chatbi-mvplisted

# ChatBI MVP Architecture ## Overview Build a ChatBI system: user asks questions in natural language → system generates SQL → executes → displays interactive charts with AI insights. **Architecture:** NL → S2SQL (semantic SQL / MQL) → Physical SQL. LLM generates S2SQL using business terms (bizName), a deterministic Translator converts to physical SQL. LLM never touches physical table/column names. **Tech stack assumed:** React (frontend) + Python/FastAPI (backend). Architecture is language-agnostic. ## Five Core Layers ``` User: "最近7天各分区播放量怎么样" │ ▼ ┌─ Layer 3: RAG ───────────────���┐ Trie + Embedding recall │ "播放量" → views (metric) │ Identify schema elements │ "分区" → category (dim) │ in user query │ "最近7天" → DateConf{-7d} │ └───────────────┬───────────────┘ │ ▼ ┌─ Layer 1: NL → S2SQL → SQL ──┐ 5-stage pipeline │ MAPPING → PARSING → │ LLM generates S2SQL (bizName) │ CORRECTING → TRANSLATING │ Translator → physical SQL │ → EXECUTE │ └───────────────┬───────────────┘ │ ┌───────┴───────┐ ▼ ▼ ┌─ Layer 2 ───┐ ┌─ Layer 5 ──────────┐ │ Multi-turn │ │ Attribution │ │ Context save │ │ LLM summary + YoY │ │ + LLM rewrite│ │ + drill-down recs │ └──────────────┘ └─────────────────────┘ │ │ └───────┬───────┘ ▼ ┌─ Layer 4: Visualization ──────┐ │ Auto chart type → ECha