pip install torch langchain sentence_transformers

# 榜单地址https://huggingface.co/spaces/mteb/leaderboard

import torchfrom typing import Any, Listfrom pydantic import Extrafrom langchain.embeddings.base import Embeddingsfrom sentence_transformers import SentenceTransformer
device = 'cpu'
class CustomEmbedding(Embeddings):
client: Any#: :meta private:tokenizer: Anycontext_sequence_length: int = 512query_sequence_length: int = 512model_name: str = ''"""Model name to use."""
def __init__(self, **kwargs: Any):"""Initialize the sentence_transformer."""# super().__init__(**kwargs)self.client = SentenceTransformer('BAAI/bge-m3',            device=device,trust_remote_code=True)self.context_sequence_length = 512self.query_sequence_length = 512
class Config:extra = Extra.forbid
@staticmethoddef mean_pooling(model_output, attention_mask):# First element of model_output contains all token embeddingstoken_embeddings = model_output[0]input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()return torch.sum(token_embeddings * input_mask_expanded, 1) / \torch.clamp(input_mask_expanded.sum(1), min=1e-9)
def embed_documents(self, texts: List[str]) -> List[List[float]]:
with torch.no_grad():embeddings = self.client.encode(texts)embeddings = embeddings.astype('float32')return embeddings.tolist()
def embed_query(self, text: str) -> List[float]:return self.embed_documents([text])[0]

# 使用测试model = CustomEmbedding()emb = model.embed_query("张三")print(len(emb))