similarity_threshold (float, optional) – the minimum similarity
for accepting a (semantic-search) match.
The default score threshold is tuned to the default metric.
Tune it carefully yourself if switching to another distance metric.
Methods
__init__(*[, collection_name, token, ...])
Initialize the cache with all relevant parameters. :param collection_name: name of the Astra DB collection to create/use. :type collection_name: str :param token: API token for Astra DB usage. :type token: Optional[str] :param api_endpoint: full URL to the API endpoint,                      such as "https://<DB-ID>-us-east1.apps.astra.datastax.com". :type api_endpoint: Optional[str] :param astra_db_client: alternative to token+api_endpoint,                         you can pass an already-created 'astrapy.db.AstraDB' instance. :type astra_db_client: Optional[Any] :param namespace: namespace (aka keyspace) where the                   collection is created. Defaults to the database's "default namespace". :type namespace: Optional[str] :param embedding: Embedding provider for semantic                   encoding and search. :type embedding: Embedding :param metric: the function to use for evaluating similarity of text embeddings. Defaults to 'cosine' (alternatives: 'euclidean', 'dot_product') :param similarity_threshold: the minimum similarity                              for accepting a (semantic-search) match. :type similarity_threshold: float, optional.
clear(**kwargs)
Clear the whole semantic cache.
delete_by_document_id(document_id)
Given this is a "similarity search" cache, an invalidation pattern that makes sense is first a lookup to get an ID, and then deleting with that ID.
lookup(prompt, llm_string)
Look up based on prompt and llm_string.
lookup_with_id(prompt, llm_string)