655 lines
21 KiB
Python
655 lines
21 KiB
Python
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"""
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Tests for OrchestratorService.
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[AC-AISVC-01, AC-AISVC-02] Test complete generation pipeline integration.
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"""
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import pytest
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from unittest.mock import AsyncMock, MagicMock, patch
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from typing import AsyncGenerator
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from app.models import ChatRequest, ChatResponse, ChannelType, ChatMessage, Role
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from app.services.orchestrator import (
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OrchestratorService,
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OrchestratorConfig,
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GenerationContext,
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set_orchestrator_service,
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)
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from app.services.llm.base import LLMClient, LLMConfig, LLMResponse, LLMStreamChunk
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from app.services.memory import MemoryService
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from app.services.retrieval.base import (
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BaseRetriever,
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RetrievalContext,
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RetrievalResult,
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RetrievalHit,
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)
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from app.services.confidence import ConfidenceCalculator, ConfidenceConfig
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from app.services.context import ContextMerger
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from app.models.entities import ChatMessage as ChatMessageEntity
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class MockLLMClient(LLMClient):
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"""Mock LLM client for testing."""
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def __init__(self, response_content: str = "Mock LLM response"):
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self._response_content = response_content
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self._generate_called = False
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self._stream_generate_called = False
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async def generate(
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self,
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messages: list[dict[str, str]],
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config: LLMConfig | None = None,
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**kwargs,
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) -> LLMResponse:
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self._generate_called = True
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return LLMResponse(
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content=self._response_content,
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model="mock-model",
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usage={"prompt_tokens": 100, "completion_tokens": 50},
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finish_reason="stop",
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)
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async def stream_generate(
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self,
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messages: list[dict[str, str]],
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config: LLMConfig | None = None,
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**kwargs,
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) -> AsyncGenerator[LLMStreamChunk, None]:
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self._stream_generate_called = True
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chunks = ["Hello", " from", " mock", " LLM"]
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for chunk in chunks:
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yield LLMStreamChunk(delta=chunk, model="mock-model")
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yield LLMStreamChunk(delta="", model="mock-model", finish_reason="stop")
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async def close(self) -> None:
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pass
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class MockRetriever(BaseRetriever):
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"""Mock retriever for testing."""
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def __init__(self, hits: list[RetrievalHit] | None = None):
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self._hits = hits or []
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async def retrieve(self, ctx: RetrievalContext) -> RetrievalResult:
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return RetrievalResult(
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hits=self._hits,
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diagnostics={"mock": True},
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)
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async def health_check(self) -> bool:
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return True
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class MockMemoryService:
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"""Mock memory service for testing."""
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def __init__(self, history: list[ChatMessageEntity] | None = None):
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self._history = history or []
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self._saved_messages: list[dict] = []
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self._session_created = False
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async def get_or_create_session(
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self,
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tenant_id: str,
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session_id: str,
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channel_type: str | None = None,
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metadata: dict | None = None,
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):
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self._session_created = True
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return MagicMock(tenant_id=tenant_id, session_id=session_id)
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async def load_history(
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self,
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tenant_id: str,
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session_id: str,
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limit: int | None = None,
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):
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return self._history
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async def append_message(
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self,
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tenant_id: str,
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session_id: str,
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role: str,
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content: str,
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):
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self._saved_messages.append({"role": role, "content": content})
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async def append_messages(
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self,
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tenant_id: str,
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session_id: str,
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messages: list[dict[str, str]],
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):
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self._saved_messages.extend(messages)
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def create_chat_request(
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message: str = "Hello",
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session_id: str = "test-session",
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history: list[ChatMessage] | None = None,
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metadata: dict | None = None,
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) -> ChatRequest:
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"""Helper to create ChatRequest."""
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return ChatRequest(
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session_id=session_id,
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current_message=message,
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channel_type=ChannelType.WECHAT,
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history=history,
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metadata=metadata,
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)
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class TestOrchestratorServiceGenerate:
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"""Tests for OrchestratorService.generate() method."""
