297 lines
11 KiB
Python
297 lines
11 KiB
Python
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"""Agent loop machinery with a scripted LLM (no API key needed):
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prompt composition, tool dispatch, transcripts, outcome classification."""
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import json
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from datetime import datetime, timedelta
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from zoneinfo import ZoneInfo
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from sqlalchemy import select
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from gogo.agent.llm import LLMResponse, ScriptedLLM, ToolUse
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from gogo.agent.loop import AgentConversation
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from gogo.agent.prompt import compose_greeting, compose_system_prompt
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from gogo.models import BookingRequest, MessageForOwner, Service
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TZ = ZoneInfo("Europe/Sarajevo")
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# -- prompt composition -------------------------------------------------------
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async def test_system_prompt_contains_tenant_data(session, tenant):
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prompt = await compose_system_prompt(session, tenant, "voice")
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assert "Salon Merima" in prompt
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assert "Šišanje i feniranje" in prompt
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assert "45 min" in prompt
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assert "25–35 KM" in prompt
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assert "ponedjeljak: 09:00–18:00" in prompt
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assert "razgovor se snima" in prompt # recording disclosure in greeting (§5.4)
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assert "phone call" in prompt # voice channel suffix
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# service ids are exposed so the model can call tools with them
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svc = (
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await session.execute(select(Service).where(Service.tenant_id == tenant.id))
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).scalars().first()
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assert str(svc.id) in prompt
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async def test_chat_prompt_requires_phone_collection(session, tenant):
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prompt = await compose_system_prompt(session, tenant, "chat")
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assert "web chat" in prompt
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assert "phone number" in prompt
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def test_greeting_variants(tenant):
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open_at = datetime(2026, 7, 15, 10, 0, tzinfo=TZ) # Wed 10:00
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closed_at = datetime(2026, 7, 15, 21, 0, tzinfo=TZ) # Wed 21:00
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g_open = compose_greeting(tenant, "voice", open_at)
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g_closed = compose_greeting(tenant, "voice", closed_at)
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g_chat = compose_greeting(tenant, "chat")
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assert "razgovor se snima" in g_open
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assert "zatvoren" not in g_open
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assert "trenutno zatvoren" in g_closed
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assert "radno vrijeme" in g_closed
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assert "razgovor se snima" not in g_chat # no recording disclosure in chat
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async def test_prompt_template_override(session, tenant):
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from gogo.models import TenantPromptOverride
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session.add(
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TenantPromptOverride(tenant_id=tenant.id, body="CUSTOM {salon_profile} END")
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)
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await session.flush()
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prompt = await compose_system_prompt(session, tenant, "voice")
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assert prompt.startswith("CUSTOM")
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assert "Salon Merima" in prompt
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# -- conversation loop --------------------------------------------------------
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async def booking_conversation(session, tenant, emails):
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"""Scripted A.1-style flow: availability check → booking request."""
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svc = (
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await session.execute(
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select(Service).where(Service.tenant_id == tenant.id, Service.name.like("Šišanje%"))
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)
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).scalar_one()
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wed = datetime(2026, 7, 15, tzinfo=TZ)
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slot_start = wed.replace(hour=17)
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llm = ScriptedLLM(
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[
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# turn 1: caller asks for a booking → model checks availability
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LLMResponse(
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text="",
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tool_uses=[
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ToolUse(
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id="tu1",
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name="check_availability",
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input={
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"service_id": str(svc.id),
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"date_from": "2026-07-15",
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"date_to": "2026-07-15",
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},
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)
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],
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stop_reason="tool_use",
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),
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LLMResponse(text="U srijedu poslijepodne slobodno je u pet. Odgovara?"),
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# turn 2: caller accepts → model submits the request and closes
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LLMResponse(
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text="",
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tool_uses=[
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ToolUse(
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id="tu2",
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name="submit_booking_request",
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input={
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"service_id": str(svc.id),
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"service_name_raw": "šišanje i feniranje",
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"client_name": "Amra Hodžić",
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"client_phone": "+38765123456",
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"slots": [
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{
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"start": slot_start.isoformat(),
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"end": (slot_start + timedelta(minutes=45)).isoformat(),
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}
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],
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"summary": "Šišanje i feniranje, srijeda u 17h.",
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},
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)
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],
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stop_reason="tool_use",
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),
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LLMResponse(
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text=(
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"Prosljeđujem salonu zahtjev: šišanje i feniranje, srijeda u pet. "
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"Kontaktiraće vas u najkraćem roku radi potvrde. Hvala i prijatno!"
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)
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),
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]
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)
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convo = AgentConversation(
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session, tenant, channel="voice", caller_phone="+38765123456", llm=llm
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)
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await convo.greeting()
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r1 = await convo.user_turn("Htjela bih zakazati šišanje i feniranje u srijedu.")
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r2 = await convo.user_turn("U pet, može. Amra Hodžić, broj je ovaj s kojeg zovem.")
