Coverage for src / local_deep_research / llm / providers / implementations / custom_openai_endpoint.py: 93%

1"""Custom OpenAI-compatible endpoint provider for Local Deep Research.""" 

2 

3from loguru import logger 

4 

5from ....config.thread_settings import ( 

6 get_llm_setting_from_snapshot as get_setting_from_snapshot, 

7) 

8from ....utilities.url_utils import normalize_url 

9from ...llm_registry import register_llm 

10from ..openai_base import OpenAICompatibleProvider 

11 

12 

13class CustomOpenAIEndpointProvider(OpenAICompatibleProvider): 

14 """Custom OpenAI-compatible endpoint provider. 

15 

16 This provider allows users to connect to any OpenAI-compatible API endpoint 

17 by specifying a custom URL in the settings. 

18 """ 

19 

20 provider_name = "Custom OpenAI Endpoint" 

21 api_key_setting = "llm.openai_endpoint.api_key" 

22 url_setting = "llm.openai_endpoint.url" # Settings key for URL 

23 default_base_url = "https://api.openai.com/v1" 

24 default_model = "gpt-3.5-turbo" 

25 

26 # Metadata for auto-discovery 

27 provider_key = "OPENAI_ENDPOINT" 

28 company_name = "Custom" 

29 is_cloud = None # Unknown — could be local or cloud 

30 

31 @classmethod 

32 def requires_auth_for_models(cls): 

33 """Custom endpoints may or may not require authentication for listing models. 

34 

35 Many OpenAI-compatible servers (vLLM, local LLMs, etc.) don't require 

36 authentication. Return False to allow model listing without an API key. 

37 If the endpoint requires auth, the OpenAI client will raise an error. 

38 """ 

39 return False 

40 

41 @classmethod 

42 def create_llm(cls, model_name=None, temperature=0.7, **kwargs): 

43 """Override to get URL from settings.""" 

44 settings_snapshot = kwargs.get("settings_snapshot") 

45 

46 # Get custom endpoint URL from settings 

47 custom_url = get_setting_from_snapshot( 

48 "llm.openai_endpoint.url", 

49 default=cls.default_base_url, 

50 settings_snapshot=settings_snapshot, 

51 ) 

52 

53 # Normalize and pass the custom URL to parent implementation 

54 kwargs["base_url"] = ( 

55 normalize_url(custom_url) if custom_url else cls.default_base_url 

56 ) 

57 

58 return super().create_llm(model_name, temperature, **kwargs) 

59 

60 

61# Keep the standalone functions for backward compatibility 

62def create_openai_endpoint_llm(model_name=None, temperature=0.7, **kwargs): 

63 """Factory function for custom OpenAI-compatible endpoint LLMs. 

64 

65 Args: 

66 model_name: Name of the model to use 

67 temperature: Model temperature (0.0-1.0) 

68 **kwargs: Additional arguments including settings_snapshot 

69 

70 Returns: 

71 A configured ChatOpenAI instance pointing to custom endpoint 

72 

73 Raises: 

74 ValueError: If API key is not configured 

75 """ 

76 return CustomOpenAIEndpointProvider.create_llm( 

77 model_name, temperature, **kwargs 

78 ) 

79 

80 

81def is_openai_endpoint_available(settings_snapshot=None): 

82 """Check if custom OpenAI endpoint is available. 

83 

84 Args: 

85 settings_snapshot: Optional settings snapshot to use 

86 

87 Returns: 

88 True if API key is configured, False otherwise 

89 """ 

90 return CustomOpenAIEndpointProvider.is_available(settings_snapshot) 

91 

92 

93def register_custom_openai_endpoint_provider(): 

94 """Register the custom OpenAI endpoint provider with the LLM registry.""" 

95 register_llm("openai_endpoint", create_openai_endpoint_llm) 

96 logger.info("Registered Custom OpenAI Endpoint LLM provider")