langchain.cache.RedisSemanticCache

class langchain.cache.RedisSemanticCache(redis_url: str, embedding: Embeddings, score_threshold: float = 0.2)

Bases: BaseCache

Cache that uses Redis as a vector-store backend.

Initialize by passing in the init GPTCache func

Parameters

• redis_url (str) – URL to connect to Redis.

• embedding (Embedding) – Embedding provider for semantic encoding and search.

• score_threshold (float, 0.2) –

Example:

import langchain

from langchain.cache import RedisSemanticCache
from langchain.embeddings import OpenAIEmbeddings

langchain.llm_cache = RedisSemanticCache(
    redis_url="redis://localhost:6379",
    embedding=OpenAIEmbeddings()
)

Methods

__init__(redis_url, embedding[, score_threshold])	Initialize by passing in the init GPTCache func
clear(**kwargs)	Clear semantic cache for a given llm_string.
lookup(prompt, llm_string)	Look up based on prompt and llm_string.
update(prompt, llm_string, return_val)	Update cache based on prompt and llm_string.

clear(**kwargs: Any) → None

Clear semantic cache for a given llm_string.

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

Look up based on prompt and llm_string.

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

Update cache based on prompt and llm_string.

Examples using RedisSemanticCache

• Redis

• Caching integrations