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# Support Redis Sentinel database connections This PR adds the support to connect not only to Redis standalone servers but High Availability Replication sets too (https://redis.io/docs/management/sentinel/) Redis Replica Sets have on Master allowing to write data and 2+ replicas with read-only access to the data. The additional Redis Sentinel instances monitor all server and reconfigure the RW-Master on the fly if it comes unavailable. Therefore all connections must be made through the Sentinels the query the current master for a read-write connection. This PR adds basic support to also allow a redis connection url specifying a Sentinel as Redis connection. Redis documentation and Jupyter notebook with Redis examples are updated to mention how to connect to a redis Replica Set with Sentinels - Remark - i did not found test cases for Redis server connections to add new cases here. Therefor i tests the new utility class locally with different kind of setups to make sure different connection urls are working as expected. But no test case here as part of this PR.
110 lines
4.2 KiB
Plaintext
110 lines
4.2 KiB
Plaintext
# 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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All wrappers needing a redis url connection string to connect to the database support either a stand alone Redis server
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or a High-Availability setup with Replication and Redis Sentinels.
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### Redis Standalone connection url
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For standalone Redis server the official redis connection url formats can be used as describe in the python redis modules
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"from_url()" method [Redis.from_url](https://redis-py.readthedocs.io/en/stable/connections.html#redis.Redis.from_url)
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Example: `redis_url = "redis://:secret-pass@localhost:6379/0"`
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### Redis Sentinel connection url
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For [Redis sentinel setups](https://redis.io/docs/management/sentinel/) the connection scheme is "redis+sentinel".
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This is an un-offical extensions to the official IANA registered protocol schemes as long as there is no connection url
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for Sentinels available.
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Example: `redis_url = "redis+sentinel://:secret-pass@sentinel-host:26379/mymaster/0"`
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The format is `redis+sentinel://[[username]:[password]]@[host-or-ip]:[port]/[service-name]/[db-number]`
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with the default values of "service-name = mymaster" and "db-number = 0" if not set explicit.
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The service-name is the redis server monitoring group name as configured within the Sentinel.
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The current url format limits the connection string to one sentinel host only (no list can be given) and
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booth Redis server and sentinel must have the same password set (if used).
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### Redis Cluster connection url
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Redis cluster is not supported right now for all methods requiring a "redis_url" parameter.
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The only way to use a Redis Cluster is with LangChain classes accepting a preconfigured Redis client like `RedisCache`
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(example below).
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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](/docs/modules/data_connection/vectorstores/integrations/redis.html).
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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](/docs/modules/memory/integrations/vectorstore_retriever_memory.html).
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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](/docs/modules/memory/integrations/redis_chat_message_history.html).
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