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96 lines
4.6 KiB
Python
96 lines
4.6 KiB
Python
"""Turn the local scan + app picks into a personalized greeting and starters.
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One cheap aux call on whatever lane the user just connected; every failure path
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returns the static fallback so the reveal can never be an error card.
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"""
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import asyncio
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import json
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from typeguard import typechecked
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from backend.apps.agents.core.aux_llm import aux_max_tokens_for, safe_resp_text
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from backend.apps.onboarding.models import PrepRequest, PrepResponse
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from backend.apps.onboarding.prep.menu import build_menu
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from backend.apps.onboarding.prep.parse_helpers import strip_dashes
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from backend.apps.onboarding.prep.parse_prep import parse_prep
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from backend.apps.onboarding.prep.prompts import PROFILE_SYSTEM, PREP_SYSTEM
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from backend.apps.onboarding.prep.scan_fallback import scan_grounded_fallback
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from backend.apps.settings.models import AppSettings
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# Below this the usage text is just titles/memories (thin); above it there is real conversation content
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# worth a distill pass. Keep the distill input bounded so the cheap call stays a couple cents.
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P_PROFILE_DISTILL_THRESHOLD = 1500
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P_PROFILE_INPUT_CAP = 140000
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@typechecked
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async def p_distill_profile(settings: AppSettings, usage_text: str) -> str:
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"""One cheap aux call: raw chat content -> a tight 'who is this person' profile. "" on any failure,
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so build_prep just falls back to feeding the raw usage text (today's behavior)."""
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try:
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from backend.apps.agents.providers.registry import resolve_aux_model
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from backend.apps.settings.credentials import get_anthropic_client_for_model
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aux_model, _ = await resolve_aux_model(settings, preferred_tier="haiku")
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client = get_anthropic_client_for_model(settings, aux_model)
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resp = await client.messages.create(
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model=aux_model,
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max_tokens=aux_max_tokens_for(aux_model, base=600),
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system=PROFILE_SYSTEM,
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messages=[{"role": "user", "content": usage_text[:P_PROFILE_INPUT_CAP]}],
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timeout=45.0,
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)
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return strip_dashes(safe_resp_text(resp).strip())
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except Exception:
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return ""
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@typechecked
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async def build_prep(settings: AppSettings, request: PrepRequest) -> PrepResponse:
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from datetime import date
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facts = request.model_dump()
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# The aux otherwise assumes its training-cutoff year and writes stale ranges like "2024-2025"
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# into research prompts; telling it today's date keeps "current" meaning current.
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facts["today"] = date.today().isoformat()
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# If the usage text carries real conversation content, distill it to a tight profile FIRST so the
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# reveal call reasons over who this person is, not raw logs (and stays in budget). Fail-open: a blank
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# profile just leaves the raw text in place, which is today's behavior.
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usage = str(facts.get("usage_summary", ""))
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if len(usage) > P_PROFILE_DISTILL_THRESHOLD:
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profile = await p_distill_profile(settings, usage)
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if profile:
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facts["usage_summary"] = profile
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# The hero's 4x4 drill-in menu rides its own parallel aux call; build_menu never raises.
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menu_task = asyncio.create_task(build_menu(settings, facts, request.scan))
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try:
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from backend.apps.agents.providers.registry import resolve_aux_model
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from backend.apps.settings.credentials import get_anthropic_client_for_model
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aux_model, _ = await resolve_aux_model(settings, preferred_tier="haiku")
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client = get_anthropic_client_for_model(settings, aux_model)
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resp = await client.messages.create(
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model=aux_model,
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# The full shape (greeting + 4 starters w/ prompts + app + 3 automations) runs ~1.5-2K
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# tokens for a rich user; 1100 truncated the JSON mid-object so parse silently fell back.
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max_tokens=aux_max_tokens_for(aux_model, base=2200),
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system=PREP_SYSTEM,
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messages=[{"role": "user", "content": json.dumps(facts)}],
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# Bound the wait: the SDK default is ~10min, so a wedged router/provider would hang the
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# auto-launch (which awaits this) instead of degrading to the static starters.
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timeout=45.0,
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)
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parsed = parse_prep(safe_resp_text(resp))
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if parsed is not None:
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parsed.menu = await menu_task
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parsed.used_llm = True
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return parsed
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except Exception:
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pass
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# Aux unusable (empty gemini/codex response on 0.3.60, provider down, no anthropic lane): still
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# ground the reveal in the real scan rather than shipping generic stubs.
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fallback = scan_grounded_fallback(request)
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fallback.menu = await menu_task
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return fallback
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