search(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
similarity_search(query[, k])
Run similarity search with AtlasDB
similarity_search_by_vector(embedding[, k])
Return docs most similar to embedding vector.
similarity_search_with_relevance_scores(query)
Return docs and relevance scores in the range [0, 1].
similarity_search_with_score(*args, **kwargs)
Run similarity search with distance.
__init__(name: str, embedding_function: Optional[Embeddings] = None, api_key: Optional[str] = None, description: str = 'A description for your project', is_public: bool = True, reset_project_if_exists: bool = False) → None[source]¶
Initialize the Atlas Client
Parameters
name (str) – The name of your project. If the project already exists,
it will be loaded.
embedding_function (Optional[Embeddings]) – An optional function used for
embedding your data. If None, data will be embedded with
Nomic’s embed model.
api_key (str) – Your nomic API key
description (str) – A description for your project.
is_public (bool) – Whether your project is publicly accessible.
True by default.
reset_project_if_exists (bool) – Whether to reset this project if it
already exists. Default False.
Generally useful during development and testing.
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]