184 lines
4.9 KiB
JSON
184 lines
4.9 KiB
JSON
{
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"name": "OperationalMemory",
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"type": "object",
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"properties": {
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"memory_id": {
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"type": "string",
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"description": "Unique memory identifier (MEM-timestamp-random)"
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},
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"trigger_event_id": {
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"type": "string",
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"description": "ID of the WorkforceEvent that triggered this memory"
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},
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"trigger_event_type": {
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"type": "string",
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"description": "Type of the triggering event"
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},
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"context_snapshot": {
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"type": "object",
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"description": "Graph state at decision time \u2014 entities, relationships, metrics",
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"additionalProperties": true
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},
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"decision": {
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"type": "object",
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"description": "The AI's decision/recommendation",
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"properties": {
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"prediction": {
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"type": "string"
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},
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"recommendation": {
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"type": "string"
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},
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"confidence_score": {
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"type": "number",
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"minimum": 0,
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"maximum": 100
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},
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"proposed_actions": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"action_type": {
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"type": "string"
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},
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"description": {
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"type": "string"
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},
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"target_entity_type": {
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"type": "string"
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},
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"target_entity_id": {
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"type": "string"
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},
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"auto_execute": {
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"type": "boolean",
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"default": false
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},
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"executed": {
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"type": "boolean",
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"default": false
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}
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}
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}
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}
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}
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},
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"reasoning": {
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"type": "string",
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"description": "Full reasoning chain \u2014 why the AI made this decision"
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},
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"causal_chain": {
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"type": "array",
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"description": "Structured causal chain tracing WHY the outcome occurred \u2014 each factor connected to the next, ending at root cause(s)",
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"items": {
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"type": "object",
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"properties": {
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"step": {
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"type": "number",
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"description": "Order in the causal chain (1 = first contributing factor, last = root cause)"
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},
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"factor": {
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"type": "string",
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"description": "The contributing factor (e.g., 'Vendor A supplied fewer experienced cooks')"
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},
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"evidence": {
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"type": "string",
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"description": "Data-driven evidence supporting this factor"
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},
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"category": {
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"type": "string",
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"enum": [
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"vendor",
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"operations",
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"workforce",
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"training",
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"demand",
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"external",
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"compliance",
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"finance",
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"equipment",
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"transportation",
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"system"
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],
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"description": "What area of the operation this factor belongs to"
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},
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"leads_to": {
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"type": "string",
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"description": "What this factor caused (the next link in the chain)"
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}
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}
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}
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},
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"root_causes": {
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"type": "array",
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"description": "The root cause(s) identified at the end of the causal chain",
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"items": {
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"type": "string"
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}
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},
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"similar_memories_consulted": {
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"type": "array",
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"description": "IDs of past memories used as reference",
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"items": {
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"type": "string"
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}
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},
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"outcome": {
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"type": "string",
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"description": "What actually happened (filled in during outcome review)"
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},
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"outcome_score": {
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"type": "number",
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"description": "Did it work? (-100 = made things worse, 0 = no effect, 100 = helped significantly)"
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},
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"outcome_metrics": {
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"type": "object",
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"description": "Quantified outcome data (e.g., overtime_reduction_pct, cost_savings)",
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"additionalProperties": true
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},
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"status": {
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"type": "string",
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"enum": [
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"pending",
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"executed",
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"outcome_pending",
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"learned",
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"rejected"
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],
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"default": "pending"
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},
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"agent_name": {
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"type": "string",
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"description": "Which agent made this decision"
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},
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"department": {
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"type": "string",
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"enum": [
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"operations",
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"procurement",
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"finance",
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"vendor",
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"compliance",
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"executive"
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],
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"description": "Department this memory belongs to"
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},
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"learned_at": {
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"type": "string",
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"format": "date-time",
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"description": "When the outcome was recorded and learning completed"
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},
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"tags": {
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"type": "array",
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"items": {
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"type": "string"
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}
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}
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},
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"required": [
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"memory_id",
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"trigger_event_id",
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"department"
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]
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} |