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@pytest.mark.asyncio
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async def test_generate_basic_without_dependencies(self):
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"""
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[AC-AISVC-01, AC-AISVC-02] Test basic generation without external dependencies.
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Should return fallback response with low confidence.
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"""
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orchestrator = OrchestratorService(
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config=OrchestratorConfig(enable_rag=False),
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)
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request = create_chat_request(message="What is the price?")
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response = await orchestrator.generate(
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tenant_id="tenant-1",
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request=request,
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)
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assert isinstance(response, ChatResponse)
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assert response.reply is not None
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assert response.confidence >= 0.0
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assert response.confidence <= 1.0
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assert isinstance(response.should_transfer, bool)
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assert "diagnostics" in response.metadata
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@pytest.mark.asyncio
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async def test_generate_with_llm_client(self):
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"""
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[AC-AISVC-02] Test generation with LLM client.
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Should use LLM response.
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"""
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mock_llm = MockLLMClient(response_content="This is the AI response.")
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orchestrator = OrchestratorService(
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llm_client=mock_llm,
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config=OrchestratorConfig(enable_rag=False),
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)
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request = create_chat_request(message="Hello")
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response = await orchestrator.generate(
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tenant_id="tenant-1",
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request=request,
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)
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assert response.reply == "This is the AI response."
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assert mock_llm._generate_called is True
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@pytest.mark.asyncio
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async def test_generate_with_memory_service(self):
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"""
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[AC-AISVC-13] Test generation with memory service.
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Should load history and save messages.
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"""
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mock_memory = MockMemoryService(
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history=[
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ChatMessageEntity(
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tenant_id="tenant-1",
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session_id="test-session",
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role="user",
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content="Previous message",
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)
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]
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)
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mock_llm = MockLLMClient()
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orchestrator = OrchestratorService(
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llm_client=mock_llm,
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memory_service=mock_memory,
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config=OrchestratorConfig(enable_rag=False),
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)
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request = create_chat_request(message="New message")
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response = await orchestrator.generate(
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tenant_id="tenant-1",
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request=request,
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)
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assert len(mock_memory._saved_messages) == 2
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assert mock_memory._saved_messages[0]["role"] == "user"
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assert mock_memory._saved_messages[1]["role"] == "assistant"
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@pytest.mark.asyncio
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async def test_generate_with_retrieval(self):
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"""
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[AC-AISVC-16, AC-AISVC-17] Test generation with RAG retrieval.
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Should include evidence in LLM prompt.
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"""
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mock_retriever = MockRetriever(
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hits=[
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RetrievalHit(
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text="Product price is $100",
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score=0.85,
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source="kb",
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)
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]
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)
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mock_llm = MockLLMClient()
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orchestrator = OrchestratorService(
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llm_client=mock_llm,
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retriever=mock_retriever,
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config=OrchestratorConfig(enable_rag=True),
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)
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request = create_chat_request(message="What is the price?")
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response = await orchestrator.generate(
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tenant_id="tenant-1",
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request=request,
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)
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assert "retrieval" in response.metadata["diagnostics"]
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assert response.metadata["diagnostics"]["retrieval"]["hit_count"] == 1
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@pytest.mark.asyncio
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async def test_generate_with_context_merging(self):
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"""
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[AC-AISVC-14, AC-AISVC-15] Test context merging with external history.
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Should merge local and external history.
