戒酒的李白
44 lines
1.5 KiB
Python
44 lines
1.5 KiB
Python
from typing import List
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import numpy as np
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from bertopic.backend import BaseEmbedder
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from langchain_core.embeddings import Embeddings
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class LangChainBackend(BaseEmbedder):
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"""LangChain Embedding Model.
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This class uses the LangChain Embedding class to embed the documents.
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Argument:
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embedding_model: A LangChain Embedding Instance.
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Examples:
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```python
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from langchain_community.embeddings import HuggingFaceInstructEmbeddings
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from bertopic.backend import LangChainBackend
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hf_embedding = HuggingFaceInstructEmbeddings()
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langchain_embedder = LangChainBackend(hf_embedding)
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```
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"""
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def __init__(self, embedding_model: Embeddings):
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self.embedding_model = embedding_model
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def embed(self, documents: List[str], verbose: bool = False) -> np.ndarray:
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"""Embed a list of n documents/words into an n-dimensional
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matrix of embeddings.
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Arguments:
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documents: A list of documents or words to be embedded
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verbose: Controls the verbosity of the process
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Returns:
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Document/words embeddings with shape (n, m) with `n` documents/words
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that each have an embeddings size of `m`
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"""
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# Prepare documents, replacing empty strings with a single space
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prepared_documents = [" " if doc == "" else doc for doc in documents]
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response = self.embedding_model.embed_documents(prepared_documents)
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return np.array(response)
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