Attributes
DEFAULT_INDEX_TYPE
embeddings
Access the query embedding object if available.
Methods
__init__(service_url, embedding[, ...])
aadd_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
aadd_embeddings(texts, embeddings[, ...])
Add embeddings to the vectorstore.
aadd_texts(texts[, metadatas, ids])
Run more texts through the embeddings and add to the vectorstore.
add_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
add_embeddings(texts, embeddings[, ...])
Add embeddings to the vectorstore.
add_texts(texts[, metadatas, ids])
Run more texts through the embeddings and add to the vectorstore.
afrom_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
afrom_embeddings(text_embeddings, embedding)
Construct TimescaleVector wrapper from raw documents and pre- generated embeddings.
afrom_texts(texts, embedding[, metadatas, ...])
Return VectorStore initialized from texts and embeddings.
amax_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
amax_marginal_relevance_search_by_vector(...)
Return docs selected using the maximal marginal relevance.
as_retriever(**kwargs)
Return VectorStoreRetriever initialized from this VectorStore.
asearch(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
asimilarity_search(query[, k, filter, ...])
Run similarity search with TimescaleVector with distance.
asimilarity_search_by_vector(embedding[, k, ...])