AzureAISearchVectorStore

class llama_index.vector_stores.AzureAISearchVectorStore(search_or_index_client: Any, id_field_key: str, chunk_field_key: str, embedding_field_key: str, metadata_string_field_key: str, doc_id_field_key: str, filterable_metadata_field_keys: Optional[Union[List[str], Dict[str, str], Dict[str, Tuple[str, MetadataIndexFieldType]]]] = None, index_name: Optional[str] = None, index_mapping: Optional[Callable[[Dict[str, str], Dict[str, Any]], Dict[str, str]]] = None, index_management: IndexManagement = IndexManagement.NO_VALIDATION, embedding_dimensionality: int = 1536, **kwargs: Any)

Bases: VectorStore

Attributes Summary

client	Get client.
flat_metadata
stores_text

Methods Summary

add(nodes, **add_kwargs)	Add nodes to index associated with the configured search client.
delete(ref_doc_id, **delete_kwargs)	Delete documents from the AI Search Index with doc_id_field_key field equal to ref_doc_id.
query(query, **kwargs)	Query vector store.

Attributes Documentation

client

Get client.

flat_metadata: bool = True

stores_text: bool = True

Methods Documentation

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

Add nodes to index associated with the configured search client.

Parameters

nodes – List[BaseNode]: nodes with embeddings

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

Delete documents from the AI Search Index with doc_id_field_key field equal to ref_doc_id.

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

Query vector store.