lookup_with_id(prompt, llm_string)
Look up based on prompt and llm_string.
lookup_with_id_through_llm(prompt, llm[, stop])
update(prompt, llm_string, return_val)
Update cache based on prompt and llm_string.
__init__(*, collection_name: str = 'langchain_astradb_semantic_cache', token: Optional[str] = None, api_endpoint: Optional[str] = None, astra_db_client: Optional[Any] = None, namespace: Optional[str] = None, embedding: Embeddings, metric: Optional[str] = None, similarity_threshold: float = 0.85)[source]¶
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”.
Parameters
astra_db_client (Optional[Any]) – alternative to token+api_endpoint,
you can pass an already-created ‘astrapy.db.AstraDB’ instance.
namespace (Optional[str]) – namespace (aka keyspace) where the
collection is created. Defaults to the database’s “default namespace”.
embedding (Embedding) – Embedding provider for semantic
encoding and search.
metric – the function to use for evaluating similarity of text embeddings.
Defaults to ‘cosine’ (alternatives: ‘euclidean’, ‘dot_product’)
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.