langchain.vectorstores.xata.XataVectorStore¶
class langchain.vectorstores.xata.XataVectorStore(api_key: str, db_url: str, embedding: Embeddings, table_name: str)[source]¶
Xata vector store.
It assumes you have a Xata database
created with the right schema. See the guide at:
https://integrations.langchain.com/vectorstores?integration_name=XataVectorStore
Initialize with Xata client.
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(api_key, db_url, embedding, table_name)
Initialize with Xata 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, ids])
Run more texts through the embeddings and add to the vectorstore.
add_vectors(vectors, documents[, ids])
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.