✨ A curated repository of code recipes, demos, tutorials and resources for basic and advanced Redis use cases in the AI ecosystem. ✨
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 |
Need quickstarts to begin your Redis AI journey?
| Recipe | 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 | | |
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 | 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 | 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 | 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 redisvl to setup best cache config | | |
Routing is a simple and effective way of preventing misuse with your AI application or for creating branching logic between data sources etc.
| Recipe | 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 redisvl 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 | Google Colab | |
|---|---|---|
| 🚪 LiteLLM Proxy - Getting started with LiteLLM proxy and Redis | | |
| Recipe | 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 | | |
| Recipe | Google Colab | |
|---|---|---|
| 👤 Facial Recognition - Build a facial recognition system using the Facenet embedding model and RedisVL | | |
| Recipe | 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 | 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.
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 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.
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.