Jorvis Target Architecture: The Cognitive SQL Agent

Status: Vision (Target State) Goal: Universal, Voice-Enabled, Self-Healing SQL Analyst with Predictive Capabilities.

1. High-Level Concept

Jorvis acts as an intelligent intermediary between humans (Voice/Text) and data warehouses. It is not just a query generator; it is a reasoning engine that understands business context, schema relationships, and data nuances.

graph TD
  User((User)) <-->|Voice/Text Stream| API[Gateway / API]
  API <-->|Context| Brain[Cognitive Engine]
  
  subgraph "The Brain (Graph Orchestrator)"
    Brain --> Intent[Intent Recognition]
    Brain --> Knowledge[Dynamic RAG]
    Brain --> SQL[Multi-Dialect SQL Gen]
    Brain --> Repair[Self-Healing Loop]
    Brain --> Forecast[Predictive Analytics]
  end
  
  subgraph "The Memory"
    Knowledge <--> SemanticCache[(Semantic Cache)]
    Knowledge <--> VectorStore[(Schema Vectors)]
    Brain --> Audit[(Audit & Compliance)]
  end
  
  subgraph "The Hands (Execution)"
    SQL --> DWH_Router{Dialect Router}
    DWH_Router --> PG[(Postgres)]
    DWH_Router --> SF[(Snowflake)]
    DWH_Router --> BQ[(BigQuery)]
    Forecast --> ML_Eng[DB-Native ML / Python Sidecar]
  end

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2. Advanced Capabilities (The "Dream" Features)

2.1. Multi-Dialect Support (Polymorphism)

Jorvis doesn't just write "SQL". He writes dialect-specific, optimized code.

• Mechanism: DialectAdapter pattern.
• Capabilities: Auto-detection of dialect nuances and specific function usage (e.g., BigQuery ML).

2.2. Voice-First Interaction

Real-time, low-latency conversation.

• Protocol: WebSockets / Server-Sent Events (SSE).
• Pipeline: STT (Speech-to-Text) -> Streaming Graph -> TTS (Text-to-Speech).

2.3. Semantic Caching (Speed & Cost)

Before asking the LLM, Jorvis checks if he answered a similar question.

• Logic: Vector similarity check (> 0.95) against successful query history.

2.4. Advanced RAG (Context 2.0)

• Business Glossary: Retrieval of business definitions and metrics.
• Value Probing: Automatic inclusion of top-N sample values for filters.

2.5. Predictive Analytics (The Future)

Jorvis evolves from analyzing the past to predicting the future.

• Forecasting Node: A specialized node in the graph that handles time-series data.
• DB-Native ML: Integration with BigQuery ML or Snowflake Cortex for reliable, enterprise-grade forecasting.
• Insight Generation: "Based on current trends, you will reach your monthly target by the 25th."

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3. The Cognitive Graph (Target Topology)

stateDiagram-v2
    [*] --> IntentClassifier
    
    state IntentClassifier {
        [*] --> IsItSQL?
        IsItSQL? --> GeneralChat: No
        IsItSQL? --> SchemaRAG: Yes
    }
    
    state SchemaRAG {
        Vector --> RRF
        Keyword --> RRF
        ValueProbing --> RRF
    }
    
    SchemaRAG --> PlanGenerator
    PlanGenerator --> ParallelExecution
    
    state ParallelExecution {
        state "SQL Generation" as SQL
        state "Forecasting (ML)" as Forecast
    }
    
    ParallelExecution --> ExecutionNode
    ExecutionNode --> ErrorHandler: Fail
    ErrorHandler --> SQL: Retry (Self-Correction)
    
    ExecutionNode --> Visualizer: Success
    Visualizer --> Synthesis
    Synthesis --> [*]

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4. Technology Stack (Future)

• Core: NestJS (Graph Engine).
• DB: Postgres + pgvector (Knowledge Base).
• ML Engine: Python Sidecar (Prophet/Statsmodels) or DB-native ML.
• Interface: Open WebUI with native MCP support.