Redis

This page covers how to use the Redis ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific Redis wrappers.

Installation and Setup

Install the Redis Python SDK with pip install redis

Wrappers

Cache

The Cache wrapper allows for Redis to be used as a remote, low-latency, in-memory cache for LLM prompts and responses.

Standard Cache

The standard cache is the Redis bread & butter of use case in production for both open source and enterprise users globally.

To import this cache:

from langchain.cache import RedisCache

To use this cache with your LLMs:

import langchain
import redis

redis_client = redis.Redis.from_url(...)
langchain.llm_cache = RedisCache(redis_client)

Semantic Cache

Semantic caching allows users to retrieve cached prompts based on semantic similarity between the user input and previously cached results. Under the hood it blends Redis as both a cache and a vectorstore.

To import this cache:

from langchain.cache import RedisSemanticCache

To use this cache with your LLMs:

import langchain
import redis

# use any embedding provider...
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings

redis_url = "redis://localhost:6379"

langchain.llm_cache = RedisSemanticCache(
    embedding=FakeEmbeddings(),
    redis_url=redis_url
)

VectorStore

The vectorstore wrapper turns Redis into a low-latency vector database for semantic search or LLM content retrieval.

To import this vectorstore:

from langchain.vectorstores import Redis

For a more detailed walkthrough of the Redis vectorstore wrapper, see this notebook.

Retriever

The Redis vector store retriever wrapper generalizes the vectorstore class to perform low-latency document retrieval. To create the retriever, simply call .as_retriever() on the base vectorstore class.

Memory

Redis can be used to persist LLM conversations.

Vector Store Retriever Memory

For a more detailed walkthrough of the VectorStoreRetrieverMemory wrapper, see this notebook.

Chat Message History Memory

For a detailed example of Redis to cache conversation message history, see this notebook.