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90 lines
3.2 KiB
Python
90 lines
3.2 KiB
Python
import os
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from langchain_community.vectorstores import FAISS
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from application.core.settings import settings
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from application.parser.schema.base import Document
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from application.vectorstore.base import BaseVectorStore
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def get_vectorstore(path: str) -> str:
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if path:
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vectorstore = os.path.join("application", "indexes", path)
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else:
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vectorstore = os.path.join("application")
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return vectorstore
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class FaissStore(BaseVectorStore):
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def __init__(self, source_id: str, embeddings_key: str, docs_init=None):
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super().__init__()
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self.source_id = source_id
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self.path = get_vectorstore(source_id)
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self.embeddings = self._get_embeddings(settings.EMBEDDINGS_NAME, embeddings_key)
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try:
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if docs_init:
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self.docsearch = FAISS.from_documents(docs_init, self.embeddings)
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else:
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self.docsearch = FAISS.load_local(
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self.path, self.embeddings, allow_dangerous_deserialization=True
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)
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except Exception:
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raise
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self.assert_embedding_dimensions(self.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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"""Check that the word embedding dimension of the docsearch index matches the dimension of the word embeddings used."""
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if (
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settings.EMBEDDINGS_NAME
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== "huggingface_sentence-transformers/all-mpnet-base-v2"
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):
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word_embedding_dimension = getattr(embeddings, "dimension", None)
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if word_embedding_dimension is None:
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raise AttributeError(
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"'dimension' attribute not found in embeddings instance."
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)
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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(
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f"Embedding dimension mismatch: embeddings.dimension ({word_embedding_dimension}) != docsearch index dimension ({docsearch_index_dimension})"
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)
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def get_chunks(self):
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chunks = []
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if self.docsearch:
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for doc_id, doc in self.docsearch.docstore._dict.items():
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chunk_data = {
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"doc_id": doc_id,
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"text": doc.page_content,
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"metadata": doc.metadata,
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}
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chunks.append(chunk_data)
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return chunks
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def add_chunk(self, text, metadata=None):
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metadata = metadata or {}
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doc = Document(text=text, extra_info=metadata).to_langchain_format()
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doc_id = self.docsearch.add_documents([doc])
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self.save_local(self.path)
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return doc_id
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def delete_chunk(self, chunk_id):
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self.delete_index([chunk_id])
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self.save_local(self.path)
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return True
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