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2414d31 657f0fa 2414d31 | 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 | """
End-to-end test through ClarifyClient (the path TRL training actually uses).
Server stays a single env instance per WebSocket session; reset+step+state
all share state. Run uvicorn first, then this script.
"""
import json
import sys
from pathlib import Path
ROOT = Path(__file__).resolve().parent.parent
sys.path.insert(0, str(ROOT))
from client import ClarifyClient
def main() -> int:
env = ClarifyClient(base_url="http://127.0.0.1:7860").sync()
with env:
print("--- list tools ---")
tools = env.list_tools()
print([t.name for t in tools])
print("\n--- reset(seed=7, task_id=medium) ---")
obs = env.reset(seed=7, task_id="medium")
print(f"reward={obs.reward} done={obs.done}")
print("observation:", obs.observation)
print("\n--- ask_question: order id ---")
result = env.call_tool("ask_question", question="what is the order id?")
print("result:", result)
print("\n--- ask_question: item issue ---")
result = env.call_tool("ask_question", question="what's wrong with the order?")
print("result:", result)
print("\n--- ask_question: refund/replace ---")
result = env.call_tool("ask_question", question="refund or replace?")
print("result:", result)
print("\n--- ask_question: urgency ---")
result = env.call_tool("ask_question", question="when do you need this?")
print("result:", result)
print("\n--- propose_plan ---")
plan = json.dumps({
"order_id": "#4521",
"item_issue": "wrong-item",
"refund_or_replace": "replace",
"urgency": "high",
})
result = env.call_tool("propose_plan", plan=plan)
print("result:", result)
print("\n--- final state ---")
state = env.state()
print(state)
return 0
if __name__ == "__main__":
sys.exit(main())
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