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
75 lines
2.6 KiB
Python
75 lines
2.6 KiB
Python
"""Persona generator: builds 15 personas (5 per tier) from a Sales Kit + scenario."""
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from __future__ import annotations
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import json
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from typing import Any
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from ..llm import LLMClient
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from .persona_prompts import PERSONA_SYSTEM
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TIERS = ["A", "B", "C"]
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PER_TIER = 5
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class PersonaGenerator:
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def __init__(self, llm: LLMClient) -> None:
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self.llm = llm
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def generate(
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self,
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*,
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sales_kit: dict[str, Any],
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language: str = "en",
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channel: str = "facebook",
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) -> list[dict[str, Any]]:
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kit_json = json.dumps(sales_kit, ensure_ascii=False)[:12000]
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lang_name = "Thai" if language == "th" else "English"
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scenario = (sales_kit.get("scenarioFrame") or "").strip() or "a general product sale"
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user_prompt = (
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f"Platform/channel preference: {channel}\n"
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f"Language: {lang_name} (all persona text in {lang_name})\n"
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f"Sales Kit:\n{kit_json}\n\n"
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f"Generate exactly 15 personas (5 per tier A/B/C) as JSON."
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)
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result = self.llm.complete_json(
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PERSONA_SYSTEM, user_prompt, temperature=0.8, max_tokens=14000
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)
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personas = result.get("personas") or []
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if not isinstance(personas, list) or not personas:
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raise ValueError("persona generator returned no personas")
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normalized, counts = [], {"A": 0, "B": 0, "C": 0}
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for idx, p in enumerate(personas, start=1):
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if not isinstance(p, dict):
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continue
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tier = p.get("tier", p.get("intent_tier"))
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if tier not in TIERS:
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tier = "B"
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if counts[tier] >= PER_TIER:
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continue # skip overflow per tier
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counts[tier] += 1
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p["id"] = f"persona-{idx:02d}"
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p["tier"] = tier
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p["channel"] = p.get("channel", channel)
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p.setdefault("initiation_mode", "customer")
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p.setdefault("special", "")
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p.setdefault("difficulty", 1)
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p.setdefault("pains", [])
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p.setdefault("negotiation_levers", [])
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p.setdefault("objections", [])
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normalized.append(p)
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# Wrap tier-C: ensure at least one wrong_text persona
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if "C" in counts and not any(
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p.get("special") == "wrong_text" for p in normalized
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):
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# find first tier-C and mark it
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for p in normalized:
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if p["tier"] == "C":
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p["special"] = "wrong_text"
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break
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if len(normalized) < 15:
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raise ValueError(f"expected 15 personas, generated {len(normalized)}")
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return normalized
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