{"id":80048,"url":"https://github.com/redis-developer/redis-ai-resources","name":"redis-ai-resources","description":"✨ A curated list of awesome community resources, integrations, and examples of Redis in the AI ecosystem.","projects_count":82,"last_synced_at":"2026-08-23T09:00:27.385Z","repository":{"id":140211410,"uuid":"596607771","full_name":"redis-developer/redis-ai-resources","owner":"redis-developer","description":"✨ A curated list of awesome community resources, integrations, and examples of Redis in the AI ecosystem.","archived":false,"fork":false,"pushed_at":"2026-08-14T22:53:31.000Z","size":72988,"stargazers_count":482,"open_issues_count":15,"forks_count":77,"subscribers_count":17,"default_branch":"main","last_synced_at":"2026-08-14T23:15:06.183Z","etag":null,"topics":["ai","awesome-list","ecosystem","feature-store","machine-learning","redis","vector-database","vector-search"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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Java AI Recipes","Feature Store","Semantic Caching","Recommendation systems","Recommendation Systems"],"readme":"\u003cdiv align=\"center\"\u003e\n\u003cimg src=\"assets/redis-logo.svg\" alt=\"AI Resources\" width=\"175px\"\u003e\n\n\u003ch1\u003eAI Resources\u003c/h1\u003e\n\n\u003cp\u003e\n  \u003ca href=\"https://opensource.org/licenses/MIT\"\u003e\u003cimg src=\"https://img.shields.io/badge/License-MIT-yellow.svg\" alt=\"License: MIT\"\u003e\u003c/a\u003e\n  \u003ca href=\"#\"\u003e\u003cimg src=\"https://img.shields.io/github/languages/top/redis-developer/redis-ai-resources\" alt=\"Language\"\u003e\u003c/a\u003e\n  \u003ca href=\"#\"\u003e\u003cimg src=\"https://img.shields.io/github/last-commit/redis-developer/redis-ai-resources\" alt=\"GitHub last commit\"\u003e\u003c/a\u003e\n  \u003ca href=\"https://discord.gg/redis\"\u003e\u003cimg src=\"https://img.shields.io/badge/Discord-Redis%20Community-blueviolet\" alt=\"Discord\"\u003e\u003c/a\u003e\n  \u003ca href=\"https://twitter.com/redisinc\"\u003e\u003cimg src=\"https://img.shields.io/badge/Twitter-@redisinc-blue\" alt=\"Twitter\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\n\u003cp\u003e\n    ✨ A curated repository of code recipes, demos, tutorials and resources for basic and advanced Redis use cases in the AI ecosystem. ✨\n\u003c/p\u003e\n\n\u003cdiv align=\"center\"\u003e\n  \u003ch3\u003e\n    \u003ca href=\"#getting-started\"\u003eGetting Started\u003c/a\u003e |\n    \u003ca href=\"#demos\"\u003eDemos\u003c/a\u003e |\n    \u003ca href=\"#recipes\"\u003eRecipes\u003c/a\u003e |\n    \u003ca href=\"#tutorials\"\u003eTutorials\u003c/a\u003e |\n    \u003ca href=\"#integrations\"\u003eIntegrations\u003c/a\u003e |\n    \u003ca href=\"#other-helpful-resources\"\u003eResources\u003c/a\u003e\n  \u003c/h3\u003e\n\u003c/div\u003e\n\n\u003c/div\u003e\n\u003cbr\u003e\n\n## Getting Started\n\nNew to Redis for AI applications? Here's how to get started:\n\n1. **First time with Redis?** Start with our [Redis Intro notebook](python-recipes/redis-intro/00_redis_intro.ipynb) \n2. **Want to try vector search?** Check our [Vector Search with RedisVL](python-recipes/vector-search/01_redisvl.ipynb) recipe\n3. **Building a RAG application?** Begin with [RAG from Scratch](python-recipes/RAG/01_redisvl.ipynb)\n4. **Ready to see it in action?** Play with the [Redis RAG Workbench](https://github.com/redis-developer/redis-rag-workbench) demo\n\n\u003chr\u003e\n\n## Demos\nNo faster way to get started than by diving in and playing around with a demo.\n\n| Demo                                                                                     | Description                                                                                                                                                                                                                                                                                                                             |\n|------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [Redis RAG Workbench](https://github.com/redis-developer/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.                                                                                                                          |\n| [Redis VSS - Simple Streamlit Demo](https://github.com/antonum/Redis-VSS-Streamlit)      | Streamlit demo of Redis Vector Search                                                                                                                                                                                                                                                                                                   |\n| [ArXiv Search](https://github.com/redis-developer/redis-arxiv-search)                    | Full stack implementation of Redis with React FE                                                                                                                                                                                                                                                                                        |\n| [Product Search](https://github.com/redis-developer/redis-product-search)                | Vector search with Redis Stack and Redis Enterprise                                                                                                                                                                                                                                                                                     |\n| [ArxivChatGuru](https://github.com/redis-developer/ArxivChatGuru)                        | Streamlit demo of RAG over Arxiv documents with Redis \u0026 OpenAI                                                                                                                                                                                                                                                                          |\n| [Redis Movies Searcher](https://github.com/redis-developer/redis-movies-searcher)        | Demo of hybrid search using Java, Spring Boot, and Redis OM                                                                                                                                                                                                                                                                             |\n| [Memory-Aware Alexa Assistant](https://github.com/redis-developer/my-jarvis-alexa-skill) | 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. |\n| [From Postgres to Redis Cloud with RDI](https://github.com/redis-developer/speedup-slowapp-with-redis-di) | 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.                                                                           |\n| [Virtual Banking Assistant with Semantic Router](https://github.com/redis-developer/banking-agent-semantic-routing-demo) | 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)      \n| [Shopping AI Agent](https://github.com/redis-developer/shopping-ai-agent-langgraph-js-demo) | 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.                                                                   |\n| [Restaurant Discovery AI Agent](https://github.com/redis-developer/restaurant-discovery-ai-agent-demo) | 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.                                                    |\n| [Podcast Chatbot with Agent Memory](https://github.com/redis-developer/podcast-chatbot-with-agent-memory-azure-demo) | 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. |\n| [Car Dealership Assistant with Agent Memory](https://github.com/redis-developer/dealership-chatbot-agent-memory-demo) | A Car Dealership chatbot  with Redis Agent Memory Server, showcasing short-term and long-term memory. |\n| [Multi-Agent AI Text Adventure Game with Agent Memory and LangGraph.js](https://github.com/redis-developer/multi-agent-ai-text-adventure-game-with-agent-memory-langgraph-js-azure) | A text-adventure game that demonstrates how to build a multi-agent AI workflows with LangGraph.js, Azure Managed Redis, and Agent Memory Server. |\n\n## Recipes\n\nNeed quickstarts to begin your Redis AI journey?\n\n### Getting started with Redis \u0026 Vector Search\n\n| Recipe | GitHub | Google Colab |\n| --- | --- | --- |\n| 🏁 **Redis Intro** - The place to start if brand new to Redis | [![Open In GitHub](https://img.shields.io/badge/View-GitHub-green)](python-recipes/redis-intro/00_redis_intro.