AstraDBCache

class langchain_community.cache.AstraDBCache(*, collection_name: str = 'langchain_astradb_cache', token: str | None = None, api_endpoint: str | None = None, astra_db_client: AstraDB | None = None, async_astra_db_client: AsyncAstraDB | None = None, namespace: str | None = None, pre_delete_collection: bool = False, setup_mode: AstraSetupMode = SetupMode.SYNC)

Deprecated since version 0.0.28: Use :class:`~langchain_astradb.AstraDBCache` instead. It will be removed in None==1.0.

Cache that uses Astra DB as a backend.

It uses a single collection as a kv store The lookup keys, combined in the _id of the documents, are:

• prompt, a string

• llm_string, a deterministic str representation of the model parameters. (needed to prevent same-prompt-different-model collisions)

Parameters:

• collection_name (str) – name of the Astra DB collection to create/use.

• token (Optional[str]) – API token for Astra DB usage.

• api_endpoint (Optional[str]) – full URL to the API endpoint, such as https://<DB-ID>-us-east1.apps.astra.datastax.com.

• astra_db_client (Optional[AstraDB]) – alternative to token+api_endpoint, you can pass an already-created ‘astrapy.db.AstraDB’ instance.

• async_astra_db_client (Optional[AsyncAstraDB]) – alternative to token+api_endpoint, you can pass an already-created ‘astrapy.db.AsyncAstraDB’ instance.

• namespace (Optional[str]) – namespace (aka keyspace) where the collection is created. Defaults to the database’s “default namespace”.

• setup_mode (AstraSetupMode) – mode used to create the Astra DB collection (SYNC, ASYNC or OFF).

• pre_delete_collection (bool) – whether to delete the collection before creating it. If False and the collection already exists, the collection will be used as is.

Methods

__init__(*[, collection_name, token, ...])	Cache that uses Astra DB as a backend.
aclear(**kwargs)	Async clear cache that can take additional keyword arguments.
adelete(prompt, llm_string)	Evict from cache if there's an entry.
adelete_through_llm(prompt, llm[, stop])	A wrapper around adelete with the LLM being passed.
alookup(prompt, llm_string)	Async look up based on prompt and llm_string.
aupdate(prompt, llm_string, return_val)	Async update cache based on prompt and llm_string.
clear(**kwargs)	Clear cache that can take additional keyword arguments.
delete(prompt, llm_string)	Evict from cache if there's an entry.
delete_through_llm(prompt, llm[, stop])	A wrapper around delete with the LLM being passed.
lookup(prompt, llm_string)	Look up based on prompt and llm_string.
update(prompt, llm_string, return_val)	Update cache based on prompt and llm_string.

__init__(*, collection_name: str = 'langchain_astradb_cache', token: str | None = None, api_endpoint: str | None = None, astra_db_client: AstraDB | None = None, async_astra_db_client: AsyncAstraDB | None = None, namespace: str | None = None, pre_delete_collection: bool = False, setup_mode: AstraSetupMode = SetupMode.SYNC)

Cache that uses Astra DB as a backend.

It uses a single collection as a kv store The lookup keys, combined in the _id of the documents, are:

• prompt, a string

• llm_string, a deterministic str representation of the model parameters. (needed to prevent same-prompt-different-model collisions)

Parameters:

• collection_name (str) – name of the Astra DB collection to create/use.

• token (Optional[str]) – API token for Astra DB usage.

• api_endpoint (Optional[str]) – full URL to the API endpoint, such as https://<DB-ID>-us-east1.apps.astra.datastax.com.

• astra_db_client (Optional[AstraDB]) – alternative to token+api_endpoint, you can pass an already-created ‘astrapy.db.AstraDB’ instance.

• async_astra_db_client (Optional[AsyncAstraDB]) – alternative to token+api_endpoint, you can pass an already-created ‘astrapy.db.AsyncAstraDB’ instance.

• namespace (Optional[str]) – namespace (aka keyspace) where the collection is created. Defaults to the database’s “default namespace”.

• setup_mode (AstraSetupMode) – mode used to create the Astra DB collection (SYNC, ASYNC or OFF).

