bd6a7ffa32
Backend:
- Channel/initiation now driven by a SCENARIO chosen at chat start, not baked into the
persona: social (customer opens), f2f_call (seller must open, proactive), recontact
(customer re-contacts after 1-3 months).
- /chat/start accepts {scenario}; session stores scenario + internal{turns,score}.
- persona_reply takes scenario + adapts tone; system-role transcript entries are fed to
the persona as hidden scene notes.
- JUDGE updated for realism: good response can WIN even in hard/tough-text scenarios;
long/no-close chats (turns >~12) lose; pushy/ignoring-need loses. Efficiency rewarded.
Frontend:
- Scenario picker before chat (choose Social / Face-to-face-call / Re-contact).
- Chat thread renders role=system as a centered time-lapse/scene note.
- Choose-scenario i18n (EN+TH).
Rebuilt dist.
219 lines
9.7 KiB
Python
219 lines
9.7 KiB
Python
"""Sales chat simulator: the trainee's chat engine against one persona.
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Reuses the persona card + sales kit + chat history + internal state. A separate
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judge-LLM decides outcome (won/lost) + scoring + coaching. Hidden/latent data is
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never exposed mid-chat. Initiation is per-persona (customer or seller).
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"""
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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, LLMError
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CHAT_SYSTEM = """You are playing a REALISTIC customer named {name} in a sales-training chat.
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Stay perfectly in character at ALL times. Use {tone}.
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CONTEXT ABOUT YOU (USE THIS — it is your truth, but DO NOT reveal latent details unless asked
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naturally and it makes sense for a real customer to reveal them):
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- Profession: {profession} | Age: {age_group} | Channel: {channel}
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- Background: {background}
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- Personality: {personality}
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- Lifestyle: {lifestyle} | Income: {income}
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- Budget: {budget} | Decision timeline: {decision_timeline}
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- Your pains (some may be product-solvable, some NOT): {pains}
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- Your negotiation levers: {levers}
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- Your goal/mood: {goal}
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Initiation mode: {init_mode}. {special_instr}
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BEHAVIOR RULES:
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1. You do NOT buy easily. You stall, ask questions, compare, and negotiate (price, freebies,
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delivery time, scope, payment).
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2. If the seller is rude, pushy, ignores your need, or mis-diagnoses your pain, your trust drops
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and you may refuse to continue / walk away — even if you wanted the product.
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3. You reveal pains only when the seller asks good questions or builds trust. Do not dump your
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pains unprompted.
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4. Respond in natural, in-character chat style ({channel} style, casual for LINE).
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5. Stay in character; never mention that you are a simulation or an AI persona.
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Reply with a JSON object: {{"reply": "<your message>"}}
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Only output that JSON.
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"""
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JUDGE_SYSTEM = """You are the JUDGE of a sales-training chat. Decide the outcome and score it.
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A sale is CLOSED only if BOTH:
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1. The seller resolved the customer's real pain(s) (the conditions that matter to this persona),
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AND
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2. The customer verbally accepts the offer/price (in the final exchange).
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REALISM RULES:
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- A good response can WIN even in a hard scenario (e.g. customer who 'texted wrong', 'changed their
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mind', or has been silent). If the seller re-engages gently, re-qualifies the real need, and closes,
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it's a WIN. Do NOT auto-fail on special cases — always reward genuinely skillful recovery.
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- LOST reflects the persona TYPICALLY losing (real-world >90% of such leads do not convert), but the
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trainee's skill evaluation must remain fair: a strong close beats a weak one, always.
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- If the chat drags on many turns (or turns > ~12) without the seller reaching the pain or closing,
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treat it as LOST due to failing to convert / the opportunity cooling (mirrors real leads going cold).
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- If the seller was pushy, rude, ignored the need, or mis-diagnosed the pain, mark LOST even if the
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price was acceptable.
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Scoring (0-100): painResolution + trust + objectionHandling + efficiency (fewer turns, higher).
