filipecosta90/redis-ai-resources
✨ A curated list of awesome community resources, integrations, and examples of Redis in the AI ecosystem.
AI Resources
Table of Contents
Demos
No faster way to get started than by diving in and playing around with one of our demos.
| Demo | Description |
|---|---|
| ArxivChatGuru | Streamlit demo of RAG over Arxiv documents with Redis & OpenAI |
| Redis VSS - Simple Streamlit Demo | Streamlit demo of Redis Vector Search |
| Vertex AI & Redis | A tutorial featuring Redis with Vertex AI |
| Agentic RAG | A tutorial focused on agentic RAG with LlamaIndex and Cohere |
| ArXiv Search | Full stack implementation of Redis with React FE |
| Product Search | Vector search with Redis Stack and Redis Enterprise |
Recipes
Need specific sample code to help get started with Redis? Start here.
Getting started with Redis & Vector Search
| Recipe | Description |
|---|---|
| /redis-intro/redis_intro.ipynb | The place to start if brand new to Redis |
| /vector-search/00_redispy.ipynb | Vector search with Redis python client |
| /vector-search/01_redisvl.ipynb | Vector search with Redis Vector Library |
Getting started with 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 | Description |
|---|---|
| /RAG/01_redisvl.ipynb | RAG from scratch with the Redis Vector Library |
| /RAG/02_langchain.ipynb | RAG using Redis and LangChain |
| /RAG/03_llamaindex.ipynb | RAG using Redis and LlamaIndex |
| /RAG/04_advanced_redisvl.ipynb | Advanced RAG with redisvl |
| /RAG/05_nvidia_ai_rag_redis.ipynb | RAG using Redis and Nvidia |
Semantic Cache
An estimated 31% of LLM queries are potentially redundant (source). Redis enables semantic caching to help cut down on LLM costs quickly.
| Recipe | Description |
|---|---|
| /semantic-cache/semantic_caching_gemini.ipynb | Build a semantic cache with Redis and Google Gemini |
Advanced RAG
For further insights on enhancing RAG applications with dense content representations, query re-writing, and other techniques.
| Recipe | Description |
|---|---|
| /RAG/04_advanced_redisvl.ipynb | Notebook for additional tips and techniques to improve RAG quality |
Recommendation systems
An exciting example of how Redis can power production-ready systems is highlighted in our collaboration with NVIDIA to construct a state-of-the-art recommendation system.
Within this repository, you'll find three examples, each escalating in complexity, showcasing the process of building such a system.
Integrations/Tools
- ⭐ 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).
- Semantic Kernel - popular lib by MSFT to integrate LLMs with plugins.
- RelevanceAI - Platform to ag, search and analyze unstructured data faster, built on Redis.
- DocArray - DocArray Integration of Redis as a VectorDB by Jina AI.
Additional content
- Vector Similarity Search: From Basics to Production - Introductory blog post to VSS and Redis as a VectorDB.
- AI-Powered Document Search - Blog post covering AI Powered Document Search Use Cases & Architectures.
- Vector Search on Azure - Using Azure Redis Enterprise for Vector Search
- Vector Databases and Large Language Models - Talk given at LLMs in Production Part 1 by Sam Partee.
- Vector Databases and AI-powered Search Talk - Video "Vector Databases and AI-powered Search" given by Sam Partee at SDSC 2023.
- Engineering Lab Review - Review of the first Redis VSS Hackathon.
- Real-Time Product Recommendations - Content-based recsys design with Redis and DocArray.
- LabLab AI Redis Tech Page
- Storing and querying for embeddings with Redis
- Building Intelligent Apps with Redis Vector Similarity Search
- RedisDays Keynote - Video "Infuse Real-Time AI Into Your "Financial Services" Application".
- RedisDays Trading Signals - Video "Using AI to Reveal Trading Signals Buried in Corporate Filings".
Benchmarks
- Vector Database Benchmarks - Jina AI VectorDB benchmarks comparing Redis against others.
- ANN Benchmarks - Standard ANN Benchmarks site. Only using single Redis OSS instance/client.
Documentation
- Redis Vector Database QuickStart
- Redis Vector Similarity Docs - Official Redis literature for Vector Similarity Search.
- Redis-py Search Docs - Redis-py client library docs for RediSearch.
- Redis-py General Docs - Redis-py client library documentation.
- Redis Stack - Redis Stack documentation.
- Redis Clients - Redis client list.