langchain_community.vectorstores.yellowbrick.Yellowbrick¶
class langchain_community.vectorstores.yellowbrick.Yellowbrick(embedding: Embeddings, connection_string: str, table: str)[source]¶
Wrapper around Yellowbrick as a vector database.
.. rubric:: Example
Initialize with yellowbrick client.
:param embedding: Embedding operator
:param connection_string: Format ‘postgres://username:password@host:port/database’
:param table: Table used to store / retrieve embeddings from
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(embedding, connection_string, table)
Initialize with yellowbrick client.
aadd_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
aadd_texts(texts[, metadatas])
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_texts(texts[, metadatas])
Add more texts to the vectorstore index.
adelete([ids])
Delete by vector ID or other criteria.
afrom_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and 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])