77 lines
2.4 KiB
Python
77 lines
2.4 KiB
Python
from __future__ import annotations
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from pathlib import Path
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from theflow import Node, Param
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from ..base import BaseComponent
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from ..base.schema import Document, RetrievedDocument
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from ..docstores import BaseDocumentStore
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from ..embeddings import BaseEmbeddings
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from ..vectorstores import BaseVectorStore
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VECTOR_STORE_FNAME = "vectorstore"
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DOC_STORE_FNAME = "docstore"
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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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doc_store: Param[BaseDocumentStore] = Param()
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embedding: Node[BaseEmbeddings] = Node()
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# TODO: refer to llama_index's storage as well
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def run(self, text: str | Document, top_k: int = 1) -> list[RetrievedDocument]:
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"""Retrieve a list of documents from vector store
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Args:
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text: the text to retrieve similar documents
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Returns:
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list[RetrievedDocument]: list of retrieved documents
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"""
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if self.doc_store is None:
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raise ValueError(
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"doc_store is not provided. Please provide a doc_store to "
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"retrieve the documents"
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)
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emb: list[float] = self.embedding(text)[0]
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_, scores, ids = self.vector_store.query(embedding=emb, top_k=top_k)
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docs = self.doc_store.get(ids)
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result = [
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RetrievedDocument(**doc.to_dict(), score=score)
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for doc, score in zip(docs, scores)
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]
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return result
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def save(
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self,
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path: str | Path,
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vectorstore_fname: str = VECTOR_STORE_FNAME,
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docstore_fname: str = DOC_STORE_FNAME,
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):
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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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if isinstance(path, str):
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path = Path(path)
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self.vector_store.save(path / vectorstore_fname)
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self.doc_store.save(path / docstore_fname)
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def load(
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self,
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path: str | Path,
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vectorstore_fname: str = VECTOR_STORE_FNAME,
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docstore_fname: str = DOC_STORE_FNAME,
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):
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"""Load all information from disk to an object"""
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if isinstance(path, str):
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path = Path(path)
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self.vector_store.load(path / vectorstore_fname)
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self.doc_store.load(path / docstore_fname)
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