System Health
API Endpoints
Tenants
Customer organisations that own one or more databases. Each tenant has a business name, the domain where their UI is hosted, and an admin contact.
New Tenant
Target Databases (shadow)
Acufy-side shadow records for tenant target databases. No credentials live here — the tenant stores those in their own registry at their site. Each shadow carries an auth key the tenant must present when provisioning the target DB locally.
Issue License Key
License Key Issued
Share the key below with the tenant admin out-of-band (e.g. email or secure message). They must paste it verbatim into their local registration tool.
This value is not re-displayed on subsequent views. If lost, re-derive via re-issue.
Client Databases
Setup Registry Database
Enter the PostgreSQL server credentials below. The application will create the registry database (if it does not already exist) and initialise all required tables.
IAM auth generates a short-lived token at connection time. SSL is enforced automatically.
The database will be created if it does not already exist.
SQL Generation Hints
Domain-specific hints injected into SQL prompts to improve query accuracy.
Entity Intelligence Prompts
Per-tenant system prompts used when the chatbot narrates an entity snapshot (member, group, plan, provider, claim, or any custom type). When no active prompt exists for an entity, the orchestrator falls back to the legacy hardcoded prompt.
Entity Detection Patterns
Python regex patterns the chatbot uses to recognise entity IDs in user questions (e.g. member IDs, NPI, claim IDs). Multiple patterns per entity_type are allowed — lowest priority wins. When no active pattern exists for a given entity_type, the detector falls back to the hardcoded healthcare defaults.
New Entity Pattern
Single-Token Router Configuration
When a user enters a single bare token (e.g. "1234", "ORD987"), the chatbot can run a deterministic SQL lookup against the configured view and bypass the LLM. At most one active config per tenant. Leave unconfigured to disable the router for that tenant.
Schema Pruner Configuration
Anchor tables are always included in the pruned schema regardless of the question. Use for central entities the LLM always needs visibility into.
Forced companions are bridge/child tables that must travel with a trigger table (when the trigger is selected, its companions are pulled in too).
When a tenant has no config row, the pruner runs purely score-driven — no tables are forced in.
New Intelligence Prompt
Query Cache Warming
Generate sample questions and pre-populate the AI cache so users get instant responses
Enter one question per line. Click Load Questions to add them to the list below.
Permanently stored question→SQL pairs (PostgreSQL). Survive server restarts. Time-relative questions are excluded.
Exact-match cached answers (Redis / in-memory). Each entry is evicted automatically when its TTL expires.
Failed Query Repair
Queries that exhausted all strategies are listed below. Re-run them after adding domain hints or corrections to fix them automatically.
Last Repair Results
Correction Memory Validation
Test each stored correct_sql against the live client DB. Identifies corrections that are now broken due to schema changes.
Provider Settings
Configure per-client LLM and embedding providers. Leave fields blank to inherit system defaults from .env.
Connection Test Results
User Feedback
Bad result reports submitted from the user UI.
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Feedback Detail
Error Rate Trend
Daily fallback % and strategy breakdown.
Daily Fallback %
| Date | Total | SQL OK | SQL Error | Vector | Fallback | Fallback % |
|---|---|---|---|---|---|---|
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Failing Questions
Most-repeated questions that triggered fallback strategy.
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LLM Cost Tracking
Daily token consumption per client.
Daily Total Tokens
| Date | Queries | Input Tokens | Output Tokens | Total Tokens | Avg / Query |
|---|---|---|---|---|---|
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Query Review
Browse all user queries. Click any row to open the Correction Workbench.
| Time | Question | Strategy | Status | Time (ms) | Actions |
|---|---|---|---|---|---|
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| Created | Question | Reason | Uses | Actions |
|---|---|---|---|---|
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Retrain System
Re-pull schema, regenerate embeddings and hints, clean up stale corrections, and flush all caches. Run this after any database schema changes or when the system needs a fresh start.
Progress
RunningCorrection Detail
Correction Workbench
Describe what's wrong — the AI will regenerate the SQL using your feedback.
Click "Run SQL" to see results
Once you're satisfied with the SQL, save it as a correction. Future similar questions will use this SQL automatically.
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