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/redis-intro/00_redis_intro.ipynb) |\n| 🔍 **Vector Search with RedisPy** - Vector search with Redis python client | [![Open In GitHub](https://img.shields.io/badge/View-GitHub-green)](python-recipes/vector-search/00_redispy.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/vector-search/00_redispy.ipynb) |\n| 📚 **Vector Search with RedisVL** - Vector search with Redis Vector Library | [![Open In GitHub](https://img.shields.io/badge/View-GitHub-green)](python-recipes/vector-search/01_redisvl.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/vector-search/01_redisvl.ipynb) |\n| 🔄 **Hybrid Search** - Hybrid search techniques with Redis (BM25 + Vector) | [![Open In GitHub](https://img.shields.io/badge/View-GitHub-green)](python-recipes/vector-search/02_hybrid_search.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/vector-search/02_hybrid_search.ipynb) |\n| 🔢 **Data Type Support** - Shows how to convert a float32 index to float16 or integer dataypes | [![Open In GitHub](https://img.shields.io/badge/View-GitHub-green)](python-recipes/vector-search/03_dtype_support.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/vector-search/03_dtype_support.ipynb) |\n| 📊 **Benchmarking Basics** - Overview of search benchmarking basics with RedisVL and Python multiprocessing | [![Open In GitHub](https://img.shields.io/badge/View-GitHub-green)](python-recipes/vector-search/04_redisvl_benchmarking_basics.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/vector-search/04_redisvl_benchmarking_basics.ipynb) |\n| 📊 **Multi Vector Search** - Overview of multi vector queries with RedisVL | [![Open In GitHub](https://img.shields.io/badge/View-GitHub-green)](python-recipes/vector-search/05_multivector_search.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/vector-search/05_multivector_search.ipynb) |\n| 🗜️ **HNSW to SVS-VAMANA Migration** - Showcase how to migrate HNSW indices to SVS-VAMANA | [![Open In GitHub](https://img.shields.io/badge/View-GitHub-green)](python-recipes/vector-search/06_hnsw_to_svs_vamana_migration.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/vector-search/06_hnsw_to_svs_vamana_migration.ipynb) |\n| 🗜️ **FLAT to SVS-VAMANA Migration** - Showcase how to migrate FLAT indices to SVS-VAMANA | [![Open In GitHub](https://img.shields.io/badge/View-GitHub-green)](python-recipes/vector-search/07_flat_to_svs_vamana_migration.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/vector-search/07_flat_to_svs_vamana_migration.ipynb) |\n\n\n### Retrieval Augmented Generation (RAG)\n\n**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.\n\nTo get started with RAG, either from scratch or using a popular framework like Llamaindex or LangChain, go with these recipes:\n\n| Recipe | GitHub | Google Colab |\n| --- | --- | --- |\n| 🧩 **RAG from Scratch** - RAG from scratch with the Redis Vector Library | [![Open In GitHub](https://img.shields.io/badge/View-GitHub-green)](python-recipes/RAG/01_redisvl.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/01_redisvl.ipynb) |\n| ⛓️ **LangChain RAG** - RAG using Redis and LangChain | [![Open In GitHub](https://img.shields.io/badge/View-GitHub-green)](python-recipes/RAG/02_langchain.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/02_langchain.ipynb) |\n| 🦙 **LlamaIndex RAG** - RAG using Redis and LlamaIndex | [![Open In GitHub](https://img.shields.io/badge/View-GitHub-green)](python-recipes/RAG/03_llamaindex.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/03_llamaindex.ipynb) |\n| 🚀 **Advanced RAG** - Advanced RAG techniques | [![Open In GitHub](https://img.shields.io/badge/View-GitHub-green)](python-recipes/RAG/04_advanced_redisvl.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/04_advanced_redisvl.ipynb) |\n| 🖥️ **NVIDIA RAG** - RAG using Redis and Nvidia NIMs | [![Open In GitHub](https://img.shields.io/badge/View-GitHub-green)](python-recipes/RAG/05_nvidia_ai_rag_redis.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/05_nvidia_ai_rag_redis.ipynb) |\n| 📊 **RAGAS Evaluation** - Utilize the RAGAS framework to evaluate RAG performance | [![Open In GitHub](https://img.shields.io/badge/View-GitHub-green)](python-recipes/RAG/06_ragas_evaluation.