0aa7c2b3a1
* feat: batch observations consolidation * feat: batch observations consolidation * docs: add CONSOLIDATION_LLM_BATCH_SIZE config flag documentation
66 lines
3.7 KiB
Python
66 lines
3.7 KiB
Python
"""Prompts for the consolidation engine."""
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# Default mission when no bank-specific mission is set
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_DEFAULT_MISSION = "Track every detail: names, numbers, dates, places, and relationships. Prefer specifics over abstractions, never generalise."
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# Processing rules — always present regardless of mission
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_PROCESSING_RULES = """Processing rules (always apply):
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- REDUNDANT: same info worded differently → UPDATE the existing observation.
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- CONTRADICTION/UPDATE: capture both states with temporal markers ("used to X, now Y").
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- RESOLVE REFERENCES: when a new fact provides a concrete value resolving a vague placeholder in an existing observation (e.g. "home country", "hometown", "birthplace", "native language", "her ex", "that city"), UPDATE the observation to embed the resolved value explicitly. Example: new fact says "grandma in Sweden" + existing observation says "moved from her home country" → update to "home country is Sweden".
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- NEVER merge observations about different people or unrelated topics."""
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# Data section — format placeholders {facts_text} and {observations_text} are substituted at call time
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_BATCH_DATA_SECTION = """
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NEW FACTS:
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{facts_text}
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EXISTING OBSERVATIONS (JSON array, pooled from recalls across all facts above):
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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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- 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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Compare the facts against existing observations:
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- Same topic as an existing observation → UPDATE it (observation_id + source_fact_ids)
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- New topic with durable knowledge → CREATE a new observation (source_fact_ids)
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- Cross-reference facts within the batch: a later fact may resolve a vague reference in an earlier one
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- Purely ephemeral facts → omit them (no create/update needed)"""
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# Output format — JSON braces escaped as {{ }} so .format() leaves them literal
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_BATCH_OUTPUT_FORMAT = """
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Output a JSON object with three arrays.
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Example (showing the required UUID format for all IDs):
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{{"creates": [{{"text": "Alice lives in Berlin", "source_fact_ids": ["a1b2c3d4-e5f6-7890-abcd-ef1234567890", "b2c3d4e5-f6a7-8901-bcde-f12345678901"]}}],
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"updates": [{{"text": "Alice works at Acme Corp as a senior engineer", "observation_id": "c3d4e5f6-a7b8-9012-cdef-123456789012", "source_fact_ids": ["d4e5f6a7-b8c9-0123-defa-234567890123"]}}],
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"deletes": [{{"observation_id": "e5f6a7b8-c9d0-1234-efab-345678901234"}}]}}
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Rules:
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- "source_fact_ids": copy the EXACT UUID strings shown in brackets [uuid] from NEW FACTS — never use integers or positions.
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- "observation_id": copy the EXACT "id" UUID string from EXISTING OBSERVATIONS.
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- One create/update may reference multiple facts when they jointly support the observation.
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- "deletes": only when an observation is directly superseded or contradicted by new facts.
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- Do NOT include "tags" — handled automatically.
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- Return {{"creates": [], "updates": [], "deletes": []}} if nothing durable is found."""
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def build_batch_consolidation_prompt(observations_mission: str | None = None) -> str:
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"""
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Build the consolidation prompt for batch mode (multiple facts per LLM call).
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The mission defines *what* to track (customisable per bank).
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Processing rules and output format are always present regardless of mission.
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"""
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mission = observations_mission or _DEFAULT_MISSION
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return (
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"You are a memory consolidation system. Synthesize facts into observations "
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"and merge with existing observations when appropriate.\n\n"
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f"## MISSION\n{mission}\n\n"
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f"{_PROCESSING_RULES}" + _BATCH_DATA_SECTION + _BATCH_OUTPUT_FORMAT
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)
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