444 lines
14 KiB
Python
444 lines
14 KiB
Python
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"""
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Unit tests for MetadataAutoInferenceService.
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"""
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import json
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import pytest
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from unittest.mock import AsyncMock, MagicMock, patch
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from dataclasses import dataclass
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from app.services.metadata_auto_inference_service import (
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AutoInferenceResult,
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InferenceFieldContext,
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MetadataAutoInferenceService,
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)
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@dataclass
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class MockFieldDefinition:
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"""Mock field definition for testing"""
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field_key: str
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label: str
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type: str
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required: bool
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options: list[str] | None = None
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class TestInferenceFieldContext:
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"""Test InferenceFieldContext dataclass."""
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def test_creation(self):
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"""Test creating InferenceFieldContext."""
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ctx = InferenceFieldContext(
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field_key="grade",
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label="年级",
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type="enum",
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required=True,
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options=["初一", "初二", "初三"],
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)
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assert ctx.field_key == "grade"
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assert ctx.label == "年级"
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assert ctx.type == "enum"
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assert ctx.required is True
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assert ctx.options == ["初一", "初二", "初三"]
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def test_default_values(self):
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"""Test default values."""
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ctx = InferenceFieldContext(
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field_key="test",
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label="Test",
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type="text",
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required=False,
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)
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assert ctx.options is None
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assert ctx.description is None
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class TestAutoInferenceResult:
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"""Test AutoInferenceResult dataclass."""
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def test_success_result(self):
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"""Test successful inference result."""
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result = AutoInferenceResult(
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inferred_metadata={"grade": "初一", "subject": "数学"},
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confidence_scores={"grade": 0.95, "subject": 0.85},
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raw_response='{"inferred_metadata": {...}}',
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success=True,
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)
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assert result.success is True
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assert result.error_message is None
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assert result.inferred_metadata["grade"] == "初一"
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def test_failure_result(self):
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"""Test failed inference result."""
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result = AutoInferenceResult(
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inferred_metadata={},
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confidence_scores={},
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raw_response="",
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success=False,
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error_message="JSON parse error",
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)
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assert result.success is False
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assert result.error_message == "JSON parse error"
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class TestMetadataAutoInferenceService:
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"""Test MetadataAutoInferenceService functionality."""
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@pytest.fixture
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def mock_session(self):
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"""Create mock session."""
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return AsyncMock()
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@pytest.fixture
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def service(self, mock_session):
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"""Create service instance."""
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return MetadataAutoInferenceService(mock_session)
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def test_build_field_contexts(self, service):
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"""Test building field contexts from definitions."""
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fields = [
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MockFieldDefinition(
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field_key="grade",
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label="年级",
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type="enum",
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required=True,
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options=["初一", "初二", "初三"],
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),
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MockFieldDefinition(
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field_key="subject",
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label="学科",
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type="enum",
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required=True,
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options=["语文", "数学", "英语"],
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),
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]
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contexts = service._build_field_contexts(fields)
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assert len(contexts) == 2
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assert contexts[0].field_key == "grade"
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assert contexts[0].options == ["初一", "初二", "初三"]
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assert contexts[1].field_key == "subject"
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def test_build_user_prompt(self, service):
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"""Test building user prompt."""
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field_contexts = [
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InferenceFieldContext(
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field_key="grade",
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label="年级",
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type="enum",
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required=True,
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options=["初一", "初二", "初三"],
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),
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]
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prompt = service._build_user_prompt(
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content="这是一道初一数学题",
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field_contexts=field_contexts,
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)
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assert "年级" in prompt
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assert "初一, 初二, 初三" in prompt
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assert "这是一道初一数学题" in prompt
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def test_build_user_prompt_with_existing_metadata(self, service):
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"""Test building user prompt with existing metadata."""
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field_contexts = [
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InferenceFieldContext(
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field_key="grade",
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label="年级",
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type="enum",
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required=True,
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options=["初一", "初二", "初三"],
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),
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]
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prompt = service._build_user_prompt(
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content="这是一道数学题",
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field_contexts=field_contexts,
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existing_metadata={"grade": "初二"},
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)
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assert "已有值: 初二" in prompt
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def test_extract_json_from_plain_json(self, service):
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"""Test extracting JSON from plain JSON response."""
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json_str = '{"inferred_metadata": {"grade": "初一"}, "confidence_scores": {"grade": 0.95}}'
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result = service._extract_json(json_str)
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assert result == json_str
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def test_extract_json_from_markdown(self, service):
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"""Test extracting JSON from markdown code block."""
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markdown = """Here is the result:
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```json
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{"inferred_metadata": {"grade": "初一"}, "confidence_scores": {"grade": 0.95}}
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```
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"""
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result = service._extract_json(markdown)
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assert "inferred_metadata" in result
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assert "grade" in result
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def test_parse_llm_response_valid(self, service):
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"""Test parsing valid LLM response."""
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response = json.dumps({
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"inferred_metadata": {
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"grade": "初一",
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"subject": "数学",
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},
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"confidence_scores": {
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"grade": 0.95,
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"subject": 0.85,
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}
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})
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field_contexts = [
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InferenceFieldContext(
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field_key="grade",
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label="年级",
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type="enum",
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required=True,
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options=["初一", "初二", "初三"],
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),
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InferenceFieldContext(
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field_key="subject",
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label="学科",
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type="enum",
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required=True,
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options=["语文", "数学", "英语"],
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),
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]
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result = service._parse_llm_response(response, field_contexts)
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assert result.success is True
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assert result.inferred_metadata["grade"] == "初一"
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assert result.inferred_metadata["subject"] == "数学"
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assert result.confidence_scores["grade"] == 0.95
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def test_parse_llm_response_invalid_option(self, service):
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"""Test parsing response with invalid enum option."""
