RiverGen AI Database Architecture (1.0.2)
Page Outline
All RiverGen AI 1.0.2 operations are persisted within a centralized PostgreSQL cluster. The schema is designed for high-concurrency metadata ingestion and supports complex relational mapping between training intents and operational decisions.
Model Studio (MSA) Schemas
These tables maintain the integrity of the RiverGen Intelligence Factory.
| Table Name | Role in RiverGen | 1.0.2 Implementation Detail |
|---|---|---|
task_intents | Declarative task spec. | Includes rg_version and spatial constraint filters. |
data_sources | Input registry. | Supports SQL, S3, and Parquet stream descriptors. |
training_jobs | Execution tracking. | Maps to the internal rg-compute-cluster hardware. |
job_status_history | Audit stream. | Phase-specific progress (Ingestion -> Analysis -> Lock). |
trained_models | Artifact registry. | The official source-of-truth for active platform models. |
Decision Intelligence (DIA) Schemas
These tables support the RiverGen Decision Brain and autonomous correction loops.
| Table Name | Role in RiverGen | 1.0.2 Implementation Detail |
|---|---|---|
decisions | Autonomous logs. | Persists the reasoning string for every platform action. |
agent_metadata | Telemetry buffer. | High-velocity storage for agent performance metrics. |
decision_guidelines | Governance layer. | Stores general boundaries and prompt instructions. |
predictions | Raw ML logs. | Stores pre-refined inference results for drift analysis. |
Intelligence Relationship Diagram
In RiverGen 1.0.2, every decision is traceable back to its training origin: