mirror of
https://github.com/jxxghp/MoviePilot.git
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226 lines
7.9 KiB
Python
226 lines
7.9 KiB
Python
import pytest
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from langchain_core.messages import AIMessage
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from app.agent import MoviePilotAgent
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from app.agent.llm.helper import LLMHelper
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from app.agent.llm.provider import LLMProviderManager
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from app.agent.middleware.usage import UsageMiddleware
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from app.chain.message import MessageChain
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def test_usage_extracts_normalized_cache_details() -> None:
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"""标准 usage_metadata 应解析缓存读取、写入和未命中 tokens。"""
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usage = UsageMiddleware._extract_usage(
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AIMessage(
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content="ok",
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usage_metadata={
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"input_tokens": 1200,
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"output_tokens": 100,
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"total_tokens": 1300,
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"input_token_details": {
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"cache_read": 700,
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"cache_creation": 0,
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"ephemeral_5m_input_tokens": 300,
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},
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},
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)
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)
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assert usage["cache_usage_available"]
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assert usage["cache_read_input_tokens"] == 700
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assert usage["cache_write_input_tokens"] == 300
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assert usage["uncached_input_tokens"] == 200
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assert usage["cache_hit_ratio"] == pytest.approx(700 / 1200)
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def test_usage_extracts_deepseek_cache_hit_and_miss_tokens() -> None:
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"""DeepSeek 原始 usage 应保留其显式缓存命中与未命中字段。"""
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usage = UsageMiddleware._extract_usage(
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AIMessage(
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content="ok",
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response_metadata={
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"token_usage": {
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"prompt_tokens": 1000,
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"completion_tokens": 50,
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"total_tokens": 1050,
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"prompt_cache_hit_tokens": 800,
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"prompt_cache_miss_tokens": 200,
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}
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},
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)
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)
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assert usage["cache_usage_available"]
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assert usage["cache_read_input_tokens"] == 800
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assert usage["cache_write_input_tokens"] == 0
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assert usage["uncached_input_tokens"] == 200
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assert usage["cache_hit_ratio"] == pytest.approx(0.8)
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def test_session_usage_aggregates_and_formats_cache_statistics() -> None:
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"""会话状态应聚合缓存统计并在状态文本中展示。"""
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agent = MoviePilotAgent(session_id="cache-session", user_id="user-1")
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agent._record_usage(
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{
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"has_usage": True,
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"cache_usage_available": True,
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"input_tokens": 100,
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"output_tokens": 10,
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"total_tokens": 110,
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"cache_read_input_tokens": 60,
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"cache_write_input_tokens": 20,
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"uncached_input_tokens": 20,
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"cache_hit_ratio": 0.6,
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}
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)
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agent._record_usage(
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{
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"has_usage": True,
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"cache_usage_available": True,
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"input_tokens": 50,
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"output_tokens": 5,
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"total_tokens": 55,
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"cache_read_input_tokens": 20,
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"cache_write_input_tokens": 0,
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"uncached_input_tokens": 30,
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"cache_hit_ratio": 0.4,
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}
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)
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status = agent.get_session_status()
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status.update({"is_processing": False, "pending_messages": 0})
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status_text = MessageChain._format_session_status_text(status)
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assert status["total_cache_read_input_tokens"] == 80
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assert status["total_cache_write_input_tokens"] == 20
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assert status["total_uncached_input_tokens"] == 50
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assert status["total_cache_hit_ratio"] == pytest.approx(80 / 150)
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assert "当前会话累计缓存: 命中 80 / 写入 20 / 未命中 50 (53.33%)" in status_text
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def test_prompt_cache_key_is_stable_and_private() -> None:
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"""提示词缓存键应在同一会话内稳定且不暴露原始标识。"""
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first = MoviePilotAgent(session_id="private-session", user_id="private-user")
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second = MoviePilotAgent(session_id="private-session", user_id="private-user")
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other = MoviePilotAgent(session_id="other-session", user_id="private-user")
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cache_key = first._build_prompt_cache_key()
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assert cache_key == second._build_prompt_cache_key()