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/06_ragas_evaluation.ipynb) |\n| 🔒 **Role-Based RAG** - Implement a simple RBAC policy with vector search using Redis | [![Open In GitHub](https://img.shields.io/badge/View-GitHub-green)](python-recipes/RAG/07_user_role_based_rag.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/07_user_role_based_rag.ipynb) |\n\n### LLM Memory\nLLMs 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.\n\n| Recipe | GitHub | Google Colab |\n| --- | --- | --- |\n| 💬 **Message History** - LLM message history with semantic similarity | [![Open In GitHub](https://img.shields.io/badge/View-GitHub-green)](python-recipes/llm-message-history/00_llm_message_history.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/llm-message-history/00_llm_message_history.ipynb) |\n| 👥 **Multiple Sessions** - Handle multiple simultaneous chats with one instance | [![Open In GitHub](https://img.shields.io/badge/View-GitHub-green)](python-recipes/llm-message-history/01_multiple_sessions.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/llm-message-history/01_multiple_sessions.ipynb) |\n\n### Semantic Caching\nAn estimated 31% of LLM queries are potentially redundant ([source](https://arxiv.org/pdf/2403.02694)). Redis enables semantic caching to help cut down on LLM costs quickly.\n\n| Recipe | GitHub | Google Colab |\n| --- | --- | --- |\n| 🧠 **Gemini Semantic Cache** - Build a semantic cache with Redis and Google Gemini | [![Open In GitHub](https://img.shields.io/badge/View-GitHub-green)](python-recipes/semantic-cache/00_semantic_caching_gemini.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/semantic-cache/00_semantic_caching_gemini.ipynb) |\n| 🦙 **Llama3.1 Doc2Cache** - Build a semantic cache using the Doc2Cache framework and Llama3.1 | [![Open In GitHub](https://img.shields.io/badge/View-GitHub-green)](python-recipes/semantic-cache/01_doc2cache_llama3_1.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/semantic-cache/01_doc2cache_llama3_1.ipynb) |\n| ⚙️ **Cache Optimization** - Use CacheThresholdOptimizer from [redis-retrieval-optimizer](https://pypi.org/project/redis-retrieval-optimizer/) to setup best cache config | [![Open In GitHub](https://img.shields.io/badge/View-GitHub-green)](python-recipes/semantic-cache/02_semantic_cache_optimization.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/semantic-cache/02_semantic_cache_optimization.ipynb) |\n| 🎯 **Context-Enabled Caching** - Context-aware semantic caching with Redis for enhanced LLM performance | [![Open In GitHub](https://img.shields.io/badge/View-GitHub-green)](python-recipes/semantic-cache/03_context_enabled_semantic_caching.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/semantic-cache/03_context_enabled_semantic_caching.ipynb) |\n\n### Semantic Routing\nRouting is a simple and effective way of preventing misuse with your AI application or for creating branching logic between data sources etc.\n\n| Recipe | GitHub | Google Colab |\n| --- | --- | --- |\n| 🔀 **Basic Routing** - Simple examples of how to build an allow/block list router in addition to a multi-topic router | [![Open In GitHub](https://img.shields.io/badge/View-GitHub-green)](python-recipes/semantic-router/00_semantic_routing.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/semantic-router/00_semantic_routing.ipynb) |\n| ⚙️ **Router Optimization** - Use RouterThresholdOptimizer from [redis-retrieval-optimizer](https://pypi.org/project/redis-retrieval-optimizer/) to setup best router config | [![Open In GitHub](https://img.shields.io/badge/View-GitHub-green)](python-recipes/semantic-router/01_routing_optimization.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/semantic-router/01_routing_optimization.ipynb) |\n| 🛠️ **Advanced Routing** - Strategies for improving routing accuracy when deployed in production | [![Open In GitHub](https://img.shields.io/badge/View-GitHub-green)](python-recipes/semantic-router/02_advanced_semantic_routing.