langchain/docs/extras/ecosystem/integrations/redis.mdx

# Redis

This page covers how to use the [Redis](https://redis.com) 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](https://redis.io) 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](https://redis.io) and [enterprise](https://redis.com) users globally.

To import this cache:
```python
from langchain.cache import RedisCache
```

To use this cache with your LLMs:
```python
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:
```python
from langchain.cache import RedisSemanticCache
```

To use this cache with your LLMs:
```python
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](https://redis.com/solutions/use-cases/vector-database/) for semantic search or LLM content retrieval.

To import this vectorstore:
```python
from langchain.vectorstores import Redis
```

For a more detailed walkthrough of the Redis vectorstore wrapper, see [this notebook](/docs/modules/data_connection/vectorstores/integrations/redis.html).

### 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](/docs/modules/memory/integrations/vectorstore_retriever_memory.html).

#### Chat Message History Memory
For a detailed example of Redis to cache conversation message history, see [this notebook](/docs/modules/memory/integrations/redis_chat_message_history.html).