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