- 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
398 KiB
398 KiB