140 lines
4.6 KiB
Python
140 lines
4.6 KiB
Python
"""
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独立测试脚本：搜索服务 + LLM处理
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不需要启动完整的FastAPI服务，直接测试搜索和LLM集成功能
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"""
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import asyncio
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import sys
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import os
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# 添加项目路径
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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# 加载环境变量
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from dotenv import load_dotenv
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load_dotenv('.env.dev')
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async def test_search_with_llm():
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"""测试搜索服务 + LLM处理"""
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print("=" * 70)
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print("搜索服务 + LLM 独立测试")
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print("=" * 70)
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# 导入服务
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from module_admin.service.search_service import SearchService, SearchServiceError
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# 测试问题
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test_question = "今天上海的天气怎么样"
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print(f"\n测试问题: {test_question}")
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print("-" * 70)
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# 步骤1: 检查关键词
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print("\n步骤1: 检查是否触发搜索服务...")
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should_handle = SearchService.should_handle(test_question)
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print(f"✓ 关键词检测: {'通过 (将使用搜索服务)' if should_handle else '未通过'}")
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if not should_handle:
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print("\n⚠️ 问题不包含搜索关键词，不会触发搜索服务")
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return
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# 步骤2: 搜索服务流式处理 (Bypass RAGFlow)
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print("\n步骤2: 测试搜索服务流式响应...")
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# 构造请求参数，启用流式
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from module_admin.entity.vo.ragflow_vo import ConverseWithChatAssistantModel
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converse_params = ConverseWithChatAssistantModel(
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chat_id="test-chat-id",
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question=test_question,
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stream=True
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)
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try:
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# 调用 handle_search_chat
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print("\n正在获取流式搜索结果...")
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response = await SearchService.handle_search_chat(converse_params, redis=None)
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# 验证是否为 StreamingResponse
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from fastapi.responses import StreamingResponse
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if isinstance(response, StreamingResponse):
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print("\n【搜索智能回答 (流式)】")
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search_context = ""
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async for line in response.body_iterator:
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line = line.strip()
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if not line: continue
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if line.startswith('data:'):
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data_str = line[5:].strip()
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try:
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data_json = json.loads(data_str)
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content = data_json.get('data', '')
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if content:
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print(content, end='', flush=True)
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search_context += content
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except:
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pass
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print("\n\n✓ 搜索流式响应完成!")
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# 注意：由于 SearchService 现在 bypass 了 RAGFlow，所以这里的 search_context 就是最终答案
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# 如果要测试 DeepSeek，我们需要手动构造另一段测试逻辑，或者模拟 RAG 流程
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else:
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print(f"\n✗ 预期返回 StreamingResponse，实际返回: {type(response)}")
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except Exception as e:
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print(f"\n✗ 搜索流式测试失败: {e}")
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import traceback
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traceback.print_exc()
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# 步骤3: 单独测试 DeepSeek 流式
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print("\n\n步骤3: 单独测试 DeepSeek LLM 流式...")
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try:
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from utils.deepseek_client import DeepSeekAPIClient
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client = DeepSeekAPIClient()
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print("\n正在调用 DeepSeek 流式生成...")
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print(f"问题: {test_question}")
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print("\n【DeepSeek 回答 (流式)】")
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messages = [{"role": "user", "content": test_question}]
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# 使用 chat_completion 开启 stream=True
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response = await client.chat_completion(
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messages=messages,
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stream=True
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)
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# 处理 AsyncStream
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async for chunk in response:
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content = chunk.choices[0].delta.content or ""
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if content:
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print(content, end='', flush=True)
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print("\n\n✓ DeepSeek 流式测试成功!")
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except Exception as e:
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print(f"\n✗ DeepSeek 流式测试失败: {e}")
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# import traceback
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# traceback.print_exc()
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print("\n" + "=" * 70)
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print("测试完成")
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print("=" * 70)
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if __name__ == "__main__":
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print("\n提示：此测试需要以下配置：")
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print("1. ✓ SEARCH_API_KEY (搜索服务)")
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print("2. ✓ DEEPSEEK_API_KEY (LLM服务)")
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print()
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try:
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asyncio.run(test_search_with_llm())
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except KeyboardInterrupt:
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print("\n\n测试被用户中断")
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except Exception as e:
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print(f"\n\n测试失败: {e}")
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import traceback
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traceback.print_exc()
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