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"""
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mock_memory = MockMemoryService(
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history=[
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ChatMessageEntity(
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tenant_id="tenant-1",
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session_id="test-session",
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role="user",
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content="Local message",
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)
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]
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)
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mock_llm = MockLLMClient()
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orchestrator = OrchestratorService(
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llm_client=mock_llm,
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memory_service=mock_memory,
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config=OrchestratorConfig(enable_rag=False),
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)
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request = create_chat_request(
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message="New message",
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history=[
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ChatMessage(role=Role.USER, content="External message"),
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ChatMessage(role=Role.ASSISTANT, content="External response"),
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],
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)
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response = await orchestrator.generate(
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tenant_id="tenant-1",
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request=request,
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)
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assert "merged_context" in response.metadata["diagnostics"]
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merged = response.metadata["diagnostics"]["merged_context"]
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assert merged["local_count"] == 1
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assert merged["external_count"] == 2
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@pytest.mark.asyncio
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async def test_generate_with_confidence_calculation(self):
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"""
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[AC-AISVC-17, AC-AISVC-18, AC-AISVC-19] Test confidence calculation.
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Should calculate confidence based on retrieval results.
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"""
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mock_retriever = MockRetriever(
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hits=[
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RetrievalHit(text="High relevance content", score=0.9, source="kb"),
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RetrievalHit(text="Medium relevance", score=0.8, source="kb"),
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]
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)
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mock_llm = MockLLMClient()
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orchestrator = OrchestratorService(
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llm_client=mock_llm,
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retriever=mock_retriever,
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config=OrchestratorConfig(enable_rag=True),
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)
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request = create_chat_request(message="Test query")
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response = await orchestrator.generate(
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tenant_id="tenant-1",
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request=request,
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)
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assert response.confidence > 0.5
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assert "confidence" in response.metadata["diagnostics"]
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@pytest.mark.asyncio
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async def test_generate_low_confidence_triggers_transfer(self):
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"""
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[AC-AISVC-18, AC-AISVC-19] Test low confidence triggers transfer.
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Should set should_transfer=True when confidence is low.
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"""
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mock_retriever = MockRetriever(hits=[])
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mock_llm = MockLLMClient()
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orchestrator = OrchestratorService(
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llm_client=mock_llm,
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retriever=mock_retriever,
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config=OrchestratorConfig(enable_rag=True),
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)
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request = create_chat_request(message="Unknown topic")
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response = await orchestrator.generate(
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tenant_id="tenant-1",
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request=request,
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)
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assert response.should_transfer is True
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assert response.transfer_reason is not None
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@pytest.mark.asyncio
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async def test_generate_handles_llm_error(self):
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"""
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[AC-AISVC-02] Test handling of LLM errors.
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Should return fallback response on error.
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"""
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mock_llm = MagicMock()
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mock_llm.generate = AsyncMock(side_effect=Exception("LLM unavailable"))
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orchestrator = OrchestratorService(
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llm_client=mock_llm,
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config=OrchestratorConfig(enable_rag=False),
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)
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request = create_chat_request(message="Hello")
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response = await orchestrator.generate(
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tenant_id="tenant-1",
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request=request,
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)
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assert response.reply is not None
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assert "llm_error" in response.metadata["diagnostics"]
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@pytest.mark.asyncio
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async def test_generate_handles_retrieval_error(self):
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|
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"""
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|
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[AC-AISVC-16] Test handling of retrieval errors.
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Should continue with empty retrieval result.
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"""
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mock_retriever = MagicMock()
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|
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mock_retriever.retrieve = AsyncMock(side_effect=Exception("Qdrant unavailable"))
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mock_llm = MockLLMClient()
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|
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orchestrator = OrchestratorService(
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llm_client=mock_llm,
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retriever=mock_retriever,
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config=OrchestratorConfig(enable_rag=True),
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)
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request = create_chat_request(message="Hello")
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response = await orchestrator.generate(
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tenant_id="tenant-1",
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request=request,
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|
|
)
|
||
|
|
|
||
|
|
assert response.reply == "Mock LLM response"
|
||
|
|
assert "retrieval_error" in response.metadata["diagnostics"]
|
||
|
|
|
||
|
|
@pytest.mark.asyncio
|
||
|
|
async def test_generate_full_pipeline_integration(self):
|
||
|
|
"""
|
||
|
|
[AC-AISVC-01, AC-AISVC-02] Test complete pipeline integration.
