620b2b03ca
Design the base interface of vector store, and apply it to the Chroma Vector Store (wrapped around llama_index's implementation). Provide the pipelines to populate and retrieve from vector store.
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 ..components 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 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: 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) -> None:
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self.vector_store.add([self.embedding(text)])
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def run_batch_raw(self, text: List[str]) -> None:
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self.vector_store.add(self.embedding(text))
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def run_document(self, text: Document) -> None:
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self.vector_store.add([self.embedding(text)])
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def run_batch_document(self, text: List[Document]) -> None:
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self.vector_store.add(self.embedding(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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