Return docs most similar to embedding vector.
asimilarity_search_with_relevance_scores(query)
Return docs and relevance scores in the range [0, 1], asynchronously.
asimilarity_search_with_score(*args, **kwargs)
Run similarity search with distance asynchronously.
delete([ids])
Delete by vector ID or other criteria.
from_collection_name(embedding, db_url, ...)
Create new empty vectorstore with collection_name.
from_documents(documents, embedding[, ...])
Return VectorStore initialized from documents.
from_texts(texts, embedding[, metadatas, ...])
Return VectorStore initialized from texts and optional metadatas.
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, distance_func])
Return docs most similar to query.
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(query[, k, ...])
Run similarity search with distance.
similarity_search_with_score_by_vector(...)
Return docs most similar to query vector, with its score.
__init__(embedding: Embeddings, dimension: int, db_url: str, collection_name: str, new_table: bool = False) → None[source]¶
async aadd_documents(documents: List[Document], **kwargs: Any) → List[str]¶