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d28f1ed cd5f60b 795c4fd cd5f60b 795c4fd d28f1ed 72289eb d28f1ed 52214c6 883546f d28f1ed 52214c6 883546f d28f1ed | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 | """LLM service - Factory for creating LLM instances."""
from typing import Optional
from langchain_openai import ChatOpenAI
from langchain_mistralai import ChatMistralAI
from langchain_core.language_models.chat_models import BaseChatModel
from domain.enums import ModelName, ModelProvider
from config import settings
class LLMService:
"""Service for managing LLM instances across different providers."""
def __init__(self):
"""Initialize LLM service."""
self._openai_api_key = settings.openai_api_key
self._mistralai_api_key = settings.mistralai_api_key
def get_llm(
self,
model_name: ModelName,
temperature: float = 0.7,
streaming: bool = False,
max_tokens: Optional[int] = None
) -> BaseChatModel:
"""
Factory method to create an LLM instance based on model name.
Args:
model_name: Model enum value
temperature: Sampling temperature (0.0 to 2.0)
streaming: Enable streaming mode
max_tokens: Maximum tokens to generate
Returns:
LLM instance (ChatOpenAI or ChatMistralAI)
Raises:
ValueError: If model provider is unknown
"""
provider = model_name.provider
if provider == ModelProvider.OPENAI:
return self._create_openai_llm(
model_name=model_name.value,
temperature=temperature,
streaming=streaming,
max_tokens=max_tokens
)
elif provider == ModelProvider.MISTRALAI:
return self._create_mistralai_llm(
model_name=model_name.value,
temperature=temperature,
streaming=streaming,
max_tokens=max_tokens
)
else:
raise ValueError(f"Unknown provider: {provider}")
def _create_openai_llm(
self,
model_name: str,
temperature: float,
streaming: bool,
max_tokens: Optional[int]
) -> ChatOpenAI:
"""Create OpenAI LLM instance.
Some OpenAI models (e.g., `gpt-5`) have specific parameter requirements:
- Only support default temperature (1.0)
- Use 'max_completion_tokens' instead of 'max_tokens'
"""
effective_temperature = temperature
# Coerce to default temperature for models that don't allow custom values
if model_name.startswith("gpt-5"):
effective_temperature = 1.0
# For gpt-5 models, use max_completion_tokens instead of max_tokens
if model_name.startswith("gpt-5"):
return ChatOpenAI(
model=model_name,
temperature=effective_temperature,
streaming=streaming,
max_completion_tokens=max_tokens,
api_key=self._openai_api_key
)
else:
return ChatOpenAI(
model=model_name,
temperature=effective_temperature,
streaming=streaming,
max_tokens=max_tokens,
api_key=self._openai_api_key
)
def _create_mistralai_llm(
self,
model_name: str,
temperature: float,
streaming: bool,
max_tokens: Optional[int]
) -> ChatMistralAI:
"""Create Mistral AI LLM instance."""
return ChatMistralAI(
model=model_name,
temperature=temperature,
streaming=streaming,
max_tokens=max_tokens,
mistral_api_key=self._mistralai_api_key
)
@staticmethod
def supports_streaming(model_name: ModelName) -> bool:
"""
Check if a model supports streaming.
Args:
model_name: Model enum value
Returns:
True if model supports streaming, False otherwise
"""
models = LLMService.list_available_models()
for model in models:
if model["name"] == model_name.value:
return model.get("supports_streaming", False)
return False
@staticmethod
def list_available_models() -> list[dict]:
"""
List all available models with their metadata.
Returns:
List of model information dictionaries
"""
models = []
# OpenAI models
openai_models = [
# {
# "name": ModelName.GPT_5.value,
# "provider": "openai",
# "description": "GPT-5",
# "supports_streaming": False,
# # "context_window": 128000
# },
# {
# "name": ModelName.GPT_5_CHAT.value,
# "provider": "openai",
# "description": "GPT-5 Chat",
# "supports_streaming": True,
# # "context_window": 128000
# },
# {
# "name": ModelName.GPT_4.value,
# "provider": "openai",
# "description": "GPT-4",
# "supports_streaming": True,
# # "context_window": 128000
# },
# {
# "name": ModelName.GPT_4_TURBO.value,
# "provider": "openai",
# "description": "GPT-4 Turbo - Fast and powerful",
# "supports_streaming": True,
# "context_window": 128000
# },
# {
# "name": ModelName.GPT_4.value,
# "provider": "openai",
# "description": "GPT-4 - High quality",
# "supports_streaming": True,
# "context_window": 8192
# },
# {
# "name": ModelName.GPT_35_TURBO.value,
# "provider": "openai",
# "description": "GPT-3.5 Turbo - Fast and efficient",
# "supports_streaming": True,
# "context_window": 16385
# }
]
# Mistral AI models
mistral_models = [
{
"name": ModelName.MISTRAL_LARGE.value,
"provider": "mistralai",
"description": "Mistral Large",
"supports_streaming": True,
# "context_window": 32000
},
{
"name": ModelName.MAGISTRAL_MEDIUM.value,
"provider": "mistralai",
"description": "Magistral Medium (reasonning)",
"supports_streaming": True,
# "context_window": 32000
}
]
models.extend(openai_models)
models.extend(mistral_models)
return models
# Singleton instance
llm_service = LLMService()
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