Quick starts on Docs

Redis as an in-memory data structure store quick start guide

Mon, 01 Jan 0001 00:00:00 +0000

This quick start guide shows you how to:

Get started with Redis
Store data under a key in Redis
Retrieve data with a key from Redis
Scan the keyspace for keys that match a specific pattern

The examples in this article refer to a simple bicycle inventory.

Setup

The easiest way to get started with Redis is to use Redis Cloud:

Redis as a document database quick start guide

Mon, 01 Jan 0001 00:00:00 +0000

This quick start guide shows you how to:

Create a secondary index
Add JSON documents
Search and query your data

The examples in this article refer to a simple bicycle inventory that contains JSON documents with the following structure:

{
  "brand": "brand name",
  "condition": "new | used | refurbished",
  "description": "description",
  "model": "model",
  "price": 0
}

Setup

The easiest way to get started with Redis is to use Redis Cloud:

Redis as a vector database quick start guide

Mon, 01 Jan 0001 00:00:00 +0000

This quick start guide helps you to:

Understand what a vector database is
Create a Redis vector database
Create vector embeddings and store vectors
Query data and perform a vector search

Note:

This guide uses RedisVL, which is a Python client library for Redis that is highly specialized for vector processing. You may also be interested in the vector query examples for our other client libraries:

RAG with Redis

Mon, 01 Jan 0001 00:00:00 +0000

What is Retrieval Augmented Generation (RAG)?

Large Language Models (LLMs) generate human-like text but are limited by the data they were trained on. RAG enhances LLMs by integrating them with external, domain-specific data stored in a Redis vector database.

Redis for GenAI apps

Mon, 01 Jan 0001 00:00:00 +0000

Redis enables high-performance, scalable, and reliable data management, making it a key component for GenAI apps, chatbots, and AI agents. By leveraging Redis for fast data retrieval, caching, and vector search capabilities, you can enhance AI-powered interactions, reduce latency, and improve user experience.

Redis excels in storing and indexing vector embeddings that semantically represent unstructured data. With vector search, Redis retrieves similar questions and relevant data, lowering LLM inference costs and latency. It fetches pertinent portions of chat history, enriching context for more accurate and relevant responses. These features make Redis an ideal choice for RAG systems and GenAI apps requiring fast data access.

Redis FAQ

Mon, 01 Jan 0001 00:00:00 +0000

How is Redis different from other key-value stores?

Redis has a different evolution path in the key-value DBs where values can contain more complex data types, with atomic operations defined on those data types. Redis data types are closely related to fundamental data structures and are exposed to the programmer as such, without additional abstraction layers.
Redis is an in-memory but persistent on disk database, so it represents a different trade off where very high write and read speed is achieved with the limitation of data sets that can't be larger than memory. Another advantage of in-memory databases is that the memory representation of complex data structures is much simpler to manipulate compared to the same data structures on disk, so Redis can do a lot with little internal complexity. At the same time the two on-disk storage formats (RDB and AOF) don't need to be suitable for random access, so they are compact and always generated in an append-only fashion (Even the AOF log rotation is an append-only operation, since the new version is generated from the copy of data in memory). However this design also involves different challenges compared to traditional on-disk stores. Being the main data representation on memory, Redis operations must be carefully handled to make sure there is always an updated version of the data set on disk.

What's the Redis memory footprint?

To give you a few examples (all obtained using 64-bit instances):