Leonid Ganeline e2d7677526

docs: compound ecosystem and integrations (#4870 )

# 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.

2023-05-18 09:29:57 -07:00

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Redis

This page covers how to use the Redis 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 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 and enterprise users globally.

To import this cache:

from langchain.cache import RedisCache

To use this cache with your LLMs:

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:

from langchain.cache import RedisSemanticCache

To use this cache with your LLMs:

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 for semantic search or LLM content retrieval.

To import this vectorstore:

from langchain.vectorstores import Redis

For a more detailed walkthrough of the Redis vectorstore wrapper, see this notebook.

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.

Chat Message History Memory

For a detailed example of Redis to cache conversation message history, see this notebook.

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