AwaDBVectorStore

class llama_index.vector_stores.AwaDBVectorStore(table_name: str = 'llamaindex_awadb', log_and_data_dir: Optional[str] = None, **kwargs: Any)

Bases: VectorStore

AwaDB vector store.

In this vector store, embeddings are stored within a AwaDB table.

During query time, the index uses AwaDB to query for the top k most similar nodes.

Parameters

chroma_collection (chromadb.api.models.Collection.Collection) – ChromaDB collection instance

Attributes Summary

DEFAULT_TABLE_NAME
client	Get AwaDB client.
flat_metadata
stores_text

Methods Summary

add(nodes, **add_kwargs)	Add nodes to AwaDB.
delete(ref_doc_id, **delete_kwargs)	Delete nodes using with ref_doc_id.
query(query, **kwargs)	Query index for top k most similar nodes.

Attributes Documentation

DEFAULT_TABLE_NAME = 'llamaindex_awadb'

client

Get AwaDB client.

flat_metadata: bool = True

stores_text: bool = True

Methods Documentation

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

Add nodes to AwaDB.

Parameters

nodes – List[BaseNode]: list of nodes with embeddings

Returns

Added node ids

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

Delete nodes using with ref_doc_id.

Parameters

ref_doc_id (str) – The doc_id of the document to delete.

Returns

None

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

Query index for top k most similar nodes.

Parameters

query – vector store query

Returns

Query results

Return type

VectorStoreQueryResult