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 |
| /RAG/06_ragas_evaluation.ipynb | Utilize RAGAS framework to evaluate RAG performance |
LLM Session Management
| LLMs are stateless. To maintain context within a conversation chat sessions must be stored and resent to the LLM. Redis manages the storage and retrieval of chat sessions to maintain context and conversational relevance. | Recipe | Description |
|---|---|---|
| /llm-session-manager/00_session_manager.ipynb | LLM session manager with semantic similarity | |
| /llm-session-manager/01_multiple_sessions.ipynb | Handle multiple simultaneous chats with one instance |
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
- Benchmarking results for vector databases - Benchmarking results for vector databases, including Redis and 7 other Vector Database players.
- 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.