59 lines
1.8 KiB
Python
59 lines
1.8 KiB
Python
from typing import List
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from theflow import Node, Param
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from ..base import BaseComponent
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from ..documents.base import Document
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from ..embeddings import BaseEmbeddings
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from ..vectorstores import BaseVectorStore
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class RetrieveDocumentFromVectorStorePipeline(BaseComponent):
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"""Retrieve list of documents from vector store"""
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vector_store: Param[BaseVectorStore] = Param()
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embedding: Node[BaseEmbeddings] = Node()
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# TODO: populate to document store as well when it's finished
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# TODO: refer to llama_index's storage as well
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def run_raw(self, text: str) -> List[str]:
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emb = self.embedding(text)
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return self.vector_store.query(embedding=emb)[2]
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def run_batch_raw(self, text: List[str]) -> List[List[str]]:
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result = []
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for each_text in text:
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emb = self.embedding(each_text)
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result.append(self.vector_store.query(embedding=emb)[2])
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return result
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def run_document(self, text: Document) -> List[str]:
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return self.run_raw(text.text)
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def run_batch_document(self, text: List[Document]) -> List[List[str]]:
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input_text = [each.text for each in text]
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return self.run_batch_raw(input_text)
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def is_document(self, text) -> bool:
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if isinstance(text, Document):
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return True
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elif isinstance(text, List) and isinstance(text[0], Document):
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return True
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return False
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def is_batch(self, text) -> bool:
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if isinstance(text, list):
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return True
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return False
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def persist(self, path: str):
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"""Save the whole state of the indexing pipeline vector store and all
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necessary information to disk
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Args:
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path (str): path to save the state
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"""
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def load(self, path: str):
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"""Load all information from disk to an object"""
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