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ecf0b852f9
Derive a standing profile of the user from session gists and inject it into
every prompt, so identity/abstract questions ("what kind of player am I",
"what are my leaks") are answered from distilled knowledge instead of noisy
single-vector recall (which finds passages, not patterns).
- memory: profile table + get/set_profile, list_summaries
- lyra/profile.py: rebuild_profile map-reduces all gists (batch -> extract
durable facts -> fold-merge) into one profile doc; `lyra-profile` CLI
- chat.build_messages injects "What you know about Brian" after the persona
Run after lyra-summarize (needs gists). Verified (stubbed): map-reduce, storage,
and prompt injection.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
85 lines
3.2 KiB
Python
85 lines
3.2 KiB
Python
"""Profile derivation: distill standing facts about the user (semantic memory).
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This is consolidation step 2. It reads every session gist and map-reduces them
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into one profile document — who Brian is as a player and person — which is then
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injected into every prompt. This is what answers identity/abstract questions
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("what kind of player am I", "what are my leaks") that raw recall handles badly,
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because those are patterns across many sessions, not facts in any single message.
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"""
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from __future__ import annotations
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from lyra import config, llm, logbus, memory
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from lyra.llm import Backend, Message
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BATCH_CHARS = 18000
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_MAP_PROMPT = """From these session summaries, extract durable facts about Brian \
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— things that are stably true, not one-off events. Cover, where present: poker \
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games/formats/stakes he plays, his playing style and strengths, recurring leaks \
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and tendencies, mental-game patterns (tilt triggers, scared money, fatigue), \
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relevant personal context, and how he likes to be coached. Terse bullet points. \
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Omit anything not supported by the summaries."""
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_REDUCE_PROMPT = """Merge these fact lists into one deduplicated profile of Brian. \
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Organize under these headings: Poker Style, Leaks & Tendencies, Mental Game, \
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Personal Context, Working With Brian. Keep it tight — bullets, no fluff, no \
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repetition. Resolve contradictions toward the more recent/frequent signal."""
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def _batch_texts(texts: list[str], budget: int) -> list[str]:
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"""Group texts into joined blocks under `budget` chars."""
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blocks, buf, size = [], [], 0
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for t in texts:
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if size + len(t) > budget and buf:
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blocks.append("\n\n".join(buf))
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buf, size = [], 0
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buf.append(t)
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size += len(t)
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if buf:
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blocks.append("\n\n".join(buf))
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return blocks
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def _call(prompt: str, body: str, backend: Backend) -> str:
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messages: list[Message] = [
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{"role": "system", "content": prompt},
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{"role": "user", "content": body},
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]
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return llm.complete(messages, backend=backend)
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def rebuild_profile(backend: Backend | None = None) -> str | None:
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"""Re-derive the profile from all current session gists and store it."""
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backend = backend or config.load().summary_backend
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summaries = memory.list_summaries()
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if not summaries:
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return None
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# MAP: extract facts from batches of gists.
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blocks = _batch_texts([s.content for s in summaries], BATCH_CHARS)
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partials = [_call(_MAP_PROMPT, b, backend) for b in blocks]
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logbus.log("info", "profile map done", batches=len(partials), sessions=len(summaries))
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# REDUCE: fold partials together until one remains.
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while len(partials) > 1:
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partials = [_call(_REDUCE_PROMPT, g, backend) for g in _batch_texts(partials, BATCH_CHARS)]
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profile = partials[0]
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memory.set_profile(profile, len(summaries))
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logbus.log("info", "profile rebuilt", sessions=len(summaries), chars=len(profile))
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return profile
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def main() -> int:
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profile = rebuild_profile()
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if profile is None:
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print("No summaries yet — run lyra-summarize first.")
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return 1
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print(profile)
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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