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/semantic-router/02_advanced_semantic_routing.ipynb) |\n| 📊 **Routing Visualization** - Visualize the semantic router and measure the quality of your route references. | [![Open In GitHub](https://img.shields.io/badge/View-GitHub-green)](python-recipes/semantic-router/03_router_visualization.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/semantic-router/03_router_visualization.ipynb) |\n\n\n### AI Gateways\nAI gateways manage LLM traffic through a centralized, managed layer that can implement routing, rate limiting, caching, and more.\n\n| Recipe | GitHub | Google Colab |\n| --- | --- | --- |\n| 🚪 **LiteLLM Proxy** - Getting started with LiteLLM proxy and Redis | [![Open In GitHub](https://img.shields.io/badge/View-GitHub-green)](python-recipes/gateway/00_litellm_proxy_redis.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/gateway/00_litellm_proxy_redis.ipynb) |\n\n\n### Agents\n\n| Recipe | GitHub | Google Colab |\n| --- | --- | --- |\n| 🕸️ **LangGraph Agents** - Notebook to get started with lang-graph and agents | [![Open In GitHub](https://img.shields.io/badge/View-GitHub-green)](python-recipes/agents/00_langgraph_redis_agentic_rag.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/agents/00_langgraph_redis_agentic_rag.ipynb) |\n| 👥 **CrewAI Agents** - Notebook to get started with CrewAI and lang-graph | [![Open In GitHub](https://img.shields.io/badge/View-GitHub-green)](python-recipes/agents/01_crewai_langgraph_redis.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/agents/01_crewai_langgraph_redis.ipynb) |\n| 🧠 **Memory Agent** - Building an agent with short term and long term memory using Redis | [![Open In GitHub](https://img.shields.io/badge/View-GitHub-green)](python-recipes/agents/03_memory_agent.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/agents/03_memory_agent.ipynb) |\n| 🛠️ **Full-Featured Agent** - Notebook builds full tool calling agent with semantic cache and router | [![Open In GitHub](https://img.shields.io/badge/View-GitHub-green)](python-recipes/agents/02_full_featured_agent.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/agents/02_full_featured_agent.ipynb) |\n| 🥗 **Autogen Agent** - Builds a blog writing agent with Autogen and Redis memory | [![Open In GitHub](https://img.shields.io/badge/View-GitHub-green)](python-recipes/agents/04_autogen_agent.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/agents/04_autogen_agent.ipynb) |\n| 🧩 **NeMo Agent Toolkit + Redis** - NVIDIA NeMo Agent Toolkit agent with self-hosted Redis Agent Memory | [![Open In GitHub](https://img.shields.io/badge/View-GitHub-green)](python-recipes/agents/06_nemo_agent_toolkit_redis.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/agents/06_nemo_agent_toolkit_redis.ipynb) |\n| ☁️ **NeMo Agent Toolkit + Redis Cloud** - Same agent backed by managed Redis Cloud Agent Memory | [![Open In GitHub](https://img.shields.io/badge/View-GitHub-green)](python-recipes/agents/07_nemo_agent_toolkit_redis_cloud.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/agents/07_nemo_agent_toolkit_redis_cloud.ipynb) |\n\n### Computer Vision\n| Recipe | GitHub | Google Colab |\n| ------ | ------ | ------------ |\n| 👤 **Facial Recognition** - Build a facial recognition system using the Facenet embedding model and RedisVL | [![Open In GitHub](https://img.shields.io/badge/View-GitHub-green)](python-recipes/computer-vision/00_facial_recognition_facenet.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/computer-vision/00_facial_recognition_facenet.ipynb) |\n\n\n### Recommendation Systems\n\n| Recipe | GitHub | Google Colab |\n| --- | --- | --- |\n| 📋 **Content Filtering** - Intro content filtering example with redisvl | [![Open In GitHub](https://img.shields.io/badge/View-GitHub-green)](python-recipes/recommendation-systems/00_content_filtering.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/recommendation-systems/00_content_filtering.ipynb) |\n| 👥 **Collaborative Filtering** - Intro collaborative filtering example with redisvl | [![Open In GitHub](https://img.shields.io/badge/View-GitHub-green)](python-recipes/recommendation-systems/01_collaborative_filtering.