mirror of
https://github.com/hwchase17/langchain
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108 lines
4.2 KiB
Python
108 lines
4.2 KiB
Python
"""
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.. warning::
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Beta Feature!
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**Cache** provides an optional caching layer for LLMs.
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Cache is useful for two reasons:
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- It can save you money by reducing the number of API calls you make to the LLM
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provider if you're often requesting the same completion multiple times.
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- It can speed up your application by reducing the number of API calls you make
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to the LLM provider.
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Cache directly competes with Memory. See documentation for Pros and Cons.
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**Class hierarchy:**
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.. code-block::
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BaseCache --> <name>Cache # Examples: InMemoryCache, RedisCache, GPTCache
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"""
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from __future__ import annotations
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from abc import ABC, abstractmethod
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from typing import Any, Optional, Sequence
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from langchain_core.outputs import Generation
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from langchain_core.runnables import run_in_executor
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RETURN_VAL_TYPE = Sequence[Generation]
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class BaseCache(ABC):
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"""This interfaces provides a caching layer for LLMs and Chat models.
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The cache interface consists of the following methods:
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- lookup: Look up a value based on a prompt and llm_string.
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- update: Update the cache based on a prompt and llm_string.
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- clear: Clear the cache.
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In addition, the cache interface provides an async version of each method.
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The default implementation of the async methods is to run the synchronous
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method in an executor. It's recommended to override the async methods
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and provide an async implementations to avoid unnecessary overhead.
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"""
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@abstractmethod
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def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]:
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"""Look up based on prompt and llm_string.
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A cache implementation is expected to generate a key from the 2-tuple
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of prompt and llm_string (e.g., by concatenating them with a delimiter).
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Args:
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prompt: a string representation of the prompt.
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In the case of a Chat model, the prompt is a non-trivial
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serialization of the prompt into the language model.
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llm_string: A string representation of the LLM configuration.
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This is used to capture the invocation parameters of the LLM
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(e.g., model name, temperature, stop tokens, max tokens, etc.).
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These invocation parameters are serialized into a string
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representation.
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Returns:
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On a cache miss, return None. On a cache hit, return the cached value.
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The cached value is a list of Generations (or subclasses).
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"""
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@abstractmethod
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def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None:
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"""Update cache based on prompt and llm_string.
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The prompt and llm_string are used to generate a key for the cache.
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The key should match that of the look up method.
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Args:
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prompt: a string representation of the prompt.
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In the case of a Chat model, the prompt is a non-trivial
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serialization of the prompt into the language model.
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llm_string: A string representation of the LLM configuration.
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This is used to capture the invocation parameters of the LLM
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(e.g., model name, temperature, stop tokens, max tokens, etc.).
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These invocation parameters are serialized into a string
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representation.
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return_val: The value to be cached. The value is a list of Generations
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(or subclasses).
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"""
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@abstractmethod
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def clear(self, **kwargs: Any) -> None:
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"""Clear cache that can take additional keyword arguments."""
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async def alookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]:
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"""Async version of lookup."""
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return await run_in_executor(None, self.lookup, prompt, llm_string)
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async def aupdate(
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self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE
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) -> None:
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"""Async version of aupdate."""
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return await run_in_executor(None, self.update, prompt, llm_string, return_val)
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async def aclear(self, **kwargs: Any) -> None:
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"""Clear cache that can take additional keyword arguments."""
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return await run_in_executor(None, self.clear, **kwargs)
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