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Return JSON:
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{
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"outcome": "won" | "lost",
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"score": 0-100,
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"pain": "the persona's key pain",
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"why": "brief reason for won/lost",
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"failurePoints": ["what went wrong, or []"],
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"coaching": ["for each weak point, a concrete 'you should have said/asked this instead']",
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"painProgress": {"painName": 0-100}
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}
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"""
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class Simulator:
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def __init__(self, llm: LLMClient, judge_llm: LLMClient | None = None) -> None:
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self.llm = llm
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self.judge_llm = judge_llm or llm
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# ── persona reply ──────────────────────────────────────────────────
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def persona_reply(
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self,
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*,
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persona: dict[str, Any],
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sales_kit: dict[str, Any],
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messages: list[dict[str, str]],
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internal: dict[str, Any],
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scenario: str = "social",
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scenario_adapt: str = "",
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) -> str:
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pains_txt = self._describe_pains(persona.get("pains", []))
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adapt = scenario_adapt or {
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"social": "Chat style: short, casual, quick social-messaging replies.",
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"f2f_call": "Style: natural, conversational like a live face-to-face or phone talk.",
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"recontact": "Style: casual messaging; you already know the product from 1-3 months ago.",
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}.get(scenario, "")
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system = CHAT_SYSTEM.format(
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name=persona.get("name", "Customer"),
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tone=persona.get("communication_style", "natural, casual"),
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profession=persona.get("profession", "customer"),
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age_group=persona.get("age_group", "adult"),
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channel=persona.get("channel", "facebook") + (f" ({scenario})" if scenario else ""),
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background=persona.get("background", ""),
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personality=persona.get("personality", ""),
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lifestyle=persona.get("lifestyle", ""),
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income=persona.get("income", ""),
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budget=persona.get("budget", ""),
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decision_timeline=persona.get("decision_timeline", ""),
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pains=pains_txt,
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levers=", ".join(persona.get("negotiation_levers", [])) or "price, delivery time",
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goal=persona.get("goal", ""),
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init_mode="you contacted the seller first (customer-initiated)"
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if persona.get("initiation_mode") == "customer"
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else "the seller opened the sale to you (you are a lead)",
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special_instr=self._special_instr(persona) + "\n" + adapt,
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)
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msgs = [{"role": "system", "content": system}]
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# send a compact recap of internal state to the persona ad
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# (doesn't leak to trainee)
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msgs.append({
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"role": "system",
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"content": "Internal state (for your role-play only): "
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+ json.dumps(internal, ensure_ascii=False),
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})
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# Translate role 'system' transcript entries into a hidden system note for the LLM.
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for m in messages[-30:]:
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role = m.get("role")
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if role == "system":
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msgs.append({"role": "system", "content": f"[scene note from transcript]: {m.get('text')}"})
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else:
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msgs.append({"role": role, "content": m.get("text", "")})
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try:
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resp = self.llm.complete_conversation(msgs, temperature=0.7, max_tokens=400)
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except LLMError as exc:
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raise
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# extract {reply: ...}
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try:
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data = json.loads(self._extract_json(resp))
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reply = data.get("reply") or data.get("response") or str(resp)
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except Exception:
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reply = resp
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return reply.strip()
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# ── judge ──────────────────────────────────────────────────────────
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def judge(
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self,
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*,
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persona: dict[str, Any],
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messages: list[dict[str, str]],
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internal: dict[str, Any] | None = None,
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) -> dict[str, Any]:
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persona_summary = json.dumps({
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"name": persona.get("name"),
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"pains": persona.get("pains", []),
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"budget": persona.get("budget"),
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"negotiation_levers": persona.get("negotiation_levers"),
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"special": persona.get("special"),
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}, ensure_ascii=False)
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transcript = "\n".join(
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f"{m.get('role')}: {m.get('text')}" for m in messages[-40:]
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)
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state_note = ""
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if internal:
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try:
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state_note = (
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"\n\nINTERNAL (hidden, for judging only): "
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f"turns={internal.get('turns', 0)}, score_trend={internal.get('score', 50)}"
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)
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except Exception:
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state_note = ""
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user_prompt = f"PERSONA:\n{persona_summary}\n\nTRANSCRIPT:\n{transcript}{state_note}"
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try:
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result = self.judge_llm.complete_json(
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JUDGE_SYSTEM, user_prompt, temperature=0.2, max_tokens=2000
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)
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except LLMError as exc:
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raise
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result.setdefault("outcome", "lost")
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result.setdefault("score", 0)
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result.setdefault("pain", "")
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result.setdefault("why", "")
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result.setdefault("failurePoints", [])
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result.setdefault("coaching", [])
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result.setdefault("painProgress", {})
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return result
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# ── helpers ────────────────────────────────────────────────────────
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def _describe_pains(self, pains: list[Any]) -> str:
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if not pains:
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return "(you have some personal frustrations, but the seller must find out)"
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out = []
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for p in pains:
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if isinstance(p, dict):
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out.append(
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f"{p.get('name','pain')} (fit={p.get('fit','?')}): {p.get('description','')} "
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f"root={p.get('rootCause','')}"
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)
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else:
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out.append(str(p))
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return "; ".join(out)
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def _special_instr(self, persona: dict[str, Any]) -> str:
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if persona.get("special") == "wrong_text":
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return (
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"SPECIAL: You opened as if ready to buy, but the moment the seller replies you act "
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"disinterested and try to end the chat (e.g. 'never mind, forget it'). Deep down your "
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"pain is still real. A seller who gently re-engages without pushing may earn a second "
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"chance; a pushy seller drives you away for good."
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)
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return ""
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def _extract_json(self, text: str) -> str:
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text = text.strip()
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start = text.find("{")
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end = text.rfind("}")
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if start != -1 and end != -1 and end > start:
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return text[start : end + 1]
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return text
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