mirror of
https://github.com/hwchase17/langchain
synced 2024-11-06 03:20:49 +00:00
e2d7677526
# 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
|
|
|
|
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).
|