Senad Uka
0d0838e6e2
- LLMProvider interface: AnthropicLLM + ScriptedLLM test fake - System prompt composition from super-admin template + structured tenant data only (§6.3); auto-generated greetings with recording disclosure, out-of-hours variant, chat variant - Agent tools (§6.5): get_salon_info, check_availability (≤8 slots to model, agent offers ≤3), submit_booking_request (caller-ID fallback, hallucinated service-id guard), take_message - Conversation loop with tool dispatch, round-limit guard, transcript capture, outcome classification (request_created/message_taken/info_only/abandoned) - Terminal playground (python -m gogo.agent.cli) + demo seed (python -m gogo.seed) - Deterministic loop tests (ScriptedLLM) + Appendix A golden-scenario acceptance tests against a real LLM (skipped without an API key) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
114 lines
3.0 KiB
Python
114 lines
3.0 KiB
Python
"""LLMProvider interface (§4) — Anthropic implementation + scripted fake for tests.
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The shape mirrors the Anthropic Messages API tool-use loop: the provider returns
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text and/or tool_use blocks; the conversation loop executes tools and feeds
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tool_result blocks back.
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from typing import Any, Protocol
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from gogo.config import get_settings
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@dataclass
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class ToolUse:
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id: str
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name: str
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input: dict[str, Any]
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@dataclass
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class LLMResponse:
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text: str # concatenated text blocks ("" if pure tool call)
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tool_uses: list[ToolUse] = field(default_factory=list)
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stop_reason: str = "end_turn"
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class LLMProvider(Protocol):
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async def complete(
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self,
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*,
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system: str,
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messages: list[dict],
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tools: list[dict],
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model: str | None = None,
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max_tokens: int = 1024,
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) -> LLMResponse: ...
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class AnthropicLLM:
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def __init__(self, api_key: str | None = None):
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import anthropic
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self._client = anthropic.AsyncAnthropic(
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api_key=api_key or get_settings().anthropic_api_key or None
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)
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async def complete(
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self,
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*,
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system: str,
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messages: list[dict],
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tools: list[dict],
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model: str | None = None,
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max_tokens: int = 1024,
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) -> LLMResponse:
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resp = await self._client.messages.create(
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model=model or get_settings().llm_model,
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system=system,
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messages=messages,
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tools=tools,
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max_tokens=max_tokens,
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)
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text_parts: list[str] = []
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tool_uses: list[ToolUse] = []
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for block in resp.content:
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if block.type == "text":
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text_parts.append(block.text)
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elif block.type == "tool_use":
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tool_uses.append(ToolUse(id=block.id, name=block.name, input=block.input))
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return LLMResponse(
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text="\n".join(text_parts).strip(),
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tool_uses=tool_uses,
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stop_reason=resp.stop_reason or "end_turn",
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)
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class ScriptedLLM:
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"""Deterministic fake: replays a fixed sequence of LLMResponses.
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Used to test the conversation loop machinery (tool dispatch, transcripts,
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guards) without a real model.
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"""
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def __init__(self, responses: list[LLMResponse]):
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self._responses = list(responses)
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self.calls: list[dict] = [] # recorded kwargs for assertions
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async def complete(self, **kwargs) -> LLMResponse:
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import copy
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self.calls.append(copy.deepcopy(kwargs)) # snapshot: the history list mutates
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if not self._responses:
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return LLMResponse(text="Doviđenja!", stop_reason="end_turn")
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return self._responses.pop(0)
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_llm: LLMProvider | None = None
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def get_llm() -> LLMProvider:
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global _llm
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if _llm is None:
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_llm = AnthropicLLM()
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return _llm
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def set_llm(provider: LLMProvider | None) -> None:
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"""Test hook."""
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global _llm
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_llm = provider
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