c3d31c06e2
- Auth/roles (no self-reg), admin user provision, JWT - Analyze: sales kit + initial pain-fit from form/upload - Persona generator: 15 personas (5/tier) w/ pain variety, negotiation, init mode, channel, latent/revealable, wrong_text special - Chat simulator: per-mode initiation, one-shot, hidden signals, judge-LLM debrief+coaching - Trainee loop: win/lose board, weak-areas, user-generated personas - Admin analytics; EN+TH Vue SPA served by Flask - Deploy: Dockerfile, docker-compose, README, eng-log + HANDOFF - Tests (mock LLM): m0/m1/routes/e2e all pass
43 lines
2.0 KiB
Python
43 lines
2.0 KiB
Python
"""Generate a user's own persona (private) from weak-area spec or a manual form."""
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from __future__ import annotations
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from typing import Any
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from ..llm import LLMClient
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OWN_PERSONA_SYSTEM = """You generate ONE customer persona for a sales-training simulator,
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PRIVATE to a specific trainee. You produce valid JSON only: {"persona": { ... }}.
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The persona dict must contain: name, tier, channel, initiation_mode, profession, age_group,
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location, product_context (revealable), plus background, income, lifestyle, personality,
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communication_style, budget, decision_timeline, goal, objections[], pains[] (with fit + rootCause
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+ resolutionConditions), negotiation_levers[], opener, difficulty, special, notes.
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The trainee wants to specifically practice against the described weakness/profile, so make this
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persona HARD in exactly that dimension (e.g. heavy price negotiation, seller-initiated cold lead,
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skeptical). Keep pains partially product-solvable for realism.
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"""
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def build_own_persona_user_prompt(*, mode: str, spec: dict[str, Any]) -> str:
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if mode == "weak-area":
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return (
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"Mode: WEAK-AREA 'lock' persona. Generate a persona specifically targeting the "
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"trainee's reported weaknesses:\n" + str(spec)
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)
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return "Mode: MANUAL. Generate a persona matching the trainee's description:\n" + str(spec)
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def generate_own_persona(llm: LLMClient, *, mode: str, spec: dict[str, Any]) -> dict[str, Any]:
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user_prompt = build_own_persona_user_prompt(mode=mode, spec=spec)
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result = llm.complete_json(OWN_PERSONA_SYSTEM, user_prompt, temperature=0.8, max_tokens=7000)
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persona = result.get("persona") or result
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if not isinstance(persona, dict):
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raise ValueError("own-persona generator returned invalid data")
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persona.setdefault("tier", "B")
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persona.setdefault("channel", "facebook")
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persona.setdefault("initiation_mode", "customer")
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persona.setdefault("pains", [])
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persona.setdefault("negotiation_levers", [])
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return persona
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