9db64ecda3
* chore: run benchmarks with reflect mode * chore: run benchmarks with reflect mode * fixes * new mm * bunch of fixes * initial commit * fixes * fixes * fixes * fix: sometimes memories gets extracted in the wrong language
91 lines
4 KiB
Python
91 lines
4 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 (mental models) 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 mental models):
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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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## TAG ROUTING RULES:
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Tags define visibility scopes. The fact and each mental model have tags (can be empty = global).
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| Fact Tags | Model Tags | Action |
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|-----------|------------|--------|
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| [alice] | [alice] | UPDATE the model (same scope) |
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| [alice] | [] | UPDATE the model (global absorbs all scopes) |
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| [alice] | [bob] | CREATE new untagged model (cross-scope insight) |
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| [] | [alice] | UPDATE the model (untagged facts can update any scope) |
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| [] | [] | UPDATE the model (global to global) |
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When NO existing model matches the fact's topic: CREATE new model with fact's tags.
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## MULTIPLE ACTIONS:
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One fact can trigger MULTIPLE actions. For example:
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- Update a scoped model [alice] about pizza preferences
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- AND update a global model [] about pizza in general
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Output an ARRAY of actions (can be empty, one, or many).
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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 mental models focused on ONE specific topic per person
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- Cross-scope insights (alice's fact about bob's topic) become UNTAGGED (global)
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- The "text" field MUST contain durable knowledge, not ephemeral state"""
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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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FACT TAGS: {fact_tags}
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EXISTING MENTAL MODELS:
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{mental_models_text}
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Instructions:
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1. First, extract the DURABLE KNOWLEDGE from the fact (not ephemeral state like "user is at X")
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2. Then compare with existing mental models:
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- If a model covers the same topic: UPDATE it with the new knowledge
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- If no model covers the topic: CREATE a new one
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- If fact is about different scope: apply tag routing rules
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Output JSON array of actions (ALWAYS an array, even for single action):
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[
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{{"action": "update", "learning_id": "uuid", "text": "updated durable knowledge", "reason": "..."}},
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{{"action": "create", "tags": ["tag"], "text": "new durable knowledge", "reason": "..."}}
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]
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If NO consolidation is needed (fact is purely ephemeral with no durable knowledge):
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[]
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If no models exist and fact contains durable knowledge:
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[{{"action": "create", "tags": {fact_tags}, "text": "durable knowledge text", "reason": "new topic"}}]"""
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