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/recommendation-systems/01_collaborative_filtering.ipynb) |\n| 🏗️ **Two Towers** - Intro deep learning two tower example with redisvl | [![Open In GitHub](https://img.shields.io/badge/View-GitHub-green)](python-recipes/recommendation-systems/02_two_towers.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/recommendation-systems/02_two_towers.ipynb) |\n\n### Fraud Detection\n| Recipe | GitHub | Google Colab |\n| --- | --- | --- |\n| 🛡️ **Fraud Detection** - Detect financial fraud with vector k-NN scoring, anomaly detection, and velocity rules using RedisVL | [![Open In GitHub](https://img.shields.io/badge/View-GitHub-green)](python-recipes/fraud-detection/00_fraud_detection.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/fraud-detection/00_fraud_detection.ipynb) |\n\n### Feature Store\n| Recipe | GitHub | Google Colab |\n| ------ | ------ | ------------ |\n| 💳 **Credit Scoring** - Credit scoring system using Feast with Redis as the online store | [![Open In GitHub](https://img.shields.io/badge/View-GitHub-green)](python-recipes/feature-store/00_feast_credit_score.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/feature-store/00_feast_credit_score.ipynb) |\n| 🔍 **Transaction Search** - Real-time transaction feature search with Redis | [![Open In GitHub](https://img.shields.io/badge/View-GitHub-green)](python-recipes/feature-store/01_card_transaction_search.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/feature-store/01_card_transaction_search.ipynb) |\n| 🕵️ **Fraud Detection** - Define, materialize, and serve features with Featureform using Redis as the online store | [![Open In GitHub](https://img.shields.io/badge/View-GitHub-green)](python-recipes/feature-store/02_featureform_fraud_detection.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/feature-store/02_featureform_fraud_detection.ipynb) |\n| 🧬 **Transformations \u0026 Lineage** - Chain Featureform SQL transformations and trace feature lineage from source to Redis | [![Open In GitHub](https://img.shields.io/badge/View-GitHub-green)](python-recipes/feature-store/03_featureform_transformations_lineage.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/feature-store/03_featureform_transformations_lineage.ipynb) |\n| 🧭 **Embeddings \u0026 Vector Search** - Materialize Featureform embedding features to Redis and run nearest-neighbor search | [![Open In GitHub](https://img.shields.io/badge/View-GitHub-green)](python-recipes/feature-store/04_featureform_embeddings_vector_search.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/feature-store/04_featureform_embeddings_vector_search.ipynb) |\n| ⚡ **On-Demand Features** - Compute Featureform features at request time, combined with values served from Redis | [![Open In GitHub](https://img.shields.io/badge/View-GitHub-green)](python-recipes/feature-store/05_featureform_ondemand_features.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/feature-store/05_featureform_ondemand_features.ipynb) |\n| 🗂️ **Feature Variants** - Version Featureform features with variants and serve multiple definitions from Redis | [![Open In GitHub](https://img.shields.io/badge/View-GitHub-green)](python-recipes/feature-store/06_featureform_feature_variants.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/feature-store/06_featureform_feature_variants.ipynb) |\n\n### Model Context Protocol (MCP)\n| Recipe | GitHub | Google Colab |\n| ------ | ------ | ------------ |\n| 🤖 **RedisVL MCP (Google ADK)** - Build an agent with Google ADK, paired with tools from RedisVL MCP Server. | [![Open In GitHub](https://img.shields.io/badge/View-GitHub-green)](python-recipes/MCP/00_google_adk_redisvl_mcp_agent.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/MCP/00_google_adk_redisvl_mcp_agent.ipynb) |\n| ✴️ **RedisVL MCP (Claude Agent SDK)** - Build an agent with Claude Agent SDK, paired with tools from RedisVL MCP Server. | [![Open In GitHub](https://img.shields.io/badge/View-GitHub-green)](python-recipes/MCP/01_claude_agent_sdk_redisvl_mcp.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/MCP/01_claude_agent_sdk_redisvl_mcp.ipynb) |\n\n### ☕️ Java AI Recipes\n\nA set of Java recipes can be found under [/java-recipes](/java-recipes/README.md).