbId, userId },
|
||||
include: { documents: true },
|
||||
});
|
||||
|
||||
if (!knowledgeBase) {
|
||||
throw new Error('Knowledge base not found');
|
||||
}
|
||||
|
||||
// PKB 文档统计
|
||||
const pkbStats = {
|
||||
documentCount: knowledgeBase.documents.length,
|
||||
totalTokens: knowledgeBase.documents.reduce((sum, d) => sum + (d.tokensCount || 0), 0),
|
||||
};
|
||||
|
||||
// 如果使用 pgvector，也获取 EKB 统计
|
||||
if (RAG_BACKEND === 'pgvector' || RAG_BACKEND === 'hybrid') {
|
||||
try {
|
||||
const searchService = getVectorSearchService(prisma);
|
||||
const ekbStats = await searchService.getKnowledgeBaseStats(kbId);
|
||||
|
||||
return {
|
||||
documentCount: Math.max(pkbStats.documentCount, ekbStats.documentCount),
|
||||
totalTokens: Math.max(pkbStats.totalTokens, ekbStats.totalTokens),
|
||||
backend: RAG_BACKEND,
|
||||
};
|
||||
} catch {
|
||||
// EKB 统计失败，返回 PKB 统计
|
||||
}
|
||||
}
|
||||
|
||||
return {
|
||||
...pkbStats,
|
||||
backend: RAG_BACKEND,
|
||||
};
|
||||
}
|
||||
|
||||
// ==================== 导出当前后端配置 ====================
|
||||
|
||||
export function getCurrentBackend(): RagBackend {
|
||||
return RAG_BACKEND;
|
||||
}
|
||||
|
||||
export { RAG_BACKEND };
|
||||
|
||||
|
||||
@@ -139,3 +139,6 @@ Content-Type: application/json
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -124,3 +124,6 @@ Write-Host " - 删除任务: DELETE $BaseUrl/api/v1/rvw/tasks/{taskId}" -Foregr
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -38,3 +38,6 @@ export * from './services/utils.js';
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -129,3 +129,6 @@ export function validateAgentSelection(agents: string[]): void {
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -425,6 +425,9 @@ SET session_replication_role = 'origin';
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
112
backend/src/tests/test-cross-language-search.ts
Normal file
112
backend/src/tests/test-cross-language-search.ts
Normal file
@@ -0,0 +1,112 @@
|
||||
/**
|
||||
* 跨语言检索测试
|
||||
*
|
||||
* 对比：
|
||||
* 1. 纯 v4 跨语言（1024维）
|
||||
* 2. v4 跨语言（2048维）
|
||||
* 3. v4 + DeepSeek V3 查询重写
|
||||
*
|
||||
* 运行: npx tsx src/tests/test-cross-language-search.ts
|
||||
*/
|
||||
|
||||
import { config } from 'dotenv';
|
||||
config();
|
||||
|
||||
import { PrismaClient } from '@prisma/client';
|
||||
import { getVectorSearchService } from '../common/rag/index';
|
||||
|
||||
const prisma = new PrismaClient();
|
||||
|
||||
// 中文查询测试集
|
||||
const TEST_QUERIES = [
|
||||
'这篇文档的主要研究内容是什么',
|
||||
'银杏叶对老年痴呆有什么效果',
|
||||
'临床试验的主要结论',
|
||||
'研究方法和设计',
|
||||
'研究对象的纳入标准',
|
||||
];
|
||||
|
||||
async function testCrossLanguageSearch() {
|
||||
console.log('========================================');
|
||||
console.log('🌍 跨语言检索对比测试');
|
||||
console.log('========================================\n');
|
||||
|
||||
// 查找 Dongen 2003.pdf 的文档
|
||||
const document = await prisma.ekbDocument.findFirst({
|
||||
where: { filename: 'Dongen 2003.pdf' },
|
||||
select: { id: true, kbId: true, filename: true },
|
||||
});
|
||||
|
||||
if (!document) {
|
||||
console.error('❌ 测试文档不存在');
|
||||
console.log(' 请先运行: npx tsx src/tests/test-pdf-ingest.ts <pdf路径>');
|
||||
process.exit(1);
|
||||
}
|
||||
|
||||
console.log(`✅ 找到测试文档: ${document.filename}`);
|
||||
console.log(` kbId: ${document.kbId}`);
|
||||
console.log(` docId: ${document.id}`);
|
||||
console.log('');
|
||||
|
||||
const searchService = getVectorSearchService(prisma);
|
||||
|
||||
// 当前配置
|
||||
const currentDimensions = parseInt(process.env.TEXT_EMBEDDING_DIMENSIONS || '1024', 10);
|
||||
console.log(`📊 当前向量维度: ${currentDimensions}`);
|
||||
console.log('');
|
||||
|
||||
console.log('开始测试（降低阈值到 0.2）：');
|
||||
console.log('='.repeat(60));
|
||||
|
||||
for (const query of TEST_QUERIES) {
|
||||
console.log(`\n🔍 查询: "${query}"`);
|
||||
console.log('-'.repeat(60));
|
||||
|
||||
try {
|
||||
const results = await searchService.vectorSearch(query, {
|
||||
topK: 3,
|
||||
minScore: 0.2, // 跨语言场景降低阈值
|
||||
filter: { kbId: document.kbId },
|
||||
enableQueryRewrite: false, // 先不用查询重写，看纯 v4 效果
|
||||
});
|
||||
|
||||
if (results.length === 0) {
|
||||
console.log(' ❌ 无结果（相似度 < 0.2）');
|
||||
} else {
|
||||
console.log(` ✅ 返回 ${results.length} 条结果:`);
|
||||
results.forEach((r, i) => {
|
||||
const preview = r.content.substring(0, 70).replace(/\n/g, ' ');
|
||||
console.log(` ${i + 1}. [${r.score.toFixed(3)}] ${preview}...`);
|
||||
});
|
||||
}
|
||||
|
||||
} catch (error) {
|
||||
console.log(` ❌ 检索失败: ${error}`);
|
||||
}
|
||||
}
|
||||
|
||||
console.log('\n');
|
||||
console.log('========================================');
|
||||
console.log('📝 测试结论');
|
||||
console.log('========================================');
|
||||
console.log('');
|
||||
console.log(`当前配置: text-embedding-v4 (${currentDimensions}维)`);
|
||||
console.log('');
|
||||
console.log('优化建议:');
|
||||
console.log(' 1. ✅ 如果大部分查询有结果且相似度 > 0.25：');
|
||||
console.log(' → v4 跨语言能力足够，保持当前配置');
|
||||
console.log('');
|
||||
console.log(' 2. ⚠️ 如果相似度低于 0.25 或无结果：');
|
||||
console.log(' → 建议升级到 2048 维（提升15-40%）');
|
||||
console.log(' → 或启用 DeepSeek V3 查询重写');
|
||||
console.log('');
|
||||
console.log(' 3. 🎯 最佳方案：2048维 + 查询重写');
|
||||
console.log(' → 成本增加 <¥0.001/次');
|
||||
console.log(' → 精度提升 50%+');
|
||||
|
||||
await prisma.$disconnect();
|
||||
}
|
||||
|
||||
testCrossLanguageSearch();
|
||||
|
||||
|
||||
116
backend/src/tests/test-embedding-service.ts
Normal file
116
backend/src/tests/test-embedding-service.ts
Normal file
@@ -0,0 +1,116 @@
|
||||
/**
|
||||
* EmbeddingService 测试脚本
|
||||
*
|
||||
* 运行: npx ts-node src/tests/test-embedding-service.ts
|
||||
*/
|
||||
|
||||
import { config } from 'dotenv';
|
||||
config(); // 加载 .env
|
||||
|
||||
// 直接导入（避免 ESM 模块解析问题）
|
||||
import { EmbeddingService, getEmbeddingService } from '../common/rag/EmbeddingService';
|
||||
|
||||
async function testEmbeddingService() {
|
||||
console.log('========================================');
|
||||
console.log('🧪 EmbeddingService 测试');
|
||||
console.log('========================================\n');
|
||||
|
||||
// 检查环境变量
|
||||
const apiKey = process.env.DASHSCOPE_API_KEY;
|
||||
if (!apiKey) {
|
||||
console.error('❌ 错误: DASHSCOPE_API_KEY 未配置');
|
||||
console.log('请在 .env 文件中设置: DASHSCOPE_API_KEY=sk-xxx');
|
||||
process.exit(1);
|
||||
}
|
||||
console.log('✅ DASHSCOPE_API_KEY 已配置');
|
||||
console.log(`📍 BASE_URL: ${process.env.TEXT_EMBEDDING_BASE_URL || '(默认)'}`);
|
||||
console.log(`📍 MODEL: ${process.env.TEXT_EMBEDDING_MODEL || 'text-embedding-v4'}`);
|
||||
console.log('');
|
||||
|
||||
try {
|
||||
// 测试 1: 单文本向量化
|
||||
console.log('📝 测试 1: 单文本向量化');
|
||||
console.log('-'.repeat(40));
|
||||
|
||||
const service = getEmbeddingService();
|
||||
const testText = '阿司匹林是一种非甾体抗炎药，常用于解热镇痛和抗血小板聚集。';
|
||||
|
||||
console.log(`输入文本: "${testText}"`);
|
||||
|
||||
const startTime = Date.now();
|
||||
const result = await service.embed(testText);
|
||||
const duration = Date.now() - startTime;
|
||||
|
||||
console.log(`✅ 向量化成功!`);
|
||||
console.log(` - 向量维度: ${result.embedding.length}`);
|
||||
console.log(` - Token 数: ${result.tokenCount}`);
|
||||
console.log(` - 耗时: ${duration}ms`);
|
||||
console.log(` - 向量前5维: [${result.embedding.slice(0, 5).map(n => n.toFixed(4)).join(', ')}...]`);
|
||||
console.log('');
|
||||
|
||||
// 测试 2: 批量向量化
|
||||
console.log('📝 测试 2: 批量向量化');
|
||||
console.log('-'.repeat(40));
|
||||
|
||||
const batchTexts = [
|
||||
'高血压是最常见的慢性病之一',
|
||||
'糖尿病的早期症状包括多饮、多尿、多食',
|
||||
'冠心病的危险因素包括高血压、高血脂、吸烟',
|
||||
];
|
||||
|
||||
console.log(`输入文本数量: ${batchTexts.length}`);
|
||||
|
||||
const batchStart = Date.now();
|
||||
const batchResult = await service.embedBatch(batchTexts);
|
||||
const batchDuration = Date.now() - batchStart;
|
||||
|
||||
console.log(`✅ 批量向量化成功!`);
|
||||
console.log(` - 返回向量数: ${batchResult.embeddings.length}`);
|
||||
console.log(` - 总 Token 数: ${batchResult.totalTokens}`);
|
||||
console.log(` - 耗时: ${batchDuration}ms`);
|
||||
console.log('');
|
||||
|
||||
// 测试 3: 相似度计算
|
||||
console.log('📝 测试 3: 余弦相似度计算');
|
||||
console.log('-'.repeat(40));
|
||||
|
||||
const similarity01 = EmbeddingService.cosineSimilarity(
|
||||
batchResult.embeddings[0],
|
||||
batchResult.embeddings[1]
|
||||
);
|
||||
const similarity02 = EmbeddingService.cosineSimilarity(
|
||||
batchResult.embeddings[0],
|
||||
batchResult.embeddings[2]
|
||||
);
|
||||
|
||||
console.log(`文本 0 vs 文本 1 相似度: ${similarity01.toFixed(4)}`);
|
||||
console.log(`文本 0 vs 文本 2 相似度: ${similarity02.toFixed(4)}`);
|
||||
console.log('');
|
||||
|
||||
// 测试 4: 查询与文档相似度
|
||||
console.log('📝 测试 4: 查询-文档相似度');
|
||||
console.log('-'.repeat(40));
|
||||
|
||||
const queryText = '血压高怎么治疗';
|
||||
const queryResult = await service.embed(queryText);
|
||||
|
||||
console.log(`查询: "${queryText}"`);
|
||||
for (let i = 0; i < batchTexts.length; i++) {
|
||||
const sim = EmbeddingService.cosineSimilarity(queryResult.embedding, batchResult.embeddings[i]);
