langchain_community.cache.GPTCache¶
- class langchain_community.cache.GPTCache(init_func: Optional[Union[Callable[[Any, str], None], Callable[[Any], None]]] = None)[source]¶
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)[source]¶
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.
- Parameters
prompt (str) –
llm_string (str) –
- 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.
- Parameters
prompt (str) –
llm_string (str) –
return_val (Sequence[Generation]) –
- Return type
None
- lookup(prompt: str, llm_string: str) Optional[Sequence[Generation]][source]¶
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[source]¶
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