\n\n#### Notebooks\n\n| Notebook                                                                                                                                      | Description                                                                                                  |\n|-----------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------|\n| [notebooks/RAG/spring_ai_redis_rag.ipynb](./java-recipes/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 |\n| [RAG/spring_ai_redis_rag.ipynb](./java-recipes/notebooks/RAG/spring_ai_redis_rag.ipynb)                                                       | Demonstrates building a RAG-based beer recommendation chatbot using Spring AI and Redis as the vector store  |\n| [semantic-routing/1_semantic_classification.ipynb](./java-recipes/notebooks/semantic-routing/1_semantic_classification.ipynb)                 | Demonstrates how to perform text classification with vector search (RedisVL) instead of LLMs                 |\n| [semantic-routing/2_semantic_tool_calling.ipynb](./java-recipes/notebooks/semantic-routing/2_semantic_tool_calling.ipynb)                     | Demonstrates how to perform tool selection with vector search (RedisVL) instead of LLMs                      |\n| [semantic-routing/3_semantic_guardrails.ipynb](./java-recipes/notebooks/semantic-routing/3_semantic_guardrails.ipynb)                         | Demonstrates how to implement guardrails with vector search (RedisVL)                                        |\n| [semantic-caching/1_pre_generated_semantic_caching.ipynb](./java-recipes/notebooks/semantic-caching/1_pre_generated_semantic_caching.ipynb)   | Demonstrates how to perform pre generated semantic caching with RedisVL                                      |\n| [semantic-caching/2_semantic_caching_with_langcache.ipynb](./java-recipes/notebooks/semantic-caching/2_semantic_caching_with_langcache.ipynb) | Demonstrates how to perform pre generated semantic caching with LangCache                                    |\n\n\n#### Applications\n\n| Application                                                                                                                                                  | Description                                                                                                               |\n|--------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------|\n| [applications/agent-long-term-memory](./java-recipes/applications/agent-long-term-memory/spring_boot_agent_memory.md)                                        | Demonstrates how to implement long-term memory for AI agents using Spring AI Advisor abstraction with Redis Vector Search |\n| [applications/agent-short-term-memory](./java-recipes/applications/agent-short-term-memory/spring_boot_agent_memory.md)                                      | Demonstrates how to implement short-term memory for AI agents using Spring AI ChatHistory abstraction                     |\n| [applications/vector-similarity-search/redis-om-spring](./java-recipes/applications/vector-similarity-search/redis-om-spring/spring_boot_redis_om_spring.md) | Demonstrates building a vector similarity search application using Spring Boot and Redis OM Spring                        |\n| [applications/vector-similarity-search/spring-ai](./java-recipes/applications/vector-similarity-search/spring-ai/spring_boot_spring_ai.md)                   | Demonstrates building a vector similarity search application using Spring Boot and Spring AI                              |\n\n\n## Tutorials\nNeed a *deeper-dive* through different use cases and topics?