mirror of
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51 lines
2.0 KiB
Python
51 lines
2.0 KiB
Python
# coding=utf-8
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from typing import Dict, List
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from urllib.parse import urlparse, ParseResult
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from langchain_core.messages import BaseMessage, get_buffer_string
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from common.config.tokenizer_manage_config import TokenizerManage
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from models_provider.base_model_provider import MaxKBBaseModel
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from models_provider.impl.base_chat_open_ai import BaseChatOpenAI
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def get_base_url(url: str):
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parse = urlparse(url)
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result_url = ParseResult(scheme=parse.scheme, netloc=parse.netloc, path=parse.path, params='',
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query='',
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fragment='').geturl()
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return result_url[:-1] if result_url.endswith("/") else result_url
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class XinferenceChatModel(MaxKBBaseModel, BaseChatOpenAI):
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@staticmethod
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def is_cache_model():
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return False
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@staticmethod
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def new_instance(model_type, model_name, model_credential: Dict[str, object], **model_kwargs):
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api_base = model_credential.get('api_base', '')
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base_url = get_base_url(api_base)
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base_url = base_url if base_url.endswith('/v1') else (base_url + '/v1')
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optional_params = MaxKBBaseModel.filter_optional_params(model_kwargs)
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return XinferenceChatModel(
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model=model_name,
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openai_api_base=base_url,
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openai_api_key=model_credential.get('api_key'),
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extra_body=optional_params
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)
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def get_num_tokens_from_messages(self, messages: List[BaseMessage]) -> int:
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if self.usage_metadata is None or self.usage_metadata == {}:
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tokenizer = TokenizerManage.get_tokenizer()
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return sum([len(tokenizer.encode(get_buffer_string([m]))) for m in messages])
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return self.usage_metadata.get('input_tokens', 0)
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def get_num_tokens(self, text: str) -> int:
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if self.usage_metadata is None or self.usage_metadata == {}:
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tokenizer = TokenizerManage.get_tokenizer()
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return len(tokenizer.encode(text))
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return self.get_last_generation_info().get('output_tokens', 0)
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