AI Overview
Sovrium ships a complete, self-hostable AI layer. Every capability is opt-in and governed by the platform's existing RBAC and field-level permissions — AI never bypasses the security model. The AI layer is disabled by default: nothing AI-related runs until you set the AI_PROVIDER environment variable.
The design philosophy mirrors the rest of the platform: operators control infrastructure via environment variables, schema authors declare intent in config. Which provider answers a call, where embeddings live, and whether the MCP server mounts are operator concerns (AI_PROVIDER, MCP_ENABLED, …). Which tables an agent may touch and which entities are AI-eligible are schema-author concerns (agents[], aiAccess).
The AI Ecosystem
Eight building blocks compose into the full AI experience.
| Capability | What it does | Docs |
|---|---|---|
| Providers | Pick the LLM and embedding backend (Anthropic, OpenAI, Mistral, Gemini, local Ollama, OpenAI-compatible). | AI Providers |
| Eco routing | Frugal-by-default provider precedence — prefer a local model, fall back to cloud. | AI Eco Routing |
| AI fields | Computed table columns that summarize, categorize, extract, translate, etc. using an LLM. | AI Fields |
| AI chat | A conversational interface over your data — query, mutate, and trigger automations in natural language. | AI Chat |
| AI agents | Autonomous virtual users with scoped tools, approval gates, schedules, and operational limits. | AI Agents |
| RAG knowledge | Ground answers in your tables and documents via vector embeddings and semantic search. | AI RAG |
| Agent memory | Conversation history, RAG-backed knowledge, and persistent learned facts per agent. | AI Memory |
| MCP integration | Expose Sovrium as an MCP server, and let agents consume external MCP tools. | MCP Integration |
AI fields are documented under Tables. The seven computed field types (ai-summary, ai-categorize, ai-extract, ai-sentiment, ai-tag, ai-translate, ai-generate) live on table records and are covered in AI Fields. This section covers the conversational, agentic, and interoperability layers.
Configuration Philosophy
AI behaves like the database (DATABASE_URL), storage (STORAGE_PROVIDER), and auth (AUTH_SECRET) layers: infrastructure is env-var config, intent is schema.
| Concern | Controlled by | Where |
|---|---|---|
| Which provider/model/key to use | Operator | AI_PROVIDER, AI_MODEL, AI_API_KEY env vars |
| Provider routing precedence (eco) | Operator | ECO_AI_PROVIDER_PRECEDENCE env var |
| Whether the MCP server mounts | Operator | MCP_ENABLED, MCP_TRANSPORT, … env vars |
| Which entities are AI-eligible | Schema author | aiAccess on tables / automations / actions |
| Agent identity, tools, approval, schedule | Schema author | app.agents[] |
| AI computed columns | Schema author | type: ai-* fields on a table |
The single master switch is AI_PROVIDER. When unset (or blanked), the entire AI layer is silently disabled — AI fields skip computation, the chat endpoint returns a disabled response, agents do not run, and RAG/embedding infrastructure is not provisioned. No errors are thrown at boot; AI simply stays dormant until configured.
# Minimal enablement: a local Ollama model (no API key, no cloud).
AI_PROVIDER=ollama
AI_BASE_URL=http://localhost:11434
AI_MODEL=llama3.1
# A cloud provider.
AI_PROVIDER=anthropic
AI_API_KEY=sk-ant-...
AI_MODEL=claude-sonnet-4-5
How the Pieces Fit Together
AI_PROVIDER (master switch)
│
┌─────────────────┬───────┴────────┬──────────────────┐
▼ ▼ ▼ ▼
AI Fields AI Chat AI Agents MCP Server
(computed cols) (conversation) (virtual users) (external clients)
│ │ │ │
│ └──── tools ─────┤ │
│ │ │
▼ ▼ ▼
RBAC + field-level permissions (always enforced)
│
▼
RAG knowledge + agent memory (pgvector / SQLite BLOB)
Every AI surface — fields, chat, agents, MCP — funnels through the same authorization layer. An agent inherits its role's permissions; a chat user can only see records their session permits; an MCP client is bounded by its token's role. There is no privileged AI bypass.
Prerequisites
| Requirement | Why |
|---|---|
AI_PROVIDER set |
Master switch. Without it the whole AI layer is dormant. |
app.auth (most) |
Agents are stored as auth users; chat and MCP RBAC require roles. AI fields work without auth. |
| pgvector / SQLite | RAG embeddings need PostgreSQL + pgvector or SQLite (Float32 BLOB + app-side cosine). No external vector DB. |
Related Pages
- AI Providers — choose and configure the LLM backend.
- AI Eco Routing — local-first provider precedence.
- AI Fields — computed columns powered by an LLM.
- AI Chat — conversational data interaction.
- AI Agents — autonomous virtual users.
- AI RAG — retrieval-augmented knowledge.
- AI Memory — agent conversation, knowledge, and facts.
- MCP Integration — Sovrium as MCP server and client.
- Environment Variables — full env-var reference.