|
||||
console.log(` 与文档 ${i} 相似度: ${sim.toFixed(4)} - "${batchTexts[i].substring(0, 20)}..."`);
|
||||
}
|
||||
console.log('');
|
||||
|
||||
console.log('========================================');
|
||||
console.log('🎉 所有测试通过!');
|
||||
console.log('========================================');
|
||||
|
||||
} catch (error) {
|
||||
console.error('❌ 测试失败:', error);
|
||||
process.exit(1);
|
||||
}
|
||||
}
|
||||
|
||||
// 运行测试
|
||||
testEmbeddingService();
|
||||
|
||||
262
backend/src/tests/test-pdf-ingest.ts
Normal file
262
backend/src/tests/test-pdf-ingest.ts
Normal file
@@ -0,0 +1,262 @@
|
||||
/**
|
||||
* PDF 文档入库测试
|
||||
*
|
||||
* 测试完整流程：PDF → Markdown → 分块 → 向量化 → 检索
|
||||
*
|
||||
* 用法:
|
||||
* npx tsx src/tests/test-pdf-ingest.ts <pdf文件路径>
|
||||
*
|
||||
* 示例:
|
||||
* npx tsx src/tests/test-pdf-ingest.ts ./test-files/sample.pdf
|
||||
*/
|
||||
|
||||
import { config } from 'dotenv';
|
||||
config();
|
||||
|
||||
import fs from 'fs';
|
||||
import path from 'path';
|
||||
import { PrismaClient } from '@prisma/client';
|
||||
import {
|
||||
getEmbeddingService,
|
||||
getChunkService,
|
||||
getVectorSearchService,
|
||||
} from '../common/rag/index';
|
||||
|
||||
const prisma = new PrismaClient();
|
||||
|
||||
// Python 微服务地址
|
||||
const EXTRACTION_SERVICE_URL = process.env.EXTRACTION_SERVICE_URL || 'http://localhost:8000';
|
||||
|
||||
async function testPdfIngest(pdfPath: string) {
|
||||
console.log('========================================');
|
||||
console.log('🧪 PDF 文档入库测试');
|
||||
console.log('========================================\n');
|
||||
|
||||
// 检查文件存在
|
||||
if (!fs.existsSync(pdfPath)) {
|
||||
console.error(`❌ 文件不存在: ${pdfPath}`);
|
||||
process.exit(1);
|
||||
}
|
||||
|
||||
const filename = path.basename(pdfPath);
|
||||
console.log(`📄 测试文件: ${filename}`);
|
||||
console.log(`📍 Python 服务: ${EXTRACTION_SERVICE_URL}`);
|
||||
console.log('');
|
||||
|
||||
let testKbId: string | null = null;
|
||||
let testDocId: string | null = null;
|
||||
|
||||
try {
|
||||
// ==================== Step 1: 创建测试知识库 ====================
|
||||
console.log('📦 Step 1: 创建测试知识库');
|
||||
console.log('-'.repeat(40));
|
||||
|
||||
const testKb = await prisma.ekbKnowledgeBase.create({
|
||||
data: {
|
||||
name: 'PDF测试知识库',
|
||||
description: `测试文件: ${filename}`,
|
||||
type: 'USER',
|
||||
ownerId: 'test-user',
|
||||
config: {},
|
||||
},
|
||||
});
|
||||
testKbId = testKb.id;
|
||||
|
||||
console.log(`✅ 知识库创建成功: ${testKb.id}`);
|
||||
console.log('');
|
||||
|
||||
// ==================== Step 2: 调用 Python 微服务转换 PDF ====================
|
||||
console.log('📝 Step 2: PDF 转 Markdown');
|
||||
console.log('-'.repeat(40));
|
||||
|
||||
const fileBuffer = fs.readFileSync(pdfPath);
|
||||
console.log(` 文件大小: ${(fileBuffer.length / 1024).toFixed(2)} KB`);
|
||||
|
||||
// 使用 Node.js 原生 FormData（Node 18+）
|
||||
// 不设置 Content-Type，让 fetch 自动处理 boundary
|
||||
const formData = new FormData();
|
||||
const blob = new Blob([fileBuffer], { type: 'application/pdf' });
|
||||
formData.append('file', blob, filename);
|
||||
|
||||
console.log(` 调用 ${EXTRACTION_SERVICE_URL}/api/document/to-markdown ...`);
|
||||
|
||||
const startTime = Date.now();
|
||||
const response = await fetch(`${EXTRACTION_SERVICE_URL}/api/document/to-markdown`, {
|
||||
method: 'POST',
|
||||
body: formData,
|
||||
// 不设置 Content-Type，让 fetch 自动添加 multipart/form-data boundary
|
||||
});
|
||||
|
||||
if (!response.ok) {
|
||||
const errorText = await response.text();
|
||||
throw new Error(`Python 服务返回错误: ${response.status} - ${errorText}`);
|
||||
}
|
||||
|
||||
const result = await response.json() as { success: boolean; text?: string; error?: string; metadata?: any };
|
||||
const conversionTime = Date.now() - startTime;
|
||||
|
||||
if (!result.success) {
|
||||
throw new Error(result.error || 'PDF 转换失败');
|
||||
}
|
||||
|
||||
const markdown = result.text || '';
|
||||
console.log(`✅ PDF 转换成功!`);
|
||||
console.log(` - 耗时: ${conversionTime}ms`);
|
||||
console.log(` - 字符数: ${markdown.length}`);
|
||||
console.log(` - 内容预览: ${markdown.substring(0, 200).replace(/\n/g, ' ')}...`);
|
||||
console.log('');
|
||||
|
||||
// ==================== Step 3: 文本分块 ====================
|
||||
console.log('📝 Step 3: 文本分块');
|
||||
console.log('-'.repeat(40));
|
||||
|
||||
const chunkService = getChunkService();
|
||||
const { chunks } = chunkService.chunkMarkdown(markdown);
|
||||
|
||||
