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- updated titles and descriptions of the `integrations/memory` notebooks into consistent and laconic format; - removed `docs/extras/integrations/memory/motorhead_memory_managed.ipynb` file as a duplicate of the `docs/extras/integrations/memory/motorhead_memory.ipynb`; - added `integrations/providers` Integration Cards for `dynamodb`, `motorhead`. - updated `integrations/providers/redis.mdx` with links - renamed several notebooks; updated `vercel.json` to reroute new names.
121 lines
4.6 KiB
Plaintext
121 lines
4.6 KiB
Plaintext
# Redis
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>[Redis (Remote Dictionary Server)](https://en.wikipedia.org/wiki/Redis) is an open-source in-memory storage,
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> used as a distributed, in-memory key–value database, cache and message broker, with optional durability.
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> Because it holds all data in memory and because of its design, `Redis` offers low-latency reads and writes,
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> making it particularly suitable for use cases that require a cache. Redis is the most popular NoSQL database,
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> and one of the most popular databases overall.
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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 Python SDK:
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```bash
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pip install redis
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```
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## Wrappers
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All wrappers need 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 unofficial 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/integrations/vectorstores/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/types/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/integrations/memory/redis_chat_message_history.html).
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