feat: 军事科技每日资讯推送系统 - Docker部署 + 日志系统 + 数据目录重组
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poiuy
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"""LLM模块测试"""
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import json
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import pytest
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import sys
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from datetime import datetime, timezone, timedelta
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from pathlib import Path
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from unittest.mock import patch, AsyncMock, MagicMock
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sys.path.insert(0, str(Path(__file__).parent.parent.parent / "src"))
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from llm import (
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load_prompt,
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_parse_llm_json_response,
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_split_entries_for_batch,
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_build_batch_prompt,
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_merge_scores,
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_score_single_batch,
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call_llm,
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check_llm_available,
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generate_immediate_push,
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score_batch,
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)
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class TestLoadPrompt:
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"""测试提示词加载"""
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def test_load_prompt_basic(self, temp_dir):
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prompt_file = temp_dir / "test.txt"
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prompt_file.write_text("Hello {name}!")
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result = load_prompt(str(prompt_file), name="World")
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assert result == "Hello World!"
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def test_load_prompt_missing_file(self):
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with pytest.raises(FileNotFoundError):
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load_prompt("nonexistent.txt")
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def test_load_prompt_with_braces(self, temp_dir):
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prompt_file = temp_dir / "test.txt"
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prompt_file.write_text("Hello {name}, curly braces: { }")
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result = load_prompt(str(prompt_file), name="World")
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assert result == "Hello World, curly braces: { }"
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def test_load_prompt_multiple_vars(self, temp_dir):
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prompt_file = temp_dir / "test.txt"
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prompt_file.write_text("{greeting} {name}, you have {count} messages")
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result = load_prompt(str(prompt_file), greeting="Hi", name="Alice", count=5)
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assert result == "Hi Alice, you have 5 messages"
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class TestParseLlmJsonResponse:
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"""测试LLM响应解析"""
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def test_parse_json_array(self):
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response = '[{"link": "https://example.com", "score": 80}]'
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result = _parse_llm_json_response(response)
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assert len(result) == 1
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assert result[0]["link"] == "https://example.com"
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assert result[0]["score"] == 80
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def test_parse_with_markdown_codeblock(self):
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response = """```json
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[{"link": "https://example.com", "score": 80}]
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```"""
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result = _parse_llm_json_response(response)
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assert len(result) == 1
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def test_parse_with_codeblock(self):
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response = """```
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[{"link": "https://example.com", "score": 80}]
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```"""
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result = _parse_llm_json_response(response)
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assert len(result) == 1
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def test_parse_invalid_response(self):
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response = "This is not JSON at all"
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with pytest.raises(ValueError):
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_parse_llm_json_response(response)
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class TestSplitEntriesForBatch:
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"""测试条目分批"""
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def test_split_empty(self):
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result = _split_entries_for_batch([])
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assert result == []
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def test_split_single_batch(self):
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entries = [
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{
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"link": f"https://example.com/{i}",
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"title": f"Title{i}",
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"content": "x" * 100,
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}
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for i in range(5)
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]
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result = _split_entries_for_batch(entries, max_prompt_chars=10000)
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assert len(result) == 1
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def test_split_multiple_batches(self):
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entries = [
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{
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"link": f"https://example.com/{i}",
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"title": f"Title{i}",
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"content": "x" * 5000,
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}
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for i in range(10)
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]
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result = _split_entries_for_batch(entries, max_prompt_chars=10000)
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assert len(result) > 1
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class TestBuildBatchPrompt:
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"""测试构建批量提示词"""
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def test_build_batch_prompt_basic(self):
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entries = [
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{
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"link": "https://example.com/1",
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"title": "Title1",
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"source": "Source1",
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"published": "2024-01-15",
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"content": "Content",
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}
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]
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result = _build_batch_prompt(entries)
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assert "Title1" in result
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assert "https://example.com/1" in result
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class TestMergeScores:
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"""测试评分合并"""
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def test_merge_scores_basic(self):
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entries = [
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{"link": "https://example.com/1", "title": "Title1"},
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{"link": "https://example.com/2", "title": "Title2"},
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]
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scores = [
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{
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"link": "https://example.com/1",
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"score": 85,
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"tags": ["AI"],
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"summary": "Summary1",
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},
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{
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"link": "https://example.com/2",
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"score": 70,
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"tags": ["Tech"],
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"summary": "Summary2",
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},
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]
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result = _merge_scores(entries, scores)
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assert result[0]["score"] == 85
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assert result[0]["tags"] == ["AI"]
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assert result[1]["score"] == 70
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def test_merge_scores_partial(self):
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entries = [
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{"link": "https://example.com/1", "title": "Title1", "score": 50},
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{"link": "https://example.com/2", "title": "Title2", "score": 60},
