7b9e0826de
- 新增 embedding_service（语义检索）、knowledge_service（RAG）、text_chunker、document_parser - 新增 tool_registry（自定义工具注册表）并完善工具市场 API（CRUD + code/http 执行） - 新增 agent_vector_memory / knowledge_base 模型及对应数据库表 - 实现 SSE 流式响应与 Agent 预算控制 - AgentChat.vue 集成 MainLayout 导航布局 - 完善测试体系：7 个新测试文件共 110 个测试覆盖 - 修复 conftest.py SQLite 内存数据库连接隔离问题 Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
125 lines
4.0 KiB
Python
125 lines
4.0 KiB
Python
"""
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知识库 RAG 单元测试
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"""
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from __future__ import annotations
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import pytest
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from unittest.mock import patch, AsyncMock, MagicMock
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class TestKnowledgeService:
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"""知识库服务测试"""
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@pytest.mark.unit
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@pytest.mark.asyncio
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async def test_search_empty_kb(self):
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"""空知识库搜索返回空列表"""
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from app.services.knowledge_service import search
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mock_db = MagicMock()
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mock_query = MagicMock()
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mock_db.query.return_value = mock_query
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mock_query.filter.return_value = mock_query
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mock_query.all.return_value = []
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mock_query.first.return_value = None
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results = await search(mock_db, kb_id="nonexistent", query="test")
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assert results == []
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@pytest.mark.unit
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@pytest.mark.asyncio
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async def test_rag_query_no_results(self):
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"""无检索结果时返回空上下文"""
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from app.services.knowledge_service import rag_query
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mock_db = MagicMock()
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mock_query = MagicMock()
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mock_db.query.return_value = mock_query
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mock_query.filter.return_value = mock_query
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mock_query.all.return_value = []
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mock_query.first.return_value = None
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result = await rag_query(mock_db, kb_id="test", query="no results")
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assert result["found"] is False
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assert result["context"] == ""
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@pytest.mark.unit
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@pytest.mark.asyncio
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async def test_search_with_content(self):
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"""模拟有内容的搜索结果"""
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from app.services.knowledge_service import search
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from app.models.knowledge_base import DocumentChunk
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mock_chunk = MagicMock(spec=DocumentChunk)
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mock_chunk.id = "chunk-1"
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mock_chunk.content = "test content about Python programming"
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mock_chunk.chunk_index = 0
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mock_chunk.document_id = "doc-1"
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mock_chunk.metadata = {"filename": "test.txt"}
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mock_db = MagicMock()
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mock_query = MagicMock()
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mock_db.query.return_value = mock_query
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mock_query.filter.return_value = mock_query
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mock_query.all.return_value = []
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with patch("app.services.knowledge_service.embedding_service.generate_embedding",
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AsyncMock(return_value=[0.1, 0.2, 0.3])):
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with patch("app.services.knowledge_service.embedding_service.similarity_search",
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AsyncMock(return_value=[
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{"content_text": "test content about Python", "score": 0.85, "metadata": {}}
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])):
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results = await search(mock_db, kb_id="test", query="Python")
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# 可能返回空（chunks filter 不匹配）但不报错
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assert isinstance(results, list)
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class TestKnowledgeModels:
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"""知识库模型测试"""
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@pytest.mark.unit
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def test_knowledge_base_model(self):
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from app.models.knowledge_base import KnowledgeBase
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kb = KnowledgeBase(
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name="Test KB",
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description="Test",
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user_id="user-1",
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chunk_size=500,
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chunk_overlap=50,
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)
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assert kb.name == "Test KB"
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assert kb.chunk_size == 500
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assert kb.chunk_overlap == 50
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@pytest.mark.unit
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def test_document_model(self):
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from app.models.knowledge_base import Document
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doc = Document(
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kb_id="kb-1",
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filename="test.txt",
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file_type="txt",
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file_size=1024,
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status="completed",
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chunk_count=5,
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)
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assert doc.filename == "test.txt"
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assert doc.status == "completed"
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assert doc.chunk_count == 5
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@pytest.mark.unit
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def test_document_chunk_model(self):
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from app.models.knowledge_base import DocumentChunk
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chunk = DocumentChunk(
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document_id="doc-1",
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kb_id="kb-1",
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chunk_index=0,
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content="test content",
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embedding="[0.1, 0.2, 0.3]",
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metadata={"source": "test"},
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)
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assert chunk.chunk_index == 0
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assert chunk.content == "test content"
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