langchain.vectorstores.myscale.MyScale¶
class langchain.vectorstores.myscale.MyScale(embedding: Embeddings, config: Optional[MyScaleSettings] = None, **kwargs: Any)[source]¶
MyScale vector store.
You need a clickhouse-connect python package, and a valid account
to connect to MyScale.
MyScale can not only search with simple vector indexes.
It also supports a complex query with multiple conditions,
constraints and even sub-queries.
For more information, please visit[myscale official site](https://docs.myscale.com/en/overview/)
MyScale Wrapper to LangChain
embedding (Embeddings):
config (MyScaleSettings): Configuration to MyScale Client
Other keyword arguments will pass into
[clickhouse-connect](https://docs.myscale.com/)
Attributes
embeddings
Access the query embedding object if available.
metadata_column
Methods
__init__(embedding[, config])
MyScale Wrapper to LangChain
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, batch_size, 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_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(...)