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from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from typing import Any, List
class MyEmbeddings(HuggingFaceEmbeddings):
def __init__(self, **kwargs: Any):
super().__init__(**kwargs)
def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Compute doc embeddings using a HuggingFace transformer model.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
texts = list(map(lambda x: x.replace("\n", " "), texts))
embeddings = self.client.encode(texts, normalize_embeddings=True)
return embeddings.tolist()
def embed_query(self, text: str) -> List[float]:
"""Compute query embeddings using a HuggingFace transformer model.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
text = text.replace("\n", " ")
embedding = self.client.encode(text, normalize_embeddings=True)
return embedding.tolist()
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