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# `langchain_community.cache`.GPTCache

class langchain_community.cache.GPTCache(init_func: Optional[Union[Callable[[Any, str], None], Callable[[Any], None]]] = None)

  Cache that uses GPTCache as a backend.

  Initialize by passing in init function (default: None).

  Parameters

    - init_func (Optional[Callable[[Any], None]]) – init GPTCache function

    - (default – None)

  Example: .. code-block:: python

  > # Initialize GPTCache with a custom init function import gptcache from gptcache.processor.pre import get_prompt from gptcache.manager.factory import get_data_manager from langchain_community.globals import set_llm_cache

  > # Avoid multiple caches using the same file, causing different llm model caches to affect each other

  > def init_gptcache(cache_obj: gptcache.Cache, llm str):
  >   cache_obj.init(

  >     pre_embedding_func=get_prompt, data_manager=manager_factory(

  >     > manager=”map”, data_dir=f”map_cache_{llm}”

  >     ),

  >   )

  > set_llm_cache(GPTCache(init_gptcache))

  Methods

  `__init__`([init_func]) | Initialize by passing in init function (default: None).
  `aclear`(**kwargs) | Clear cache that can take additional keyword arguments.
  `alookup`(prompt, llm_string) | Look up based on prompt and llm_string.
  `aupdate`(prompt, llm_string, return_val) | Update cache based on prompt and llm_string.
  `clear`(**kwargs) | Clear cache.
  `lookup`(prompt, llm_string) | Look up the cache data.
  `update`(prompt, llm_string, return_val) | Update cache.

  __init__(init_func: Optional[Union[Callable[[Any, str], None], Callable[[Any], None]]] = None)

    Initialize by passing in init function (default: None).

    Parameters

      - init_func (Optional[Callable[[Any], None]]) – init GPTCache function

      - (default – None)

    Example: .. code-block:: python

    > # Initialize GPTCache with a custom init function import gptcache from gptcache.processor.pre import get_prompt from gptcache.manager.factory import get_data_manager from langchain_community.globals import set_llm_cache

    > # Avoid multiple caches using the same file, causing different llm model caches to affect each other

    > def init_gptcache(cache_obj: gptcache.Cache, llm str):
    >   cache_obj.init(

    >     pre_embedding_func=get_prompt, data_manager=manager_factory(

    >     > manager=”map”, data_dir=f”map_cache_{llm}”

    >     ),

    >   )

    > set_llm_cache(GPTCache(init_gptcache))

  async aclear(**kwargs: Any) → None

    Clear cache that can take additional keyword arguments.

    Parameters

      kwargs (Any) –

    Return type

      None

  async alookup(prompt: str, llm_string: str) → Optional[Sequence[Generation]]

    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

    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.

    Parameters

      kwargs (Any) –

    Return type

      None

  lookup(prompt: str, llm_string: str) → Optional[Sequence[Generation]]

    Look up the cache data. First, retrieve the corresponding cache object using the llm_string parameter, and then retrieve the data from the cache based on the prompt.

    Parameters

      - prompt (str) –

      - llm_string (str) –

    Return type

      Optional[Sequence[Generation]]

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

    Update cache. First, retrieve the corresponding cache object using the llm_string parameter, and then store the prompt and return_val in the cache object.

    Parameters

      - prompt (str) –

      - llm_string (str) –

      - return_val (Sequence[Generation]) –

    Return type

      None

# Examples using GPTCache

- To make the caching really obvious, lets use a slower model.