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]
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scores = [{"link": "https://example.com/1", "score": 85}]
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result = _merge_scores(entries, scores)
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assert result[0]["score"] == 85
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assert result[1]["score"] == 60
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class TestCallLlm:
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"""测试LLM调用"""
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@pytest.mark.asyncio
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async def test_call_llm_success(self):
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config = {
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"model": "gpt-4",
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"baseUrl": "https://api.openai.com/v1",
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"apiKeyName": "OPENAI_API_KEY",
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}
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with patch("llm.call_llm", new_callable=AsyncMock) as mock_call:
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mock_call.return_value = "Test response"
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result = await mock_call("Test prompt", config)
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assert result == "Test response"
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@pytest.mark.asyncio
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async def test_call_llm_missing_key(self):
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config = {"model": "gpt-4", "apiKeyName": "MISSING_KEY"}
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with pytest.raises(ValueError, match="未设置MISSING_KEY"):
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await call_llm("Test prompt", config)
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class TestLlmHealthCheck:
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"""测试LLM可用性检查"""
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@pytest.mark.asyncio
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async def test_check_llm_available_success(self, sample_config):
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with patch("llm.call_llm", new_callable=AsyncMock) as mock_call:
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mock_call.return_value = "OK"
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result = await check_llm_available(sample_config["llm"])
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assert result == "OK"
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@pytest.mark.asyncio
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async def test_check_llm_available_empty_response(self, sample_config):
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with patch("llm.call_llm", new_callable=AsyncMock) as mock_call:
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mock_call.return_value = " "
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with pytest.raises(RuntimeError, match="返回空响应"):
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await check_llm_available(sample_config["llm"])
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class TestImmediatePush:
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"""测试即时推送生成"""
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@pytest.mark.asyncio
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async def test_generate_immediate_push_failure_returns_error(
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self, sample_entries, sample_config
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):
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with patch("llm.load_prompt", return_value="prompt"), patch(
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"llm.call_llm", new_callable=AsyncMock
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) as mock_call:
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mock_call.side_effect = RuntimeError("boom")
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content, error = await generate_immediate_push(
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sample_entries[:1], sample_config["llm"], recent_push_context=""
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)
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assert content == ""
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assert error == "生成即时推送失败: boom"
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class TestScoreBatch:
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"""测试批量评分"""
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@pytest.mark.asyncio
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async def test_score_batch_empty(self, sample_config):
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result, errors = await score_batch([], sample_config["llm"])
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assert result == []
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assert errors == []
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@pytest.mark.asyncio
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async def test_score_batch_single(self, sample_entries, sample_config):
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entries = sample_entries[:1]
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mock_scores = [
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{
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"link": entries[0]["link"],
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"score": 85,
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"tags": ["AI"],
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"summary": "Test summary",
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}
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]
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with patch("llm._score_single_batch", new_callable=AsyncMock) as mock_score:
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mock_score.return_value = (mock_scores, [])
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result, errors = await score_batch(entries, sample_config["llm"])
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assert len(result) == 1
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assert result[0]["score"] == 85
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assert errors == []
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@pytest.mark.asyncio
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async def test_score_single_batch_failure_returns_empty_results(
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self, sample_entries, sample_config
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):
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with patch("llm.call_llm", new_callable=AsyncMock) as mock_call:
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mock_call.side_effect = RuntimeError("boom")
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results, errors = await _score_single_batch(
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sample_entries[:2], sample_config["llm"]
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)
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assert results == []
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assert errors == ["批次1 评分失败: boom"]
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@pytest.mark.asyncio
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async def test_score_single_batch_reconcile_partial_results(
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self, sample_entries, sample_config
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):
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entries = sample_entries[:2]
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llm_results = [
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{
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"link": entries[0]["link"],
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"score": 91,
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"tags": ["AI"],
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"summary": "Matched result",
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}
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]
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with patch("llm.call_llm", new_callable=AsyncMock) as mock_call:
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mock_call.return_value = json.dumps(llm_results, ensure_ascii=False)
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results, errors = await _score_single_batch(entries, sample_config["llm"])
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assert len(results) == 1
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assert results[0]["score"] == 91
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assert len(errors) == 1
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assert "评分结果异常" in errors[0]
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assert "输入2" in errors[0]
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assert "返回1" in errors[0]
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assert "匹配1" in errors[0]
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@pytest.mark.asyncio
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async def test_score_single_batch_keeps_full_results(self, sample_entries, sample_config):
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entries = sample_entries[:3]
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llm_results = [
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{
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"link": entry["link"],
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"score": 88,
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"tags": ["AI"],
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"summary": f"Summary for {index}",
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}
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for index, entry in enumerate(entries, start=1)
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]
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with patch("llm.call_llm", new_callable=AsyncMock) as mock_call:
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mock_call.return_value = json.dumps(llm_results, ensure_ascii=False)
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results, errors = await _score_single_batch(entries, sample_config["llm"])
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assert len(results) == 3
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assert [result["link"] for result in results] == [entry["link"] for entry in entries]
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assert errors == []
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