|
||
|
|
All components working together.
|
||
|
|
"""
|
||
|
|
mock_memory = MockMemoryService(
|
||
|
|
history=[
|
||
|
|
ChatMessageEntity(
|
||
|
|
tenant_id="tenant-1",
|
||
|
|
session_id="test-session",
|
||
|
|
role="user",
|
||
|
|
content="Previous question",
|
||
|
|
),
|
||
|
|
ChatMessageEntity(
|
||
|
|
tenant_id="tenant-1",
|
||
|
|
session_id="test-session",
|
||
|
|
role="assistant",
|
||
|
|
content="Previous answer",
|
||
|
|
),
|
||
|
|
]
|
||
|
|
)
|
||
|
|
mock_retriever = MockRetriever(
|
||
|
|
hits=[
|
||
|
|
RetrievalHit(text="Knowledge base content", score=0.85, source="kb"),
|
||
|
|
]
|
||
|
|
)
|
||
|
|
mock_llm = MockLLMClient(response_content="AI generated response")
|
||
|
|
|
||
|
|
orchestrator = OrchestratorService(
|
||
|
|
llm_client=mock_llm,
|
||
|
|
memory_service=mock_memory,
|
||
|
|
retriever=mock_retriever,
|
||
|
|
config=OrchestratorConfig(enable_rag=True),
|
||
|
|
)
|
||
|
|
|
||
|
|
request = create_chat_request(
|
||
|
|
message="New question",
|
||
|
|
history=[
|
||
|
|
ChatMessage(role=Role.USER, content="External history"),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
response = await orchestrator.generate(
|
||
|
|
tenant_id="tenant-1",
|
||
|
|
request=request,
|
||
|
|
)
|
||
|
|
|
||
|
|
assert response.reply == "AI generated response"
|
||
|
|
assert response.confidence > 0.0
|
||
|
|
assert len(mock_memory._saved_messages) == 2
|
||
|
|
|
||
|
|
diagnostics = response.metadata["diagnostics"]
|
||
|
|
assert diagnostics["memory_enabled"] is True
|
||
|
|
assert diagnostics["retrieval"]["hit_count"] == 1
|
||
|
|
assert diagnostics["llm_mode"] == "live"
|
||
|
|
|
||
|
|
|
||
|
|
class TestOrchestratorServiceGenerationContext:
|
||
|
|
"""Tests for GenerationContext dataclass."""
|
||
|
|
|
||
|
|
def test_generation_context_initialization(self):
|
||
|
|
"""Test GenerationContext initialization."""
|
||
|
|
ctx = GenerationContext(
|
||
|
|
tenant_id="tenant-1",
|
||
|
|
session_id="session-1",
|
||
|
|
current_message="Hello",
|
||
|
|
channel_type="wechat",
|
||
|
|
)
|
||
|
|
|
||
|
|
assert ctx.tenant_id == "tenant-1"
|
||
|
|
assert ctx.session_id == "session-1"
|
||
|
|
assert ctx.current_message == "Hello"
|
||
|
|
assert ctx.channel_type == "wechat"
|
||
|
|
assert ctx.local_history == []
|
||
|
|
assert ctx.diagnostics == {}
|
||
|
|
|
||
|
|
def test_generation_context_with_metadata(self):
|
||
|
|
"""Test GenerationContext with metadata."""
|
||
|
|
ctx = GenerationContext(
|
||
|
|
tenant_id="tenant-1",
|
||
|
|
session_id="session-1",
|
||
|
|
current_message="Hello",
|
||
|
|
channel_type="wechat",
|
||
|
|
request_metadata={"user_id": "user-123"},
|
||
|
|
)
|
||
|
|
|
||
|
|
assert ctx.request_metadata == {"user_id": "user-123"}
|
||
|
|
|
||
|
|
|
||
|
|
class TestOrchestratorConfig:
|
||
|
|
"""Tests for OrchestratorConfig dataclass."""
