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- from typing import Dict, List
- from langchain_core.messages import get_buffer_string, BaseMessage
- from common.config.tokenizer_manage_config import TokenizerManage
- from models_provider.base_model_provider import MaxKBBaseModel
- from models_provider.impl.base_chat_open_ai import BaseChatOpenAI
- class VllmImage(MaxKBBaseModel, BaseChatOpenAI):
- @staticmethod
- def new_instance(model_type, model_name, model_credential: Dict[str, object], **model_kwargs):
- optional_params = MaxKBBaseModel.filter_optional_params(model_kwargs)
- return VllmImage(
- model_name=model_name,
- openai_api_base=model_credential.get('api_base'),
- openai_api_key=model_credential.get('api_key'),
- # stream_options={"include_usage": True},
- streaming=True,
- stream_usage=True,
- **optional_params,
- )
- def is_cache_model(self):
- return False
- def get_num_tokens_from_messages(self, messages: List[BaseMessage]) -> int:
- if self.usage_metadata is None or self.usage_metadata == {}:
- tokenizer = TokenizerManage.get_tokenizer()
- return sum([len(tokenizer.encode(get_buffer_string([m]))) for m in messages])
- return self.usage_metadata.get('input_tokens', 0)
- def get_num_tokens(self, text: str) -> int:
- if self.usage_metadata is None or self.usage_metadata == {}:
- tokenizer = TokenizerManage.get_tokenizer()
- return len(tokenizer.encode(text))
- return self.get_last_generation_info().get('output_tokens', 0)
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