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return convo, llm, r1, r2
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async def test_booking_flow_creates_request(session, tenant, emails, sms, clean_mock_provider):
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convo, llm, r1, r2 = await booking_conversation(session, tenant, emails)
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await session.commit()
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assert "slobodno je u pet" in r1
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assert "prijatno" in r2.lower()
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# tool_result was fed back to the model
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tool_result_msg = llm.calls[1]["messages"][-2] # assistant tool_use, then user tool_result
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assert tool_result_msg["role"] == "assistant"
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results = llm.calls[1]["messages"][-1]
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assert results["role"] == "user"
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assert results["content"][0]["type"] == "tool_result"
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payload = json.loads(results["content"][0]["content"])
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assert "slots" in payload
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# a real BookingRequest exists and the proposal email went out
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req = (await session.execute(select(BookingRequest))).scalar_one()
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assert req.client_name == "Amra Hodžić"
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assert req.source == "voice"
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assert len(emails) == 1
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assert convo.outcome == "request_created"
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# transcript captures roles, text and tool calls
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roles = [t["role"] for t in convo.transcript]
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assert roles == ["assistant", "user", "assistant", "user", "assistant"]
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assert convo.transcript[-1]["tool_calls"][0]["name"] == "submit_booking_request"
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async def test_take_message_outcome(session, tenant, emails, sms):
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llm = ScriptedLLM(
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[
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LLMResponse(
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text="",
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tool_uses=[
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ToolUse(
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id="tu1",
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name="take_message",
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input={
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"text": "Selma Kovač otkazuje sutrašnji termin u deset.",
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"client_name": "Selma Kovač",
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},
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)
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],
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stop_reason="tool_use",
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),
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LLMResponse(text="Prosljeđujem salonu poruku. Prijatno!"),
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]
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)
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convo = AgentConversation(
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session, tenant, channel="voice", caller_phone="+38765111222", llm=llm
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)
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await convo.user_turn("Trebala bih otkazati termin za sutra u deset, Selma Kovač.")
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await session.commit()
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msg = (await session.execute(select(MessageForOwner))).scalar_one()
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assert "otkazuje" in msg.text
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assert msg.client_phone == "+38765111222" # caller-ID fallback
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assert convo.outcome == "message_taken"
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async def test_info_only_outcome(session, tenant):
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llm = ScriptedLLM([LLMResponse(text="Farbanje je od šezdeset do devedeset maraka.")])
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convo = AgentConversation(session, tenant, channel="voice", llm=llm)
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await convo.user_turn("Koliko košta farbanje?")
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assert convo.outcome == "info_only"
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async def test_submit_without_phone_fails_in_chat(session, tenant, emails):
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"""Chat has no caller-ID: submitting without a phone returns an error to the model."""
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llm = ScriptedLLM(
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[
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LLMResponse(
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text="",
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tool_uses=[
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ToolUse(
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id="tu1",
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name="submit_booking_request",
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input={"client_name": "Ana", "summary": "manikir"},
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)
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],
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stop_reason="tool_use",
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),
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LLMResponse(text="Koji je vaš broj telefona za kontakt?"),
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]
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)
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convo = AgentConversation(session, tenant, channel="chat", llm=llm)
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await convo.user_turn("Može manikir sutra?")
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results = llm.calls[1]["messages"][-1]
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payload = json.loads(results["content"][0]["content"])
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assert payload["error"] == "missing_phone"
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assert (await session.execute(select(BookingRequest))).scalar_one_or_none() is None
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assert convo.outcome == "info_only"
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async def test_hallucinated_service_id_is_dropped(session, tenant, emails):
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llm = ScriptedLLM(
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[
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LLMResponse(
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text="",
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tool_uses=[
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ToolUse(
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id="tu1",
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name="submit_booking_request",
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input={
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"service_id": "not-a-real-uuid",
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"service_name_raw": "nešto čudno",
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"client_name": "Ana",
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"client_phone": "+38761000111",
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"summary": "test",
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},
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)
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],
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stop_reason="tool_use",
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),
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LLMResponse(text="Zahtjev proslijeđen."),
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]
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)
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convo = AgentConversation(session, tenant, channel="chat", llm=llm)
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await convo.user_turn("Zakazi mi nešto čudno")
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await session.commit()
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req = (await session.execute(select(BookingRequest))).scalar_one()
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assert req.service_id is None
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assert req.service_name_raw == "nešto čudno"
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async def test_tool_round_limit_guard(session, tenant):
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"""A model stuck in a tool loop cannot spin forever."""
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endless = LLMResponse(
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text="",
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tool_uses=[ToolUse(id="x", name="get_salon_info", input={})],
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stop_reason="tool_use",
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)
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llm = ScriptedLLM([endless] * 20)
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convo = AgentConversation(session, tenant, channel="chat", llm=llm)
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reply = await convo.user_turn("zdravo")
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assert len(llm.calls) == 6 # MAX_TOOL_ROUNDS_PER_TURN
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assert reply # graceful fallback text, not an exception
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async def test_unknown_tool_returns_error(session, tenant):
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llm = ScriptedLLM(
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[
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LLMResponse(
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text="",
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tool_uses=[ToolUse(id="x", name="book_now", input={})],
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stop_reason="tool_use",
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),
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LLMResponse(text="Izvinite."),
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]
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)
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convo = AgentConversation(session, tenant, channel="chat", llm=llm)
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await convo.user_turn("test")
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payload = json.loads(llm.calls[1]["messages"][-1]["content"][0]["content"])
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assert "unknown tool" in payload["error"]
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