• pre_delete_collection (bool) – whether to delete the collection before creating it. If False and the collection already exists, the collection will be used as is.

async aclear(**kwargs: Any) → None

Async clear cache that can take additional keyword arguments.

Parameters:

kwargs (Any)

Return type:

None

async adelete(prompt: str, llm_string: str) → None

Evict from cache if there’s an entry.

Parameters:

• prompt (str)

• llm_string (str)

Return type:

None

async adelete_through_llm(prompt: str, llm: LLM, stop: List[str] | None = None) → None

A wrapper around adelete with the LLM being passed. In case the llm.invoke(prompt) calls have a stop param, you should pass it here

Parameters:

• prompt (str)

• llm (LLM)

• stop (List[str] | None)

Return type:

None

async alookup(prompt: str, llm_string: str) → Sequence[Generation] | None

Async look up based on prompt and llm_string.

A cache implementation is expected to generate a key from the 2-tuple of prompt and llm_string (e.g., by concatenating them with a delimiter).

Parameters:

• prompt (str) – a string representation of the prompt. In the case of a Chat model, the prompt is a non-trivial serialization of the prompt into the language model.

• llm_string (str) – A string representation of the LLM configuration. This is used to capture the invocation parameters of the LLM (e.g., model name, temperature, stop tokens, max tokens, etc.). These invocation parameters are serialized into a string representation.

Returns:

On a cache miss, return None. On a cache hit, return the cached value. The cached value is a list of Generations (or subclasses).

Return type:

Sequence[Generation] | None

async aupdate(prompt: str, llm_string: str, return_val: Sequence[Generation]) → None

Async update cache based on prompt and llm_string.

The prompt and llm_string are used to generate a key for the cache. The key should match that of the look up method.

Parameters:

• prompt (str) – a string representation of the prompt. In the case of a Chat model, the prompt is a non-trivial serialization of the prompt into the language model.

• llm_string (str) – A string representation of the LLM configuration. This is used to capture the invocation parameters of the LLM (e.g., model name, temperature, stop tokens, max tokens, etc.). These invocation parameters are serialized into a string representation.

• return_val (Sequence[Generation]) – The value to be cached. The value is a list of Generations (or subclasses).

Return type:

None

clear(**kwargs: Any) → None

Clear cache that can take additional keyword arguments.

Parameters:

kwargs (Any)

Return type:

None

delete(prompt: str, llm_string: str) → None

Evict from cache if there’s an entry.

Parameters:

• prompt (str)

• llm_string (str)

Return type:

None

delete_through_llm(prompt: str, llm: LLM, stop: List[str] | None = None) → None

A wrapper around delete with the LLM being passed. In case the llm.invoke(prompt) calls have a stop param, you should pass it here

Parameters:

• prompt (str)

• llm (LLM)

• stop (List[str] | None)

Return type:

None

lookup(prompt: str, llm_string: str) → Sequence[Generation] | None

Look up based on prompt and llm_string.

A cache implementation is expected to generate a key from the 2-tuple of prompt and llm_string (e.g., by concatenating them with a delimiter).

Parameters:

• prompt (str) – a string representation of the prompt. In the case of a Chat model, the prompt is a non-trivial serialization of the prompt into the language model.

• llm_string (str) – A string representation of the LLM configuration. This is used to capture the invocation parameters of the LLM (e.g., model name, temperature, stop tokens, max tokens, etc.). These invocation parameters are serialized into a string representation.

Returns:

On a cache miss, return None. On a cache hit, return the cached value. The cached value is a list of Generations (or subclasses).

Return type:

Sequence[Generation] | None

update(prompt: str, llm_string: str, return_val: Sequence[Generation]) → None

Update cache based on prompt and llm_string.

The prompt and llm_string are used to generate a key for the cache. The key should match that of the lookup method.

Parameters:

• prompt (str) – a string representation of the prompt. In the case of a Chat model, the prompt is a non-trivial serialization of the prompt into the language model.

• llm_string (str) – A string representation of the LLM configuration. This is used to capture the invocation parameters of the LLM (e.g., model name, temperature, stop tokens, max tokens, etc.). These invocation parameters are serialized into a string representation.

• return_val (Sequence[Generation]) – The value to be cached. The value is a list of Generations (or subclasses).

Return type:

None