langchain_astradb.cache.AstraDBCache¶

class langchain_astradb.cache.AstraDBCache(*, collection_name: str = 'langchain_astradb_cache', token: str | TokenProvider | None = None, api_endpoint: str | None = None, environment: 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: SetupMode = SetupMode.SYNC)[source]¶

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 (str | TokenProvider | None) – API token for Astra DB usage, either in the form of a string or a subclass of astrapy.authentication.TokenProvider. If not provided, the environment variable ASTRA_DB_APPLICATION_TOKEN is inspected.

  • api_endpoint (str | None) – full URL to the API endpoint, such as https://<DB-ID>-us-east1.apps.astra.datastax.com. If not provided, the environment variable ASTRA_DB_API_ENDPOINT is inspected.

  • environment (str | None) – a string specifying the environment of the target Data API. If omitted, defaults to “prod” (Astra DB production). Other values are in astrapy.constants.Environment enum class.

  • astra_db_client (AstraDB | None) – DEPRECATED starting from version 0.3.5. Please use ‘token’, ‘api_endpoint’ and optionally ‘environment’. you can pass an already-created ‘astrapy.db.AstraDB’ instance (alternatively to ‘token’, ‘api_endpoint’ and ‘environment’).

  • async_astra_db_client (AsyncAstraDB | None) – DEPRECATED starting from version 0.3.5. Please use ‘token’, ‘api_endpoint’ and optionally ‘environment’. you can pass an already-created ‘astrapy.db.AsyncAstraDB’ instance (alternatively to ‘token’, ‘api_endpoint’ and ‘environment’).

  • namespace (str | None) – namespace (aka keyspace) where the collection is created. If not provided, the environment variable ASTRA_DB_KEYSPACE is inspected. Defaults to the database’s “default namespace”.

  • setup_mode (SetupMode) – 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 | TokenProvider | None = None, api_endpoint: str | None = None, environment: 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: SetupMode = SetupMode.SYNC)[source]¶

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 (str | TokenProvider | None) – API token for Astra DB usage, either in the form of a string or a subclass of astrapy.authentication.TokenProvider. If not provided, the environment variable ASTRA_DB_APPLICATION_TOKEN is inspected.

  • api_endpoint (str | None) – full URL to the API endpoint, such as https://<DB-ID>-us-east1.apps.astra.datastax.com. If not provided, the environment variable ASTRA_DB_API_ENDPOINT is inspected.

  • environment (str | None) – a string specifying the environment of the target Data API. If omitted, defaults to “prod” (Astra DB production). Other values are in astrapy.constants.Environment enum class.

  • astra_db_client (AstraDB | None) – DEPRECATED starting from version 0.3.5. Please use ‘token’, ‘api_endpoint’ and optionally ‘environment’. you can pass an already-created ‘astrapy.db.AstraDB’ instance (alternatively to ‘token’, ‘api_endpoint’ and ‘environment’).

  • async_astra_db_client (AsyncAstraDB | None) – DEPRECATED starting from version 0.3.5. Please use ‘token’, ‘api_endpoint’ and optionally ‘environment’. you can pass an already-created ‘astrapy.db.AsyncAstraDB’ instance (alternatively to ‘token’, ‘api_endpoint’ and ‘environment’).

  • namespace (str | None) – namespace (aka keyspace) where the collection is created. If not provided, the environment variable ASTRA_DB_KEYSPACE is inspected. Defaults to the database’s “default namespace”.

  • setup_mode (SetupMode) – 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[source]¶

Async clear cache that can take additional keyword arguments.

Parameters

kwargs (Any) –

Return type

None

async adelete(prompt: str, llm_string: str) → None[source]¶

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[source]¶

A wrapper around adelete with the LLM being passed.

In case the llm(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) → Optional[Sequence[Generation]][source]¶

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

Optional[Sequence[Generation]]

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

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[source]¶

Clear cache that can take additional keyword arguments.

Parameters

kwargs (Any) –

Return type

None

delete(prompt: str, llm_string: str) → None[source]¶

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[source]¶

A wrapper around delete with the LLM being passed.

In case the llm(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) → Optional[Sequence[Generation]][source]¶

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

Optional[Sequence[Generation]]

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

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