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46 lines
1.9 KiB
Python
46 lines
1.9 KiB
Python
from langchain_community.vectorstores import FAISS
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from application.vectorstore.base import BaseVectorStore
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from application.core.settings import settings
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class FaissStore(BaseVectorStore):
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def __init__(self, path, embeddings_key, docs_init=None):
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super().__init__()
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self.path = path
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embeddings = self._get_embeddings(settings.EMBEDDINGS_NAME, embeddings_key)
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if docs_init:
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self.docsearch = FAISS.from_documents(
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docs_init, embeddings
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)
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else:
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self.docsearch = FAISS.load_local(
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self.path, embeddings
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)
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self.assert_embedding_dimensions(embeddings)
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def search(self, *args, **kwargs):
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return self.docsearch.similarity_search(*args, **kwargs)
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def add_texts(self, *args, **kwargs):
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return self.docsearch.add_texts(*args, **kwargs)
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def save_local(self, *args, **kwargs):
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return self.docsearch.save_local(*args, **kwargs)
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def delete_index(self, *args, **kwargs):
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return self.docsearch.delete(*args, **kwargs)
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def assert_embedding_dimensions(self, embeddings):
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"""
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Check that the word embedding dimension of the docsearch index matches
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the dimension of the word embeddings used
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"""
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if settings.EMBEDDINGS_NAME == "huggingface_sentence-transformers/all-mpnet-base-v2":
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try:
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word_embedding_dimension = embeddings.client[1].word_embedding_dimension
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except AttributeError as e:
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raise AttributeError("word_embedding_dimension not found in embeddings.client[1]") from e
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docsearch_index_dimension = self.docsearch.index.d
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if word_embedding_dimension != docsearch_index_dimension:
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raise ValueError(f"word_embedding_dimension ({word_embedding_dimension}) " +
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f"!= docsearch_index_word_embedding_dimension ({docsearch_index_dimension})") |