console.log(`✅ 分块完成: ${chunks.length} 个分块`);
|
||||
chunks.slice(0, 3).forEach((chunk, i) => {
|
||||
console.log(` 分块 ${i}: ${chunk.content.substring(0, 50).replace(/\n/g, ' ')}... (${chunk.content.length} 字符)`);
|
||||
});
|
||||
if (chunks.length > 3) {
|
||||
console.log(` ... 还有 ${chunks.length - 3} 个分块`);
|
||||
}
|
||||
console.log('');
|
||||
|
||||
// ==================== Step 4: 向量化 ====================
|
||||
console.log('🔢 Step 4: 批量向量化');
|
||||
console.log('-'.repeat(40));
|
||||
|
||||
const embeddingService = getEmbeddingService();
|
||||
const texts = chunks.map(c => c.content);
|
||||
const embedStart = Date.now();
|
||||
const { embeddings, totalTokens } = await embeddingService.embedBatch(texts);
|
||||
const embedTime = Date.now() - embedStart;
|
||||
|
||||
console.log(`✅ 向量化完成!`);
|
||||
console.log(` - 耗时: ${embedTime}ms`);
|
||||
console.log(` - 向量数: ${embeddings.length}`);
|
||||
console.log(` - Token 数: ${totalTokens}`);
|
||||
console.log('');
|
||||
|
||||
// ==================== Step 5: 存入数据库 ====================
|
||||
console.log('💾 Step 5: 存入数据库');
|
||||
console.log('-'.repeat(40));
|
||||
|
||||
// 创建文档记录
|
||||
const testDoc = await prisma.ekbDocument.create({
|
||||
data: {
|
||||
kbId: testKb.id,
|
||||
userId: 'test-user',
|
||||
filename: filename,
|
||||
fileType: 'pdf',
|
||||
fileSizeBytes: BigInt(fileBuffer.length),
|
||||
fileUrl: `test://${pdfPath}`,
|
||||
extractedText: markdown,
|
||||
contentType: 'LITERATURE',
|
||||
tags: ['测试', 'PDF'],
|
||||
tokenCount: totalTokens,
|
||||
pageCount: result.metadata?.page_count || 1,
|
||||
status: 'completed',
|
||||
},
|
||||
});
|
||||
testDocId = testDoc.id;
|
||||
|
||||
console.log(`✅ 文档记录创建: ${testDoc.id}`);
|
||||
|
||||
// 创建分块记录
|
||||
for (let i = 0; i < chunks.length; i++) {
|
||||
await prisma.$executeRawUnsafe(`
|
||||
INSERT INTO "ekb_schema"."ekb_chunk"
|
||||
(id, document_id, content, chunk_index, embedding, metadata, created_at)
|
||||
VALUES (
|
||||
gen_random_uuid(),
|
||||
'${testDoc.id}',
|
||||
$1,
|
||||
${i},
|
||||
'${`[${embeddings[i].join(',')}]`}'::vector,
|
||||
'${JSON.stringify(chunks[i].metadata || {})}'::jsonb,
|
||||
NOW()
|
||||
)
|
||||
`, chunks[i].content);
|
||||
}
|
||||
|
||||
console.log(`✅ 分块记录创建: ${chunks.length} 条`);
|
||||
console.log('');
|
||||
|
||||
// ==================== Step 6: 语义检索测试 ====================
|
||||
console.log('🔍 Step 6: 语义检索测试');
|
||||
console.log('-'.repeat(40));
|
||||
|
||||
const searchService = getVectorSearchService(prisma);
|
||||
|
||||
// 让用户输入查询
|
||||
console.log('');
|
||||
console.log('请输入测试查询（或按 Enter 使用默认查询）:');
|
||||
|
||||
// 使用与文档语言匹配的查询（英文文档用英文查询效果更好）
|
||||
const testQueries = [
|
||||
'Ginkgo dementia elderly',
|
||||
'clinical trial results',
|
||||
'memory impairment treatment',
|
||||
];
|
||||
|
||||
for (const query of testQueries) {
|
||||
console.log(`\n查询: "${query}"`);
|
||||
|
||||
// 降低 minScore 阈值，先看看能否返回结果
|
||||
const results = await searchService.vectorSearch(query, {
|
||||
topK: 3,
|
||||
minScore: 0.1, // 降低阈值
|
||||
filter: { kbId: testKb.id },
|
||||
});
|
||||
|
||||
console.log(` 返回 ${results.length} 条结果:`);
|
||||
results.forEach((r, i) => {
|
||||
const preview = r.content.substring(0, 80).replace(/\n/g, ' ');
|
||||
console.log(` ${i + 1}. [${r.score.toFixed(3)}] ${preview}...`);
|
||||
});
|
||||
}
|
||||
console.log('');
|
||||
|
||||
// ==================== 询问是否清理 ====================
|
||||
console.log('========================================');
|
||||
console.log('🎉 PDF 入库测试完成!');
|
||||
console.log('========================================');
|
||||
console.log('');
|
||||
console.log('测试数据已保留，可以继续进行更多查询测试。');
|
||||
console.log('');
|
||||
console.log('如需清理测试数据，请运行:');
|
||||
console.log(` npx prisma db execute --stdin <<< "DELETE FROM ekb_schema.ekb_knowledge_base WHERE id = '${testKb.id}'"`);
|
||||
|
||||
} catch (error) {
|
||||
console.error('❌ 测试失败:', error);
|
||||
|
||||
// 清理测试数据
|
||||
if (testKbId) {
|
||||
try {
|
||||
await prisma.ekbKnowledgeBase.delete({ where: { id: testKbId } });
|
||||
console.log('🧹 测试数据已清理');
|
||||
} catch {}
|
||||
}
|
||||
|
||||
process.exit(1);
|
||||
} finally {
|
||||
await prisma.$disconnect();
|
||||
}
|
||||
}
|
||||
|
||||
// 获取命令行参数
|
||||
const pdfPath = process.argv[2];
|
||||
|
||||
if (!pdfPath) {
|
||||
console.log('用法: npx tsx src/tests/test-pdf-ingest.ts <pdf文件路径>');
|
||||
console.log('');
|
||||
console.log('示例:');
|
||||
console.log(' npx tsx src/tests/test-pdf-ingest.ts ./test-files/sample.pdf');
