AI Resources

✨ A curated repository of code recipes, demos, tutorials and resources for basic and advanced Redis use cases in the AI ecosystem. ✨

Getting Started | Demos | Recipes | Tutorials | Integrations | Resources

Getting Started

New to Redis for AI applications? Here's how to get started:

1. First time with Redis? Start with our Redis Intro notebook
2. Want to try vector search? Check our Vector Search with RedisVL recipe
3. Building a RAG application? Begin with RAG from Scratch
4. Ready to see it in action? Play with the Redis RAG Workbench demo

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Demos

No faster way to get started than by diving in and playing around with a demo.

Demo	Description
Redis RAG Workbench	Interactive demo to build a RAG-based chatbot over a user-uploaded PDF. Toggle different settings and configurations to improve chatbot performance and quality. Utilizes RedisVL, LangChain, RAGAs, and more.
Redis VSS - Simple Streamlit Demo	Streamlit demo of Redis Vector Search
ArXiv Search	Full stack implementation of Redis with React FE
Product Search	Vector search with Redis Stack and Redis Enterprise
ArxivChatGuru	Streamlit demo of RAG over Arxiv documents with Redis & OpenAI
Redis Movies Searcher	Demo of hybrid search using Java, Spring Boot, and Redis OM
My Jarvis Alexa Skill	Complete example of an Alexa skill that can recall previously stored conversations and memories to provide contextual responses to users. Utilizes Redis Agent Memory Server, LangChain4J, Terraform, and AWS. It showcases how to implement context engineering to dynamically leverage RAG, tools, short-term and long-term memories.

Recipes

Need quickstarts to begin your Redis AI journey?

Getting started with Redis & Vector Search

Recipe	GitHub	Google Colab
🏁 Redis Intro - The place to start if brand new to Redis
🔍 Vector Search with RedisPy - Vector search with Redis python client
📚 Vector Search with RedisVL - Vector search with Redis Vector Library
🔄 Hybrid Search - Hybrid search techniques with Redis (BM25 + Vector)
🔢 Data Type Support - Shows how to convert a float32 index to float16 or integer dataypes
📊 Benchmarking Basics - Overview of search benchmarking basics with RedisVL and Python multiprocessing

Retrieval Augmented Generation (RAG)

Retrieval Augmented Generation (aka RAG) is a technique to enhance the ability of an LLM to respond to user queries. The retrieval part of RAG is supported by a vector database, which can return semantically relevant results to a user's query, serving as contextual information to augment the generative capabilities of an LLM.

To get started with RAG, either from scratch or using a popular framework like Llamaindex or LangChain, go with these recipes:

Recipe	GitHub	Google Colab
🧩 RAG from Scratch - RAG from scratch with the Redis Vector Library
⛓️ LangChain RAG - RAG using Redis and LangChain
🦙 LlamaIndex RAG - RAG using Redis and LlamaIndex
🚀 Advanced RAG - Advanced RAG techniques
🖥️ NVIDIA RAG - RAG using Redis and Nvidia NIMs
📊 RAGAS Evaluation - Utilize the RAGAS framework to evaluate RAG performance
🔒 Role-Based RAG - Implement a simple RBAC policy with vector search using Redis

LLM Memory

LLMs are stateless. To maintain context within a conversation chat sessions must be stored and re-sent to the LLM. Redis manages the storage and retrieval of message histories to maintain context and conversational relevance.

Recipe	GitHub	Google Colab
💬 Message History - LLM message history with semantic similarity
👥 Multiple Sessions - Handle multiple simultaneous chats with one instance

Semantic Caching

An estimated 31% of LLM queries are potentially redundant (source). Redis enables semantic caching to help cut down on LLM costs quickly.

Recipe	GitHub	Google Colab
🧠 Gemini Semantic Cache - Build a semantic cache with Redis and Google Gemini
🦙 Llama3.1 Doc2Cache - Build a semantic cache using the Doc2Cache framework and Llama3.1
⚙️ Cache Optimization - Use CacheThresholdOptimizer from redis-retrieval-optimizer to setup best cache config
🎯 Context-Enabled Caching - Context-aware semantic caching with Redis for enhanced LLM performance

Semantic Routing

Routing is a simple and effective way of preventing misuse with your AI application or for creating branching logic between data sources etc.

