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
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@@ -579,6 +579,9 @@ df['creatinine'] = pd.to_numeric(df['creatinine'], errors='coerce')
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@@ -417,6 +417,9 @@ npm run dev
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@@ -994,6 +994,9 @@ export const aiController = new AIController();
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@@ -1328,6 +1328,9 @@ npm install react-markdown
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@@ -236,6 +236,9 @@ FMA___基线 | FMA___1个月 | FMA___2个月
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@@ -394,6 +394,9 @@ formula = "FMA总分(0-100) / 100"
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@@ -228,6 +228,9 @@ async handleFillnaMice(request, reply) {
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@@ -200,6 +200,9 @@ method: 'mean' | 'median' | 'mode' | 'constant' | 'ffill' | 'bfill'
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