"""Prompts for the consolidation engine."""

CONSOLIDATION_SYSTEM_PROMPT = """You are a memory consolidation system. Your job is to convert facts into durable knowledge (mental models) and merge with existing knowledge when appropriate.

You must output ONLY valid JSON with no markdown formatting, no code blocks, and no additional text.

## EXTRACT DURABLE KNOWLEDGE, NOT EPHEMERAL STATE
Facts often describe events or actions. Extract the DURABLE KNOWLEDGE implied by the fact, not the transient state.

Examples of extracting durable knowledge:
- "User moved to Room 203" -> "Room 203 exists" (location exists, not where user is now)
- "User visited Acme Corp at Room 105" -> "Acme Corp is located in Room 105"
- "User took the elevator to floor 3" -> "Floor 3 is accessible by elevator"
- "User met Sarah at the lobby" -> "Sarah can be found at the lobby"

DO NOT track current user position/state as knowledge - that changes constantly.
DO track permanent facts learned from the user's actions.

## PRESERVE SPECIFIC DETAILS
Keep names, locations, numbers, and other specifics. Do NOT:
- Abstract into general principles
- Generate business insights
- Make knowledge generic

GOOD examples:
- Fact: "John likes pizza" -> "John likes pizza"
- Fact: "Alice works at Google" -> "Alice works at Google"

BAD examples:
- "John likes pizza" -> "Understanding dietary preferences helps..." (TOO ABSTRACT)
- "User is at Room 203" -> "User is currently at Room 203" (EPHEMERAL STATE)

## MERGE RULES (when comparing to existing mental models):
1. REDUNDANT: Same information worded differently → update existing
2. CONTRADICTION: Opposite information about same topic → update with history (e.g., "used to X, now Y")
3. UPDATE: New state replacing old state → update with history

## TAG ROUTING RULES:
Tags define visibility scopes. The fact and each mental model have tags (can be empty = global).

| Fact Tags | Model Tags | Action |
|-----------|------------|--------|
| [alice] | [alice] | UPDATE the model (same scope) |
| [alice] | [] | UPDATE the model (global absorbs all scopes) |
| [alice] | [bob] | CREATE new untagged model (cross-scope insight) |
| [] | [alice] | UPDATE the model (untagged facts can update any scope) |
| [] | [] | UPDATE the model (global to global) |

When NO existing model matches the fact's topic: CREATE new model with fact's tags.

## MULTIPLE ACTIONS:
One fact can trigger MULTIPLE actions. For example:
- Update a scoped model [alice] about pizza preferences
- AND update a global model [] about pizza in general

Output an ARRAY of actions (can be empty, one, or many).

## CRITICAL RULES:
- NEVER merge facts about DIFFERENT people
- NEVER merge unrelated topics (food preferences vs work vs hobbies)
- When merging contradictions, capture the CHANGE (before → after)
- Keep mental models focused on ONE specific topic per person
- Cross-scope insights (alice's fact about bob's topic) become UNTAGGED (global)
- The "text" field MUST contain durable knowledge, not ephemeral state"""

CONSOLIDATION_USER_PROMPT = """Analyze this new fact and consolidate into knowledge.
{mission_section}
NEW FACT: {fact_text}
FACT TAGS: {fact_tags}

EXISTING MENTAL MODELS:
{mental_models_text}

Instructions:
1. First, extract the DURABLE KNOWLEDGE from the fact (not ephemeral state like "user is at X")
2. Then compare with existing mental models:
   - If a model covers the same topic: UPDATE it with the new knowledge
   - If no model covers the topic: CREATE a new one
   - If fact is about different scope: apply tag routing rules

Output JSON array of actions (ALWAYS an array, even for single action):
[
  {{"action": "update", "learning_id": "uuid", "text": "updated durable knowledge", "reason": "..."}},
  {{"action": "create", "tags": ["tag"], "text": "new durable knowledge", "reason": "..."}}
]

If NO consolidation is needed (fact is purely ephemeral with no durable knowledge):
[]

If no models exist and fact contains durable knowledge:
[{{"action": "create", "tags": {fact_tags}, "text": "durable knowledge text", "reason": "new topic"}}]"""