完成和大模型的通信

tests/test_llm.py

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+"""LLM 模块测试。"""
+
+from unittest.mock import MagicMock, patch
+
+import sys
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).parent.parent / "src"))
+
+
+class TestCreateLLM:
+    """测试 LLM 实例创建。"""
+
+    @patch("config.get_llm_config")
+    @patch("llama_index.llms.openai_like.OpenAILike")
+    def test_create_llm_uses_config(self, mock_openai_like, mock_get_config):
+        """测试 _create_llm 使用配置文件中的参数。"""
+        mock_get_config.return_value = {
+            "model": "qwen/qwen3.6-27b",
+            "api_base": "http://100.123.83.113:1234/v1",
+            "api_key": "123456",
+        }
+
+        from llm import _create_llm
+
+        _create_llm()
+
+        mock_openai_like.assert_called_once_with(
+            model="qwen/qwen3.6-27b",
+            api_base="http://100.123.83.113:1234/v1",
+            api_key="123456",
+            temperature=0.1,
+            max_tokens=128000,
+            request_timeout=300.0,
+            is_chat_model=True,
+        )
+
+
+class TestQueryAnalysis:
+    """测试 query_analysis 端到端流程。"""
+
+    @patch("llm._create_llm")
+    @patch("llm.build_analysis_prompt")
+    def test_query_analysis_calls_llm(self, mock_prompt, mock_create_llm):
+        """测试 query_analysis 正确调用 prompt 构建和 LLM stream_chat。"""
+        mock_prompt.return_value = "test prompt"
+        mock_llm = MagicMock()
+        mock_resp = MagicMock()
+        mock_resp.delta = "analysis result"
+        mock_llm.stream_chat.return_value = [mock_resp]
+        mock_create_llm.return_value = mock_llm
+
+        from llm import query_analysis
+
+        result = query_analysis(purpose="test purpose", index="test index")
+
+        mock_prompt.assert_called_once_with(
+            "test purpose",
+            "test index",
+            source_content="",
+            configured_language=None,
+        )
+        assert mock_llm.stream_chat.called
+        assert result == "analysis result"
+
+    @patch("llm._create_llm")
+    @patch("llm.build_analysis_prompt")
+    def test_query_analysis_passes_source_content(self, mock_prompt, mock_create_llm):
+        """测试 source_content 正确传递。"""
+        mock_prompt.return_value = "test prompt"
+        mock_llm = MagicMock()
+        mock_resp = MagicMock()
+        mock_resp.delta = "result"
+        mock_llm.stream_chat.return_value = [mock_resp]
+        mock_create_llm.return_value = mock_llm
+
+        from llm import query_analysis
+
+        query_analysis(
+            purpose="",
+            index="",
+            source_content="some source",
+            configured_language="English",
+        )
+
+        mock_prompt.assert_called_once_with(
+            "",
+            "",
+            source_content="some source",
+            configured_language="English",
+        )
+
+    @patch("llm._create_llm")
+    @patch("llm.build_analysis_prompt")
+    def test_query_analysis_returns_text(self, mock_prompt, mock_create_llm):
+        """测试返回值是纯文本字符串。"""
+        mock_prompt.return_value = "prompt"
+        mock_llm = MagicMock()
+        mock_resp1 = MagicMock()
+        mock_resp1.delta = "## 关键实体\n"
+        mock_resp2 = MagicMock()
+        mock_resp2.delta = "- 测试实体"
+        mock_llm.stream_chat.return_value = [mock_resp1, mock_resp2]
+        mock_create_llm.return_value = mock_llm
+
+        from llm import query_analysis
+
+        result = query_analysis(purpose="", index="")
+
+        assert isinstance(result, str)
+        assert "关键实体" in result
+
+    @patch("llm._create_llm")
+    @patch("llm.build_analysis_prompt")
+    def test_query_analysis_empty_response(self, mock_prompt, mock_create_llm):
+        """测试 LLM 返回空字符串时不会崩溃。"""
+        mock_prompt.return_value = "prompt"
+        mock_llm = MagicMock()
+        mock_resp = MagicMock()
+        mock_resp.delta = ""
+        mock_llm.stream_chat.return_value = [mock_resp]
+        mock_create_llm.return_value = mock_llm
+
+        from llm import query_analysis
+
+        result = query_analysis(purpose="", index="")
+
+        assert result == ""