redis ai resources

AI Resources

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

Demos | Recipes | Tutorials | Integrations | Content | Benchmarks | Docs

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

Recipes

Need quickstarts to begin your Redis AI journey? Start here.

Getting started with Redis & Vector Search

Recipe	Description
/redis-intro/00_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
/vector-search/02_hybrid_search.ipynb	Hybrid search techniques with Redis (BM25 + Vector)
/vector-search/03_float16_support.ipynb	Shows how to convert a float32 index to use float16

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	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 techniques
/RAG/05_nvidia_ai_rag_redis.ipynb	RAG using Redis and Nvidia NIMs
/RAG/06_ragas_evaluation.ipynb	Utilize the RAGAS framework to evaluate RAG performance

LLM Memory

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/doc2cache_llama3_1.ipynb	Build a semantic cache using the Doc2Cache framework and Llama3.1
/semantic-cache/semantic_caching_gemini.ipynb	Build a semantic cache with Redis and Google Gemini

Agents

Recipe	Description
/agents/00_langgraph_redis_agentic_rag.ipynb	Notebook to get started with lang-graph and agents
/agents/01_crewai_langgraph_redis.ipynb	Notebook to get started with lang-graph and agents

Computer Vision

Recipe	Description
/computer-vision/00_facial_recognition_facenet.ipynb	Build a facial recognition system using the Facenet embedding model and RedisVL.

Recommendation Systems

Recipe	Description
/recommendation-systems/00_content_filtering.ipynb	Intro content filtering example with redisvl
/recommendation-systems/01_collaborative_filtering.ipynb	Intro collaborative filtering example with redisvl

Tutorials

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

Tutorial	Description
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 w/ NVIDIA Merlin & Redis	Three examples, each escalating in complexity, showcasing the process of building a realtime recsys with NVIDIA and Redis

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

Content

• Vector Similarity Search: From Basics to Production - Introductory blog post to VSS and Redis as a VectorDB.
• Improving RAG quality with RAGAs
• Level-up RAG with RedisVL
• 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.
• Real-Time Product Recommendations - Content-based recsys design with Redis and DocArray.
• NVIDIA Recsys with Redis
• LabLab AI Redis Tech Page
• Storing and querying for embeddings with Redis
• Building Intelligent Apps with Redis Vector Similarity Search
• 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.

Docs

• Redis Vector Library Docs
• 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.