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# Docs: compound ecosystem and integrations **Problem statement:** We have a big overlap between the References/Integrations and Ecosystem/LongChain Ecosystem pages. It confuses users. It creates a situation when new integration is added only on one of these pages, which creates even more confusion. - removed References/Integrations page (but move all its information into the individual integration pages - in the next PR). - renamed Ecosystem/LongChain Ecosystem into Integrations/Integrations. I like the Ecosystem term. It is more generic and semantically richer than the Integration term. But it mentally overloads users. The `integration` term is more concrete. UPDATE: after discussion, the Ecosystem is the term. Ecosystem/Integrations is the page (in place of Ecosystem/LongChain Ecosystem). As a result, a user gets a single place to start with the individual integration.
80 lines
2.5 KiB
Markdown
80 lines
2.5 KiB
Markdown
# Redis
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This page covers how to use the [Redis](https://redis.com) ecosystem within LangChain.
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It is broken into two parts: installation and setup, and then references to specific Redis wrappers.
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## Installation and Setup
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- Install the Redis Python SDK with `pip install redis`
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## Wrappers
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### Cache
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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.
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#### Standard Cache
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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.
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To import this cache:
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```python
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from langchain.cache import RedisCache
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```
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To use this cache with your LLMs:
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```python
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import langchain
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import redis
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redis_client = redis.Redis.from_url(...)
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langchain.llm_cache = RedisCache(redis_client)
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```
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#### Semantic Cache
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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.
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To import this cache:
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```python
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from langchain.cache import RedisSemanticCache
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```
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To use this cache with your LLMs:
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```python
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import langchain
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import redis
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# use any embedding provider...
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from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
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redis_url = "redis://localhost:6379"
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langchain.llm_cache = RedisSemanticCache(
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embedding=FakeEmbeddings(),
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redis_url=redis_url
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)
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```
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### VectorStore
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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.
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To import this vectorstore:
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```python
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from langchain.vectorstores import Redis
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```
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For a more detailed walkthrough of the Redis vectorstore wrapper, see [this notebook](../modules/indexes/vectorstores/examples/redis.ipynb).
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### Retriever
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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.
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### Memory
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Redis can be used to persist LLM conversations.
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#### Vector Store Retriever Memory
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For a more detailed walkthrough of the `VectorStoreRetrieverMemory` wrapper, see [this notebook](../modules/memory/types/vectorstore_retriever_memory.ipynb).
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#### Chat Message History Memory
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For a detailed example of Redis to cache conversation message history, see [this notebook](../modules/memory/examples/redis_chat_message_history.ipynb).
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