Return docs most similar to embedding vector.
asimilarity_search_with_relevance_scores(query)
Return docs most similar to query.
create_index_if_not_exist(dim, ...)
delete([ids])
Delete by vector ID or other criteria.
drop_index([index_name])
Drop an existing index.
from_documents(documents, embedding[, ...])
Return VectorStore initialized from documents and embeddings.
from_existing_index(embedding[, index_name, ...])
Connect to an existing Tair index.
from_texts(texts, embedding[, metadatas, ...])
Return VectorStore initialized from texts and embeddings.
max_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
max_marginal_relevance_search_by_vector(...)
Return docs selected using the maximal marginal relevance.
search(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
similarity_search(query[, k])
Returns the most similar indexed documents to the query text.
similarity_search_by_vector(embedding[, k])
Return docs most similar to embedding vector.
similarity_search_with_relevance_scores(query)
Return docs and relevance scores in the range [0, 1].
similarity_search_with_score(*args, **kwargs)
Run similarity search with distance.
__init__(embedding_function: Embeddings, url: str, index_name: str, content_key: str = 'content', metadata_key: str = 'metadata', search_params: Optional[dict] = None, **kwargs: Any)[source]¶
async aadd_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters