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chiguyong e33dc25ad3 feat(memory): RAG pipeline optimization — 5 Implementation Units 
U1: QueryTransformer — LLM/rule-based query rewriting + sub-query decomposition
U2: HttpRAGService enhanced_search() — rerank + compression via /bases/{kb_id}/retrieve
U3: Structured context injection — source attribution headers in RAG results
U4: RetrieveKnowledgeTool — built-in tool for mid-reasoning knowledge retrieval
U5: Configurable retrieval params + per-KB weights + CJK token estimation

Config example:
  memory:
    retrieval:
      top_k: 5
      token_budget: 2000
      context_template: structured
    query_transform:
      enabled: true
      strategy: llm
    semantic:
      search_mode: enhanced
      use_rerank: true
      kb_weights:
        industry-kb-id: 1.2
        enterprise-kb-id: 0.8

Tests: 1037 passed, 18 skipped, 0 failed
 		2026-06-06 19:27:09 +08:00
..
brainstorms feat(agentkit): v2 Phase 1 - ReAct/LLM Gateway/Skill/Server + review fixes 2026-06-05 23:32:16 +08:00
plans feat(memory): RAG pipeline optimization — 5 Implementation Units 2026-06-06 19:27:09 +08:00
GEO-INTEGRATION-GUIDE.md feat(agentkit): Phase 3 upgrade - persistence, memory, evolution, observability 2026-06-06 17:17:45 +08:00