2 Commits

Author	SHA1	Message	Date
HaHafeng	40c2f8e148	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	2026-01-21 20:24:29 +08:00
HaHafeng	1f5bf2cd65	docs(rag-engine): update architecture design with building-blocks principle ... - Add core design principle: provide building blocks, no strategy selection - Remove chat() method, strategy determined by business modules - Add new capabilities: getDocumentFullText(), getAllDocumentsText() - Add new capabilities: getDocumentSummary(), getAllDocumentsSummaries() - Add business module strategy examples (PKB/AIA/ASL/RVW) - Add strategy selection guide (by scale, by scenario) - Update data model with summary and tokenCount fields - Add SummaryService to code structure	2026-01-20 20:35:26 +08:00