similarity_search_with_relevance_scores(query)
Perform a similarity search with ClickHouse
similarity_search_with_score(*args, **kwargs)
Run similarity search with distance.
__init__(embedding: Embeddings, config: Optional[ClickhouseSettings] = None, **kwargs: Any) → None[source]¶
ClickHouse Wrapper to LangChain
embedding_function (Embeddings):
config (ClickHouseSettings): Configuration to ClickHouse Client
Other keyword arguments will pass into
[clickhouse-connect](https://docs.clickhouse.com/)
async aadd_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
(List[Document] (documents) – Documents to add to the vectorstore.
Returns
List of IDs of the added texts.
Return type
List[str]
async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str]¶
Run more texts through the embeddings and add to the vectorstore.
add_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
(List[Document] (documents) – Documents to add to the vectorstore.
Returns
List of IDs of the added texts.
Return type
List[str]
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, batch_size: int = 32, ids: Optional[Iterable[str]] = None, **kwargs: Any) → List[str][source]¶
Insert 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.