- Auto-detect embedding model per table dimension (1536/3072) for search queries
- Embedding cache: reuse same embedding for tables with same model (avoid redundant API calls)
- Tenancy filter: strict JSON_VALUE match, no IS NULL fallback (prevents old data leaking)
- Global tables (cisrecom, engineerknowledgebase): no tenancy filter (generic knowledge)
- ADB connection timeout: 15s connect, 30s query
- RAG context: includes extract_date per document, relevance score, source table name
- Context size: 12000 chars max, distributed proportionally across top 8 docs
- ADB offline handling: LLM informed when bases are unavailable
- System prompt updated: clear hierarchy (findings > cisrecom > engineerknowledgebase)
- RAG vs MCP Tools differentiation: stored data vs real-time scan instructions
- Temporal awareness: model prioritizes recent data, supports date comparison
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