0a3fc4b228
* Correct abstractmethod usage * Update interface * Specify minimal llama-index version [ignore cache] * Update examples
80 lines
2.4 KiB
Python
80 lines
2.4 KiB
Python
from __future__ import annotations
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import uuid
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from pathlib import Path
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from typing import cast
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from ..base import BaseComponent, Document
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from ..embeddings import BaseEmbeddings
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from ..storages import BaseDocumentStore, BaseVectorStore
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VECTOR_STORE_FNAME = "vectorstore"
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DOC_STORE_FNAME = "docstore"
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class IndexVectorStoreFromDocumentPipeline(BaseComponent):
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"""Ingest the document, run through the embedding, and store the embedding in a
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vector store.
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This pipeline supports the following set of inputs:
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- List of documents
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- List of texts
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"""
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vector_store: BaseVectorStore
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doc_store: BaseDocumentStore
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embedding: BaseEmbeddings
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# TODO: refer to llama_index's storage as well
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def run(self, text: str | list[str] | Document | list[Document]) -> None:
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input_: list[Document] = []
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if not isinstance(text, list):
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text = [text]
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for item in cast(list, text):
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if isinstance(item, str):
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input_.append(Document(text=item, id_=str(uuid.uuid4())))
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elif isinstance(item, Document):
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input_.append(item)
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else:
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raise ValueError(
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f"Invalid input type {type(item)}, should be str or Document"
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)
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embeddings = self.embedding(input_)
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self.vector_store.add(
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embeddings=embeddings,
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ids=[t.id_ for t in input_],
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)
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if self.doc_store:
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self.doc_store.add(input_)
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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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