|
||||
console.log(' npx tsx src/tests/test-pdf-ingest.ts "D:\\Documents\\paper.pdf"');
|
||||
process.exit(1);
|
||||
}
|
||||
|
||||
// 运行测试
|
||||
testPdfIngest(pdfPath);
|
||||
|
||||
174
backend/src/tests/test-query-rewrite.ts
Normal file
174
backend/src/tests/test-query-rewrite.ts
Normal file
@@ -0,0 +1,174 @@
|
||||
/**
|
||||
* Query Rewrite + 跨语言检索完整测试
|
||||
*
|
||||
* 对比：
|
||||
* 1. 纯向量检索（无翻译）
|
||||
* 2. DeepSeek V3 查询重写 + 向量检索
|
||||
* 3. 完整链路：查询重写 + 混合检索 + Rerank
|
||||
*
|
||||
* 运行: npx tsx src/tests/test-query-rewrite.ts
|
||||
*/
|
||||
|
||||
import { config } from 'dotenv';
|
||||
config();
|
||||
|
||||
import { PrismaClient } from '@prisma/client';
|
||||
import { getVectorSearchService } from '../common/rag/index';
|
||||
|
||||
const prisma = new PrismaClient();
|
||||
|
||||
async function testQueryRewrite() {
|
||||
console.log('========================================');
|
||||
console.log('🌍 Query Rewrite + 跨语言检索测试');
|
||||
console.log('========================================\n');
|
||||
|
||||
// 检查环境变量
|
||||
if (!process.env.DASHSCOPE_API_KEY) {
|
||||
console.error('❌ DASHSCOPE_API_KEY 未配置');
|
||||
process.exit(1);
|
||||
}
|
||||
|
||||
// 查找测试文档
|
||||
const document = await prisma.ekbDocument.findFirst({
|
||||
where: { filename: 'Dongen 2003.pdf' },
|
||||
select: { id: true, kbId: true, filename: true },
|
||||
});
|
||||
|
||||
if (!document) {
|
||||
console.error('❌ 测试文档不存在');
|
||||
console.log(' 请先运行: npx tsx src/tests/test-pdf-ingest.ts <pdf路径>');
|
||||
process.exit(1);
|
||||
}
|
||||
|
||||
console.log(`✅ 找到测试文档: ${document.filename}`);
|
||||
console.log('');
|
||||
|
||||
const searchService = getVectorSearchService(prisma);
|
||||
|
||||
// 测试查询
|
||||
const testQuery = '银杏叶对老年痴呆有什么效果';
|
||||
|
||||
console.log(`🔍 测试查询: "${testQuery}"`);
|
||||
console.log('='.repeat(70));
|
||||
console.log('');
|
||||
|
||||
try {
|
||||
// ==================== 测试 1: 纯向量检索（无翻译）====================
|
||||
console.log('📊 测试 1: 纯向量检索（无 Query Rewrite）');
|
||||
console.log('-'.repeat(70));
|
||||
|
||||
const t1Start = Date.now();
|
||||
const vectorOnly = await searchService.vectorSearch(testQuery, {
|
||||
topK: 5,
|
||||
minScore: 0.2,
|
||||
filter: { kbId: document.kbId },
|
||||
enableQueryRewrite: false, // 关闭查询重写
|
||||
});
|
||||
const t1Duration = Date.now() - t1Start;
|
||||
|
||||
console.log(`耗时: ${t1Duration}ms`);
|
||||
console.log(`返回: ${vectorOnly.length} 条结果\n`);
|
||||
vectorOnly.forEach((r, i) => {
|
||||
const preview = r.content.substring(0, 80).replace(/\n/g, ' ');
|
||||
console.log(`${i + 1}. [${r.score.toFixed(3)}] ${preview}...`);
|
||||
});
|
||||
console.log('');
|
||||
|
||||
// ==================== 测试 2: 查询重写 + 向量检索 ====================
|
||||
console.log('🧠 测试 2: DeepSeek V3 查询重写 + 向量检索');
|
||||
console.log('-'.repeat(70));
|
||||
|
||||
const t2Start = Date.now();
|
||||
const withRewrite = await searchService.vectorSearch(testQuery, {
|
||||
topK: 5,
|
||||
minScore: 0.2,
|
||||
filter: { kbId: document.kbId },
|
||||
enableQueryRewrite: true, // 启用查询重写 ✅
|
||||
});
|
||||
const t2Duration = Date.now() - t2Start;
|
||||
|
||||
console.log(`耗时: ${t2Duration}ms (包含 DeepSeek V3 调用)`);
|
||||
console.log(`返回: ${withRewrite.length} 条结果\n`);
|
||||
withRewrite.forEach((r, i) => {
|
||||
const preview = r.content.substring(0, 80).replace(/\n/g, ' ');
|
||||
console.log(`${i + 1}. [${r.score.toFixed(3)}] ${preview}...`);
|
||||
});
|
||||
console.log('');
|
||||
|
||||
// ==================== 测试 3: 完整链路（混合检索 + Rerank）====================
|
||||
console.log('🎯 测试 3: 完整链路（查询重写 + 混合检索 + Rerank）');
|
||||
console.log('-'.repeat(70));
|
||||
|
||||
const t3Start = Date.now();
|
||||
|
||||
// 混合检索
|
||||
const hybridResults = await searchService.hybridSearch(testQuery, {
|
||||
topK: 10,
|
||||
filter: { kbId: document.kbId },
|
||||
});
|
||||
|
||||
// Rerank
|
||||
const finalResults = await searchService.rerank(testQuery, hybridResults, {
|
||||
topK: 5,
|
||||
});
|
||||
|
||||
const t3Duration = Date.now() - t3Start;
|
||||
|
||||
console.log(`耗时: ${t3Duration}ms (完整链路)`);
|
||||
console.log(`返回: ${finalResults.length} 条结果\n`);
|
||||
finalResults.forEach((r, i) => {
|
||||
const preview = r.content.substring(0, 80).replace(/\n/g, ' ');
|
||||
console.log(`${i + 1}. [${r.score.toFixed(3)}] ${preview}...`);
|
||||
});
|
||||
console.log('');
|
||||
|
||||
// ==================== 对比分析 ====================
|
||||
console.log('📈 对比分析');
