aa2eb8dd37
The conversation now ENDS when the persona makes a decision (option C), not when the
trainee clicks a button:
- persona_id replies carry {reply, decision(none/buy/walk), mood}; when decision is
buy/walk the session auto-finishes (won/lost) with a debrief that reveals latent
details + per-turn 'turning points'.
- personas have a tolerance (1-5, 'temper'): impatient personas walk away fast after
poor answers (fed via internal.misses on mood<=-1); tough 'wrong text' cases can
still be won by a strong, gentle response (judge realism).
- trainee 'Finish' button removed; if they leave mid-chat an active session is resumed
via /chat/resume (continue, not restart). One-shot lock still enforced once decided.
- mock/tests updated: persona deciding buy -> send auto-finishes won.
Rebuilt dist.
117 lines
4.6 KiB
Python
117 lines
4.6 KiB
Python
"""Mock LLM for deterministic end-to-end tests (no external API needed).
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Substitutes for app.llm.LLMClient. Returns canned JSON for structured calls and
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simple replies for chat calls, so the full analyze→persona→chat→debrief flow runs.
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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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SAMPLE_SALES_KIT = {
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"productName": "CloudPOS",
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"category": "POS software",
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"valueProps": ["faster checkout", "inventory sync"],
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"features": ["tablets", "reports"],
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"pricingAnchors": ["1,000 THB/month"],
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"targetAudience": {"segment": "SME restaurants", "demographics": "", "useCases": ["front counter"]},
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"objectionHandlers": ["free trial", "setup included"],
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"initialPainFit": [
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{"pain": "slow checkout queues", "fit": "strong", "evidence": "faster checkout"},
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{"pain": "lost sales from stockouts", "fit": "partial", "evidence": "inventory sync"},
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],
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"scenarioFrame": "Cloud POS sold over LINE to Bangkok SME restaurants.",
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}
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def _sample_persona(idx: int, tier: str) -> dict[str, Any]:
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return {
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"id": f"persona-{idx:02d}",
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"name": f"Persona {idx}",
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"tier": tier,
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"channel": "line",
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"initiation_mode": "customer" if idx % 3 else "seller",
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"profession": "restaurant owner",
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"age_group": "30s",
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"location": "Bangkok",
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"product_context": "running a small noodle shop",
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"background": "Runs a family noodle shop for 8 years.",
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"income": "60k THB/month",
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"lifestyle": "works long hours",
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"personality": "practical and cautious",
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"communication_style": "short, direct, casual",
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"budget": "1,500 THB/month max",
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"decision_timeline": "within 2 weeks",
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"goal": "reduce lunch-rush queues",
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"objections": ["too expensive", "hard to learn"],
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"pains": [
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{"id": "p1", "name": "slow checkout", "fit": "strong",
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"description": "Long queues at lunch", "rootCause": "manual order taking",
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"resolutionConditions": ["show faster checkout", "offer a trial"]},
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{"id": "p2", "name": "stockouts", "fit": "partial",
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"description": "Runs out of ingredients", "rootCause": "no inventory tracking",
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"resolutionConditions": ["show inventory feature"]},
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],
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"negotiation_levers": ["price reduction", "free setup"],
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"opener": "Hi, I saw your POS ad. Does it work with small shops?",
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"special": "wrong_text" if (tier == "C" and idx % 5 == 4) else "",
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"difficulty": 2 if tier == "A" else (3 if tier == "B" else 4),
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"notes": "sample",
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}
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def make_personas() -> list[dict[str, Any]]:
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out = []
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idx = 1
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for tier in ["A", "B", "C"]:
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for _ in range(5):
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out.append(_sample_persona(idx, tier))
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idx += 1
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return out
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class MockLLM:
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"""Drop-in for app.llm.LLMClient — reads config the same way."""
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persona_count = 0
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def __init__(self, **kwargs):
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pass
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def complete(self, system_prompt: str, user_prompt: str, **kw) -> str:
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if "Persona generation prompts" in system_prompt or "persona designer" in system_prompt.lower():
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return json.dumps({"personas": make_personas()}, ensure_ascii=False)
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if "market-research persona designer" in system_prompt.lower():
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return json.dumps({"personas": make_personas()}, ensure_ascii=False)
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return "ok"
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def complete_json(self, system_prompt: str, user_prompt: str, **kw) -> dict[str, Any]:
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sp = system_prompt.lower()
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if "ecommerce/b2b analyst" in sp:
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return dict(SAMPLE_SALES_KIT)
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if "market-research persona designer" in sp:
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return {"personas": make_personas()}
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if "sales-training simulator" in sp and "PRIVATE" in system_prompt:
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return {"persona": _sample_persona(99, "C")}
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if "judge" in sp and "sales-training chat" in sp:
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return {
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"outcome": "won",
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"score": 82,
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"pain": "slow checkout queues",
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"why": "resolved the pain and secured acceptance",
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"failurePoints": [],
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"coaching": [],
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"painProgress": {"slow checkout": 100},
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}
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return {}
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def complete_conversation(self, messages, **kw) -> str:
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# persona chat: echo a short in-character reply with a decision.
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# On the first send, the persona decides to buy (so E2E auto-finishes as won).
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return json.dumps({
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"reply": "I see. Tell me more about the price then.",
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"decision": "buy",
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"mood": 1,
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}, ensure_ascii=False)
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