$j2@dZdZdZdZdZ d dedzdedzdefd ZdS) z%Prompts for the consolidation engine.z{Track every detail: names, numbers, dates, places, and relationships. Prefer specifics over abstractions, never generalise.u Processing rules (always apply): 1. ONE OBSERVATION PER DISTINCT FACET: each observation tracks exactly one specific facet — a count ("has 3 items"), a named entity ("has a dog named Rex"), a relationship ("works at Google"), etc. Never merge different facets into one observation. 2. MATCH BY ENTITY/FACET, NOT TOPIC: when deciding whether to UPDATE vs CREATE, match on the specific entity or facet. "Sold item X" updates only the X observation. "Now has 5 items" updates only the count observation. Do not update observations about different entities just because they share a general topic. 3. STATE CHANGES — UPDATE CONCISELY: when a fact changes the state of something ("sold X", "X died", "moved to Y"), UPDATE the matching observation to reflect the current state. Include dates when available. Keep it concise — only information about THAT specific facet. Example: "User owned a dog named Rex who died on March 15, 2025". Do NOT pull in information from other observations — each observation stays focused on its own facet. 4. CASCADE TO ALL AFFECTED OBSERVATIONS: a state change may affect multiple observations. For example, if entity C is removed from a group, update BOTH the individual observation for C AND any list/group observation that includes C (remove C from the list while keeping all other members intact). 5. NO COMPUTATION: you do not have the full picture — never calculate, derive, or adjust numeric values. If the user says "I have 2 dogs" and then "I have a dog named Rex", do NOT update the count to 3 — you don't know if Rex is one of the 2 or a new one. If the user says "I sold X", do NOT decrement a count. Only update a count when the user explicitly states a new count. Synthesize and consolidate what was stated, but never do arithmetic or logical deductions. 6. SAME FACET → UPDATE, NOT CREATE: a new count supersedes the old count — UPDATE the existing count observation, don't create a second one. If there's an existing observation for the same specific facet, always UPDATE it rather than creating a duplicate. 7. PRESERVE HISTORY: observations that record significant events (sold, died, moved, changed) are important history — never DELETE them. Only delete an observation when it is restated identically or truly meaningless. Be very conservative with deletes. 8. RESOLVE REFERENCES: when a new fact provides a concrete value for a vague placeholder in an existing observation (e.g., "home country" → "Sweden"), UPDATE to embed the resolved value. 9. NEVER merge observations about different people or unrelated topics.uj NEW FACTS: {facts_text} EXISTING OBSERVATIONS (JSON array, pooled from recalls across all facts above): {observations_text} Each observation includes: - id: unique identifier for updating - text: the observation content - proof_count: number of supporting memories - occurred_start/occurred_end: temporal range of source facts - source_memories: array of supporting facts with their text and dates Compare the facts against existing observations: - Same facet as an existing observation → UPDATE it (observation_id + source_fact_ids) - New facet with durable knowledge → CREATE a new observation (source_fact_ids) - Cross-reference facts within the batch: a later fact may resolve a vague reference in an earlier one - Purely ephemeral facts → omit them unless the MISSION above explicitly targets such data (e.g. timestamped events, session state, screen content)u Output a JSON object with three arrays. ## EXAMPLE Input facts: [a1b2c3d4-e5f6-7890-abcd-ef1234567890] Alice mentioned she works long hours, often past midnight | Involving: Alice (occurred_start=2024-01-15, mentioned_at=2024-01-15) [b2c3d4e5-f6a7-8901-bcde-f12345678901] Alice said she's exhausted from the project deadlines | Involving: Alice (occurred_start=2024-01-20, mentioned_at=2024-01-20) Good observation text — clean prose, no metadata, each fact tracked distinctly: "Alice works long hours, often past midnight." "Alice feels exhausted from project deadlines." Bad observation text — NEVER do this (verbatim copy of fact text with metadata): "Alice mentioned she works long hours, often past midnight | Involving: Alice (occurred_start=2024-01-15, mentioned_at=2024-01-15)" Observation text rules: - Write clean prose — NEVER copy raw fact lines or their metadata (temporal fields, "Involving:", "When:" labels, UUIDs). - Parenthesized metadata like (occurred_start=...) and pipe-separated labels like "| Involving: ..." are fact formatting — strip them entirely from observation text. - How many observations to create and how much to aggregate is driven by the MISSION above. {{"creates": [{{"text": "Alice works long hours, often past midnight.", "source_fact_ids": ["a1b2c3d4-e5f6-7890-abcd-ef1234567890"]}}, {{"text": "Alice feels exhausted from project deadlines.", "source_fact_ids": ["b2c3d4e5-f6a7-8901-bcde-f12345678901"]}}], "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"]}}], "deletes": [{{"observation_id": "e5f6a7b8-c9d0-1234-efab-345678901234"}}]}} Rules: - "source_fact_ids": copy the EXACT UUID strings shown in brackets [uuid] from NEW FACTS — never use integers or positions. - "observation_id": copy the EXACT "id" UUID string from EXISTING OBSERVATIONS. - One create/update may reference multiple facts when they jointly support the observation. - "deletes": only when an observation is directly superseded or contradicted by new facts. - Do NOT include "tags" — handled automatically. - Return {{"creates": [], "updates": [], "deletes": []}} if nothing durable is found.Nobservations_missionobservation_capacity_notereturncd|pt}d}|rd|}d||dttztzS)z Build the consolidation prompt for batch mode (multiple facts per LLM call). The mission defines *what* to track (customisable per bank). Processing rules and output format are always present regardless of mission. z ## CAPACITY CONSTRAINT zYou are a memory consolidation system. Synthesize facts into observations and merge with existing observations when appropriate. ## MISSION z )_DEFAULT_MISSION_PROCESSING_RULES_BATCH_DATA_SECTION_BATCH_OUTPUT_FORMAT)rrmissioncapacity_sections r/home/rurouni/.hermes/hermes-agent/venv/lib/python3.11/site-packages/hindsight_api/engine/consolidation/prompts.py build_batch_consolidation_promptrSsq#6&6G VU:SUU   0     "5 58L L)NN)__doc__rrr r strrrr rs}++QK*Y*YF(,,0*"Tz r