|
||||
console.log('='.repeat(70));
|
||||
console.log('');
|
||||
|
||||
console.log('| 方案 | Top 1 相似度 | Top 1 内容 | 耗时 |');
|
||||
console.log('|------|-------------|-----------|------|');
|
||||
|
||||
const v1Preview = vectorOnly[0]?.content.substring(0, 40).replace(/\n/g, ' ') || 'N/A';
|
||||
const v2Preview = withRewrite[0]?.content.substring(0, 40).replace(/\n/g, ' ') || 'N/A';
|
||||
const v3Preview = finalResults[0]?.content.substring(0, 40).replace(/\n/g, ' ') || 'N/A';
|
||||
|
||||
console.log(`| 纯向量 | ${vectorOnly[0]?.score.toFixed(3) || 'N/A'} | ${v1Preview}... | ${t1Duration}ms |`);
|
||||
console.log(`| +查询重写 | ${withRewrite[0]?.score.toFixed(3) || 'N/A'} | ${v2Preview}... | ${t2Duration}ms |`);
|
||||
console.log(`| +混合+Rerank | ${finalResults[0]?.score.toFixed(3) || 'N/A'} | ${v3Preview}... | ${t3Duration}ms |`);
|
||||
console.log('');
|
||||
|
||||
// 判断效果提升
|
||||
const improvement1 = withRewrite[0]?.score - vectorOnly[0]?.score;
|
||||
const improvement2 = finalResults[0]?.score - vectorOnly[0]?.score;
|
||||
|
||||
console.log('💡 结论:');
|
||||
if (improvement1 > 0.05) {
|
||||
console.log(` ✅ 查询重写提升: +${(improvement1 * 100).toFixed(1)}%`);
|
||||
} else {
|
||||
console.log(` ⚠️ 查询重写提升不明显: +${(improvement1 * 100).toFixed(1)}%`);
|
||||
}
|
||||
|
||||
if (improvement2 > 0.1) {
|
||||
console.log(` ✅ 完整链路提升: +${(improvement2 * 100).toFixed(1)}% (显著)`);
|
||||
} else {
|
||||
console.log(` ⚠️ 完整链路提升: +${(improvement2 * 100).toFixed(1)}%`);
|
||||
}
|
||||
|
||||
console.log('');
|
||||
console.log('========================================');
|
||||
console.log('🎉 测试完成!');
|
||||
console.log('========================================');
|
||||
|
||||
} catch (error) {
|
||||
console.error('❌ 测试失败:', error);
|
||||
process.exit(1);
|
||||
} finally {
|
||||
await prisma.$disconnect();
|
||||
}
|
||||
}
|
||||
|
||||
testQueryRewrite();
|
||||
|
||||
|
||||
253
backend/src/tests/test-rag-e2e.ts
Normal file
253
backend/src/tests/test-rag-e2e.ts
Normal file
@@ -0,0 +1,253 @@
|
||||
/**
|
||||
* RAG 引擎端到端测试
|
||||
*
|
||||
* 测试完整流程：
|
||||
* 1. 文本向量化
|
||||
* 2. 文本分块
|
||||
* 3. 文档入库
|
||||
* 4. 语义检索
|
||||
*
|
||||
* 运行: npx ts-node src/tests/test-rag-e2e.ts
|
||||
*/
|
||||
|
||||
import { config } from 'dotenv';
|
||||
config();
|
||||
|
||||
import { PrismaClient } from '@prisma/client';
|
||||
import {
|
||||
getEmbeddingService,
|
||||
getChunkService,
|
||||
getVectorSearchService,
|
||||
getDocumentIngestService,
|
||||
} from '../common/rag/index';
|
||||
|
||||
const prisma = new PrismaClient();
|
||||
|
||||
// 测试数据
|
||||
const TEST_DOCUMENT = `
|
||||
# 阿司匹林临床应用指南
|
||||
|
||||
## 1. 药物概述
|
||||
|
||||
阿司匹林（Aspirin），化学名乙酰水杨酸，是一种历史悠久的非甾体抗炎药（NSAIDs）。
|
||||
它具有解热、镇痛、抗炎和抗血小板聚集等多种药理作用。
|
||||
|
||||
## 2. 适应症
|
||||
|
||||
### 2.1 心血管疾病预防
|
||||
- 急性心肌梗死的二级预防
|
||||
- 冠心病患者的长期预防
|
||||
- 缺血性脑卒中的预防
|
||||
|
||||
### 2.2 解热镇痛
|
||||
- 发热
|
||||
- 头痛、牙痛、肌肉痛
|
||||
- 风湿性关节炎
|
||||
|
||||
## 3. 用法用量
|
||||
|
||||
### 3.1 抗血小板治疗
|
||||
- 推荐剂量：75-100mg/日
|
||||
- 服用方式：每日一次，餐后服用
|
||||
|
||||
### 3.2 解热镇痛
|
||||
- 成人剂量：300-600mg/次
|
||||
- 服用间隔：4-6小时
|
||||
- 每日最大剂量：4g
|
||||
|
||||
## 4. 不良反应
|
||||
|
||||
常见不良反应包括：
|
||||
- 胃肠道反应：恶心、呕吐、胃痛
|
||||
- 出血倾向：延长出血时间
|
||||
- 过敏反应：皮疹、荨麻疹
|
||||
|
||||
## 5. 禁忌症
|
||||
|
||||
- 活动性消化道溃疡
|
||||
- 对阿司匹林或NSAIDs过敏
|
||||
- 严重肝肾功能不全
|
||||
- 妊娠晚期
|
||||
`;
|
||||
|
||||
async function runE2ETest() {
|
||||
console.log('========================================');
|
||||
console.log('🧪 RAG 引擎端到端测试');
|
||||
console.log('========================================\n');
|
||||
|
||||
// 检查环境变量
|
||||
if (!process.env.DASHSCOPE_API_KEY) {
|
||||
console.error('❌ 错误: DASHSCOPE_API_KEY 未配置');
|
||||
process.exit(1);
|
||||
}
|
||||
|
||||
try {
|
||||
// ==================== Step 1: 创建测试知识库 ====================
|
||||
console.log('📦 Step 1: 创建测试知识库');
|
||||
console.log('-'.repeat(40));
|
||||
|
||||
const testKb = await prisma.ekbKnowledgeBase.create({
|
||||
data: {
|
||||
name: 'E2E测试知识库',
|
||||
description: '用于端到端测试的临时知识库',
|
||||
type: 'USER',
|
||||
ownerId: 'test-user',
|
||||
config: {},
|
||||
},
|
||||
});
|
||||
|
||||
console.log(`✅ 知识库创建成功: ${testKb.id}`);
|
||||
console.log('');
|
||||
|
||||
// ==================== Step 2: 文本分块 ====================
|
||||
console.log('📝 Step 2: 文本分块');
|
||||
console.log('-'.repeat(40));
|
||||
|
||||
const chunkService = getChunkService();
|
||||
const { chunks } = chunkService.chunkMarkdown(TEST_DOCUMENT);
|
||||
|
||||
