Files
sales-trainer/backend/app/services/own_persona.py
Macky 8771438aef fix(scenario): recontact chats normally first then re-engages mid-chat; channel default=social; remove 'add more' button (15 personas auto)
- recontact persona now opens as a NORMAL customer (no 'I asked before' in the opening);
  the mid-chat time-lapse system note (turn 2) makes them re-engage warmer instead.
- channel default changed facebook->social everywhere (create/analyze/store/simulator/me).
- 15 personas auto-generated (TARGET=15); removed the 'สร้างบุคคลต้นแบบเพิ่มเติม' button/guide.
All 9 backend suites pass. Rebuilt dist.
2026-08-09 12:37:43 +07:00

43 lines
2.0 KiB
Python

"""Generate a user's own persona (private) from weak-area spec or a manual form."""
from __future__ import annotations
from typing import Any
from ..llm import LLMClient
OWN_PERSONA_SYSTEM = """You generate ONE customer persona for a sales-training simulator,
PRIVATE to a specific trainee. You produce valid JSON only: {"persona": { ... }}.
The persona dict must contain: name, tier, channel, initiation_mode, profession, age_group,
location, product_context (revealable), plus background, income, lifestyle, personality,
communication_style, budget, decision_timeline, goal, objections[], pains[] (with fit + rootCause
+ resolutionConditions), negotiation_levers[], opener, difficulty, special, notes.
The trainee wants to specifically practice against the described weakness/profile, so make this
persona HARD in exactly that dimension (e.g. heavy price negotiation, seller-initiated cold lead,
skeptical). Keep pains partially product-solvable for realism.
"""
def build_own_persona_user_prompt(*, mode: str, spec: dict[str, Any]) -> str:
if mode == "weak-area":
return (
"Mode: WEAK-AREA 'lock' persona. Generate a persona specifically targeting the "
"trainee's reported weaknesses:\n" + str(spec)
)
return "Mode: MANUAL. Generate a persona matching the trainee's description:\n" + str(spec)
def generate_own_persona(llm: LLMClient, *, mode: str, spec: dict[str, Any]) -> dict[str, Any]:
user_prompt = build_own_persona_user_prompt(mode=mode, spec=spec)
result = llm.complete_json(OWN_PERSONA_SYSTEM, user_prompt, temperature=0.8, max_tokens=7000)
persona = result.get("persona") or result
if not isinstance(persona, dict):
raise ValueError("own-persona generator returned invalid data")
persona.setdefault("tier", "B")
persona.setdefault("channel", "social")
persona.setdefault("initiation_mode", "customer")
persona.setdefault("pains", [])
persona.setdefault("negotiation_levers", [])
return persona