Parameters
query – Text to look up documents similar to.
k – Optional Number of Documents to return. Defaults to 4.
pre_filter – Optional Dictionary of argument(s) to prefilter on document
fields.
post_filter_pipeline – Optional Pipeline of MongoDB aggregation stages
following the knnBeta search.
Returns
List of Documents most similar to the query and score for each
class langchain.vectorstores.MyScale(embedding: langchain.embeddings.base.Embeddings, config: Optional[langchain.vectorstores.myscale.MyScaleSettings] = None, **kwargs: Any)[source]#
Wrapper around MyScale vector database
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 complex query with multiple conditions,
constraints and even sub-queries.
For more information, please visit[myscale official site](https://docs.myscale.com/en/overview/)
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, batch_size: int = 32, ids: Optional[Iterable[str]] = None, **kwargs: Any) → List[str][source]#
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts – Iterable of strings to add to the vectorstore.
ids – Optional list of ids to associate with the texts.
batch_size – Batch size of insertion
metadata – Optional column data to be inserted
Returns
List of ids from adding the texts into the vectorstore.
drop() → None[source]#
Helper function: Drop data
escape_str(value: str) → str[source]#
rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html