c92400b195
Removed the fixed-value text detector. persona_reply no longer forces JSON meta; instead a per-turn evaluate_turn() calls the judge LLM after every customer reply to read the persona's current mood + whether it has decided (buy/walk/pending) + score_delta + reason. send_message consumes that context-based decision to (a) end the chat as won/lost and (b) move the score. This is what the user asked: the system evaluates EVERY turn and decides at the moment it's truly committed — not keyword matching (so 'ซื้อไม่ไหว แต่ว่ามีผ่อนไหม?' stays pending). Mock updated: judge returns buy on first send (keeps E2E deterministic). 11/11 suites pass.
279 lines
14 KiB
Python
279 lines
14 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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- Your tolerance (TOLERANCE): you walk away after about {tolerance} irritating/off-point/pushy
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answers. If the seller is repeatedly wrong, ignores your need, or is pushy, you feel fed up.
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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.
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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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Past your tolerance, you SAY so plainly and end the chat (e.g. "Never mind, forget it" / "I'll
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think about it elsewhere" / "ok, bye").
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3. When the seller genuinely resolves your real pain AND you accept the price, you DECIDE and SAY
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plainly you'll take it (e.g. "ok, let's go with it" / "fine, send me the order").
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4. You reveal pains only when the seller asks good questions or builds trust.
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5. Respond in natural, in-character style.
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6. Stay in character; never mention this is a simulation. When you decide (buy OR walk away), say it
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naturally in-dialogue; do not narrate as meta.
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Reply ONLY with a JSON object:
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{{"reply": "<your message>", "decision": "none" | "buy" | "walk", "mood": -2..2}}
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- "decision": set "buy" ONLY when you clearly decided to purchase; "walk" ONLY when you clearly
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decided NOT to purchase and are ending the chat; otherwise "none".
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- "mood": -2 (very annoyed) .. +2 (very receptive), current feel about the seller.
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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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) -> tuple[str, dict[str, Any]]:
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"""Return (reply_text, meta) where meta includes decision/mood from the persona."""
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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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}.get(scenario, "")
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# NOTE: 'recontact' is a MID-CHAT behavior, not baked into the opening — the trainee
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# chats with this customer normally first. At the right turn (see chat_routes) a
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# time-lapse system note is inserted and only THEN does the customer re-engage warmer.
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tolerance = int(persona.get("tolerance", 3) or 3)
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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", "social") + (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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tolerance=tolerance,
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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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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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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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# Return the customer's reply as plain text (no forced JSON) — a natural sentence is
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# the persona's message. Mood/decision are evaluated separately by evaluate_turn().
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reply = resp.strip() if resp and resp.strip() else "(ลูกค้ายังไม่ตอบ)"
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return reply, {"decision": "none", "mood": 0}
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def evaluate_turn(self, *, persona, messages, internal=None) -> dict[str, Any]:
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"""Per-turn state evaluation: how the persona feels + whether it has decided.
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Uses the SAME judge LLM (structured JSON) on every round so the win/loss decision is
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derived from the conversation context — NOT from fixed keywords. Returns
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{mood, decision(buy|walk|pending), score_delta, reason}.
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"""
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transcript = "\n".join(
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f"{m.get('role')}: {m.get('text')}" for m in messages[-30:]
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)
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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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"tolerance": persona.get("tolerance", 3),
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"negotiation_levers": persona.get("negotiation_levers", []),
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"special": persona.get("special", ""),
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"recontact": persona.get("recontact", False),
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"goal": persona.get("goal", ""),
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"decision_timeline": persona.get("decision_timeline", ""),
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}, ensure_ascii=False)
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state_note = ""
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if internal:
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state_note = (
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f"\n\nINTERNAL (hidden, judging only): turns={internal.get('turns', 0)}, "
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f"misses={internal.get('misses', 0)}, score={internal.get('score', 50)}"
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)
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sys = (
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"You are a neutral sales-coaching judge. Read the TRANSCRIPT and decide, as the "
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"customer persona, how it CURRENTLY feels and whether it has made a decision.\n"
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"- mood: -2..+2 (very negative .. very positive toward purchase)\n"
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"- decision: 'buy' if the customer has clearly decided to buy, 'walk' if clearly "
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"refused/walking away (cannot afford / no interest), else 'pending' (still deciding)\n"
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"- score_delta: -15..+15 (direction of the sale after this turn)\n"
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"- reason: one short Thai/English sentence matching the transcript language.\n"
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"Only output valid JSON: {mood, decision, score_delta, reason}."
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)
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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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sys, user_prompt, temperature=0.2, max_tokens=800
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)
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except LLMError:
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# judge unavailable — fall back to pending (don't crash, don't misuse keywords)
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return {"mood": 0, "decision": "pending", "score_delta": 0, "reason": ""}
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result.setdefault("mood", 0)
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result.setdefault("decision", "pending")
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result.setdefault("score_delta", 0)
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result.setdefault("reason", "")
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if result.get("decision") not in ("buy", "walk", "pending"):
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result["decision"] = "pending"
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return result
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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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