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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 Contents
  
Installation and Setup
Wrappers
Cache
Standard Cache
Semantic Cache
VectorStore
Retriever
Memory
Vector Store Retriever Memory
Chat Message History Memory
By Harrison Chase
    
      © Copyright 2023, Harrison Chase.
      
  Last updated on Jun 16, 2023.
rtdocs_stable/api.python.langchain.com/en/stable/integrations/redis.html