Mike-Solar
e0db5ab2cf
- Core types: Rational time, EntityId, ActionBatch, PngBytes (no Pillow dep) - Config: ~/.quercus/config.toml with 0600 perms, env var override for CI - LLMProvider: Claude (anthropic SDK) and OpenAI-compatible endpoint (covers custom gateways and llama.cpp server mode); graceful degradation without API keys - Tool schemas: 26 curated tools with JSON Schema and read_only flags - AgentLoop: sync dialogue loop with confirmation gate (read-only batches bypass, rejected mutations are reported back to the LLM), PNG frame feedback for multimodal providers - Session log: JSONL recording with frame sha256 hashes, replayable - HostAdapter ABC (signatures only, aligned with OPP/1 method families) - Mock LLM provider (scripted/replay/record) for network-free CI Tests: 69 passed via uv run pytest (no network access)
105 lines
3.2 KiB
Python
105 lines
3.2 KiB
Python
"""进程内 Mock LLM：脚本化回复 + 录制模式（CI 无 key 无网络跑通 agent loop 的底座）。
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脚本文件格式（JSON 列表，每项一个 AssistantTurn）::
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[
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{"text": "", "tool_calls": [{"id": "call_1", "name": "get_frame",
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"arguments": {"time": {"num": 1, "den": 1}}}]},
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{"text": "完成", "tool_calls": []}
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]
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"""
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from __future__ import annotations
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import json
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from pathlib import Path
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from typing import Any
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from quercus_core.providers.base import AssistantTurn, LLMProvider, Message, ToolCall, ToolSchema
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def _turn_to_dict(turn: AssistantTurn) -> dict[str, Any]:
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return {
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"text": turn.text,
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"tool_calls": [
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{"id": tc.id, "name": tc.name, "arguments": tc.arguments}
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for tc in turn.tool_calls
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],
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}
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def _turn_from_dict(data: dict[str, Any]) -> AssistantTurn:
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calls = tuple(
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ToolCall(id=item["id"], name=item["name"], arguments=item.get("arguments") or {})
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for item in data.get("tool_calls") or []
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)
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return AssistantTurn(text=data.get("text", ""), tool_calls=calls)
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class MockProvider(LLMProvider):
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"""按预置脚本依次返回 AssistantTurn；记录每次收到的消息供断言。"""
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name = "mock"
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def __init__(
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self,
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script: list[AssistantTurn] | None = None,
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script_file: Path | str | None = None,
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) -> None:
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self._script = list(script) if script is not None else []
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if script_file is not None:
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self._script = self.load_script(script_file)
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self._index = 0
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self.observed: list[list[Message]] = [] # 每次 generate 收到的消息历史
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def generate(
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self,
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messages: list[Message],
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tools: list[ToolSchema] | None = None,
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) -> AssistantTurn:
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self.observed.append(list(messages))
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if self._index >= len(self._script):
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return AssistantTurn(text="(mock: 脚本已耗尽，结束本轮)")
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turn = self._script[self._index]
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self._index += 1
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return turn
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@property
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def exhausted(self) -> bool:
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return self._index >= len(self._script)
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# ---- 脚本文件 ----
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@staticmethod
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def dump_script(turns: list[AssistantTurn], path: Path | str) -> Path:
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path = Path(path)
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path.parent.mkdir(parents=True, exist_ok=True)
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path.write_text(
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json.dumps([_turn_to_dict(t) for t in turns], ensure_ascii=False, indent=2),
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encoding="utf-8",
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)
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return path
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@staticmethod
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def load_script(path: Path | str) -> list[AssistantTurn]:
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data = json.loads(Path(path).read_text(encoding="utf-8"))
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return [_turn_from_dict(item) for item in data]
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class RecordingProvider(LLMProvider):
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"""录制模式：包装真实（或 mock）provider，记录其响应序列。"""
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name = "recording"
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def __init__(self, inner: LLMProvider) -> None:
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self._inner = inner
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self.recorded: list[AssistantTurn] = []
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def generate(
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self,
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messages: list[Message],
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tools: list[ToolSchema] | None = None,
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) -> AssistantTurn:
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turn = self._inner.generate(messages, tools=tools)
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self.recorded.append(turn)
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return turn
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