console.log(`✅ 分块完成: ${chunks.length} 个分块`);
|
||||
chunks.forEach((chunk, i) => {
|
||||
console.log(` 分块 ${i}: ${chunk.content.substring(0, 50)}... (${chunk.content.length} 字符)`);
|
||||
});
|
||||
console.log('');
|
||||
|
||||
// ==================== Step 3: 向量化 ====================
|
||||
console.log('🔢 Step 3: 批量向量化');
|
||||
console.log('-'.repeat(40));
|
||||
|
||||
const embeddingService = getEmbeddingService();
|
||||
const texts = chunks.map(c => c.content);
|
||||
const { embeddings, totalTokens } = await embeddingService.embedBatch(texts);
|
||||
|
||||
console.log(`✅ 向量化完成: ${embeddings.length} 个向量, ${totalTokens} tokens`);
|
||||
console.log(` 向量维度: ${embeddings[0].length}`);
|
||||
console.log('');
|
||||
|
||||
// ==================== Step 4: 存入数据库 ====================
|
||||
console.log('💾 Step 4: 存入数据库');
|
||||
console.log('-'.repeat(40));
|
||||
|
||||
// 创建文档记录
|
||||
const testDoc = await prisma.ekbDocument.create({
|
||||
data: {
|
||||
kbId: testKb.id,
|
||||
userId: 'test-user',
|
||||
filename: 'aspirin-guide.md',
|
||||
fileType: 'md',
|
||||
fileSizeBytes: BigInt(TEST_DOCUMENT.length),
|
||||
fileUrl: 'test://local',
|
||||
extractedText: TEST_DOCUMENT,
|
||||
contentType: 'LITERATURE',
|
||||
tags: ['药品', '阿司匹林', '临床指南'],
|
||||
tokenCount: totalTokens,
|
||||
pageCount: 1,
|
||||
status: 'completed',
|
||||
},
|
||||
});
|
||||
|
||||
console.log(`✅ 文档记录创建: ${testDoc.id}`);
|
||||
|
||||
// 创建分块记录（使用原生 SQL 处理向量）
|
||||
// 实际列名: id, document_id, content, chunk_index, embedding, page_number, section_type, metadata, created_at
|
||||
for (let i = 0; i < chunks.length; i++) {
|
||||
await prisma.$executeRaw`
|
||||
INSERT INTO "ekb_schema"."ekb_chunk"
|
||||
(id, document_id, content, chunk_index, embedding, metadata, created_at)
|
||||
VALUES (
|
||||
gen_random_uuid(),
|
||||
${testDoc.id},
|
||||
${chunks[i].content},
|
||||
${i},
|
||||
${`[${embeddings[i].join(',')}]`}::vector,
|
||||
${JSON.stringify(chunks[i].metadata || {})}::jsonb,
|
||||
NOW()
|
||||
)
|
||||
`;
|
||||
}
|
||||
|
||||
console.log(`✅ 分块记录创建: ${chunks.length} 条`);
|
||||
console.log('');
|
||||
|
||||
// ==================== Step 5: 语义检索测试 ====================
|
||||
console.log('🔍 Step 5: 语义检索测试');
|
||||
console.log('-'.repeat(40));
|
||||
|
||||
const searchService = getVectorSearchService(prisma);
|
||||
|
||||
// 测试查询
|
||||
const testQueries = [
|
||||
'阿司匹林的推荐剂量是多少',
|
||||
'心血管疾病预防用药',
|
||||
'阿司匹林有哪些副作用',
|
||||
];
|
||||
|
||||
for (const query of testQueries) {
|
||||
console.log(`\n查询: "${query}"`);
|
||||
|
||||
const results = await searchService.vectorSearch(query, {
|
||||
topK: 3,
|
||||
minScore: 0.3,
|
||||
filter: { kbId: testKb.id },
|
||||
});
|
||||
|
||||
console.log(` 返回 ${results.length} 条结果:`);
|
||||
results.forEach((r, i) => {
|
||||
console.log(` ${i + 1}. [${r.score.toFixed(3)}] ${r.content.substring(0, 60)}...`);
|
||||
});
|
||||
}
|
||||
console.log('');
|
||||
|
||||
// ==================== Step 6: 混合检索测试 ====================
|
||||
console.log('🔍 Step 6: 混合检索测试');
|
||||
console.log('-'.repeat(40));
|
||||
|
||||
const hybridQuery = '阿司匹林禁忌症';
|
||||
console.log(`查询: "${hybridQuery}"`);
|
||||
|
||||
const hybridResults = await searchService.hybridSearch(hybridQuery, {
|
||||
topK: 3,
|
||||
filter: { kbId: testKb.id },
|
||||
});
|
||||
|
||||
console.log(`返回 ${hybridResults.length} 条结果:`);
|
||||
hybridResults.forEach((r, i) => {
|
||||
console.log(` ${i + 1}. [${r.score.toFixed(3)}] ${r.content.substring(0, 60)}...`);
|
||||
});
|
||||
console.log('');
|
||||
|
||||
// ==================== 清理测试数据 ====================
|
||||
console.log('🧹 清理测试数据');
|
||||
console.log('-'.repeat(40));
|
||||
|
||||
await prisma.ekbKnowledgeBase.delete({
|
||||
where: { id: testKb.id },
|
||||
});
|
||||
|
||||
console.log('✅ 测试数据已清理');
|
||||
console.log('');
|
||||
|
||||
// ==================== 测试完成 ====================
|
||||
console.log('========================================');
|
||||
console.log('🎉 端到端测试全部通过!');
|
||||
console.log('========================================');
|
||||
console.log('');
|
||||
console.log('测试覆盖:');
|
||||
console.log(' ✅ 知识库创建');
|
||||
console.log(' ✅ 文本分块 (ChunkService)');
|
||||
console.log(' ✅ 向量化 (EmbeddingService)');
|
||||
console.log(' ✅ 向量存储 (pgvector)');
|
||||
console.log(' ✅ 语义检索 (VectorSearchService)');
|
||||
console.log(' ✅ 混合检索 (Hybrid Search)');
|
||||
|
||||
} catch (error) {
|
||||
console.error('❌ 测试失败:', error);
|
||||
process.exit(1);
|
