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style: more refs fixes (#33730)

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Mason Daugherty
2025-10-29 16:34:46 -04:00
committed by GitHub
parent 6a1dca113e
commit 123e29dc26
49 changed files with 586 additions and 394 deletions
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19
libs/core/langchain_core/caches.py
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@@ -2,8 +2,8 @@
Distinct from provider-based [prompt caching](https://docs.langchain.com/oss/python/langchain/models#prompt-caching).
!!! warning
This is a beta feature! Please be wary of deploying experimental code to production
!!! warning "Beta feature"
This is a beta feature. Please be wary of deploying experimental code to production
unless you've taken appropriate precautions.
A cache is useful for two reasons:
@@ -49,17 +49,18 @@ class BaseCache(ABC):
"""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).
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.
These invocation parameters are serialized into a string representation.
Returns:
On a cache miss, return `None`. On a cache hit, return the cached value.
@@ -78,8 +79,10 @@ class BaseCache(ABC):
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 `Generation`
@@ -94,15 +97,17 @@ class BaseCache(ABC):
"""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).
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.
@@ -125,8 +130,10 @@ class BaseCache(ABC):
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 `Generation`