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- # coding=utf-8
- from typing import Dict, List
- from urllib.parse import urlparse, ParseResult
- from langchain_core.messages import BaseMessage, get_buffer_string
- 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
- def get_base_url(url: str):
- parse = urlparse(url)
- result_url = ParseResult(scheme=parse.scheme, netloc=parse.netloc, path=parse.path, params='',
- query='',
- fragment='').geturl()
- return result_url[:-1] if result_url.endswith("/") else result_url
- class XinferenceChatModel(MaxKBBaseModel, BaseChatOpenAI):
- @staticmethod
- def is_cache_model():
- return False
- @staticmethod
- def new_instance(model_type, model_name, model_credential: Dict[str, object], **model_kwargs):
- api_base = model_credential.get('api_base', '')
- base_url = get_base_url(api_base)
- base_url = base_url if base_url.endswith('/v1') else (base_url + '/v1')
- optional_params = MaxKBBaseModel.filter_optional_params(model_kwargs)
- return XinferenceChatModel(
- model=model_name,
- openai_api_base=base_url,
- openai_api_key=model_credential.get('api_key'),
- **optional_params,
- )
- 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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