\n\n\u003ctable\u003e\n  \u003ctr\u003e\n    \u003ctd align=\"center\" width=\"33%\"\u003e\n      \u003cb\u003e\u003ca href=\"https://github.com/redis-developer/agentic-rag\"\u003e🤖 Agentic RAG\u003c/a\u003e\u003c/b\u003e\n      \u003cbr\u003e\n      A tutorial focused on agentic RAG with LlamaIndex and Cohere\n    \u003c/td\u003e\n    \u003ctd align=\"center\" width=\"33%\"\u003e\n      \u003cb\u003e\u003ca href=\"https://github.com/redis-developer/gcp-redis-llm-stack/tree/main\"\u003e☁️ RAG on VertexAI\u003c/a\u003e\u003c/b\u003e\n      \u003cbr\u003e\n      A RAG tutorial featuring Redis with Vertex AI\n    \u003c/td\u003e\n    \u003ctd align=\"center\" width=\"33%\"\u003e\n      \u003cb\u003e\u003ca href=\"https://github.com/redis-developer/redis-nvidia-recsys\"\u003e🔍 Recommendation Systems\u003c/a\u003e\u003c/b\u003e\n      \u003cbr\u003e\n      Building realtime recsys with NVIDIA Merlin \u0026 Redis\n    \u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd align=\"center\" width=\"33%\"\u003e\n      \u003cb\u003e\u003ca href=\"https://github.com/redis-developer/redis-movies-searcher-workshop\"\u003e🧑🏻‍💻 Redis Movies Searcher Workshop\u003c/a\u003e\u003c/b\u003e\n      \u003cbr\u003e\n      A hands-on workshop to create the Redis Movies Searcher application\n  \u003c/tr\u003e\n\u003c/table\u003e\n\n\u003chr\u003e\n\n## Integrations\nRedis integrates with many different players in the AI ecosystem. Here's a curated list below:\n\n| Integration | Description |\n| --- | --- |\n| [RedisVL](https://github.com/redis/redis-vl-python) | A dedicated Python client lib for Redis as a Vector DB |\n| [AWS Bedrock](https://redis.io/docs/latest/integrate/amazon-bedrock/) | Streamlines GenAI deployment by offering foundational models as a unified API |\n| [LangChain Python](https://github.com/langchain-ai/langchain) | Popular Python client lib for building LLM applications powered by Redis |\n| [LangChain JS](https://github.com/langchain-ai/langchainjs) | Popular JS client lib for building LLM applications powered by Redis |\n| [LlamaIndex](https://gpt-index.readthedocs.io/en/latest/examples/vector_stores/RedisIndexDemo.html) | LlamaIndex Integration for Redis as a vector Database (formerly GPT-index) |\n| [LiteLLM](https://www.litellm.ai/) | Popular LLM proxy layer to help manage and streamline usage of multiple foundation models |\n| [Semantic Kernel](https://github.com/microsoft/semantic-kernel/tree/main) | Popular lib by MSFT to integrate LLMs with plugins |\n| [RelevanceAI](https://relevance.ai/) | Platform to tag, search and analyze unstructured data faster, built on Redis |\n| [DocArray](https://docs.docarray.org/user_guide/storing/index_redis/) | DocArray Integration of Redis as a VectorDB by Jina AI |\n\n\u003chr\u003e\n\n# Other Helpful Resources\n\n- [Vector Databases and Large Language Models](https://youtu.be/GJDN8u3Y-T4) - Talk given at LLMs in Production Part 1 by Sam Partee.\n- [Level-up RAG with RedisVL](https://redis.io/blog/level-up-rag-apps-with-redis-vector-library/)\n- [Improving RAG quality with RAGAs](https://redis.io/blog/get-better-rag-responses-with-ragas/)\n- [Vector Databases and AI-powered Search Talk](https://www.youtube.com/watch?v=g2bNHLeKlAg) - Video \"Vector Databases and AI-powered Search\" given by Sam Partee at SDSC 2023.\n- [NVIDIA RecSys with Redis](https://developer.nvidia.com/blog/offline-to-online-feature-storage-for-real-time-recommendation-systems-with-nvidia-merlin/)\n- [Benchmarking results for vector databases](https://redis.io/blog/benchmarking-results-for-vector-databases/) - Benchmarking results for vector databases, including Redis and 7 other Vector Database players.\n- [Redis Vector Library Docs](https://docs.redisvl.com)\n- [Redis Vector Search API Docs](https://redis.io/docs/interact/search-and-query/advanced-concepts/vectors/) - Official Redis literature for Vector Similarity Search.\n- [Redis Retrieval Optimizer](https://pypi.org/project/redis-retrieval-optimizer/) - Library for optimizing index, embedding, and search method usage within Redis.\n\n\u003chr\u003e\n\n## Contributing\n\nWe welcome contributions to Redis AI Resources! Here's how you can help:\n\n1. **Add a new recipe**: Create a Jupyter notebook demonstrating a Redis AI use case\n2. **Improve documentation**: Enhance existing notebooks or README with clearer explanations\n3. **Fix bugs**: Address issues in code samples or documentation\n4. **Suggest improvements**: Open an issue with ideas for new content or enhancements\n\nTo contribute:\n1. Fork the repository\n2. Create a feature branch\n3. Make your changes\n4. Submit a pull request\n\nPlease follow the existing style and format of the repository when adding content.\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/redis-developer%2Fredis-ai-resources/projects"}