✨ 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
New to Redis for AI applications? Here's how to get started:
- First time with Redis? Start with our Redis Intro notebook
- Want to try vector search? Check our Vector Search with RedisVL recipe
- Building a RAG application? Begin with RAG from Scratch
- Ready to see it in action? Play with the Redis RAG Workbench demo
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 |
| Memory-Aware Alexa Assistant | 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. |
| From Postgres to Redis Cloud with RDI | This demo shows how to deploy RDI on Kubernetes running locally so it can be used to build a moving/transformation data pipeline to enable data on Redis Cloud. The source database lives on-premises, and the data is movied and transformed to Redis Cloud. |
| Virtual Banking Assistant with Semantic Router | A Banking Agent demo that uses semantic routing to route user queries to right banking tool. (Eg: Credit Card Recommendation, Loan EMI calculation, Banking FAQ etc) |
| Shopping AI Agent | An Agentic AI grocery-shopping platform demo that combines Redis's speed with LangGraph's workflow orchestration. Get personalized recipe recommendations, smart product suggestions, and lightning-fast responses through semantic caching. |
| Restaurant Discovery AI Agent | An Agentic AI restaurant discovery platform that combines Redis's speed with LangGraph's intelligent workflow orchestration. Get personalized restaurant recommendations, make reservations, and get lightning-fast responses through semantic caching. |
| Podcast Chatbot with Agent Memory | A chatbot that discusses and recommends podcasts. Affectionately called Podbot, it demonstrates how to use Redis Agent Memory Server to build context-aware agents with Azure Managed Redis. |
| Car Dealership Assistant with Agent Memory | A Car Dealership chatbot with Redis Agent Memory Server, showcasing short-term and long-term memory. |
Need quickstarts to begin your Redis AI journey?
| 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 | | |
| 📊 Multi Vector Search - Overview of multi vector queries with RedisVL | | |
| 🗜️ HNSW to SVS-VAMANA Migration - Showcase how to migrate HNSW indices to SVS-VAMANA | | |
| 🗜️ FLAT to SVS-VAMANA Migration - Showcase how to migrate FLAT indices to SVS-VAMANA | | |
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 | | |
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 | | |
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 | | |
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 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 | | |
| 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 | | |
| Recipe | GitHub | Google Colab |
|---|---|---|
| 👤 Facial Recognition - Build a facial recognition system using the Facenet embedding model and RedisVL | | |
| 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 | | |
| 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 | | |
A set of Java recipes can be found under /java-recipes.
| Notebook | Description |
|---|---|
| notebooks/RAG/spring_ai_redis_rag.ipynb | Demonstrates building a RAG-ba sed beer recommendation chatbot using Spring AI and Redis as the vector store |
| RAG/spring_ai_redis_rag.ipynb | Demonstrates building a RAG-based beer recommendation chatbot using Spring AI and Redis as the vector store |
| semantic-routing/1_semantic_classification.ipynb | Demonstrates how to perform text classification with vector search (RedisVL) instead of LLMs |
| semantic-routing/2_semantic_tool_calling.ipynb | Demonstrates how to perform tool selection with vector search (RedisVL) instead of LLMs |
| semantic-routing/3_semantic_guardrails.ipynb | Demonstrates how to implement guardrails with vector search (RedisVL) |
| semantic-caching/1_pre_generated_semantic_caching.ipynb | Demonstrates how to perform pre generated semantic caching with RedisVL |
| semantic-caching/2_semantic_caching_with_langcache.ipynb | Demonstrates how to perform pre generated semantic caching with LangCache |
| Application | Description |
|---|---|
| applications/agent-long-term-memory | Demonstrates how to implement long-term memory for AI agents using Spring AI Advisor abstraction with Redis Vector Search |
| applications/agent-short-term-memory | Demonstrates how to implement short-term memory for AI agents using Spring AI ChatHistory abstraction |
| applications/vector-similarity-search/redis-om-spring | Demonstrates building a vector similarity search application using Spring Boot and Redis OM Spring |
| applications/vector-similarity-search/spring-ai | Demonstrates building a vector similarity search application using Spring Boot and Spring AI |
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 |
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 |
- 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.
We welcome contributions to Redis AI Resources! Here's how you can help:
- Add a new recipe: Create a Jupyter notebook demonstrating a Redis AI use case
- Improve documentation: Enhance existing notebooks or README with clearer explanations
- Fix bugs: Address issues in code samples or documentation
- Suggest improvements: Open an issue with ideas for new content or enhancements
To contribute:
- Fork the repository
- Create a feature branch
- Make your changes
- Submit a pull request
Please follow the existing style and format of the repository when adding content.