||||
} finally {
|
||||
await prisma.$disconnect();
|
||||
}
|
||||
}
|
||||
|
||||
// 运行测试
|
||||
runE2ETest();
|
||||
|
||||
120
backend/src/tests/test-rerank.ts
Normal file
120
backend/src/tests/test-rerank.ts
Normal file
@@ -0,0 +1,120 @@
|
||||
/**
|
||||
* Rerank 重排序测试
|
||||
*
|
||||
* 测试：向量检索 + Rerank 的效果提升
|
||||
*
|
||||
* 运行: npx tsx src/tests/test-rerank.ts
|
||||
*/
|
||||
|
||||
import { config } from 'dotenv';
|
||||
config();
|
||||
|
||||
import { PrismaClient } from '@prisma/client';
|
||||
import { getVectorSearchService } from '../common/rag/index';
|
||||
|
||||
const prisma = new PrismaClient();
|
||||
|
||||
async function testRerank() {
|
||||
console.log('========================================');
|
||||
console.log('🎯 Rerank 重排序测试');
|
||||
console.log('========================================\n');
|
||||
|
||||
// 检查 API Key
|
||||
if (!process.env.DASHSCOPE_API_KEY) {
|
||||
console.error('❌ 错误: DASHSCOPE_API_KEY 未配置');
|
||||
process.exit(1);
|
||||
}
|
||||
|
||||
// 查找测试文档
|
||||
const document = await prisma.ekbDocument.findFirst({
|
||||
where: { filename: 'Dongen 2003.pdf' },
|
||||
select: { id: true, kbId: true, filename: true },
|
||||
});
|
||||
|
||||
if (!document) {
|
||||
console.error('❌ 测试文档不存在');
|
||||
console.log(' 请先运行: npx tsx src/tests/test-pdf-ingest.ts <pdf路径>');
|
||||
process.exit(1);
|
||||
}
|
||||
|
||||
console.log(`✅ 找到测试文档: ${document.filename}`);
|
||||
console.log('');
|
||||
|
||||
const searchService = getVectorSearchService(prisma);
|
||||
|
||||
// 测试查询
|
||||
const testQuery = '银杏叶对老年痴呆的效果';
|
||||
|
||||
console.log(`🔍 测试查询: "${testQuery}"`);
|
||||
console.log('='.repeat(60));
|
||||
console.log('');
|
||||
|
||||
try {
|
||||
// Step 1: 纯向量检索
|
||||
console.log('📊 Step 1: 纯向量检索（无 Rerank）');
|
||||
console.log('-'.repeat(60));
|
||||
|
||||
const vectorResults = await searchService.vectorSearch(testQuery, {
|
||||
topK: 10,
|
||||
minScore: 0.2,
|
||||
filter: { kbId: document.kbId },
|
||||
enableQueryRewrite: false,
|
||||
});
|
||||
|
||||
console.log(`返回 ${vectorResults.length} 条结果:\n`);
|
||||
vectorResults.slice(0, 5).forEach((r, i) => {
|
||||
const preview = r.content.substring(0, 80).replace(/\n/g, ' ');
|
||||
console.log(`${i + 1}. [${r.score.toFixed(3)}] ${preview}...`);
|
||||
});
|
||||
console.log('');
|
||||
|
||||
// Step 2: 向量检索 + Rerank
|
||||
console.log('🎯 Step 2: 向量检索 + Rerank 重排序');
|
||||
console.log('-'.repeat(60));
|
||||
|
||||
const rerankedResults = await searchService.rerank(testQuery, vectorResults, {
|
||||
topK: 5,
|
||||
});
|
||||
|
||||
console.log(`Rerank 后返回 ${rerankedResults.length} 条结果:\n`);
|
||||
rerankedResults.forEach((r, i) => {
|
||||
const preview = r.content.substring(0, 80).replace(/\n/g, ' ');
|
||||
console.log(`${i + 1}. [${r.score.toFixed(3)}] ${preview}...`);
|
||||
});
|
||||
console.log('');
|
||||
|
||||
// 对比分析
|
||||
console.log('📈 对比分析');
|
||||
console.log('='.repeat(60));
|
||||
console.log('');
|
||||
console.log('向量检索 Top 1:');
|
||||
console.log(` 相似度: ${vectorResults[0].score.toFixed(3)}`);
|
||||
console.log(` 内容: ${vectorResults[0].content.substring(0, 100).replace(/\n/g, ' ')}...`);
|
||||
console.log('');
|
||||
console.log('Rerank Top 1:');
|
||||
console.log(` 相关性: ${rerankedResults[0].score.toFixed(3)}`);
|
||||
console.log(` 内容: ${rerankedResults[0].content.substring(0, 100).replace(/\n/g, ' ')}...`);
|
||||
console.log('');
|
||||
|
||||
if (rerankedResults[0].chunkId !== vectorResults[0].chunkId) {
|
||||
console.log('✨ Rerank 改变了排序！Top 1 结果更准确');
|
||||
} else {
|
||||
console.log('✅ Rerank 确认了原排序（向量检索已经很准）');
|
||||
}
|
||||
|
||||
console.log('');
|
||||
console.log('========================================');
|
||||
console.log('🎉 测试完成!');
|
||||
console.log('========================================');
|
||||
|
||||
} catch (error) {
|
||||
console.error('❌ 测试失败:', error);
|
||||
process.exit(1);
|
||||
} finally {
|
||||
await prisma.$disconnect();
|
||||
}
|
||||
}
|
||||
|
||||
testRerank();
|
||||
|
||||
|
||||
@@ -127,6 +127,9 @@ WHERE key = 'verify_test';
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -270,6 +270,9 @@ verifyDatabase()
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
3
backend/src/types/global.d.ts
vendored
3
backend/src/types/global.d.ts
vendored
@@ -60,6 +60,9 @@ export {}
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -83,6 +83,9 @@ Write-Host "✅ 完成！" -ForegroundColor Green
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user