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124 lines
4.3 KiB
Python
124 lines
4.3 KiB
Python
from __future__ import annotations
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from types import SimpleNamespace
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import pytest
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from deeptutor.agents.chat.agentic_pipeline import AgenticChatPipeline
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from deeptutor.agents.chat.chat_agent import ChatAgent
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from deeptutor.agents.chat.prompt_blocks import ChatPromptAssembler
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@pytest.fixture(autouse=True)
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def _fake_llm_config(monkeypatch: pytest.MonkeyPatch) -> None:
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cfg = SimpleNamespace(
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binding="openai",
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model="gpt-test",
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api_key="sk-test",
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base_url="https://example.test/v1",
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api_version=None,
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)
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monkeypatch.setattr(
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"deeptutor.agents.chat.agentic_pipeline.get_llm_config",
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lambda: cfg,
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)
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monkeypatch.setattr("deeptutor.agents.base_agent.get_llm_config", lambda: cfg)
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def test_agentic_chat_final_prompt_uses_selected_language(
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monkeypatch: pytest.MonkeyPatch,
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) -> None:
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class FakeRegistry:
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def build_prompt_text(self, *_args, **_kwargs) -> str:
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return "- tool"
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monkeypatch.setattr(
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"deeptutor.agents.chat.agentic_pipeline.get_tool_registry",
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lambda: FakeRegistry(),
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)
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from deeptutor.core.context import UnifiedContext
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ctx = UnifiedContext()
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zh_prompt = AgenticChatPipeline(language="zh")._build_system_prompt([], ctx)
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en_prompt = AgenticChatPipeline(language="en")._build_system_prompt([], ctx)
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# Prompt blocks are phase-specific, but the shared language directive
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# still runs at the end, so per-language imperatives must surface.
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assert "请严格使用中文" in zh_prompt
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assert "Write ALL reader-facing text" in en_prompt
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# Persona phrasing differs by language so the prompts are not just
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# English text with a Chinese tail appended.
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assert "你是 DeepTutor" in zh_prompt
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assert "You are DeepTutor" in en_prompt
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def test_mastery_plugin_system_prompt_uses_localized_fallback(
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monkeypatch: pytest.MonkeyPatch,
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) -> None:
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class FakeRegistry:
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def build_prompt_text(self, *_args, **_kwargs) -> str:
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return "- tool"
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monkeypatch.setattr(
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"deeptutor.agents.chat.agentic_pipeline.get_tool_registry",
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lambda: FakeRegistry(),
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)
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from deeptutor.core.context import UnifiedContext
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ctx = UnifiedContext(metadata={"mastery_mode": True, "mastery_path_id": "p1"})
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zh_prompt = AgenticChatPipeline(language="zh")._build_system_prompt([], ctx)
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en_prompt = AgenticChatPipeline(language="en")._build_system_prompt([], ctx)
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assert "## mastery_tutor" in zh_prompt
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assert "精通导师模式" in zh_prompt
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assert "## mastery_tutor" in en_prompt
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assert "Mastery Tutor mode" in en_prompt
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def test_legacy_chat_agent_system_prompt_uses_selected_language() -> None:
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zh_messages = ChatAgent(language="zh", config={}).build_messages(
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message="解释梯度下降",
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history=[],
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)
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en_messages = ChatAgent(language="en", config={}).build_messages(
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message="Explain gradient descent",
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history=[],
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)
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assert "你是 DeepTutor" in zh_messages[0]["content"]
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assert "请严格使用中文" in zh_messages[0]["content"]
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assert "You are DeepTutor" in en_messages[0]["content"]
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assert "Write ALL reader-facing text" in en_messages[0]["content"]
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def test_prompt_blocks_include_localized_optional_context() -> None:
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from deeptutor.core.context import UnifiedContext
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prompts = {
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"general": "通用",
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"runtime_policy": "策略",
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"loop": {
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"system": "循环",
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"user": "用户说：{user_message}",
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"finish_exhausted": "预算已用完，请直接回答。",
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},
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}
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ctx = UnifiedContext(
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user_message="解释光合作用",
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persona_context="用苏格拉底式提问",
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memory_context="学生喜欢例子",
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)
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assembler = ChatPromptAssembler(prompts=prompts, language="zh")
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blocks = assembler.blocks(context=ctx, tool_manifest="", workspace_note="工作区可用")
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names = [block.name for block in blocks]
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assert names[:3] == ["general", "runtime_policy", "loop"]
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assert "persona_style" in names
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assert "memory" in names
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assert "workspace" in names
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assert assembler.user_message(context=ctx) == "用户说：解释光合作用"
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assert assembler.finish_exhausted_instruction() == "预算已用完，请直接回答。"
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