TimescaleVectorStore

class llama_index.vector_stores.TimescaleVectorStore(service_url: str, table_name: str, num_dimensions: int = 1536, time_partition_interval: Optional[timedelta] = None)

Bases: VectorStore

Attributes Summary

DEFAULT_INDEX_TYPE
flat_metadata
stores_text

Methods Summary

add(nodes, **add_kwargs)	Add nodes with embedding to vector store.
aquery(query, **kwargs)	Asynchronously query vector store.
async_add(nodes, **add_kwargs)	Asynchronously add nodes with embedding to vector store.
close()
create_index([index_type])
date_to_range_filter(**kwargs)
delete(ref_doc_id, **delete_kwargs)	Delete nodes using with ref_doc_id.
drop_index()
from_params(service_url, table_name[, ...])
query(query, **kwargs)	Query vector store.

Attributes Documentation

DEFAULT_INDEX_TYPE = 1

flat_metadata = False

stores_text: bool = True

Methods Documentation

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

Add nodes with embedding to vector store.

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

Asynchronously query vector store. NOTE: this is not implemented for all vector stores. If not implemented, it will just call query synchronously.

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

Asynchronously add nodes with embedding to vector store. NOTE: this is not implemented for all vector stores. If not implemented, it will just call add synchronously.

async close() → None

create_index(index_type: IndexType = IndexType.TIMESCALE_VECTOR, **kwargs: Any) → None

date_to_range_filter(**kwargs: Any) → Any

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

Delete nodes using with ref_doc_id.

drop_index() → None

classmethod from_params(service_url: str, table_name: str, num_dimensions: int = 1536, time_partition_interval: Optional[timedelta] = None) → TimescaleVectorStore

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

Query vector store.