|
||
|
|
|
||
|
|
def test_default_config(self):
|
||
|
|
"""Test default configuration values."""
|
||
|
|
config = OrchestratorConfig()
|
||
|
|
|
||
|
|
assert config.max_history_tokens == 4000
|
||
|
|
assert config.max_evidence_tokens == 2000
|
||
|
|
assert config.enable_rag is True
|
||
|
|
assert "智能客服" in config.system_prompt
|
||
|
|
|
||
|
|
def test_custom_config(self):
|
||
|
|
"""Test custom configuration values."""
|
||
|
|
config = OrchestratorConfig(
|
||
|
|
max_history_tokens=8000,
|
||
|
|
enable_rag=False,
|
||
|
|
system_prompt="Custom prompt",
|
||
|
|
)
|
||
|
|
|
||
|
|
assert config.max_history_tokens == 8000
|
||
|
|
assert config.enable_rag is False
|
||
|
|
assert config.system_prompt == "Custom prompt"
|
||
|
|
|
||
|
|
|
||
|
|
class TestOrchestratorServiceHelperMethods:
|
||
|
|
"""Tests for OrchestratorService helper methods."""
|
||
|
|
|
||
|
|
def test_build_llm_messages_basic(self):
|
||
|
|
"""Test _build_llm_messages with basic context."""
|
||
|
|
orchestrator = OrchestratorService(
|
||
|
|
config=OrchestratorConfig(enable_rag=False),
|
||
|
|
)
|
||
|
|
|
||
|
|
ctx = GenerationContext(
|
||
|
|
tenant_id="tenant-1",
|
||
|
|
session_id="session-1",
|
||
|
|
current_message="Hello",
|
||
|
|
channel_type="wechat",
|
||
|
|
)
|
||
|
|
|
||
|
|
messages = orchestrator._build_llm_messages(ctx)
|
||
|
|
|
||
|
|
assert len(messages) == 2
|
||
|
|
assert messages[0]["role"] == "system"
|
||
|
|
assert messages[1]["role"] == "user"
|
||
|
|
assert messages[1]["content"] == "Hello"
|
||
|
|
|
||
|
|
def test_build_llm_messages_with_evidence(self):
|
||
|
|
"""Test _build_llm_messages includes evidence from retrieval."""
|
||
|
|
orchestrator = OrchestratorService(
|
||
|
|
config=OrchestratorConfig(enable_rag=True),
|
||
|
|
)
|
||
|
|
|
||
|
|
ctx = GenerationContext(
|
||
|
|
tenant_id="tenant-1",
|
||
|
|
session_id="session-1",
|
||
|
|
current_message="What is the price?",
|
||
|
|
channel_type="wechat",
|
||
|
|
retrieval_result=RetrievalResult(
|
||
|
|
hits=[
|
||
|
|
RetrievalHit(text="Price is $100", score=0.9, source="kb"),
|
||
|
|
]
|
||
|
|
),
|
||
|
|
)
|
||
|
|
|
||
|
|
messages = orchestrator._build_llm_messages(ctx)
|
||
|
|
|
||
|
|
assert "知识库参考内容" in messages[0]["content"]
|
||
|
|
assert "Price is $100" in messages[0]["content"]
|
||
|
|
|
||
|
|
def test_build_llm_messages_with_history(self):
|
||
|
|
"""Test _build_llm_messages includes merged history."""
