**kwargs – kwargs to be passed to similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs
Returns
List of Tuples of (doc, similarity_score)
async asimilarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]¶
Run similarity search with distance asynchronously.
create_index_if_not_exist(dim: int, distance_type: str, index_type: str, data_type: str, **kwargs: Any) → bool[source]¶
delete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]¶
Delete by vector ID or other criteria.
Parameters
ids – List of ids to delete.
**kwargs – Other keyword arguments that subclasses might use.
Returns
True if deletion is successful,
False otherwise, None if not implemented.
Return type
Optional[bool]
static drop_index(index_name: str = 'langchain', **kwargs: Any) → bool[source]¶
Drop an existing index.
Parameters
index_name (str) – Name of the index to drop.
Returns
True if the index is dropped successfully.
Return type
bool
classmethod from_documents(documents: List[Document], embedding: Embeddings, metadatas: Optional[List[dict]] = None, index_name: str = 'langchain', content_key: str = 'content', metadata_key: str = 'metadata', **kwargs: Any) → Tair[source]¶
Return VectorStore initialized from documents and embeddings.
classmethod from_existing_index(embedding: Embeddings, index_name: str = 'langchain', content_key: str = 'content', metadata_key: str = 'metadata', **kwargs: Any) → Tair[source]¶
Connect to an existing Tair index.