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assert cache_key != other._build_prompt_cache_key()
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assert "private-session" not in cache_key
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assert "private-user" not in cache_key
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def test_openai_prompt_cache_options_only_target_official_endpoints() -> None:
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"""OpenAI 专属缓存参数不得泄露到第三方兼容端点。"""
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headers, kwargs = LLMHelper._build_openai_prompt_cache_options(
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provider="openai",
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base_url="https://api.openai.com/v1",
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use_responses_api=False,
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prompt_cache_key="cache-key",
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default_headers={"User-Agent": "MoviePilot"},
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model_kwargs={"extra_body": {"existing": True}},
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)
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_, compatible_kwargs = LLMHelper._build_openai_prompt_cache_options(
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provider="openai",
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base_url="https://api.openai.com.example/v1",
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use_responses_api=False,
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prompt_cache_key="cache-key",
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default_headers=None,
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model_kwargs={},
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)
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assert headers == {"User-Agent": "MoviePilot"}
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assert kwargs["extra_body"] == {
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"existing": True,
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"prompt_cache_key": "cache-key",
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}
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assert compatible_kwargs == {}
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def test_xai_chat_completions_uses_stable_conversation_header() -> None:
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"""xAI Chat Completions 应使用官方会话缓存路由请求头。"""
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headers, kwargs = LLMHelper._build_openai_prompt_cache_options(
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provider="xai",
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base_url="https://api.x.ai/v1",
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use_responses_api=None,
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prompt_cache_key="cache-key",
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default_headers=None,
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model_kwargs={},
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)
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assert headers == {"x-grok-conv-id": "cache-key"}
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assert kwargs == {}
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def test_prompt_cache_adapter_preserves_control_after_tool_binding() -> None:
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"""Provider 缓存参数应在 Agent 最终绑定工具时仍然存在。"""
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class FakeModel:
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"""模拟通过 bind_tools 再调用 bind 的 LangChain 模型。"""
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def bind(self, **kwargs):
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"""返回最终绑定参数。"""
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return kwargs
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def bind_tools(self, tools, **kwargs):
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"""模拟模型的工具绑定流程。"""
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return self.bind(tools=tools, **kwargs)
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cached_model_cls = LLMHelper._with_prompt_cache_control(
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FakeModel,
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{"type": "default"},
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)
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result = cached_model_cls().bind_tools([{"name": "tool"}])
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assert result["tools"] == [{"name": "tool"}]
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assert result["cache_control"] == {"type": "default"}
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def test_anthropic_cache_control_only_targets_official_endpoint() -> None:
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"""Anthropic 原生缓存控制不得发送到第三方兼容端点。"""
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official = LLMHelper._use_anthropic_prompt_cache(
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provider="anthropic",
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runtime={
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"runtime": "anthropic_compatible",
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"base_url": "https://api.anthropic.com/v1",
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},
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prompt_cache_key="cache-key",
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)
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compatible = LLMHelper._use_anthropic_prompt_cache(
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provider="minimax",
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runtime={
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"runtime": "anthropic_compatible",
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"base_url": "https://api.minimax.io/anthropic/v1",
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},
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prompt_cache_key="cache-key",
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)
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assert official
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assert not compatible
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def test_provider_metadata_declares_prompt_cache_without_model_allowlist() -> None:
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"""Provider 应通过模型能力元数据判断缓存支持，不依赖模型 ID 白名单。"""
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assert LLMProviderManager._metadata_supports_prompt_cache(
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{"cost": {"input": 1, "cache_read": 0.1}}
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)
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assert LLMProviderManager._metadata_supports_prompt_cache(
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{"capabilities": {"prompt_cache": True}}
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)
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assert not LLMProviderManager._metadata_supports_prompt_cache(
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{"cost": {"input": 1, "output": 2}}
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)
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def test_bedrock_model_metadata_candidates_remove_region_prefix() -> None:
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"""Bedrock 跨区域模型应自动回落到基础模型的能力元数据。"""
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candidates = LLMProviderManager._models_dev_model_candidates(
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"amazon-bedrock",
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"us.vendor.model-version",
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)
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assert candidates == ("us.vendor.model-version", "vendor.model-version")
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