VectorStoreQuery#

class llama_index.core.vector_stores.VectorStoreQuery(query_embedding: Optional[List[float]] = None, similarity_top_k: int = 1, doc_ids: Optional[List[str]] = None, node_ids: Optional[List[str]] = None, query_str: Optional[str] = None, output_fields: Optional[List[str]] = None, embedding_field: Optional[str] = None, mode: VectorStoreQueryMode = VectorStoreQueryMode.DEFAULT, alpha: Optional[float] = None, filters: Optional[MetadataFilters] = None, mmr_threshold: Optional[float] = None, sparse_top_k: Optional[int] = None, hybrid_top_k: Optional[int] = None)#

Bases: object

Vector store query.

Attributes Summary

`alpha`
`doc_ids`
`embedding_field`
`filters`
`hybrid_top_k`
`mmr_threshold`
`mode`
`node_ids`
`output_fields`
`query_embedding`
`query_str`
`similarity_top_k`
`sparse_top_k`

Attributes Documentation

alpha: Optional[float] = None#

doc_ids: Optional[List[str]] = None#

embedding_field: Optional[str] = None#

filters: Optional[MetadataFilters] = None#

hybrid_top_k: Optional[int] = None#

mmr_threshold: Optional[float] = None#

mode: VectorStoreQueryMode = 'default'#

node_ids: Optional[List[str]] = None#

output_fields: Optional[List[str]] = None#

query_embedding: Optional[List[float]] = None#

query_str: Optional[str] = None#

similarity_top_k: int = 1#

sparse_top_k: Optional[int] = None#