Defaults to 0.5.
Returns
List of Documents selected by maximal marginal relevance.
similarity_search(query: str, k: int = 4, **kwargs: Any) → List[langchain.schema.Document][source]#
Return docs most similar to query.
Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
Returns
List of Documents most similar to the query.
similarity_search_by_text(query: str, k: int = 4, **kwargs: Any) → List[langchain.schema.Document][source]#
Return docs most similar to query.
Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
Returns
List of Documents most similar to the query.
similarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[langchain.schema.Document][source]#
Look up similar documents by embedding vector in Weaviate.
similarity_search_with_score(query: str, k: int = 4, **kwargs: Any) → List[Tuple[langchain.schema.Document, float]][source]#
Return list of documents most similar to the query
text and cosine distance in float for each.
Lower score represents more similarity.
class langchain.vectorstores.Zilliz(embedding_function: langchain.embeddings.base.Embeddings, collection_name: str = 'LangChainCollection', connection_args: Optional[dict[str, Any]] = None, consistency_level: str = 'Session', index_params: Optional[dict] = None, search_params: Optional[dict] = None, drop_old: Optional[bool] = False)[source]#
rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html