langchain.cache.RedisSemanticCache¶
class langchain.cache.RedisSemanticCache(redis_url: str, embedding: Embeddings, score_threshold: float = 0.2)[source]¶
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()
)
Attributes
DEFAULT_SCHEMA
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.
__init__(redis_url: str, embedding: Embeddings, score_threshold: float = 0.2)[source]¶
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()
)