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response = json.dumps({
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"inferred_metadata": {
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"grade": "高一", # Not in options
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},
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"confidence_scores": {
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"grade": 0.90,
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}
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})
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field_contexts = [
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InferenceFieldContext(
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field_key="grade",
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label="年级",
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type="enum",
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required=True,
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options=["初一", "初二", "初三"],
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),
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]
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result = service._parse_llm_response(response, field_contexts)
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assert result.success is True
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assert "grade" not in result.inferred_metadata
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def test_parse_llm_response_invalid_json(self, service):
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"""Test parsing invalid JSON response."""
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response = "This is not valid JSON"
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field_contexts = [
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InferenceFieldContext(
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field_key="grade",
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label="年级",
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type="text",
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required=False,
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),
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]
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result = service._parse_llm_response(response, field_contexts)
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assert result.success is False
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assert "JSON parse error" in result.error_message
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def test_validate_field_value_text(self, service):
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"""Test validating text field value."""
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ctx = InferenceFieldContext(
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field_key="title",
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label="标题",
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type="text",
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required=False,
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)
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result = service._validate_field_value(ctx, "测试标题")
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assert result == "测试标题"
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def test_validate_field_value_number(self, service):
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"""Test validating number field value."""
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ctx = InferenceFieldContext(
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field_key="count",
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label="数量",
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type="number",
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required=False,
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)
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assert service._validate_field_value(ctx, 42) == 42
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assert service._validate_field_value(ctx, "3.14") == 3.14
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assert service._validate_field_value(ctx, "invalid") is None
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def test_validate_field_value_boolean(self, service):
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"""Test validating boolean field value."""
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ctx = InferenceFieldContext(
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field_key="active",
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label="是否激活",
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type="boolean",
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required=False,
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)
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assert service._validate_field_value(ctx, True) is True
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assert service._validate_field_value(ctx, "true") is True
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assert service._validate_field_value(ctx, "false") is False
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assert service._validate_field_value(ctx, 1) is True
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def test_validate_field_value_enum(self, service):
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"""Test validating enum field value."""
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ctx = InferenceFieldContext(
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field_key="grade",
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label="年级",
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type="enum",
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required=False,
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options=["初一", "初二", "初三"],
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)
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assert service._validate_field_value(ctx, "初一") == "初一"
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assert service._validate_field_value(ctx, "高一") is None
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def test_validate_field_value_array_enum(self, service):
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"""Test validating array_enum field value."""
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ctx = InferenceFieldContext(
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field_key="tags",
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label="标签",
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type="array_enum",
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required=False,
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options=["重点", "难点", "易错"],
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)
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result = service._validate_field_value(ctx, ["重点", "难点"])
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assert result == ["重点", "难点"]
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result = service._validate_field_value(ctx, ["重点", "不存在"])
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assert result == ["重点"]
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result = service._validate_field_value(ctx, "重点")
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assert result == ["重点"]
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class TestIntegrationScenarios:
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"""Test integration scenarios."""
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|
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@pytest.fixture
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def mock_session(self):
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|
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"""Create mock session."""
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return AsyncMock()
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|
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@pytest.fixture
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def service(self, mock_session):
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"""Create service instance."""
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return MetadataAutoInferenceService(mock_session)
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def test_education_scenario(self, service):
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"""Test education scenario with grade and subject."""
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response = json.dumps({
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"inferred_metadata": {
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"grade": "初二",
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"subject": "物理",
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"type": "痛点",
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},
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"confidence_scores": {
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"grade": 0.95,
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"subject": 0.90,
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"type": 0.85,
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}
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})
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field_contexts = [
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InferenceFieldContext(
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field_key="grade",
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label="年级",
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type="enum",
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required=True,
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options=["初一", "初二", "初三"],
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),
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InferenceFieldContext(
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field_key="subject",
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label="学科",
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type="enum",
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required=True,
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options=["语文", "数学", "英语", "物理", "化学"],
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),
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InferenceFieldContext(
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field_key="type",
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label="类型",
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type="enum",
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required=False,
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options=["痛点", "重点", "难点"],
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),
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]
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result = service._parse_llm_response(response, field_contexts)
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assert result.success is True
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assert result.inferred_metadata == {
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"grade": "初二",
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"subject": "物理",
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"type": "痛点",
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}
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assert result.confidence_scores["grade"] == 0.95
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def test_partial_inference(self, service):
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|
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"""Test partial inference when some fields cannot be inferred."""
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|
|
response = json.dumps({
|
||
|
|
"inferred_metadata": {
|
||
|
|
"grade": "初一",
|
||
|
|
},
|
||
|
|
"confidence_scores": {
|
||
|
|
"grade": 0.90,
|
||
|
|
}
|
||
|
|
})
|
||
|
|
|
||
|
|
field_contexts = [
|
||
|
|
InferenceFieldContext(
|
||
|
|
field_key="grade",
|
||
|
|
label="年级",
|
||
|
|
type="enum",
|
||
|
|
required=True,
|
||
|
|
options=["初一", "初二", "初三"],
|
||
|
|
),
|
||
|
|
InferenceFieldContext(
|
||
|
|
field_key="subject",
|
||
|
|
label="学科",
|
||
|
|
type="enum",
|
||
|
|
required=True,
|
||
|
|
options=["语文", "数学", "英语"],
|
||
|
|
),
|
||
|
|
]
|
||
|
|
|
||
|
|
result = service._parse_llm_response(response, field_contexts)
|
||
|
|
|
||
|
|
assert result.success is True
|
||
|
|
assert "grade" in result.inferred_metadata
|
||
|
|
assert "subject" not in result.inferred_metadata
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
pytest.main([__file__, "-v"])
|