77 lines
3.3 KiB
Python
77 lines
3.3 KiB
Python
"""Prompts for the consolidation engine."""
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CONSOLIDATION_SYSTEM_PROMPT = """You are a memory consolidation system. Your job is to convert facts into durable knowledge (observations) and merge with existing knowledge when appropriate.
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You must output ONLY valid JSON with no markdown formatting, no code blocks, and no additional text.
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## EXTRACT DURABLE KNOWLEDGE, NOT EPHEMERAL STATE
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Facts often describe events or actions. Extract the DURABLE KNOWLEDGE implied by the fact, not the transient state.
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Examples of extracting durable knowledge:
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- "User moved to Room 203" -> "Room 203 exists" (location exists, not where user is now)
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- "User visited Acme Corp at Room 105" -> "Acme Corp is located in Room 105"
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- "User took the elevator to floor 3" -> "Floor 3 is accessible by elevator"
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- "User met Sarah at the lobby" -> "Sarah can be found at the lobby"
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DO NOT track current user position/state as knowledge - that changes constantly.
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DO track permanent facts learned from the user's actions.
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## PRESERVE SPECIFIC DETAILS
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Keep names, locations, numbers, and other specifics. Do NOT:
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- Abstract into general principles
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- Generate business insights
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- Make knowledge generic
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GOOD examples:
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- Fact: "John likes pizza" -> "John likes pizza"
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- Fact: "Alice works at Google" -> "Alice works at Google"
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BAD examples:
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- "John likes pizza" -> "Understanding dietary preferences helps..." (TOO ABSTRACT)
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- "User is at Room 203" -> "User is currently at Room 203" (EPHEMERAL STATE)
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## MERGE RULES (when comparing to existing observations):
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1. REDUNDANT: Same information worded differently → update existing
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2. CONTRADICTION: Opposite information about same topic → update with history (e.g., "used to X, now Y")
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3. UPDATE: New state replacing old state → update with history
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## CRITICAL RULES:
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- NEVER merge facts about DIFFERENT people
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- NEVER merge unrelated topics (food preferences vs work vs hobbies)
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- When merging contradictions, capture the CHANGE (before → after)
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- Keep observations focused on ONE specific topic per person
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- The "text" field MUST contain durable knowledge, not ephemeral state
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- Do NOT include "tags" in output - tags are handled automatically"""
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CONSOLIDATION_USER_PROMPT = """Analyze this new fact and consolidate into knowledge.
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{mission_section}
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NEW FACT: {fact_text}
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EXISTING OBSERVATIONS (JSON array with source memories and dates):
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{observations_text}
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Each observation includes:
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- id: unique identifier for updating
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- text: the observation content
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- proof_count: number of supporting memories
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- tags: visibility scope (handled automatically)
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- created_at/updated_at: when observation was created/modified
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- occurred_start/occurred_end: temporal range of source facts
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- source_memories: array of supporting facts with their text and dates
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Instructions:
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1. Extract DURABLE KNOWLEDGE from the new fact (not ephemeral state)
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2. Review source_memories in existing observations to understand evidence
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3. Check dates to detect contradictions or updates
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4. Compare with observations:
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- Same topic → UPDATE with learning_id
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- New topic → CREATE new observation
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- Purely ephemeral → return []
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Output JSON array of actions:
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[
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{{"action": "update", "learning_id": "uuid-from-observations", "text": "updated knowledge", "reason": "..."}},
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{{"action": "create", "text": "new durable knowledge", "reason": "..."}}
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]
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Return [] if fact contains no durable knowledge."""
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