Recipe	GitHub	Google Colab
🔀 Basic Routing - Simple examples of how to build an allow/block list router in addition to a multi-topic router
⚙️ Router Optimization - Use RouterThresholdOptimizer from redis-retrieval-optimizer to setup best router config

AI Gateways

AI gateways manage LLM traffic through a centralized, managed layer that can implement routing, rate limiting, caching, and more.

Recipe	GitHub	Google Colab
🚪 LiteLLM Proxy - Getting started with LiteLLM proxy and Redis

Agents

Recipe	GitHub	Google Colab
🕸️ LangGraph Agents - Notebook to get started with lang-graph and agents
👥 CrewAI Agents - Notebook to get started with CrewAI and lang-graph
🧠 Memory Agent - Building an agent with short term and long term memory using Redis
🛠️ Full-Featured Agent - Notebook builds full tool calling agent with semantic cache and router
🥗 Autogen Agent - Builds a blog writing agent with Autogen and Redis memory

Computer Vision

Recipe	GitHub	Google Colab
👤 Facial Recognition - Build a facial recognition system using the Facenet embedding model and RedisVL

Recommendation Systems

Recipe	GitHub	Google Colab
📋 Content Filtering - Intro content filtering example with redisvl
👥 Collaborative Filtering - Intro collaborative filtering example with redisvl
🏗️ Two Towers - Intro deep learning two tower example with redisvl

Feature Store

Recipe	GitHub	Google Colab
💳 Credit Scoring - Credit scoring system using Feast with Redis as the online store
🔍 Transaction Search - Real-time transaction feature search with Redis

☕️ Java AI Recipes

A set of Java recipes can be found under /java-recipes.

Tutorials

Need a deeper-dive through different use cases and topics?

🤖 Agentic RAG A tutorial focused on agentic RAG with LlamaIndex and Cohere	☁️ RAG on VertexAI A RAG tutorial featuring Redis with Vertex AI	🔍 Recommendation Systems Building realtime recsys with NVIDIA Merlin & Redis
🧑🏻‍💻 Redis Movies Searcher Workshop A hands-on workshop to create the Redis Movies Searcher application

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Integrations

Redis integrates with many different players in the AI ecosystem. Here's a curated list below:

Integration	Description
RedisVL	A dedicated Python client lib for Redis as a Vector DB
AWS Bedrock	Streamlines GenAI deployment by offering foundational models as a unified API
LangChain Python	Popular Python client lib for building LLM applications powered by Redis
LangChain JS	Popular JS client lib for building LLM applications powered by Redis
LlamaIndex	LlamaIndex Integration for Redis as a vector Database (formerly GPT-index)
LiteLLM	Popular LLM proxy layer to help manage and streamline usage of multiple foundation models
Semantic Kernel	Popular lib by MSFT to integrate LLMs with plugins
RelevanceAI	Platform to tag, search and analyze unstructured data faster, built on Redis
DocArray	DocArray Integration of Redis as a VectorDB by Jina AI

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Other Helpful Resources

• Vector Databases and Large Language Models - Talk given at LLMs in Production Part 1 by Sam Partee.
• Level-up RAG with RedisVL
• Improving RAG quality with RAGAs
• Vector Databases and AI-powered Search Talk - Video "Vector Databases and AI-powered Search" given by Sam Partee at SDSC 2023.
• NVIDIA RecSys with Redis
• Benchmarking results for vector databases - Benchmarking results for vector databases, including Redis and 7 other Vector Database players.
• Redis Vector Library Docs
• Redis Vector Search API Docs - Official Redis literature for Vector Similarity Search.
• Redis Retrieval Optimizer - Library for optimizing index, embedding, and search method usage within Redis.

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Contributing

We welcome contributions to Redis AI Resources! Here's how you can help:

1. Add a new recipe: Create a Jupyter notebook demonstrating a Redis AI use case
2. Improve documentation: Enhance existing notebooks or README with clearer explanations
3. Fix bugs: Address issues in code samples or documentation
4. Suggest improvements: Open an issue with ideas for new content or enhancements

To contribute:

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a pull request

Please follow the existing style and format of the repository when adding content.