similarity_search_with_score(*args, **kwargs)
Run similarity search with distance.
vector_search(query[, k])
Returns the most similar indexed documents to the query text.
vector_search_with_score(query[, k, filters])
Return docs most similar to query.
__init__(azure_search_endpoint: str, azure_search_key: str, index_name: str, embedding_function: Callable, search_type: str = 'hybrid', semantic_configuration_name: Optional[str] = None, semantic_query_language: str = 'en-us', fields: Optional[List[SearchField]] = None, vector_search: Optional[VectorSearch] = None, semantic_settings: Optional[SemanticSettings] = None, scoring_profiles: Optional[List[ScoringProfile]] = None, default_scoring_profile: Optional[str] = None, **kwargs: Any)[source]¶
async aadd_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
(List[Document] (documents) – Documents to add to the vectorstore.
Returns
List of IDs of the added texts.
Return type
List[str]
async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str]¶
Run more texts through the embeddings and add to the vectorstore.
add_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
(List[Document] (documents) – Documents to add to the vectorstore.
Returns
List of IDs of the added texts.
Return type
List[str]