test_deepseek.py 1.9 KB

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  1. # test_deepseek.py
  2. import requests
  3. import json
  4. import time
  5. def test_deepseek_model():
  6. """测试 DeepSeek 模型"""
  7. base_url = "http://localhost:11434"
  8. # 首先确保模型已下载
  9. print("检查模型列表...")
  10. try:
  11. response = requests.get(f"{base_url}/api/tags")
  12. models = response.json().get("models", [])
  13. print(f"已安装模型: {[model['name'] for model in models]}")
  14. except Exception as e:
  15. print(f"获取模型列表失败: {e}")
  16. return False
  17. # 测试模型响应
  18. test_prompt = "你好,请用中文介绍一下你自己,并说明你能在浏览器自动化中提供什么帮助?"
  19. try:
  20. print("测试 DeepSeek 模型响应...")
  21. response = requests.post(
  22. f"{base_url}/api/generate",
  23. json={
  24. "model": "deepseek-coder:6.7b",
  25. "prompt": test_prompt,
  26. "stream": False,
  27. "options": {
  28. "temperature": 0.7,
  29. "top_p": 0.9
  30. }
  31. },
  32. timeout=60
  33. )
  34. if response.status_code == 200:
  35. result = response.json()
  36. print("=" * 50)
  37. print("DeepSeek 模型测试成功!")
  38. print(f"回复: {result.get('response', '')}")
  39. print("=" * 50)
  40. return True
  41. else:
  42. print(f"模型响应失败: {response.status_code}")
  43. print(f"错误信息: {response.text}")
  44. return False
  45. except Exception as e:
  46. print(f"测试失败: {e}")
  47. return False
  48. if __name__ == "__main__":
  49. # 等待服务启动
  50. print("等待 Ollama 服务启动...")
  51. time.sleep(10)
  52. success = test_deepseek_model()
  53. if success:
  54. print("DeepSeek 模型部署成功!")
  55. else:
  56. print("DeepSeek 模型部署失败,请检查。")