d79b3744cb
This change remove `BaseComponent`'s: - run_raw - run_batch_raw - run_document - run_batch_document - is_document - is_batch Each component is expected to support multiple types of inputs and a single type of output. Since we want the component to work out-of-the-box with both standardized and customized use cases, supporting multiple types of inputs are expected. At the same time, to reduce the complexity of understanding how to use a component, we restrict a component to only have a single output type. To accommodate these changes, we also refactor some components to remove their run_raw, run_batch_raw... methods, and to decide the common output interface for those components. Tests are updated accordingly. Commit changes: * Add kwargs to vector store's query * Simplify the BaseComponent * Update tests * Remove support for Python 3.8 and 3.9 * Bump version 0.3.0 * Fix github PR caching still use old environment after bumping version --------- Co-authored-by: ian <ian@cinnamon.is>
83 lines
2.5 KiB
Python
83 lines
2.5 KiB
Python
from __future__ import annotations
|
|
|
|
import uuid
|
|
from pathlib import Path
|
|
|
|
from theflow import Node, Param
|
|
|
|
from ..base import BaseComponent
|
|
from ..docstores import BaseDocumentStore
|
|
from ..documents.base import Document
|
|
from ..embeddings import BaseEmbeddings
|
|
from ..vectorstores import BaseVectorStore
|
|
|
|
VECTOR_STORE_FNAME = "vectorstore"
|
|
DOC_STORE_FNAME = "docstore"
|
|
|
|
|
|
class IndexVectorStoreFromDocumentPipeline(BaseComponent):
|
|
"""Ingest the document, run through the embedding, and store the embedding in a
|
|
vector store.
|
|
|
|
This pipeline supports the following set of inputs:
|
|
- List of documents
|
|
- List of texts
|
|
"""
|
|
|
|
vector_store: Param[BaseVectorStore] = Param()
|
|
doc_store: Param[BaseDocumentStore] = Param()
|
|
embedding: Node[BaseEmbeddings] = Node()
|
|
# TODO: refer to llama_index's storage as well
|
|
|
|
def run(self, text: str | list[str] | Document | list[Document]) -> None:
|
|
input_: list[Document] = []
|
|
if not isinstance(text, list):
|
|
text = [text]
|
|
|
|
for item in text:
|
|
if isinstance(item, str):
|
|
input_.append(Document(text=item, id_=str(uuid.uuid4())))
|
|
elif isinstance(item, Document):
|
|
input_.append(item)
|
|
else:
|
|
raise ValueError(
|
|
f"Invalid input type {type(item)}, should be str or Document"
|
|
)
|
|
|
|
embeddings = self.embedding(input_)
|
|
self.vector_store.add(
|
|
embeddings=embeddings,
|
|
ids=[t.id_ for t in input_],
|
|
)
|
|
if self.doc_store:
|
|
self.doc_store.add(input_)
|
|
|
|
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)
|