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Agentic BI v4.0 — LangGraph + PG Supernode

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Enterprise NL2SQL with three-layer semantic governance, multi-agent orchestration, and defense-in-depth security.

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Talk to Data — Query Workspace
You

上周各门店的销售排名是什么？

AI
rankingstoresaleslast_week
SELECT store_name, SUM(sales_gmv) AS total_sales
FROM fact_sales JOIN dim_store USING (store_id)
WHERE status = 'completed'
ORDER BY total_sales DESC

Store A leads with ¥1.2M sales (+15% WoW) · 15 rows · 340ms

21Agent Nodes
< 8sP90 Latency
95%Query Success
7Security Layers

Semantic Governance

Three-Layer Data Governance

A structured semantic layer that transforms raw metadata into governed, auditable knowledge — from schema definitions to compliance rules.

Layer 1

Canonical Metadata

Table schemas, column definitions, metric contracts, join rules, and few-shot NL2SQL examples — version-controlled as YAML, ingested into PostgreSQL.

table_assetmetric_assetjoin_rulefew_shot_example
Layer 2

Term Governance

Business term lifecycle management, bilingual disambiguation, synonym networks, and ambiguity matrices — powered by pgvector embeddings.

business_termterm_relationshipambiguity_matrix
Layer 3

Business Knowledge

Compliance rules, business context assumptions, industry archetypes, and regulatory constraints — enforced via Apache AGE graph traversal.

business_rulebusiness_contextcompliance_pack

Governance as Code — Git-Managed YAML → PG Supernode

YAML Metadata
Schema Validation
Cross-Layer Refs
Version Check
CI Pipeline
PG Ingest (R+V+G)

PG Supernode

R/V/G Triple-Engine Retrieval

One PostgreSQL instance, three retrieval paradigms. Relational precision, vector semantics, and graph governance — unified in a single transaction.

R

Relational

Engine

Structured DDL, metric definitions, join rules, lifecycle tracking in PostgreSQL

V

Vector

Engine

7 embedding types with pgvector — cosine similarity search for fuzzy term matching

G

Graph

Engine

Apache AGE property graph enforces governance boundaries and relationship traversal

Four-Stage RAG Pipeline

  • Precision matching — exact term lookup in relational tables
  • Semantic recall — cosine similarity across 7 embedding types
  • Graph traversal — relationship discovery + boundary enforcement
  • Reranker — relevance re-rank, column pruning, token budgeting
Graph (AGE)Vector (pgvector)Relational (PG)

Multi-Agent System

21-Node Agentic Orchestration

LangGraph Supervisor choreographs specialized sub-agents — each optimized for its task with dedicated models, tools, and evaluation gates.

Supervisor

Embedding + governance context

Query Understanding

12-type intent classification

Router

6-route intelligent dispatch

Data Retrieval

R/V/G hybrid search

Reranker

4-stage RAG post-processing

SQL Generation

Code-specialized LLM

Guardrails

7-layer security validation

Visualization

Auto chart recommendation

6 Intelligent Route Decisions

KPI LookupNL2SQL QueryDeep AnalysisGraph ReasoningKnowledge QAClarification

Defense in Depth

7-Layer Security

Every query passes through seven independent security checks — from JWT authentication to result-level PII masking. Fail-safe by default: when in doubt, reject.

GDPRHIPAAPCI-DSSSOX
1

JWT Authentication

Role-based access control + rate limiting

2

Permission Filter

Domain-scoped data access boundaries

3

Metadata Safety

PII exposure policy (hidden / masked / visible)

4

Graph Constraints

Apache AGE enforces governance joins

5

SQL Policy Engine

SELECT-only whitelist + column validation

6

Execution Isolation

Dedicated workgroup + statement timeout

7

Result Masking

Column-level PII masking (SHA256 / partial)

Capabilities

Built for Enterprise Scale

Multi-Model Strategy

Smart model routing — DeepSeek for understanding, Qwen3-Coder for SQL, Qwen3-Max for insights. Automatic fallback chains.

SSE Streaming

Real-time pipeline progress via Server-Sent Events. See each agent step as it happens, not a blank loading screen.

Three-Layer Memory

Short-term dialog context, long-term user preferences, and semantic SQL cache with pgvector matching.

Corrective Retrieval

On SQL error, auto-detect pattern (missing table, ambiguous column) and compensate with targeted re-retrieval.

Bilingual Native

Chinese + English semantic understanding. Bilingual term governance, disambiguation, and query rewriting built-in.

Audit by Default

Structured JSON logging for every pipeline step — question, retrieval path, SQL, execution, result. S3 archival for compliance.

Ready to talk to your data?

Transform natural language into governed, auditable SQL queries. Deploy on your own infrastructure with full data sovereignty.

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