|
||
|
|
from app.services.context import MergedContext
|
||
|
|
|
||
|
|
orchestrator = OrchestratorService(
|
||
|
|
config=OrchestratorConfig(enable_rag=False),
|
||
|
|
)
|
||
|
|
|
||
|
|
ctx = GenerationContext(
|
||
|
|
tenant_id="tenant-1",
|
||
|
|
session_id="session-1",
|
||
|
|
current_message="New question",
|
||
|
|
channel_type="wechat",
|
||
|
|
merged_context=MergedContext(
|
||
|
|
messages=[
|
||
|
|
{"role": "user", "content": "Previous question"},
|
||
|
|
{"role": "assistant", "content": "Previous answer"},
|
||
|
|
]
|
||
|
|
),
|
||
|
|
)
|
||
|
|
|
||
|
|
messages = orchestrator._build_llm_messages(ctx)
|
||
|
|
|
||
|
|
assert len(messages) == 4
|
||
|
|
assert messages[1]["role"] == "user"
|
||
|
|
assert messages[1]["content"] == "Previous question"
|
||
|
|
assert messages[2]["role"] == "assistant"
|
||
|
|
assert messages[3]["role"] == "user"
|
||
|
|
assert messages[3]["content"] == "New question"
|
||
|
|
|
||
|
|
def test_fallback_response_with_evidence(self):
|
||
|
|
"""Test _fallback_response when retrieval has evidence."""
|
||
|
|
orchestrator = OrchestratorService()
|
||
|
|
|
||
|
|
ctx = GenerationContext(
|
||
|
|
tenant_id="tenant-1",
|
||
|
|
session_id="session-1",
|
||
|
|
current_message="Question",
|
||
|
|
channel_type="wechat",
|
||
|
|
retrieval_result=RetrievalResult(
|
||
|
|
hits=[RetrievalHit(text="Evidence", score=0.8, source="kb")]
|
||
|
|
),
|
||
|
|
)
|
||
|
|
|
||
|
|
fallback = orchestrator._fallback_response(ctx)
|
||
|
|
assert "知识库" in fallback
|
||
|
|
|
||
|
|
def test_fallback_response_without_evidence(self):
|
||
|
|
"""Test _fallback_response when no retrieval evidence."""
|
||
|
|
orchestrator = OrchestratorService()
|
||
|
|
|
||
|
|
ctx = GenerationContext(
|
||
|
|
tenant_id="tenant-1",
|
||
|
|
session_id="session-1",
|
||
|
|
current_message="Question",
|
||
|
|
channel_type="wechat",
|
||
|
|
retrieval_result=RetrievalResult(hits=[]),
|
||
|
|
)
|
||
|
|
|
||
|
|
fallback = orchestrator._fallback_response(ctx)
|
||
|
|
assert "无法处理" in fallback or "人工客服" in fallback
|
||
|
|
|
||
|
|
def test_format_evidence(self):
|
||
|
|
"""Test _format_evidence formats hits correctly."""
|
||
|
|
orchestrator = OrchestratorService()
|
||
|
|
|
||
|
|
result = RetrievalResult(
|
||
|
|
hits=[
|
||
|
|
RetrievalHit(text="First result", score=0.9, source="kb"),
|
||
|
|
RetrievalHit(text="Second result", score=0.8, source="kb"),
|
||
|
|
]
|
||
|
|
)
|
||
|
|
|
||
|
|
formatted = orchestrator._format_evidence(result)
|
||
|
|
|
||
|
|
assert "[1]" in formatted
|
||
|
|
assert "[2]" in formatted
|
||
|
|
assert "First result" in formatted
|
||
|
|
assert "Second result" in formatted
|
||
|
|
|
||
|
|
|
||
|
|
class TestOrchestratorServiceSetInstance:
|
||
|
|
"""Tests for set_orchestrator_service function."""
|
||
|
|
|
||
|
|
def test_set_orchestrator_service(self):
|
||
|
|
"""Test setting orchestrator service instance."""
|
||
|
|
custom_orchestrator = OrchestratorService(
|
||
|
|
config=OrchestratorConfig(enable_rag=False),
|
||
|
|
)
|
||
|
|
|
||
|
|
set_orchestrator_service(custom_orchestrator)
|
||
|
|
|
||
|
|
from app.services.orchestrator import get_orchestrator_service
|
||
|
|
|
||
|
|
instance = get_orchestrator_service()
|
||
|
|
assert instance is custom_orchestrator
|