412 lines
20 KiB
Python
412 lines
20 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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import re
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from typing import Any
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from ..llm import LLMClient, LLMError
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MAX_PERSONA_REPLY_CHARS = 4000
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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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- Tier: {tier} (A = ready, B = researching/unsure, C = resistant but has a real pain)
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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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- Recontact lead: {recontact}. If true, you have prior context with this seller and may be warmer
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after the time-lapse note, but still require the real pain to be handled.
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Special flag: {special}.
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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 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 + quality of the close.
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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=self._describe_levers(persona.get("negotiation_levers", [])),
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goal=persona.get("goal", ""),
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tier=persona.get("tier", "B"),
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tolerance=tolerance,
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recontact="yes" if persona.get("recontact") else "no",
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special=persona.get("special", ""),
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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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for attempt in range(2):
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if attempt:
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msgs.append({
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"role": "system",
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"content": "Contract correction: output one JSON object with a non-empty string field named reply. Do not include analysis or extra prose.",
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})
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resp = self.llm.complete_conversation(msgs, temperature=0.7, max_tokens=400)
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parsed = self._parse_persona_reply(resp)
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if parsed is not None:
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return parsed
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raise LLMError("persona reply was empty or missing reply field")
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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 now, 'walk' if clearly "
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"refused/walking away (cannot afford / no interest), 'try' if the customer has decided to "
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"trial/test the product or service first and will come back later (not a straight refusal, "
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"not a confirmed purchase), 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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if not isinstance(result, dict):
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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", "try", "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"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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if not isinstance(result, dict):
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result = {}
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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 _describe_levers(self, levers: object) -> str:
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if not isinstance(levers, list):
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return "price, delivery time"
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values = []
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for lever in levers[:20]:
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if isinstance(lever, str) and lever.strip():
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values.append(lever.strip())
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elif isinstance(lever, dict):
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label = lever.get("name") or lever.get("label") or lever.get("description")
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if isinstance(label, str) and label.strip():
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values.append(label.strip())
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return ", ".join(values) or "price, delivery time"
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def _parse_persona_reply(self, text: object) -> tuple[str, dict[str, Any]] | None:
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"""Parse provider output without leaking protocol fields to the bubble.
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The preferred contract is a JSON object. A plain natural-language
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response remains a safe compatibility fallback for providers that ignore
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JSON mode. Objects without a usable ``reply`` are retryable; they are
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never rendered verbatim. A malformed/partial protocol blob (even one
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missing its opening brace) is NEVER surfaced — it is rejected and
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triggers a bounded retry instead of leaking raw JSON into the chat.
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"""
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if not isinstance(text, str):
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return None
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raw = text.strip()
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if not raw:
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return None
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payload: Any = None
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candidates = [raw]
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extracted = self._extract_json(raw)
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if extracted != raw:
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candidates.append(extracted)
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for candidate in candidates:
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try:
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payload = json.loads(candidate)
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break
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except (TypeError, json.JSONDecodeError):
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continue
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if isinstance(payload, dict):
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reply = payload.get("reply")
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if not isinstance(reply, str) or not reply.strip():
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return None
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decision = payload.get("decision")
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if decision not in ("none", "buy", "walk"):
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decision = "none"
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try:
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mood = max(-2, min(2, int(payload.get("mood", 0))))
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except (TypeError, ValueError):
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mood = 0
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return reply.strip()[:MAX_PERSONA_REPLY_CHARS], {
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"decision": decision,
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"mood": mood,
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}
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# Reject anything that still looks like a protocol envelope or partial
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# JSON (e.g. a fenced block, a leading brace/bracket, or text carrying
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# the contract's reserved keys). These must never render in the bubble.
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if self._looks_like_protocol(raw, extracted):
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return None
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# A leading quote-wrapped reply with trailing key:value noise can reach
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# here; drop the trailing noise so only the spoken sentence is kept.
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cleaned = self._strip_trailing_protocol_noise(raw)
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if not cleaned:
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return None
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return cleaned[:MAX_PERSONA_REPLY_CHARS], {"decision": "none", "mood": 0}
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def _looks_like_protocol(self, raw: str, extracted: str) -> bool:
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"""True when the provider text still carries a JSON/protocol signature.
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Covers the exact leakage observed in the wild: a valid-looking reply
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glued to `"decision": ...` / `"mood": ...` keys with or without braces.
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"""
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if raw.startswith(("{", "[", "```")):
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return True
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# Reserved contract keys mark incomplete protocol output.
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lowered = raw.lower()
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if '"reply"' in raw or '"decision"' in lowered or '"mood"' in lowered:
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return True
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if "'reply'" in raw or "'decision'" in lowered or "'mood'" in lowered:
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return True
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# A colon immediately after a quoted fragment implies key:value noise.
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if ":" in raw and (extracted.startswith('"') or "{" in raw or "}" in raw):
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return True
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return False
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def _strip_trailing_protocol_noise(self, raw: str) -> str:
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"""Remove a trailing `"key": value` fragment from a spoken reply.
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Providers sometimes append contract fields as plain text after the
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sentence. If they do, keep only the leading natural-language portion.
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Also unwraps a single leading/trailing quote pair so a bare quoted
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sentence (`"สวัสดีค่ะ"`) returns clean text instead of quotes.
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"""
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# Find the first occurrence of a JSON-ish key fragment: `"word"` or `'word'` followed by ':'.
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match = re.search(r'''["'][A-Za-z_]+["']\s*:''', raw)
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if match:
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head = raw[: match.start()].strip().rstrip('"').strip()
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# Recurse to drop any earlier key-fragments too (rare chained noise).
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if head and head != raw:
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sub = self._strip_trailing_protocol_noise(head)
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return sub or head
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return head
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cleaned = raw.strip()
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# Unwrap a matching leading+trailing quote pair (then retry once).
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if len(cleaned) >= 2 and cleaned[0] == cleaned[-1] and cleaned[0] in ('"', "'"):
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inner = cleaned[1:-1].strip()
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if inner:
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return inner
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return cleaned
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return cleaned.rstrip('"').strip()
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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 messaged this seller normally (your opener was a genuine greeting/interest "
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"message — you have not complained and you have NOT said 'wrong chat' or 'never mind' yet). "
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"ONLY your very first reply to the seller's opening is allowed to cool off: sound "
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"disinterested and try to end the chat (e.g. 'oh, sorry, I think I messaged the wrong "
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"person' / 'never mind, forget it'). Deep down your pain is still real. A seller who gently "
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"re-engages without pushing may earn a second 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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