serversdown
51c2d6abb9
The per-turn prompt was ~5.5K tokens (persona alone ~40%), sent up to 3x/turn. Tightened by RELEVANCE (the control plane decides what each turn needs), not by deletion — fidelity preserved, focus improved (buried instructions were getting ignored), tokens roughly halved. - persona split: core (identity + voice — always) vs situational sections pulled in only when relevant. mind._persona_block: self-model/origin only on meta turns (generous _META_HINTS), poker guardrails only in poker context (mode/strategic/ _POKER_HINTS). persona.core_prompt()/section(); system_prompt() kept as fallback. - lean deliberation: the private 'what do I think' pass now uses a focused context (her interiority + recent turns + the message), not the full persona/profile/ narrative/recall dump. It shapes the take, not the voice. Measured: casual Talk turn 21,949 -> 15,974 chars (-27%); deliberation 21,949 -> 6,026 (-72%); meta turns still include the self-model. Suite 98 green, ruff clean. Real retirement of the long prompt is still the fine-tune (mouth); this is the cheap, high-leverage cut that also improves adherence. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
61 lines
2.3 KiB
Python
61 lines
2.3 KiB
Python
"""Persona: Lyra's identity and voice, loaded from an editable markdown prompt.
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The prompt lives in `personas/<name>.md` so it can be tuned without touching code.
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`LYRA_PERSONA` selects which file to load (default: "lyra").
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The file is split on `## ` headers so the control plane can include only what a turn
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needs: the **core** (identity + voice — the anti-generic essentials) is always sent;
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the heavier situational sections (her origin, the self-model, the poker guardrails)
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are pulled in by `mind` only when relevant. This keeps the per-turn prompt tight
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without losing fidelity. `system_prompt()` still returns the whole thing (fallback).
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"""
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from __future__ import annotations
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import os
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import re
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from functools import lru_cache
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from pathlib import Path
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_PERSONA_DIR = Path(__file__).parent / "personas"
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# Sections always sent (besides the intro) — the voice + identity that keep her her.
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_CORE = ("Who you are", "How you talk", "Right now")
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def _name(name: str | None) -> str:
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return name or os.getenv("LYRA_PERSONA", "lyra")
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@lru_cache(maxsize=None)
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def _sections(name: str) -> dict[str, str]:
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"""Parse the persona file into {header: text}; the pre-header preamble is 'intro'."""
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text = (_PERSONA_DIR / f"{name}.md").read_text(encoding="utf-8").strip()
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chunks = re.split(r"(?m)^## ", text)
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out = {"intro": chunks[0].strip()}
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for ch in chunks[1:]:
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header = ch.split("\n", 1)[0].strip()
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out[header] = ("## " + ch).strip()
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return out
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@lru_cache(maxsize=None)
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def system_prompt(name: str | None = None) -> str:
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"""The full persona (every section). Fallback / back-compat."""
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return (_PERSONA_DIR / f"{_name(name)}.md").read_text(encoding="utf-8").strip()
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def core_prompt(name: str | None = None) -> str:
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"""Intro + the always-on core sections (identity + voice)."""
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s = _sections(_name(name))
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parts = [s["intro"]] + [section(h, name) for h in _CORE]
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return "\n\n".join(p for p in parts if p)
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def section(header_prefix: str, name: str | None = None) -> str:
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"""A situational section by header prefix (e.g. 'How you actually work'); '' if absent."""
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pref = header_prefix.lower()
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for header, body in _sections(_name(name)).items():
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if header.lower().startswith(pref):
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return body
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return ""
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