Returns
True if deletion is successful,
False otherwise.
classmethod from_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST¶
Return VectorStore initialized from documents and embeddings.
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, dashvector_api_key: Optional[str] = None, collection_name: str = 'langchain', text_field: str = 'text', batch_size: int = 25, ids: Optional[List[str]] = None, **kwargs: Any) → DashVector[source]¶
Return DashVector VectorStore initialized from texts and embeddings.
This is the quick way to get started with dashvector vector store.
Example
from langchain.vectorstores import DashVector
from langchain.embeddings import OpenAIEmbeddings
import dashvector
embeddings = OpenAIEmbeddings()
dashvector = DashVector.from_documents(
docs,
embeddings,
dashvector_api_key=”{DASHVECTOR_API_KEY}”
)
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[dict] = None, **kwargs: Any) → List[Document][source]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
fetch_k – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult – Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.