core[patch]: Document BaseCache abstraction in code (#20046)

Document the base cache abstraction in the cache.
This commit is contained in:
Eugene Yurtsev
2024-04-05 10:56:57 -04:00
committed by GitHub
parent 4d8a6a27a3
commit e4fc0e7502

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@@ -31,28 +31,75 @@ RETURN_VAL_TYPE = Sequence[Generation]
class BaseCache(ABC):
"""Base interface for cache."""
"""This interfaces provides a caching layer for LLMs and Chat models.
The cache interface consists of the following methods:
- lookup: Look up a value based on a prompt and llm_string.
- update: Update the cache based on a prompt and llm_string.
- clear: Clear the cache.
In addition, the cache interface provides an async version of each method.
The default implementation of the async methods is to run the synchronous
method in an executor. It's recommended to override the async methods
and provide an async implementations to avoid unnecessary overhead.
"""
@abstractmethod
def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]:
"""Look up based on prompt and llm_string."""
"""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).
Args:
prompt: 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: 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).
"""
@abstractmethod
def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None:
"""Update cache based on prompt and llm_string."""
"""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.
Args:
prompt: 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: 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: The value to be cached. The value is a list of Generations
(or subclasses).
"""
@abstractmethod
def clear(self, **kwargs: Any) -> None:
"""Clear cache that can take additional keyword arguments."""
async def alookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]:
"""Look up based on prompt and llm_string."""
"""Async version of lookup."""
return await run_in_executor(None, self.lookup, prompt, llm_string)
async def aupdate(
self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE
) -> None:
"""Update cache based on prompt and llm_string."""
"""Async version of aupdate."""
return await run_in_executor(None, self.update, prompt, llm_string, return_val)
async def aclear(self, **kwargs: Any) -> None: