TairVectorStore

class llama_index.vector_stores.TairVectorStore(tair_url: str, index_name: str, index_type: str = 'HNSW', index_args: Optional[Dict[str, Any]] = None, overwrite: bool = False, **kwargs: Any)

Bases: VectorStore

Attributes Summary

client	Return the Tair client instance.
flat_metadata
stores_node
stores_text

Methods Summary

add(nodes, **add_kwargs)	Add nodes to the index.
delete(ref_doc_id, **delete_kwargs)	Delete a document.
delete_index()	Delete the index and all documents.
query(query, **kwargs)	Query the index.

Attributes Documentation

client

Return the Tair client instance.

flat_metadata = False

stores_node = True

stores_text: bool = True

Methods Documentation

add(nodes: List[BaseNode], **add_kwargs: Any) → List[str]

Add nodes to the index.

Parameters

nodes (List[BaseNode]) – List of nodes with embeddings

Returns

List of ids of the documents added to the index.

Return type

List[str]

delete(ref_doc_id: str, **delete_kwargs: Any) → None

Delete a document.

Parameters

doc_id (str) – document id

delete_index() → None

Delete the index and all documents.

query(query: VectorStoreQuery, **kwargs: Any) → VectorStoreQueryResult

Query the index.

Parameters

query (VectorStoreQuery) – query object

Returns

query result

Return type

VectorStoreQueryResult

Raises

ValueError – If query.query_embedding is None.