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.
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.
For a more detailed walkthrough of the `VectorStoreRetrieverMemory` wrapper, see [this notebook](/docs/modules/memory/integrations/vectorstore_retriever_memory.html).
