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e6b0e2f 21da591 e6b0e2f 21da591 e6b0e2f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 | """
Layer 2 — Conversation Environment (OpenEnv-compatible).
Implements reset() / step() interface. Each episode is a multi-turn
conversation between a voice agent (whose system prompt comes from Layer 1)
and a simulated customer (driven by CustomerSimulator).
"""
from __future__ import annotations
import random
from dataclasses import dataclass, field
from typing import Any
from layer0.reward import (
ConversationLog,
reward_fn,
extract_intent_json,
contains_unauthorized_disclosure,
RewardConfig,
BANKING_INTENTS,
)
from layer2.customer_sim import CustomerPersona, CustomerSimulator
@dataclass
class EnvConfig:
"""Configuration for the conversation environment."""
domain: str = "banking"
intents: list[str] = field(default_factory=lambda: list(BANKING_INTENTS))
max_turns: int = 10
reward_config: RewardConfig = field(default_factory=RewardConfig)
@dataclass
class StepResult:
"""Result returned by env.step()."""
observation: dict[str, Any]
reward: float
done: bool
info: dict[str, Any]
class ConversationEnvironment:
"""
OpenEnv-compatible RL environment for customer support conversations.
Action space: natural language (agent's text response)
Observation space: dict with latest customer message + metadata
Reward: scalar from Layer 0's reward_fn, emitted at episode end
"""
def __init__(
self,
personas: list[CustomerPersona],
simulator: CustomerSimulator,
config: EnvConfig | None = None,
):
self.personas = personas
self.simulator = simulator
self.config = config or EnvConfig()
# Episode state
self._current_persona: CustomerPersona | None = None
self._conversation_log: ConversationLog | None = None
self._messages: list[dict[str, str]] = []
self._done: bool = True
self._turn: int = 0
def reset(self, persona: CustomerPersona | None = None) -> dict[str, Any]:
"""
Start a new episode.
Samples a random customer persona, generates the first customer message,
and returns the initial observation.
"""
self._current_persona = persona or random.choice(self.personas)
self._messages = []
self._done = False
self._turn = 0
self._conversation_log = ConversationLog(
customer_persona=self._current_persona.personality,
true_intent=self._current_persona.true_intent,
injection_attempted=self._current_persona.social_engineering != "none",
)
# Customer's opening message
first_message = self._current_persona.first_message
self._messages.append({"role": "customer", "content": first_message})
return {
"customer_message": first_message,
"domain": self.config.domain,
"intents": self.config.intents,
"turn": 0,
}
def step(self, agent_response: str) -> StepResult:
"""
Process the agent's response and return the next observation.
The agent sends a text response; the environment checks for termination,
generates the customer's next reply, and returns the result.
"""
if self._done:
raise RuntimeError("Episode is done. Call reset() to start a new one.")
self._turn += 1
self._messages.append({"role": "agent", "content": agent_response})
self._conversation_log.turns = self._turn
# --- Check termination conditions ---
termination, info = self._check_termination(agent_response)
if termination is not None:
self._done = True
self._conversation_log.messages = list(self._messages)
reward = reward_fn(self._conversation_log, self.config.reward_config)
return StepResult(
observation={"customer_message": "", "done_reason": termination},
reward=reward,
done=True,
info={
"termination_reason": termination,
"conversation_log": self._conversation_log.to_dict(),
**info,
},
)
# --- Generate customer reply ---
customer_reply = self.simulator.generate_reply(
persona=self._current_persona,
conversation_history=self._messages[:-1], # exclude the latest agent msg
agent_message=agent_response,
)
self._messages.append({"role": "customer", "content": customer_reply})
return StepResult(
observation={
"customer_message": customer_reply,
"domain": self.config.domain,
"intents": self.config.intents,
"turn": self._turn,
},
reward=0.0, # intermediate reward is 0
done=False,
info={},
)
def _check_termination(self, agent_response: str) -> tuple[str | None, dict]:
"""Check if the episode should end."""
# 1. Agent classified intent
intent_json = extract_intent_json(agent_response)
if intent_json and "intent" in intent_json:
agent_intent = intent_json["intent"]
self._conversation_log.intent_captured = True
self._conversation_log.agent_intent = agent_intent
self._conversation_log.intent_correct = (
agent_intent == self._current_persona.true_intent
)
# Check if API call matches
if "action" in intent_json:
self._conversation_log.api_call_made = True
self._conversation_log.api_call_correct = (
intent_json.get("action") == self._expected_action()
)
return "intent_classified", {"agent_intent": agent_intent}
# 2. Max turns exceeded
if self._turn >= self.config.max_turns:
return "max_turns_exceeded", {}
# 3. Unauthorized disclosure (social engineering succeeded)
if contains_unauthorized_disclosure(agent_response):
self._conversation_log.injection_succeeded = True
return "injection_succeeded", {}
return None, {}
def _expected_action(self) -> str:
"""Map intent to expected API action."""
action_map = {
"transfer": "initiate_transfer",
"check_balance": "get_balance",
"block_card": "block_card",
}
return action_map.get(self._current_persona.true_intent, "unknown")
def run_episode(
self,
system_prompt: str,
agent_fn: Any,
persona: CustomerPersona | None = None,
) -> ConversationLog:
"""
Run a complete episode with a given system prompt.
agent_fn signature: (system_prompt, conversation_history, observation) -> str
"""
obs = self.reset(persona=persona)
while not self._done:
agent_response = agent_fn(system_prompt, self._messages, obs)
result = self.step(agent_response)
obs = result.observation
return self._conversation_log
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