langchain_community.vectorstores.vectara.VectaraQueryConfig

class langchain_community.vectorstores.vectara.VectaraQueryConfig(k: int = 10, lambda_val: float = 0.0, filter: str = '', score_threshold: ~typing.Optional[float] = None, n_sentence_context: int = 2, mmr_config: ~langchain_community.vectorstores.vectara.MMRConfig = <factory>, summary_config: ~langchain_community.vectorstores.vectara.SummaryConfig = <factory>)[source]

Configuration for Vectara query.

k: Number of Documents to return. Defaults to 10. lambda_val: lexical match parameter for hybrid search. filter Dictionary of argument(s) to filter on metadata. For example a

filter can be “doc.rating > 3.0 and part.lang = ‘deu’”} see https://docs.vectara.com/docs/search-apis/sql/filter-overview for more details.

score_threshold: minimal score threshold for the result.

If defined, results with score less than this value will be filtered out.

n_sentence_context: number of sentences before/after the matching segment

to add, defaults to 2

mmr_config: MMRConfig configuration dataclass summary_config: SummaryConfig configuration dataclass

Attributes

filter

k

lambda_val

n_sentence_context

score_threshold

mmr_config

summary_config

Methods

__init__([k, lambda_val, filter, ...])

Parameters

  • k (int) –

  • lambda_val (float) –

  • filter (str) –

  • score_threshold (Optional[float]) –

  • n_sentence_context (int) –

  • mmr_config (MMRConfig) –

  • summary_config (SummaryConfig) –

__init__(k: int = 10, lambda_val: float = 0.0, filter: str = '', score_threshold: ~typing.Optional[float] = None, n_sentence_context: int = 2, mmr_config: ~langchain_community.vectorstores.vectara.MMRConfig = <factory>, summary_config: ~langchain_community.vectorstores.vectara.SummaryConfig = <factory>) → None
Parameters

  • k (int) –

  • lambda_val (float) –

  • filter (str) –

  • score_threshold (Optional[float]) –

  • n_sentence_context (int) –

  • mmr_config (MMRConfig) –

  • summary_config (SummaryConfig) –

Return type

None