langchain/docs/ecosystem/redis.md

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# 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](../modules/indexes/vectorstores/examples/redis.ipynb).
### 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](../modules/memory/types/vectorstore_retriever_memory.ipynb).
#### Chat Message History Memory
For a detailed example of Redis to cache conversation message history, see [this notebook](../modules/memory/examples/redis_chat_message_history.ipynb).